**Meet the editors**

Wengui Yan has been a Research Geneticist since 1996 at the USDA-ARS Dale Bumpers National Rice Research Center. Dr. Yan received both B.S. (1981) and M.S. (1984) in Agronomy from Sichuan Agricultural University, China and Ph.D (1992) in Plant Breeding and Genetics from University of Arkansas, USA. His research interests cover phenotypic and genotypic characterization of over

20,000 rice accessions originated from 116 countries in the USDA Plant Germplasm System http://www.ars-grin.gov/npgs/index.html, germplasm identification for various traits essential for resistances to biotic and abiotic stresses, and grain yield, quality and nutritional values, and gene and/or QTL mapping along with molecular marker development for those traits to assist cultivar improvement using genomic technologies. The research program led by him has published 83 peer-reviewed scientific papers, 4 book chapters, 104 proceedings and abstracts for academic conferences and 1 patent in USA, China, Philippines, etc. At present, he is serving as academic editor for PLOS ONE, Plant Omics Journal and The Crop Journal, and reviewer for tens of scientific journals.

Jinsong Bao is a professor of Plant Genetics and Biotechnology at Zhejiang University, China. He received his B.S. (1993) and M.S. (1996) degrees in horticulture and Ph.D. (1999) degree in biophysics from Zhejiang University. His research interests are in molecular genetics of rice quality, more specifically in the areas of starch quality, nutritional quality, genetic mapping, and molecular

breeding. He has published more than 60 peer-reviewed research articles and five book chapters in these areas, and has received two professional awards for his achievements in the genetic study and molecular improvement of rice quality from Zhejiang Provincial Government. He has awarded the Young Scientist Research Award from the American Association of Cereal Chemists International, and Zhejiang Young Science and Technology Award from the Zhejiang Association of Science and Technology. He is a member of the editorial board of the Cereal Chemistry, Genes & Genomics, and Open Journal of Genetics.

## Contents

#### **Preface XI**


Liyong Cao and Xiaodeng Zhan

## Preface

Chapter 8 **Rice Straighthead Disease – Prevention, Germplasm, Gene Mapping and DNA Markers for Breeding 219**

Chapter 9 **Genes and QTLs for Rice Grain Quality Improvement 239**

Chapter 10 **Chinese Experiences in Breeding Three-Line, Two-Line and**

Bihu Huang

**VI** Contents

Jinsong Bao

**Super Hybrid Rice 279** Liyong Cao and Xiaodeng Zhan

Wengui Yan, Karen Moldenhauer, Wei Zhou, Haizheng Xiong and

Rice is a staple food for half of the world's population mostly in Asia. Productivity of rice has largely been improved since the Green Revolution in 1960s. Further improvement of rice yield is necessary to keep pace with population growth, which is a challenging task for breeders. This book, Rice - Germplasm, Genetics and Improvement, as its name implies, comprehensively reviews current knowledge in germplasm exploration, genetic basis of complex traits, and molecular breeding strategies in rice. In the germplasm part, we high‐ light the application of wild rice in rice breeding. In the genetics part, most of the complex traits related with yield, disease, quality have been covered. In the improvement part, Chi‐ nese experiences in hybrid rice breeding have been summarized together with many molec‐ ular breeding practices scattering in different chapters.

> **Dr. Jinsong Bao** College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China

> **Dr. Wengui Yan** USDA-ARS Dale Bumpers National Rice Research Center, USA

## **Unraveling the Secrets of Rice Wild Species**

Ehsan Shakiba and Georgia C. Eizenga

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58393

## **1. Introduction**

The world is facing a new challenge with global population predicted to plateau at nine billion people by the middle of this century (Godfray et al. 2010). Increasing food production to feed the world's population is an even greater challenge considering that agriculture is experiencing greater competition for land, water and energy, as well as, the effects of substantial climate change and the unintended effects of crop production on the environment. Part of the solution to increasing food production on the same or less cultivated land lies in exploiting the subset of genes lost during the domestication process and subsequent targeted breeding. Currently, these valuable genes are found only in the progenitor species genepool for crop cultivars. Cultivated plants having desirable genes were utilized in intensive breeding projects focused on increasing yield for particular environments and management systems but this process has narrowed the genetic diversity (Rausher 2001). For cultivated plants, this unexploited genetic material includes both landraces and the more exotic wild relatives. Improving our under‐ standing of this tertiary gene pool and exploiting it for crop improvement is paramount to meeting the challenges of feeding the world in this century through the integration of classical genetics and genomics-enabled research paradigms.

The loss of genetic diversity can be more problematic for self-pollinated plant species where the rate of cross pollination is below five percent, thus making it more difficult to reintroduce the lost diversity. In the case of the two major grain crops, rice (*Oryza sativa* L.) and wheat (*Triticum aestivum* L.), both self-pollinated, the re-introduction genetic diversity from the wild is central to the continued success of breeding, given that viruses, fungi, and bacteria, three main causal agents of biotic stress, are constantly evolving to cause the breakdown of the host plant's defense mechanisms (Rausher 2001).

Abiotic stress, including salinity, aluminum toxicity and acid sulfate soils, as well as, temper‐ ature and drought, complicate the difficulty of improving crop yields, especially in the face of

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

global warming (Brooker 2006; Tilman and Lehman 2001), which makes modern cultivars even more vulnerable. Genetic sources of resistance or tolerance offer the most promising mecha‐ nism to protect plants against these unfavorable conditions. Often wild species are not included as parental lines in cultivar development because it is relatively difficult to harness desirable genes by genetic recombination and many undesirable genes are introgressed from the wild parent resulting in inferior yield, undesirable plant architecture, and/or poor grain quality (Tanksley and McCouch 1997). Recent studies, however, in rice (McCouch et al. 2007) and tomato, *Lycopersicon esculentum* Mill. (Grandillo and Tanksley 2003), have shown that wild species contain genomic components that could result in genetic gains in terms of agronomic performance.

The rapid advancement in molecular technologies allows for genotyping plants much more quickly and inexpensively than ever before. The availability of high resolution genotypic information creates the opportunity to further explore an expanding number of accessions in a greater depth, and harness this information to enhance the efficiency and accuracy of introgression. These developments create opportunities not previously possible, to identify molecular markers associated with desirable traits in wild species and transfer these traits into elite lines and/or varieties, as well as, to unravel multi-genic traits for crop improvement (Tanksley and McCouch 1997; McCouch et al. 2012).

Our main objective is to summarize efforts over the past 15 years to identify useful novel alleles in the *Oryza* species that were lost during evolution and domestication, genetically dissect the traits encoded by these alleles through chromosome mapping, and incorporate these traits or alleles into an agronomically useful genetic background. To do this we will (a) briefly describe the relationships among the species in the genus *Oryza*, (b) describe the types of populations that have been developed for mapping desirable traits identified in the wild *Oryza* species to a chromosome location, and (c) summarize the quantitative trait locus (QTL) studies focused on mapping the useful traits and novel alleles to specific locations in the genomes of *Oryza* species.

## **2. Phylogeny of the** *Oryza* **genus**

The *Oryza* genus includes two cultivated species, Asian rice, *O. sativa*, which is grown throughout the tropical and temperate climates of the world, and African rice, *O. glaberrima*, which is found in sub-Saharan Africa along the Niger River. The 22 wild species composing the *Oryza* genus are characterized by eleven different genomes identified as the A-, B-, C-, D-, E-, F-, G-, H-, J-, K-and L-genomes and arranged in the following 10 genome types AA, BB, CC, BBCC, CCDD, EE, FF, GG, HHJJ and KKLL. Four of the wild *Oryza* species are tetraploid and the remaining 18 are diploid, as well as, the two cultivated species (Table 1).


global warming (Brooker 2006; Tilman and Lehman 2001), which makes modern cultivars even more vulnerable. Genetic sources of resistance or tolerance offer the most promising mecha‐ nism to protect plants against these unfavorable conditions. Often wild species are not included as parental lines in cultivar development because it is relatively difficult to harness desirable genes by genetic recombination and many undesirable genes are introgressed from the wild parent resulting in inferior yield, undesirable plant architecture, and/or poor grain quality (Tanksley and McCouch 1997). Recent studies, however, in rice (McCouch et al. 2007) and tomato, *Lycopersicon esculentum* Mill. (Grandillo and Tanksley 2003), have shown that wild species contain genomic components that could result in genetic gains in terms of agronomic

The rapid advancement in molecular technologies allows for genotyping plants much more quickly and inexpensively than ever before. The availability of high resolution genotypic information creates the opportunity to further explore an expanding number of accessions in a greater depth, and harness this information to enhance the efficiency and accuracy of introgression. These developments create opportunities not previously possible, to identify molecular markers associated with desirable traits in wild species and transfer these traits into elite lines and/or varieties, as well as, to unravel multi-genic traits for crop improvement

Our main objective is to summarize efforts over the past 15 years to identify useful novel alleles in the *Oryza* species that were lost during evolution and domestication, genetically dissect the traits encoded by these alleles through chromosome mapping, and incorporate these traits or alleles into an agronomically useful genetic background. To do this we will (a) briefly describe the relationships among the species in the genus *Oryza*, (b) describe the types of populations that have been developed for mapping desirable traits identified in the wild *Oryza* species to a chromosome location, and (c) summarize the quantitative trait locus (QTL) studies focused on mapping the useful traits and novel alleles to specific locations in the genomes of *Oryza*

The *Oryza* genus includes two cultivated species, Asian rice, *O. sativa*, which is grown throughout the tropical and temperate climates of the world, and African rice, *O. glaberrima*, which is found in sub-Saharan Africa along the Niger River. The 22 wild species composing the *Oryza* genus are characterized by eleven different genomes identified as the A-, B-, C-, D-, E-, F-, G-, H-, J-, K-and L-genomes and arranged in the following 10 genome types AA, BB, CC, BBCC, CCDD, EE, FF, GG, HHJJ and KKLL. Four of the wild *Oryza* species are tetraploid

and the remaining 18 are diploid, as well as, the two cultivated species (Table 1).

(Tanksley and McCouch 1997; McCouch et al. 2012).

**2. Phylogeny of the** *Oryza* **genus**

performance.

2 Rice - Germplasm, Genetics and Improvement

species.


† Classification for species, genome designation and distribution based on Brar and Singh (2011), Lu et al. (2014), Sanchez et al. (2013) and Vaughan (2003). The superscripts for the A-genome indicate a variation of the type of A-genome.

**‡** Genome size based on the following: *O. sativa* subsp. *japonica* (Goff et al. 2002), *O. sativa* subsp. *indica* (Yu et al. 2002) and *Oryza* species (Ammiraju et al. 2010).

**§***O. barthii* is also classified as *O. breviligulata* A. Chev. et Roehr.

**Table 1.** Taxonomic classification of *Oryza* species including the chromosome number, genome designation, genome size and distribution for each species.

Rice is the only major cereal found in the ancient lineage of the Bambusoideae and is currently placed in the subfamily Erhartoideae. Historically, the grass family, Poaceae, is thought to have evolved about 70-55 mya (million years ago) with the tribes Oryzeae and Pooideae (wheat and oats) diverging about 35 mya [reviewed by Kellogg (2009) and Vaughan et al. (2008)]. The Oryzinae and Zizaninae subtribes diverged about 20-22 mya and the *Oryza* and *Leersia* genera about 14.2 mya. The genus *Oryza* is divided into three sections: *Padia*, *Brachyantha* and *Oryza*. *Padia* includes the forest-dwelling *Oryza*, which are distributed into the *O. granulata* (GG), *O. ridleyi* (HHJJ) and *O. schlechteria* (KKLL) complexes. The *O. granulata* complex is thought to have diverged from the other *Oryza* species about 8 mya. *O. brachyantha* (FF) is the only species in the section *Brachyantha*. This species is widely distributed across Africa, growing in ironpan rock pools.

Section *Oryza* consists of two species complexes, the *O. officinalis* complex with the B-, C-, Dand E-genomes and the *O. sativa* complex, which includes all the A-genome species. Within the *O. officinalis* complex, *O. australiensis* (EE) is the most diverged and *O. eichingeri* (CC) appears to be the most basal of the C-genome species.

**Species†**

*Section Padia*

*Oryza granulata complex*

(Zoll. et (Mor. ex Steud.) Baill.)

4 Rice - Germplasm, Genetics and Improvement

*O. meyeriana*

*Oryza ridleyi complex*

*Oryza schlechteria complex*

and *Oryza* species (Ammiraju et al. 2010).

size and distribution for each species.

pan rock pools.

**§***O. barthii* is also classified as *O. breviligulata* A. Chev. et Roehr.

**No. of chromosomes (2n)†**

**Genome† Genome size**

24 GG Southeast Asia

*O. granulata* Nees et Am. ex Watt 24 GG 862 South and Southeast Asia

*O. longiglumis* Jansen <sup>48</sup> HHJJ Irian Jaya, Indonesia, Papua

*O. ridleyi* Hook. F. 48 HHJJ 1283 South Asia

*O. coarctata* Tateoka 48 KKLL 771 Asian coastal area

*O. schlerchteri* Pilger 48 KKLL Papua New Guinea

† Classification for species, genome designation and distribution based on Brar and Singh (2011), Lu et al. (2014), Sanchez et al. (2013) and Vaughan (2003). The superscripts for the A-genome indicate a variation of the type of A-genome. **‡** Genome size based on the following: *O. sativa* subsp. *japonica* (Goff et al. 2002), *O. sativa* subsp. *indica* (Yu et al. 2002)

**Table 1.** Taxonomic classification of *Oryza* species including the chromosome number, genome designation, genome

Rice is the only major cereal found in the ancient lineage of the Bambusoideae and is currently placed in the subfamily Erhartoideae. Historically, the grass family, Poaceae, is thought to have evolved about 70-55 mya (million years ago) with the tribes Oryzeae and Pooideae (wheat and oats) diverging about 35 mya [reviewed by Kellogg (2009) and Vaughan et al. (2008)]. The Oryzinae and Zizaninae subtribes diverged about 20-22 mya and the *Oryza* and *Leersia* genera about 14.2 mya. The genus *Oryza* is divided into three sections: *Padia*, *Brachyantha* and *Oryza*. *Padia* includes the forest-dwelling *Oryza*, which are distributed into the *O. granulata* (GG), *O. ridleyi* (HHJJ) and *O. schlechteria* (KKLL) complexes. The *O. granulata* complex is thought to have diverged from the other *Oryza* species about 8 mya. *O. brachyantha* (FF) is the only species in the section *Brachyantha*. This species is widely distributed across Africa, growing in iron-

Section *Oryza* consists of two species complexes, the *O. officinalis* complex with the B-, C-, Dand E-genomes and the *O. sativa* complex, which includes all the A-genome species. Within

**(Mbp)‡ Distribution†**

New Guinea

The species in the *O. sativa* complex prefer full sun, and grow near lakes, rivers and seasonal pools of water. Molecular data suggests that *O. meridionalis* diverged from the other A-genome species about 2 mya. Also, the perennial African species, *O. longistaminata*, diverged from the Asian A-genome species about the same time period, 2-3 mya. The second divergence between the Asian and African A-genome species, *O. barthii* and *O. glaberrima*, occurred 0.6 to 0.7 mya. More recently, possibly about 0.4 mya (or more than 0.2 mya), the *O. rufipogon* clade(s) that eventually diverged into the *O. sativa* subspecies (subsp.) *Japonica* and *Indica*. Later, the *Indica* subspecies differentiated into the *indica* and *aus* subpopulations and the *Japonica* subspecies into the *aromatic* (Group V), *tropical japonica* and *temperate japonica* subpopulations (Garris et al. 2005, Zhao et al. 2011, Huang et al. 2012). Archaeobotanical evidence from spikelet bases and changes in grain size document this domestication process (Fuller et al. 2010). Recently, based on genome sequences of 446 geographically diverse *O. rufipogon* accessions, Huang et al. (2012) further subdivided *O. rufipogon* accessions into three major *O. rufipogon* clades: one closely aligned with *O. sativa* subsp. *japonica*, one aligned with *O. sativa* subsp. *indica*, and the third clade was independent of *O. sativa*. Furthermore, as part of this study, a neighbor-joining tree constructed from sequence differences of 15 representative A-genome accessions sug‐ gested within *Indica*, different *O. rufipogon* clades were associated with the *aus* and *indica* subpopulations, whereas the three *Japonica* subpopulations arose from a single *O. rufipogon* clade. This phylogenetic tree also supported the aforementioned genetic distance between *O. meridionalis*, *O. longistaminata*, *O. barthii* and *O. glaberrima*.

Rice, *O. sativa*, the first monocot plant with a reference genome, is the central comparative genomics model for all grasses, and has been compared to all major cereals. To lay the foundation for interrogating the rice wild relatives, 18 bacterial artificial chromosome (BAC) libraries for 16 different *Oryza* species spanning all 10 *Oryza* genome types including the AAgenome species (*O. nivara*, *O. rufipogon*, *O. glaberrima*, *O. barthii*, *O. glumaepatula*, *O. longista‐ minata*, *O. meridionalis*), *O. punctata* (BB), *O. officinalis* (CC), *O. minuta* (BBCC), *O. alta* (CCDD), *O. australiensis* (EE), *O. brachyantha* (FF), *O. granulata* (GG), *O. ridleyi* (HHJJ) and *O. coarctata* (HHKK), were generated through the *Oryza* Map Alignment Project (OMAP) as summarized by Ammiraju et al. (2010). Subsequently, the International OMAP consortium was formed in 2007 to (a) generate reference sequences and transcriptome data sets of the eight A-genome species and representative species of the other genome types, (b) generate advanced mapping populations for the A-genome species, and (c) identify naturally occurring populations of the wild *Oryza* species for diversity and evolutionary analyses, as well as, conservation (Jacquemin et al. 2013; Sanchez et al. 2013). The species included in the sequencing effort were A-genome species (*O. nivara*, *O. rufipogon*, *O. barthii*, *O. glaberrima*, *O. glumaepatula*, *O. longistaminata*, *O. meridionalis* and both *O. sativa* subsp. *indica* and subsp. *japonica*), *O. punctata* (BB), C-genome species (*O. officinalis*, *O. eichingeri*, *O. rhizomatis*), *O. australiensis* (EE), *O. brachyantha* (FF), *O. granulata* (GG), and the outgroup, *Leersia perrieri*. To date, the sequencing of nine genomes and *L. perrieri* has been completed, in addition to the established reference sequences for *O. sativa* subsp. *japonica* and subsp. *indica* genomes (Wing 2013). Currently, two additional *O. sativa*  subsp. *indica* cultivars are being sequenced representing the *aus* (DJ123) and *indica* (IR64) subpopulations (McCombie 2013).

## **3. Methods for developing** *Oryza* **interspecific mapping populations**

Traits are classified as either qualitative or quantitative traits. Qualitative traits are controlled by one or a few genes with major effects while quantitative characters are controlled by many genes with minor effects (Poehlman and Sleper 1994). Identification of genes associated with quantitative traits is always more complicated compared to those involving qualitative traits.

Interspecific and intergenomic hybridization, hybridization between species with the same or different genomes, have been used to transfer desirable genes or QTL associated with simple or complex traits from wild species into a cultivated genetic background (Brar and Khush 1997; Dalmacio et al. 1995; Tanksley and McCouch 1997). Nevertheless hybridization success can be hindered by genomic incompatibilities and sterility barriers (Ishii et al. 1994; McCouch et al. 2007; Wang et al. 2005). The utilization of embryo rescue and other methods of producing viable and fertile hybrids combined with robust molecular markers and associated computa‐ tional and statistical analyses, led to the successful generation of interspecific genetic popula‐ tions that were used to link desirable traits to molecular markers and subsequent identification of the actual genes controlling the traits of interest (Ali et al. 2010; Chen et al. 2010; Ghesquière et al. 1997; Guo et al. 2013; Lexer and Fay 2005; McCouch et al. 2007). Six types of mapping populations are generated from interspecific crosses between *Oryza* species and *O. sativa* including (a) recombinant inbred line (RIL), (b) advanced backcross (AB), (c) backcross inbred line (BIL), (d) chromosome segment substitution line (CSSL), (e) near isogenic line (NIL) and (f) multi-parent advanced generation inter-cross (MAGIC). A discussion of each of these populations follows and examples are included in the third section describing agronomically important traits attributed to the *Oryza* species donor.

#### **3.1. Recombinant Inbred Line (RIL) population**

RIL populations have been the most common type of mapping population used in rice genetics and breeding when both parents are *O. sativa* but a limited number of interspecif‐ ic populations have been reported. To develop a RIL population, two contrasting culti‐ vars or accessions for the trait(s) to be mapped are crossed together to create an F1 hybrid. By successive self-pollination starting from the F1 generation, subsequent generations of segregants are produced (up to F3), representing multiple rounds of recombination and eventually fixation to homozygosity towards either of the parental alleles (Fig. 1). This derived population is advanced for several generations by the single seed descent (SSD) method, where a single F3 seed from each F2 plant is planted to produce the F4 genera‐ tion, subsequently a single F4 seed is selected from each line to produce the F5 generation with the SSD method usually continuing until F8 seed are produced. At the F7, the RILs exhibit genetic homogeneity, such that the genomic contribution of each parent is fixed,

subsp. *indica* cultivars are being sequenced representing the *aus* (DJ123) and *indica* (IR64)

Traits are classified as either qualitative or quantitative traits. Qualitative traits are controlled by one or a few genes with major effects while quantitative characters are controlled by many genes with minor effects (Poehlman and Sleper 1994). Identification of genes associated with quantitative traits is always more complicated compared to those

Interspecific and intergenomic hybridization, hybridization between species with the same or different genomes, have been used to transfer desirable genes or QTL associated with simple or complex traits from wild species into a cultivated genetic background (Brar and Khush 1997; Dalmacio et al. 1995; Tanksley and McCouch 1997). Nevertheless hybridization success can be hindered by genomic incompatibilities and sterility barriers (Ishii et al. 1994; McCouch et al. 2007; Wang et al. 2005). The utilization of embryo rescue and other methods of producing viable and fertile hybrids combined with robust molecular markers and associated computa‐ tional and statistical analyses, led to the successful generation of interspecific genetic popula‐ tions that were used to link desirable traits to molecular markers and subsequent identification of the actual genes controlling the traits of interest (Ali et al. 2010; Chen et al. 2010; Ghesquière et al. 1997; Guo et al. 2013; Lexer and Fay 2005; McCouch et al. 2007). Six types of mapping populations are generated from interspecific crosses between *Oryza* species and *O. sativa* including (a) recombinant inbred line (RIL), (b) advanced backcross (AB), (c) backcross inbred line (BIL), (d) chromosome segment substitution line (CSSL), (e) near isogenic line (NIL) and (f) multi-parent advanced generation inter-cross (MAGIC). A discussion of each of these populations follows and examples are included in the third section describing agronomically

RIL populations have been the most common type of mapping population used in rice genetics and breeding when both parents are *O. sativa* but a limited number of interspecif‐ ic populations have been reported. To develop a RIL population, two contrasting culti‐ vars or accessions for the trait(s) to be mapped are crossed together to create an F1 hybrid. By successive self-pollination starting from the F1 generation, subsequent generations of segregants are produced (up to F3), representing multiple rounds of recombination and eventually fixation to homozygosity towards either of the parental alleles (Fig. 1). This derived population is advanced for several generations by the single seed descent (SSD) method, where a single F3 seed from each F2 plant is planted to produce the F4 genera‐ tion, subsequently a single F4 seed is selected from each line to produce the F5 generation with the SSD method usually continuing until F8 seed are produced. At the F7, the RILs exhibit genetic homogeneity, such that the genomic contribution of each parent is fixed,

**3. Methods for developing** *Oryza* **interspecific mapping populations**

subpopulations (McCombie 2013).

6 Rice - Germplasm, Genetics and Improvement

involving qualitative traits.

important traits attributed to the *Oryza* species donor.

**3.1. Recombinant Inbred Line (RIL) population**

**Figure 1.** A comparison of the methods for creating primary and advanced bi-parental mapping populations, includ‐ ing recombinant inbred lines (RILs), backcross inbred lines (BILs), chromosome segment substitution lines (CSSLs) and near isogenic lines (NILs) as summarized by Fukuoka et al. (2010). Also shown are the number of backcrosses (BC) re‐ quired and the genotypes of the lines obtained by each method. Karyotypes of the three CSSLs illustrate how chromo‐ some 1 of the donor can be introgressed into the recurrent parent. The three NIL genotypes are based on the JeffersonNILs, each with a different *O. rufipogon* (IRGC105491) introgression selected for a different yield QTL (Imai et al. 2013).

and together these RILs compose a mapping population. If selections are being made for improved lines with a particular trait(s), this selection often begins in the F5-F6 if individu‐ al plants can be selected for the trait; otherwise, the selection is postponed to later generations (F7-Fn) (Nguyen et al. 2003, Poehlman and Sleper 1995). The procedure continues until the superior lines with desirable traits are produced.

The main advantage of the RIL method is that no backcrossing is necessary but when a wild *Oryza* species is a parent, often undesirable traits associated with the wild parent, especially shattering and sterility are problematic, thus it is often necessary to backcross. RIL populations are suitable for identifying major gene(s) or QTL(s), and to detect genetic interactions such as epistasis (Fukuoka et al. 2010). Other advantages are, the individual RIL may contain more than one introgressed segment in their chromosomes, representing different recombination events and a higher recombination frequency. As a result, fewer progeny lines are required to cover the complete donor genome as compared to other types of bi-parental mapping populations that include a backcross generation. Moreover, epistatic effects can be detected in RILs due to the presence of several introgressed segments in each line (Keurentjes et al. 2007). Because several segments of each parent are present in each individual line composing the population, there is less homogeneity in RIL populations as compared to most other types of populations. This heterogeneity is easy to observe and provides an excellent opportunity for phenotypic evaluation. In summary, the RIL method has proven to be useful when both parents are *O. sativa* but with interspecific and intergenomic crosses, backcrossing is often necessary (Fukuoka et al. 2010).

Commonly used softwares for creating the linkage map from the genotypic (molecular marker) data of the population for QTL analyses include MapMaker-QTL (Lander and Botstein 1989), JoinMap (Van Ooijen 2006) and MapDisto (Lorieux 2012). The possible chromosome location of the QTL for the trait being evaluated is based on the QTL having a significant LOD [loga‐ rithm (base 10) of odds] score with the LOD score detecting linkage between the molecular marker and the trait of interest. Several softwares are freely available for conducting the QTL analysis, including MapMaker-QTL (Lander and Botstein 1989), QTLCartographer (Wang et al. 2012), QGene (Joehanes and Nelson 2008), MapDisto (Lorieux 2012) and QTLNetwork (Yang et al. 2008). It is important to confirm that the software being used for QTL analysis can correctly analyze the population type since some cannot be used with BC2F2 populations based on differences in fundamental assumptions. Most recent QTL analyses with rice have been performed using either composite interval mapping (CIM) (Zeng 1994) or multiple interval mapping (MIM) (Kao and Zeng 1999) with single point analysis (SPA) (Tanksley et al. 1982), marker regression (Kearsey and Hyne 1994) and interval mapping (IM) (Haley and Knott 1992; Lander and Botstein 1989) being used in earlier analyses.

#### **3.2. Advanced Backcross (AB) population**

The advanced backcross (AB)-quantitative trait locus (QTL) analysis is a powerful strategy to map desirable trait(s) discovered in the wild species (Tanksley and Nelson 1996). This method was first applied to QTL mapping in tomato, and subsequently to several other crops, including rice (Grandillo and Tanksley 2003; McCouch et al. 2007). In the process of developing the AB populations used for QTL analysis, plants or lines with unfavorable genes derived from donor parents like sterility and sometimes shattering, are often eliminated from the population after phenotypic and genotypic evaluation. Due to artificial selection in favor of lines with desirable alleles and the genetic background from the recurrent parent, the distribution can be skewed toward the recurrent parent, therefore, after the BC3 generation, the power of the statistical analysis to detect QTL decreases. Since sequential backcrossing in AB-QTL removes epistatic interactions, the chance of detecting QTLs with epistatic interactions among alleles from the donor parent decreases, while the ability to detect additive QTLs increases (Tanksley and Nelson 1996; Grandillo and Tanksley 2003).

To create an AB mapping population, one parent, usually the wild *Oryza* species, identified as the donor parent, is crossed with the recurrent parent, usually an elite cultivar, which will be crossed with the hybrid parent in subsequent crosses (illustrated in Ali et al. 2010). Often the donor parent is used as a male and the recurrent parent as the female to avoid the cytoplasmic male sterility and because it is usually easier to emasculate the cultivated parent. The F1 plant(s) is one parent in the second generation and it is crossed with the recurrent parent, which is defined as backcrossing. The resulting first backcross generation (BC1) may be backcrossed again with the recurrent parent to generate a BC2 population. If the BC2 progeny are sterile, it is best to advance the population to the BC3 generation by crossing the BC2 plants to the recurrent parent a third time. After the progeny lines are advanced to the BC2 (or BC3) generation and allowed to self pollinate, these BC2F2 (or BC3F2) progeny plants are grown to collect phenotypic and genotypic data for the QTL analysis. After the AB-QTL mapping, the AB population can be advanced by (a) allowing all the progeny lines to self-pollinate and be advanced by SSD for three to four additional generations, thus developing a BIL population or (b) backcrossing the progeny lines additional generations to develop a library of CSSLs or NILs for targeted traits (Fig. 1).

#### **3.3. Backcross Inbred Line (BIL) population**

RIL may contain more than one introgressed segment in their chromosomes, representing different recombination events and a higher recombination frequency. As a result, fewer progeny lines are required to cover the complete donor genome as compared to other types of bi-parental mapping populations that include a backcross generation. Moreover, epistatic effects can be detected in RILs due to the presence of several introgressed segments in each line (Keurentjes et al. 2007). Because several segments of each parent are present in each individual line composing the population, there is less homogeneity in RIL populations as compared to most other types of populations. This heterogeneity is easy to observe and provides an excellent opportunity for phenotypic evaluation. In summary, the RIL method has proven to be useful when both parents are *O. sativa* but with interspecific and

Commonly used softwares for creating the linkage map from the genotypic (molecular marker) data of the population for QTL analyses include MapMaker-QTL (Lander and Botstein 1989), JoinMap (Van Ooijen 2006) and MapDisto (Lorieux 2012). The possible chromosome location of the QTL for the trait being evaluated is based on the QTL having a significant LOD [loga‐ rithm (base 10) of odds] score with the LOD score detecting linkage between the molecular marker and the trait of interest. Several softwares are freely available for conducting the QTL analysis, including MapMaker-QTL (Lander and Botstein 1989), QTLCartographer (Wang et al. 2012), QGene (Joehanes and Nelson 2008), MapDisto (Lorieux 2012) and QTLNetwork (Yang et al. 2008). It is important to confirm that the software being used for QTL analysis can correctly analyze the population type since some cannot be used with BC2F2 populations based on differences in fundamental assumptions. Most recent QTL analyses with rice have been performed using either composite interval mapping (CIM) (Zeng 1994) or multiple interval mapping (MIM) (Kao and Zeng 1999) with single point analysis (SPA) (Tanksley et al. 1982), marker regression (Kearsey and Hyne 1994) and interval mapping (IM) (Haley and Knott

The advanced backcross (AB)-quantitative trait locus (QTL) analysis is a powerful strategy to map desirable trait(s) discovered in the wild species (Tanksley and Nelson 1996). This method was first applied to QTL mapping in tomato, and subsequently to several other crops, including rice (Grandillo and Tanksley 2003; McCouch et al. 2007). In the process of developing the AB populations used for QTL analysis, plants or lines with unfavorable genes derived from donor parents like sterility and sometimes shattering, are often eliminated from the population after phenotypic and genotypic evaluation. Due to artificial selection in favor of lines with desirable alleles and the genetic background from the recurrent parent, the distribution can be skewed toward the recurrent parent, therefore, after the BC3 generation, the power of the statistical analysis to detect QTL decreases. Since sequential backcrossing in AB-QTL removes epistatic interactions, the chance of detecting QTLs with epistatic interactions among alleles from the donor parent decreases, while the ability to detect additive QTLs increases (Tanksley

intergenomic crosses, backcrossing is often necessary (Fukuoka et al. 2010).

1992; Lander and Botstein 1989) being used in earlier analyses.

**3.2. Advanced Backcross (AB) population**

8 Rice - Germplasm, Genetics and Improvement

and Nelson 1996; Grandillo and Tanksley 2003).

BIL populations are used to introgress desirable traits from the wild *Oryza* species donor into rice with the potential of improving the agronomic performance of elite cultivars and develop mapping populations (Fig. 1). After backcrossing, as described in the aforementioned AB population development, the individual lines, BC1, BC2 or BC3 generation, are self-pollinated for about four generations to the BC2F5, as described in the RIL population development. If a specific trait is being selected, the BILs will be screened for that trait and backcrossed as described in the NIL section (Blanco et al. 2003; Fukuoka et al. 2010; Fulton et al. 1997; Bernacchi et al. 1998; Talamè et al. 2004).

The advantages of utilizing BILs are that the method is relatively straightforward and the lines are more homogeneous, having less linkage drag and fewer untargeted segments from the donor parent as compared to RILs. Furthermore, BIL populations can be used to identify major QTLs and single genes, detect QTLs with epistatic or additive effects, as well as, provide an accurate estimation of genotype x environment interactions. It takes more time to develop a BIL population than a RIL population but less time than developing CSSLs and NILs because there are fewer backcrosses to do and less emphasis on targeted segments (Fukuoka et al. 2010; Fulton et al. 1997; Jaquemin et al. 2013). Some disadvantages of this method are the genetic background of the donor parent is higher in the BILs as compared to the CSSLs and NILs, and the lines require more phenotypic evaluation but less genotypic characterization. As a result, mapping in a BIL population is more labor intensive and costly compared to RILs but less costly than NILs and CSSLs. Unfortunately, only limited success has been reported for improving quantitative traits with low heritability and identifying minor QTLs. Also, it is difficult to transfer a relatively large number of genes or QTLs associated with the desirable traits from the wild donor to an elite cultivar using lines selected from a BIL population.

## **3.4. Chromosome Segment Substitution Line (CSSL) library**

A CSSL "library" is a set of near isogenic lines, often ranging from 26 to 80 lines, which cover the entire donor genome when the segments included in each introgression line are in the background of the recurrent parent (Fig. 1; Ali et al. 2010). The concept of CSSL libraries was initially proposed by Eshed and Zamir (1995) as introgression lines and Ghesquière et al. (1997) as contig lines. To develop CSSLs, the initial crossing follows the same scheme as described for AB and BIL populations where the wild, unadapted *Oryza* species is the donor parent and the recurrent parent is usually an elite cultivar. To confirm the entire donor genome is included in the CSSL library, a set of polymorphic markers is often used to assist in selecting lines for each generation, beginning with the BC1F1 generation. To develop a CSSL library usually requires backcrossing to the recurrent parent for three to four additional generations (BC4F1 or BC5F1). The set of polymorphic markers can be used each generation to confirm the targeted segment is present in each line composing the CSSL library as illustrated in Ali et al. (2010). Alternatively, several hundred lines can be backcrossed for 4 to 5 generations and a CSSL library can be selected after genotyping in the BC4 or BC5 generation. Once the desired BC4:5F1 lines are selected, the lines are self-pollinated to achieve homozygosity and the lines homozygous for the individual targeted segment are selected from the BC4:5F2 progeny lines. The BC4:5F3 seed is used to establish the CSSL library composed of a set near isogenic lines covering the entire donor genome (Ali et al. 2010; Fukuoka et al. 2010).

A CSSL library has several advantages compared to BILs or an AB mapping population in that it can be used for fine mapping, to identify both major and minor QTLs, and vali‐ date genetic interactions. Also, due to the recurrent parent background in CSSLs, linkage drag and its negative effects on the QTL studies are significantly reduced or eliminated. This uniform genetic background enables one to make rapid progress in linkage mapping of targeted QTLs. Lastly, individual CSSLs which carry a specific trait can be used for fine mapping and gene pyramiding (Ali et al. 2010; Fukuoka et al. 2010), as illustrated by the identification of the rice stripe necrosis virus resistance introgression from *O. glaberrima* (Gutiérrez et al. 2010).

The rice universal core genetic map is a set of uniformly distributed polymorphic SSR markers that clearly differentiate *O. sativa* cultivars and wild *Oryza* species accessions, especially within the AA genome (Orjuela et al. 2010). If polymorphic SSR (simple sequence repeat) markers for several different CSSL libraries or other mapping populations are selected from the core map, such that the markers are in approximately the same location, comparisons can be made across several different CSSL libraries or mapping populations. More recently, SNP (single nucleotide polymorphism) markers have been used to genotype the putative lines being selected for the CSSL libraries. For this purpose, several different 384-SNP assays have been used to identify the target donor segment and recurrent parent background (Ali et al. 2010; Tung et al. 2010; McCouch et al. 2010) and most recently a single 6,000 SNP assay is being employed (Zhou et al. 2013; SR McCouch, Cornell University, personal communication).

## **3.5. Near Isogenic Lines (NILs)**

**3.4. Chromosome Segment Substitution Line (CSSL) library**

10 Rice - Germplasm, Genetics and Improvement

donor genome (Ali et al. 2010; Fukuoka et al. 2010).

(Gutiérrez et al. 2010).

A CSSL "library" is a set of near isogenic lines, often ranging from 26 to 80 lines, which cover the entire donor genome when the segments included in each introgression line are in the background of the recurrent parent (Fig. 1; Ali et al. 2010). The concept of CSSL libraries was initially proposed by Eshed and Zamir (1995) as introgression lines and Ghesquière et al. (1997) as contig lines. To develop CSSLs, the initial crossing follows the same scheme as described for AB and BIL populations where the wild, unadapted *Oryza* species is the donor parent and the recurrent parent is usually an elite cultivar. To confirm the entire donor genome is included in the CSSL library, a set of polymorphic markers is often used to assist in selecting lines for each generation, beginning with the BC1F1 generation. To develop a CSSL library usually requires backcrossing to the recurrent parent for three to four additional generations (BC4F1 or BC5F1). The set of polymorphic markers can be used each generation to confirm the targeted segment is present in each line composing the CSSL library as illustrated in Ali et al. (2010). Alternatively, several hundred lines can be backcrossed for 4 to 5 generations and a CSSL library can be selected after genotyping in the BC4 or BC5 generation. Once the desired BC4:5F1 lines are selected, the lines are self-pollinated to achieve homozygosity and the lines homozygous for the individual targeted segment are selected from the BC4:5F2 progeny lines. The BC4:5F3 seed is used to establish the CSSL library composed of a set near isogenic lines covering the entire

A CSSL library has several advantages compared to BILs or an AB mapping population in that it can be used for fine mapping, to identify both major and minor QTLs, and vali‐ date genetic interactions. Also, due to the recurrent parent background in CSSLs, linkage drag and its negative effects on the QTL studies are significantly reduced or eliminated. This uniform genetic background enables one to make rapid progress in linkage mapping of targeted QTLs. Lastly, individual CSSLs which carry a specific trait can be used for fine mapping and gene pyramiding (Ali et al. 2010; Fukuoka et al. 2010), as illustrated by the identification of the rice stripe necrosis virus resistance introgression from *O. glaberrima*

The rice universal core genetic map is a set of uniformly distributed polymorphic SSR markers that clearly differentiate *O. sativa* cultivars and wild *Oryza* species accessions, especially within the AA genome (Orjuela et al. 2010). If polymorphic SSR (simple sequence repeat) markers for several different CSSL libraries or other mapping populations are selected from the core map, such that the markers are in approximately the same location, comparisons can be made across several different CSSL libraries or mapping populations. More recently, SNP (single nucleotide polymorphism) markers have been used to genotype the putative lines being selected for the CSSL libraries. For this purpose, several different 384-SNP assays have been used to identify the target donor segment and recurrent parent background (Ali et al. 2010; Tung et al. 2010; McCouch et al. 2010) and most recently a single 6,000 SNP assay is being employed (Zhou et

al. 2013; SR McCouch, Cornell University, personal communication).

The procedure for developing a set of NILs is similar to CSSLs except the number of back‐ crosses is unlimited because the focus is on incorporating a single segment with the trait(s) of interest identified in the *Oryza* species donor into the background of the recurrent parent (Fig. 1). With NILs, the focus is on a particular set of lines for the trait(s) of interest, not covering the entire donor genome as with a CSSL library. As with CSSLs, once the targeted segment is introgressed into the recurrent parent background, the pre-NIL lines are allowed to selfpollinate, so that the NILs will be homozygous for the targeted segment. Molecular markers, such as SSRs and SNPs, are used to select for the targeted segment and determine the number of chromosomal segments from the donor parent remaining in the background (Fukuoka et al. 2010).

NILs are often developed to fine map QTLs identified in primary mapping populations, like RIL or BIL, because the QTLs can be mapped precisely as single Mendelian factors (McCouch et al. 2007). Use of NILs, like CSSLs, increases the power to detect small-effect QTL and can overcome or minimize genetic incompatibility, linkage drag, cytoplasmic sterility and epistatic effects, all of which are common obstacles in wide hybridization efforts because the genetic background is more or less uniform. Although developing NILs, like CSSLs, is labor intensive, time consuming, and expensive, NILs are a valuable tool for exploring the genes underlying QTLs because the epistatic effects are removed or minimized making it easier to measure gene expression (Keurentjes et al. 2007). Finally, those NILs with valuable genes introgressed from the wild *Oryza* species donor, can be used as parental lines in breeding programs.

## **3.6. Multi-parent Advanced Generation Inter-Cross (MAGIC) population**

Recently, some efforts have turned to MAGIC populations (Cavanagh et al. 2008; Kover et al. 2009) which can serve the dual purpose of permanent mapping populations for precise QTL mapping, and for direct or indirect use in variety development, especially when the parents used to develop the population are the source of agronomically useful traits (Bandillo et al. 2013). MAGIC populations are developed by systematically crossing several F1 hybrids involving four to sixteen different parental lines to create a set of double crosses, then system‐ atically crossing the double cross hybrids to create a set of 4-, 8-or 16-way crosses. As the final step, the lines composing the population are advanced four or more generations by single seed descent to obtain a set of advanced intercrossed lines (AILs). Bandillo et al. (2013) reported four different types of MAGIC populations being developed in rice (*O. sativa*) at the Interna‐ tional Rice Research Institute (IRRI) which are described as (1) *Indica* MAGIC composed of 1,831 S8 AILs; (2) MAGIC Plus with 2,214 S6 AILs; (3) *Japonica* MAGIC with approximately 400 S6 AILs; and (4) MAGIC Global with 1,402 AILs in the S5 generation. Currently, a Wild MAGIC population is being developed by a team at IRRI (K. Jena, H. Leung, K. McNally) in collabo‐ ration with J. Hibberd (University of Cambridge, U.K.), and I. Mackay (NIAB, Cambridge, U.K.) using multiple accessions of all eight A-genome species (McNally, personnel commu‐ nication). In most cases, for this population, the initial crosses had *O. sativa* as the female parent, and the goal is to produce 16-way crosses with highly mixed genomes.

## **4. Useful agronomic traits mapped in** *Oryza* **species and transferred into cultivated rice**



**4. Useful agronomic traits mapped in** *Oryza* **species and transferred into**

**Type of mapping population†**

**QTL mapping analysis‡**

**Chromosome location§**

7, 8

6, 8

1, 2, 3, 4, 10

CIM 2, 7 RFLP,

ANOVA 6, 9 SSR

RFLP, SSR

SSR

CIM

**Type of marker¶**

SSR

8, 11 SSR, STS

**Reference**

Ishii et al. (1994)

al. (2011)

Suh et al. (2005)

Sanchez et al. (2003)

Yoon et al. (2006)

Chen et al. (2012)

(2013)

SSR Eizenga et al. (2013)

(accepted)

Thomson et al. (2003)

Septiningsih et al. (2003)

Xiao et al. (1998)

Jin et al. (2009), Xie et al. (2008)

Wickneswari et al. (2012)

Yuan et al. (2009)

**Recurrent parent(s)**

*O. glaberrima* IRGC103544 Milyang 23 BC3F2 SPA 1, 4,

*O. meyeriana* Y73 IR24 RIL CIM 6, 7,

*O. nivara* IRGC100898 Bengal AB-QTL MIM 3, 4,

*O. rufipogon* IRGC105491 Jefferson AB-QTL SPA, IM,

*O. rufipogon* IRGC105491 IR64 AB-QTL SPA, IM,

*O. rufipogon* IRGC105491 Hwaseongbyeo AB-QTL, NIL SPA, IM,

*O. rufipogon* IRGC105491 V20A, V20B BC2 ANOVA 6, 12 RFLP

*O. rufipogon* IRGC105491 MR219 AB-QTL CIM 3 SSR

*O. rufipogon* W1944 Hwayeongbyeo IL SPA, IM 1 SPA, IM

*O. australiensis* IRGC100882 IR31917-45-3-2 IL 10 RFLP

heading *O. glaberrima* IR64 BC2F3 SPA 2, 10 SSR, STS Bimpong et

*glumaepatula* Taichung 65 BC4F2 <sup>7</sup> RFLP

*O. grandiglumis* IRGC101154 Hwaseongbyeo AB-QTL SPA 6 SSR

*O. nivara* IRGC100898 Bengal AB-QTL MIM 3, 6 SSR Eizenga et al.

*O. nivara* IRGC100195 M-202 AB-QTL MIM 3, 8 SSR Eizenga et al.

**cultivated rice**

**Trait Donor species**

12 Rice - Germplasm, Genetics and Improvement

**Vegetative Growth Stages**

*O.*

Days to flowering

Days to

**Donor accession**



14 Rice - Germplasm, Genetics and Improvement

*O. minuta*

*O.*

*O.*

**Panicle Development**

density *O. rufipogon*

*O.*

*O.*

*glumaepatula*

*glumaepatula*

Panicle

Panicle

Panicle

*glumaepatula*

*glumaepatula*

*O. minuta*

Plant type (Culm habit or tiller angle)

Flag leaf length

Third node width

Tiller

**Donor accession**

IR71033-121

IR71033-121

IRGC

**Recurrent parent(s)**

*O. rufipogon* IRGC105491 MR219 AB-QTL CIM 1, 3, 9 SSR

*O. rufipogon* IC22015 IR 58025A AB-QTL IM, CIM 1 SSR

*O. rufipogon* W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR

number *O. glaberrima* IR64 BC2F3 SPA 2, 7 SSR, STS Bimpong et

*O. rufipogon* IRGC105491 MR219 AB-QTL CIM 2, 5, 8 SSR

exsertion *O. rufipogon* W1944 Hwayeongbyeo RIL SPA, CIM <sup>1</sup> SSR

number *O. glaberrima* IRGC103544 Milyang 23 BC3F2 SPA <sup>4</sup> SSR

RS-16 BG90-2 BC2F2 SPA, IM 4, 5, 7,

**Type of mapping population†**

*O. nivara* IRGC100898 Bengal AB-QTL MIM <sup>9</sup> SSR Eizenga et al.

*O. nivara* IRGC104705 Bengal AB-QTL MIM <sup>9</sup> SSR Eizenga et al.

*O. nivara* IRGC100195 M-202 AB-QTL MIM <sup>9</sup> SSR Eizenga et al.


RS-16 Cica8 BC2F2-9 CIM 7, 11 SSR Rangel et al.


<sup>105491</sup> Hwaseongbyeo NIL ANOVA <sup>9</sup> SSR

RS-16 BG90-2 BC2F2 SPA, IM 5, 8, 11 SSR, STS

RS-16 Cica8 BC2F2-9 CIM 7, 11 SSR Rangel et al.

**QTL mapping analysis‡**

**Chromosome location§** **Type of marker¶**

**Reference**

Wickneswari et al. (2012)

Marri et al. (2005)

(2013)

(2013)

(accepted)

Lee et al. (2005)

al. (2011)

(2013)

Brondani et al. (2002)

Rahman et al. (2007)

Wickneswari et al. (2012)

Lee et al. (2005)

Xie et al. (2008)

Suh et al. (2005)

Brondani et al. (2002)

(2013)

8, 11 SSR, STS

Rahman et al. (2007)



16 Rice - Germplasm, Genetics and Improvement

**Reproductive Growth Stages**

*glumaepatula*

*longistaminata*

*O.*

*O.*

Primary branches per panicle

Secondary branches per panicle

Pollen (male) sterility

Hybrid breakdow n locus

Panicle

Productive panicle number

Spikelets per plant

**Donor accession** **Recurrent parent(s)**

*O. rufipogon* W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR

*O. rufipogon* W1944 Hwayeongbyeo IL SPA, IM, 1, 2 SPA, IM

*O. minuta* IRGC101144 Hwaseongbyeo NIL 7 SSR

*O. rufipogon* W1944 Hwayeongbyeo RIL SPA, CIM 1 SSR

*O. rufipogon* IRGC105491 Hwaseongbyeo AB-QTL SPA, IM 6, 8 SSR

IRGC105688 Taichung 65 BC4F2 2, 7 RFLP

RD23 BC7F2 CIM 6 SSR

*O. rufipogon* W1944 Hwayeongbyeo RIL, IL SPA, IM,

*O. nivara* IRGC105444 Taichung 65 IL-BC4F1 4, 8, 12

*O. nivara* IRGC105444 Koshihikari BC4F3 <sup>2</sup> SSR,

fertility *O. glaberrima* IR64 BC2F3 SPA 2, 10 SSR, STS Bimpong et

*O. minuta* IRGC101144 Hwaseongbyeo NIL 7 SSR

*O. rufipogon* IC 22015 IR 58025A AB-QTL IM, CIM 2, 5 SSR

*O. rufipogon* G52-9 Yuexiangzhan AB-QTL CIM 2, 3, 7 SSR Jing et al.

*O. rufipogon* G52-9 Yuexiangzhan AB-QTL CIM <sup>2</sup> SSR Jing et al.

**Type of mapping population†**

**QTL mapping analysis‡**

**Chromosome location§**

CIM 1, 2, 9 SSR

**Type of marker¶**

**Reference**

Lee et al. (2005)

Yuan et al. (2009)

Balkunde et al. (2013)

Lee et al. (2005)

Jin et al. (2009)

Lee et al. (2005), Yuan et al. (2009)

Sobrizal et al. (2000a, 2000b)

Win et al. (2009; 2011)

Chen et al. (2009)

Miura et al. (2008)

al. (2011)

(2010)

(2010)

Marri et al. (2005)

Balkunde et al. (2013)

RFLP, SSR, SNP

SNP



18 Rice - Germplasm, Genetics and Improvement

Grains per panicle

Percent

Awn length

*O. minuta*

**Donor accession**

Shattering *O. rufipogon* IRGC105491 Jefferson AB-QTL SPA, IM,

*O. rufipogon* W1944 Hwayeongbyeo IL, RIL SPA, IM,

*O. rufipogon* IRGC105491 Jefferson AB-QTL SPA, IM,

*O. rufipogon* IRGC105491 Hwaseongbyeo AB-QTL, NIL SPA, IM,

*O. rufipogon* YJCW 93-11 AB-QTL SPA, IM,

*O. rufipogon* W1944 Hwayeongbyeo IL, RIL SPA, IM,

IR71033-121

seed set *O. meyeriana* Y73 IR24 RIL CIM <sup>8</sup> SSR, STS

*O. rufipogon* IRGC105491 MR219 AB-QTL CIM 3 SSR

*O. rufipogon* IRGC105491 V20A, V20B BC2 ANOVA 2, 4 RFLP

*O. minuta* IRGC101144 Hwayeongbyeo AB-QTL SPA, CIM 6, 9 SSR

**Recurrent parent(s)**

*O. rufipogon* W1944 Hwayeongbyeo RIL SPA, CIM 1, 3, 6 SSR

*O. minuta* IRGC101144 Hwaseongbyeo NIL 7 SSR

*O. rufipogon* IRGC105491 V20A, V20B BC2 ANOVA 1, 8, 12 RFLP

*O. rufipogon* IC22015 IR 58025A AB-QTL IM, CIM 2, 5 SSR

*O. rufipogon* G52-9 Yuexiangzhan AB-QTL CIM 4, 10, 11 SSR Jing et al.

**Type of mapping population†**

**QTL mapping analysis‡**

CIM

CIM

CIM


**Chromosome location§**

CIM 1, 4, 5 SSR

2, 3, 8, 9

ANOVA 8, 9 SSR

CIM 1, 3 SSR

10 SSR

RFLP, SSR

**Type of marker¶**

<sup>8</sup> RFLP, SSR

**Reference**

Thomson et al. (2003)

Lee et al. (2005)

Yuan et al. (2009), Lee et al. (2005)

Balkunde et al. (2013)

Thomson et al. (2003)

Xiao et al. (1998)

Jin et al. (2009), Xie et al. (2008)

Marri et al. (2005)

(2010)

Fu et al. (2010)

Chen et al. (2012)

Wickneswari et al. (2012)

Xiao et al. (1998)

Lee et al. (2005)

Linh et al. (2004)

Rahman et al. (2007)



20 Rice - Germplasm, Genetics and Improvement

color *O. rufipogon* IRGC105491

*O. minuta*

Grain thickness

Pericarp

**Yield Traits**

Grain weight

**Donor accession** **Recurrent parent(s)**

**Type of mapping population†**

*O. nivara* IRGC100195 M-202 AB-QTL MIM 1, 5 SSR Eizenga et al.

*O. nivara* IRGC81848 Swarna BC2F2 IM, CIM <sup>12</sup> SSR Swamy et al.

*O. grandiglumis* IRGC101154 Hwaseongbyeo AB-QTL SPA 6, 11 SSR

*O. rufipogon* W1944 Hwayeongbyeo RIL SPA, CIM 1, 12 SSR

*O. rufipogon* W1944 Hwayeongbyeo IL SPA, IM, 1, 7 SPA, IM

*O. glaberrima* IRGC103544 Milyang 23 BC3F2 SPA 2, 3 SSR

*O. meyeriana* Y73 IR24 RIL CIM 3, 9, 11 SSR, STS


ANOVA

*O. nivara* IRGC100195 M-202 AB-QTL MIM <sup>10</sup> SSR Eizenga et al.

*O. grandiglumis* IRGC101154 Hwaseongbyeo AB-QTL SPA 3, 6,

*O. rufipogon* IRGC105491 Hwaseongbyeo NIL SPA, IM,

*O. rufipogon* IRGC105491 Jefferson AB-QTL SPA, IM,

*O. rufipogon* IRGC105491 IR64 AB-QTL SPA, IM,

*O. rufipogon* IRGC105491 V20A, V20B BC2 ANOVA 4, 8, 9,

*O. rufipogon* IRGC105491 MR219 AB-QTL CIM 6 SSR

IR71033-121

Ce64, Caiapó, Hwacheong, Jefferson, IR64

*O. rufipogon* IRGC105491 Hwaseongbyeo NIL SPA, IM,

**QTL mapping analysis‡**

ANOVA

AB-QTL IM, CIM 7 SSR

8, 11

SSR

8 SSR

SSR

SSR

RFLP

CIM 1, 5 RFLP,

CIM 1, 3 RFLP,

11, 12

**Chromosome location§** **Type of marker¶**

8 SSR

**Reference**

(accepted)

Yoon et al. (2006)

Xie et al. (2006)

Lee et al. (2005)

McCouch et al. (2007)

Yuan et al. (2009)

Suh et al. (2005)

Yoon et al. (2006)

Chen et al. (2012)

Rahman et al. (2007)

(accepted)

Thomson et al. (2003)

Septiningsih et al. (2003)

Xiao et al. (1998)

Wickneswari et al. (2012)

Xie et al. (2006)

(2012)


† Abbreviations for mapping population types are: AB-QTL, advanced backcross-quantitative trait locus; IL, inbred line; NIL, near isogenic line; RIL, recombinant inbred line.

‡ Abbreviations for QTL analysis method are: ANOVA, analysis of variance; CIM, composite interval mapping; IM, interval mapping; MIM, multiple interval mapping; SPA, single point analysis.

§ Only the chromosomes where the QTL increase is attributed to the wild parent are listed.

Abbreviations for types of markers are: RAPD, random amplified polymorphic DNA; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism; SSR, simple sequence repeat; STS, sequence-tagged site.

**Table 2.** Summary of QTLs for improved yield and yield components attributed to the non-*O. sativa* parent.

#### **4.1. Yield enhancing QTL from exotic** *Oryza* **genomes**

Several plant traits directly or indirectly affect rice grain yield including days to heading and maturity; plant height; panicle length; number of panicles per plant, spikelets per panicle and grains per panicle; seed set; grain weight; grain size and shape; and shattering (Table 2). The most important yield components in rice are the number of panicles per plant, number of grains per panicle, and grain weight (Chen et al. 2012; Lee et al. 2004; Septiningsih et al. 2003; Thomson et al. 2003). Yield improvement can be achieved as a result of the vast allelic diversity for these traits found in interspecific populations, especially number of grains per panicle which has proven to have the greatest relevance for rice breeding programs (Li et al. 1998; Liu et al. 2008; Tian et al. 2006).

**Trait Donor species**

22 Rice - Germplasm, Genetics and Improvement

Harvest

NIL, near isogenic line; RIL, recombinant inbred line.

mapping; MIM, multiple interval mapping; SPA, single point analysis.

**4.1. Yield enhancing QTL from exotic** *Oryza* **genomes**

**Donor accession** **Recurrent parent(s)**

*O. grandiglumis* IRGC101154 Hwaseongbyeo AB-QTL SPA 2 SSR

*O. minuta* IRGC101144 Hwaseongbyeo NIL 7 SSR

*O. meyeriana* Y73 IR24 RIL CIM 6 SSR, STS

*O. rufipogon* IC22015 IR58025A AB-QTL IM, CIM 1, 2, 8 SSR

*O. rufipogon* IRGC105491 Jefferson AB-QTL SPA, IM,

*O. rufipogon* IRGC105491 IR64 AB-QTL SPA, IM,

*O. rufipogon* YJCW 93-11 AB-QTL SPA, IM,

§ Only the chromosomes where the QTL increase is attributed to the wild parent are listed.

*O. rufipogon* IRGC105491 V20A, V20B BC2 ANOVA 1, 2,

*O. rufipogon* IRGC105491 Hwaseongbyeo AB-QTL SPA, IM 8 SSR

index *O. glaberrima* IR64 BC2F3 SPA 2, 7 SSR, STS Bimpong et

*O. rufipogon* IC22015 IR58025A AB-QTL IM, CIM 2 SSR

† Abbreviations for mapping population types are: AB-QTL, advanced backcross-quantitative trait locus; IL, inbred line;

‡ Abbreviations for QTL analysis method are: ANOVA, analysis of variance; CIM, composite interval mapping; IM, interval

Abbreviations for types of markers are: RAPD, random amplified polymorphic DNA; RFLP, restriction fragment length polymorphism; SNP, single nucleotide polymorphism; SSR, simple sequence repeat; STS, sequence-tagged site.

Several plant traits directly or indirectly affect rice grain yield including days to heading and maturity; plant height; panicle length; number of panicles per plant, spikelets per panicle and

**Table 2.** Summary of QTLs for improved yield and yield components attributed to the non-*O. sativa* parent.

*O. rufipogon* MR219 AB-QTL <sup>4</sup> SSR Bhuiyan et al.

**Type of mapping population†**

**QTL mapping analysis‡**

CIM

CIM

CIM

2, 3, 6, 9

8, 12

RFLP, SSR

RFLP

<sup>1</sup> RFLP, SSR

1 SSR

**Chromosome location§** **Type of marker¶**

**Reference**

Yoon et al. (2006)

Balkunde et al. (2013)

Chen et al. (2012)

(2011)

Marri et al. (2005)

Thomson et al. (2003)

Septiningsih et al. (2003)

Xiao et al. (1998)

Jin et al. (2009)

Fu et al. (2010)

al. (2011)

Marri et al. (2005)

Modern rice varieties are developed after an extensive selection process to improve a few targeted traits related to cultivation and end-use quality but primarily those associated with yield components, such as resistance to shattering, compact growth habit and improved seed germination (Tanksley and McCouch 1997). This prolonged breeding procedure can lead to a reduction in the genetic variability found in modern cultivated rice (Rangel et al. 2008), thus identifying genetic sources for agronomically important traits from wild *Oryza* species and introgressing them into cultivated rice is desirable and necessary. Although wild *Oryza* species are inferior in grain yield, especially when compared to cultivated rice, transgressive segre‐ gation resulting from a cross between cultivated rice and a wild *Oryza* species, especially the ancestral species, *O. rufipogon* and *O. nivara*, revealed the presence of favorable alleles from the wild parent that can increase yield in the genetic background of cultivated rice (Brar and Singh 2011). Xiao et al. (1996) developed a genetic population by initially crossing the cyto‐ plasmic male sterile parent, V20A, with *O. rufipogon* (IRGC105491), the donor, as male parent. Subsequently, F1 plants and later BC1 plants selected for vigor, plant type, maturity and fertility, were backcrossed to V20B (maintainer line of V20A). A selected subset of 300 BC2F1 lines was crossed with Ce64 to create the genotype of the Chinese hybrid rice variety V/64, developed from V20A x Ce64. Xiao et al. (1996) reported that 15% of the testcross families outperformed the recurrent parent, 14% of the yield improvement was related to grains per plant and 56% was related to 1000-grain weight. Subsequently, Xiao et al. (1998) identified 35 QTLs associated with yield improvement, 19 of which, including *yld1.1* and *yld 2.1*, were located on chromosomes 1 and 2, respectively. They also observed no undesirable alleles causing negative effects on yield components, thus the presence of alleles in wild *O. rufipo‐ gon* can improve rice yields.

Other AB-QTL populations developed using the same *O. rufipogon* (IRGC105491) donor parent with recurrent parents representing various *O. sativa* subpopulations including *indica* with IR64 (Septiningsih et al. (2003), upland *tropical japonica* with Caiapó (Moncada et al. 2001), irrigated *tropical japonica* with Jefferson (Thomas et al. 2003), and *temperate japonica* with Hwaseongbyeo (Xie et al. 2006 & 2008), revealed enhanced yield and yield components attributed to the *O. rufipogon* donor parent. Selected progeny lines were advanced to BILs or NILs and this yield enhancement was confirmed in field studies with the recurrent parents IR64 (Cheema et al. 2008a), Jefferson (Imai et al. 2013), and Hwaseongbyeo (Jin et al. 2011). Also, an epistatic interaction was noted between the QTLs for grain weight on chromosomes 8 and 9 in the Hwaseongbyeo background (Jin et al. 2011).

Tian et al. (2006) selected an introgression line, SIL040, from the BC4F4 lines of *O. rufipogon* (Dongxiang) x Guichao 2, an *indica* rice cultivar. High resolution QTL mapping and analysis in the SIL040 x Guichao 2 F2 progeny for yield components revealed *gpa7* (grains per panicle) on the short arm of chromosome 7. This QTL contained five putative genes associated with five panicle traits: panicle length, number of primary and secondary branches per panicle, and number of grains on primary and secondary branches, in the same region. These findings supported the importance of *gpa7* in rice domestication and yield enhancement.

Two AB-QTL populations were developed using the *O. rufipogon* identified as YJCWR (collected from Yuanjiang County, Yunnan Province, P.R. China) as a donor, and TeQing, a popular *indica* cultivar (Tan et al. 2008) and 93-11, a two-line elite *indica* restorer (Fu et al. 2010), as the recurrent parents. Both studies revealed QTL attributed to *O. rufipogon* having a beneficial effect on yield related traits. A CSSL library of 120 lines selected from the TeQing AB-QTL population and evaluated at two locations confirmed a yield advantage associated with *O. rufipogon* alleles for all traits evaluated except 1000-grain weight (Tan et al. 2007).

Similarly, a CSSL library composed of 133 lines selected from an AB-QTL population with an *O. rufipogon* collected in Hainan Province, P.R. China, as the donor, and TeQing as the recurrent parent was used to identify *spd6*, a gene on chromosome 6 responsible for the small panicle and dwarf traits (Shan et al. 2009). This gene, *spd6*, had pleiotropic effects on panicle number per plant, grain size, grain weight, grain number per panicle and plant height, suggesting it may have played a role in the domestication of rice.

To identify the genetic potential of *O. glumaepatula* (AgpAgp genome) as a genetic resource for cultivar improvement, Brondani et al. (2002) developed an AB-QTL population using RS-16, an accession of the Amazonian native rice wild species, *O. glumaepatula*, as the donor parent, and BG90-2, a Latin American *indica* rice, as the recurrent parent. QTL analysis of 96 BC2F2 progenies for eleven agronomic traits with *O. glumaepatula* alleles revealed none were posi‐ tively associated with yield traits. However, several BC2F2 lines indicated the presence of introgressed alleles related to yield improvement which were not detected in the QTL analysis. Later, Rangel et al. (2008) evaluated the agronomic performance of 35 BC2F8 ILs selected from this population over two years and in multiple locations by measuring grain yield and grain quality traits. The six highest yielding ILs had the highest percentage of recurrent parent genomic background. One of the six ILs, CNAi 9930, was recommended for cultivar release due to its good cooking and milling qualities, and high yield stability. Also, all six ILs contained novel alleles, thus were incorporated as parents in the breeding program for developing high yielding cultivars.

BILs in the BC5F5:6 were derived from *O. grandiglumis* (IRGC101154; CCDD) as the donor parent, and Hwaseongbyeo (Yoon et al. 2006). One BIL, HG101, was backcrossed, and evaluation of the targeted IL, CR1242, revealed the beneficial effect of the *O. grandiglumis* allele on yield and yield components. Further analysis of the QTL, *tgw11*, on chromosome 11 associated with 1000-grain weight showed that a single gene in this QTL controls three grain traits: grain weight, grain width and grain thickness (Oh et al. 2011).

To evaluate the effect of *O. minuta* (IRGC101141) with a BBCC genome on yield components, a single plant, WH79006, was selected from the Hwaseongbyeo x *O. minuta* BC5F3 families and selfed (Jin et al. 2004). QTL analysis of Hwaseongbyeo x WH79006 F2:3 progeny identified four QTLs, *sw7* (seed width), *sl11* (seed length), *tsw7* (1000-seed weight) and *lw10* (seed length to width ratio). Similarly, WH29001 was selected from the BC5F3 families, selfed and by QTL analysis the co-located QTLs for days to heading, *dth6* and *dth8*, and awn length, *awn6* and *awn8*, were identified on chromosomes 6 and 8, respectively (Linh et al. 2006). Subsequently, a new QTL, *spp7*, for spikelets per panicle, was detected on the long arm of chromosome 7 with the allele attributed to the *O. minuta* parent and validated in the F3 and F4 progeny (Linh et al. 2008). Similarly the introgression line IR71033-121-15 was selected from the BC3 progeny of the same *O. minuta* (IRGC101141) x *indica* cultivar, IR31917. To introgress the *O. minuta* genome into *japonica*, IR71033-121-15 was crossed with Junambyeo, a Korean *japonica* cultivar, and QTL analysis of F2 progeny identified 14 QTLs associated with agronomic traits reported in previous studies and 22 novel QTLs related to yield components (Rahman et al. 2007).

in the SIL040 x Guichao 2 F2 progeny for yield components revealed *gpa7* (grains per panicle) on the short arm of chromosome 7. This QTL contained five putative genes associated with five panicle traits: panicle length, number of primary and secondary branches per panicle, and number of grains on primary and secondary branches, in the same region. These findings

Two AB-QTL populations were developed using the *O. rufipogon* identified as YJCWR (collected from Yuanjiang County, Yunnan Province, P.R. China) as a donor, and TeQing, a popular *indica* cultivar (Tan et al. 2008) and 93-11, a two-line elite *indica* restorer (Fu et al. 2010), as the recurrent parents. Both studies revealed QTL attributed to *O. rufipogon* having a beneficial effect on yield related traits. A CSSL library of 120 lines selected from the TeQing AB-QTL population and evaluated at two locations confirmed a yield advantage associated with *O. rufipogon* alleles for all traits evaluated except 1000-grain weight (Tan et al. 2007).

Similarly, a CSSL library composed of 133 lines selected from an AB-QTL population with an *O. rufipogon* collected in Hainan Province, P.R. China, as the donor, and TeQing as the recurrent parent was used to identify *spd6*, a gene on chromosome 6 responsible for the small panicle and dwarf traits (Shan et al. 2009). This gene, *spd6*, had pleiotropic effects on panicle number per plant, grain size, grain weight, grain number per panicle and plant height, suggesting it

To identify the genetic potential of *O. glumaepatula* (AgpAgp genome) as a genetic resource for cultivar improvement, Brondani et al. (2002) developed an AB-QTL population using RS-16, an accession of the Amazonian native rice wild species, *O. glumaepatula*, as the donor parent, and BG90-2, a Latin American *indica* rice, as the recurrent parent. QTL analysis of 96 BC2F2 progenies for eleven agronomic traits with *O. glumaepatula* alleles revealed none were posi‐ tively associated with yield traits. However, several BC2F2 lines indicated the presence of introgressed alleles related to yield improvement which were not detected in the QTL analysis. Later, Rangel et al. (2008) evaluated the agronomic performance of 35 BC2F8 ILs selected from this population over two years and in multiple locations by measuring grain yield and grain quality traits. The six highest yielding ILs had the highest percentage of recurrent parent genomic background. One of the six ILs, CNAi 9930, was recommended for cultivar release due to its good cooking and milling qualities, and high yield stability. Also, all six ILs contained novel alleles, thus were incorporated as parents in the breeding program for developing high

BILs in the BC5F5:6 were derived from *O. grandiglumis* (IRGC101154; CCDD) as the donor parent, and Hwaseongbyeo (Yoon et al. 2006). One BIL, HG101, was backcrossed, and evaluation of the targeted IL, CR1242, revealed the beneficial effect of the *O. grandiglumis* allele on yield and yield components. Further analysis of the QTL, *tgw11*, on chromosome 11 associated with 1000-grain weight showed that a single gene in this QTL controls three grain

To evaluate the effect of *O. minuta* (IRGC101141) with a BBCC genome on yield components, a single plant, WH79006, was selected from the Hwaseongbyeo x *O. minuta* BC5F3 families and selfed (Jin et al. 2004). QTL analysis of Hwaseongbyeo x WH79006 F2:3 progeny identified four

traits: grain weight, grain width and grain thickness (Oh et al. 2011).

supported the importance of *gpa7* in rice domestication and yield enhancement.

may have played a role in the domestication of rice.

24 Rice - Germplasm, Genetics and Improvement

yielding cultivars.

Awns are an important trait in wild rice species because it protects the seeds from birds and other animals. By contrast, the majority of modern rice cultivars have short awns so that it is easier to harvest the seed. This trait is reported to be controlled by several genes located in different chromosomes, including *An-1* on chromosome 3, *An-2* and *an-5(t)* on chromosome 4, and *An-3* on chromosome 5 (Hu et al. 2011; Nagao and Takahashi 1963; Takamure and Kinoshita 1991). *O. meridionalis* has long awns, ranging in length from 7.8-10.3 cm (Vaughan 1994) and two genes, *An7* and *An8*, associated with the trait, were identified on chromosomes 5 and 4, respectively (Kurakazu et al. 2001). Analysis of *O. meridionalis* x *O. sativa* BC4F2:6 and BC4F2:8 revealed the presence of two dominant genes at different locations on chromosome 1, *An9* and *An10*, and a new allele, *An6-mer* on chromosome 6 (Matsushita et al. 2003a). Another study of an *O. sativa* x *O. glumaepatula* population also identified new alleles, *An7* and *An8* (Matsushita et al. 2003b), thus confirming awn length is controlled by several genes.

A doubled haploid (DH) population was developed from Caiapó (*tropical japonica*, recurrent parent) x *O. glaberrima* (donor parent, IRGC103544, MG12) BC3F1 progeny (Aluko et al. 2004). This population was evaluated for agronomic traits including yield and yield components in Colombia and Louisiana, USA (Gutiérrez et al. 2010). Strong segregation distortion was found on chromosomes 3 and 6 indicating the presence of interspecific sterility genes. Evaluation of the phenotypic data revealed transgressive segregation for several traits. A set of 34 CSSLs was selected from Koshihikari, an elite *temperate japonica* rice cultivar (recurrent parent) x *O. glaberrima* (donor parent, IRGC104038) BC5F1 progeny, advanced to the F7 generation, and genotyped with 142 SNP markers (Shim et al. 2010). QTL analysis of phenotypic data from field grown plants revealed 105 putative QTL of which 84 were positive with 64 being related to grain yield components, suggesting the possible use of favorable alleles in *O. glaberrima* for improvement of cultivated rice.

These studies give several examples of QTL or genes for yield and yield components being attributed to the wild donor parent not only the ancestral A-genome species, *O. rufipogon* or *O. nivara*, but also in the more distant tetraploid *O. minuta* with a BBCC genome. These observations confirm that not only single genes and alleles are affecting these traits but there are epistatic interactions and epigenetic interactions, as well as environmental factors affecting many of these traits, resulting in the phenomenon often described as transgressive variation. Currently, six CSSL libraries are under development with three different *O. rufipogon/O.* *nivara* donor accessions, representing the *Indica*, *Japonica* and independent groups of *O. rufipogon* (Huang et al. 2012), and two recurrent parents, IR64, an *indica* representing the *Indica* subspecies, and Cybonnet, a U.S. *tropical japonica*, representing the *Japonica* subspecies to further explore these interactions resulting in transgressive variation (Tung et al. 2010; SR McCouch, Cornell University and GC Eizenga, personal communication).

## **4.2. Genes for sterility**

Reproductive barriers, such as crossability, hybrid seed inviability, hybrid sterility and hybrid breakdown, have significantly limited the success of interspecific hybridization between *O. sativa* and non-A genome *Oryza* species. Several studies reported the production of F1 seeds by crossing male sterile lines and *Oryza* species (Huang et al. 2001; Luo et al. 2000). The crossability rate between *O. sativa* and other *Oryza* species vary; however, the rate of crossa‐ bility between A-genome and non-A genome diploid *Oryza* species is higher than with tetraploid *Oryza* species, none of which has an A-genome (Jena and Khush 1989 & 1990; Yasui and Iwata 1991).

The phenomenon of transmission ratio distortion (TRD) where one allele is transmitted more frequently than the opposite allele in interspecific and intraspecific hybrids has been discov‐ ered in a broad range of organisms and is often a reproductive barrier (Koide et al. 2012). Recently, Koide et al. (2012) identified a unique sex-independent TRD (*si*TRD) where one allele is preferentially transmitted through both the male and female parent derived from *O. rufipogon* (W593). This research showed the S6 allele on chromosome 6 is responsible for the *si*TRD allele and influenced by other unlinked modifiers. The locus, *sa1*, conferring pollen sterility was found in *O. glaberrima* (W025) x T65*wx* progeny (Sano 1990) where T65*wx* is an NIL derived from Taichung 65 (*japonica* rice) x Peiku (*indica* rice) with a Taichung 65 back‐ ground and the Peiku *waxy* gene on chromosome 6. Other studies identified several pollen sterility loci, *S-1, S3, S18, S19, S20, S21, s25, s27, S29, S29(t)* and *s36*, in populations resulting from interspecific hybridizations between various *O. sativa* cultivars and the *Oryza* species, *O. glumaepatula*, *O. glaberrima* and *O. nivara* (Doi et al. 1998 & 1999; Hu et al. 2006, Sano 1983 & 1986; Taguchi et al. 1999; Win et al. 2009). To overcome such barriers, several methods have been suggested including anther culture, backcrossing, marker-assisted selection (MAS) and asymmetric somatic hybridization, (Fu et al. 2008; Sarla et al. 2005). Also, Deng et al. (2010) demonstrated the fertility in *O. glaberrima* x *O. sativa* crosses could be improved by using an *O. sativa* bridging parent. The bridging parent had the *O. glaberrima* sterility gene, *S1-g* on chromosome 6, introgressed into the particular *O. sativa* cultivar background.

## **4.3. Grain quality traits**

Acceptable rice grain quality is a major goal of rice breeding programs worldwide because it determines the acceptability of cooked rice to the consumer. Grain quality is a combination of several components including milling efficiency, physical appearance, cooking and eating characteristics, and nutritional quality (Aluko et al. 2004; Li et al. 2004). A few interspecific populations were evaluated for grain quality traits (Table 3). These studies showed the *Oryza* parent affects the apparent amylose content, alkali spreading value, protein content, rice bran percentage, milled rice percentage and seed size. What is desirable for these traits is determined for the most part, by consumer preference and marketing classes. When selecting for these traits, often the grain quality of the recurrent parent is preferred.

*nivara* donor accessions, representing the *Indica*, *Japonica* and independent groups of *O. rufipogon* (Huang et al. 2012), and two recurrent parents, IR64, an *indica* representing the *Indica* subspecies, and Cybonnet, a U.S. *tropical japonica*, representing the *Japonica* subspecies to further explore these interactions resulting in transgressive variation (Tung et al. 2010; SR

Reproductive barriers, such as crossability, hybrid seed inviability, hybrid sterility and hybrid breakdown, have significantly limited the success of interspecific hybridization between *O. sativa* and non-A genome *Oryza* species. Several studies reported the production of F1 seeds by crossing male sterile lines and *Oryza* species (Huang et al. 2001; Luo et al. 2000). The crossability rate between *O. sativa* and other *Oryza* species vary; however, the rate of crossa‐ bility between A-genome and non-A genome diploid *Oryza* species is higher than with tetraploid *Oryza* species, none of which has an A-genome (Jena and Khush 1989 & 1990; Yasui

The phenomenon of transmission ratio distortion (TRD) where one allele is transmitted more frequently than the opposite allele in interspecific and intraspecific hybrids has been discov‐ ered in a broad range of organisms and is often a reproductive barrier (Koide et al. 2012). Recently, Koide et al. (2012) identified a unique sex-independent TRD (*si*TRD) where one allele is preferentially transmitted through both the male and female parent derived from *O. rufipogon* (W593). This research showed the S6 allele on chromosome 6 is responsible for the *si*TRD allele and influenced by other unlinked modifiers. The locus, *sa1*, conferring pollen sterility was found in *O. glaberrima* (W025) x T65*wx* progeny (Sano 1990) where T65*wx* is an NIL derived from Taichung 65 (*japonica* rice) x Peiku (*indica* rice) with a Taichung 65 back‐ ground and the Peiku *waxy* gene on chromosome 6. Other studies identified several pollen sterility loci, *S-1, S3, S18, S19, S20, S21, s25, s27, S29, S29(t)* and *s36*, in populations resulting from interspecific hybridizations between various *O. sativa* cultivars and the *Oryza* species, *O. glumaepatula*, *O. glaberrima* and *O. nivara* (Doi et al. 1998 & 1999; Hu et al. 2006, Sano 1983 & 1986; Taguchi et al. 1999; Win et al. 2009). To overcome such barriers, several methods have been suggested including anther culture, backcrossing, marker-assisted selection (MAS) and asymmetric somatic hybridization, (Fu et al. 2008; Sarla et al. 2005). Also, Deng et al. (2010) demonstrated the fertility in *O. glaberrima* x *O. sativa* crosses could be improved by using an *O. sativa* bridging parent. The bridging parent had the *O. glaberrima* sterility gene, *S1-g* on

chromosome 6, introgressed into the particular *O. sativa* cultivar background.

Acceptable rice grain quality is a major goal of rice breeding programs worldwide because it determines the acceptability of cooked rice to the consumer. Grain quality is a combination of several components including milling efficiency, physical appearance, cooking and eating characteristics, and nutritional quality (Aluko et al. 2004; Li et al. 2004). A few interspecific populations were evaluated for grain quality traits (Table 3). These studies showed the *Oryza* parent affects the apparent amylose content, alkali spreading value, protein content, rice bran

McCouch, Cornell University and GC Eizenga, personal communication).

**4.2. Genes for sterility**

26 Rice - Germplasm, Genetics and Improvement

and Iwata 1991).

**4.3. Grain quality traits**

Most interesting was the BC3F1 progeny of the Caiapó x *O. glaberrima* (IRGC103544, MG12) doubled haploid population (Aluko et al. 2004). For this population, the QTL analysis revealed 27 QTLs associated with rice quality of which seven QTLs including percent rice bran, percent milled rice, alkali spreading value (inversely related to gelatinization temperature), percent protein and grain dimensions (length to width ratio), were traced to alleles originating from the *O. glaberrima* parent.




28 Rice - Germplasm, Genetics and Improvement

Percent

Percent

Bacterial blight

**Biotic Stress-Diseases**

*O. australiensis*

O. brachyantha

longistaminata O. officinalis

*longistaminata*

*longistaminata*

*O. latifolia* IRGC100914

Blast disease *O. australiensis* IRGC100882 Lijiangxintuan

O.

*O.*

*O.*

**Donor accession** **Recurrent parent(s)**

IR31917-45-3- 2

IR31917-45-3- 2

*O. meyeriana* Y73 IR24 RIL CIM

*O. minuta* 78-1-5 IR24 F2- BC1 6

*O. nivara* IRGC81825 PR114 RIL, BIL, IL SMA-IM 4 SSR, STS


*O. minuta* IRGC101141 IR31917 F2 6 RAPD

*O. nivara* IRGC100898 Bengal AB-QTL MIM <sup>8</sup> SSR Eizenga et al.

*O. nivara* IRGC104705 Bengal AB-QTL MIM 8, 12 SSR Eizenga et al.

WLO2 BS125 NIL 11

rice bran *O. glaberrima* IRGC103544 Caiapó BC3F1 IM, CIM 4, 7 SSR

milled rice *O. glaberrima* IRGC103544 Caiapó BC3F1 IM, CIM <sup>5</sup> SSR

**Mapping population†**

*O. nivara* IRGC81848 Swarna BC2F2 IM, CIM <sup>1</sup> SSR Swamy et al.

AIL

**QTL mapping analysis‡**

MAAL 12

AIL ANOVA

IR24 11

**Chromosome location§**

> 5, 6, 8, 11

12, others

1, 3, 5, 10, 11

6

SSR

SSR, SNP, STS, InDel

RFLP, RAPD

SSR, STS

RAPD, AFLP

CAPS, SSR, STS

**Type of marker¶**

**References**

Aluko et al. (2004)

Aluko et al. (2004)

(2012)

Multani et al. (1994)

Hechanova et al. (2008)

Angeles-Shim et al. (accepted)

Ronald et al. (1992)

Khush et al. (1990)

Chen et al. (2012)

Gu et al. (2004)

Cheema et al. (2008)

Jeung et al. (2007)

Liu et al. (2002)

(2013)

(2013)



† Abbreviations for mapping population types are: AB-QTL, advanced backcross-quantitative trait locus; AIL, alien in‐ trogression line; BIL, backcrossed inbred line; CSSL, chromosome segment substitution line; IL, inbred line; MAAL, monosomic alien addition line; NIL, near isogenic line; RIL, recombinant inbred line.

‡ Abbreviations for QTL analysis method are: ANOVA, analysis of variance; CIM, composite interval mapping; IM, inter‐ val mapping; MIM, multiple interval mapping; SMR, single marker analysis; SPA, single point analysis.

§ Only the chromosomes where the QTL increase is attributed to the wild parent are listed.

Abbreviations for types of markers are: AFLP, amplified fragment length polymorphism; CAPS, cleaved amplified poly‐ morphic sequence, InDel, insertion-deletion polymorphism, RAPD, random amplified polymorphic DNA; RFLP, restric‐ tion fragment length polymorphism; SNP, single nucleotide polymorphism; SSR, simple sequence repeat; STS, sequence-tagged site.

**Table 3.** Summary of QTLs for grain quality, biotic stress tolerance, abiotic stress tolerance and biomass attributed to the non-*O.sativa* parent.

#### **4.4. Resistance to biotic stress**

#### *4.4.1. Disease resistance*

**Trait Donor species**

30 Rice - Germplasm, Genetics and Improvement

*O. officinalis*

*O. officinalis*

*O. rufipogon*

Green rice

White-backed planthopper

**Abiotic stress** Aluminum

Drought

*O. officinalis* IRGC100896

**Donor accession**

IRGC101412, IRGC102385

IR54745-2-21-12-1 7-6

IR54745-2-21-12-1 7-6

leafhopper *O. glaberrima* IRGC104038 Taichung 65 NIL IM, CIM

BILs-DWR/

tolerance *O. rufipogon* IRGC106424 IR64 RIL IM

**Recurrent parent(s)**

*O. officinalis* B5 1826, 93-11 3, 4 SSR

IR31917-45-3- 2

*O. officinalis* B5 RIL 4

*O. rufipogon* WBO1 Minghui 63 F2 4, 8 SSR

*O. officinalis* B5 Minghui 63 RIL 3, 4 SSR

tolerance *O. rufipogon* Guichao 2 IL SMR 2, 12 SSR Zhang et al.

*O. rufipogon* W630 Nipponbare BIL IM 1, 5 SSR

Dingxiang XieqingzaoB BIL

*O. officinalis* <sup>3</sup> RFLP Hirabayshi et

*O. officinalis* B5 Minghui 63 F2 <sup>3</sup> RFLP Huang et al.

*O. rufipogon* W1962 Taichung 65 NIL, BC4F2 <sup>8</sup> SSR Fujita et al.

**Mapping population†**

IR50 3

**QTL mapping analysis‡**

**Chromosome location§**

F2 11 RAPD

IR50 RIL <sup>3</sup> RAPD Renganayaki

3, 7, 9, 10

2, 5, 9 SSR

1, 3, 9 (2, 7, 8)

CIM, MIM **Type of marker¶**

RAPD, STS

**References**

Renganayaki et al. (2002)

Li et al. (2006)

Jena et al. (2002)

al. (1998)

et al. (2002)

(2001)

Yang et al. (2004)

Hou et al. (2011)

SSR Fujita et al. (2010)

(2006)

Tan et al. (2006b)

Chen et al. (2010)

RFLP Nguyen et al. (2003)

(2006)

Thanh et al. (2011)

AFLP, RFLP, SSR

> Pathogenic microorganisms, such as fungi, oomycetes, viruses and bacteria, and pests, such as insect herbivores, significantly reduce rice seed yield and quality. The most destructive rice diseases include bacterial blight caused by *Xanthomonas oryzae* pv. *oryzae* Ishiyama Dye (Cheema et al. 2008), blast caused by the fungus *Magnaporthe oryzae* B. Couch (Couch and Kohn 2002)*,* and sheath blight caused by the soil-borne fungus *Rhizoctonia solani* Kühn (Zhang 2007). The first reported successful introduction of an agronomically important trait from a

wild *Oryza* species was the introgression of grassy stunt virus resistance from the AA-genome species *O. nivara* into the cultivated *O. sativa* genetic background (Khush et al. 1977). The resistance mechanism was subsequently introduced into several other rice cultivars (Sanchez et al. 2013). Since this first introduction, wild *Oryza* accessions have been screened as a potential source of resistance genes to biotic and abiotic stresses, as well as, yield and yield components, as previously discussed. These screening efforts, including successful introduction of stress resistance genes from *Oryza* species were recently summarized by Ali et al. (2010), Brar and Singh (2011) and Sanchez et al. (2013). Table 3 summarizes the efforts to identify the chromo‐ some location of stress resistance genes introduced from the wild *Oryza* species by QTL and fine mapping analyses.

Seed yield losses due to bacterial blight were reported to be up to 75% in India, Indonesia, and the Philippines, and 20 to 30% in Japan (Mew et al. 1993; Nino-Liu et al. 2006). Thus far 31 bacterial blight resistance genes have been reported and six of these were identified in species other than *O. sativa*. These resistance genes were identified in several wild *Oryza* species, including *Xa21* in *O. longistaminata*, *Xa23* in *O. rufipogon*, *Xa27* in *O. minuta* (IRGC101141), *Xa29*(t) in *O. officinalis* (B5), and *Xa30*(t) in *O. nivara* (IRGC81825) (Brar and Singh 2011; Cheema et al. 2008b; Gu et al. 2004; Khush et al. 1990; Tan et al. 2004; Zhang et al. 1998). Most recently, *Xa34*(t) exhibiting broad spectrum resistance, was identified in *O. brachyantha* (IRGC101232) as a single dominant gene after examining the crossed progeny of two resistant introgression lines derived from IR56 (recurrent parent) and *O. brachyantha* (Ram et al. 2010a).

Both bacterial blight and blast resistance were identified in the tetraploid CCDD genome species, *O. minuta* (IRGC101141). To transfer these resistance genes into the background of diploid, cultivated rice, Amante-Bordeos (1992) used embryo rescue and backcrossing to produce interspecific hybrids between the elite *O. sativa* line, IR31917-45-3-2 (recurrent parent) and *O. minuta* (donor parent).

Lastly, the line Y73 was selected for a high level of bacterial blight resistance from the hybrid progeny of an asymmetric somatic hybridization between a resistant *O. meyeriana* and a *O. sativa* subsp. *japonica* cultivar (Yan et al. 2004). Subsequently, Chen et al. (2012) developed a RIL population from Y73 x IR24 (*O. sativa*) and identified five QTLs, two were major QTLs located on chromosomes 1 and 5, and three were minor QTLs on chromosomes 3, 10 and 11. They also mapped 21 additional QTLs related to yield components and yield.

Blast is considered the most destructive fungal disease in irrigated rice. The symptoms include lesions on leaves, stems, peduncles, panicles, seeds and roots (Savary and Willocquet 2000; Khush et al. 2009). To date, 41 blast resistance genes have been reported; however, there are only two genes, *Pi9* and *Pi40(t)*, that have been identified in wild *Oryza* species, *O. minuta* and *O. australiensis*, respectively. *Pi40(t),* which confers broad spectrum of blast resistance, was introgressed into the elite breeding line, IR65482-4-136-2-2 (Liu et al. 2002; Jeung et al. 2007). Qu et al. (2006) cloned the *Pi9* gene via a positional (map-based) cloning technique and found the gene is a member of a group of six resistance-like genes, which encodes a nucleotidebinding site (NBS) and leucine-rich repeats (LRRs). Silué et al. (1992) screened 99 *O. glaberri‐ ma* accessions for blast resistance by inoculating with ten *M. oryzae* strains from Cote d'Ivoire. The results revealed that nine accessions were resistant to all *M. oryzae* strains and 32 accessions were moderately resistant, suggesting these accessions may be the source of novel resistance genes. Eizenga et al. (2009) identified resistance to U.S. blast races in some *Oryza* species but no accession exhibited resistance to all races. Subsequently, two AB-QTL populations with two different resistant *O. nivara* (IRGC100898; IRGC104705) accessions as donor parents x Bengal, a U.S. medium grain *tropical japonica*, as recurrent parent were evaluated for reaction to two U.S. blast races. QTL analysis of these populations mapped resistance to U.S. leaf blast races *IB1* and *IB49* to chromosomes 8 and 12 (Eizenga et al. 2013).

wild *Oryza* species was the introgression of grassy stunt virus resistance from the AA-genome species *O. nivara* into the cultivated *O. sativa* genetic background (Khush et al. 1977). The resistance mechanism was subsequently introduced into several other rice cultivars (Sanchez et al. 2013). Since this first introduction, wild *Oryza* accessions have been screened as a potential source of resistance genes to biotic and abiotic stresses, as well as, yield and yield components, as previously discussed. These screening efforts, including successful introduction of stress resistance genes from *Oryza* species were recently summarized by Ali et al. (2010), Brar and Singh (2011) and Sanchez et al. (2013). Table 3 summarizes the efforts to identify the chromo‐ some location of stress resistance genes introduced from the wild *Oryza* species by QTL and

Seed yield losses due to bacterial blight were reported to be up to 75% in India, Indonesia, and the Philippines, and 20 to 30% in Japan (Mew et al. 1993; Nino-Liu et al. 2006). Thus far 31 bacterial blight resistance genes have been reported and six of these were identified in species other than *O. sativa*. These resistance genes were identified in several wild *Oryza* species, including *Xa21* in *O. longistaminata*, *Xa23* in *O. rufipogon*, *Xa27* in *O. minuta* (IRGC101141), *Xa29*(t) in *O. officinalis* (B5), and *Xa30*(t) in *O. nivara* (IRGC81825) (Brar and Singh 2011; Cheema et al. 2008b; Gu et al. 2004; Khush et al. 1990; Tan et al. 2004; Zhang et al. 1998). Most recently, *Xa34*(t) exhibiting broad spectrum resistance, was identified in *O. brachyantha* (IRGC101232) as a single dominant gene after examining the crossed progeny of two resistant introgression

Both bacterial blight and blast resistance were identified in the tetraploid CCDD genome species, *O. minuta* (IRGC101141). To transfer these resistance genes into the background of diploid, cultivated rice, Amante-Bordeos (1992) used embryo rescue and backcrossing to produce interspecific hybrids between the elite *O. sativa* line, IR31917-45-3-2 (recurrent parent)

Lastly, the line Y73 was selected for a high level of bacterial blight resistance from the hybrid progeny of an asymmetric somatic hybridization between a resistant *O. meyeriana* and a *O. sativa* subsp. *japonica* cultivar (Yan et al. 2004). Subsequently, Chen et al. (2012) developed a RIL population from Y73 x IR24 (*O. sativa*) and identified five QTLs, two were major QTLs located on chromosomes 1 and 5, and three were minor QTLs on chromosomes 3, 10 and 11.

Blast is considered the most destructive fungal disease in irrigated rice. The symptoms include lesions on leaves, stems, peduncles, panicles, seeds and roots (Savary and Willocquet 2000; Khush et al. 2009). To date, 41 blast resistance genes have been reported; however, there are only two genes, *Pi9* and *Pi40(t)*, that have been identified in wild *Oryza* species, *O. minuta* and *O. australiensis*, respectively. *Pi40(t),* which confers broad spectrum of blast resistance, was introgressed into the elite breeding line, IR65482-4-136-2-2 (Liu et al. 2002; Jeung et al. 2007). Qu et al. (2006) cloned the *Pi9* gene via a positional (map-based) cloning technique and found the gene is a member of a group of six resistance-like genes, which encodes a nucleotidebinding site (NBS) and leucine-rich repeats (LRRs). Silué et al. (1992) screened 99 *O. glaberri‐ ma* accessions for blast resistance by inoculating with ten *M. oryzae* strains from Cote d'Ivoire. The results revealed that nine accessions were resistant to all *M. oryzae* strains and 32 accessions

lines derived from IR56 (recurrent parent) and *O. brachyantha* (Ram et al. 2010a).

They also mapped 21 additional QTLs related to yield components and yield.

fine mapping analyses.

32 Rice - Germplasm, Genetics and Improvement

and *O. minuta* (donor parent).

Rice sheath blight, *Rhizoctonia solani* Kühn, was reported for the first time in Japan in 1910 and since then, it has spread to many rice growing areas worldwide (Lee and Rush 1983; Savary et al. 2000). To date, no major resistance gene(s) that confers complete resistance to sheath blight has been discovered, only genes conferring partial resistance (Pinson et al. 2005; Jia et al. 2009). Several *Oryza* species accessions were screened for sheath blight resistance at the International Rice Research Institute (IRRI) in the Philippines with resistance being identified in accessions of *O. minuta* and *O. rufipogon* (Amante et al. 1990), and a resistant *O. officinalis* accession being the donor of sheath blight resistance genes in *O. sativa* introgression lines (Lakshmanan, 1991). Prasad and Eizenga (2008) screened a collection of 73 *Oryza* species accessions using three different methods and identified seven accessions with moderate resistance including three *O. nivara* accessions and one each of *O. barthii*, *O. meridionalis*, *O. nivara/O. sativa* hybrid, and *O. officinalis*. Based on these results, Eizenga et al. (2013) developed two AB-QTL populations using two of these *O. nivara* accessions (IRGC100898; IRGC104705) as the donor parents, and Bengal as the recurrent parent for both populations. QTL analysis identified a major QTL, *qShB6*, associated with sheath blight attributed to the wild rice parent. Two other minor QTLs, *qShB1* and *qShB3*, were identified but not attributed to the *O. nivara* parent in all sheath blight tests. A third AB-QTL population with the more distant A-genome species, *O. meridionalis* (IRGC105306), as the donor parent, and Lemont, a U.S. long grain *tropical japonica*, as the recurrent parent, was evaluated for reaction to sheath blight disease and the QTL analysis is currently underway (Eizenga, unpublished data).

Stem rot, a fungal disease caused by *Sclerotium oryzae* Catt., causes yield losses by reduced tillering, more unfilled grains per panicle, chalky grain, lower milling yields and increased lodging (Ou 1985). The germplasm line 87-Y-550 (PI566666) was derived from a cross between the stem rot resistant *O. rufipogon* (IRGC100912) and L-201, a long grain California (USA) *temperate japonica* cultivar (Tseng and Oster 1994). To identify the location of this resistance gene and associated molecular markers, Ni et al. (2001) developed four RIL populations from crosses between 87-Y-550 and four susceptible long grain *O. sativa* breeding lines. Following the bulk segregant analysis method, QTLs revealed an association between the AFLP marker, TAA/GTA167, on chromosome 2 and SSR marker, RM232, on chromosome 3.

African cultivated rice, *O. glaberrima*, is an excellent, potential donor of genes to improve the tolerance of Asian cultivated rice, *O. sativa*, to biotic stresses, including bacterial blight, rice blast, rice stripe necrosis virus (RSNV), nematodes (*Meloidogyne graminicola* Golden and Birchfield) and especially rice yellow mottle virus, RYMV (Djedatin et al. 2011; Gutiérrez et al. 2010; Ndjiondjop and Alber 1999; Plowright et al. 1999; Silue et al. 1992; Thiémélé et al. 2010). A set of 64 CSSLs selected from a Caiapó x *O. glaberrima* (IRGC103544) DH, BC3F1 population was used to identify a major QTL controlling RSNV resistance on chromosome 11 (Gutiérrez et al. 2010). RYMV is one of the most destructive rice viral diseases. Few RYMV resistance genes have been found in *O. sativa* accessions, but 8% of the *O. glaberrima* accessions that were screened exhibited resistant to the virus with three recessive resistance alleles *rymv1-3*, *rymv1-4*, and *rymv1-5* and one dominant resistance allele, *RYMV1*, were identified. Three lines, TOG5681, TOG5672 and TOG7291 initially derived from the wild *Oryza glaberrima* showed resistance to RYMV, blast, and lodging (Futakuchi et al. 2008; Sié et al. 2002; Thiémélé et al. 2010).

#### *4.4.2. Insect resistance*

Genetic resistance is an effective method of protecting rice from insect pests in Asia and the Americas (Kiritani 1979; Way 1990) and avoids the possible environmental contamination associated with chemical control (Zhang 2007). The brown planthopper, *Nilaparvata lugens* (Stål), one of the most devastating herbivores of rice in Asia, causes damage by feeding on rice plants and transmitting two viruses, rice ragged stunt virus and rice grassy stunt virus. A total of 26 brown leafhopper resistance genes have been reported, of which 10 genes are recessive and 18 are dominant. Of the 26 genes, 12 genes, *Bph1, bph2, Bph3, bph4*, *bph5*, *Bph6*, *bph7*, *bph8*, *Bph9*, *bph19, Bph25* and *Bph26,* are found in *O. sativa;* two genes, *Bph10* and *Bph18,* are found in *O. australiensis;* seven genes*, Bph11*, *bph11, bph12, Bph14, Bph15, bph16* and *Bph17,* in *O. officinalis;* one gene, *Bph13*, in *O. eichingeri;* one gene, *Bph17*, in *O. latifolia*; two genes, *Bph20(t)* and *Bph21(t)*, in *O. minuta*; one gene, *bph22(t)* in *O. glaberrima*; and three genes, *Bph22(t), Bph23(t)* and *Bph24(t),* in *O. rufipogon* (Deen et al. 2010; Jena 2010; Oryzabase 2014; Ram et al. 2010, Zhang 2007).

Early efforts to evaluate the *Oryza* species accessions as a source of brown planthopper resistance and incorporation of this resistance into *O. sativa* were in a large part due to the efforts at IRRI. Early reports include introgression of resistance from *O. australiensis* through backcrossing into an *O. sativa* background and using RFLP markers to resolve the position of the gene in chromosome 12 (Ishii et al. 1993). Introgression of resistance to three brown planthopper biotypes from five different *O. officinalis* accessions into cultivated *O. sativa* breeding lines resulted in 52 BC2F8 ILs (Jena and Khush 1990; Jena et al. 1992). Genotyping of these lines with RFLP markers showed *O. officinalis*introgressions in 11 of the 12 rice chromo‐ somes with markers on chromosomes 4, 10 and 12 appearing to be associated with BPH resistance but not unequivocally. More recently, a single dominant gene, *bph22(t)*, was discovered in *O. glaberrima* and transferred into *O. sativa* (Ram et al. 2010).

The white-backed planthopper, *Sogatella furcifera* (Horvath), is another serious insect pest of rice in Asia (Chen et al. 2010). Six major QTLs, *Wbp1, Wbp2, Wbp3, wbh4*, *Wbp5* and *Wbp6*, have been identified (Angeles et al. 1981; Hernandez and Khush 1981; Oryzabase 2014; Ravindar et al. 1982; Sidhu and Khush 1979; Min et al. 1991; Wu and Khush 1985). *O. officinalis* has been reported as a source of resistance to both white-backed and brown planthoppers. Further study identified two QTLs, *Wph8(t)* and *Bph15* on chromosome 4, with *wph7*(t) and *Bph14* on chromosome 3 (Huang et al. 2001; Oryzabase 2014; Tan et al. 2004). Chen et al. (2010) developed a BIL population derived from *O. sativa* x *O. rufipogon* and found three QTLs from wild *Oryza* including *qWph2* on the short arm of chromosome 2, *qWph5* on the long arm of chromosome 5, and *qWph9*, which confers high resistance on the long arm of chromosome 9. In addition, these wild alleles reduced the rate of seedling mortality.

Guo et al. (2013) analyzed 131 BC4F2 ILs resulting from a cross between *O. minuta* (IRGC101133) x IR24 (*O. sativa*) by using 164 SSR markers, and estimated the average length of introgressed segments to be 14.78 cM. They observed a range of morphological and yield traits, as well as, resistance to bacterial blight, brown planthopper, and white-backed planthopper among the populations.

Rice monosomic alien addition lines (MAALs) contain the complete *O. sativa* chromosome complement (2n=24AA) plus an additional, unpaired chromosome from a wild *Oryza* (alien) donor, thus the designation 2n=24AA+1alien (Jena 2010). MAALs have been used to identify important genes conferring resistance to biotic stresses, such as bacterial blight, brown planthopper and white-backed planthopper, from *O. australiensis*(EE) and *O. latifolia* (CCDD) into cultivated *O. sativa* (Multani et al. 1994 & 2003). Recently, Angeles-Shim (accepted) evaluated a set of 27 alien introgression lines developed from the *O. sativa* breeding line IR31917-45-3-2 x *O. latifolia* (IRGC100194) MAALs and identified putative QTLs for resistance to four Philippine races of bacterial blight, as well as, yield components and stem strength.

Green leafhopper [*Nephotettix virescens* (Distant)] is an insect found in wetlands including irrigated and rainfed environments. Six resistance loci, *Grh1, Grh2, Grh3, Grh4, Grh5* and *Grh6*, and one QTL, *qGRH4*, conferring resistance to green leafhopper have been reported. *Grh5* was identified in the wild rice, *O. rufipogon*, for the first time. *Ghr6* was identified in both cultivated rice and *O. nivara* (Fujita et al. 2003, 2004 & 2010; Fukuta et al. 1998; Saka et al. 1997; Tamura et al. 1999; Yasui and Yoshimura 1999; Yazawa et al. 1998).

The wild *Oryza* species have been used successfully as a source of novel alleles conferring resistance to both rice diseases and insect pests because in many instances these alleles could be transferred to *O. sativa* by backcrossing and screening the progeny. In fact, several of these alleles were successfully transferred even before the advent of molecular markers. With molecular markers, it is now possible to expedite the introduction of these novel alleles because marker-assisted breeding techniques can be used. With the molecular tools currently available, it should be possible to unravel those resistances which are quantitatively inherited like sheath blight.

## **4.5. Genes for abiotic stress**

was used to identify a major QTL controlling RSNV resistance on chromosome 11 (Gutiérrez et al. 2010). RYMV is one of the most destructive rice viral diseases. Few RYMV resistance genes have been found in *O. sativa* accessions, but 8% of the *O. glaberrima* accessions that were screened exhibited resistant to the virus with three recessive resistance alleles *rymv1-3*, *rymv1-4*, and *rymv1-5* and one dominant resistance allele, *RYMV1*, were identified. Three lines, TOG5681, TOG5672 and TOG7291 initially derived from the wild *Oryza glaberrima* showed resistance to RYMV, blast, and lodging (Futakuchi et al. 2008; Sié et al. 2002; Thiémélé et al.

Genetic resistance is an effective method of protecting rice from insect pests in Asia and the Americas (Kiritani 1979; Way 1990) and avoids the possible environmental contamination associated with chemical control (Zhang 2007). The brown planthopper, *Nilaparvata lugens* (Stål), one of the most devastating herbivores of rice in Asia, causes damage by feeding on rice plants and transmitting two viruses, rice ragged stunt virus and rice grassy stunt virus. A total of 26 brown leafhopper resistance genes have been reported, of which 10 genes are recessive and 18 are dominant. Of the 26 genes, 12 genes, *Bph1, bph2, Bph3, bph4*, *bph5*, *Bph6*, *bph7*, *bph8*, *Bph9*, *bph19, Bph25* and *Bph26,* are found in *O. sativa;* two genes, *Bph10* and *Bph18,* are found in *O. australiensis;* seven genes*, Bph11*, *bph11, bph12, Bph14, Bph15, bph16* and *Bph17,* in *O. officinalis;* one gene, *Bph13*, in *O. eichingeri;* one gene, *Bph17*, in *O. latifolia*; two genes, *Bph20(t)* and *Bph21(t)*, in *O. minuta*; one gene, *bph22(t)* in *O. glaberrima*; and three genes, *Bph22(t), Bph23(t)* and *Bph24(t),* in *O. rufipogon* (Deen et al. 2010; Jena 2010; Oryzabase 2014; Ram et al.

Early efforts to evaluate the *Oryza* species accessions as a source of brown planthopper resistance and incorporation of this resistance into *O. sativa* were in a large part due to the efforts at IRRI. Early reports include introgression of resistance from *O. australiensis* through backcrossing into an *O. sativa* background and using RFLP markers to resolve the position of the gene in chromosome 12 (Ishii et al. 1993). Introgression of resistance to three brown planthopper biotypes from five different *O. officinalis* accessions into cultivated *O. sativa* breeding lines resulted in 52 BC2F8 ILs (Jena and Khush 1990; Jena et al. 1992). Genotyping of these lines with RFLP markers showed *O. officinalis*introgressions in 11 of the 12 rice chromo‐ somes with markers on chromosomes 4, 10 and 12 appearing to be associated with BPH resistance but not unequivocally. More recently, a single dominant gene, *bph22(t)*, was

The white-backed planthopper, *Sogatella furcifera* (Horvath), is another serious insect pest of rice in Asia (Chen et al. 2010). Six major QTLs, *Wbp1, Wbp2, Wbp3, wbh4*, *Wbp5* and *Wbp6*, have been identified (Angeles et al. 1981; Hernandez and Khush 1981; Oryzabase 2014; Ravindar et al. 1982; Sidhu and Khush 1979; Min et al. 1991; Wu and Khush 1985). *O. officinalis* has been reported as a source of resistance to both white-backed and brown planthoppers. Further study identified two QTLs, *Wph8(t)* and *Bph15* on chromosome 4, with *wph7*(t) and *Bph14* on chromosome 3 (Huang et al. 2001; Oryzabase 2014; Tan et al. 2004). Chen et al. (2010) developed a BIL population derived from *O. sativa* x *O. rufipogon* and found three QTLs from wild *Oryza*

discovered in *O. glaberrima* and transferred into *O. sativa* (Ram et al. 2010).

2010).

*4.4.2. Insect resistance*

34 Rice - Germplasm, Genetics and Improvement

2010, Zhang 2007).

Abiotic stresses, including drought and flood, high and low temperatures, high salinity, high aluminum and acid sulfate soils, have a negative impact on rice productivity worldwide. Rice, like other plant species, has evolved to adapt to different environmental stresses using different mechanisms and strategies with multiple sensors. When the sensors identify a stress, a signal transduction pathway is invoked, which activates genes conferring the initial response for short term or long term tolerance to the stress (Grennan 2006; Lexer and Fay 2005). Recent studies identified many genes involved in plant tolerance to abiotic stress, which are classified into two groups based on their products. The first group includes genes that protect the cells via synthesis of chaperones, a group of proteins that help non-covalent folding and unfolding of other proteins in the cell under stress conditions, and enzymes for protecting metabolites and proteins. The second group includes those genes that regulate stress responses acting as transcriptional factors to control stress genes or by producing hormones (Grennan 2006).

## *4.5.1. Tolerance to drought and heat*

Drought reduces grain yield and affects yield stability in many rainfed regions by decreasing the number of tillers per plant, plant height, number of leaves and leaf width; and delaying anthesis and maturity as shown by Ndjiondjop et al. (2010) using 202 BILs derived from WAB56-104 (*O. sativa*) x CG14 (*O. glaberrima*) to identify the effect of drought on rice agronomic traits. Despite the fact that African rice (*O. glaberrima*) has low productivity and grain yield, it is an excellent source of genes associated with drought tolerance (Blum 1998; Hanamaratti et al. 2008; Manneh et al. 2007; Ndjiondjop et al. 2010).

Bocco et al. (2012) evaluated the morphological and agronomical traits of 60 genotypes including 54 BC3F6 introgression lines from IR64 (recurrent parent, elite *indica* cultivar) x TOG5681 (*O. glaberrima*), two parents (IR64 and TOG5681) and four NERICA-L cultivars derived from the same parents, for comparison as controls. These genotypes were evaluated in two time periods corresponding to the dry season under irrigated (control) and drought conditions to identify the most drought tolerance introgression lines. Plant height, spikelet fertility, grain yield and leaf area at harvesting were consistently reduced by drought and values for leaf temperature, leaf rolling, leaf tip drying, leaf blast disease, days to flowering and days to maturity were increased under drought conditions. From this evaluation, five BC3F6 lines were identified that out yielded the four NERICA-L cultivars described as drought tolerant.

Several accessions of *O. barthii*, *O. meridionalis* and *O. australiensis* were screened for heat and drought tolerance at the University of Arizona, which is located in a desert environment at Tuscon, Arizona, USA (Sanchez et al. 2013). One of the most tolerant *O. meridionalis* accessions was crossed with M-202, a California (USA) medium grain, *temperate japonica* cultivar. From the backcross progeny, two heat-tolerant advanced backcross lines resembling the M-202 parent were selected for variety release as 'Arizona Rice-1' and 'Arizona Rice-2'.

#### *4.5.2. Tolerance to low temperatures*

Low temperatures during the rice growing season causes poor germination, slow growth, withering and anther injury (Andaya et al. 2007; Hu et al. 2008). To cope with cold stress, many plant species including rice have developed several physiological and biochemical pathways for surviving and adapting to stress conditions (Ingram and Bartels 1996; Pastori and Foyer 2002; Hu et al. 2008). Rice is predominately grown in tropical and sub-tropical regions; therefore, many cultivars are sensitive to cold temperature especially during the seedling stage. The optimum temperature range for germination and early seedling growth is 25-30o C, and temperatures below 15-17o C during this period delay plant establishment, reduce plant competitive ability against weeds, delay plant maturity, and decrease grain yield. Improving cold tolerance at this stage is one of the most effective ways to achieve yield stability and genetic tolerance is the most promising strategy (Andaya and Mackill 2003; Fujino et al. 2004; Koseki et al. 2009). Overall, the *Indica* subspecies is more sensitive to cold stress than *Japonica* rice. Several QTLs associated with cold tolerance have been identified, especially in populations derived from crosses between *Japonica* and *Indica* cultivars (Lu et al. 2007; Zhang et al. 2005).

Wild rice species, such as *O. rufipogon*, contain QTLs that can be integrated into cultivated rice to improve cold tolerance (Koseki et al. 2010). Lee et al. (2005) constructed a RIL population consisting of 120 BC1F7 lines derived from a cross between the *japonica* cultivar, Hwayeongbyeo and *O. rufipogon* (W1944). The population was genotyped with 124 SSR markers and evaluated for 20 agronomic traits including leaf discoloration which is associated with cold stress. Of the 63 QTLs identified, there were two QTLs for decreased leaf discoloration, in other words, increased cold tolerance, attributed to the *O. rufipogon* parent. These QTLs, *dc2* located on chromosome 2 and *dc5* on chromosome 5, accounted for 11.2% and 11.1% of the phenotypic variation, respectively. The *O. rufipogon* parent also contributed favorable alleles to panicle length, spikelets per panicle and days to heading.

Koseki et al. (2010) analyzed 184 F2 introgression lines from crosses of Guang-lu-ai 4 (cold sensitive, *indica* cultivar) x W1943 (cold tolerant, *O. rufipogon*) for cold tolerance at the seedling stage (CTSS). Three *Ctss*-QTLs were detected with those on chromosomes 3 (*qCtss 3*) and 11 (*qCtss*11) attributed to the *O. rufipogon* parent, and on chromosome 10 (*qCtss10*) to Guang-luai 4. The major QTL, *qCtss11*, explained 40% of the phenotypic variation and using backcross progenies, it was fine-mapped to a 60kb candidate region defined by markers AK24 and GP0030 with Os11g0615600 and/or Os11g0615900 hypothesized as the causal gene(s) for cold tolerance.

Seedling cold tolerance was measured in the M-202 (medium grain, U.S. *temperate japonica*) x *O. nivara* (IRGC100195) AB-QTL population using a slant board method [Jones and Petersen 1976; Eizenga et al. (accepted)]. In this study, QTLs for increasing coleoptile length and shoot length were identified in the same region on chromosome 5 and attributed to the *O. nivara* parent. QTLs for increased shoot length and root length were found on chromosome 8 and 6, respectively, and attributed to the M-202 parent.

## *4.5.3. Tolerance to aluminum and acid soils*

via synthesis of chaperones, a group of proteins that help non-covalent folding and unfolding of other proteins in the cell under stress conditions, and enzymes for protecting metabolites and proteins. The second group includes those genes that regulate stress responses acting as transcriptional factors to control stress genes or by producing hormones (Grennan 2006).

Drought reduces grain yield and affects yield stability in many rainfed regions by decreasing the number of tillers per plant, plant height, number of leaves and leaf width; and delaying anthesis and maturity as shown by Ndjiondjop et al. (2010) using 202 BILs derived from WAB56-104 (*O. sativa*) x CG14 (*O. glaberrima*) to identify the effect of drought on rice agronomic traits. Despite the fact that African rice (*O. glaberrima*) has low productivity and grain yield, it is an excellent source of genes associated with drought tolerance (Blum 1998; Hanamaratti et

Bocco et al. (2012) evaluated the morphological and agronomical traits of 60 genotypes including 54 BC3F6 introgression lines from IR64 (recurrent parent, elite *indica* cultivar) x TOG5681 (*O. glaberrima*), two parents (IR64 and TOG5681) and four NERICA-L cultivars derived from the same parents, for comparison as controls. These genotypes were evaluated in two time periods corresponding to the dry season under irrigated (control) and drought conditions to identify the most drought tolerance introgression lines. Plant height, spikelet fertility, grain yield and leaf area at harvesting were consistently reduced by drought and values for leaf temperature, leaf rolling, leaf tip drying, leaf blast disease, days to flowering and days to maturity were increased under drought conditions. From this evaluation, five BC3F6 lines were identified that out yielded the four NERICA-L cultivars described as drought

Several accessions of *O. barthii*, *O. meridionalis* and *O. australiensis* were screened for heat and drought tolerance at the University of Arizona, which is located in a desert environment at Tuscon, Arizona, USA (Sanchez et al. 2013). One of the most tolerant *O. meridionalis* accessions was crossed with M-202, a California (USA) medium grain, *temperate japonica* cultivar. From the backcross progeny, two heat-tolerant advanced backcross lines resembling the M-202

Low temperatures during the rice growing season causes poor germination, slow growth, withering and anther injury (Andaya et al. 2007; Hu et al. 2008). To cope with cold stress, many plant species including rice have developed several physiological and biochemical pathways for surviving and adapting to stress conditions (Ingram and Bartels 1996; Pastori and Foyer 2002; Hu et al. 2008). Rice is predominately grown in tropical and sub-tropical regions; therefore, many cultivars are sensitive to cold temperature especially during the seedling stage. The optimum temperature range for germination and early seedling growth is 25-30o

competitive ability against weeds, delay plant maturity, and decrease grain yield. Improving

C during this period delay plant establishment, reduce plant

C, and

parent were selected for variety release as 'Arizona Rice-1' and 'Arizona Rice-2'.

*4.5.1. Tolerance to drought and heat*

36 Rice - Germplasm, Genetics and Improvement

*4.5.2. Tolerance to low temperatures*

temperatures below 15-17o

tolerant.

al. 2008; Manneh et al. 2007; Ndjiondjop et al. 2010).

Aluminum toxicity is another abiotic stress that causes grain yield reduction especially when rice is grown in an acidic soil (IRRI 1978). If the soil pH falls below 5.5, aluminum will more likely separate from the soil colloids and come into a solution phase. Aluminum at toxic levels slows root development, reduces the plant's ability to take up water and nutrients, and decreases plant growth, consequently reducing grain yield and grain quality (Foy 1992). Application of lime to the soil, reduces soil acidity and improves soil fertility but the results have showed limited success in overcoming the effects of aluminum toxicity. Aluminum tolerance is a quantitative trait and varies among rice species. Both additive and dominance effects contribute to the genetic heritability of aluminum tolerance as documented by the importance of both general combining ability and specific combining ability (Howeler and Cadavid 1976; Wu et al. 1997).

In the past decade, one *O. rufipogon* (IRGC106424) accession found growing in an acid sulfate soil in Vietnam (Sanchez et al. 2013) has proven to be valuable for improving tolerance to both aluminum and acid sulfate soils in cultivated rice. Initially, Nguyen et al. (2003) evaluated 171 F6 RILs derived from IR64 (*indica*, susceptible) x *O. rufipogon* (IRGC106424, tolerant) for aluminum tolerance. QTL analysis revealed QTLs for root length under stress conditions attributed to the *O. rufipogon* parent in six different chromosomal regions on chromosomes 1, 2, 3, 7, 8 and 9 that individually explained 9.0–24.9% of the phenotypic variation and were controlled by additive effects. The major QTL on chromosome 3, explaining 24.9% of the variation, was found to be conserved across cereal species. During the same time period, the tolerance to acid sulfate soils identified in this *O. rufipogon* accession was introgressed into the IR64 background through breeding efforts. The selected introgression line, IR73678-6-9-B, was released by IRRI as variety AS996 (Sanchez et al. 2013). AS996 is currently grown on 100,000 ha in the Mekong Delta and described as moderately tolerant to acid sulfate soils and tolerant to brown planthopper and blast.

Even though traits associated with abiotic stress are more difficult to evaluate because of environmental effects and interactions between genes, the development of the AS996 variety is an exciting success story. The release of Arizona-1 and Arizona-2 could make significant contributions to improving rice yields in areas where high temperatures routinely lower yield. With the improved molecular techniques for dissecting these traits and the gene functions related to abiotic stress, more significant advances should be made in the near future, especially as the scientific community provides the tools for rice producers to deal with global climate change.

## **5. Conclusions**

The repositories of *Oryza* species accessions found around the world are a storehouse of novel alleles and traits lost during the evolution and domestication of cultivated rice as we know it today. The fact that introgression lines derived from crosses between Asian rice and it's ancestral species, *O. rufipogon* and *O. nivara*, exhibited notable improvement in yield and yield components through the phenomenon known as transgressive variation, was surprising and unexpected. The identification of novel alleles related to biotic stress, especially insect pests like brown planthopper and bacterial leaf blight, and more recently abiotic stresses like acid sulfate soils and drought, underscore the importance of mining these collections. The advent of molecular marker technology and development of mapping populations, especially AB-QTL and CSSL, have made it possible to map many of these alleles to chromosome location and begin to dissect the interactions between various genes. The fact that high quality genome sequences are now available or will soon be available, make it possible to interrogate the wild *Oryza* species accessions at a level that was not possible before. These resources will allow us to move swiftly beyond the first step of QTL identification to fine mapping traits of interest; introgressing desirable traits into elite breeding lines using markers within the gene, thus decreasing linkage drag; identifying genotype by environment interactions; determining the effect of epistasis (non-allelic genes) on traits of interest; discovering epigenetic effects such as histone modification or DNA methylation; and finally unraveling other genetic phenomenon like gene silencing. In summary, the interspecific and intergenomic mapping populations available or soon to be available, and the increased availability of SNP data, resequencing data and advanced statistical software, create even more opportunities to investigate novel alleles for agronomically important traits discovered in the *Oryza* species and increase our under‐ standing of the mechanisms underlying these traits to deal with the challenges of climate change and feeding nine billion people.

## **Acknowledgements**

importance of both general combining ability and specific combining ability (Howeler and

In the past decade, one *O. rufipogon* (IRGC106424) accession found growing in an acid sulfate soil in Vietnam (Sanchez et al. 2013) has proven to be valuable for improving tolerance to both aluminum and acid sulfate soils in cultivated rice. Initially, Nguyen et al. (2003) evaluated 171 F6 RILs derived from IR64 (*indica*, susceptible) x *O. rufipogon* (IRGC106424, tolerant) for aluminum tolerance. QTL analysis revealed QTLs for root length under stress conditions attributed to the *O. rufipogon* parent in six different chromosomal regions on chromosomes 1, 2, 3, 7, 8 and 9 that individually explained 9.0–24.9% of the phenotypic variation and were controlled by additive effects. The major QTL on chromosome 3, explaining 24.9% of the variation, was found to be conserved across cereal species. During the same time period, the tolerance to acid sulfate soils identified in this *O. rufipogon* accession was introgressed into the IR64 background through breeding efforts. The selected introgression line, IR73678-6-9-B, was released by IRRI as variety AS996 (Sanchez et al. 2013). AS996 is currently grown on 100,000 ha in the Mekong Delta and described as moderately tolerant to acid sulfate soils and tolerant

Even though traits associated with abiotic stress are more difficult to evaluate because of environmental effects and interactions between genes, the development of the AS996 variety is an exciting success story. The release of Arizona-1 and Arizona-2 could make significant contributions to improving rice yields in areas where high temperatures routinely lower yield. With the improved molecular techniques for dissecting these traits and the gene functions related to abiotic stress, more significant advances should be made in the near future, especially as the scientific community provides the tools for rice producers to deal with global climate

The repositories of *Oryza* species accessions found around the world are a storehouse of novel alleles and traits lost during the evolution and domestication of cultivated rice as we know it today. The fact that introgression lines derived from crosses between Asian rice and it's ancestral species, *O. rufipogon* and *O. nivara*, exhibited notable improvement in yield and yield components through the phenomenon known as transgressive variation, was surprising and unexpected. The identification of novel alleles related to biotic stress, especially insect pests like brown planthopper and bacterial leaf blight, and more recently abiotic stresses like acid sulfate soils and drought, underscore the importance of mining these collections. The advent of molecular marker technology and development of mapping populations, especially AB-QTL and CSSL, have made it possible to map many of these alleles to chromosome location and begin to dissect the interactions between various genes. The fact that high quality genome sequences are now available or will soon be available, make it possible to interrogate the wild *Oryza* species accessions at a level that was not possible before. These resources will allow us

Cadavid 1976; Wu et al. 1997).

38 Rice - Germplasm, Genetics and Improvement

to brown planthopper and blast.

change.

**5. Conclusions**

The support of National Science Foundation-Plant Genome Project: "The Genetic Basis of Transgressive Variation in Rice" (Award no. 1026555) to Ehsan Shakiba is gratefully acknowl‐ edged. Dr. Paul L. Sanchez and Dr. Benildo G. de los Reyes are acknowledged for their critical reading of this manuscript.

## **Author details**

Ehsan Shakiba2 and Georgia C. Eizenga1\*

\*Address all correspondence to: georgia.eizenga@ars.usda.gov

1 USDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR, USA

2 University of Arkansas Rice Research and Extension Center, Stuttgart, AR, USA

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## **Genes and QTLs Resistant to Biotic and Abiotic Stresses from Wild Rice and Their Applications in Cultivar Improvements**

Fantao Zhang and Jiankun Xie

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56825

## **1. Introduction**

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58 Rice - Germplasm, Genetics and Improvement

Rice (*Oryza sativa* L.) is one of the most important food crops for mankind because it feeds more than half the world population, especially in developing countries (Maclean et al. 2002). Although rice production in the world has increased markedly in the past decades, it is still insufficient to cope with the ever-increasing global demands (Sasaki et al. 2000). This is not an easy task in view of the fact that the land available for cultivation is decreasing year by year, especially in Asia where 90 percent of the world's rice is produced and consumed (Khush 1997; Fischer et al. 2005). Meanwhile, since rice is grown worldwide and under a wide range of agro-climatic conditions, its productivity is affected by many abiotic and biotic stresses. Major biotic stresses including insect pests, such as brown planthopper (*Nilaparvata lugens* Stål), green rice leafhopper (*Nephotettix cincticeps*), and stem borer (*Chilo suppressalis*); and diseases, such as sheath blight, bacterial leaf blight, tungro virus; and abiotic stresses including salinity, acidity, drought, cold, and iron toxicity severely affect rice production.

During domestication process from wild species to cultivated rice, selecting desirableagronomic traits to keep achieving high yield allows many genes to be either directly selected or filtered out, resulting in a significant reduction of genetic diversity in rice gene pool (Brar et al. 2003). Sun et al (2001) revealed that the number of alleles in cultivated rice had been reduced by 50-60% compared to wild rice. Thus, it is necessary to broaden the gene pool in rice breeding from diverse sources, especially from wild rice.

In the genus of Oryza, there are two cultivated species and more than 20 wild species. Both of the cultivated species, O. sativa and O. glaberrima, are diploid (2n = 24) and have the AA genome. Wild species have evolved in a wide range of environments over millions of years

(Stebbins 1981). The wild species have either 2n = 24 or 2n = 48 chromosomes, and seven genomes (AA, BB, CC, BBCC, CCDD, EE, and FF) have so far been designated for 17 species (Vaughan 1994; Brar et al. 1997). Common wild rice (Oryza rufipogon Griff.), due to its longterm growth in the wild conditions, possesses numerous advantages such as genetic diversity, excellent agronomic traits, and resistance against various biotic and abiotic stresses, proved to be an important resource for genetic improvement of rice (Song et al. 2005). Dongxiang wild rice (O. rufipogon Griff.) is in the northern most habitats among O. rufipogon populations to be discovered in the world (Chen et al. 2008; Xie et al. 2010; Figure 1), and displays strong tolerance to low temperature (Figure 2). It is for certain that many valuable traits exist in the wild rice species, but the most challenges to us are how to explore the valuable genes from wild rice and effectively transfer them into the cultivated rice for diversifying genetic basis of cultivated rice. Recently, many genes and QTLs have been mined from the wild rice, which functions include disease and insect resistances, abiotic stress tolerances, high yield, and so on. In this chapter, we will summarize current research progresses in mining elite genes and QTLs from wild rice for cultivar improvement in breeding programs.

**Figure 1.** Dongxiang wild rice (*Oryza ru*fi*pogon* Griff.) is a common wild rice located at 28°14′N latitude and 116°30′E longitude in Dongxiang county, Jiangxi province, China, which is considered to be the northernmost region in the world where *O*. *ru*fi*pogon* is found.

**Figure 2.** Dongxiang wild rice can survive under freezing conditions.

## **2. Disease resistance genes and QTLs in wild rice**

Rice diseases such as blast, bacterial blight and sheath blight are major obstacles for achieving optimal yields. To complement conventional breeding method, molecular or transgenic method represents an increasingly important approach for genetic improvement of disease resistance and reduction of pesticide usage. During the past two decades, a wide variety of genes and mechanisms involved in rice defense response have been identified and elucidated. However, most of the cloned genes confer high level of race specific resistance in a gene-forgene manner, and the resistance is effective against one or a few related races or strains of the pathogens. The resistance is effective for only few years because the pathogen race or strain keeps changing for survival in nature. Therefore, there is an urgent need to broaden the rice gene pool from diverse resources, of which the wild rice is an ideal option.

## **2.1. Rice blast resistance**

(Stebbins 1981). The wild species have either 2n = 24 or 2n = 48 chromosomes, and seven genomes (AA, BB, CC, BBCC, CCDD, EE, and FF) have so far been designated for 17 species (Vaughan 1994; Brar et al. 1997). Common wild rice (Oryza rufipogon Griff.), due to its longterm growth in the wild conditions, possesses numerous advantages such as genetic diversity, excellent agronomic traits, and resistance against various biotic and abiotic stresses, proved to be an important resource for genetic improvement of rice (Song et al. 2005). Dongxiang wild rice (O. rufipogon Griff.) is in the northern most habitats among O. rufipogon populations to be discovered in the world (Chen et al. 2008; Xie et al. 2010; Figure 1), and displays strong tolerance to low temperature (Figure 2). It is for certain that many valuable traits exist in the wild rice species, but the most challenges to us are how to explore the valuable genes from wild rice and effectively transfer them into the cultivated rice for diversifying genetic basis of cultivated rice. Recently, many genes and QTLs have been mined from the wild rice, which functions include disease and insect resistances, abiotic stress tolerances, high yield, and so on. In this chapter, we will summarize current research progresses in mining elite genes and

QTLs from wild rice for cultivar improvement in breeding programs.

**Figure 1.** Dongxiang wild rice (*Oryza ru*

60 Rice - Germplasm, Genetics and Improvement

fi

*pogon* is found.

world where *O*. *ru*

fi

longitude in Dongxiang county, Jiangxi province, China, which is considered to be the northernmost region in the

*pogon* Griff.) is a common wild rice located at 28°14′N latitude and 116°30′E

Rice blast, caused by pathogen *Xanthomonas oryzae* pv. *oryzae*, is considered as a major disease of rice because of its wide distribution and destructiveness under favorable conditions. Rice blast causes lesion symptoms on leaves, stems, peduncles, panicles, seeds, and even roots. The potential threat for cropping failure makes this disease ranked among the most devastating diseases in rice. It is reported that the rice blast disease can lead to lose one million hectares annually in China alone (Savary et al. 2000; Khush et al. 2009). Exploitation of resistance gene resources for rice breeding is one of the most important ways to control the disease.

*Oryza minuta* J. S. Presl ex C. B. Presl is a tetraploid wild rice, highly resistant to rice blast. By genetic analysis, Amante-Bordeos et al (1992) found that the disease resistance was controlled by a single dominant gene, named *Pi9*. Subsequently, Liu et al (2002) mapped *Pi9* in an approximately 100-kb region on chromosome 6, tightly linked with RFLP markers R2132 and RG6. Finally, this broad-spectrum rice blast resistance gene was cloned using a map-based cloning strategy. It turns out that *Pi9* encodes a nucleotide-binding site-leucine-rich repeat protein, as a member of a multi-gene family in rice (Qu et al. 2006).

Jeung et al (2007) identified a new gene in the introgression line IR65482-4-136-2-2 that has inherited the resistance gene from an EE genome wild *Oryza* species, *O*. *australiensis* (Acc. 100882). Genetic and molecular analysis localized a major resistance gene, *Pi40*(*t*), on the short arm of chromosome 6, within 70-Kb chromosomal region narrowed by two molecular markers RM3330 and S2539. *Pi40*(*t*) was validated using the most virulent isolates identified in Korea and the Philippines, suggesting a broad resistance spectrum.

Li et al (2009) evaluated blast resistance for 21 progenies from crossing with common wild rice, and obtained three stably resistance progenies. Preliminary analysis showed that the rice blast resistance was controlled by dominant genes. Geng et al (2008) cloned rice blast resistance gene *Pi-ta+* from Jinghong erect type of common wild rice. The *Pi-ta+* coding region shares 99.86% and 98.78% of homologous identity with Japanese waxy cultivar Yashiro-mochi and Yuanjiang type of common wild rice, respectively in the corresponding regions. There are four nucleoti‐ des difference in the coding region and six in intron of the cloned *Pi-ta+* gene, compared with *Pi-ta* from Yashiro-mochi. The allele in the Jinghong *Pi-ta+* gene is very rare in nature, because there is an alanine rather than a serine at position 918 within the predicted amino acid sequence of *Pi-ta+* . The *Pi-ta+* allele can display disease resistance in response to blast pathogens in rice plant cells.

## **2.2. Bacterial blight resistance**

Bacterial blight is caused by *Xanthomonas oryzae* pv. *oryzae*. Yield losses due to bacterial blight are variable, heavily dependent on the cultivar used and the environment. In Japan, yield losses ranged typically between 20% and 30% after distribution of high-yielding dwarf varieties (Mew et al. 1993). Among tropical climates, yield losses up to 75% were reported in Indonesia, India, and the Philippines (Nino-Liu et al. 2006). It is of great importance to explore elite bacterial blight resistance genes in rice. By now, a total of 35 related genes have been reported, and nine of which were from wild rice, i.e. *Xa21*(*t*), *Xa23*(*t*), *Xa27*(*t*), *Xa29*(*t*), *Xa30*(*t*), *Xa32*(*t*), *xa32*(*t*), *Xa35*(*t*) and *Xa36*(*t*).

In 1977, Dr. S. Devadath found that a strain of *Oryza barthii* from Mali is resistant to all the races of bacterial blight in India. Then, Khush et al (1989) found that this strain is akin to *O*. *longistaminata*, thus crossed it with IR24, which is susceptible to six races of bacterial blight in Philippines. The F1 was resistant to the six races, thereby showing that the resistance of *O*. *longistaminata* was dominant. They designated this gene as *Xa21*(*t*). By developing nearly isogenic lines of *Xa21*(*t*), Ronald et al (1992) mapped locus *Xa21*(*t*) to a region larger than 270 kb on chromosome 11. By positional cloning, Song et al (1995) isolated *Xa21*(*t*). The sequence of the predicted protein, which carries both a leucine-rich repeat motif and a serine-threonine kinase-like domain, suggests a role in cell surface recognition of a pathogen ligand and subsequent activation of an intracellular defense response. Furthermore, they demonstrated that the transgenic rice plants carrying the cloned *Xa21*(*t*) gene display high levels of resistance to the pathogen.

*Oryza minuta* J. S. Presl ex C. B. Presl is a tetraploid wild rice, highly resistant to rice blast. By genetic analysis, Amante-Bordeos et al (1992) found that the disease resistance was controlled by a single dominant gene, named *Pi9*. Subsequently, Liu et al (2002) mapped *Pi9* in an approximately 100-kb region on chromosome 6, tightly linked with RFLP markers R2132 and RG6. Finally, this broad-spectrum rice blast resistance gene was cloned using a map-based cloning strategy. It turns out that *Pi9* encodes a nucleotide-binding site-leucine-rich repeat

Jeung et al (2007) identified a new gene in the introgression line IR65482-4-136-2-2 that has inherited the resistance gene from an EE genome wild *Oryza* species, *O*. *australiensis* (Acc. 100882). Genetic and molecular analysis localized a major resistance gene, *Pi40*(*t*), on the short arm of chromosome 6, within 70-Kb chromosomal region narrowed by two molecular markers RM3330 and S2539. *Pi40*(*t*) was validated using the most virulent isolates identified in Korea

Li et al (2009) evaluated blast resistance for 21 progenies from crossing with common wild rice, and obtained three stably resistance progenies. Preliminary analysis showed that the rice blast resistance was controlled by dominant genes. Geng et al (2008) cloned rice blast resistance gene

*Pi-ta* from Yashiro-mochi. The allele in the Jinghong *Pi-ta+* gene is very rare in nature, because there is an alanine rather than a serine at position 918 within the predicted amino acid sequence

Bacterial blight is caused by *Xanthomonas oryzae* pv. *oryzae*. Yield losses due to bacterial blight are variable, heavily dependent on the cultivar used and the environment. In Japan, yield losses ranged typically between 20% and 30% after distribution of high-yielding dwarf varieties (Mew et al. 1993). Among tropical climates, yield losses up to 75% were reported in Indonesia, India, and the Philippines (Nino-Liu et al. 2006). It is of great importance to explore elite bacterial blight resistance genes in rice. By now, a total of 35 related genes have been reported, and nine of which were from wild rice, i.e. *Xa21*(*t*), *Xa23*(*t*), *Xa27*(*t*), *Xa29*(*t*), *Xa30*(*t*), *Xa32*(*t*),

In 1977, Dr. S. Devadath found that a strain of *Oryza barthii* from Mali is resistant to all the races of bacterial blight in India. Then, Khush et al (1989) found that this strain is akin to *O*. *longistaminata*, thus crossed it with IR24, which is susceptible to six races of bacterial blight in Philippines. The F1 was resistant to the six races, thereby showing that the resistance of *O*. *longistaminata* was dominant. They designated this gene as *Xa21*(*t*). By developing nearly isogenic lines of *Xa21*(*t*), Ronald et al (1992) mapped locus *Xa21*(*t*) to a region larger than 270 kb on chromosome 11. By positional cloning, Song et al (1995) isolated *Xa21*(*t*). The sequence of the predicted protein, which carries both a leucine-rich repeat motif and a serine-threonine

 from Jinghong erect type of common wild rice. The *Pi-ta+* coding region shares 99.86% and 98.78% of homologous identity with Japanese waxy cultivar Yashiro-mochi and Yuanjiang type of common wild rice, respectively in the corresponding regions. There are four nucleoti‐

. The *Pi-ta+* allele can display disease resistance in response to blast pathogens in rice

gene, compared with

protein, as a member of a multi-gene family in rice (Qu et al. 2006).

and the Philippines, suggesting a broad resistance spectrum.

des difference in the coding region and six in intron of the cloned *Pi-ta+*

*Pi-ta+*

of *Pi-ta+*

plant cells.

**2.2. Bacterial blight resistance**

62 Rice - Germplasm, Genetics and Improvement

*xa32*(*t*), *Xa35*(*t*) and *Xa36*(*t*).

*Xa23*(*t*) was first detected from *O*. *rufipogon* by Zhang (2005), showing resistance to race 6 of bacterial blight in the Philippines. Wang et al (2005) constructed a F2 population of JG30/CBB23 for molecule mapping of the *Xa23*(*t*) in rice. Based on their previous mapping of *Xa23*(*t*) gene, 12 EST markers from Rice Genome Program (RGP) database were surveyed in the susceptible F2 plants and two markers, C189 and CP02662, were found to flank *Xa23*(*t*) gene, with genetic distance of 0.8 cM and 1.3 cM, respectively.

Jin et al (2007) identified a rice bacterial blight resistance germplasm (Y238) from the wild rice species *Oryza rufipogon*, and then they transferred the resistance locus into the cultivated rice to breed near isogenic line. By molecular mapping, the gene *Xa30*(*t*) was mapped on the long arm of rice chromosome 11. Linkage analysis revealed that four molecular markers RM1341, V88, C189 and 03STS located on the same side of *Xa30*(*t*), with genetic distances of 11.4 cM, 11.4 cM, 4.4 cM and 2.0 cM to the candidate gene, respectively.

Gu et al (2004) performed disease evaluation to a *Xa27*(*t*) near-isogenic line, IRBB27 with 35 *Xanthomonas oryzae* pv. *oryzae* strains collected from 11 countries. The *Xa27*(*t*) gene conferred a high level of resistance to 27 strains and moderate resistance to three strains. Resistance of the *Xa27*(*t*) gene was developmentally regulated in IRBB27 and showed semi-dominant or a dosage effect in the cv. CO39 genetic background. Molecular mapping located *Xa27*(*t*) within a genetic interval of 0.052 cM, flanked by markers M964 and M1197 and co-segregated with markers M631, M1230, and M449.

Guo et al (2010) transferred a new rice bacterial blight resistance gene *Xa35*(*t*) from the wild rice species *Oryza minuta* (Acc. No. 101133) into *Oryza sativa* L. (IR24). Through genetic analysis and identification of resistance spectrum, *Xa35*(*t*) showed a high level of resistance to PXO61, PXO112 and PXO339, but was susceptible to PXO86 and PXO99 after inoculation with the five strains of *Xanthomonas oryzae* pv. *oryzae*. With SSR marker analysis, the *Xa35*(*t*) locus was mapped to a 1.80 cM region. This locus was co-segregated with marker RM144, and was 0.7 cM from marker RM6293 on one side and 1.1 cM from marker RM7654 on the other side on rice chromosome 11.

*Xa29*(*t*), which was detected from the wild rice *Oryza officinalis*, has a high resistance to bacterial blight. By molecular mapping, the *Xa29*(*t*) gene was mapped within a 1.3 cM region flanked by RFLP markers C904 and R596 on chromosome 1 (Tan et al. 2004). *Xa32*(*t*), a bacterial blight resistance gene from *Oryzae ustraliensis*, was resistant to *Xanthmonas oryzae* pv. *oryzae* strains P1 (PXO61), P4 (PXO71), P5 (PXO112), P6 (PXO99), P7 (PXO145), P8 (PXO280), P9 (PXO339), and KX085, but susceptible to P2 (PXO86) and P3 (PXO79). *Xa32*(*t*) was mapped within a 2.0 cM interval flanked by two SSR markers RM2064 and RM6293 on the long arm of rice chro‐ mosome 11 (Zheng et al. 2009). Miao et al (2010) detected that the rice germplasm C4059 harbored a bacterial blight resistance gene, and designated it tentatively as *Xa36*(*t*). By analyzing the mapping populations, the gene *Xa36*(*t*) was mapped within a length of 4.5 cM flanked by RM224 and RM2136 on the long arm of rice chromosome 11.

#### **2.3. Others**

Bacterial leaf streak (BLS) is caused by *Xanthomonas oryzae* pv. *Oryzicola* in rice. BLS occurs in Asia and West Africa, and yield losses are up to 30 percent. The symptoms of BLS include translucent interveinal streaks extending to orange lesions which may kill the leaf. Yellowish bacterial exudates may be seen. Bacteria may enter through small wounds on the leaf surface, including insect damage. Plants are susceptible at all stages, but infection is most damaging at the tillering stage. BLS is often prevalent in the rainy season. In order to determine if the resistance genes to the BLS disease were from Guangxi wild rice in China, Huang et al (2008) screened 1655 accessions of Guangxi *Oryza rufipogon Griss*, and identified 57 (1.87%) accessions to be resistant. In another screening, 15 (48.4%) out of 31 accessions of *O*. *officinalis* Wall. ex Watt were resistant.

Sheath blight disease, caused by a soilborne necrotrophic fungus *Rhizoctonia solani* Kühn, is one of the most important diseases in cultivated rice. This disease was first reported in Japan in 1910 and subsequently discovered worldwide (Rush et al. 1992). At present, rice sheath blight widely occurs in most rice-growing areas, including temperate, tropical and subtropical regions in diverse rice production systems (Lee et al. 1983). Sheath blight disease causes approximately 50% yield reduction in test plots of susceptible cultivars (Savary et al. 1996). To identify resistant germplasm to sheath blight disease, Prasad et al (2008) reported seven *Oryza spp*. accessions as moderately resistant, three were *O*. *nivara* accessions (IRGC104705, IRGC100898, and IRGC104443), *O*. *barthii* (IRGC100223), *O*. *meridionalis* (IRGC105306), *O*. *nivara*/*O*. *sativa* (IRGC100943), and *O*. *officinalis* (IRGC105979). Greater effort should be paid to search sheath blight resistant germplasm from wild rice and to transfer the resistant genes into the cultivated rice in the future.

## **3. Insect resistance genes and QTLs identified in wild rice**

Insects are serious constraints to rice production. In Asia alone, yield loss due to insects has been estimated at about 25% (Savary et al. 2000). Insects not only damage the plant by feeding on its tissue, but also are vectors of devastating rice viruses in many cases. All portions of the plant, from panicle to root, are possibly attacked by various insects. And all growth stages of the rice plant, from the seedling to mature stages, are vulnerable. Even after harvest, the grain in store might face the attack from insects (Cramer et al. 1967). Because the resistance sources in cultivated rice are limited, it is important to keep exploring resistant germplasm from wild rice species for cultivar improvements.

Brown planthopper (BPH) is a destructive insect pest to rice in Asian countries where most rice is produced in the world, including China, India, the Philippines, Japan, Korea, Vietnam, etc (Khush 1984). BPH directly damages the plant phloem by using its piercing-sucking mouthparts, resulting in "hopper burn" in the most serious cases. Furthermore, it is also a vector for rice grassy stunt virus and ragged stunt virus, which may cause further yield losses in many Asian countries (Chelliah et al. 1993). Identification and incorporation of new BPH resistance genes from wild rice into modern cultivars are important breeding strategies to control the damage caused by the BPH.

analyzing the mapping populations, the gene *Xa36*(*t*) was mapped within a length of 4.5 cM

Bacterial leaf streak (BLS) is caused by *Xanthomonas oryzae* pv. *Oryzicola* in rice. BLS occurs in Asia and West Africa, and yield losses are up to 30 percent. The symptoms of BLS include translucent interveinal streaks extending to orange lesions which may kill the leaf. Yellowish bacterial exudates may be seen. Bacteria may enter through small wounds on the leaf surface, including insect damage. Plants are susceptible at all stages, but infection is most damaging at the tillering stage. BLS is often prevalent in the rainy season. In order to determine if the resistance genes to the BLS disease were from Guangxi wild rice in China, Huang et al (2008) screened 1655 accessions of Guangxi *Oryza rufipogon Griss*, and identified 57 (1.87%) accessions to be resistant. In another screening, 15 (48.4%) out of 31 accessions of *O*. *officinalis* Wall. ex

Sheath blight disease, caused by a soilborne necrotrophic fungus *Rhizoctonia solani* Kühn, is one of the most important diseases in cultivated rice. This disease was first reported in Japan in 1910 and subsequently discovered worldwide (Rush et al. 1992). At present, rice sheath blight widely occurs in most rice-growing areas, including temperate, tropical and subtropical regions in diverse rice production systems (Lee et al. 1983). Sheath blight disease causes approximately 50% yield reduction in test plots of susceptible cultivars (Savary et al. 1996). To identify resistant germplasm to sheath blight disease, Prasad et al (2008) reported seven *Oryza spp*. accessions as moderately resistant, three were *O*. *nivara* accessions (IRGC104705, IRGC100898, and IRGC104443), *O*. *barthii* (IRGC100223), *O*. *meridionalis* (IRGC105306), *O*. *nivara*/*O*. *sativa* (IRGC100943), and *O*. *officinalis* (IRGC105979). Greater effort should be paid to search sheath blight resistant germplasm from wild rice and to transfer the resistant genes into

Insects are serious constraints to rice production. In Asia alone, yield loss due to insects has been estimated at about 25% (Savary et al. 2000). Insects not only damage the plant by feeding on its tissue, but also are vectors of devastating rice viruses in many cases. All portions of the plant, from panicle to root, are possibly attacked by various insects. And all growth stages of the rice plant, from the seedling to mature stages, are vulnerable. Even after harvest, the grain in store might face the attack from insects (Cramer et al. 1967). Because the resistance sources in cultivated rice are limited, it is important to keep exploring resistant germplasm from wild

Brown planthopper (BPH) is a destructive insect pest to rice in Asian countries where most rice is produced in the world, including China, India, the Philippines, Japan, Korea, Vietnam, etc (Khush 1984). BPH directly damages the plant phloem by using its piercing-sucking mouthparts, resulting in "hopper burn" in the most serious cases. Furthermore, it is also a

flanked by RM224 and RM2136 on the long arm of rice chromosome 11.

**3. Insect resistance genes and QTLs identified in wild rice**

**2.3. Others**

64 Rice - Germplasm, Genetics and Improvement

Watt were resistant.

the cultivated rice in the future.

rice species for cultivar improvements.

Ishii et al (1994) found an introgression line from wild species *Oryza australiensis* resistant to three biotypes of BPH, and named the gene *Bph10*(*t*). RFLP analysis resulted in a linkage of the gene *Bph10*(*t*) with RG457 on chromosome 12 at a distance of 3.68 +/- 1.29 cM. A BPH biotype-4 resistance gene *Bph13*(*t*) was identified from *Oryza officinalis* Wall. Using RILs where parents "IR50" (cultivar which is susceptible to BPH Biotype-4) and "IR54745-2-21-12-17-6" (a line with *Oryza officinalis*-derived resistance to BPH biotype-4) are included, *Bph13*(*t*) was located on chromosome 3, linked with a RAPD marker AJ09b with the distance of 1.3 cM (Renganayaki et al. 2002).

Later, Jena et al (2006) identified a major BPH resistance gene *Bph18*(*t*) from an introgression line (IR65482-7-216-1-2) with wild species *Oryza australiensis*. Genetic analysis concluded that *Bph18*(*t*) is a dominant gene located within a 0.843 Mb physical interval flanked by markers R10289S and RM6869 on the long arm of chromosome 12, where three BAC clones are present.,. Subsequently, Jena et al (2010) successful cloned the *Bph18*(*t*) gene. *Bph14* is a BPH resistance gene at seedling and maturity stages. Du et al (2009) cloned *Bph14* gene to encode a coiled-coil, nucleotide-binding, and leucine-rich repeat (CC-NB-LRR) protein. Sequence comparison indicates that *Bph14* carries a unique LRR domain that might function in recognizing the BPH insect invasion and activating the defense response. *Bph14* is predominantly expressed in vascular bundles, the site of BPH feeding. Expression of *Bph14* activates the salicylic acid signaling pathway and induces callose deposition in phloem cells and trypsin inhibitor production after BPH infestation, thus reducing the BPH feeding to yield low growth rate and longevity of BPH insects.

Rahman et al (2009) conducted a genetic analysis of BPH resistance using an F2 population derived from a cross between an introgression line, IR71033-121-15 from *Oryza minuta* (Accession number 101141) and a susceptible Korean *japonica* variety, Junambyeo. Two major QTLs were identified for BPH resistance. One was mapped to 193.4 kb region located on the short arm of chromosome 4, and the other was mapped to a 194.0 kb region on the long arm of chromosome 12.

## **4. Abiotic stress resistance genes and QTLs identified in wild rice**

Abiotic stresses including high salinity, drought and flood, high and low temperatures are largely limiting productivity of rice crops in large areas of the world. According to Hossain (1996), abiotic stresses affect rice cultivation more than the biotic stresses. Improving the resistance to abiotic stresses will increase agricultural productivity and extend cultivatable areas of rice. There is, therefore, a strong demand for rice cultivars resistant to abiotic stresses.

Based on physiological studies on stress responses, recent progress in plant molecular biology has enabled discovery of many genes involved in stress tolerance. These genes include functional genes which protect the cell (e.g., enzymes for generating protective metabolites and proteins), and regulatory genes which regulate stress response (e.g., transcription factors and protein kinases). Wild rice is the ancestor of cultivated rice, having been an important gene pool due to its survival ability in wild conditions and suffering from natural selection. Therefore, it is of great significance to study genetic basis of abiotic stress resistance as well as to explore new related genes in wild rice.

## **4.1. Cold resistance**

Cold stress is a common problem for rice cultivation, and is a significant factor affecting global food production since cold stress can cause poor germination, slow growth, withering, and anthers injury on rice plants (Andaya et al. 2007). Annually, about 15 million hectares of rice in the world suffered from cold damage (Zhang et al. 2005). In south Asia, about 7 million hectares cannot be planted timely because of the low temperature stress (Sthapit et al. 1998). Consequently, development of rice cultivars with cold tolerance is recognized as one of the important breeding objectives.

Various methods have been adapted to improve rice resistance to low temperature stress (Bertin et al. 1997; Takesawa et al. 2002). With increasing emphasis on F1 hybrid rice production in public institutions and private breeding companies, lots of landraces with diversified genetic background continue to decrease, which makes the genetic base of parental materials become more and more narrower. As a result, development of cultivars for strong cold tolerance becomes increasingly difficult using intra-variation. There is thus an urgent need to study the cold-tolerance character and excavate related genes in wild rice to broaden rice gene pool for developing cold tolerance cultivars.

Genetic analysis of cold tolerance at seedling and/or booting stage has resulted in the iden‐ tification of many QTLs (Lou et al. 2007; Zeng et al. 2009). Zheng et al (2011) constructed chromosome segment substitution line (CSSL) populations using two core accessions of common wild rice (DP15 and DP30) as donor parents and cultivar 9311 as recipient parent. Thus, they identified cold tolerance QTLs effective at the seedling stage. Two donor lines, DP15 and DP30, are different in the number, location and effect of QTLs for cold tolerance. A total of 19 cold tolerance QTLs were detected, and clustered on chromosome 3 and chromosome 8. The survival rates ranged 8 – 74% after cold treatment among the CSSLs. A major QTL *qSCT-3-1* was mapped between SSR markers RM15031 and RM3400, near the centromere of chromosome 3 on the long arm with a distance of 1.8 cM.

Dongxiang wild rice can winter over successful in Wuhan, Hubei province, China, where the lowest temperature can be down to -12C in winter (Liu et al. 2003). In order to transfer cold tolerance gene from Dongxiang wild rice, we have developed introgression lines (ILs) through a backcrossing and single-seed descent program using an elite *indica* restoring cultivar Xieqingzao B (*O*. *sativa* L.) as recipient and Dongxiang wild rice as donor parent (Jian et al. 2011). Analyzing the introgression lines found that the IL5243 and IL5335 were the best for cold tolerant ability (Chen et al. 2013). Genetic analysis using SSR markers further confirmed that a part of alien DNA has been transferred from the common wild rice into IL5243 and IL5335. Therefore, IL5243 and IL5335 might be excellent bridging germplasm for breeding programs to improve cultivar tolerance to cold stress.

## **4.2. Soil salinity resistance**

functional genes which protect the cell (e.g., enzymes for generating protective metabolites and proteins), and regulatory genes which regulate stress response (e.g., transcription factors and protein kinases). Wild rice is the ancestor of cultivated rice, having been an important gene pool due to its survival ability in wild conditions and suffering from natural selection. Therefore, it is of great significance to study genetic basis of abiotic stress resistance as well as

Cold stress is a common problem for rice cultivation, and is a significant factor affecting global food production since cold stress can cause poor germination, slow growth, withering, and anthers injury on rice plants (Andaya et al. 2007). Annually, about 15 million hectares of rice in the world suffered from cold damage (Zhang et al. 2005). In south Asia, about 7 million hectares cannot be planted timely because of the low temperature stress (Sthapit et al. 1998). Consequently, development of rice cultivars with cold tolerance is recognized as one of the

Various methods have been adapted to improve rice resistance to low temperature stress (Bertin et al. 1997; Takesawa et al. 2002). With increasing emphasis on F1 hybrid rice production in public institutions and private breeding companies, lots of landraces with diversified genetic background continue to decrease, which makes the genetic base of parental materials become more and more narrower. As a result, development of cultivars for strong cold tolerance becomes increasingly difficult using intra-variation. There is thus an urgent need to study the cold-tolerance character and excavate related genes in wild rice to broaden rice gene pool for

Genetic analysis of cold tolerance at seedling and/or booting stage has resulted in the iden‐ tification of many QTLs (Lou et al. 2007; Zeng et al. 2009). Zheng et al (2011) constructed chromosome segment substitution line (CSSL) populations using two core accessions of common wild rice (DP15 and DP30) as donor parents and cultivar 9311 as recipient parent. Thus, they identified cold tolerance QTLs effective at the seedling stage. Two donor lines, DP15 and DP30, are different in the number, location and effect of QTLs for cold tolerance. A total of 19 cold tolerance QTLs were detected, and clustered on chromosome 3 and chromosome 8. The survival rates ranged 8 – 74% after cold treatment among the CSSLs. A major QTL *qSCT-3-1* was mapped between SSR markers RM15031 and RM3400, near the centromere of chromosome

Dongxiang wild rice can winter over successful in Wuhan, Hubei province, China, where the lowest temperature can be down to -12C in winter (Liu et al. 2003). In order to transfer cold tolerance gene from Dongxiang wild rice, we have developed introgression lines (ILs) through a backcrossing and single-seed descent program using an elite *indica* restoring cultivar Xieqingzao B (*O*. *sativa* L.) as recipient and Dongxiang wild rice as donor parent (Jian et al. 2011). Analyzing the introgression lines found that the IL5243 and IL5335 were the best for cold tolerant ability (Chen et al. 2013). Genetic analysis using SSR markers further confirmed that a part of alien DNA has been transferred from the common wild rice into IL5243 and

to explore new related genes in wild rice.

66 Rice - Germplasm, Genetics and Improvement

**4.1. Cold resistance**

important breeding objectives.

developing cold tolerance cultivars.

3 on the long arm with a distance of 1.8 cM.

Soil salinity is one of the major agricultural problems affecting crop productivity worldwide (Rozema et al. 2008). Of the cereals, rice is one of the most salt-sensitive crops (Shelden et al. 2013). The effects of salinity on rice have been reported to reduce seed germination (Hakim et al. 2010), decrease growth and survival of seedlings (Lutts et al. 1995), damage the structure of chloroplasts (Yamane et al. 2008), reduce photosynthesis (Moradi et al. 2007) and inhibit seed set and grain yield (Asch et al. 2000). Improving evaluation methodologies to identify genetic sources and excavating responsible genes for improving cultivar salt resistance is of continuing importance in rice. *Oryza coaretata* is an Asian wild rice species, occurring mostly in the coastal areas of India. This species is highly resistant to salt because of survival ability in the coastal environments. *O*. *coarctata* has some special unicellular hairs (trichomes) on the adaxial surface of leaves. The hairs efficiently maintain a low concentration of toxic salts in the plant tissue (Bal et al. 1986).

#### **4.3. Low-phosphorus resistance**

Phosphorus is one of essential nutritive elements for rice growth and development (Abel et al. 2002). The phosphorus content may be too little in the soil to be able to meet the needs of rice growth. It has been estimated that 5.7 billion hectares of land are deficient in phosphorus worldwide. Phosphorus deficiency is considered as one of the greatest limitations in agricul‐ tural production (Schachtman et al. 1998; Lynch et al. 2008).

Chen et al (2011) identified the low-phosphorus resistance ability of Dongxiang wild rice at the seedling stage by using the cultivated low-phosphorus sensitive varieties as the control. The results showed that Dongxiang wild rice has strong low-phosphorus resistance ability. And then, they developed BILs by using Dongxiang wild rice as donor parent and the lowphosphorus sensitive variety Xieqingzao B as recurrent parent. By analyzing the morpholog‐ ical indices, they found that the low-phosphorus resistance lines under low-phosphorus stress had higher values of relative leaf age, relative plant height, relative shoot dry mass, and relative soluble content, but low values of relative yellow leaf number and relative malondialdehyde content, suggesting that the low-phosphorus resistance capability of the low-phosphorus resistance lines was mainly attributed to the high phosphorus utilization efficiency of the lines, namely, low-phosphorus resistance lines had stronger capability in synthesizing dry mass with per unit phosphorus uptake (Chen et al. 2011).

## **4.4. Drought resistance**

Because of global climate warming and increasing scarcity of water resource, drought stress and water scarcity have severely impacted the security of rice production (Farooq et al. 2009). At least 23 million hectares of rice area in Asia are estimated to be drought-prone (Pandey et al. 2005). To date, however, the major challenge for research communities is the relatively limited progress achieved in developing high yielding rice cultivars with drought resistance (Rabello et al. 2008). Therefore, the improvement of drought resistance in newly developed cultivars, for the wide adaptability across rice-growing ecologies, has become a major priority in rice breeding programs. Accordingly, identifying genes from new germplasm resources such as wild rice has become extremely important for drought resistance, which will lay the foundation for utilization of drought resistance gene and genetic improvement of drought resistance (Xie et al. 2004).

Our group has already carried out preliminary experiments for many years on characterization of Dongxiang wild rice for genetic differentiation and conservation, and utilization (Xie et al. 2010). We proved that Dongxiang wild rice has strong drought resistance (Figure 3). Subse‐ quently, Hu et al (2013) constructed BIL population using *Indica* restorer line R974 (*Oryza sativa* L.) and Dongxiang wild rice. Using a mixed inheritance model for both major genes and minor genes, they found that the inheritance of drought-resistance at seedling stage was controlled by two independent genes plus polygenes. Therefore, Dongxiang wild rice could be precious resource for genetic improvement of drought resistance in cultivar development.

**Figure 3.** Dongxiang wild rice has strong drought resistance.

## **5. Yield-enhancing QTLs from wild rice**

In general, wild rice has smaller seeds and other undesirable traits compared to cultivars, and thus appears not to be appropriate for a donor to enhance yield in cultivars. Howev‐ er, molecular studies have demonstrated that phenotypically poor wild rice contains some genes important for improving cultivar yield (Tanksley et al. 1996). Some wild-QTL alleles

are favorable for some traits, but may be associated with deleterious effects on other traits. The positive QTLs from *O. rufipogon* may be potentially useful for breeding high yield cultivar if the disadvantage linkage drag could be broken through careful selection. In addition, other potentially beneficial QTLs for yield-related traits are often linked to the QTLs conferring negative traits. For example, *gpp1.1* with yield increasing effect is closely linked with a negative QTL to increase plant height because this QTL is closely linked to *sd1* locus (Cho et al. 2003). Brondani et al (2002) detected specific marker regions to strongly associate with multiple yield-related traits including panicle number, spikelets per pani‐ cle, seed set percentage, 100-grain weight, grain yield per plant, filled grain number per panicle and grain yield per panicle.

By using a BC2F5 population derived from the cross between Zhenshan 97 and a wild rice, Wu et al (2012) identified a QTL region flanked by SSR marker RM481 and RM2 on chromosome 7. This QTL has pleiotropic effects on heading date, spikelets per panicle, and grain yield per plant. The alleles from wild rice have increasing effects on these phenotypic traits contributable to grain yield.

Fu et al (2010) developed an advanced backcross population by using an accession of common wild rice collected from Yuanjiang County, Yunnan Province, China, as the donor and an elite cultivar 9311 as the recurrent parent. From this population, several QTLs originating from *O. rufipogon* display beneficial effects for yield-related traits in the 9311 genetic background. In addition, five QTLs controlling yield and its components are newly identified, and they are potentially novel alleles in Yuanjiang common wild rice. Three regions underling significant QTLs for several yield-related traits are detected on chromosome 1 (RM212-RM5362), 7 (RM125-RM1135) and 12 (RM7003-RM277).

Xiao et al (1998) identified two yield-enhancing QTLs, *yld1.1* and *yld2.1*, from *O. ru*fi*pogon* using BC2 populations. QTLs *yld1.1* and *yld2.1* have been transferred to the elite restorer lines Ce64-7, 9311 and Minghui63 by marker-assisted selection (MAS), and they are confirmed to produce significant yield-enhancing effects in field tests. Xie et al (2006) fine mapped a yield-enhancing QTL cluster using a BC3F4 population derived from a cross between the Korean *japonica* cultivar Hwaseongbyeo and *O. ru*fi*pogon.* The cluster contained seven QTLs for 1000-grain weight, spikelets per panicle, grains per panicle, panicle length, spikelet density, heading date and plant height. The alleles from the low-yielding *O. ru*fi*pogon* parent are beneficial in the Hwaseongbyeo background.

## **6. Present problems and future directions**

limited progress achieved in developing high yielding rice cultivars with drought resistance (Rabello et al. 2008). Therefore, the improvement of drought resistance in newly developed cultivars, for the wide adaptability across rice-growing ecologies, has become a major priority in rice breeding programs. Accordingly, identifying genes from new germplasm resources such as wild rice has become extremely important for drought resistance, which will lay the foundation for utilization of drought resistance gene and genetic improvement of drought

Our group has already carried out preliminary experiments for many years on characterization of Dongxiang wild rice for genetic differentiation and conservation, and utilization (Xie et al. 2010). We proved that Dongxiang wild rice has strong drought resistance (Figure 3). Subse‐ quently, Hu et al (2013) constructed BIL population using *Indica* restorer line R974 (*Oryza sativa* L.) and Dongxiang wild rice. Using a mixed inheritance model for both major genes and minor genes, they found that the inheritance of drought-resistance at seedling stage was controlled by two independent genes plus polygenes. Therefore, Dongxiang wild rice could be precious resource for genetic improvement of drought resistance in cultivar development.

In general, wild rice has smaller seeds and other undesirable traits compared to cultivars, and thus appears not to be appropriate for a donor to enhance yield in cultivars. Howev‐ er, molecular studies have demonstrated that phenotypically poor wild rice contains some genes important for improving cultivar yield (Tanksley et al. 1996). Some wild-QTL alleles

resistance (Xie et al. 2004).

68 Rice - Germplasm, Genetics and Improvement

**Figure 3.** Dongxiang wild rice has strong drought resistance.

**5. Yield-enhancing QTLs from wild rice**

As the wild relatives and ancestor of cultivated rice, wild rice carries various characteris‐ tics resistant to biotic and abiotic stresses, beneficial agronomic traits, and abundant genetic diversity, which have been lost in the cultivated rice due to breeding activities (Sakai et al. 2010). Thus, it is an extremely important resource for improving important traits in cultivated rice (Xie et al. 2004). However, loss of wild rice genetic diversity was sped up by increasing deterioration of original habitat. For example, the Dongxiang wild rice was sharply reduced from nine populations in nine isolated areas in 1978 to three in 1995 (Hu et al. 2011). The dramatic reduction makes the unique gene pool endangered. Therefore, it is necessary to accelerate a rational conservation for effective utilization of these survived genetic resources.

Breeders have long recognized the intrinsic value of wild rice for improving the traits of modern cultivars. The most successful examples to utilize wild rice in the history of rice breeding include the use of *Oryza nivara* genes to provide long-lasting resistance to grassy stunt virus (Plucknett et al. 1987), and the use of *O. spontanea* as a source of wild abortive cytoplasmic male sterility, which has made a cornerstone for today's hybrid rice (Li et al. 1988). However, despite these successes, it is still difficult to utilize wild rice for the improvement of quantita‐ tively inherited traits. Great progress of molecular markers and maps makes it possible to identify individual QTL associated with elite traits from wild rice, which will help transfer the valuable QTLs into modern cultivars to improve their qualities (Tanksley et al. 1996).

Nowadays, QTL studies for mining favorable genes from wild rice species are receiving more and more attentions in global rice community. Several studies have successfully identified and introduced the QTL enhancing alleles from wild rice for yield-related traits into high-yielding elite cultivars (He et al. 2006; Deng et al. 2007; Tan et al. 2008). In addition, some QTLs related to rice quality traits were also detected using wild rice introgression lines (Hao et al. 2006; Garcia-Oliveira et al. 2009). Molecular mapping of these good genes will help discover and make full use of the elite resources of wild species to broaden the genetic base of modern cultivars. However, only a few genes have been cloned from wild rice, and the mechanism for those excellent traits from wild rice are far from being clarified. Cloning more genes from wild rice should be emphasized in the future, which will help make full use of these elite resources more effectively.

In summary, as a rare germplasm resource, wild rice is of great significance to our agricultural heritage and biodiversity protection. Research reveals that wild rice not only has many elite genes which have lost in cultivated rice, but also maintains a greater genetic diversity than cultivated rice. We should use the wild rice to broaden genetic diversity of cultivated rice, by which new cultivars could withstand biotic and abiotic stresses. This is of great significance to assure both high yield and quality in rice production.

## **Author details**

Fantao Zhang and Jiankun Xie\*

\*Address all correspondence to: xiejiankun@yahoo.com

College of Life Sciences, Jiangxi Normal University, China

## **References**

by increasing deterioration of original habitat. For example, the Dongxiang wild rice was sharply reduced from nine populations in nine isolated areas in 1978 to three in 1995 (Hu et al. 2011). The dramatic reduction makes the unique gene pool endangered. Therefore, it is necessary to accelerate a rational conservation for effective utilization of these survived

Breeders have long recognized the intrinsic value of wild rice for improving the traits of modern cultivars. The most successful examples to utilize wild rice in the history of rice breeding include the use of *Oryza nivara* genes to provide long-lasting resistance to grassy stunt virus (Plucknett et al. 1987), and the use of *O. spontanea* as a source of wild abortive cytoplasmic male sterility, which has made a cornerstone for today's hybrid rice (Li et al. 1988). However, despite these successes, it is still difficult to utilize wild rice for the improvement of quantita‐ tively inherited traits. Great progress of molecular markers and maps makes it possible to identify individual QTL associated with elite traits from wild rice, which will help transfer the

valuable QTLs into modern cultivars to improve their qualities (Tanksley et al. 1996).

Nowadays, QTL studies for mining favorable genes from wild rice species are receiving more and more attentions in global rice community. Several studies have successfully identified and introduced the QTL enhancing alleles from wild rice for yield-related traits into high-yielding elite cultivars (He et al. 2006; Deng et al. 2007; Tan et al. 2008). In addition, some QTLs related to rice quality traits were also detected using wild rice introgression lines (Hao et al. 2006; Garcia-Oliveira et al. 2009). Molecular mapping of these good genes will help discover and make full use of the elite resources of wild species to broaden the genetic base of modern cultivars. However, only a few genes have been cloned from wild rice, and the mechanism for those excellent traits from wild rice are far from being clarified. Cloning more genes from wild rice should be emphasized in the future, which will help make full use of these elite resources

In summary, as a rare germplasm resource, wild rice is of great significance to our agricultural heritage and biodiversity protection. Research reveals that wild rice not only has many elite genes which have lost in cultivated rice, but also maintains a greater genetic diversity than cultivated rice. We should use the wild rice to broaden genetic diversity of cultivated rice, by which new cultivars could withstand biotic and abiotic stresses. This is of great significance to

genetic resources.

70 Rice - Germplasm, Genetics and Improvement

more effectively.

**Author details**

Fantao Zhang and Jiankun Xie\*

assure both high yield and quality in rice production.

\*Address all correspondence to: xiejiankun@yahoo.com

College of Life Sciences, Jiangxi Normal University, China


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## **Rice Germplasm in Korea and Association Mapping**

Aye Aye Khaing, Gang Li, Xiao Qiang Wang, Min Young Yoon, Soon Wook Kwon, Chang Yong Lee, Beom Seok Park and Yong Jin Park

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56614

## **1. Introduction**

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(*Oryza sativa* L.) grown under salinity. Plant Prod Sci 11:139–145

rice revealed by SSR. Genes Genom 31:143–154

Plant Sci 168:527–534

78 Rice - Germplasm, Genetics and Improvement

Rice is a traditional staple food crop in Korea and many other countries. Although the center of rice origin is still unclear, it is believed to be introduced from China to the Korean Peninsula in the early Bronze Age via one of two possible routes—across the West Sea or along the northeastern seashore from China according to Hammer (2005) and Vavilov (1935). Rice germplasm has evolved through several millennia of cultivation and selection by our farming ancestors. An important consequence of the domestication of both plants and animals is a reduction of genetic variability (Hancock, 2004). Maintaining biodiversity is an important worldwide problem and different countries have various policies intended to preserve biodiversity. Because conservation of biodiversity and ecosystems is closely linked to the quality of human life, the preservation and improvement of ecosystems are problematic for agriculture.

Genetic diversity in a crop species is essential for sustained high productivity. Breeding efforts have been devoted to improving grain quality, yield potential, resistance to diseases and insect pests, and environmental stress tolerance. Progress in plant breeding requires a continuous supply of genes or gene-complexes. In this respect, the researcher is often handicapped by the limited availability of germplasm resources. The assembly of large varietal collections, systematic screening for desired traits and subsequent incorporation of the relevant genes into existing cultivars is imperative to meet these needs. The use of landrace varieties has increased in recent years. Wild rice accessions have contributed greatly to rice breeding as a source of resistance genes (*e.g.*, *Xa21, BPH14, BPH15*) (Ronald *et al*., 1992; Song *et al*., 1995; Yang *et al*., 2004; Du *et al*., 2009; Hu *et al*., 2012). Much of the diversity in the rice gene pool is contained in gene banks around the world. Molecular biology has contributed significantly to an increased

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

understanding of many aspects of plant biology by generating technologies and methods of analysis that provide new approaches or supplement classical methods of analysis. Plant genetic resource scientists and other researchers are increasingly aware of the potential benefits of applying new technologies to germplasm conservation and research.

The integration of genetic data with molecular biotechnology will help breeders produce new rice varieties with the desired traits and make the conservation of rice genetic resources more efficient. Because of newly developed methods for association mapping of genes or QTLs related to desired traits, many genome-wide association analyses have been conducted in rice and resulted in valuable genome-wide association maps to describe the genetic architecture of complex traits. However, further efforts are needed to obtain more genomic information to fill in the gaps of our knowledge and meet the needs and challenges of rice breeding. This chapter will focus on the status of rice germplasm preservation activities, research programs, and outcomes of association mapping in rice in Korea.

## **2. Research on rice germplasm in Korea**

The Ministry of Foreign Affairs and Trade (2009) had outlined eight major environmental issues as current threats to Korea; global warming, desertification, wildlife extinction, rain forest reduction, acid rain, depletion of the ozone layer, marine pollution, and air pollution. The rate of climate change is faster in Korea than the global average, leading to a rapid reduction in national biodiversity. One hundred and ninety families comprising 4000 species of vascular plants and ferns occur in Korea (Lee and Yim, 1978). Approximately 3700 different kinds of flowering plants are estimated to occur naturally (Chung, 1957; Lee, 1980). Four hundred and seven different endemic taxa in six genera are distributed throughout Korea (Lee, 1982). However, some plant species are on the verge of extinction because of pollution and a wide range of developmental activities during the last 20 years in Korea (Ministry of Envi‐ ronment, 1994), highlighting the importance of conservation efforts (Ahn *et al*., 1994). Conser‐ vation programs usually involve activities such as collection, characterization, evaluation, regeneration, documentation, and storage of each germplasm accession.

The National Biodiversity Strategy was implemented in 1997 to integrate and consolidate plans formulated by various ministries and government institutes, including Comprehensive Biological Resources Conservation Plans. The Rural Development Administration (RDA) Gene Bank is one of the institutions responsible for these plans. Rice research programs covering agronomic practices, physiology, post-harvest technology, grain quality evaluation, rice breeding and genetics, and biotechnology, are led by the National Institute of Crop Science (NICS) under the RDA. Other institutions affiliated with NICS carry out rice research programs to target specific problems in various regions of Korea. From 1980 to 1990, rice sheath blight (*Acrocylindirum oryzae*) was the most destructive disease affecting production from damaging approximately 555,000 hectares of rice paddy fields in Korea. Furthermore, rice pests including brown plant hopper, white-backed plant hopper, and small brown plant hopper attacked 586,000 hectares of rice nationwide during the same period (NASTI, 1996). A continuous cultivation of only five or six cultivars countrywide should be responsible to the extensive losses from the pests.

understanding of many aspects of plant biology by generating technologies and methods of analysis that provide new approaches or supplement classical methods of analysis. Plant genetic resource scientists and other researchers are increasingly aware of the potential benefits

The integration of genetic data with molecular biotechnology will help breeders produce new rice varieties with the desired traits and make the conservation of rice genetic resources more efficient. Because of newly developed methods for association mapping of genes or QTLs related to desired traits, many genome-wide association analyses have been conducted in rice and resulted in valuable genome-wide association maps to describe the genetic architecture of complex traits. However, further efforts are needed to obtain more genomic information to fill in the gaps of our knowledge and meet the needs and challenges of rice breeding. This chapter will focus on the status of rice germplasm preservation activities, research programs,

The Ministry of Foreign Affairs and Trade (2009) had outlined eight major environmental issues as current threats to Korea; global warming, desertification, wildlife extinction, rain forest reduction, acid rain, depletion of the ozone layer, marine pollution, and air pollution. The rate of climate change is faster in Korea than the global average, leading to a rapid reduction in national biodiversity. One hundred and ninety families comprising 4000 species of vascular plants and ferns occur in Korea (Lee and Yim, 1978). Approximately 3700 different kinds of flowering plants are estimated to occur naturally (Chung, 1957; Lee, 1980). Four hundred and seven different endemic taxa in six genera are distributed throughout Korea (Lee, 1982). However, some plant species are on the verge of extinction because of pollution and a wide range of developmental activities during the last 20 years in Korea (Ministry of Envi‐ ronment, 1994), highlighting the importance of conservation efforts (Ahn *et al*., 1994). Conser‐ vation programs usually involve activities such as collection, characterization, evaluation,

The National Biodiversity Strategy was implemented in 1997 to integrate and consolidate plans formulated by various ministries and government institutes, including Comprehensive Biological Resources Conservation Plans. The Rural Development Administration (RDA) Gene Bank is one of the institutions responsible for these plans. Rice research programs covering agronomic practices, physiology, post-harvest technology, grain quality evaluation, rice breeding and genetics, and biotechnology, are led by the National Institute of Crop Science (NICS) under the RDA. Other institutions affiliated with NICS carry out rice research programs to target specific problems in various regions of Korea. From 1980 to 1990, rice sheath blight (*Acrocylindirum oryzae*) was the most destructive disease affecting production from damaging approximately 555,000 hectares of rice paddy fields in Korea. Furthermore, rice pests including brown plant hopper, white-backed plant hopper, and small brown plant hopper attacked 586,000 hectares of rice nationwide during the same period (NASTI, 1996). A continuous

of applying new technologies to germplasm conservation and research.

regeneration, documentation, and storage of each germplasm accession.

and outcomes of association mapping in rice in Korea.

**2. Research on rice germplasm in Korea**

80 Rice - Germplasm, Genetics and Improvement

Rice season normally begins in mid-April and ends in mid-October in Korea. The lowest temperature in both April and October is 13°C. Because of the unprecedented yield loss due to cold damage in 1980 (damage to 80% of total rice hectarage and a yield reduction of 3.9 tons per hectare), cultivation of high-yielding "Tongil-type" rice cultivars declined rapidly, and only high-yielding japonica cultivars have been grown since 1990. In 2010, 20 mid- to latematuring japonica cultivars were grown on 891,493 hectares, accounting for 92.9% of the total rice production area (Kang and Kim, 2012). Large decrease of temperature also occurred in 1971 and 2003, causing damage to 17% and 20% of total rice hectarage, respectively. Preharvest sprouting may become a serious problem for rice production, as well as for other crops, because of the trend in recent years for frequent and unusually heavy rain at harvest time. Breeders are making efforts to address this problem.

Rice breeders see the development of genetically improved cultivars using modern breeding techniques as an efficient way to reduce the losses in rice production caused by biotic and abiotic stresses. Sequencing the rice genome for genotyping and developing marker-assisted selection (MAS) system have fast-tracked research efforts. In the past, most national programs gave a lower priority to collecting wild relatives of rice than to collecting rice cultivars. Wild rice resources are agronomically unattractive, relatively expensive to conserve, and difficult to use. However, wild rice germplasms are known to contain a broad array of useful genes (Hodgkin, 1991). The benefits for the landrace germplasms to be used in breeding new cultivars in response to climate and environment changes in Korea are resistances to diseases in order to maintain superior qualities suited to consumers' preferences. Plant germplasm resource activities in Korea are performed by The Basic Conservation Programme for Nature and Environment (1994–2003) under the Ministry of Environment (NASTI, 1996).

The RDA Gene Bank conserves 24,673 rice accessions, including Korean landraces and wild types. Many gene banks are having financial difficulty to maintain germplasm collections due to a rapid increase of accession number. These problems may restrict a full exploitation, evaluation, and utilization of these accessions, thus managing such collections presents major challenges (Holden, 1984). The concept of a core collection for resolving these problems has received increased attention over the last few years. Germplasm sampling methods include sequential, stratified, biased (for example, by ecology or country), and random sampling. An understanding of factors underlying the traits being sought will help reduce the time required for identification of useful genes. For very rare traits, such as some associated with resistance to virus infection, searching among wild *Oryza* species and *O. glaberrima* may be most appro‐ priate. Efficient methods for evaluation of germplasm to identify genes for crop improvement will promote the use of conserved germplasm. Frankel and Brown (1984) proposed the concept of a core set of lines to resolve such problems. A desire core set should include the maximum genetic diversity in a crop species including its wild relatives with minimum repetition and provide a manageable set of accessions to gene bank managers, plant breeders, and research scientists. Such a core collection would become the focus of the search for desirable new characteristics, detailed evaluation, and development of new techniques. An initial set of 4406 rice accessions was selected based on ecological types and accession passport information, including their countries of origin. Using simple sequence repeat (SSR) genotype information, a final core set comprising the 166 conserved accessions currently used by the RDA was generated by a heuristic approach using the PowerCore software developed by Kim *et al*. (2007). Based on this resulted core set, some association mapping studies have been conducted and further researches are still being undertaken.

## **3. Association mapping in rice**

Association mapping analyzes loci in diverse populations and associates them with both one another and with phenotypes. It is a powerful genetic mapping tool for crops and provides high-resolution, broad allele coverage, and cost-effective gene tagging for the evaluation of plant germplasm resources. Genetic mapping of QTL can be performed in two main ways (Ross-Ibarra *et al*., 2007): (1) Linkage-mapping as well as "gene tagging" using experimental populations (also referred to as "biparental" mapping populations) and (2) LD-mapping or "association mapping" using diverse lines from the natural populations or germplasm collections (Abdurakhmonov and Abdukarimov, 2008). LD mapping is based on identification of associations between phenotype and allele frequencies. The advantage of LD mapping for the breeder is that mapping and commercial variety development can be conducted simulta‐ neously (Langridge and Chalmers, 2005). For phenotypes or traits that are governed by multiple genes or QTLs, diverse alleles or advantageous allele combinations should be mined by association mapping followed by gene-tagging efforts using biparental crosses.

The localization of alleles relies on creating a statistical association between markers and QTL alleles and on the efficacy of markers. For markers to be effective, they must be closely linked to the target locus and be able to detect polymorphisms in material likely to be used in a breeding program. Improvements in marker screening techniques have facilitated the tracking of genes (Subudhi *et al*., 2006). Isoenzyme and other protein-based marker systems had in wide long been used before DNA markers became popular (Langridge and Chalmers, 2005). Since then, a variety of DNA-based molecular markers have been developed, including restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNAs (RAPDs) (Williams *et al*., 1990), amplified fragment length polymorphisms (AFLPs) (Vos *et al*., 1995), SSRs (Litt and Luty, 1989), single-strand conformational polymorphisms (SSCPs), cleaved amplified polymorphic sequence (CAPS) markers (Koniecyzn and Asubel, 1993), sequence tagged sites (STSs) (Olson *et al*. 1989), sequence-characterized amplified region (SCAR) markers (Martin *et al*., 1991), and single nucleotide polymorphisms (SNPs) (Brookes, 1999). A total of 2740 SSRs were experimentally confirmed for rice in 2002 or approximately one SSR for every 157 kb (McCouch, 2002). The highly polymorphic nature of many microsatellites is of particular value (Banni *et al*., 2012, Yoon *et al*., 2012, Moe and Park, 2012, Zhao *et al*., 2012a; Khaing *et al*., 2013) for analysis of closely related genotypes or within narrowly adapted gene pools. Thus, the availability of a high-density SSR map is a valuable public resource for interpretation of the functional significance of the rapidly emerging rice genome sequence information.

The next generation of genetic markers is based on SNPs, which provide an attractive tool for QTL mapping studies and marker-assisted selection in plant breeding programs (Mohler and Schwarz, 2005). SNP discovery is performed primarily *in silico* or using new sequencing approaches (Henry and Edwards, 2009). Large-scale SNP analysis is now possible in plants using a range of platforms. The increasing ease of sequencing and automated genotyping has made association mapping in plants a more attractive option by altering the conventional plant genome mapping method, which involves linkage analysis in a segregating population. This trend is likely to continue as the sequencing of genomes increases. Recently, genome-wide association studies (GWAS) with SNP variants have been conducted using new sequencing platforms (Table 1).

## **3.1. International rice association-mapping activity**

including their countries of origin. Using simple sequence repeat (SSR) genotype information, a final core set comprising the 166 conserved accessions currently used by the RDA was generated by a heuristic approach using the PowerCore software developed by Kim *et al*. (2007). Based on this resulted core set, some association mapping studies have been conducted

Association mapping analyzes loci in diverse populations and associates them with both one another and with phenotypes. It is a powerful genetic mapping tool for crops and provides high-resolution, broad allele coverage, and cost-effective gene tagging for the evaluation of plant germplasm resources. Genetic mapping of QTL can be performed in two main ways (Ross-Ibarra *et al*., 2007): (1) Linkage-mapping as well as "gene tagging" using experimental populations (also referred to as "biparental" mapping populations) and (2) LD-mapping or "association mapping" using diverse lines from the natural populations or germplasm collections (Abdurakhmonov and Abdukarimov, 2008). LD mapping is based on identification of associations between phenotype and allele frequencies. The advantage of LD mapping for the breeder is that mapping and commercial variety development can be conducted simulta‐ neously (Langridge and Chalmers, 2005). For phenotypes or traits that are governed by multiple genes or QTLs, diverse alleles or advantageous allele combinations should be mined

by association mapping followed by gene-tagging efforts using biparental crosses.

The localization of alleles relies on creating a statistical association between markers and QTL alleles and on the efficacy of markers. For markers to be effective, they must be closely linked to the target locus and be able to detect polymorphisms in material likely to be used in a breeding program. Improvements in marker screening techniques have facilitated the tracking of genes (Subudhi *et al*., 2006). Isoenzyme and other protein-based marker systems had in wide long been used before DNA markers became popular (Langridge and Chalmers, 2005). Since then, a variety of DNA-based molecular markers have been developed, including restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNAs (RAPDs) (Williams *et al*., 1990), amplified fragment length polymorphisms (AFLPs) (Vos *et al*., 1995), SSRs (Litt and Luty, 1989), single-strand conformational polymorphisms (SSCPs), cleaved amplified polymorphic sequence (CAPS) markers (Koniecyzn and Asubel, 1993), sequence tagged sites (STSs) (Olson *et al*. 1989), sequence-characterized amplified region (SCAR) markers (Martin *et al*., 1991), and single nucleotide polymorphisms (SNPs) (Brookes, 1999). A total of 2740 SSRs were experimentally confirmed for rice in 2002 or approximately one SSR for every 157 kb (McCouch, 2002). The highly polymorphic nature of many microsatellites is of particular value (Banni *et al*., 2012, Yoon *et al*., 2012, Moe and Park, 2012, Zhao *et al*., 2012a; Khaing *et al*., 2013) for analysis of closely related genotypes or within narrowly adapted gene pools. Thus, the availability of a high-density SSR map is a valuable public resource for interpretation of the functional significance of the rapidly emerging rice genome sequence

The next generation of genetic markers is based on SNPs, which provide an attractive tool for QTL mapping studies and marker-assisted selection in plant breeding programs (Mohler and

and further researches are still being undertaken.

**3. Association mapping in rice**

82 Rice - Germplasm, Genetics and Improvement

information.

Genome mapping of rice was first attempted using linkage analysis of appearance or pheno‐ type (Nagao and Takahashi, 1963). Nowadays, improvement of the linkage map has been achieved using isozymes (Nakagahra, 1977) and RFLPs and SSRs (McCouch *et al*., 1988; Saito *et al*., 1991; McCouch *et al*., 1991 and Yu *et al*., 1991, Tanksley *et al*., 1991, Causse *et al*., 1994; Kurata *et al*., 1994; Harushima *et al*., 1998; McCouch *et al*., 2002). Relatively few associationmapping studies in rice have been performed. Some rice association-mapping studies using various populations and molecular markers are summarized in Table 1 in which most are conducted using SSR markers.

Whole-genome resequencing is a promising strategy to identify the relationship between sequence variation and normal or mutant phenotypes. High-throughput genome resequenc‐ ing - if accurate - has the advantage of allowing researchers to identify the specific nucleotides associated with a given phenotype, and allowing the effective detection and analysis of genetic variations important for molecular breeding. An important application of NGS is the rese‐ quencing of targeted regions for the identification of mutant alleles, and we believe that mapping by sequencing will become a centerpiece in efforts to discover the genes responsible for QTLs. Generally speaking, the availability of a wide range of low- and high-multiplex single nucleotide polymorphism (SNP) assay methods (sequencing accuracy and depth of coverage relies on the experimental design) makes SNPs an ideal marker option for QTL mapping, association analysis, MAS, and the construction of high-density genetic maps for fine mapping and cloning of agronomically important genes (McCouch *et al*., 2010).

SNP discovery by resequencing whole-genome or subgenome of sample materials is often among the first use of a reference genome sequence. For inbreeding species such as rice, lines to be resequenced are normally purified through 1 or 2 generations of inbreeding (via single seed descent). After a DNA sample is resequenced using NGS technology, SNPs can be identified by comparing the sequenced genome with a reference genome like Nipponbare for japonica rice. For example, using information on the features of the B73, Gore *et al*. (2009) targeted the gene fraction of the maize genome for resequencing in the founder inbred lines of the nested association mapping population. Two data sets comprising 3.3 million SNPs were used to produce a first haplotype map ("HapMap") and to analyze the distribution of recom‐ bination and diversity along the maize chromosomes.

A suitable example is the construction of a comprehensive HapMap for rice that was used for the genome-wide associate study of 14 agronomic traits, such as heading date and tillering (Huang *et al*., 2010). The researchers made use of low-coverage (1-fold per rice line) sequence data across lines, for a combined coverage of ~508-fold, and detected 3.6 million SNPs which can explain ~36% of the phenotypic variance for 14 agronomic traits. This work provided a new approach to low-fold sequence coverage, which can be used to detect not only SNPs but also more complex polymorphisms, and partially overcome the need for deeper sequence coverage (Clark, 2010). Further study was performed with the similar strategy for 950 world‐ wide rice varieties by Huang et al. (2012) and thirty-two novel loci associated with flowering time and ten grain-related traits were identified. Additionally, 40 cultivated accessions selected from the major groups of rice and 10 from their wild progenitors (*O. rufipogon* and *O. nivara*) were resequenced to >15X raw data coverage (Xu *et al*., 2012). After mapping the sequence read back to an IRGSP reference genome, the authors investigated the genome-wide variation pattern in a comparative analysis. The data revealed examples of structural variation in genomes and included 6.5 million high-quality SNPs after excluding sites with missing data in any accession. Using these population and SNP data, the authors also identified thousands of new rice genes and tracked down those with a significantly lower diversity in cultivated, but not wild rice. These variants represent a valuable resource for those interested in improving rice cultivars.

Preferences in terms of the processing, cooking, and eating qualities of rice differ globally. Plant breeders are attempting to fulfill consumer demand for rice varieties with specific qualities. The major components of rice grain quality include appearance, milling, cooking, eating, and nutritional aspects. The chemical composition of rice grain is important because of its relationship with eating quality of rice. Amylose content is one of the most important traits that determine cooking quality, which is controlled by a major locus waxy (*Wx*) on chromosome 6 (Wang *et al*., 1992; He *et al*., 1999; Tan *et al*., 1999). Genes associated with amylose content, such as starch synthase IIa (*SSIIa*) and *Wx*, are of particular interest. Sano *et al*. (1986) identified two alleles of the *Wx* locus using RFLP markers that correspond to the indica and japonica subspecies. Most grain quality mapping studies have used the *O. sativa* germplasm (He *et al*., 1999; Tan *et al*., 1999, 2000, 2001; Zhou *et al*., 2003). Borba *et al*. (2010) conducted association mapping study on yield traits and grain quality traits including amylose content, and the significant association detected between amylose content and RM190 was in agreement with previous QTL analyses. Zhao *et al.*(2011) identified 44,100 SNP variants across 413 diverse rice accessions collected from 82 countries and observed GWAS for six categories of traits covering morphology related traits; yield-related traits; seed and grain morphology related traits; stress-related phenotypes; cooking, eating and nutritional-quality-related traits; and plant development, represented by flowering time. This study demonstrated that different traits have different genetic architectures.

Olsen and Purugganan (2002) elucidated the origin and evolution of glutinous rice based on the haplotype of the *Wx* gene. By using dCAPS markers, waxy mutations and waxy rice cultivation were shown to have occurred predominantly in the japonica line during the evolution of domestic rice cultivation (Yamanaka *et al.*, 2004). Genetic polymorphisms of starch-synthesis genes have been demonstrated to be associated with starch physicochemical properties using molecular markers such as SSRs, SNPs, and STSs. These markers can be extremely useful in marker-assisted breeding (Bao *et al*., 2002; Bao *et al*., 2006a). *SSIIa* was identified as the major gene responsible for determination of gelatinization temperature (GT). Among four SNPs in the *SSIIa* gene, some that cause amino acid substitutions have been identified. The GC/TT SNP is strongly associated with GT (Bao *et a*l., 2006b; Nakamura *et al*., 2005; Umemoto and Aoki, 2005; Waters *et al*., 2006).

data across lines, for a combined coverage of ~508-fold, and detected 3.6 million SNPs which can explain ~36% of the phenotypic variance for 14 agronomic traits. This work provided a new approach to low-fold sequence coverage, which can be used to detect not only SNPs but also more complex polymorphisms, and partially overcome the need for deeper sequence coverage (Clark, 2010). Further study was performed with the similar strategy for 950 world‐ wide rice varieties by Huang et al. (2012) and thirty-two novel loci associated with flowering time and ten grain-related traits were identified. Additionally, 40 cultivated accessions selected from the major groups of rice and 10 from their wild progenitors (*O. rufipogon* and *O. nivara*) were resequenced to >15X raw data coverage (Xu *et al*., 2012). After mapping the sequence read back to an IRGSP reference genome, the authors investigated the genome-wide variation pattern in a comparative analysis. The data revealed examples of structural variation in genomes and included 6.5 million high-quality SNPs after excluding sites with missing data in any accession. Using these population and SNP data, the authors also identified thousands of new rice genes and tracked down those with a significantly lower diversity in cultivated, but not wild rice. These variants represent a valuable resource for those interested in improving

Preferences in terms of the processing, cooking, and eating qualities of rice differ globally. Plant breeders are attempting to fulfill consumer demand for rice varieties with specific qualities. The major components of rice grain quality include appearance, milling, cooking, eating, and nutritional aspects. The chemical composition of rice grain is important because of its relationship with eating quality of rice. Amylose content is one of the most important traits that determine cooking quality, which is controlled by a major locus waxy (*Wx*) on chromosome 6 (Wang *et al*., 1992; He *et al*., 1999; Tan *et al*., 1999). Genes associated with amylose content, such as starch synthase IIa (*SSIIa*) and *Wx*, are of particular interest. Sano *et al*. (1986) identified two alleles of the *Wx* locus using RFLP markers that correspond to the indica and japonica subspecies. Most grain quality mapping studies have used the *O. sativa* germplasm (He *et al*., 1999; Tan *et al*., 1999, 2000, 2001; Zhou *et al*., 2003). Borba *et al*. (2010) conducted association mapping study on yield traits and grain quality traits including amylose content, and the significant association detected between amylose content and RM190 was in agreement with previous QTL analyses. Zhao *et al.*(2011) identified 44,100 SNP variants across 413 diverse rice accessions collected from 82 countries and observed GWAS for six categories of traits covering morphology related traits; yield-related traits; seed and grain morphology related traits; stress-related phenotypes; cooking, eating and nutritional-quality-related traits; and plant development, represented by flowering time. This study demonstrated that different

Olsen and Purugganan (2002) elucidated the origin and evolution of glutinous rice based on the haplotype of the *Wx* gene. By using dCAPS markers, waxy mutations and waxy rice cultivation were shown to have occurred predominantly in the japonica line during the evolution of domestic rice cultivation (Yamanaka *et al.*, 2004). Genetic polymorphisms of starch-synthesis genes have been demonstrated to be associated with starch physicochemical properties using molecular markers such as SSRs, SNPs, and STSs. These markers can be extremely useful in marker-assisted breeding (Bao *et al*., 2002; Bao *et al*., 2006a). *SSIIa* was identified as the major gene responsible for determination of gelatinization temperature (GT). Among four SNPs in the *SSIIa* gene, some that cause amino acid substitutions have been

rice cultivars.

84 Rice - Germplasm, Genetics and Improvement

traits have different genetic architectures.

Rice nutritional quality is another important factor for consumer acceptance. In developing countries where rice is the main food, its nutrient content makes a significant contribution to the intake of some essential nutrients. Interest in natural antioxidants in rice is growing due to their role in preventing oxidative stress-related diseases (Aguilar-Garcia *et al*., 2007; Willcox *et al*., 2004; Zhang *et al*., 2013). Rice contains antioxidant compounds such as γ-oryzanols, tocols, and polyphenols, which are associated with a reduced risk of developing chronic diseases such as cardio vascular disease, type 2 diabetes, and some cancers (Liu, 2007; Tan *et al*., 2001; Toyokuni *et al*., 2002). Pigments and flavonoids in colored rice are positively correlated with the antioxidant capacity (Xia *et al*., 2003; Yawadio *et al*., 2007). Association mapping of pigments and flavonoid contents was carried out in brown rice using SSR markers. Significant correla‐ tions between phytochemical content and marker loci were found and markers associated with multi-phenotypic traits such as grain color, phenolic content and antioxidant capacity were identified (Shao *et al*., 2011).

The amino acid composition of rice grain is an important characteristic related to nutrient quality. Environmental conditions, potash, and nitrogen dramatically influence the amino acid and protein contents of rice (Wu *et al*., 2004). Few reports of mapping of QTLs for the contents of protein and amino acids in rice grain have been published. Twelve main effect QTLs (M-QTLs) were identified for 10 components of amino acid content in milled rice. Most of the main effect QTLs for amino acid content tended to co-localize within the genome (Lu et al., 2009).

Although many QTL analyses and genetic mapping studies of grain quality have been conducted, association-mapping studies of biotic and abiotic traits in rice are few in number. The genes or QTLs related to these traits are complex. Genetic mapping, including association mapping and linkage mapping, are useful methods of identifying alleles for these traits. As shown in Table 1, most association-mapping studies focused on morphological and agronomic characteristics. Four studies were related to grain and eating quality and only one addressed disease resistance and aluminum tolerance. Biotic and abiotic stress-tolerance traits remain to be explored by association mapping.




**Table 1.** International association-mapping studies of various traits using various markers in rice.

#### **3.2. Association mapping of rice in Korea**

**Reference**

Agrama *et al*., 2007

Huang *et al*., 2010

Ordonez Jr. *et al*.,

Famoso *et al*., 2011

Bryant *et al*., 2011

2010

Borba *et al*., 2010 242 inbred lines,

86 Rice - Germplasm, Genetics and Improvement

worldwide germplasms

517 landraces including japonica and indica

base

Zhao *et al*., 2010 395 diverse O. sativa accessions

Hu *et al*., 2011 303 *O. sativa*

192 elite rice breeding lines and tropical japonica germplasm

373 diverse *O. sativa*

Lou *et al*., 2011 48 accessions 218 markers (SSRs +

including 361 white rice, 50 red rice, and 6

consisting of 150 rice

black rice

varieties

varieties

accessions

accessions

Zhao *et al*., 2011 413 diverse accessions of *O. sativa*

Shao *et al*., 2011 416 rice accessions

Zhang *et al*., 2011 A core collection

Zhou *et al*., 2012 128 japonica rice

**Number of accessions and population type**

**Number and types of markers used**

Yan *et al*., 2009 90 accessions 108 SSRs + 1 indel Single, dual and total stigma exsertions and spikelet

Wen *et al*., 2009 170 rice accessions 126 SSRs, 6 indels Heading date, plant height, panicle length

Iwata *et al*., 2010 332 rice accessions 179 RFLPs Grain shape variation

Jin *et al*., 2010 416 rice accessions 100 SSRs Grain color

183 rice accessions 123 SSRs Grain length and width, grain length/width ratio,

~3.6 million SNPs Fourteen agronomic traits

97 SSRs Grain quality and flowering time

44,100 SNPs Thirty-four traits of agronomic characteristics,

Grain metabolites

100 SSRs Color parameters of brown rice grain, phenolic

274 SSRs Six morphological traits: glume hair, phenol

152 SSRs Eleven quantitative traits of agronomic and economic importance

cooking and eating quality, disease resistance

content, flavonoid content and antioxidant activity

reaction, length of 1st-2nd rachis internode, glume color at heading, leaf hair, and grain length/width

1,536 SNPs Amylose content

36,901 SNPs Al tolerance

24 SSRs Awness

174 accessions 156 SSRs Silica concentration in rice hulls

Li *et al*., 2011 217 accessions 154 SSRs and 1 indel Yield and yield components among 14 traits

indels)

characteristics

86 SSRs Yield, amylose content, head-milled rice

Iwata *et al*., 2007 332 rice accessions 179 RFLPs Size and shape of milled rice grains

**Traits**

100 grain weight, grain thickness

To identify useful alleles from a representative core set of rice lines for transferring into elite lines, an allele-mining set of 166 accessions (Zhao et al., 2010) was successfully developed from 4046 rice accessions which were selected from 10368 accessions in the Korea RDA Gene Bank by 39 phenotype traits (Chung and Park, 2009), through a modified heuristic algorithm approach based on 15 SSR markers using the PowerCore software (Kim *et al*., 2007). Chung et al. also employed the PowerCore software of Kim et al. to develop the first preliminary core set by phenotypes. The gene diversity and population structure (Q) were analyzed using PowerMarker 3.25 (Liu and Muse, 2005) and Structure 2.2 (Evanno *et al*., 2005) based on 170 SSR markers. Analysis of these data identified the major substruc‐ ture groups when the number of populations was set at four (Fig. 1).

Association mapping was conducted on this core set of lines over the past 2 years (as shown in Table 2). Zhao *et al*. (2012b) analyzed 130 accessions from the core set using 170 SSR markers for association analysis of physicochemical traits related to eating quality. Linkage disequilibrium (LD) patterns and distributions are of fundamental importance for genomewide mapping associations. The mean *r*2 value for all intrachromosomal loci pairs was 0.0940. LD between linked markers decreased with distance. Marker–trait associations were investigated using the unified mixed-model approach considering both Q and kinship (*K*). In total, 101 marker–trait associations (*P* <0.05) were identified using 52 SSR markers covering 12 chromosomes (Fig. 2.). Although direct comparisons of the chromosomal locations of marker–trait associations with previously reported QTLs are difficult because different materials and mapping molecular markers were used, most marker–trait associa‐ tions were located in regions containing QTLs associated with a given trait. Indeed, some were located in similar or proximal regions related to starch synthesis. The new markers related to eating quality will facilitate the understanding of QTLs and marker-assisted selection (Zhao *et al*., 2012b).

**Figure 1.** Values of Δ*K*, with its modal value used to detect the true *K* of four groups (*K* = 4). For each *K* value, five independent runs (blue diamonds) were considered and averaged over the replicates (Zhao *et al*., 2012b).


**Table 2.** Rice association-mapping studies for various traits and marker types in Korea.

Association analysis of candidate genes has been used to trace the origin of agronomically important traits. Lu *et al*. (2012a) used the rice core lines for association-mapping to investigate the relationship between sequence variations from parts of 10 SSRGs and the amylose content (AC) and rapid viscosity analysis (RVA) profiles. Eighty-six sequence variations were found in 10 sequenced amplicons including 79 SNPs, six insertion-deletions (indels), and one polymorphic SSR. Among them, 61 variations were exon-based, of which 41 should lead to amino acid changes. The association mapping results showed a sum of four significant associations between three phenotypic indices and three sequence variations. An ADP glucose pyrophosphorylase small subunit 1 (*OsAGPS1*) SNP (A to G) was significantly associated with increased AC (*P* <0.001, *r*<sup>2</sup> = 15.6%) while a 12-bp deletion of an *AGPase* large subunit 4 (OsAGPL4) (Table 3) was significantly related to decreased breakdown viscosity (*P* <0.001, *r*<sup>2</sup> = 16.6%) in both general linear model (GLM) and mixed linear model (MLM) (Lu *et al*., 2012a). One SNP with a g/c transversion at the 63rd nucleotide downstream of the *OsBEIIb* gene termination codon on rice chromosome 2 was significantly associated with multiple trait

#### Rice Germplasm in Korea and Association Mapping http://dx.doi.org/10.5772/56614 89

**Figure 2.** The positions of markers used and marker–trait associations on 12 chromosomes except unmapped mark‐ ers. Genetic distances are indicated as cM on the left of each map and the corresponding trait-marker names are indi‐ cated on the right. AC, amylase content; PKV, peak viscosity; TV, trough viscosity; BD, breakdown viscosity; FV, final viscosity; SBV, setback viscosity; PKT, peak time; fa, degree of polymerization (DP) ≤12; fb2, 24<DP ≤36; fb3, DP<36 (Zhao *et al*., 2012b).

indices in both the GLM and MLM, including the final viscosity (*P* <0.001, *r*<sup>2</sup> = 23.87%), in both 2009 and 2010, and AC (*P* <0.01, *r*<sup>2</sup> = 11.25%) and trough viscosity (*P* <0.01, *r*<sup>2</sup> = 20.43) in 2010 (Table 4). This study provided a new perspective on the use of allele mining in breeding strategies based on marker-assisted selection (Lu *et al*., 2012b).


AAc, amino acid changes; P\_GLM, adjusted *P*-values with 1000 permutations; P\_MLM, *P*-values significant in the FDR test; amino acid code: S, serine; A, alanine; N, asparagine; D, aspartic acid; BDV, breakdown viscosity; AC, amylase content; FV, final viscosity (Lu and Park, 2012a).

**Table 3.** Association between sequence variations and phenotype

**Figure 1.** Values of Δ*K*, with its modal value used to detect the true *K* of four groups (*K* = 4). For each *K* value, five

**population type Number and types of markers used Traits**

Association analysis of candidate genes has been used to trace the origin of agronomically important traits. Lu *et al*. (2012a) used the rice core lines for association-mapping to investigate the relationship between sequence variations from parts of 10 SSRGs and the amylose content (AC) and rapid viscosity analysis (RVA) profiles. Eighty-six sequence variations were found in 10 sequenced amplicons including 79 SNPs, six insertion-deletions (indels), and one polymorphic SSR. Among them, 61 variations were exon-based, of which 41 should lead to amino acid changes. The association mapping results showed a sum of four significant associations between three phenotypic indices and three sequence variations. An ADP glucose pyrophosphorylase small subunit 1 (*OsAGPS1*) SNP (A to G) was significantly associated with increased AC (*P* <0.001, *r*<sup>2</sup> = 15.6%) while a 12-bp deletion of an *AGPase* large subunit 4 (OsAGPL4) (Table 3) was significantly related to decreased breakdown viscosity (*P* <0.001, *r*<sup>2</sup> = 16.6%) in both general linear model (GLM) and mixed linear model (MLM) (Lu *et al*., 2012a). One SNP with a g/c transversion at the 63rd nucleotide downstream of the *OsBEIIb* gene termination codon on rice chromosome 2 was significantly associated with multiple trait

25 SSRs 16 amino acids

170 SSRs Eating quality

86 SNPs and indels Amylose content, RVA

83 SNPs, indels, and SSRs Amylose content, RVA

independent runs (blue diamonds) were considered and averaged over the replicates (Zhao *et al*., 2012b).

**Numbers of lines and**

84 accessions from land race core set

130 accessions from

104 accessions from

107 accessions from

**Table 2.** Rice association-mapping studies for various traits and marker types in Korea.

core set

88 Rice - Germplasm, Genetics and Improvement

core set

core set

**Reference**

Zhao *et al*., 2009

Zhao *et al*., 2012

Lu *et al*., 2012a

Lu *et al*., 2012b


P\_GLM: adjusted *P*-values with 10,000 permutations in GLM; P\_MLM: nominal *P*-values in MLM; Q value: adjusted nominal *P*-value in MLM by false discovery rate; AC: amylose content; PV: peak viscosity; TV: trough viscosity; FV: final viscosity (Lu and Park, 2012b).

**Table 4.** Associations between sequence variations and eating quality indicators.

Zhao *et al*. (2009) evaluated the contents of 16 amino acids in brown rice by genotyping using 25 SSR markers. A total of 42 marker-trait associations for amino acid content covering three chromosomes (*P* <0.05) were identified by the MLM model (Fig. 4), which accounted for more than 40% of the total variation (Zhao *et al*., 2009). In our research group, association mapping of rice traits related to cold-stress tolerance during germination, preharvest sprouting resist‐ ance, salt tolerance, blast disease resistance, and grain physicochemical properties are under‐ taken using SSRs and SNP variants on advanced resequencing platforms.

In conclusion, association mapping is a promising approach to overcoming the limitations of conventional linkage mapping in plant breeding. Recent research has demonstrated the significant potential of LD-based association mapping of physicochemical traits and other important agronomic traits in rice accessions using SSR/SNP markers. This type of mapping could be a useful alternative to linkage mapping for the detection of marker–trait associations, and lead to implementation of marker-assisted selection in rice breeding programs.

## **4. Future directions**

#### **4.1. Genomics and GWAS in germplasm research**

With the development of next- and third-generation sequencing technologies, the whole genomes of individual rice accessions can now be sequenced with less than \$ 1000 (US dollar).

**Figure 3.** Three regions of putative marker–trait associations on three chromosomes (3, 7, and 8) for amino acid con‐ tent in brown rice. Genetic distances are indicated in cM on the left of each map and the corresponding marker names are indicated on the right. Ala, alanine; Arg, arginine; Asp, aspartic acid; Glu, glutamine; Gly, glycine; His, histidine; Ile, isoleucine; Leu, leucine; Lys, lysine; Met, methionine; Phe, phenylalanine; Pro, proline; Ser, serine; Thr, threonine; Tyr, tyrosine; Val, valine (Zhao *et al*., 2009).

P\_GLM: adjusted *P*-values with 10,000 permutations in GLM; P\_MLM: nominal *P*-values in MLM; Q value: adjusted nominal *P*-value in MLM by false discovery rate; AC: amylose content; PV: peak viscosity; TV: trough viscosity; FV: final

Zhao *et al*. (2009) evaluated the contents of 16 amino acids in brown rice by genotyping using 25 SSR markers. A total of 42 marker-trait associations for amino acid content covering three chromosomes (*P* <0.05) were identified by the MLM model (Fig. 4), which accounted for more than 40% of the total variation (Zhao *et al*., 2009). In our research group, association mapping of rice traits related to cold-stress tolerance during germination, preharvest sprouting resist‐ ance, salt tolerance, blast disease resistance, and grain physicochemical properties are under‐

In conclusion, association mapping is a promising approach to overcoming the limitations of conventional linkage mapping in plant breeding. Recent research has demonstrated the significant potential of LD-based association mapping of physicochemical traits and other important agronomic traits in rice accessions using SSR/SNP markers. This type of mapping could be a useful alternative to linkage mapping for the detection of marker–trait associations,

With the development of next- and third-generation sequencing technologies, the whole genomes of individual rice accessions can now be sequenced with less than \$ 1000 (US dollar).

and lead to implementation of marker-assisted selection in rice breeding programs.

**Table 4.** Associations between sequence variations and eating quality indicators.

taken using SSRs and SNP variants on advanced resequencing platforms.

viscosity (Lu and Park, 2012b).

90 Rice - Germplasm, Genetics and Improvement

**4. Future directions**

**4.1. Genomics and GWAS in germplasm research**

Also, new efficient genotyping technologies, such as RADs (Restriction Associated DNAs) (Baird et al., 2008) and GBS (Genotyping-by-Sequencing) allow the generation of genotyping data for up to 40,000 genes at low cost in few days.

Natural alleles and alleles obtained from artificially mutagenized populations provide an important resource for crop breeding. By using all available alleles and detailed phenotyp‐ ic data from core sets of rice lines, new genes and useful traits can be identified. Molecu‐ lar tags for useful traits developed using GWASs based on genotypic and phenotypic information can be used to track target traits during segregation of populations in rice breeding (Figure 4).

To identify new alleles from a representative core set of rice lines and transfer them into elite lines, we finally selected 166 from ~25,000 accessions in the RDA Gene Bank. We completed whole-genome resequencing of 84 core accessions with 7x coverage in 2012. We plan to resequence the whole genomes of the remaining 82 core accessions in addition to 84 bred varieties from a validation set in 2013. We are currently undertaking the phenotyping of the core accessions for agronomic traits, and chemical composition for the GWAS analysis with the resequence information. We are also planning to improve the software algorithm for the association analysis to increase the ability to identify alleles from the core set of lines using whole genomic SNP or indel genotype data and phenotypic information. More precise characterization of rice traits that confer resistance to stress from climate change is required to

**Figure 4.** A schematic illustration of inter-disciplinary relationships between genomic research and other fields in the breeding of crop species.

screen useful alleles using GWASs. Using whole-genome genotype information, we are able to develop large numbers of molecular tags across 12 different rice linkage groups based on their contributions to specific phenotypes.

## **4.2. Strategy for identification of major and minor QTLs for molecular breeding**

The core accessions are highly diverse with many traits useful for rice breeding. Upon selection of an accession with a desirable trait, bi-parental mapping populations will be developed using two japonica varieties (Shindongjinbyeo and Junambyeo) and one indica variety (Hanareum‐ byeo). Major QTLs will be surveyed with an F8 RIL-segregating population using wholegenome resequencing of 96 samples for first mapping, and then, we can resequence this target region using the expanded 3000 to 5000 samples for fine mapping till the targeted gene can be cloned. We expect that all major QTLs will contribute more than 10% to target traits. To identify minor QTLs that contribute less than 10% to a target trait, BC4F1 population will be first developed, and then, selfing will be done till BC4F8. The recurrent parent will be an elite line for the purposes of QTL mapping and for transferring target traits into the elite lines. Mapping of minor QTLs will be performed using a BC4F8 segregating population (as shown in Fig. 5).

Natural variation results from the expression of different alleles during evolution. As a result of the contributions to farmers over the past ~8000 years, many important traits have been accumulated in the natural germplasm collections currently maintained in seed banks. Whole genome resequencing allows efficient identification of unused alleles from conserved germ‐ plasm. We are at present developing a platform for allele mining in rice breeding systems using GWAS approaches and diverse germplasm accessions with the support of the Next-Generation BioGreen 21 Program (No.PJ009099) from Rural Development Administration, Republic of Korea. We believe our effort will facilitate the molecular breeding of rice.

screen useful alleles using GWASs. Using whole-genome genotype information, we are able to develop large numbers of molecular tags across 12 different rice linkage groups based on

**Figure 4.** A schematic illustration of inter-disciplinary relationships between genomic research and other fields in the

The core accessions are highly diverse with many traits useful for rice breeding. Upon selection of an accession with a desirable trait, bi-parental mapping populations will be developed using two japonica varieties (Shindongjinbyeo and Junambyeo) and one indica variety (Hanareum‐ byeo). Major QTLs will be surveyed with an F8 RIL-segregating population using wholegenome resequencing of 96 samples for first mapping, and then, we can resequence this target region using the expanded 3000 to 5000 samples for fine mapping till the targeted gene can be cloned. We expect that all major QTLs will contribute more than 10% to target traits. To identify minor QTLs that contribute less than 10% to a target trait, BC4F1 population will be first developed, and then, selfing will be done till BC4F8. The recurrent parent will be an elite line for the purposes of QTL mapping and for transferring target traits into the elite lines. Mapping of minor QTLs will be performed using a BC4F8 segregating population (as shown in Fig. 5).

**4.2. Strategy for identification of major and minor QTLs for molecular breeding**

their contributions to specific phenotypes.

breeding of crop species.

92 Rice - Germplasm, Genetics and Improvement

**Figure 5.** Strategies for identification of major and minor QTLs in rice from selected accessions carrying useful traits through GWAS. The major QTLs will be localized and tagged by molecular markers in the F8 generation. Minor QTLs will be localized using a BC4F8 population.

## **Author details**

Aye Aye Khaing1 , Gang Li1 , Xiao Qiang Wang1 , Min Young Yoon1 , Soon Wook Kwon2 , Chang Yong Lee3 , Beom Seok Park4 and Yong Jin Park1,5\*

\*Address all correspondence to: yjpark@kongju.ac.kr

1 Department of Plant Resources, College of Industrial Sciences, Kongju National Universi‐ ty, Yesan, Republic of Korea

2 Department of Plant Bioscience, College of Natural Resources & Life Science, Pusan Na‐ tional University, Milyang, Republic of Korea

3 Department of Industrial and Systems Engineering, Kongju National University, Kongju, Republic of Korea

4 Department of Agricultural Bio-resources, National Academy of Agricultural Science (NAAS), Rural Development Administration (RDA), Suwon, Republic of Korea

5 Legume Bio-Resource Center of Green Manure (LBRCGM), Kongju National University, Yesan, Republic of Korea

## **References**


**Author details**

94 Rice - Germplasm, Genetics and Improvement

Aye Aye Khaing1

Chang Yong Lee3

Republic of Korea

Yesan, Republic of Korea

**References**

1232

pp.13-27

ty, Yesan, Republic of Korea

, Gang Li1

tional University, Milyang, Republic of Korea

Journal of Plant Genomics. Vol. 2008, 1–18

quenced RAD markers, Plos One 3:3376-84

, Beom Seok Park4

\*Address all correspondence to: yjpark@kongju.ac.kr

, Xiao Qiang Wang1

and Yong Jin Park1,5\*

1 Department of Plant Resources, College of Industrial Sciences, Kongju National Universi‐

2 Department of Plant Bioscience, College of Natural Resources & Life Science, Pusan Na‐

3 Department of Industrial and Systems Engineering, Kongju National University, Kongju,

4 Department of Agricultural Bio-resources, National Academy of Agricultural Science

5 Legume Bio-Resource Center of Green Manure (LBRCGM), Kongju National University,

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102 Rice - Germplasm, Genetics and Improvement


## **Association Mapping of Four Important Traits Using the USDA Rice Mini-Core Collection**

Wengui Yan, Aaron Jackson, Melissa Jia, Wei Zhou, Haizheng Xiong and Rolfe Bryant

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56830

## **1. Introduction**

Classical QTL mapping reveals only a slice of the genetic architecture for a trait because only two alleles that differ between the two parental lines segregate. A comprehensive analysis of genetic architecture requires consideration of a diverse population that represents genetic variation in a species. Association mapping provides an effective method to identify QTL that have effects across a broad spectrum of germplasm (Yu et al. 2006). Many studies have used association mapping for important traits since it was introduced from human genetics (Yu et al. 2006; Kim et al. 2006; Huang et al. 2010; Kang et al. 2008). Genome-wide association scans are expected to be effective when linkage disequilibrium (LD) and marker density are sufficiently high, so that the random markers have a greater chance of being in disequilibrium with QTL across diverse genetic materials (Kim et al. 2006). A substantial number of QTL at close to gene resolution for important traits have been identified by genome-wide association studies (GWAS) in rice (Zhao et al. 2007). Recently, the USDA Rice Mini-Core (URMC) collection was developed and serves as a genetically diversified panel for mining genes of interest (Li et al. 2010). The URMC was derived from 1,794 accessions in the USDA rice core collection using PowerCore software based on 26 phenotypic traits and 70 molecular markers (Agrama et al. 2009). The core collection represents over 18,000 accessions in the USDA global genebank of rice (Yan et al. 2007). The URMC contains 217 accessions originating from 76 countries and covering 14 geographic regions worldwide. The Objective of this review is to analyze the genetic diversity and differentiation of the URMC for genome-wide association mapping of harvest index, grain yield, sheath blight resistance and hull silica concentration.

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **2. Materials and methods**

## **2.1. Rice association panel**

Of 217 accessions in the URMC, 203 belong to *sativa* whereas the remaining belong to other species in *Oryza*, including 8 to *O. glaberrima,* 2 each to *O*. *nivara* and *rufipogon*, and 1 each to *O. glumaepatula* and *latifolia* (Agrama et al. 2009). Pure seed of these accessions were provided by the Genetic Stock *Oryza* Collection (GSOR) (www.ars.usda.gov/spa/dbnrrc/gsor). In this study, 217 accessions were used for genetic structure and diversity analyses, but only 203 *O. sativa* accessions were used for association mapping analyses because the wild relatives, *O. glaberrima*, *nivara, rufipogon, glumaepatula* and *latifolia*, contain many rare alleles, and rare alleles are one of the factors that increase the risk of type I errors or spurious associations (Breseghello and Sorrells 2006).

## **2.2. Location and field experiment**

Evaluations were conducted for 14 traits in two field locations, USDA-ARS Dale Bumpers National Rice Research Center near Stuttgart, Arkansas and USDA-ARS Rice Research Unit near Beaumont, Texas during the growing season of 2009. The Stuttgart test site is located at N 340 27'44" and W 910 24'59", representing a temperate climate with a 243 d frost free period and average temperature of 23.9 C during the growing season. The Beaumont test site is located at N 300 03'47" and W 940 17'45", representing a subtropical climate with a 253 d frost free period and an average temperature of 26.1 C during the growing season. The experiments at both locations utilized a randomized complete block design having three replications with nine plants spaced 0.3×0.6 m in each plot. Li et al. (2012) had a detail description of experimental methods and field managements.

## **2.3. Phenotyping**

Data collection followed procedures described by Yan et al. (2005a; 2005b) with modifications. Fourteen characteristics were recorded using the methods described by Li et al. (2010; 2011; 2012), including heading days, plant height, plant weight, tiller per plant, grain yield per plant, harvest index, main panicle length, panicle branches, Grain per panicle, seed set percentage, 1000 grain weight, grains per cm panicle, grains per branch panicle, and grain weight per panicle.

## **2.4. Genotyping**

Bulk tissue from five plants was collected from each accession as described by Brondani et al. (2006) and total genomic DNA was extracted using a rapid alkali extraction procedure (Xin et al. 2003) and a CTAB method as described in Hulbert and Bennetzen (1991). The bulked DNA allowed identification of the origin of heterogeneity, which can result from the presence of heterozygous individuals or from a mix of individuals with different homozygous alleles (Borba et al. 2005). A total of 155 molecular markers covering the entire rice genome, with approximately one marker per 10 cM on average, were used to genotype the URMC accessions. Among the markers, 149 SSRs were obtained from the Gramene database (http:// www.gramene.org/), and five SSRs (AP5652-1, AP5652-2, AL606682-1, con673 and LJSSR1) were identified by Li et al(2011). The remaining marker was an *indel* at the *Rc* locus, named *Rid 12* and is responsible for rice pericarp color (Sweeny et al. 2006). Polymerase chain reaction (PCR) marker amplifications were performed as described in Agrama et al. (2009).

#### **2.5. Statistical analysis marker and phenotype profile**

Genetic distance was calculated from 155 molecular markers using Nei's method (Nei and Takezaki 1983). Phylogenetic reconstruction was based on the UPGMA method implemented in *PowerMarker* version 3.25 (Liu and Muse 2005). *PowerMarker* was also used to calculate the average number of alleles, gene diversity, and polymorphism information content (PIC) values. The tree to visualize the phylogenetic distribution of accessions and ancestry groups was constructed using MEGA version 4 (Tamura et al. 2007).

Each of the 14 phenotypic traits was modeled independently with the MIXED procedure in SASv.9.2, where genotype, location and interaction of location with genotype were defined as fixed effects while replication within a location (block effect) was a random effect. Broad-sense heritability was calculated using formula H2 = σ<sup>g</sup> <sup>2</sup> /(σ<sup>g</sup> <sup>2</sup> +σ<sup>e</sup> <sup>2</sup> /n), where σ<sup>g</sup> <sup>2</sup> as the genotypic variance, σ<sup>e</sup> <sup>2</sup> as the environmental variance and n as the number of replications (Wang et al. 2007). Spearman rank correlation coefficients between each pair of the 14 traits were calculated using the mean of 9 plants, 3 in each of three replications for an accession, using the CORR procedure in SASv.9.2. Correlation coefficients were graphically displayed based on un‐ weighted pair-group method using arithmetic average (UPGMA) by *NTSYSpc* software version 2.11V (Rohlf 2000).

#### **2.6. Population structure**

**2. Materials and methods**

106 Rice - Germplasm, Genetics and Improvement

Of 217 accessions in the URMC, 203 belong to *sativa* whereas the remaining belong to other species in *Oryza*, including 8 to *O. glaberrima,* 2 each to *O*. *nivara* and *rufipogon*, and 1 each to *O. glumaepatula* and *latifolia* (Agrama et al. 2009). Pure seed of these accessions were provided by the Genetic Stock *Oryza* Collection (GSOR) (www.ars.usda.gov/spa/dbnrrc/gsor). In this study, 217 accessions were used for genetic structure and diversity analyses, but only 203 *O. sativa* accessions were used for association mapping analyses because the wild relatives, *O. glaberrima*, *nivara, rufipogon, glumaepatula* and *latifolia*, contain many rare alleles, and rare alleles are one of the factors that increase the risk of type I errors or spurious associations (Breseghello

Evaluations were conducted for 14 traits in two field locations, USDA-ARS Dale Bumpers National Rice Research Center near Stuttgart, Arkansas and USDA-ARS Rice Research Unit near Beaumont, Texas during the growing season of 2009. The Stuttgart test site is located at

and average temperature of 23.9 C during the growing season. The Beaumont test site is located

and an average temperature of 26.1 C during the growing season. The experiments at both locations utilized a randomized complete block design having three replications with nine plants spaced 0.3×0.6 m in each plot. Li et al. (2012) had a detail description of experimental

Data collection followed procedures described by Yan et al. (2005a; 2005b) with modifications. Fourteen characteristics were recorded using the methods described by Li et al. (2010; 2011; 2012), including heading days, plant height, plant weight, tiller per plant, grain yield per plant, harvest index, main panicle length, panicle branches, Grain per panicle, seed set percentage, 1000 grain weight, grains per cm panicle, grains per branch panicle, and grain weight per

Bulk tissue from five plants was collected from each accession as described by Brondani et al. (2006) and total genomic DNA was extracted using a rapid alkali extraction procedure (Xin et al. 2003) and a CTAB method as described in Hulbert and Bennetzen (1991). The bulked DNA allowed identification of the origin of heterogeneity, which can result from the presence of heterozygous individuals or from a mix of individuals with different homozygous alleles (Borba et al. 2005). A total of 155 molecular markers covering the entire rice genome, with approximately one marker per 10 cM on average, were used to genotype the URMC accessions.

24'59", representing a temperate climate with a 243 d frost free period

17'45", representing a subtropical climate with a 253 d frost free period

**2.1. Rice association panel**

and Sorrells 2006).

N 340

at N 300

panicle.

**2.4. Genotyping**

**2.3. Phenotyping**

**2.2. Location and field experiment**

27'44" and W 910

03'47" and W 940

methods and field managements.

The model-based program *STRUCTURE* (Prichard et al. 2000) was used to infer population structure using a burn-in of 100,000, a run length of 100,000, and a model allowing for admixture and correlated allele frequencies. The number of groups (K) was set from 1 to 10, with ten independent runs each. The most probable structure number of (K) was calculated based on Evanno et al. (2005) using an *ad hoc* statistic *D(K)*, assisted with *L(K)*, *L'(K)* and *(L"K)*. The *D(K)* perceives the rate of change in log probability of the data between successive (K) values rather than just the log probability of the data. Determination of mixed ancestry (an accession unable to be clearly assigned to only one group) was based on 60% (Q) as a threshold to consider an individual with its inferred ancestry from one single group. Principal compo‐ nent analysis (PCA), that summarizes the major patterns of variation in a multi-locus data set, was performed with *NTSYSpc* software version 2.11 (Rohlf 2000). Two principal coordinates were used to visualize the dispersion of the mini core accessions graphically. *Fst* indicative of ancestral relationship between genetic groups was calculated using an AMOVA approach in Arlequin V2.000 (Weir 1996; Schneider and Excoffier 1999). The number of private alleles was estimated by Genetic Data Analysis (GDA) program (Lewis and Zaykin 2001).

Fourteen phenotypic characteristics were used to calculate Mahalanobis distance as a meas‐ urement of genetic differentiation among the groups (Kouame and Quesenberry 1993). The Mahalanobis distance and Canonical discriminant analysis were performed by the procedures PROC CANDISC of the *SAS* version 9.1 statistical packages. Eventually, the correlation of genetic structure differentiation resulting from the genotypic markers with phenotypic traits was assessed using the Mantel test (Mantel 1967) performed by PowerMarker.

## **2.7. Model comparisons and association analysis**

The flexible mixed model (Yu et al. 2006) was used to control population structure. The methods for model comparisons and association mapping are referred to Li et al. (2012) for harvest index, Li et al. (2011) for grain yield, Jia et al. (2012) for sheath blight resistance and Bryant et al. (2011) for silica concentration in rice hulls.

## **3. Analysis of genetic structure and genetic diversity**

## **3.1. Profile of DNA markers**

Among 217 accessions in the URMC, the average number of alleles per locus was 13.5 ranging from 2 for RM338 to 57 for con673. PIC mean was 0.71 ranging from 0.30 for AP5625-1 to 0.97 for con673 among these markers. Since every accession was analyzed as a bulk of five plants, 54 (42.19%) loci showed heterozygosity and 38 (17.51%) accessions showed heterogeneity for at least one locus. Nei genetic distance (Nei and Takezaki 1983) was estimated for each pair of the 217 rice accessions which ranged from 0.021 to 1.000, with an average 0.752.

In previous studies, the average number of alleles per locus was 5.1 in Cho et al. (2000), 7.8 in Jain et al. (2004), 11.9 in Xu et al. (2004) and 11.8 in Garris et al. (2005). Recently, 13 alleles per locus were reported in the rice population studied by Thomson et al. (2007), 5.5 by Thomson et al. (2009) and 12.4 by Borba et al. (2009). The PIC in the URMC was 0.71, larger than it in the population studied by Cho et al. (2000) (0.56 PIC), Jain et al. (2004) (0.60), Xu et al. (2004) (0.66), Garris et al. (2005) (0.67), Thomson et al. (2007) (0.66), and Thomson et al. (2009) (0.45). The PIC was slightly less in our study than in the population studied by Borba et al. (2009) (0.75). Both the average allele number and PIC values are indicative of genetic diversity or gene richness in a germplasm collection. The higher the genetic diversity is in a collection, the greater the probability is for a gene of interest to be mined from the collection. Greater genetic diversity in the URMC is due to its global originations, multiple *Oryza* species, and the way of sampling with PowerCore software (Kim et al. 2007) based on 26 phenotypic traits and 70 SSR markers in order to capture the most diversity in the core collection (Agrama et al. 2009). Most other rice collections are either for a single country (Thomson et al. 2007), or for certain groups (Jain et al. 2004) and regions in a country (Thomson et al. 2009), or for special interests (Xu et al. 2004; Garris et al. 2005).

## **3.2. Genetic structure and differentiation derived from DNA markers**

Fourteen phenotypic characteristics were used to calculate Mahalanobis distance as a meas‐ urement of genetic differentiation among the groups (Kouame and Quesenberry 1993). The Mahalanobis distance and Canonical discriminant analysis were performed by the procedures PROC CANDISC of the *SAS* version 9.1 statistical packages. Eventually, the correlation of genetic structure differentiation resulting from the genotypic markers with phenotypic traits

The flexible mixed model (Yu et al. 2006) was used to control population structure. The methods for model comparisons and association mapping are referred to Li et al. (2012) for harvest index, Li et al. (2011) for grain yield, Jia et al. (2012) for sheath blight resistance and

Among 217 accessions in the URMC, the average number of alleles per locus was 13.5 ranging from 2 for RM338 to 57 for con673. PIC mean was 0.71 ranging from 0.30 for AP5625-1 to 0.97 for con673 among these markers. Since every accession was analyzed as a bulk of five plants, 54 (42.19%) loci showed heterozygosity and 38 (17.51%) accessions showed heterogeneity for at least one locus. Nei genetic distance (Nei and Takezaki 1983) was estimated for each pair of

In previous studies, the average number of alleles per locus was 5.1 in Cho et al. (2000), 7.8 in Jain et al. (2004), 11.9 in Xu et al. (2004) and 11.8 in Garris et al. (2005). Recently, 13 alleles per locus were reported in the rice population studied by Thomson et al. (2007), 5.5 by Thomson et al. (2009) and 12.4 by Borba et al. (2009). The PIC in the URMC was 0.71, larger than it in the population studied by Cho et al. (2000) (0.56 PIC), Jain et al. (2004) (0.60), Xu et al. (2004) (0.66), Garris et al. (2005) (0.67), Thomson et al. (2007) (0.66), and Thomson et al. (2009) (0.45). The PIC was slightly less in our study than in the population studied by Borba et al. (2009) (0.75). Both the average allele number and PIC values are indicative of genetic diversity or gene richness in a germplasm collection. The higher the genetic diversity is in a collection, the greater the probability is for a gene of interest to be mined from the collection. Greater genetic diversity in the URMC is due to its global originations, multiple *Oryza* species, and the way of sampling with PowerCore software (Kim et al. 2007) based on 26 phenotypic traits and 70 SSR markers in order to capture the most diversity in the core collection (Agrama et al. 2009). Most other rice collections are either for a single country (Thomson et al. 2007), or for certain groups (Jain et al. 2004) and regions in a country (Thomson et al. 2009), or for special interests (Xu et al.

the 217 rice accessions which ranged from 0.021 to 1.000, with an average 0.752.

was assessed using the Mantel test (Mantel 1967) performed by PowerMarker.

**2.7. Model comparisons and association analysis**

Bryant et al. (2011) for silica concentration in rice hulls.

**3.1. Profile of DNA markers**

108 Rice - Germplasm, Genetics and Improvement

2004; Garris et al. 2005).

**3. Analysis of genetic structure and genetic diversity**

UPGMA tree showed that the accessions of *Oryza sativa* were classified to two main branch‐ es equivalent to *lowland* and *upland* cultivars, respectively. Ecogeographically, *indica* is pri‐ marily known as *lowland* rice and is grown throughout tropical Asia, while *japonica* often referred to as *upland* rice is typically found in temperate East Asia, upland geographic re‐ gions of Southeast Asia, and high elevations of South Asia (Garris et al. 2005). The *lowland* branch was further distinguished into two minor groups corresponding to AUS and IND ac‐ cessions, while the *upland* branched into three groups, TEJ, TRJ and ARO (Fig. 1a). Wild germplasm cluster separates from the two main branches. Eight accessions of *O. glaberrima* stayed together, distinguishable from *O. latifolia*, and *glumaepatula* on one side, and *O.nivara* and *O.sativa* (PI 430909) grouped together on the other side of the tree*.* Although PI 430909 from Pakistan was classified *O*. *sativa* in the Germplasm Resources Information Network (GRIN) at www.ars-grin.gov, it exhibited shattering, had a spreading plant type, black hulls with fulllong awns, and small red kernels; all of which are typical characteristics of wild rice. Surprisingly, PI 590422 from Myanmar in 1995 and PI 346371 from Brazil in 1969 were classified as *O. rufipogon* in GRIN, but the former was clustered with *indica* (Q-indica = 0.77) and the latter with an admixture of *aus* and *indica* (Q-aus = 0.59, Q-indica = 0.41). The disa‐ greement of cluster analysis in the study with traditional classification in GRIN is worthy of further attention.

**Figure 1.** UPGMA tree (a) and principal coordinate analysis for 217 accessions in the USDA rice mini-core collection, both visualizing six main groups, AUS *aus*, IND *indica*, ARO *aromatic*, TEJ *temperate japonica*, TRJ *tropical japonica*, WD *wild rice*, ADA *admix of AUS and IND*, and ADJ *admix of TEJ and TRJ* (Li et al. 2010).

The ancestry of each accession was inferred from the Q value and classified into one of the six groups which corresponded to *aromatic* (ARO), *aus* (AUS), *indica* (IND), *temperate japonica* (TEJ), *tropical japonica* (TRJ) and *wild rice* (WD) based on reference cultivars reported previously by Garris et al. (2005), Agrama and Eizenga (2008), Agrama and Yan (2009). The classification was clear for a single group when the Q value was greater than 60%, otherwise an accession of germplasm was considered admixture with another group. In total, 21 accessions (9.68%) in the URMC had admixed ancestry either between TEJ and TRJ (ADJ) or between AUS and IND (ADA) (Fig. 1a, b).

The first-two axes in PCoA with 83.2% of total variation sufficiently discriminated the six main groups and two admixture groups (Fig. 1b). Each main group was distinguishable from another, but overlaps existed either among *temperate* and *tropic japonica* and their admixtures, or among *indica, aus* and their admixtures. The PCoA visualization and UPGMA tree were in agreement, which demonstrates a correct division of genetic structure in the URMC.

**Figure 2.** Geographic distribution of each mini-core accession with ancestral classification based on the latitude and longitude of germplasm origination, location of phenotypic evaluation : Stuttgart AR, : Beaumont TX (Li et al. 2012).

Each accession with ancestry information was plotted on a world map using its latitude and longitude of geographic origin (Fig. 2). TEJ accessions were mainly distributed between latitudes 30 and 50 degrees north and south of the equator (i.e. temperate zone) while the other four groups scattered between latitude N 30 and S 30 degrees (i.e. tropical and subtropical zone).

In the URMC, the majority of accessions were IND (33%), followed by TRJ and AUS (18% each), TEJ (15%), WD (6%) and ARO with only six accessions (Fig. 3). All the marker loci were polymorphic in IND (Table 1). TRJ had 99% polymorphic loci, followed by WD, AUS, TEJ and ARO. IND had the most alleles per locus, TRJ and AUS the second most, TEJ and WD the third most and ARO had the fewest alleles. The largest number of private alleles per locus (alleles unique in one group and not found in another group) were found in WD (41.89%), followed by IND (23.78%) and AUS (17.66%). TRJ and TEJ had about equal private alleles, and the least was found in ARO. Gene diversity averaged 0.47 among the groups ranging from 0.37 in ARO to 0.52 in both IND and AUS. TRJ and WD had the same diversity (0.50), slightly greater than TEJ (0.43).

Results from the AMOVA showed that 37.92% of total variation was due to differences among groups, 61.21% within groups and 0.88% within individuals. Pair-wise estimates of *Fst* using the AMOVA approach indicated a high degree of differentiation among the six main modelbased groups (Fig. 3a). The mean *Fst* of all group pairs was 0.39 ranging from 0.24 between TRJ and TEJ to 0.48 between ARO and WD (Table 2). All pair-wise *Fst* values for the six groups were significant. The greatest genetic distance (0.990) among the 217 mini-core accessions was observed for PI 590413, an *O. glumaepatula* accession from the WD with 22 IND accessions and three accessions admixed AUS and IND, followed by the distance of 0.981 for PI 590413 with 9 AUS and 28 IND accessions, and the distance of 0.981 for PI 269727, an *O. latifolia* accession from the WD with 4 TEJ accessions. Two IND accessions, PI 202864 and PI 214077 had the shortest distance (0.021).

**Figure 3.** Dendrogram of genetic differentiation based AMOVA (Fst) (a) using DNA markers for six main groups (b), estimated group structure with each individual represented by *a horizontal bar* (c) and two admixture groups (d) (Li et al. 2010).


ARO, *aromatic*; AUS: *aus*; IND, *indica*; TEJ, *temperate japonica*; TRJ, *Tropic japonica*; WD, *wild rice*.

\* Excluding admixed accessions.

The first-two axes in PCoA with 83.2% of total variation sufficiently discriminated the six main groups and two admixture groups (Fig. 1b). Each main group was distinguishable from another, but overlaps existed either among *temperate* and *tropic japonica* and their admixtures, or among *indica, aus* and their admixtures. The PCoA visualization and UPGMA tree were in

agreement, which demonstrates a correct division of genetic structure in the URMC.

L o n g it u d e

Latitude

110 Rice - Germplasm, Genetics and Improvement

2012).

zone).

TEJ (0.43).

ARO AUS IND TEJ TRJ AUS-IND TEJ-TRJ TRJ-IND ARO-TEJ-TRJ AUS-TRJ-IND

**Figure 2.** Geographic distribution of each mini-core accession with ancestral classification based on the latitude and longitude of germplasm origination, location of phenotypic evaluation : Stuttgart AR, : Beaumont TX (Li et al.

Each accession with ancestry information was plotted on a world map using its latitude and longitude of geographic origin (Fig. 2). TEJ accessions were mainly distributed between latitudes 30 and 50 degrees north and south of the equator (i.e. temperate zone) while the other four groups scattered between latitude N 30 and S 30 degrees (i.e. tropical and subtropical

In the URMC, the majority of accessions were IND (33%), followed by TRJ and AUS (18% each), TEJ (15%), WD (6%) and ARO with only six accessions (Fig. 3). All the marker loci were polymorphic in IND (Table 1). TRJ had 99% polymorphic loci, followed by WD, AUS, TEJ and ARO. IND had the most alleles per locus, TRJ and AUS the second most, TEJ and WD the third most and ARO had the fewest alleles. The largest number of private alleles per locus (alleles unique in one group and not found in another group) were found in WD (41.89%), followed by IND (23.78%) and AUS (17.66%). TRJ and TEJ had about equal private alleles, and the least was found in ARO. Gene diversity averaged 0.47 among the groups ranging from 0.37 in ARO to 0.52 in both IND and AUS. TRJ and WD had the same diversity (0.50), slightly greater than

Results from the AMOVA showed that 37.92% of total variation was due to differences among groups, 61.21% within groups and 0.88% within individuals. Pair-wise estimates of *Fst* using

**Table 1.** Analysis of genetic diversity among structural groups for 217 accessions in the USDA rice mini-core collection genotyped with DNA markers (Li et al. 2010).

#### **3.3. Phenotypic analysis**

Statistical analysis using a mixed model demonstrated that the differences due to genotypes and genotype × location interactions were highly significant at the 0.001 level of probability for all of the 14 traits (Table 2). The differences due to location were also significant for all traits except for panicle branches and seed set. Heritability was very high for all 14 traits. Heading had the highest heritability which was close to 100%. Although seed set had the lowest heritability, it was still above 70%. Heritability ranged from 77 to 97% among the other 12 traits. Harvest index had a heritability of 83% at Stuttgart and 90% at Beaumont. Correlation coefficients for each pair of the 14 traits were calculated using Spearman rank in each location for visualizing their relationships using PCA where the first two axes accounted for more than 50% phenotypic variation (Fig. 4a, b). At Stuttgart, 47 out of 91 correlations among the 14 traits were significant (<0.0001) (Fig. 4a), and 40 correlations were significant at Beaumont (Fig. 4b). Thirty four correlations were uniformly significant across two locations and their corre‐ lation directions (positive or negative) were also same across two locations.

**Figure 4.** Relationship map constructed by PCA for 14 traits at Stuttgart, Arkansas (A) and Beaumont, TX (B). The dis‐ tance between traits is inversely proportional to the sizes of the correlation coefficients. *Solid* and *dashed lines* indi‐ cate positive and negative correlations, respectively. Trait number corresponds to Table 2, and red numbers show the significant correlation coefficient with Trait 6: Harvest Index (Li et al. 2012).

#### **3.4. Genetic structure and differentiation derived from phenotypic traits**

Canonical discriminant analysis of 14 phenotypic traits for the mini-core accessions clearly separated the six plus two admixture model-based genetic groups derived from molecular data (Fig. 5). The first four significant (*P* < 0.001) canonical discriminant functions (CAN) explained 92.02% of the total variance, 54.87 % by the first CAN and 18.08% by the second CAN function, respectively. The accessions in group of AUS, ARO, IND, TEJ, TRJ and WD were clustered into their groups with various overlaps. The *upland* (ARO, TEJ and TRJ) were obviously discriminated from the *lowland* (AUS, IND), and the admixed groups ADA was scattered across AUS and IND while ADJ across TEJ and TRJ.

All 14 traits were significantly different among the eight (six plus two admixtures) modelbased genetic groups. However, only three traits, plant weight (biomass), tillers and grain yield

Association Mapping of Harvest Index, Grain Yield, Sheath Blight Resistance and… http://dx.doi.org/10.5772/56830 113


**3.3. Phenotypic analysis**

112 Rice - Germplasm, Genetics and Improvement

Statistical analysis using a mixed model demonstrated that the differences due to genotypes and genotype × location interactions were highly significant at the 0.001 level of probability for all of the 14 traits (Table 2). The differences due to location were also significant for all traits except for panicle branches and seed set. Heritability was very high for all 14 traits. Heading had the highest heritability which was close to 100%. Although seed set had the lowest heritability, it was still above 70%. Heritability ranged from 77 to 97% among the other 12 traits. Harvest index had a heritability of 83% at Stuttgart and 90% at Beaumont. Correlation coefficients for each pair of the 14 traits were calculated using Spearman rank in each location for visualizing their relationships using PCA where the first two axes accounted for more than 50% phenotypic variation (Fig. 4a, b). At Stuttgart, 47 out of 91 correlations among the 14 traits were significant (<0.0001) (Fig. 4a), and 40 correlations were significant at Beaumont (Fig. 4b). Thirty four correlations were uniformly significant across two locations and their corre‐

**Figure 4.** Relationship map constructed by PCA for 14 traits at Stuttgart, Arkansas (A) and Beaumont, TX (B). The dis‐ tance between traits is inversely proportional to the sizes of the correlation coefficients. *Solid* and *dashed lines* indi‐ cate positive and negative correlations, respectively. Trait number corresponds to Table 2, and red numbers show the

Canonical discriminant analysis of 14 phenotypic traits for the mini-core accessions clearly separated the six plus two admixture model-based genetic groups derived from molecular data (Fig. 5). The first four significant (*P* < 0.001) canonical discriminant functions (CAN) explained 92.02% of the total variance, 54.87 % by the first CAN and 18.08% by the second CAN function, respectively. The accessions in group of AUS, ARO, IND, TEJ, TRJ and WD were clustered into their groups with various overlaps. The *upland* (ARO, TEJ and TRJ) were obviously discriminated from the *lowland* (AUS, IND), and the admixed groups ADA was

All 14 traits were significantly different among the eight (six plus two admixtures) modelbased genetic groups. However, only three traits, plant weight (biomass), tillers and grain yield

lation directions (positive or negative) were also same across two locations.

significant correlation coefficient with Trait 6: Harvest Index (Li et al. 2012).

scattered across AUS and IND while ADJ across TEJ and TRJ.

**3.4. Genetic structure and differentiation derived from phenotypic traits**

**Table 2.** Statistical analysis of 14 traits generated at Stuttgart, Arkansas and Beaumont, Texas in 2009 among 203 *O. sativa* accessions in the USDA rice mini-core collection (Li et al. 2011).

per plant, had larger variation among groups than within groups. Therefore, they are consid‐ ered the main discriminatory characters (*r*<sup>2</sup> >=0.49) in differentiating these genetic groups. The first canonical loading was 0.81 for grain yield and tillers and 0.78 for plant weight. The second canonical loading was dominated by panicle length (0.59), heading days (0.55) and seed weight (0.51).

The most tillers were observed in AUS accessions PI 385697 (93) and 352687 (86), while the lowest were in TRJ PI 584567 (9) and PI 154464 (10). WD had the most tillers (60), followed by AUS (46), ADA (44), AUS (46), IND (38), ARO (27), TEJ (24), ADJ (21) and TRJ (18). The greatest plant weight was 731 g for PI 549215 (IND), and the lowest was 37g for PI 281630 (TEJ). Again, WD had the most plant weight (442g) and TEJ had the lowest (127g). PI 373335 (IND) had the highest grain yield per plant at 175g and PI 389933 (IND) had the lowest at 11g. ADA had the most grain yield per plant (127 g per plant), while TEJ had the lowest (55g).

#### **3.5. Relationship between genetic and phenotypic differentiation**

Both the dendrograms based on the Mahalanobis distance (D2 ) using the 14 phenotypic traits (Fig. 5) and based on the *Fst* genetic differentiation from AMOVA using DNA markers (Fig. 3a) produced similar results. The two dendrograms differentiated the *lowland* including IND, AUS and their admixtures from the *upland* having TEJ, TRJ, ARO and their admixtures. The WD or non-*sativa* accessions remained independent from the others.

**Figure 5.** Dendrogram of differentiation based on Mahalanobis distance (left) and Canonical discriminant analysis (CDA) (right) using 14 phenotypic traits among structural groups for 217 accessions in the USDA rice mini-core collec‐ tion (Li et al. 2010).


All AMOVA-based Fst estimates from 110 permutations were significant (P<0.001), and all Mahalanobis distance (D2) estimates were significant (P<0.001).

**Table 3.** Pairwise comparison of Fst values above the diagonal based on DNA markers and Mahalanobis distance (D2) below the diagonal based on 14 phenotypic traits among structural groups for 217 accessions in the USDA rice minicore collection (Li et al. 2010).

Analysis developed by Mantel (1967) is widely used to describe the genetic relationship between genotypic and phenotypic measurements (Gaudeul et al. 2000, Gizaw et al. 2007). In our study, genetic distance derived from the DNA markers among the six plus two admixture model-based groups was highly and significantly correlated with the distance derived from 14 phenotypic traits (*r* = 0.85, *P =*0.000<0.001). This explains the correspondence of the two dendrograms in Fig. 3a and Fig. 5(left), and similar pattern of D2 and *Fst* in Table 3.

In rice ancestry, structure and genetic diversity of germplasm collections has been studied using a variety of molecular markers such as SNP (Zhao et al. 2011), SSR (Cho et al. 2000; Jain et al. 2004; Xu et al. 2004; Garris et al. 2005; Thomson et al. 2007; 2009; Borba et al. 2009), RAPD (Mackill 1995) and isozyme (Glaszmann 1987) markers. Phenotypic characteristics are rarely used to analyze genetic diversity or structure in rice germplasm collections. Zeng et al. (2003) collected samples from each of six genetic groups for a diversity analysis using 31 phenotypic traits, but failed to reveal their genetic differentiations.

However, assessment of genetic diversity and structure using both genotypic and phenotyp‐ ic characterization and relationship or accuracy between the genotypic and phenotypic assessments has long been attractive to the scientific community. Elias et al. (2001a) reported a significant positive association between genotypic and phenotypic distances (*r* = 0.204, *p* = 0.054) using eight SSRs and 14 traits for 38 accessions of cultivated cassava (*Manihot esculenta* Crantz). The association was improved (*r* = 0.283, *p <* 0.01) in a set of 29 cassava accessions genotyped with AFLP markers and phenotyped for 14 morphological and four agronomic traits (Elias et al. 2001b). A set of 68 sweet sorghum and four grain sorghum (*Sorghum bicolor* L.) accessions were genotyped with 41 SSRs and phenotyped for six traits (Ali et al. 2008). The genotypic analysis classified the 72 accessions in 10 clusters and the phenotypic variation among the clusters was described. Similarly, 15 morpho-physiologi‐ cal traits were used to describe four major groups of 61 tomato (*Solanum lycopersicum* L.) accessions classified by genotypic data of 29 SSRs (Mazzucato et al. 2008). In barley (*Hordeum vulgare* L.), based on five cultivars phenotyped for 18 traits and genotyped with 11 AFLP markers, trait relationships were demonstrated using simple correlation, path analysis and GGE biplot. The cultivars were clustered based on genetic dissimilarity estimated by the AFLP markers (Akash and Kang 2009).

We use both genotypic and phenotypic characterizations to analyze genetic differentiation in a plant germplasm collection. The present study in rice has a much greater association (*r* = 0.85, *P =*0.000<0.001) of genetic distance derived from genotypic characterizations with phenotypic characterizations than the previous study in cassava.

## **4. Association mapping of harvest index and components**

Analysis developed by Mantel (1967) is widely used to describe the genetic relationship between genotypic and phenotypic measurements (Gaudeul et al. 2000, Gizaw et al. 2007). In our study, genetic distance derived from the DNA markers among the six plus two admixture model-based groups was highly and significantly correlated with the distance derived from 14 phenotypic traits (*r* = 0.85, *P =*0.000<0.001). This explains the correspondence of the two

**Table 3.** Pairwise comparison of Fst values above the diagonal based on DNA markers and Mahalanobis distance (D2) below the diagonal based on 14 phenotypic traits among structural groups for 217 accessions in the USDA rice mini-

**Figure 5.** Dendrogram of differentiation based on Mahalanobis distance (left) and Canonical discriminant analysis (CDA) (right) using 14 phenotypic traits among structural groups for 217 accessions in the USDA rice mini-core collec‐

**Group ARO AUS IND TEJ TRJ WD** ARO - 0.37 0.41 0.38 0.31 0.48 AUS 12.90 - 0.31 0.44 0.40 0.38 IND 10.96 3.36 - 0.45 0.40 0.38 TEJ 13.08 15.92 9.59 - 0.24 0.46 TRJ 9.37 16.08 9.36 8.03 - 0.41 WD 21.47 14.90 17.10 22.70 22.57 - All AMOVA-based Fst estimates from 110 permutations were significant (P<0.001), and all Mahalanobis distance (D2)

In rice ancestry, structure and genetic diversity of germplasm collections has been studied using a variety of molecular markers such as SNP (Zhao et al. 2011), SSR (Cho et al. 2000; Jain et al. 2004; Xu et al. 2004; Garris et al. 2005; Thomson et al. 2007; 2009; Borba et al. 2009), RAPD (Mackill 1995) and isozyme (Glaszmann 1987) markers. Phenotypic characteristics are rarely used to analyze genetic diversity or structure in rice germplasm collections. Zeng et al. (2003)

and *Fst* in Table 3.

dendrograms in Fig. 3a and Fig. 5(left), and similar pattern of D2

tion (Li et al. 2010).

114 Rice - Germplasm, Genetics and Improvement

estimates were significant (P<0.001).

core collection (Li et al. 2010).

Harvest index is a ratio of grain yield to total biomass, which measures farming success in partitioning assimilated photosynthate to harvestable product (Hay 1995; Sinclair 1998). In cereal crops, dramatic improvements of harvest index during domestication have made commercial cultivars dramatically different from their wild ancestors (Gepts 2004). Rice (*Oryza sativa* L.) is one of the most important staple foods (Tyagi et al. 2004), and can be highly productive if high harvest index genotypes are grown with optimal management practices (Raes et al. 2009). Harvest index is one of the most complex traits in rice involving number of panicles per unit area, number of spikelets per panicle, percentage of fully ripened grains, kernel size (Terao et al. 2010) and plant height (Marri et al. 2005). Marri et al. (2005) found that harvest index was negatively correlated with plant height, but positively correlated with grain number per panicle, tiller number per plant, seed set, kernel size and grain yield per plant in rice. Similarly in maize, harvest index is negatively correlated with plant height, and positively correlated with grain yield (Can and Yoshida 1999). In sorghum, harvest index is negatively correlated with forage yield (Mohammad et al. 1993), but positively correlated with growth rate and grain filling rate (Soltani et al. 2001). The correlated traits are interrelated in most cases, so that increases in one component may lead to either decreases or increases in others. Therefore, scientists aim to identify genes or QTL that increase one aspect of a target trait without affecting others, or improve the target trait indirectly through an improvement of its related traits.

In rice, previous studies on harvest index have identified numerous QTL all using a classic linkage-mapping strategy with two parents. Mao et al. (2003) reported four main QTL on chromosome (Chr) 1, 4, 8 and 11 and an epistatic interaction between two QTL respectively on Chr 1 and Chr 5. Sabouri et al. (1999) identified three QTL each on Chr 2, 3 and 5, and two QTL close to each other on Chr 4. Lanceras et al. (2004) described harvest index QTL on Chr 1 and 3. However, mapping populations developed from different parental combinations and/ or experiments conducted in different environments often result in partly or wholly nonoverlapping sets of QTL (Hao et al. 2010).

## **4.1. Traits correlated with harvest index in our study**

Six traits were significantly correlated with harvest index and these correlation directions were the same across the two locations. The correlations with harvest index were negative for heading (-0.46 at Stuttgart and -0.61 at Beaumont), plant height (-0.50 and -0.50), plant weight (-0.36 and -0.30), panicle length (-0.45 and -0.32), while positive for seed set (0.52 and 0.61) and grain weight/panicle (0.32 and 0.40) (Fig. 4a, b). In the PCA based on phenotypic traits of 203 mini-core accessions, four traits negatively correlated with harvest index were plotted on opposing axis from harvest index (Fig. 4a, b). Conversely, two traits positively correlated with harvest index were plotted in the same axis relatively close to harvest index.

## **4.2. Marker-trait associations**

At Stuttgart, a total of 36 markers were significantly associated with harvest index traits at the 6.45×10-3 level of probability (the Bonferroni corrected significance level). Among 36 markers, seven were associated with harvest index, five with heading, three with plant height, six with plant weight, five with panicle length, nine with seed set and one with grain weight/panicle. Eight trait-marker associations have been reported by previous linkage mappings. Addition‐ ally, seven markers were associated with two or more harvest index traits, named "consistent" markers (Pinto et al. 2010). Out of the seven consistent markers, RM600, RM5 and RM302 were co-associated with harvest index and seed set, RM431 with heading and seed set, RM341 with plant height and panicle length, RM471 with heading and plant weight, and RM510 with three traits, plant height, harvest index and seed set.

At Beaumont, we identified 28 markers significantly associated with harvest index's traits. Among 28 markers, two were associated with harvest index, three with heading, nine with plant height, six with plant weight, four with panicle length, three with seed set and one with grain weight/panicle. Similarly with Stuttgart, 11 trait-marker associations have been identi‐ fied in previous QTL studies. Two consistent markers were RM208 co-associated with harvest index and seed set, and RM55 co-associated with plant height and plant weight.

Associations of RM431 with plant height, Rid12 and RM471 with plant weight and RM24011 with panicle length were found in both locations. The four markers that associated with the same trait across both locations are called "constitutive QTL" markers, while others that associated with a certain trait only at one location are called "adaptive QTL" markers (Mao et al. 2003).

## **4.3. Allelic effects**

cases, so that increases in one component may lead to either decreases or increases in others. Therefore, scientists aim to identify genes or QTL that increase one aspect of a target trait without affecting others, or improve the target trait indirectly through an improvement of its

In rice, previous studies on harvest index have identified numerous QTL all using a classic linkage-mapping strategy with two parents. Mao et al. (2003) reported four main QTL on chromosome (Chr) 1, 4, 8 and 11 and an epistatic interaction between two QTL respectively on Chr 1 and Chr 5. Sabouri et al. (1999) identified three QTL each on Chr 2, 3 and 5, and two QTL close to each other on Chr 4. Lanceras et al. (2004) described harvest index QTL on Chr 1 and 3. However, mapping populations developed from different parental combinations and/ or experiments conducted in different environments often result in partly or wholly non-

Six traits were significantly correlated with harvest index and these correlation directions were the same across the two locations. The correlations with harvest index were negative for heading (-0.46 at Stuttgart and -0.61 at Beaumont), plant height (-0.50 and -0.50), plant weight (-0.36 and -0.30), panicle length (-0.45 and -0.32), while positive for seed set (0.52 and 0.61) and grain weight/panicle (0.32 and 0.40) (Fig. 4a, b). In the PCA based on phenotypic traits of 203 mini-core accessions, four traits negatively correlated with harvest index were plotted on opposing axis from harvest index (Fig. 4a, b). Conversely, two traits positively correlated with

At Stuttgart, a total of 36 markers were significantly associated with harvest index traits at the 6.45×10-3 level of probability (the Bonferroni corrected significance level). Among 36 markers, seven were associated with harvest index, five with heading, three with plant height, six with plant weight, five with panicle length, nine with seed set and one with grain weight/panicle. Eight trait-marker associations have been reported by previous linkage mappings. Addition‐ ally, seven markers were associated with two or more harvest index traits, named "consistent" markers (Pinto et al. 2010). Out of the seven consistent markers, RM600, RM5 and RM302 were co-associated with harvest index and seed set, RM431 with heading and seed set, RM341 with plant height and panicle length, RM471 with heading and plant weight, and RM510 with three

At Beaumont, we identified 28 markers significantly associated with harvest index's traits. Among 28 markers, two were associated with harvest index, three with heading, nine with plant height, six with plant weight, four with panicle length, three with seed set and one with grain weight/panicle. Similarly with Stuttgart, 11 trait-marker associations have been identi‐ fied in previous QTL studies. Two consistent markers were RM208 co-associated with harvest

index and seed set, and RM55 co-associated with plant height and plant weight.

harvest index were plotted in the same axis relatively close to harvest index.

related traits.

116 Rice - Germplasm, Genetics and Improvement

overlapping sets of QTL (Hao et al. 2010).

**4.2. Marker-trait associations**

traits, plant height, harvest index and seed set.

**4.1. Traits correlated with harvest index in our study**

The allelic effects of the constitutive markers associated with their traits were estimated with the least square mean (LSMEAN) of phenotypic value and presented in Fig. 6. Meanwhile, an algorithm was employed to generate a letter-based representation of all-pairwise comparisons for allelic effect. For RM431, allele 253bp had a significantly larger effect than all other 6 alleles at Beaumont and than 4 others at Stuttgart to reduce plant height. For RM24011, allele 390bp had the greatest effect on decreasing panicle length while allele 411bp had the largest effect on increasing panicle length at both locations. However, for Rid12, the allelic effects were opposite between two locations. Allele 151bp of Rid12 had a decreasing effect on plant weight at Stuttgart, but an increasing effect at Beaumont instead. The 165 allele of Rid12 had an opposite effect to 151bp on plant weight. For RM471, the allelic effects on plant weight were not consistent from one location to another. The 109bp allele had the largest effect on decreasing plant weight at Stuttgart, but a fairly larger effect on increasing plant weight at Beaumont.

## **4.4. Genetic dissection of harvest index**

Harvest index is an integrative trait including the net effect of all physiological processes during the crop cycle and its phenotypic expression is generally affected by genes respon‐ sible for non-target traits, such as heading (Lanceras et al. 2004; Hemamalini et al. 2000), plant height (Lanceras et al. 2004) and panicle architecture (Ando et al. 2008). The magni‐ tude and direction of these gene functions on different phenotypes would bear heavily on the utility of such genes for improvement of these traits. In the current study, the traits like heading, plant height, plant weight and panicle length had a strong negative correlation with harvest index, while seed set and grain weight/panicle were positively correlated with harvest index. These phenotypic correlations were consistently reflected in the identifica‐ tion of molecular markers associated with harvest index and related traits. For example, four consistent markers at Stuttgart, RM600, RM302, RM25, and RM431, were associated with not only harvest index itself, but also for one or more additional traits correlated with harvest index. Another consistent marker, Rid12, associated with both heading and plant weight was close to a reported QTL "*qHID7-1*" responsible for harvest index and the gene "*Ghd7*" which effects grains per panicle, plant height and heading in rice (Hittalmani et al. 2003). At Beaumont, the consistent marker RM55 associated with both plant height and plant weight was adjacent to a QTL "*qHID3-2*" for control of harvest index (Hemamalini et al. 2000). RM431 co-associated with plant height and harvest index in this study has been reported to be closely linked to gene ''*sd1*'' (Xue et al. 2008; Peng et al. 2009). *sd1* is involved in gibberellic acid biosynthesis, decreases plant height and thus increases harvest index. The decreased height confered by *sd1*allows the plant to have a reduced risk of lodging, be more tolerant to heavy doses of nitrogen fertilizer, and allows for planting increased stand densities. The *sd1* gene has greatlyimproved grain yield and has contributed to the Green Revolution in cereal crops including rice (Fu et al. 2010).

Other markers were associated with the traits correlated with harvest index, but not with harvest index directly in this study. These markers have been reported either nearby or flanking the QTL for harvest index. RM5, which was associated with plant height in the Stuttgart study, was close to a reported QTL for harvest index on Chr 1 (Marri et al. 2005). RM471 associated with plant weight was close to the reported *qHID4-1* and *qHID4-2* for harvest index (Hemamalini et al. 2000). Furthermore, RM257 and RM22559 associated with seed set were co-localized with a known QTL on Chr 9 (Marri et al. 2005), and with *qHID8-1* (Hema‐ malini et al. 2000) for harvest index, respectively. Similarly, at Beaumont, RM44 associated with plant height was close to *qHID8-1* (Hemamalini et al. 2000), and RM263 associated with heading was adjacent to *hi2.1* (Marri et al. 2005). The chromosomal regions where numerous correlated traits are mapped indicate either pleiotropy of a single gene or tight linkage of multiple genes. Fine-mapping of such chromosomal regions would help discern the actual genetic control of these congruent traits. Development of markers for such traits in specific regions could lead to a highly effective strategy of marker-assisted selection for improving harvest index.

**Figure 6.** Comparisons of allelic effects of four constitutive marker loci RM431 (a) associated with plant height, RM471 (b) and Rid12 (c) associated with plant weight, RM24011 (d) associated with panicle length constitutively at both Stuttgart, Arkansas and Beaumont, Texas (Li et al. 2012).

## **5. Association mapping of grain yield and components**

be more tolerant to heavy doses of nitrogen fertilizer, and allows for planting increased stand densities. The *sd1* gene has greatlyimproved grain yield and has contributed to the

Other markers were associated with the traits correlated with harvest index, but not with harvest index directly in this study. These markers have been reported either nearby or flanking the QTL for harvest index. RM5, which was associated with plant height in the Stuttgart study, was close to a reported QTL for harvest index on Chr 1 (Marri et al. 2005). RM471 associated with plant weight was close to the reported *qHID4-1* and *qHID4-2* for harvest index (Hemamalini et al. 2000). Furthermore, RM257 and RM22559 associated with seed set were co-localized with a known QTL on Chr 9 (Marri et al. 2005), and with *qHID8-1* (Hema‐ malini et al. 2000) for harvest index, respectively. Similarly, at Beaumont, RM44 associated with plant height was close to *qHID8-1* (Hemamalini et al. 2000), and RM263 associated with heading was adjacent to *hi2.1* (Marri et al. 2005). The chromosomal regions where numerous correlated traits are mapped indicate either pleiotropy of a single gene or tight linkage of multiple genes. Fine-mapping of such chromosomal regions would help discern the actual genetic control of these congruent traits. Development of markers for such traits in specific regions could lead to a highly effective strategy of marker-assisted selection for improving

**Figure 6.** Comparisons of allelic effects of four constitutive marker loci RM431 (a) associated with plant height, RM471 (b) and Rid12 (c) associated with plant weight, RM24011 (d) associated with panicle length constitutively at both

Stuttgart, Arkansas and Beaumont, Texas (Li et al. 2012).

Green Revolution in cereal crops including rice (Fu et al. 2010).

118 Rice - Germplasm, Genetics and Improvement

harvest index.

Yield is one of the most important and complex traits in crops that does not evolve independ‐ ently but shows correlations with other traits. Thus, breeders have to consider correlated traits in breeding programs. Yield and its related traits are quantitatively inherited and controlled by many genes with small effects subject to environmental effects (Inostroza et al. 2009; Shi et al. 2009). Many studies have focused on the improvement and inheritance of agronomically important yield-related traits for achieving greater yield (Gravois and McNew 1993; Samonte et al. 1998). Other traits such as biomass, plant architecture, adaptation, and resistance to biotic and abiotic constraints may also indirectly affect yield through yield components or other physical and physiological mechanisms. Hence, estimation of the positions and effects of quantitative trait loci (QTL) for traits related to yield is of central importance for markerassisted selection for yield improvement. In rice genetics, most QTLs related to yield have been identified through classic linkage mapping approaches (Moncada et al. 2001; Brondani et al. 2002; Thomson et al. 2003; Jiang et al. 2004; Suh et al. 2005). With a few notable exceptions, most of these QTLs have not been successfully validated or consistently used in crop im‐ provement (Bernardo 2008). The classic approaches are too simplistic to effectively model most of the genetic variation for complex traits because they are unable to reflect the genetic realities of these traits (Cooper et al. 2005; Holland 2007).



#: The marker was associated with two or more traits

\*: The marker was reported to be associated with the corresponding trait previously

GY\_QTL Marker: The markers were identified to be linked or close to yield QTL in previous studies

**Table 4.** Marker loci significantly associated with grain yield and its related traits mapped in the USDA rice mini-core collection (Li et al. 2011)

## **5.1. Traits correlated with grain yield per plant in our study**

The traits significantly correlated with grain yield were plant height (0.43), plant weight (0.81), tillers (0.77), panicle length (0.30) and kernels/branch (0.40). All these traits were clustered into one branch except kernels/branch. This exploratory assessment showed that grain yield and the set of five correlated traits would serve as an appropriate base population for an association mapping application.

#### **5.2. Marker-yield trait associations**

Using the selected PCA model, a total of 30 marker loci were identified to have significant marker-trait associations at the 6.45×10-3 level of probability (the Bonferroni corrected signifi‐ cance level) for yield and its correlated traits (Table 4). Out of the 30 markers, four were associated with grain yield, three with plant height, six with plant weight, nine with tillers, five with panicle length and three with kernels/branch. Six markers were co-localized with previous identified QTL (Thomson et al. 2003; Jiang et al. 2004; Xue et al. 2008; Fu et al. 2010; Borba et al. 2010; Moncada et al. 2001) (Table 4).

Most importantly, eight of the 30 markers were synchronously associated with two or more traits (Table 4). RM471 was co-associated with three traits, grain yield, plant weight and kernels/branch. Three markers Rid12, RM224 and RM279 were co-associated with plant weight and tillers. RM431 was co-associated with plant height and tillers; RM509 with plant height and panicle length; RM7003 with grain yield and plant weight; and OSR13 with grain yield and kernels/branch. Three markers, OSR13, RM471 and RM7003 were included for the allelic analysis because they were not only associated with grain yield directly, but also co-associated with other yield correlated traits (Fig. 7). The allelic effect of each loci associated with the traits was estimated with mean of phenotypic value for each allele. For marker locus RM471, allele 126bp had the highest effect on all three traits (93.48 for grain yield, 266.23 for plant weight and 25.36 for kernels/branch), while two other alleles 109bp and 113bp had the lowest effect on grain yield with 48.19 and 49.90, and plant weight with 17.54 and 19.82, respectively (Fig. 7a and b). For OSR13, allele 123bp had a large effect on both grain yield and kernels/branch with 66.37 and 19.91, respectively while allele 115 had the highest effect on kernels/branch and the lowest on grain yield (Fig. 7c). For RM7003, allele 108bp had the highest effect on both traits (66.37 for grain yield and of 228.05 for plant weight) while the allele 106bp had the lowest effect on both traits (43.19 for grain yield and with 154.48 for plant weight) (Fig. 7d).

## **5.3. Trait-trait and marker-trait associations**

**Trait Locus Chr. no. Position (cM) Prob F Annotation**

Rid12 7 41.0 0.0001 # GY\_QTL Marker (Xue et al. 2008) RM125 <sup>7</sup> 41.0 0.0012 GY\_QTL Marker (Jiang et al. 2004;

RM245 9 91.8 0.0040 GY\_QTL Marker (Suh et al. 2005)

Fu et al. 2010; Borba et al. 2010)

GY\_QTL Marker (Moncada et al.

2001)

RM341 2 70.0 0.0023 \*

RM3558 4 69.8 0.0024

RM484 10 71.4 0.0005

RM287 11 68.0 0.0050

RM24011 9 27.4 0.0033

RM3739 12 97.0 0.0009

GY\_QTL Marker: The markers were identified to be linked or close to yield QTL in previous studies

**Table 4.** Marker loci significantly associated with grain yield and its related traits mapped in the USDA rice mini-core

The traits significantly correlated with grain yield were plant height (0.43), plant weight (0.81), tillers (0.77), panicle length (0.30) and kernels/branch (0.40). All these traits were clustered into one branch except kernels/branch. This exploratory assessment showed that grain yield and the set of five correlated traits would serve as an appropriate base population for an association

Using the selected PCA model, a total of 30 marker loci were identified to have significant marker-trait associations at the 6.45×10-3 level of probability (the Bonferroni corrected signifi‐ cance level) for yield and its correlated traits (Table 4). Out of the 30 markers, four were associated with grain yield, three with plant height, six with plant weight, nine with tillers, five with panicle length and three with kernels/branch. Six markers were co-localized with

Panicle length RM509 5 59.0 0.0036 # (5 markers) RM510 6 11.5 0.0003

Kernels/branch OSR13 3 36.0 0.0016 # (3 markers) RM471 4 45.0 0.0048 # □ RM1335 7 106.0 0.0005

\*: The marker was reported to be associated with the corresponding trait previously

**5.1. Traits correlated with grain yield per plant in our study**

#: The marker was associated with two or more traits

120 Rice - Germplasm, Genetics and Improvement

collection (Li et al. 2011)

mapping application.

**5.2. Marker-yield trait associations**

RM224 11 115.0 0.0002 #

Correlation among phenotypic traits is a common phenomenon in biology. Plant breeders need to consider trait correlations for either improving numerous correlated traits simultaneously or reducing undesirable side effects when their goal is only one of the correlated traits (Chen and Lubberstedt 2010). In this study, 34 of 91 pairs (37.36%) of 14 traits were observed to have significant correlation, and five traits were correlated with grain yield among 203 mini-core accessions. The correlations exhibited a complex network among these traits. Numerous researchers have concluded that rice yield is highly dependent on the number of productive tillers or panicles (Sharma and Choubey 1985; Dhanraj and Jagadish 1987), which is recently verified with a high correlation between tillers and yield (r=0.88; p < 0.01) by Borba et al. (2010). Panicle characters including panicle length, number of primary branches, secondary branches per primary branch, total kernels and seed set in a panicle, are reported to be tightly related to yield performance (Thomson et al. 2003; Ando et al. 2008; Terao et al. 2010). Although seed set and kernel weight per panicle were not directly correlated with yield in this study, they may be correlated in other panels of germplasm or may be indirectly contributable to yield. For example, seed weight per panicle, seed set and 1000 kernel weight are identified to be highly correlated with yield in wild rice (Oryza *rufipogon* Griff.) (Fu et al. 2010). Similarly, seed weight per panicle and seed set have correlations with yield in an advanced backcross population between *Oryza rufipogon* and the *Oryza sativa* cultivar Jefferson (Thomson et al. 2003). These different results could be expected since different materials were used in those studies.

**Figure 7.** Comparisons of allelic effects of three marker loci on yield traits: RM471 (a, b), OSR13 (c) and RM7003 (d). RM471 co-associated with grain yield, plant weight and kernels/branch panicle; OSR13 co-associated with grain yield and kernels/branch; RM7003 co-associated with grain yield and plant weight; bars are the standard error (Li et al. 2011).

Morphological correlations could be explained by either pleiotropy or linkage disequilibrium. The former describes the impact of a single gene on multiple phenotypic traits. The latter deals with influence of two or more genes on multiple traits, where the genes are physically located so close to each other, that they cannot be practically separated (Chen and Lubberstedt 2010). Co-association of a single gene (or two linked genes) with multiple traits that are phenotypi‐ cally correlated has occurred in numerous studies. Yan et al. (2009) reported five SSRs that were co-associated with two correlated traits affecting stigma exertion, another five SSRs with two traits correlated to spikelets, and one SSR with three correlated traits to spikelets in rice. Similarly, Terao et al. (2010) identified the gene of *APO1* that increases both the primary rachis branches and grains per panicle in rice. Gene *DEP1* increases both rachis branches and grain yield in rice (Huang et al. 2009). Gene *Ghd7* has major effects on grains per panicle, plant height and heading date in rice (Xue et al. 2008). Further, developmentally related traits (like number of tillers and roots) have been mapped to the same chromosome regions (Hemamalini et al. 2000; Brondani et al. 2002, Li et al. 2006; Thomson et al. 2003, Fu et al. 2010). In this study, eight markers were co-associated with two or more correlated traits and some QTLs related to yield and yield components have been reported to be near these regions. RM7003 co-associated with grain yield and plant weight is reported to flank a major yield QTL (*yld12.1*) (Thomson et al. 2003; Fu et al. 2010). Also, RM7003 is near the QTL *gpp12.1* which influences grains per panicle (Thomson et al. 2003), the QTL *pss12.1* which effects seed set (Fu et al. 2010) and another QTL *qFG12-2* which is involved with filled grain number (Li et al. 2002). Interestingly, five particular markers were not associated with yield directly in this study, but they were all identified to be the markers flanking grain yield QTL in other studies. RM431, RM340, and RM245 were found to be associated with yield QTLs, *yld1.1* (Fu et al. 2010), *qYI-6-1* and *qYI-9* (Suh et al. 2005), respectively. Rid12 co-associated with tillers and plant weight was found to be very close to *Ghd7* that had major effects on grain yield, plant height and heading date (Xue et al. 2008) in addition to its function for rice pericarp color (Sweeney et al. 2006; Brooks et al. 2008). RM125 associated with tillers was also identified to have a strong association with yield (Borba et al. 2010, Jiang et al. 2004). RM431 co-associated with plant height and tillers in this study has been reported to be closely linked with a QTL "*sd1*" to decrease plant height and increase yield (Peng et al. 1999; Fu et al. 2010). The chromosomal regions where numerous traits are mapped indicate either pleiotropy resulting from a single gene or tight linkage of multiple genes. Fine-mapping of such chromosomal regions would help discern the actual genetic control of these congruent traits. Development of markers for such traits in these regions could lead to a highly effective strategy of marker-assisted selection.

Several genes for grain yield and its related traits have been recently cloned, and each of these genes has a clearly distinct biological function (Li et al. 2003; Ashikari et al. 2005; Fan et al. 2006; Song et al. 2007). Molecular cloning and functional analyses of several genes have shown that these genes are mostly related to the synthesis and regulation of the phytohormone gibberellin (Peng et al. 1999; Ashikari et al. 1999; Spielmeyer et al. 2002; Itoh et al. 2004). For example, a semidwarf QTL "*sd-1"* close to RM431 contains a defective gibberellin 20-oxidase gene responsible for height reduction. The shorter statured plants have a decreases lodging threat and tolerates higher dosags of nitrogen fertilization, thus dramatically increases grain yield. Furthermore, the photoperiod pathway controls flowering time or heading directly, thus affects plant weight and yield indirectly (Xue et al. 2008). Two other genes regulating heading have been identified. One is *Hd6* which encodes a subunit of protein kinase CK2 (Takahashi et al. 2001), and the other is *Ehd1* which encodes a B-type response regulator (Doi et al. 2004). Also, a gene *GHD7* has been identified to simultaneously control yield, plant height and heading in rice (Xue et al. 2008). This gene locates close to Rid12 and encodes a CCT (CO, Colike and Timing of CAB1) domain protein. These findings demonstrate that genes regulating yield usually share some common pathways for traits that contribute to yield. Regions with either tightly linked QTLs or pleiotropic effects would become QTL hot spots, worth further investigation.

**Figure 7.** Comparisons of allelic effects of three marker loci on yield traits: RM471 (a, b), OSR13 (c) and RM7003 (d). RM471 co-associated with grain yield, plant weight and kernels/branch panicle; OSR13 co-associated with grain yield and kernels/branch; RM7003 co-associated with grain yield and plant weight; bars are the standard error (Li et al.

Morphological correlations could be explained by either pleiotropy or linkage disequilibrium. The former describes the impact of a single gene on multiple phenotypic traits. The latter deals with influence of two or more genes on multiple traits, where the genes are physically located so close to each other, that they cannot be practically separated (Chen and Lubberstedt 2010). Co-association of a single gene (or two linked genes) with multiple traits that are phenotypi‐ cally correlated has occurred in numerous studies. Yan et al. (2009) reported five SSRs that were co-associated with two correlated traits affecting stigma exertion, another five SSRs with two traits correlated to spikelets, and one SSR with three correlated traits to spikelets in rice. Similarly, Terao et al. (2010) identified the gene of *APO1* that increases both the primary rachis branches and grains per panicle in rice. Gene *DEP1* increases both rachis branches and grain yield in rice (Huang et al. 2009). Gene *Ghd7* has major effects on grains per panicle, plant height and heading date in rice (Xue et al. 2008). Further, developmentally related traits (like number of tillers and roots) have been mapped to the same chromosome regions (Hemamalini et al. 2000; Brondani et al. 2002, Li et al. 2006; Thomson et al. 2003, Fu et al. 2010). In this study, eight markers were co-associated with two or more correlated traits and some QTLs related to yield and yield components have been reported to be near these regions. RM7003 co-associated with grain yield and plant weight is reported to flank a major yield QTL (*yld12.1*) (Thomson et al. 2003; Fu et al. 2010). Also, RM7003 is near the QTL *gpp12.1* which influences grains per panicle (Thomson et al. 2003), the QTL *pss12.1* which effects seed set (Fu et al. 2010) and another QTL *qFG12-2* which is involved with filled grain number (Li et al. 2002). Interestingly, five particular

2011).

122 Rice - Germplasm, Genetics and Improvement

Comparison of the allelic effect among different alleles at the same locus could determine which specific alleles would be most informative for marker assisted selection. For example, allele 126bp of RM471 and 108bp of RM7003 were considered major alleles with a positive effecton increasing yield among all the alleles in the loci (Fig. 7). Howeve, the allele 106bp of RM7003 would be less desirable because it had a negetaive effect which is associated with a decrease of both grain yield and plant weight among accessions containing the allele. Results of the present study demonstrated that genome-wide association mapping in the USDA rice mini-core collection could complement and enhance the information from linkage-based QTL studies, and help increase yield through improvement of these related traits by marker-assisted selection either directly or indirectly.

## **6. Association mapping of resistance to Sheath Blight disease**

Rice sheath blight (ShB), caused by the soil-borne fungal pathogen *Rhizoctonia solani* Kühn, is a major disease of rice that greatly reduces yield and grain quality worldwide (Savary et al. 2006). Due to the high cost of cultural practices and the phytotoxic influence associated with the application of fungicides, the use of ShB resistant cultivars is considered the most eco‐ nomical and environmentally sound strategy in managing this disease. Understandings of genetic control will facilitate cultivar improvement for this disease and secure global food production.

The necrotrophic ShB pathogen has a broad host range and no complete resistance has been identified in either commercial rice cultivars or wild related species (Mew et al. 2004; Eizenga et al. 2002). However, substantial differences in susceptibility to ShB among rice cultivars have been observed under field conditions (Jia et al. 2007). Differential levels of resistance and the associated resistance genes have been studied among rice germplasm accessions (Manosalva et al. 2009). Rice ShB resistance is believed to be controlled by multiple genes or quantitative trait loci (QTLs) (Pinson et al. 2005). Since Li et al. (1995) first identified ShB QTLs using restricted fragment length polymorphism (RFLP) markers under field conditions, over 30 resistant ShB QTLs have been reported using various mapping populations, such as F2s (Sharma et al. 2009; Che et al. 2003), double haploid (DH) lines (Kunihiro et al. 2002), recombi‐ nant inbred lines (RILs) (Liu et al. 2009; Jia et al. 2007; Prasad and Eizenga 2008), near-isogenic introgression lines (NIL) (Loan et al. 2004) and backcross populations (Zuo et al. 2007; Sato et al. 2004). 'Teqing' and 'Jasmine 85' have been repeatedly involved in these studies as the ShB resistant parents. We are the first to map rice ShB QTLs using an association mapping strategy in a global germplasm collection (Jia et al. 2012).

## **6.1. Phenotypic evaluation of Sheath Blight resistance**

The isolate RR0140-1 of *R. solani* was selected from 102 isolates collected state-wide from Arkansas rice fields due to its slow growing phenotype (Wamishe et al. 2007). Field evaluations have showed similar disease reactions between slow growing and fast growing isolates (Wamishe et al. 2007). Further, the RR0140-1 isolate has been adapted by numerous studies (Liu et al. 2009; Jia et al. 2007; Prasad and Eizenga 2008). Pathogen preparation and inoculation are referred to Jia et al. (2007; 2011; 2012).

Plant response to the sheath blight pathogen was measured using the ratio between the height of the pathogen growing up the plant and the height of the leaf collar on the last emerged leaf. Because mature plant height varied from 70 to 202 cm in this collection (Yan et al. 2007), the ratio excluded possible interference of plant height in scoring disease response. Therefore, the smaller the ratio, the greater the resistance was for an entry. Measurements were taken when the ratio reached 1.0 for 75% of the susceptible check plants, Lemont, so that maximum susceptibility was scored as 1.0.

ShB rating data were analyzed using the GLIMMIX procedure in SAS version 9.1.3. The experimental design of randomized incomplete block formed the basis of the statistical model, where the accession is a fixed effect and block is treated as random effect. The LSMEANS option was used to calculate the least-square means (LSMs) from 18 plant scores in 6 replicates of each entry and the LSMs were used for the association mapping. The statistical differences of the accession to each check (Jasmine 85 and Lemont) were determined by a Dunnett's multiple comparison test, using the diff=control option.

## **6.2. Phenotypic variation of Sheath Blight severity ratings**

**6. Association mapping of resistance to Sheath Blight disease**

production.

124 Rice - Germplasm, Genetics and Improvement

in a global germplasm collection (Jia et al. 2012).

are referred to Jia et al. (2007; 2011; 2012).

susceptibility was scored as 1.0.

**6.1. Phenotypic evaluation of Sheath Blight resistance**

Rice sheath blight (ShB), caused by the soil-borne fungal pathogen *Rhizoctonia solani* Kühn, is a major disease of rice that greatly reduces yield and grain quality worldwide (Savary et al. 2006). Due to the high cost of cultural practices and the phytotoxic influence associated with the application of fungicides, the use of ShB resistant cultivars is considered the most eco‐ nomical and environmentally sound strategy in managing this disease. Understandings of genetic control will facilitate cultivar improvement for this disease and secure global food

The necrotrophic ShB pathogen has a broad host range and no complete resistance has been identified in either commercial rice cultivars or wild related species (Mew et al. 2004; Eizenga et al. 2002). However, substantial differences in susceptibility to ShB among rice cultivars have been observed under field conditions (Jia et al. 2007). Differential levels of resistance and the associated resistance genes have been studied among rice germplasm accessions (Manosalva et al. 2009). Rice ShB resistance is believed to be controlled by multiple genes or quantitative trait loci (QTLs) (Pinson et al. 2005). Since Li et al. (1995) first identified ShB QTLs using restricted fragment length polymorphism (RFLP) markers under field conditions, over 30 resistant ShB QTLs have been reported using various mapping populations, such as F2s (Sharma et al. 2009; Che et al. 2003), double haploid (DH) lines (Kunihiro et al. 2002), recombi‐ nant inbred lines (RILs) (Liu et al. 2009; Jia et al. 2007; Prasad and Eizenga 2008), near-isogenic introgression lines (NIL) (Loan et al. 2004) and backcross populations (Zuo et al. 2007; Sato et al. 2004). 'Teqing' and 'Jasmine 85' have been repeatedly involved in these studies as the ShB resistant parents. We are the first to map rice ShB QTLs using an association mapping strategy

The isolate RR0140-1 of *R. solani* was selected from 102 isolates collected state-wide from Arkansas rice fields due to its slow growing phenotype (Wamishe et al. 2007). Field evaluations have showed similar disease reactions between slow growing and fast growing isolates (Wamishe et al. 2007). Further, the RR0140-1 isolate has been adapted by numerous studies (Liu et al. 2009; Jia et al. 2007; Prasad and Eizenga 2008). Pathogen preparation and inoculation

Plant response to the sheath blight pathogen was measured using the ratio between the height of the pathogen growing up the plant and the height of the leaf collar on the last emerged leaf. Because mature plant height varied from 70 to 202 cm in this collection (Yan et al. 2007), the ratio excluded possible interference of plant height in scoring disease response. Therefore, the smaller the ratio, the greater the resistance was for an entry. Measurements were taken when the ratio reached 1.0 for 75% of the susceptible check plants, Lemont, so that maximum

ShB rating data were analyzed using the GLIMMIX procedure in SAS version 9.1.3. The experimental design of randomized incomplete block formed the basis of the statistical model, The ShB severity ratings among the 217 entries were distributed normally, ranging from 0.256 ± 0.111 to 0.909 ± 0.096 with an average of 0.521 ± 0.008 (Fig. 8). The resistant check Jasmine 85 was rated 0.472 ± 0.021 and susceptible check Lemont was rated 0.946 ± 0.080. Twenty-four entries (11.1 %) were significantly more resistant to ShB than Jasmine 85 at the 5% level of probability while 54 others (24.9%) had similar resistance.

**Figure 8.** Distribution of sheath blight severity ratings among 217 mini-core accessions averaged over 18 plant scores, 3 in each of 6 replicates using a micro-chamber method with resistant check Jasmine 85 and susceptible Lemont (Jia et al. 2012).

#### **6.3. Marker loci and their alleles associated with Sheath Blight resistance**

Ten marker loci were identified to be significantly associated with ShB resistance at the probability level of 5% or lower, three on chromosome (Chr) 11, two on Chr1, and one each on Chr2, 4, 5, 6 and 8 (Table 5). RM237 on Chr1 at 27.1 Mb had the highest significance rating for ShB at the 0.002 level of probability. RM11229 on the long arm of Chr1 explained the most phenotypic variation (9.5%) with significance at the 0.044 level of probability. RM11229 and 1233 each had six alleles, the most among the 217 mini-core entries, followed by RM341 and 254 (five alleles), RM237, 8217,146 and 408 (four), RM133 (three) and RM7203 (two) (Table 5).

Among the six alleles of RM11229, allele 158 was present in 18 entries that had the lowest average ShB rating (0.414), and thus, it was designated as the 'putative resistant allele' of this marker locus. Accordingly, ten alleles, one each from the ten associated marker loci, were noted as the putative resistant allele in Table 5 because they had the greatest effect to decrease ShB among all the alleles for their respective loci (Table 5). ShB rating was the smallest for putative resistant allele 158 of RM11229 among the ten putative resistant alleles. Of the other five putative resistant alleles, 139 of RM341 (present in 17 entries), 340 of RM146 (28 entries), 88 of RM7203 (120 entries), 169 of RM254 (12 entries) and 177 of RM1233 (35 entries), had lower ShB means ranging 0.447 - 0.470 than the resistant check Jasmine 85 (0.472), suggesting a stronger effect for resistance to ShB than Jasmine 85. The remaining four putative resistant alleles had similar ShB ratings with Jasmine 85, suggesting a similar effect for the level of ShB control.


Association Mapping of Harvest Index, Grain Yield, Sheath Blight Resistance and… http://dx.doi.org/10.5772/56830 127


a Rsq\_Marker - total explained phenotypic variation.

phenotypic variation (9.5%) with significance at the 0.044 level of probability. RM11229 and 1233 each had six alleles, the most among the 217 mini-core entries, followed by RM341 and 254 (five alleles), RM237, 8217,146 and 408 (four), RM133 (three) and RM7203 (two) (Table 5).

Among the six alleles of RM11229, allele 158 was present in 18 entries that had the lowest average ShB rating (0.414), and thus, it was designated as the 'putative resistant allele' of this marker locus. Accordingly, ten alleles, one each from the ten associated marker loci, were noted as the putative resistant allele in Table 5 because they had the greatest effect to decrease ShB among all the alleles for their respective loci (Table 5). ShB rating was the smallest for putative resistant allele 158 of RM11229 among the ten putative resistant alleles. Of the other five putative resistant alleles, 139 of RM341 (present in 17 entries), 340 of RM146 (28 entries), 88 of RM7203 (120 entries), 169 of RM254 (12 entries) and 177 of RM1233 (35 entries), had lower ShB means ranging 0.447 - 0.470 than the resistant check Jasmine 85 (0.472), suggesting a stronger effect for resistance to ShB than Jasmine 85. The remaining four putative resistant alleles had similar ShB ratings with Jasmine 85, suggesting a similar effect for the level of ShB control.

**P value Rsq\_Marker a Allele (bp)**

RM11229 1 22.4 0.044 9.5% 158\* 18 0.414

RM237 1 26.8 0.002 6.9% 122 19 0.526

RM341 2 19.3 0.041 4.1% 135 89 0.558

RM8217 4 32.7 0.044 3.2% 178 67 0.581

RM146 5 18.1 0.021 3.8% 330 26 0.539

**Number of Entries**

192 21 0.515 195 21 0.473 198 13 0.532 207 14 0.608 224 12 0.466

128\* 32 0.473 130 105 0.515 132 20 0.635

138 39 0.545 139\* 17 0.447 141 15 0.579 171 39 0.461

182 19 0.534 184 65 0.482 186\* 49 0.476

**ShB Mean b**

**Marker Chr.**

126 Rice - Germplasm, Genetics and Improvement

**Position (Mb)**

b The mean of ShB severity rating for the entries with the allele.

Allele\*: Putative resistant allele which had the lowest ShB mean at the marker locus.

**Table 5.** Marker loci significantly associated with sheath blight resistance, their physical locations on chromosomes (Chr), allele size in 217 mini-core entries, number of entries with the allele, and their mean sheath blight (ShB) rating (Jia et al. 2012)

Among the ten putative resistant alleles, allele 88 of RM7203 was the most prevalent and existed in 120 (55%) of 217 entries in the mapping panel, followed by allele 230 of RM133 and 119 of RM408 (48% of the lines), allele 186 of RM8217 (23%), allele 340 of RM146, 128 of RM237 and 177 of RM1233 (13-16%), allele 139 of RM341 and 158 of RM1229 (8%), and allele 169 of RM254 (6%).

**Figure 9.** UPGMA tree based on Nei genetic distance for 217 mini-core entries where 24 marked with ● were signifi‐ cantly more resistant to sheath blight than the resistant check 'Jasmine 85'. Presence of 'putative resistant alleles' is distinguished by branch color: Red= eight putative resistant alleles, Pink= seven, Blue= six, Green= five, and Orange = four (Jia et al. 2012).

## **6.4. Number of putative resistant alleles and Sheath Blight resistance**

The number of putative resistant alleles increased along with an increase of sheath blight resistance in an accession of rice germplasm. GSOR 310389 from Korea contained the most putative resistant alleles, eight out of ten, and had a ShB rating of 0.351 which was significantly more resistant than the resistant check Jasmine 85 which contained three putative resistant alleles and had a ShB rating of 0.472. Among seven entries containing six putative resistant alleles with a mean of 0.386 ShB, GSOR 310475 and 311475 were more resistant than Jasmine 85 and had ShB ratings of 0.324 and 0.336, respectively. Among 28 entries having five putative resistant alleles with a mean ShB rating of 0.444, seven were significantly more resistant than Jasmine 85. Seven, out of 35 entries which carried four putative resistant alleles and had a mean ShB of 0.466, were identified to be significantly more resistant than Jasmine 85. The mean ShB ratings for entries containing three, two, one and zero putative resistant alleles were 0.483, 0.535, 0.582 and 0.598, respectively. There was a strong and negative correlation between the ShB severity rating and number of putative resistant alleles (*r* = -0.535, *p*<0.0001).

Our mapping results showed that most entries containing a large number of putative resistant alleles were IND (Fig. 9). All entries with six or more putative resistant alleles were IND with only one exception of AUS. Among 28 entries with five putative resistant alleles, 25 were IND and the remaining three were AUS. There were 35 entries with four putative resistant alleles, nine were AUS, one was admix of TRJ, AUS and IND, and the remaining 25 were IND. Among 35 entries with three putative resistant alleles, 18 were IND, eight AUS, seven TRJ and two admixes of IND. However, among 51 entries without a single putative resistant allele, 26 were TEJ, 18 TRJ, four ARO and two admixes of TRJ-TEJ-ARO, and one IND. Among 72 entries that carried four or more putative resistant alleles, 58 (81%) were IND and 13 AUS (18%) plus admix of TRJ-AUS-IND.

## **6.5. Putative resistant alleles and ancestry background for Sheath Blight resistance**

Jia et al. (2011) reported 52 entries that are significantly more resistant to ShB than Jasmine 85. The resistant entries were identified from 1,794 entries of the USDA rice core collection that has 35% *indica*, 27% *temperate japonica*, 24% *tropical japonica*, 10% *aus* and 4% *aromatic* genotypes (Agrama et al. 2010). Based on the ancestry classification, there are 621 *indica* entries in the core and 45 of them are included in the resistant list, making a resistance frequency of 7.2% for *indica* germplasm. Accordingly, the resistance frequency is 2.8% for *aromatic*, 1.7% for *aus*, and 0.2% each for *temperate japonica* and *tropical japonica*. In a study conducted by Zuo et al. (2007), *japonica* cultivars showed higher sheath blight severity than *indica* cultivars. They describe a general observation that *japonica* rice is more susceptible than *indica* rice. Furthermore, Jasmine 85, Tetep and Teqing, used as parents in many studies on mapping ShB resistance, are all *indica*.

Our study demonstrated that: 1) a majority of the ShB putative resistant alleles existed in *indica* germplasm*,* 2) most of the resistant entries with a large number of putative resistant alleles were *indica*, conversely 3) only a very small portion of putative resistant alleles existed in *japonica,* and 4) the most susceptible entries with very few or no putative resistant alleles were *japonica* (Fig. 8). Entry GSOR 310389 is an example which had eight out of ten putative resistant alleles, showed a high level of resistance to ShB, and is *indica*. The results from association mapping match well with the phenotypic observation that most resistant genotypes are *indica* and resistant germplasm is rare in *japonica*.

## **7. Association mapping of silica concentration in rice hulls**

**Figure 9.** UPGMA tree based on Nei genetic distance for 217 mini-core entries where 24 marked with ● were signifi‐ cantly more resistant to sheath blight than the resistant check 'Jasmine 85'. Presence of 'putative resistant alleles' is distinguished by branch color: Red= eight putative resistant alleles, Pink= seven, Blue= six, Green= five, and Orange =

The number of putative resistant alleles increased along with an increase of sheath blight resistance in an accession of rice germplasm. GSOR 310389 from Korea contained the most putative resistant alleles, eight out of ten, and had a ShB rating of 0.351 which was significantly more resistant than the resistant check Jasmine 85 which contained three putative resistant alleles and had a ShB rating of 0.472. Among seven entries containing six putative resistant alleles with a mean of 0.386 ShB, GSOR 310475 and 311475 were more resistant than Jasmine 85 and had ShB ratings of 0.324 and 0.336, respectively. Among 28 entries having five putative resistant alleles with a mean ShB rating of 0.444, seven were significantly more resistant than Jasmine 85. Seven, out of 35 entries which carried four putative resistant alleles and had a mean ShB of 0.466, were identified to be significantly more resistant than Jasmine 85. The mean ShB ratings for entries containing three, two, one and zero putative resistant alleles were 0.483, 0.535, 0.582 and 0.598, respectively. There was a strong and negative correlation between the

**6.4. Number of putative resistant alleles and Sheath Blight resistance**

ShB severity rating and number of putative resistant alleles (*r* = -0.535, *p*<0.0001).

Our mapping results showed that most entries containing a large number of putative resistant alleles were IND (Fig. 9). All entries with six or more putative resistant alleles were IND with only one exception of AUS. Among 28 entries with five putative resistant alleles, 25 were IND and the remaining three were AUS. There were 35 entries with four putative resistant alleles,

four (Jia et al. 2012).

128 Rice - Germplasm, Genetics and Improvement

Rice (*Oryza sativa* L.) accumulates silicon (Si) in various tissues including hulls. Although Si is not an essential nutrient, it plays an important role in the growth and health of rice plants. Silicic acid is actively taken up by rice roots, which is then translocated in the form of mono‐ silicic acid (silica gel) through the xylem (Mitani et al. 2005; Ma and Yamaji 2006) to the leaves, stems, hulls and grains of the plant where it converted to silica (SiO2) (Ma et al. 2007). Often, rice hulls are burned in the mills to produce steam or electricity. However, disposal of the rice hull ash is difficult due to the high silica content (70-95%) (Marshall 2004). Unused hulls and ash are taken to a landfill where they remain for years due to their chemical stability. Another approach for reducing the amount of hulls and ash going to the landfill is to use the silica for value-added products. Rice hulls have been used to produce particle board, poultry bedding, brick making, package cushioning, and absorbents. Due to the high silicon content, rice hulls and ash are good raw materials in the production of silicon-based industrial materials with high economic value, including silicon carbide, silica, silicon nitride, silicon tetrachloride, pure silicon, and zeolite (Sun and Gong 2001). Since the Si in rice hulls is amorphous, it can be extracted at lower temperatures than Si derived from other conventional sources, thus reducing the cost of Si production (Kalapathy et al. 2002). Understanding the genetic control of Si content in rice will facilitate the development of new varieties with either high or low Si content. Varieties with high silica content in their hulls would be useful for raw material for silica based industrial compounds, while varities with low silica hulls would be more biode‐ gradable and better suited for energy producing purposes (i.e. cleaner energy production at the mills and possible use in bioenergy production).

## **7.1. Chemical analysis of silica concentration in rice hulls**

The rough rice samples from test plots were dehulled with a Satake Rice Machine (Satake Engineering Co., LTD, Ueno Taito-Ku, Tokyo). After drying at 80o C for 2 hr, the hulls (~3g) were stored in 50 ml polypropylene tubes (Cat. # 05-539-5, Fisher Scientific, Houston, TX) at room temp. (22o C) until analyzed. Silica was determined using the molybdenum yellow method described by Saito et al. (2005) and Bryant et al. (2011).

## **7.2. Variation of silica concentration in the USDA rice mini-core collection**

Si content averaged 200 mg g-1 and ranged from 118 mg g-1 for ACNO 430909, an Admixture of *aus* (AUS), *indica* (IND) and *wild rice* (WD) from the Punjab region of Pakistan, to 249 mg g-1 for ACNO 353722, an AUS accession from Assam, India. The non-Admix accession with the lowest Si was ACNO 439683, a TEJ from Eastern Europe, having a Si of 147 mg g-1. Wide variation of Si was seen in all genetic groups. Mean Si of the TRJ (219 mg g-1) and AUS (208 mg g-1) was greater while other groups were less than the overall mean. All the accessions native to Central America region (n = 9), except a TRJ ACNO 2169 from Guatemala, were above the Mini-Core mean value, whereas the Si contents of accessions native to the Mideast (n = 5), Eastern Europe (n = 8), Central Asia (n = 9) and North America (n = 3) were below the Mini-Core mean with a few exceptions. The variation due to genetics (accessions) accounted for 32.4% of the total Si variation in the Mini-Core. The silica content of samples grown in Beaumont, TX (186±1.3 mg g-1 ) was lower than those grown in Stuttgart, AR (211±1.2 mg g-1), with Location accounting for 19.5% of the silica content variation.

#### **7.3. Marker loci associated with silica concentration**

We identified four associated markers in AR, and they were different from the four identified in TX (Table 6). Three out of four AR markers were among seven associated markers mapped in the combined location, whereas none of the TX markers were in. The 19.5% of the total silica content variation due to the difference of AR from TX might be responsible for the mapping results. It is known that the amount of silica present in the soil, the presence of other elements and/or nutrients, the amount of light, and temperature are all factors that affect silica concen‐ trations in the plant (Ma and Takahashi 2002; Ma et al. 2002). RM263 from AR, RM6544 from both AR and Combined location and RM5371 from TX are all within a 1.5 Mb region where additive by additive QTL effects were previously identified by Dai et al. (2005). In summary, five of the marker-trait associations found in this study are within 1.5 Mb of the reported QTLs for silica concentrations from linkage mapping studies, and one marker-trait association (RM5371 on chromosome 6 at 25.83 Mb) overlaps with a QTL involved in grain arsenic concentration as well as silica concentration (Dai et al. 2005). The present study demonstrates that association mapping of the diverse germplasm in the USDA rice Mini-Core collection is an effective method for identifying new genetic markers and validating previously reported marker regions associated with silica concentration.

high economic value, including silicon carbide, silica, silicon nitride, silicon tetrachloride, pure silicon, and zeolite (Sun and Gong 2001). Since the Si in rice hulls is amorphous, it can be extracted at lower temperatures than Si derived from other conventional sources, thus reducing the cost of Si production (Kalapathy et al. 2002). Understanding the genetic control of Si content in rice will facilitate the development of new varieties with either high or low Si content. Varieties with high silica content in their hulls would be useful for raw material for silica based industrial compounds, while varities with low silica hulls would be more biode‐ gradable and better suited for energy producing purposes (i.e. cleaner energy production at

The rough rice samples from test plots were dehulled with a Satake Rice Machine (Satake

were stored in 50 ml polypropylene tubes (Cat. # 05-539-5, Fisher Scientific, Houston, TX) at

Si content averaged 200 mg g-1 and ranged from 118 mg g-1 for ACNO 430909, an Admixture of *aus* (AUS), *indica* (IND) and *wild rice* (WD) from the Punjab region of Pakistan, to 249 mg g-1 for ACNO 353722, an AUS accession from Assam, India. The non-Admix accession with the lowest Si was ACNO 439683, a TEJ from Eastern Europe, having a Si of 147 mg g-1. Wide variation of Si was seen in all genetic groups. Mean Si of the TRJ (219 mg g-1) and AUS (208 mg g-1) was greater while other groups were less than the overall mean. All the accessions native to Central America region (n = 9), except a TRJ ACNO 2169 from Guatemala, were above the Mini-Core mean value, whereas the Si contents of accessions native to the Mideast (n = 5), Eastern Europe (n = 8), Central Asia (n = 9) and North America (n = 3) were below the Mini-Core mean with a few exceptions. The variation due to genetics (accessions) accounted for 32.4% of the total Si variation in the Mini-Core. The silica content of samples grown in Beaumont, TX (186±1.3 mg g-1 ) was lower than those grown in Stuttgart, AR (211±1.2 mg g-1),

We identified four associated markers in AR, and they were different from the four identified in TX (Table 6). Three out of four AR markers were among seven associated markers mapped in the combined location, whereas none of the TX markers were in. The 19.5% of the total silica content variation due to the difference of AR from TX might be responsible for the mapping results. It is known that the amount of silica present in the soil, the presence of other elements and/or nutrients, the amount of light, and temperature are all factors that affect silica concen‐ trations in the plant (Ma and Takahashi 2002; Ma et al. 2002). RM263 from AR, RM6544 from both AR and Combined location and RM5371 from TX are all within a 1.5 Mb region where additive by additive QTL effects were previously identified by Dai et al. (2005). In summary,

C) until analyzed. Silica was determined using the molybdenum yellow

C for 2 hr, the hulls (~3g)

the mills and possible use in bioenergy production).

130 Rice - Germplasm, Genetics and Improvement

room temp. (22o

**7.1. Chemical analysis of silica concentration in rice hulls**

Engineering Co., LTD, Ueno Taito-Ku, Tokyo). After drying at 80o

**7.2. Variation of silica concentration in the USDA rice mini-core collection**

method described by Saito et al. (2005) and Bryant et al. (2011).

with Location accounting for 19.5% of the silica content variation.

**7.3. Marker loci associated with silica concentration**


a Markers that occur within 1.5 Mb or less of previously identified silica QTL's.

b Markers that occur within 1.5 Mb or less of previously identified additive-by-additive QTL regions.

c Marker that occur within 1.5 Mb or less of previously identified arsenic QTL's.

**Table 6.** Marker loci associated with hull silica content at less than 0.01 probability mapped among 174 mini-core accessions genotyped with 164 SSR markers and phenotyped at Stuttgart, Arkansas (AR) and Beaumont, Texas (TX) (Bryant et al. 2011)

## **Acknowledgements**

The authors thank Tiffany Sookaserm, Tony Beaty, Yao Zhou, Biaolin Hu, LaDuska Simpson, Curtis Kerns, Sarah Hendrix, Bill Luebke, Jodie Cammack, Kip Landry, Carl Henry, Jason Bonnette, and Piper Roberts for technical assistance; Xiaobai Li, Limeng Jia, Chengsong Zhu, Robert Fjellstrom and Anna McClung for professional assistance.

## **Author details**

Wengui Yan1 , Aaron Jackson1 , Melissa Jia1 , Wei Zhou1,2, Haizheng Xiong1,3 and Rolfe Bryant1

1 United States Department of Agriculture, Agricultural Research Service, Dale Bumpers National Rice Research Center, Stuttgart, Arkansas, USA

2 University of Arkansas at Pine Bluff, Pine Bluff, Arkansas, USA

3 Zhejiang University, State Key Lab of Rice Biology, Institute of Nuclear-Agricultural Scien‐ ces, Hangzhou, Zhejiang, China

USDA is an equal opportunity provider and employer

## **References**


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**Author details**

, Aaron Jackson1

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National Rice Research Center, Stuttgart, Arkansas, USA

USDA is an equal opportunity provider and employer

2 University of Arkansas at Pine Bluff, Pine Bluff, Arkansas, USA

Wengui Yan1

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## **Identification and Utilization of Elite Genes from Elite Germplasms for Yield Improvement**

Dawei Xue, Qian Qian and Sheng Teng

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56390

## **1. Introduction**

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Rice Sci 21: 136-142

142 Rice - Germplasm, Genetics and Improvement

Rice is a major food crop in the world. Half of the world's population relies on rice as their staple food. Due to continuous growth of human population, the area of arable land decreases every year. Therefore, ensuring adequate grain production has become a challenge for many countries. Rice production has an important role in global food security, poverty alleviation and rural employment. The current rate of increase in mean rice yield per annum is only 0.8%, which falls behind the rate of population growth annually. An annual mean increase in rice production of 1.2% is required between 2001 and 2030 in order to catch up with the growing food demand resulting from increase in population[1,2].

Constrained by lack of water resources and arable land, the area for rice cultivation has decreased in the context of economic development and urbanization. It is evident that rice yield cannot be increased by simply expanding the cultivation area. In light of this, it is important to pay attention to increasing rice yield per unit area. There are two major ap‐ proaches for achieving this goal: improving cultivation conditions and technology, and breeding rice varieties with higher yield potential. In order to improve the cultivation techni‐ que, selecting superior cultivars is essential. Practices in rice science and production have shown that high-yield breeding of rice is essential for yield increase, and a breakthrough is usually made through discovery and effective utilization of specific germplasm (gene). The first leap in rice yield per unit area came from breeding semidwarf rice varieties and their popularization. In the 1940s, with rapid development of the chemical industry, chemical fertilizers were applied extensively in rice production. Tall rice varieties showed very low potential for yield increase due to their low tolerance for fertilizers and easily lodging. Chinese rice breeders first proposed the strategy of dwarfing breeding. In the late 1950s, successful breeding of Taichung Native 1 (TN1), Aijiaonante and Guangchangai rice varieties, which were

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

high-yielding dwarf rice varieties, marked a new epoch in dwarf rice breeding[80]. Later, the International Rice Research Institute cross-bred the Dijiaowujian rice as a dwarf-gene source with tall Pitai rice. The dwarf hybrid IR8, known as miracle rice, was developed in 1964. Compared with tall rice varieties, dwarf rice varieties have several advantages, including fertilizer tolerance, anti-lodging, erect leaves, more panicles and high harvest index. The yield per unit area was increased over 30% by the dwarf rice varieties. The successful breeding of dwarf rice varieties was just the beginning of the global "green revolution" [3]. Academician Yuan Longping discovered wild-abortive cytoplasmic male sterile line, and was the first to realize three-line combination in 1973 with the establishment of hybrid rice seed production system. The success of three line combination directly resulted in yield per mu exceeding 500 kg in many areas of China. The yield increase was over 15%-20% compared with conventional varieties. The discovery of photoperiod-thermo sensitive genic sterile gene facilitates the transition from three-line system to two-line system, which is especially useful for developing hybrids. Their effective utilization enabled the second leap in rice yield, which is also known as the second "Green Revolution" in rice production history.

The global rice yield of current varieties seems to be at a standstill. Reduction of arable land and global warming are also threatening rice production. For this reason, increasing yield per unit area is very important for boosting total yield. Rice yield per unit area heavily depends on the yield potential of the rice variety. In order to achieve the third leap in yield per unit area, many countries have successively put forward the plan of super rice breeding by adopting the technical route of combining ideal plant type with indica-japonica heterosis utilization [4].

In 1918, Japanese rice breeders had suggested super high-yield breeding of rice via indicajaponica cross. The International Rice Research Institute launched a new plant type breeding program based on Javanica rice in 1989. They proppsed morphological indices for higher yield rate were specified as follows: low tillering (3-4 tillers), no unproductive tiller, large panicle, sturdy stem, dark green, thick and erect leaves, vigorous root systems and high harvest index [5]. In 1996, China started the super rice research program which consisted of two parts: conventional super rice employing the technical route of indica-japonica cross, and super hybrid rice breeding with the combination of plant type improvement, intersubspecific heterosis utilization and distant favorable genes. Chinese rice breeders proposed different ideal plant types based on long-term production practice. These ideal plant types included an erect large spike type in north China, an early-growth deep-root type in south China, a sparseseeding heavy-panicle type in the upper reach of Yangtze River, a functional-leaf erect type and later-stage functional type in middle and lower reaches of Yangtze River [4]. All ideal plant types of rice share some common features: low tiller number, no ineffective tiller, robust stalk, anti-lodging, large panicle, large grain number per spike, high yield per spike, high biological production, high harvest index and vigorous root systems.

Exploitation of F1 hybrid heterosis for the purpose of reaping economic benefits is referred to as hybrid heterosis exploitation. Research on the mechanism of hybrid heterosis is of great significance for the exploitation of heterosis of hybrids in crop genetic breeding. Pei'ai 64S is a low thermo-sensitive dual-purpose genic male sterile line developed by China National Hybrid Rice Engineering Research Center, with Nongken 58S as the female parent and Indica Pei'ai 64 as the male parent. Pei'ai 64S was obtained after crossing and backcrossing, as well as multi-generation selection. Because of its stable sterility, seed production security, combi‐ nation freedom and strong hybrid heterosis, it has been extensively applied in breeding. Pei'ai 64S is China's first indica male sterile line with practical utilization value. Medium maturity indica rice variety 9311 (Yangdao 6) was bred by the Research Institute of Agricultural Sciences in the Lixia River region of Jiangsu Province. It has been extensively applied in rice production for its high quality, high yield, multiple resistance and strong combing ability. Yangdao 6 has served as the male parent for key hybrid varieties including Liangyoupeijiu, Fengliangyou 1, Yangliangyou 6 and Yueyou 938, and it is also the first indica variety used in the sequencing of rice genome framework under the name of 9311.

Effective tiller, grain number per spike and 1000 grain weight are the three major elements used for determining rice yield. These three metrics are also the indicators of hybrid heterosis. Spike number is closely related to rice tillering. Grain number per spike is associated with spike length and grain density. Grain weight depends on grain length, width and thickness. These yield components are considered in ideal plant type. Plant height, tiller, panicle type and grain weight are determined by the interaction between growth, hormone and environ‐ ment. Generally, these yield-related traits are controlled by quantitative trait loci (QTL) or major genes. At present, some QTL/genes related to yield and heterosis have been cloned and the regulatory mechanism of ideal plant type and hybrid heterosis at the molecular level is being revealed. Knowledge of these mechanisms is especially important in the context of rice breeding.

## **2. Plant type**

high-yielding dwarf rice varieties, marked a new epoch in dwarf rice breeding[80]. Later, the International Rice Research Institute cross-bred the Dijiaowujian rice as a dwarf-gene source with tall Pitai rice. The dwarf hybrid IR8, known as miracle rice, was developed in 1964. Compared with tall rice varieties, dwarf rice varieties have several advantages, including fertilizer tolerance, anti-lodging, erect leaves, more panicles and high harvest index. The yield per unit area was increased over 30% by the dwarf rice varieties. The successful breeding of dwarf rice varieties was just the beginning of the global "green revolution" [3]. Academician Yuan Longping discovered wild-abortive cytoplasmic male sterile line, and was the first to realize three-line combination in 1973 with the establishment of hybrid rice seed production system. The success of three line combination directly resulted in yield per mu exceeding 500 kg in many areas of China. The yield increase was over 15%-20% compared with conventional varieties. The discovery of photoperiod-thermo sensitive genic sterile gene facilitates the transition from three-line system to two-line system, which is especially useful for developing hybrids. Their effective utilization enabled the second leap in rice yield, which is also known

The global rice yield of current varieties seems to be at a standstill. Reduction of arable land and global warming are also threatening rice production. For this reason, increasing yield per unit area is very important for boosting total yield. Rice yield per unit area heavily depends on the yield potential of the rice variety. In order to achieve the third leap in yield per unit area, many countries have successively put forward the plan of super rice breeding by adopting the technical route of combining ideal plant type with indica-japonica heterosis

In 1918, Japanese rice breeders had suggested super high-yield breeding of rice via indicajaponica cross. The International Rice Research Institute launched a new plant type breeding program based on Javanica rice in 1989. They proppsed morphological indices for higher yield rate were specified as follows: low tillering (3-4 tillers), no unproductive tiller, large panicle, sturdy stem, dark green, thick and erect leaves, vigorous root systems and high harvest index [5]. In 1996, China started the super rice research program which consisted of two parts: conventional super rice employing the technical route of indica-japonica cross, and super hybrid rice breeding with the combination of plant type improvement, intersubspecific heterosis utilization and distant favorable genes. Chinese rice breeders proposed different ideal plant types based on long-term production practice. These ideal plant types included an erect large spike type in north China, an early-growth deep-root type in south China, a sparseseeding heavy-panicle type in the upper reach of Yangtze River, a functional-leaf erect type and later-stage functional type in middle and lower reaches of Yangtze River [4]. All ideal plant types of rice share some common features: low tiller number, no ineffective tiller, robust stalk, anti-lodging, large panicle, large grain number per spike, high yield per spike, high

Exploitation of F1 hybrid heterosis for the purpose of reaping economic benefits is referred to as hybrid heterosis exploitation. Research on the mechanism of hybrid heterosis is of great significance for the exploitation of heterosis of hybrids in crop genetic breeding. Pei'ai 64S is a low thermo-sensitive dual-purpose genic male sterile line developed by China National

as the second "Green Revolution" in rice production history.

biological production, high harvest index and vigorous root systems.

utilization [4].

144 Rice - Germplasm, Genetics and Improvement

The ideal plant type was first proposed in 1968. It is defined as the combination of several traits favorable for photosynthesis, growth and grain yield in one plant type. In such ideal plant type, competition between individuals is reduced and solar energy utilization in the popula‐ tion is maximized, leading to minimal consumption and maximum dry matter accumulation [6]. In a narrow sense, plant type of a crop refers to plant morphology and spatial arrangement of the tillering and leaves. It is also related to other plant traits including plant height, tiller number, tiller angle, panicle type, leaf morphology and leaf angle. In a general sense, plant type may also consist of functional traits related to solar energy utilization of the population, including nitrogen content, photosynthetic efficiency, chlorophyll content, stoma density and extinction coefficient [7].

Apart from rice morphology and spatial arrangement, plant type also covers some functional traits directly related to solar energy utilization of the population. Breeders from the Interna‐ tional Rice Research Institute announced a new plant type (NPT) in 1989. It had fewer tillers (5-6 per plant) and no ineffective tiller; large spike, with grain number reaching 150-200 per panicle; plant height of 90-100 cm; anti-lodging robust stalk; thick, erect and dark green leaves; and well-developed root systems [8]. Chinese rice breeders also conducted a series of research on ideal plant type in different ecological conditions for high-yield breeding and cultivation. Several models of ideal plant type were developed. Prof. Yang Shouren of Shenyang Agricul‐ tural University proposed the "short-branch and erect-leaf, large and straight panicle" type for japonica rice in north China [9]. In 1997, Prof. Zhou Kaida of Sichuan Agricultural University proposed "intersubspecific large-spike type", which is adapted to conditions of less wind, high moisture and high temperature in the Sichuan Basin [10]. Similarly, Yuan Longping of China National Hybrid Rice Engineering Research Center proposed "high-canopy and low-spikelayer type", which is also referred to as the "gold hidden in the leaf" type, for the ecological area of middle and lower reaches of the Yangtze River [11]. Huang Yaoxiang of the Research Institute of Guangdong Academy of Agricultural Sciences proposed the "semi-dwarf clustered high-growth super high yield type" for indica rice in south China in 2001 [12]. The China National Rice Research Institute [13], by combining various components of super-high-yield plant type, proposed the "later-stage functional super rice type". In-depth analysis showed that the ideal plant types of China and other countries share the following characteristics: reduced tiller number, fewer ineffective tillers, robust stem, anti-lodging, large spike, large grain number per spike, large grain yield per spike, increased biomass yield, high harvest index and highly developed root system.

Breeding ideal plant types is the goal of super high yield rice breeding in the future. Con‐ struction of the ideal plant type in rice has to be done with consideration for traits such as tiller, stem, leaf shape and panicle type. Spike number is used for determining rice yield, and is closely related to tillering. Grain number per spike is associated with spike length and grain density. Grain weight is determined by grain length, width and thickness. These yield components are covered by the factors of the ideal plant type of rice. Plant type, tiller, panicle type and grain weight are determined by interactions between growth hormones and the environment. Successful cloning of QTL/genes related to yield has greatly contributed to our understanding of the regulatory mechanism of ideal plant type at the molecular level.

## **2.1. Plant height**

Plant height is an important component of plant type in rice. Since the end of the 1950s when the first "green revolution" in rice yield was triggered by dwarf breeding, rice grain yield per unit area has increased substantially. This achievement can be attributed to the application of dwarf germplasm, especially semi-dwarf germplasm in breeding. Several studies have shown that dwarfism in many dwarf and semi-dwarf rice varieties is controlled by a recessive major gene and is subject to certain modifier genes [14]. For indica rice, application of the semi-dwarf gene sd-1 is a major contributor to rice yield improvement. The major dwarf cultivars of indica rice are Aijiaonante and Aizizhan. The majority of semi-dwarf indica rice varieties that have been bred are either directly or indirectly derived from the above two cultivars [15]. In 2002, three research groups successively published results of map-based cloning of the sd-1 gene, which supports the first green revolution in molecular level. The sd-1 gene controls plant height in rice. Mutation of sd-1 directly leads to different degrees of dwarfism in rice. The sd-1 protein is involved in the biosynthesis of gibberellin, encoding GA20 oxidase (GA20ox) composed of 389 amino acids. GA20ox is a key enzyme in the gibberellin synthetic pathway, which catalyzes the conversion of GA53 to GA20. The sd-1 gene is located in chromosome 1 in rice, corresponding to 38381423 - 38384165 map position (5'-3') in Nipponbare. Monna et al. [16] showed that Nipponbare, Sasanishiki and Calrose rice, which are ordinary wild-type rice, contain three exons, with sizes of 558, 318 and 291 bp, and 2 introns with sizes of 105 and 1471 bp. Deo-geo-woo-gen type semi-dwarf varieties IR24, Habataki and Milyang 23 have a 383 bp deletion in the middle of exon 1, covering 278 bp in exon 1 and 2, and 105 bp in the intron. Calrose76 is the outcome of CTC-to-TTC mutation at position 265 in exon 2, which changes a Leu (leucine) residue to Phe (phenyl alanine) residue. Sasaki et al. [17] proposed that the GA20ox-1 gene is independent of sd-1, while the newly discovered GA20-ox-2 is linked to sd-1. Sequences of the sd-1 gene in four cultivars and one wild-type variety were compared. It was found that the length of three exons in SD-1 gene of wild-type variety were 557, 321 and 291 bp respectively while the length of two introns were 103 and 1472 bp. A 383 bp deletion was found in the dwarf Deo-geo-woo-gen variety and its derivative, including 103 bp of intron 1. A GGG-to-GTG mutation at position 94 in Jikkoku rice results in change of glycine to valine. A CTC-to-TTC mutation at position 266 in Calorose 76 rice leads to change of leucine to phenyl alanine. A GAC-to-CAC mutation at position 349 in the dwarf variety Remei leads to a change from aspartic acid to histidine. GA20ox-2 is strongly expressed in the leaves, stem and unbloomed flowers of rice. However, GA20ox-1 is only expressed in unbloomed flowers, which is why the sd-1 gene controls plant height but not the yield. Spielmeyer et al. [18] reported that the 3 sd-1 exons in wild-type variety have lengths of 557, 322 and 291 bp. A 280 bp deletion was found in the GA20ox2 coding region (exon 1 and 2) in the DGWG semi-dwarf indica rice variety. Until now, a total of 5 alleles have been discovered for the sd-1 locus including sd-1 in the wild-type variety, sd-1-d from Deo-geo-woo-gen and its derivative, sd-1 r from Reimei, sd-1-c from Calrose 76 and sd-1-j from Jikkoku. Recently, Asano et al. [77] found that *SD-1* has been subjected to artificial selection in rice evolution and that ancient humans took advantage of functional nucleotide polymorphisms (FNPs) from two SNPs in sd-1 in *japonica* rice.

## **2.2. Tillering**

on ideal plant type in different ecological conditions for high-yield breeding and cultivation. Several models of ideal plant type were developed. Prof. Yang Shouren of Shenyang Agricul‐ tural University proposed the "short-branch and erect-leaf, large and straight panicle" type for japonica rice in north China [9]. In 1997, Prof. Zhou Kaida of Sichuan Agricultural University proposed "intersubspecific large-spike type", which is adapted to conditions of less wind, high moisture and high temperature in the Sichuan Basin [10]. Similarly, Yuan Longping of China National Hybrid Rice Engineering Research Center proposed "high-canopy and low-spikelayer type", which is also referred to as the "gold hidden in the leaf" type, for the ecological area of middle and lower reaches of the Yangtze River [11]. Huang Yaoxiang of the Research Institute of Guangdong Academy of Agricultural Sciences proposed the "semi-dwarf clustered high-growth super high yield type" for indica rice in south China in 2001 [12]. The China National Rice Research Institute [13], by combining various components of super-high-yield plant type, proposed the "later-stage functional super rice type". In-depth analysis showed that the ideal plant types of China and other countries share the following characteristics: reduced tiller number, fewer ineffective tillers, robust stem, anti-lodging, large spike, large grain number per spike, large grain yield per spike, increased biomass yield, high harvest index and

Breeding ideal plant types is the goal of super high yield rice breeding in the future. Con‐ struction of the ideal plant type in rice has to be done with consideration for traits such as tiller, stem, leaf shape and panicle type. Spike number is used for determining rice yield, and is closely related to tillering. Grain number per spike is associated with spike length and grain density. Grain weight is determined by grain length, width and thickness. These yield components are covered by the factors of the ideal plant type of rice. Plant type, tiller, panicle type and grain weight are determined by interactions between growth hormones and the environment. Successful cloning of QTL/genes related to yield has greatly contributed to our understanding of the regulatory mechanism of ideal plant type at the molecular level.

Plant height is an important component of plant type in rice. Since the end of the 1950s when the first "green revolution" in rice yield was triggered by dwarf breeding, rice grain yield per unit area has increased substantially. This achievement can be attributed to the application of dwarf germplasm, especially semi-dwarf germplasm in breeding. Several studies have shown that dwarfism in many dwarf and semi-dwarf rice varieties is controlled by a recessive major gene and is subject to certain modifier genes [14]. For indica rice, application of the semi-dwarf gene sd-1 is a major contributor to rice yield improvement. The major dwarf cultivars of indica rice are Aijiaonante and Aizizhan. The majority of semi-dwarf indica rice varieties that have been bred are either directly or indirectly derived from the above two cultivars [15]. In 2002, three research groups successively published results of map-based cloning of the sd-1 gene, which supports the first green revolution in molecular level. The sd-1 gene controls plant height in rice. Mutation of sd-1 directly leads to different degrees of dwarfism in rice. The sd-1 protein is involved in the biosynthesis of gibberellin, encoding GA20 oxidase (GA20ox) composed of 389 amino acids. GA20ox is a key enzyme in the gibberellin synthetic pathway,

highly developed root system.

146 Rice - Germplasm, Genetics and Improvement

**2.1. Plant height**

Rice tillering is an important agronomic character in rice production. Effective tiller number per unit area determines the spike number, which is among the three components of rice yield per unit area, the other two being grain number per spike and 1000 grain weight. Therefore, reducing unproductive tiller is important to improve rice yield. MOC1 was the first gene identified to be related to rice tillering. Li et al. [19] employed map-based cloning to narrow down the location of MOC1 to a 20 kb region in the long arm of Chromosome 6. MOC1 is a member of the GRAS transcription factor family. It is closely related to the LAS gene in Arabidopsis thaliana and the 1s gene in tomato. MOC1 is necessary for initiation of axillary meristem. Loss of MOC1 leads to defects in tiller bud initiation and consequently to complete absence of tillering. Over-expression of MOC1 results in massive tillering in transgenic plants. *OsTB1* and *OSH1* are expressed at lower levels in loss-of-function *MOC1* mutants. Therefore, *MOC1* gene may act as a master regulator in the control of rice tillering. In addition to affecting tillering of rice stem, MOC1 also significantly reduces panicle branches. Cloning of *MOC1* has facilitated our understanding of the regulatory mechanism of rice tillering. However, the molecular mechanisms regulating *MOC1* remains to be elucidated. Recently, two Chinese research groups found that TAD1/TE directly regulates MOC1, thus revealing an important molecular mechanism of regulation of rice tillers [78,79]. *TAD1/TE* encodes a co-activator of the anaphase-promoting complex (APC/C), a multi-subunit E3 ligase. TAD1 interacts with MOC1, forms a complex with OsAPC10, and functions as a co-activator of APC/C to target MOC1 for degradation in a cell cycle-dependent manner. These findings uncovered a new mechanism underlying shoot branching and found novel determinants of plant architecture and grain yield.

#### **2.3. Panicle type**

Grain number per spike of rice is an important agronomic character of rice spike that is directly related to rice yield. Increasing the grain number per spike is part of the goal of high yield breeding. At present, most high yield varieties have significantly increased grain number per spike. Some genes, including Gn1, DEP1 and DEP2, that have a major role in influencing grain number per spike, have been successfully cloned.

QTL-Gn1 was cloned by a Japanese research group [20]. Map-based cloning and sequencing showed that QTL-Gn1 encodes cytokinin oxidase (OsCKX2). Using a cross between the japonica rice cultivar Koshihikari and the indica rice cultivar Habataki, they located QTL-Gn1, a major gene controlling grain number, on the short arm of Chromosome 1. This gene accounts for 44% of phenotypic variation in grain number per panicle. NIL-Gn1 heterozygous plants (Gn1/gn1) were used to generate 96 F2 plants, to decompose Gn1 into two loci, Gn1a and Gn1b, which are of equal effect. Gn1a is located in a region less than 2 cM between R3192 and C12072S, and Gn1b is located upstream of Gn1a. NIL-Gn1a heterozygous plants (Gn1a/gn1a) were used to generate 13000 F2 plants and then finely map the Gn1a gene to a 6.3 kb region between 3A28 and 3A20. There is only one open reading frame in this region, and it belongs to the OsCKX2 gene, which is highly homologous to cytokinin oxidase/dehydrogenase. Sequencing analysis showed that compared with Koshihikari, there are deletions of 16 bases and 6 bases in the 5' untranslated region and exon 1, respectively, of OsCKX2 in Habataki. A 3-base substitution in exon 4 causes changes in the protein product was also found in Habataki. These results suggested the loss of function or deletion of OsCKX2 may lead to increase in rice yield. Complementation tests showed that OsCKX2 is the same gene as Gn1a. OsCKX2 is strongly expressed in leaf, stem, inflorescence meristem and flower, but weakly in apical meristem of rice plants; it is not expressed in the root and embryo. The Gn1a locus is an allele of Habataki, which has increased grain number per spike. Loss of this gene leads to significantly increased content of cytokinin in spikes and increased number of spikelets, i.e. grain number, and consequently increased rice yield. Ashikari et al. [21] obtained NIL-Gn1-Sd-1 by crossing and screening using Koshihikari as the genetic background and NIL-Gn1 and NIL-Sd-1, which are Gn1 alleles from Habataki controlling grain number and Sd-1 allele controlling plant height, respectively. This line has 26% higher grain yield and 18% lower plant height compared to Koshihikari.

Spike characters include spike shape, spike-layer uniformity and grain density. High spikelayer uniformity is conducive to ventilation and photopermeability of the lower part of the plant population, which provides a favorable condition for consistent maturity. Developed primary branches and moderate grain density helps reduce spike length, lower the center of gravity of plants, and ensure the consistency of grain maturity. Panicle type is an important agronomic character, depending on the morphology and number of primary and secondary branches. Panicle type can be erect, semi-erect and curved. It is generally believed that erect panicle type has higher solar energy utilization, and is conducive for CO2 diffusion. It can also modify the biological environment of the population, adjust inter-plant temperature and reduce moisture. The erect-panicle type has higher accumulation of photosynthestic products, better fertilizer tolerance and anti-lodging properties. Yang Shouren et al. first proposed a super high yield japonica rice type with erect panicles. These new lines, which include Shennong 265 and Shennong 89366 that are bred based on this plant type, feature high yield potential [22] and erect panicle, which are desired traits in rice in north China. Therefore, these lines have attracted increasing attention from breeders in that region.

molecular mechanisms regulating *MOC1* remains to be elucidated. Recently, two Chinese research groups found that TAD1/TE directly regulates MOC1, thus revealing an important molecular mechanism of regulation of rice tillers [78,79]. *TAD1/TE* encodes a co-activator of the anaphase-promoting complex (APC/C), a multi-subunit E3 ligase. TAD1 interacts with MOC1, forms a complex with OsAPC10, and functions as a co-activator of APC/C to target MOC1 for degradation in a cell cycle-dependent manner. These findings uncovered a new mechanism underlying shoot branching and found novel determinants of plant architecture

Grain number per spike of rice is an important agronomic character of rice spike that is directly related to rice yield. Increasing the grain number per spike is part of the goal of high yield breeding. At present, most high yield varieties have significantly increased grain number per spike. Some genes, including Gn1, DEP1 and DEP2, that have a major role in influencing grain

QTL-Gn1 was cloned by a Japanese research group [20]. Map-based cloning and sequencing showed that QTL-Gn1 encodes cytokinin oxidase (OsCKX2). Using a cross between the japonica rice cultivar Koshihikari and the indica rice cultivar Habataki, they located QTL-Gn1, a major gene controlling grain number, on the short arm of Chromosome 1. This gene accounts for 44% of phenotypic variation in grain number per panicle. NIL-Gn1 heterozygous plants (Gn1/gn1) were used to generate 96 F2 plants, to decompose Gn1 into two loci, Gn1a and Gn1b, which are of equal effect. Gn1a is located in a region less than 2 cM between R3192 and C12072S, and Gn1b is located upstream of Gn1a. NIL-Gn1a heterozygous plants (Gn1a/gn1a) were used to generate 13000 F2 plants and then finely map the Gn1a gene to a 6.3 kb region between 3A28 and 3A20. There is only one open reading frame in this region, and it belongs to the OsCKX2 gene, which is highly homologous to cytokinin oxidase/dehydrogenase. Sequencing analysis showed that compared with Koshihikari, there are deletions of 16 bases and 6 bases in the 5' untranslated region and exon 1, respectively, of OsCKX2 in Habataki. A 3-base substitution in exon 4 causes changes in the protein product was also found in Habataki. These results suggested the loss of function or deletion of OsCKX2 may lead to increase in rice yield. Complementation tests showed that OsCKX2 is the same gene as Gn1a. OsCKX2 is strongly expressed in leaf, stem, inflorescence meristem and flower, but weakly in apical meristem of rice plants; it is not expressed in the root and embryo. The Gn1a locus is an allele of Habataki, which has increased grain number per spike. Loss of this gene leads to significantly increased content of cytokinin in spikes and increased number of spikelets, i.e. grain number, and consequently increased rice yield. Ashikari et al. [21] obtained NIL-Gn1-Sd-1 by crossing and screening using Koshihikari as the genetic background and NIL-Gn1 and NIL-Sd-1, which are Gn1 alleles from Habataki controlling grain number and Sd-1 allele controlling plant height, respectively. This line has 26% higher grain yield and 18% lower plant height compared to

Spike characters include spike shape, spike-layer uniformity and grain density. High spikelayer uniformity is conducive to ventilation and photopermeability of the lower part of the

and grain yield.

148 Rice - Germplasm, Genetics and Improvement

**2.3. Panicle type**

Koshihikari.

number per spike, have been successfully cloned.

DEP1 is a key pleiotropic gene isolated from Shennong 265 (super rice variety in north China) controlling rice yield. The DEP1 locus is a major QTL controlling yield-related trait of rice, located between SSR markers RM3770 and RM7424 on Chromosome 9. DEP1 corresponds to the 16410553 - 16414701 map position (5'-3') in Nipponbare, as a qPE9-1 allele [24]. The dominant gene at this locus is caused by an acquired mutation, which results in failure to encode a protein similar to phosphatidylethanolamine-binding protein. This mutant DEP1 promotes cell division, reduces the length of neck-panicle node and increases grain density in a panicle. Moreover, a higher branch number and grain number per panicle results in rice yield increase by 15%-20%. Researchers have found that mutation in DEP1 is widely present in erect and semi-erect-panicle high-yield rice that is grown in the middle and lower reaches of the Yangtze River. Thus, it is evident that the DEP1 gene has played an important role in rice yield increase in China [23]. DEP1 not only increases the yield in rice, but also in other crops, such as wheat and barley. Thus, DEP1 is very important in high-yield molecular breeding and for breeding new high-yielding varieties of crops.

*DEP2* is responsible for the trait of erect and dense spike in rice. It is located in Chromosome 7, has 10 exons and encodes a protein with 1365 amino acids and unknown function. Sequence analysis of dep2-1 mutant showed that there is a 31-base deletion in exon 6 and a G/A transversion in intron 2. The deletion of 31 bases leads to shift in the reading frame, while the G/A transversion changes the editing position in intron 2, causing another frame shift. DEP2 mainly influences rachis development, promoting the elongation of primary and secondary branches. Mutation in DEP2 causes cell proliferation disorder in spike differentiation, resulting in the phenotype of erect and dense spikes [25]. It has also been shown that DEP2 is at the same locus as the small and round seed 1 (SRS1) gene [26] and EP2 [27]. SRS1/DEP2 not only regulates spike type in rice, but also its seed size. SRS1/DEP2 is mainly expressed in young tissues, such as young spikes. Mutation in srs1/dep2 leads to erect panicle, and small and round seed.

Recently, dense and erect panicle 3 (DEP3) was identified by map-based cloning. It is located in Chromosome 6. *dep3* is an erect and dense panicle mutant of the japonica rice variety. In the wild-type variety, the panicle begins to droop after flowering, which is accompanied by changes in panicle length, grain shape and grain number per spike. However, dep3 mutants have smaller vascular bundles and thicker stems, which account for the erect-panicle pheno‐ type. Thus, the erect and dense panicle phenotype in rice is controlled by a single recessive gene dep3. It is predicted to encode a patatin-like phospholipase A2 (PLA2) super family domain-containing protein. The mutant allele of dep3 has a deletion of 408 bp at LOC\_Os06g46350, which covers 47 bp after the coding region in exon 3 and 361 bp before the 3' untranslated region [28].

#### **2.4. Ideal plant type**

Yield of rice is determined by wide diversity of agronomical traits including tiller number, grain number per spike, grain weight, grain-filling rate, plant type, etc.. It is a complex quantitative trait controlled coordinately by multiple genes and environment. In order to improve the yield potential of rice, concept of new plant type is proposed by rice breeders. New plant type is also called as ideal plant type and its key characteristics include decreased tillering, no ineffective tillering, increased grain number per spike, thick and strong stem and developed root system. Theoretical analysis shows that the yield of rice varieties of ideal plant type could increase by 25% than that of the current variety under the equatorial drought conditions. It is commendable to find the favorable mutant of ideal plant type. Using the japonica line "Shaoniejing" possessing characteristics of ideal plant type, Chinese scientists isolated and cloned the major quantitative trait gene IPA1 (Ideal Plant Architecture 1) which controls the ideal plant type of rice in 2010 [29]. Compared with the conventional cultivar such as indica rice TN1, "Shaoniejing" has less tillering, larger spikes, higher grain number per spike, thicker stem and more developed root system with the characteristics of ideal plant type. Backcrossing was performed between Shaoniej‐ ing, TN1 (recurrent parent) and conventional japonica rice Hui7 (recurrent parent). It was found that traits including plant height, tiller number stem thickness and panicle type have a co-segregation relationship. Thus, it is indicated that the phenotypic difference be‐ tween Shaoniejing and conventional variety might be controlled by the same major gene. Analysis of near isogenic lines in Shaoniejing and Hui7 showed that phenotype of plant with IPA1 locus in heterozygote state (IPA1/ipal) was between homozygous wild type (IPA1/IPA1)and homozygous mutant type (ipal/ipal), indicating that IPA1 is a semidomi‐ nant gene. Using mapping population of BC2F2 constructed by Shaoniejing and TN1, a major QTL—QTL8 was identified and cloned on chromosome 8 using map-based clon‐ ing. Sequence analysis found that the third extron in QTL8 of Shaoniejing had a C to A point mutation, causing amino acid to change from leucine to isoleucine. Through constructing plasmid gIPA1 carrying full-length gene and transforming Nipponbare as a receptor, it was found that transgenic plants had decreased tiller, thick stem, increased branch number and grain number per spike. Contrarily, when IPA1 expression was downregulated in RI 22 (it had the same point mutation as Shaoneijing), it was found that the transgenic plant had increased tillering, decreased plant height and thin stem with significant declining in branch and grain numbers. Sequence analysis revealed that IPA1

encoded the transcription factor OsSPL14 which contained the SBP structural domain. IPA1 was located in the nucleus with the transcriptional activity. Analysis of mRNA *in situ* expression showed that IPA1 had the highest expression in stem tips during vegetative growth period and branch primodium during reproductive growing period. IPA1 con‐ tains target site of miR156 and could be regulated by miR156 *in vivo* by means of transcriptional segmentation and translational repression. As point mutation of Shaoniej‐ ing occurs at the target site of miR156, the two regulatory channels are influenced, causing simultaneous increase of transcript and protein amount in IPA1. Transgenic study revealed that, although point mutation induced changes in amino acid, it caused no influence on the function of IPA1 protein. Application value of IPA1 gene was explored and ipa1 mutant gene was introduced into rice "Xiushui 11" through backcrossing. Analysis of near isogenic lines of backcross offspring found that strains carrying ipa1 mutant gene had the typical characteristics of ideal plant type and their yield had an increase of over 10% in the field plot experiment compared with their parent strain "Xiushui 11". Therefore, mutation of this gene has induced decreased tillering, thick stem and obvious increase in grain number per spike and 1000 grain weight, and the rice variety had the typical features of ideal plant type. It is a powerful tool for improving the plant type of current rice cultivars and enhancing the rice yield with great application potential in rice breeding[29]. At the same time, this gene was also cloned successfully by the Japanese research group using "Nipponbare" which was widely used in Japan and high-yield rice variety"ST-12". This allelic gene was introduced into "Nipponbare" whose yield was low with an average production of 2200 grains per plant. Heading number of "Nipponbare" was strengthened after introducing this gene and its yield reached to 3100 grains by about 40% [30]. Using hybrid segregating population of Nipponbare and ST-12, they found gene Gn1a which controlled the grain number per spike on chromosome 1 and WFP gene which control‐ led the primary branch number on chromosome 8. Through selecting lines with 4 different combinations of Gn1a and WFP genes from BC2F2 population, primary branch number and grain number per spike among different lines were compared. It was found that the pyramiding of Gn1a and WFP from ST-12 could increase the grain number per spike by 40% - 50%, which effectively improved the rice yield [30].

#### **2.5. Summary**

changes in panicle length, grain shape and grain number per spike. However, dep3 mutants have smaller vascular bundles and thicker stems, which account for the erect-panicle pheno‐ type. Thus, the erect and dense panicle phenotype in rice is controlled by a single recessive gene dep3. It is predicted to encode a patatin-like phospholipase A2 (PLA2) super family domain-containing protein. The mutant allele of dep3 has a deletion of 408 bp at LOC\_Os06g46350, which covers 47 bp after the coding region in exon 3 and 361 bp before the

Yield of rice is determined by wide diversity of agronomical traits including tiller number, grain number per spike, grain weight, grain-filling rate, plant type, etc.. It is a complex quantitative trait controlled coordinately by multiple genes and environment. In order to improve the yield potential of rice, concept of new plant type is proposed by rice breeders. New plant type is also called as ideal plant type and its key characteristics include decreased tillering, no ineffective tillering, increased grain number per spike, thick and strong stem and developed root system. Theoretical analysis shows that the yield of rice varieties of ideal plant type could increase by 25% than that of the current variety under the equatorial drought conditions. It is commendable to find the favorable mutant of ideal plant type. Using the japonica line "Shaoniejing" possessing characteristics of ideal plant type, Chinese scientists isolated and cloned the major quantitative trait gene IPA1 (Ideal Plant Architecture 1) which controls the ideal plant type of rice in 2010 [29]. Compared with the conventional cultivar such as indica rice TN1, "Shaoniejing" has less tillering, larger spikes, higher grain number per spike, thicker stem and more developed root system with the characteristics of ideal plant type. Backcrossing was performed between Shaoniej‐ ing, TN1 (recurrent parent) and conventional japonica rice Hui7 (recurrent parent). It was found that traits including plant height, tiller number stem thickness and panicle type have a co-segregation relationship. Thus, it is indicated that the phenotypic difference be‐ tween Shaoniejing and conventional variety might be controlled by the same major gene. Analysis of near isogenic lines in Shaoniejing and Hui7 showed that phenotype of plant with IPA1 locus in heterozygote state (IPA1/ipal) was between homozygous wild type (IPA1/IPA1)and homozygous mutant type (ipal/ipal), indicating that IPA1 is a semidomi‐ nant gene. Using mapping population of BC2F2 constructed by Shaoniejing and TN1, a major QTL—QTL8 was identified and cloned on chromosome 8 using map-based clon‐ ing. Sequence analysis found that the third extron in QTL8 of Shaoniejing had a C to A point mutation, causing amino acid to change from leucine to isoleucine. Through constructing plasmid gIPA1 carrying full-length gene and transforming Nipponbare as a receptor, it was found that transgenic plants had decreased tiller, thick stem, increased branch number and grain number per spike. Contrarily, when IPA1 expression was downregulated in RI 22 (it had the same point mutation as Shaoneijing), it was found that the transgenic plant had increased tillering, decreased plant height and thin stem with significant declining in branch and grain numbers. Sequence analysis revealed that IPA1

3' untranslated region [28].

150 Rice - Germplasm, Genetics and Improvement

**2.4. Ideal plant type**

Improving yield of rice is an important means to ensure food security in the world today. In order to further strengthen the yield potential of current cultivars to meet people's food demand, super-high-yield breeding based on ideal plant type has become the goal of rice breeders. Studying the control mechanism of ideal plant type to clone the related control gene is of great significance in breeding higher-yield rice variety using genetic engineering. Meanwhile, as model crop of monocotyledon, research of control gene in ideal plant type will contribute to clarifying the molecular mechanism of growth and development of monocoty‐ ledon significantly.

## **3. Hybrid heterosis**

The phenomenon of plant heterosis was first described by Shull as the promoting effect of plant development after copulation of gametes of different genotypes[31]. Heterosis of crops was first discovered in tobacco in the middle of the 18th century. Rice heterosis was initially reported by American scientist Jones, who found that some F1 hybrids of rice had increased tillering and higher yield compared with the parents [32]. Later, heterosis in self-pollinated plants was studied and confirmed by more scientists. In the late 1950s, in the context of successful commerical exploitation of corn heterosis in America, rice breeders broaden the exploration channel of heterosis. In 1960s, scientists in India, US, Japan and China began to study the rice heterosis and its application in commercial production successively. For the first time, Xincheng Changyou from Japan achieved three-line combination of japonica rice in 1968. Study on heterosis application of rice began in China when male sterile plant was discovered by Yuan Longping et al. in 1964. Li Bihu in 1970 discovered a wild-type rice with pollen abortion in Nanhong Farm in Yaxian County of Hainan, which was a major breakthrough for the breeding and selection of male sterile rice in China[11]. Three-line combination of hybrid indica rice was achieved successfully in China in 1973. Indica hybrid rice began to receive extensive popularization in China in 1976 and China became the first country in the world to realize the commerical utilization of rice heterosis. Hybrid rice planting resulted in large rice yield increase in China from 1976 to 1995, which was a significant achievement. Cumulative planting area of hybrid rice reached 250 million hm2 in 1999 with an increased crop production of 370 million tons[11]. Hybrid rice had made great contribution to the food production of China and the world.

## **3.1. Cytoplasmic male sterility and the fertility-restoring genes**

Cytoplasmic male sterility (CMS) refers to the biological phenomena that the male reproduc‐ tive system of plant cannot develop normally to produce the viable pollen, but the female reproductive system has normal development and vegetative growth. As the major type of hybrid rice combination, cytoplasmic male sterility in rice has attracted more and more attention. Sterile line of rice can be divided into genic male sterility and cytoplasmic male sterility based on sterility mechanism. Genic male sterility can be further divided into domi‐ nant male genetic sterility, recessive genic male sterility and environmental sterility. Based on genetic characters of male sterility, cytoplasmic male sterility is classified into sporophyte sterility and gametophyte sterility. Gametophyte sterility mainly consists of Baotai type (BT type), Dian type, Honglian type(HL) and Lide type. Source of sterile cytoplasm in sporophyte is abundant, and wild abortion type (WA), dwarf abortion type (DA), D type, G type, K type, Indonesia paddy type, etc. have large planting area in China (Table 1)[77].

As cytoplasmic male sterility and its fertility restoration are a basis for three-line hybrid rice breeding and production application. Topics on the mechanism of cytoplasmic male sterility and its fertility restoration in rice have attracted attentions of many scientists. Cytoplasmic male sterility is manifested as maternal inheritance and generally related with abnormal open reading frame of mitochondrial genome. In most cases, male sterility could be restored by the


#### **Table 1.** The CMS types

**3. Hybrid heterosis**

152 Rice - Germplasm, Genetics and Improvement

China and the world.

**3.1. Cytoplasmic male sterility and the fertility-restoring genes**

Indonesia paddy type, etc. have large planting area in China (Table 1)[77].

The phenomenon of plant heterosis was first described by Shull as the promoting effect of plant development after copulation of gametes of different genotypes[31]. Heterosis of crops was first discovered in tobacco in the middle of the 18th century. Rice heterosis was initially reported by American scientist Jones, who found that some F1 hybrids of rice had increased tillering and higher yield compared with the parents [32]. Later, heterosis in self-pollinated plants was studied and confirmed by more scientists. In the late 1950s, in the context of successful commerical exploitation of corn heterosis in America, rice breeders broaden the exploration channel of heterosis. In 1960s, scientists in India, US, Japan and China began to study the rice heterosis and its application in commercial production successively. For the first time, Xincheng Changyou from Japan achieved three-line combination of japonica rice in 1968. Study on heterosis application of rice began in China when male sterile plant was discovered by Yuan Longping et al. in 1964. Li Bihu in 1970 discovered a wild-type rice with pollen abortion in Nanhong Farm in Yaxian County of Hainan, which was a major breakthrough for the breeding and selection of male sterile rice in China[11]. Three-line combination of hybrid indica rice was achieved successfully in China in 1973. Indica hybrid rice began to receive extensive popularization in China in 1976 and China became the first country in the world to realize the commerical utilization of rice heterosis. Hybrid rice planting resulted in large rice yield increase in China from 1976 to 1995, which was a significant achievement. Cumulative planting area of hybrid rice reached 250 million hm2 in 1999 with an increased crop production of 370 million tons[11]. Hybrid rice had made great contribution to the food production of

Cytoplasmic male sterility (CMS) refers to the biological phenomena that the male reproduc‐ tive system of plant cannot develop normally to produce the viable pollen, but the female reproductive system has normal development and vegetative growth. As the major type of hybrid rice combination, cytoplasmic male sterility in rice has attracted more and more attention. Sterile line of rice can be divided into genic male sterility and cytoplasmic male sterility based on sterility mechanism. Genic male sterility can be further divided into domi‐ nant male genetic sterility, recessive genic male sterility and environmental sterility. Based on genetic characters of male sterility, cytoplasmic male sterility is classified into sporophyte sterility and gametophyte sterility. Gametophyte sterility mainly consists of Baotai type (BT type), Dian type, Honglian type(HL) and Lide type. Source of sterile cytoplasm in sporophyte is abundant, and wild abortion type (WA), dwarf abortion type (DA), D type, G type, K type,

As cytoplasmic male sterility and its fertility restoration are a basis for three-line hybrid rice breeding and production application. Topics on the mechanism of cytoplasmic male sterility and its fertility restoration in rice have attracted attentions of many scientists. Cytoplasmic male sterility is manifested as maternal inheritance and generally related with abnormal open reading frame of mitochondrial genome. In most cases, male sterility could be restored by the fertility-restoring gene (Rf) encoded by nucleus [33]. Therefore, CMS/Rf system is the ideal model for studying the interaction between mitochondrial genome and nuclei genome. It has been widely applied in hybrid breeding in order to improve the yield of crop. Various types of CMS have been found in rice and the main applied types in indica hybrid rice include wild abortion type (CMS-WA), Hongling type(CMS-HL), dwarf abortion type (CMS-DA) and so on. The typical representatives are Zhenshan 97A, Congguang 41A and Xieqingzao A. Main applied types in japonica hybrid rice are Baotai type(BT) and Dian type, representatives being Fengjin A and Liuqianxin A[79].

At present, fertility-restoring gene in cytoplasmic male sterility has been positioned and cloned in *Zea mays*, *Petunia hybrida*, *Daucus carota* and other plants [34, 35, 36]. For rice, two research groups in Japan [37, 38] have reported the fine positioning and cloning of fertility-restoring gene Rf-1 for BT type. The results showed that Rf-1 (PRR791) gene also encodes a mitochondial positioning protein which contains PPR. Recently, fertility-restoring gene Rf5 which could restore the cytoplasmic male sterile line of Honglian type was obtained by Hu et al. [39] through map-based cloning and proved to be consistent with Rf1a. Results by Akagi et al. [38] also indicated that a PRR homologous gene (Rf-1b) exists beside this Rf-1 gene (also called as Rf-1a). However, it was supposed that Rf-1b has no restoring function. Study of cytoplasmic male sterility mechanism and functional analysis of fertility-restoring gene in rice CMS-BT was published by Chinese research group in 2006[40]. Results revealed that cytoplasmic male sterile line of BT type contains an abnormal mitochondrial open reading frame—orf79. There is a cotranscription with atp6 gene to encode a cytotoxic peptide. Using transgenic plant, it was proved that the expression of orf79 in rice caused male gametophyte sterility of pollen. A polygene cluster encoding PPR protein was found in Rf-1 loci of chromosome 10. At least 2 members including Rfla (it was reported as PRR791 by the Japanese research group) and Rf1b were proved to have fertility restoring function for BT type.

Studies showed that Rf1 could restore the fertility of cytoplasmic male sterile line of Baotai type. Rf5 could also restore the fertility of cytoplasmic male sterile line of Honglian type. For the near isogenic line with cytoplasmic male sterility, the pollen is fertile if it carries Rf-1 gene, and otherwise, sterile. Varieties carrying Rf-1 gene such as IR24, IR36 and MTC-18R could correct the cytoplasmic male sterility of BT type but varieties carrying recessive gene rf-1 could not, such as Nipponbare. Rf-1 cDNA has a full length of 2760bp in MTC-10R. It only contains 1 exon which encodes a protein product composed of 791 amino acids. The product contains 16 trigonous pentapeptide repeat sequence motifs and mitochondrial targeting peptides. The near isogenic line MTC-10R(Rf-1/Rf-1)could restore the cytoplasmic male sterility, but the near isogenic line MTC-10A(rf-1/rf-1)with 1bp and 547bp deletion in Rf-1A locus could not restore the cytoplasmic male sterility [38]. With a full length of 3870bp, Rf1b cDNA only has one exon in restoring line Minghui 63. The exon encodes a protein product composed of 506 amino acids containing PPR motif and mitochondrial targeting peptide. The proteins encoded by fertilityrestoring allele of 6 restorer lines (male sterile line or maintainer line) are different on 9 amino acids from non-fertility restoring allele of 6 non-restorer lines. The shared difference is that the base A at position 1235 in the fertility-restoring allele Rf1b is replaced by G in the non-fertility restoring allele, causing the changing of asparaginate into serine at position 412 [40]. Fertilityrestoring allele Rf1a in the restorer line could encode the complete protein. However, due to frameshift mutation, allele of non-restorer line of japonica rice encodes a truncated protein which only contains 266 amino acids. Protein encoded by allele of non-restorer line of indica rice has a transformation of 55 amino acids [40].

Two open reading frames of Rf-1A and Rf-1B are found in isogenic line MTC-10R which could correct the cytoplasmic male sterility. Due to the presence of Rf-1B, the terminator codon occurs in advance, causing the formation of a short protein with no mitochondrial targeting peptide, Rf-1A is exactly the Rf-1 gene. Rf-1A encodes a protein which contains 16 trigonous penta‐ peptide repeat (PPR) motifs and is targeted to mitochondrion. Rf-1A is expressed in inflores‐ cence during booting stage and PPR motif with tandem duplication is considered to be capable of having specific binding with RNA and DNA. Therefore, Rf-1A is a fertility-restoring gene through processing the atp6/orf79 transcript from mitochondrial genome in BT type. Cyto‐ plasmic male sterility of BT type could be corrected by rice varieties carrying Rf-1 gene, which has no effect on WA type [38]. In Boro II rice, abnormal mitochondrial open reading frame orf79 has co-transcription with doubled atp6 gene, encoding a cytotoxic peptide. Specific accumulation of this toxic polypeptide causes male sterility of gametophyte. The two related fertility-restoring genes Rf1a and Rf1b are located in the typical Rf-1 locus as members of polygenic cluster, encoding the trigonous pentapeptide repeat protein. RF1A and RF1B are both targeted to the mitochondrion and they prevent the formation of toxic peptide to restore the fertility by restriction and decomposition of B-atp6/orf79 mRNA. For decomposing mRNA, RF1A is epistatic over RF1B. Besides, RF1A could not only degrade B-atp6/orf79 mRNA but also promote the editing of atp6 mRNAs [40].

Rf1a and Rf1b are both fertility restoring-gene with expression in spikes, leaves and roots. Proteins RF1A and RF1B encoded by them are both targeted at mitochondrion. Via restriction, B-atp6/orf79 mRNA is blocked by RF1A to prevent the generation of ORF79 protein to restore the fertility. And fertility is restored by RF1B via degrading B-atp6/orf79 mRNA. When RF1A and RF1B are present simultaneously, RFIA functions with preference, that is, RF1A is epistatic over RF1B in mRNA processing. In addition to the function of dissecting B-atp6/orf79 mRNA, RF1A could also improve atp6 mRNA editing. It is presumed that the latter is the basic function of RF1A and the former the new function developed during the evolution [40].

Through forming the complex with GRP162-rich glycine protein, trigonous pentapeptide protein RF5 restores the fertility of cytoplasmic male sterile line of Honglian type [39]. Two non-allelic nuclear restorer genes including Rf5 and Rf6 are involved in the gametophyte fertility restoring model of Honglian type (Rf6 is a new restorer gene locus located in the short arm on chromosome 8). Half of the pollens in F1 plants carrying either Rf5 or Rf6 are fertile and fertility of 75% pollens is normal in hybrid carrying both Rf5 and Rf6. Seed setting rate of F1 plants carrying 2 non-allelic genes is higher than that of F1 carrying only 1 restorer gene under adverse environment [41].

## **3.2. Photoperiod (thermo)-sensitive male sterile**

and otherwise, sterile. Varieties carrying Rf-1 gene such as IR24, IR36 and MTC-18R could correct the cytoplasmic male sterility of BT type but varieties carrying recessive gene rf-1 could not, such as Nipponbare. Rf-1 cDNA has a full length of 2760bp in MTC-10R. It only contains 1 exon which encodes a protein product composed of 791 amino acids. The product contains 16 trigonous pentapeptide repeat sequence motifs and mitochondrial targeting peptides. The near isogenic line MTC-10R(Rf-1/Rf-1)could restore the cytoplasmic male sterility, but the near isogenic line MTC-10A(rf-1/rf-1)with 1bp and 547bp deletion in Rf-1A locus could not restore the cytoplasmic male sterility [38]. With a full length of 3870bp, Rf1b cDNA only has one exon in restoring line Minghui 63. The exon encodes a protein product composed of 506 amino acids containing PPR motif and mitochondrial targeting peptide. The proteins encoded by fertilityrestoring allele of 6 restorer lines (male sterile line or maintainer line) are different on 9 amino acids from non-fertility restoring allele of 6 non-restorer lines. The shared difference is that the base A at position 1235 in the fertility-restoring allele Rf1b is replaced by G in the non-fertility restoring allele, causing the changing of asparaginate into serine at position 412 [40]. Fertilityrestoring allele Rf1a in the restorer line could encode the complete protein. However, due to frameshift mutation, allele of non-restorer line of japonica rice encodes a truncated protein which only contains 266 amino acids. Protein encoded by allele of non-restorer line of indica

Two open reading frames of Rf-1A and Rf-1B are found in isogenic line MTC-10R which could correct the cytoplasmic male sterility. Due to the presence of Rf-1B, the terminator codon occurs in advance, causing the formation of a short protein with no mitochondrial targeting peptide, Rf-1A is exactly the Rf-1 gene. Rf-1A encodes a protein which contains 16 trigonous penta‐ peptide repeat (PPR) motifs and is targeted to mitochondrion. Rf-1A is expressed in inflores‐ cence during booting stage and PPR motif with tandem duplication is considered to be capable of having specific binding with RNA and DNA. Therefore, Rf-1A is a fertility-restoring gene through processing the atp6/orf79 transcript from mitochondrial genome in BT type. Cyto‐ plasmic male sterility of BT type could be corrected by rice varieties carrying Rf-1 gene, which has no effect on WA type [38]. In Boro II rice, abnormal mitochondrial open reading frame orf79 has co-transcription with doubled atp6 gene, encoding a cytotoxic peptide. Specific accumulation of this toxic polypeptide causes male sterility of gametophyte. The two related fertility-restoring genes Rf1a and Rf1b are located in the typical Rf-1 locus as members of polygenic cluster, encoding the trigonous pentapeptide repeat protein. RF1A and RF1B are both targeted to the mitochondrion and they prevent the formation of toxic peptide to restore the fertility by restriction and decomposition of B-atp6/orf79 mRNA. For decomposing mRNA, RF1A is epistatic over RF1B. Besides, RF1A could not only degrade B-atp6/orf79 mRNA but

Rf1a and Rf1b are both fertility restoring-gene with expression in spikes, leaves and roots. Proteins RF1A and RF1B encoded by them are both targeted at mitochondrion. Via restriction, B-atp6/orf79 mRNA is blocked by RF1A to prevent the generation of ORF79 protein to restore the fertility. And fertility is restored by RF1B via degrading B-atp6/orf79 mRNA. When RF1A and RF1B are present simultaneously, RFIA functions with preference, that is, RF1A is epistatic over RF1B in mRNA processing. In addition to the function of dissecting B-atp6/orf79 mRNA,

rice has a transformation of 55 amino acids [40].

154 Rice - Germplasm, Genetics and Improvement

also promote the editing of atp6 mRNAs [40].

For the first time in 1973, natural nuclear male sterile line Nongken 58S which was mediated by photoperiod and thermal was discovered by Shi Mingsong in a late japonica rice field in Hubei province. The discovery and effective utilization of photoperiod (thermo)-sensitive genic male sterile (PTGMS) line Nongken 58S opened a new chapter in China's hybrid rice research. Because the PTGMS line could be dually used as sterile and maintainer lines, the maintainer line is no longer needed in the two-line hybrid rice cultivation. Under different thermal and photoperoid conditions, the PTGMS line could be used not only as sterile line for hybrid seed production, but also as maintainer line for self reproduction. Thus, process of seed reproduction and breeding are simplified, reducing the production cost of hybrid seeds. Besides, it is not restricted by restoring and maintaining relationship. Therefore, it could strengthen the degree of genetic complexity of breeding parents in the rice hybrid breeding and expand the genetic distance between the 2 parents. So it is favorable for selecting and breeding strong and optical combination with higher heterosis. However, study of fertility transition mechanism of PTGMS line is still weak and could not adapt to the development of application studies on two-line hybrid rice. Especially, the studies on genetic mechanism and regulatory mechanism in photoperiod thermo-sensitive genic male sterile line are not very intensive. Therefore, strengthening the studies on fertility transition mechanism of photope‐ roid thermo-sensitive genic male sterile line of rice, especially the studies on the genetics and molecular biology, and finding the gene and protein closely related with fertility transition regulation are important. The achievements made in these respect will promote the breeding, selection and mating of photoperiod thermo-sensitive genic male sterile lines and the utiliza‐ tion of heterosis in other crops in future. Photoperiod thermo-sensitive genic male sterile lines show diversity in genetics. This is because sterility is a kind of biological phenomenon related t photoperoid and thermal ecological conditions and expression of sterile gene requires optimal light and temperature conditions. Researchers have already carried out a great number of studies on the sterility inheritance rules of all kinds of photoperiod thermo-sensitive male sterile resources including Nongken 58S, Annong S-1, Hengnong S-1 and 546OS.Some basic inheritance rules have been clarified.

At present, gene pms3 which controls the photoperoid-sensitive male sterility in japonica rice Nongken 58S [42] and gene p/tms12-1 which controls the thermo-sensitive male sterility in indica rice Peiai 64S [43] already have been cloned. Studies proved that located at the same locus, they are a non-coding RNA. Researchers in Huazhong Agricultural University success‐ fully cloned gene pms3 controlling the photoperoid-sensitive genetic sterile line of rice in 2012. They found that it is a long non-coding RNA that controls the sterility of Nongken 58S; pms3 is the transcript 1 of LOC\_12g36030. Studies indicated that it is a RNA molecule associated with male sterility specific to long-time lighting with a length of 1236bp (LDMAR). For normal rice under long-day condition, the expression of this gene could ensure normal pollen development and male fertility. However, for photoperoid-sensitive genic male sterile line of rice, base mutation of pms3 interval causes methylation of promotor interval in this gene with decreased expression. As a result, it could not meet the requirement of pollen development. Thus, this causes the male sterility under long-day condition [42]. Gene p/tms12-1 which controls the thermo-sensitive male sterility was cloned from Peiai 64S, which was the parent of thermo-sensitive genic male sterile line for two-line hybrid indica rice with the largest planting area by researches from South China Agricultural University. This gene is a noncoding RNA gene and its original transcript produces a small RNA after processing at least 2 times. Compared with the normal rice variety, thermo-sensitive male sterile line of rice had a single base mutation in this small RNA. It was further revealed by the studies that Nongken 58S also has the same gene mutation and this single base mutation is the common cause for thermo-sensitive male sterility of indica rice and photoperoid-sensitive male sterility of japonica rice. In normal rice, the expression of wild-type P/TMS12-1 restrains the occurrence of thermo-sensitive or photoperoid-sensitive male sterility. However, for thermo-sensitive and photoperoid-sensitive male sterile line of rice, the expression level of small RNA and its interaction with target gene are influenced by mutation of p/tms12-1, causing male sterility [43]. Successful cloning of pms3(p/tms12-1)gene had a very great significance for accelerating the breeding of two-line male sterile varieties of rice and promoting the research and devel‐ opment of crop heterosis utilization.

#### **3.3. Wide and specific compatibility genes and subspecies heterosis**

Making full utilization of heterosis between subspecies of indica and japonica rice is a major and effective means to increase the rice yield per unit area. However, this utilization is restricted by the low fertility of indica-japonica hybrid F1. Asian cultivated rice is divided into 2 subspecies, *indica* and *japonica*. Heterosis of intersubspecific indica-japonica hybrid is far greater than that of intrasubspecific hybrid. However, because reproductive isolation exists widely between subspecies in nature, hybrid fertility of the intersubspecific hybrid declines, which results in low seed setting rate. Breeding of hybrid rice had been limited within the subspecies for a long time because of this restriction because of the difficulty to utilize the stronger intersubspecies heterosis. Later, rice resources which could break the reproductive isolation are discovered by scientists, and known as wide compatibility varieties. Using indica rice varieties IR36 and IR50 and japonica rice varieties Qiuguang and Ribenyou as testers, Japanese scientist IKehashi, et al. [44] performed the hybrid fertility identification for 74 intermediate varieties. Six varieties including Ketan NangKa, Cpslo-17, etc. and hybrid F1 of indica rice and japonica rice all had high seed setting rate. They were believed to have wide compatibility gene (WCG), and named as wide compatibility varieties (WCV). After extensive testing by Chinese researchers, Balila, Qiuguang, Nantehao and IR36 were officially assigned as the testers for wide compatibility in China in 1989[78]. At present, a great number of wide compatibility lines are selected for hybrid rice breeding through different ways. For example, WA type cytoplasmic male sterile line 02428A, Reyan 1A, Peiai 64S and other wide compatible sterile lines were bred through backcrossing between wide compatible materials and genecytoplasmic male sterile lines. Wide compatibility restorer lines including H108, H64, H921, D069, P26, JM-2, Zhong 413, T2070, 9308 were bred with japonica-indica rice cross.

indica rice Peiai 64S [43] already have been cloned. Studies proved that located at the same locus, they are a non-coding RNA. Researchers in Huazhong Agricultural University success‐ fully cloned gene pms3 controlling the photoperoid-sensitive genetic sterile line of rice in 2012. They found that it is a long non-coding RNA that controls the sterility of Nongken 58S; pms3 is the transcript 1 of LOC\_12g36030. Studies indicated that it is a RNA molecule associated with male sterility specific to long-time lighting with a length of 1236bp (LDMAR). For normal rice under long-day condition, the expression of this gene could ensure normal pollen development and male fertility. However, for photoperoid-sensitive genic male sterile line of rice, base mutation of pms3 interval causes methylation of promotor interval in this gene with decreased expression. As a result, it could not meet the requirement of pollen development. Thus, this causes the male sterility under long-day condition [42]. Gene p/tms12-1 which controls the thermo-sensitive male sterility was cloned from Peiai 64S, which was the parent of thermo-sensitive genic male sterile line for two-line hybrid indica rice with the largest planting area by researches from South China Agricultural University. This gene is a noncoding RNA gene and its original transcript produces a small RNA after processing at least 2 times. Compared with the normal rice variety, thermo-sensitive male sterile line of rice had a single base mutation in this small RNA. It was further revealed by the studies that Nongken 58S also has the same gene mutation and this single base mutation is the common cause for thermo-sensitive male sterility of indica rice and photoperoid-sensitive male sterility of japonica rice. In normal rice, the expression of wild-type P/TMS12-1 restrains the occurrence of thermo-sensitive or photoperoid-sensitive male sterility. However, for thermo-sensitive and photoperoid-sensitive male sterile line of rice, the expression level of small RNA and its interaction with target gene are influenced by mutation of p/tms12-1, causing male sterility [43]. Successful cloning of pms3(p/tms12-1)gene had a very great significance for accelerating the breeding of two-line male sterile varieties of rice and promoting the research and devel‐

opment of crop heterosis utilization.

156 Rice - Germplasm, Genetics and Improvement

**3.3. Wide and specific compatibility genes and subspecies heterosis**

Making full utilization of heterosis between subspecies of indica and japonica rice is a major and effective means to increase the rice yield per unit area. However, this utilization is restricted by the low fertility of indica-japonica hybrid F1. Asian cultivated rice is divided into 2 subspecies, *indica* and *japonica*. Heterosis of intersubspecific indica-japonica hybrid is far greater than that of intrasubspecific hybrid. However, because reproductive isolation exists widely between subspecies in nature, hybrid fertility of the intersubspecific hybrid declines, which results in low seed setting rate. Breeding of hybrid rice had been limited within the subspecies for a long time because of this restriction because of the difficulty to utilize the stronger intersubspecies heterosis. Later, rice resources which could break the reproductive isolation are discovered by scientists, and known as wide compatibility varieties. Using indica rice varieties IR36 and IR50 and japonica rice varieties Qiuguang and Ribenyou as testers, Japanese scientist IKehashi, et al. [44] performed the hybrid fertility identification for 74 intermediate varieties. Six varieties including Ketan NangKa, Cpslo-17, etc. and hybrid F1 of indica rice and japonica rice all had high seed setting rate. They were believed to have wide compatibility gene (WCG), and named as wide compatibility varieties (WCV). After extensive

Sterility of indica-japonica hybrid is the key obstacle to taking advantage of hybrid vigor, and its mechanism has for a long time remained as one of the research hotspots for rice breeding and molecular genetics. For the past decades, genetic analysis has already located a host of loci related to the sterility of hybrid rice, but still little is known about the molecular mechanism for the reproductive segregation between the two rice subspecies. In 1984, Japan's rice breeding expert Ikehashi argued that the sterility of indica-japonica hybrid is mainly controlled by the allele at S5 locus on Chromosome 6. S5-n is known as a WCG, and the rice variety containing S5-n gene is a WCV, whose hybrids with indica and japonica show normal fertility [45]. In 2008 after many years of extensive research, Chinese scientists successfully cloned S5 gene and preliminarily illuminated the molecular mechanism for S5 to regulate the sterility of hybrid [46]. The research shows that S5-j is located on Chromosome 6, cDNA having a total length of 2495 bp and containing three exons. It encodes aspartyl protease made up of 472 amino acids and the product contains signal peptides, central domain, N terminal and C terminal. S5 is not expressed in leaves, but in the developing panicle. *In-situ* hybridization shows that S5 is expressed in various organs of ovule, including nucleus, integument, macrospore mother cell and embryo sac. S5 gene regulates seed setting percentage by controlling the sterility of female gamete. Protein s5-i and s5-j of indica and japonica are different on two amino acids. Located in the central domain, both two amino acids may have an effect on the activity or stability of aspartyl protease. Just like Nipponbare and Balilla, in indica rice-japonica hybrid, locus 273 is lLeucine and locus 471 is valine; for indica Nanjing 11, locus 273 is phenylalanine and locus 471 is alanine. At locus 172 bp in the downstream of terminator coden, there is deletion of an A; wide-compatibility variety 02428: deletion appears at 67 pb before ATG and 69 bp after ATG transcription start site, totaling 136bp, resulting in the deletion of 115 amino acids at N terminal of signal peptides and rendering it unable to be located on the cell wall. Therefore, the deletion of large segment on S5 gene of wide-compatibility variety has led to loss of function. Neither the hybrid with indica nor with japonica can affect hybrid fertility. Sequencing of 16 different varieties (including indica and japonica and wide-compatibility variety) has further confirmed the above results. At locus S5-i and S5-j of indica and japonica, indica-japonica differentiation occurs due to the infertility of indica-japonica hybrid, thus creating rich diversity of rice varieties and leading to reproductive segregation. However, the existence of wide-compati‐ bility gene S5-n has provided a bridge for the gene exchange between sub-species of indicajaponica hybrid, maintaining the integrity of rice variety. Wide-compatibility gene S5-n enjoys bright prospect for application in the breed improvement of rice variety, for it can be directly used to develop other wide-compatibility genes and also in breeding wide-compatibility varieties as molecular marker. Effective application of wide-compatibility genes can help overcome the infertility of the hybrid between indica and japonica rice subspecies so as to improve rice yield by relying on the strong hybrid vigor of indica-japonica sub-species[47].

It's worth noting that the research findings of Aradidopsis indicate that aspartyl protease is mainly involved in the transduction of disease resistant signal and the programmed cell death of regenerative tissues. Although the current research has failed to fully reveal the functional mechanism of S5, they can be sure that S5 has close ties with the emergence and survival of macrospore. According to the analysis of crystalline structure, aspartyl protease has three structural domains, namely, central structural domain, ring structure of N-terminal and ring structure of C-terminal. Sequence alignment and analysis show that the two mutational sites amino acid 273 and 471 in S5 are located in the central domain. However, the problems of the decreasing extent in the activity of aspartyl protease are connected with the fertility of female gamete (embryo sac), and the reason forthe functionally deficient S5-n not to affect the fertility of female gamete (embryo sac) in homozygosity and heterozygosity need further research. Recently, researchers discover a "killer-protector" system encoded by three closely interlocked open reading frames (ORF3, ORF4 and ORF5), which controls the fertility of indica-hybrid hybrid. ORF5 gene plays the "killer" role, assisted by ORF4. Conversely, ORF3, as the protector, has the opposite function. In the forming process of gynospore, the action of ORF5+ ("killer") and ORF4+ ("partner") can cause the stress response of endoplasmic reticulum (ER), while ORF3+ ("protector") blocks the ER stress response in cells and facilitates cells to produce normal gamete. But ORF3- cannot block ER stress response, thus causing programmed cell death and embryo sac abortion to happen in advance [48]. This research has given a relatively complete elaboration of the molecular mechanism of S5, and revealed the molecular mechanism for controlling the fertility of indica-japonica hybrid. It provides reference for studying the sterility of indica-japonica hybrid, molecular mechanism for reproductive segregation and biological evolution. This killer-protector system regulates the sterility of a hybrid from two subspecies. The non-fatal combination of ORF4 and ORF5 allows the indica-japonica hybrid to pass its genes to the next generation, thus overcoming the hybrid sterility and laying the foundation for the development of ideal rice varieties. This finding has vast application potential in improving rice varieties. The relevant information can be directly used to develop other widecompatibility genes and breed wide-compatibility varieties. It will help fix reproductive segregation, overcome sterility of hybrid between indica-japonica sub-species and make use of hybrid vigor of indica-japonica sub-species to increase rice yield.

Besides wide-compatibility genes, there are also some specific-compatibility genes present in rice. Based on the systematic research on pollen fertility, Zhang Guiquan et al. [49] put forward the theory of specific compatibility genes, holding that the pollen fertility of indica-japonica hybrid is controlled by at least six loci, namely, S-a, s-b, S-c, S-d, S-e and S-f. The pollen sterility of hybrid is mainly determined by the number of heterozygous loci and the differentiation distance of alleles. Heterozygous alleles lead to sterility, while homozygous alleles lead to compatibility. Such gene is called specific compatibility gene. On these loci, indica variety often carries Si /Si , while japonica carries Sj /Sj . In their hybrid, the interaction of Si gene and Sj gene causes the abortion of Sj -carrying male gamete. [50]. Sa gene locus affects the fertility of F1 hybrid between indica-japonica subspecies and the interaction of indica-japonica alleles leads to the abortion of male gamete and reduces the seed setting percentage. Using cultivar Taichung 65 and isogenic F1 sterile line TISL4 as the materials, Zhuang Chuxiong et al (51) employed such technologies as RFLP and RAPD to locate S-a locus on Chromosome 1 and the genetic distance from CDO548 is 6.4 cM.

overcome the infertility of the hybrid between indica and japonica rice subspecies so as to improve rice yield by relying on the strong hybrid vigor of indica-japonica sub-species[47].

It's worth noting that the research findings of Aradidopsis indicate that aspartyl protease is mainly involved in the transduction of disease resistant signal and the programmed cell death of regenerative tissues. Although the current research has failed to fully reveal the functional mechanism of S5, they can be sure that S5 has close ties with the emergence and survival of macrospore. According to the analysis of crystalline structure, aspartyl protease has three structural domains, namely, central structural domain, ring structure of N-terminal and ring structure of C-terminal. Sequence alignment and analysis show that the two mutational sites amino acid 273 and 471 in S5 are located in the central domain. However, the problems of the decreasing extent in the activity of aspartyl protease are connected with the fertility of female gamete (embryo sac), and the reason forthe functionally deficient S5-n not to affect the fertility of female gamete (embryo sac) in homozygosity and heterozygosity need further research. Recently, researchers discover a "killer-protector" system encoded by three closely interlocked open reading frames (ORF3, ORF4 and ORF5), which controls the fertility of indica-hybrid hybrid. ORF5 gene plays the "killer" role, assisted by ORF4. Conversely, ORF3, as the protector, has the opposite function. In the forming process of gynospore, the action of ORF5+ ("killer") and ORF4+ ("partner") can cause the stress response of endoplasmic reticulum (ER), while ORF3+ ("protector") blocks the ER stress response in cells and facilitates cells to produce normal gamete. But ORF3- cannot block ER stress response, thus causing programmed cell death and embryo sac abortion to happen in advance [48]. This research has given a relatively complete elaboration of the molecular mechanism of S5, and revealed the molecular mechanism for controlling the fertility of indica-japonica hybrid. It provides reference for studying the sterility of indica-japonica hybrid, molecular mechanism for reproductive segregation and biological evolution. This killer-protector system regulates the sterility of a hybrid from two subspecies. The non-fatal combination of ORF4 and ORF5 allows the indica-japonica hybrid to pass its genes to the next generation, thus overcoming the hybrid sterility and laying the foundation for the development of ideal rice varieties. This finding has vast application potential in improving rice varieties. The relevant information can be directly used to develop other widecompatibility genes and breed wide-compatibility varieties. It will help fix reproductive segregation, overcome sterility of hybrid between indica-japonica sub-species and make use

of hybrid vigor of indica-japonica sub-species to increase rice yield.

/Sj

carries Si

/Si

causes the abortion of Sj

158 Rice - Germplasm, Genetics and Improvement

, while japonica carries Sj

Besides wide-compatibility genes, there are also some specific-compatibility genes present in rice. Based on the systematic research on pollen fertility, Zhang Guiquan et al. [49] put forward the theory of specific compatibility genes, holding that the pollen fertility of indica-japonica hybrid is controlled by at least six loci, namely, S-a, s-b, S-c, S-d, S-e and S-f. The pollen sterility of hybrid is mainly determined by the number of heterozygous loci and the differentiation distance of alleles. Heterozygous alleles lead to sterility, while homozygous alleles lead to compatibility. Such gene is called specific compatibility gene. On these loci, indica variety often

hybrid between indica-japonica subspecies and the interaction of indica-japonica alleles leads

. In their hybrid, the interaction of Si


gene and Sj gene

Further research found that Sa locus is actually made up of two adjacent gene loci SaM and SaF, encoding ubiquitin-like modifier E3 ligase and F-box proteins[52]. Allele SaM+ encodes an ubiquitin-like modifier E3 ligase made up of 257 amino acids, while a G→T single site mutation at intron 5 in SaM- , causing premature termination of translation and the end product, is only made of 217 amino acids. SaF encodes a Fbox protein composed of 476 amino acids. Compared with SaF+ , a single nucleotide mutation occurs in SaF- , resulting in phenylalanine for serine substitution at position 287[52]. The haplotype in most indica varieties is SaM+ SaF+ , while SaM - SaF in all japonica varieties. The semi-sterility of indica-japonica hybrid is due to SaF+ 's direct interaction with SaM and indirect interaction with SaM+ , which has led to the abortion of pollen that carries SaM- . Due to the existence of repression domain, SaM+ does not directly interact with SaF+ , but SaM+ will inevitably cause male sterility. Male sterility would be impossible if any of SaM+ , SaM or SaF+ is lacking. This "two pairs of alleles/three elements" interaction model has provided a satisfactory explanation for the incompatibility of indicajaponica hybrid [52].

In F1 plant, combinations of alleles at adjacent positions (SaM+ SaF+ or SaM-SaF- ) separate in the haploid microspore. Therefore, only the protein migration between spores can result in the concurrence of SaM+ , SaM and SaF+ . It may be impossible for SaM to migrate due to the deletion of a domain in its truncated proteins, so SaF+ and SaM protein need transport from its own microspore to the microspore that carries SaM for the interaction to happen. SaF+ SaMcomplex further interacts with SaM+ , leading to male sterility by resulting in killing the microspore that carriers SaM- . Since the male developmental defect of hybrid occurs in the early period of microspores, the transport of these proteins may occur via the cytoplasmic channel during the tetrad period. The SNPs analysis of SaF and SaM shows that the functional variation on SaF has already existed before the evolution and seperation of most rice varieties. The mutation on SaM occurs in the population of ordinary wildtype rice (Oryza rufipogon) that carries SuM+ SuF in south China, thus creating SuM-SaF haplotype. Through analysis, the authors conclude that their research data agree with the recently presented assumption that indica and japonica originate from different wild rice populations [53]. Some varieties containing SaM+ SaF haplotype have also been found in indica. Since its hybrid with indica or japonica lacks SaM+ or SaF+ , it is fertile. Therefore, SaM+ and SaF can be defined as compatibility locus San. San (SaM+ SaF- ), Sa-i (SaM+ SaF+ ) and Sa-j (SaM-SaF- ) are similar to S5 locus, thus forming a three-allele system to control rice hybrid's male sterility and fertility (compatibility). The molecular mechanisms for the sterility of rice hybrid are thus unified.

Considering that indica-japonica hybrid has great application prospect in improving rice variety, the obtaining of relevant information about Sa and S5 genes can facilitate its use as molecular marker in large-scale screening for compatible germplasm of rice varieties. Or people can also use transgenic technology to create new compatibility hybrid lines. The breakthrough in the research of relevant molecular mechanism for the sterility of indicajaponica rice hybrid has laid a solid foundation for making use of the strong hybrid vigor of inica-japonica subspecies to increase rice yield.

## **4. Grain shape**

Rice's grain shape traits are important agronomic characters directly related to yield, so to reveal the genetic and development mechanism of grain shape and apply it in breeding is an important means to increase the per unit yield of rice. Since grain shape in rice is closely connected with its appearance, processing quality, cooking and edible qualities, grain shape traits affect not only rice yield, but also rice qualities, playing an important role in the forming of yield and quality in rice [54]. The grain shape of the world's rice varieties can be divided into several types: coarse grain, fine grain, short grain, long grain and ultra-large grain. Grain shape traits mainly include grain's length, width, length/width ratio and length/thickness ratio. Many tests show that the inheritance of grain length is controlled by single gene, double gene, polygene and minorgene. Grain width and thickness are mostly in normal distribution, indicating that this trait is controlled by polygene; grain weight is one of the important factors to constitute yield-related trait as well as the integrated indicator of grain length, grain width and grain thickness. It is generally believed that grain weight is controlled by polygene. Therefore, grain's length, width, thickness, length/width ratio and weight belong to quantita‐ tive traits controlled by polygene. Meanwhile, there is correlation between different traits [55]. QTL positioning is an important means to analyze the inheritance of quantitative traits. Up to now, the number of already positioned QTL for controlling rice's grain shape has exceeded 200 [56]. The positioning, cloning and functional analysis of the important genes that control rice's yield-related traits can help improve the molecular genetics of rice's yield-related traits and increase per unit yield of rice. Grain size is an important determinant factor of the yield of rice grain as well as the objective trait for crop domestication and artificial breeding. At present, some grain shape-related genes have been cloned by means of map-based cloning, such as GS3, GW2, GW5, GS5 and GW8.

#### **4.1. GS3**

GS3 is the first cloned major QTL controlling grain length and weight and also the minor QTL controlling grain width and plumpness. Fan et al [57] used Minghui 63 (large grain) as the recurrent parent in continuous cross breeding and backcrossing with Chuan 7 (small grain) and constructed the near-isogenic lines for positioning GS3. Through analysis of 201 random samples in the offsprings of BC3F2, it is found that GS3 has accounted for the 80-90% variation in the grain weight and length of the population. They built advanced backcross population BC3F1 and selected recombinants for the target zones. They conducted fine mapping in the Minghui 63-based BC3F2 (GS3-NIL) plant population, and selected single plants that display recessive phenotype for recombinant screen, positioning GS3 within the range of 7.9 kb. Spanning over a length of 956 bp, GS3 cDNA contains 5 exons, encoding a transmembrane protein made up of 232 amino acids. The protein product consists of the following four structural domains: a structural domain for adjusting the size of organs unique to plants (OSR),

a transmembrane domain, the cysteine-rich homologous region of tumor necrosis factor recipient/nerve growth factor receptor (TNFR/NGFR) and von willebrand factor type C at C terminal (VWFC module). OSR domain was previously called PEBP domain. Sequence analysis shows that compared with small grain varieties, the coden IGC encoding cysteine at position 55 in the second exon in large grain varieties mutates to stop coden TGA and causes the advance termination of protein translation (deletion of 178 animo acids). Finally, this results in the deletion of PEBP-like domains and other three domains. Apparently, GS3-encoded protein can negatively regulate grain weight [57]. It is found in the latest database software analysis that GS3 does not belong to the PEBP family. By comparison, they found that the predicted GS3 PEBP is only about 1/3 of the actual PEBP, with 20.3%-28.4% similarity. By comparing to a database sequence, it is shown that the N terminal of GS3 has a highly similar and conserved 66 aa structural domain in most angiosperm, e.g. DEP 1 for controlling panicle type. The author temporarily re-names the domain as OSR [58]. GS3 acts as negative regulator for the size of rice grain and organ. In-situ hybridization shows that GS3 is expressed in young panicles and decreased as the panicles grow. It is also slightly expressed in other tissues like embryo, apical meristem, leaves and stalk, but largely expressed in roots and crowns. Realtime PCR has also proved the above results. Wild-type allele contains four presumed structural domains: OSR domain at N terminal, a transmembrane domain, the TNFR/NGFR family cysteine-rich domain and VWFC at C terminal. It is found that the protein encoded by this gene consists of two confrontational parts and the "gaming" of the two parts at the beginning and end of GS3 protein finally determines the size of grains. The rice varieties without GS3 protein (or the protein is non-functional) is long-grain type (about 10 mm long); the rice varieties containing complete GS3 protein belongs to medium-grain type (about 8mm); the rice varieties containing only ORS belongs to short-grain type (about 6 mm) [58]. The research also found that almost all the excellent indica rice varieties contain complete GS3 protein, and therefore are medium-grain type. The GS3 protein is not functional in long-grain-type indica varieties. Gene transfer and substitution can effectively change the grain shape of rice variety, indicating that GS3 plays a decisive role in the yield and quality of rice and also in the mutation and evolution of grain shape. Homologous gene to GS3 is also found in other species, including corn, barley and soybean, while OSR exits in all these homologous genes, indicating that these genes may also control the seed size of corresponding species. Therefore, this finding will have significant prospect of application. First of all, genetic variation can be directly used in breeding varieties for desired grain size and improving the yield of rice. Secondly, based on the research information about rice, the GS3 homologous genes of other species can be cloned so as to guide the breed improvement of corresponding species. Researchers have already been done on how to apply GS3 gene in rice breeding design. Yang et al [59] used the single-segment substitution line developed from indica variety " Huaxian 74" carrying GS3 gene, to perform pyramiding breeding with single-segment substitution line carrying other favorable genes. Twenty-six homozygous pyramided lines containing GS3 and other favorable genes were obtained in F4. The measurement of grain length confirmed that these pyramided lines have desired long grain length, with much improved appearance qualities. This indicates that using singlesegment substitution lines and GS3 can help realize rice breeding design aim to modify the grain length.

japonica rice hybrid has laid a solid foundation for making use of the strong hybrid vigor of

Rice's grain shape traits are important agronomic characters directly related to yield, so to reveal the genetic and development mechanism of grain shape and apply it in breeding is an important means to increase the per unit yield of rice. Since grain shape in rice is closely connected with its appearance, processing quality, cooking and edible qualities, grain shape traits affect not only rice yield, but also rice qualities, playing an important role in the forming of yield and quality in rice [54]. The grain shape of the world's rice varieties can be divided into several types: coarse grain, fine grain, short grain, long grain and ultra-large grain. Grain shape traits mainly include grain's length, width, length/width ratio and length/thickness ratio. Many tests show that the inheritance of grain length is controlled by single gene, double gene, polygene and minorgene. Grain width and thickness are mostly in normal distribution, indicating that this trait is controlled by polygene; grain weight is one of the important factors to constitute yield-related trait as well as the integrated indicator of grain length, grain width and grain thickness. It is generally believed that grain weight is controlled by polygene. Therefore, grain's length, width, thickness, length/width ratio and weight belong to quantita‐ tive traits controlled by polygene. Meanwhile, there is correlation between different traits [55]. QTL positioning is an important means to analyze the inheritance of quantitative traits. Up to now, the number of already positioned QTL for controlling rice's grain shape has exceeded 200 [56]. The positioning, cloning and functional analysis of the important genes that control rice's yield-related traits can help improve the molecular genetics of rice's yield-related traits and increase per unit yield of rice. Grain size is an important determinant factor of the yield of rice grain as well as the objective trait for crop domestication and artificial breeding. At present, some grain shape-related genes have been cloned by means of map-based cloning,

GS3 is the first cloned major QTL controlling grain length and weight and also the minor QTL controlling grain width and plumpness. Fan et al [57] used Minghui 63 (large grain) as the recurrent parent in continuous cross breeding and backcrossing with Chuan 7 (small grain) and constructed the near-isogenic lines for positioning GS3. Through analysis of 201 random samples in the offsprings of BC3F2, it is found that GS3 has accounted for the 80-90% variation in the grain weight and length of the population. They built advanced backcross population BC3F1 and selected recombinants for the target zones. They conducted fine mapping in the Minghui 63-based BC3F2 (GS3-NIL) plant population, and selected single plants that display recessive phenotype for recombinant screen, positioning GS3 within the range of 7.9 kb. Spanning over a length of 956 bp, GS3 cDNA contains 5 exons, encoding a transmembrane protein made up of 232 amino acids. The protein product consists of the following four structural domains: a structural domain for adjusting the size of organs unique to plants (OSR),

inica-japonica subspecies to increase rice yield.

160 Rice - Germplasm, Genetics and Improvement

such as GS3, GW2, GW5, GS5 and GW8.

**4.1. GS3**

**4. Grain shape**

## **4.2. GW2**

GW2 is a major gene controlling grain width and weight. Song et al [60] used the offsprings of F2 between WY3 and FAZ1 for preliminary QTL positioning. It is found that the allele coming from WY3 has significantly increased the grain width and weight. Advanced backcross population and the screened recessive single plants were used to position GW2 within the range of 8.2 kb. GW2 in FAZ1 contains eight exons. With a total length of 1634 bp, cDNA encodes the protein composed of 425 amino acids of 47kDa. The deletion of a base on exon 4 causes GW2 allele to terminate the translation in advance, and the product is only composed of 115 amino acids. GW2 encodes a ring-type E3 ubiquitin ligase in the cytoplasm and performs negative regulation on cell division by anchoring the substrate to the proteasome for degra‐ dation. The absence of GW2 function renders it impossible to transfer ubiquitin to target protein, making the supposedly degradable substrate hard for specific recognition, activating cell division in spikelet and increasing the width of husk. On the other hand, the grain filling rate is also raised, followed by the growth in the size of endosperm. As a result, the width of the husk, the weight of the grain and yield all increased. The histological analysis of AZ1 and NIL (GW2) indicates that larger rice husk of NIL (GW2) is mainly due to the growth in the number rather than the size of cells. The growth in the endosperm of NIL (GW2) is mainly caused by the growth in the size rather than the number of cells. Compared with FAZ1 (recurrent parents), near isogenic line NIL(GW2) can significantly increase the width and tiller number of grains. GW2 (WY3) allele has significantly increased grain width and 1000 seed weight, thus raising single plant yield. This allele can also increase panicles per plant and prolong the growth period while greatly reducing the seeds per panicle and the length of main panicle, indicating pleiotropism of GW2. Through molecular-marker-assisted selection, researchers transferred the GW2 gene in large-grain varieties to small-grain variety FAZ1 to breed new line. By comparison of the yield of NIL (GW2) and small-grain parent FAZ1, it is found that plant yield of NIL (GW2) increased by 19.7% and plot yield increased by 15.9% over small-grain parent, indicating that this gene is valuable in high-yield breeding. In order to prove whether the growth in grain size and yield in NIL (GW2) can affect rice quality, the rice quality was compared between NIL (GW2) and small-grain parent (FAZ1). It turned out that GW2 large-grains allele had an effect on the appearance of rice grains, while the physical and chemical indicators remained unchanged. It is speculated that the edible and cooking qualities are not much affected. Meanwhile, it is also found that both corns and wheat contain GW2 allele, so the discovery of this gene will greatly advance the research on high-yield breeding of crops [60].

#### **4.3. GW5**

Another cloned gene for controlling gain width is GW5, which affects the grain width and weight of rice. The allele coming from Asominori significantly increases grain width and weight [61]. GW5 is preliminarily located between SSR marker RM3328 and RMw513 on the short arm of Chromosome 5 at 2.33 cM and 0.37 cM, respectively. By expanding the population and developing CAPS marker, GW5 was narrowed down to OJ1097-A12 and between CAPS markers Cw5 and Cw6. Within this region, compared with wide-type rice with slender grains, 1212 bp nucleotide is deleted in wide-grain variety. GW5 for controlling grain width is exactly in this deleted sequence [61]. GW5 encodes a nuclear-localised protein made up of 144 amino acids, which contains a nuclear localization signal and a histidine-rich domain. The yeast twohybrid experiment proved that GW5 interacts with polyubiquitin chain, indicating that GW5 may regulate grain width and weight through ubiquitin proteasome. The lack of GW5 function renders it impossible to transfer ubiquitin to target protein, making the supposedly degradable substrate hard for specific recognition and thus activating cell division in spikelet. As a result, the husk width, grain weight and yield all increase [61].

## **4.4. GS5**

**4.2. GW2**

162 Rice - Germplasm, Genetics and Improvement

of crops [60].

**4.3. GW5**

GW2 is a major gene controlling grain width and weight. Song et al [60] used the offsprings of F2 between WY3 and FAZ1 for preliminary QTL positioning. It is found that the allele coming from WY3 has significantly increased the grain width and weight. Advanced backcross population and the screened recessive single plants were used to position GW2 within the range of 8.2 kb. GW2 in FAZ1 contains eight exons. With a total length of 1634 bp, cDNA encodes the protein composed of 425 amino acids of 47kDa. The deletion of a base on exon 4 causes GW2 allele to terminate the translation in advance, and the product is only composed of 115 amino acids. GW2 encodes a ring-type E3 ubiquitin ligase in the cytoplasm and performs negative regulation on cell division by anchoring the substrate to the proteasome for degra‐ dation. The absence of GW2 function renders it impossible to transfer ubiquitin to target protein, making the supposedly degradable substrate hard for specific recognition, activating cell division in spikelet and increasing the width of husk. On the other hand, the grain filling rate is also raised, followed by the growth in the size of endosperm. As a result, the width of the husk, the weight of the grain and yield all increased. The histological analysis of AZ1 and NIL (GW2) indicates that larger rice husk of NIL (GW2) is mainly due to the growth in the number rather than the size of cells. The growth in the endosperm of NIL (GW2) is mainly caused by the growth in the size rather than the number of cells. Compared with FAZ1 (recurrent parents), near isogenic line NIL(GW2) can significantly increase the width and tiller number of grains. GW2 (WY3) allele has significantly increased grain width and 1000 seed weight, thus raising single plant yield. This allele can also increase panicles per plant and prolong the growth period while greatly reducing the seeds per panicle and the length of main panicle, indicating pleiotropism of GW2. Through molecular-marker-assisted selection, researchers transferred the GW2 gene in large-grain varieties to small-grain variety FAZ1 to breed new line. By comparison of the yield of NIL (GW2) and small-grain parent FAZ1, it is found that plant yield of NIL (GW2) increased by 19.7% and plot yield increased by 15.9% over small-grain parent, indicating that this gene is valuable in high-yield breeding. In order to prove whether the growth in grain size and yield in NIL (GW2) can affect rice quality, the rice quality was compared between NIL (GW2) and small-grain parent (FAZ1). It turned out that GW2 large-grains allele had an effect on the appearance of rice grains, while the physical and chemical indicators remained unchanged. It is speculated that the edible and cooking qualities are not much affected. Meanwhile, it is also found that both corns and wheat contain GW2 allele, so the discovery of this gene will greatly advance the research on high-yield breeding

Another cloned gene for controlling gain width is GW5, which affects the grain width and weight of rice. The allele coming from Asominori significantly increases grain width and weight [61]. GW5 is preliminarily located between SSR marker RM3328 and RMw513 on the short arm of Chromosome 5 at 2.33 cM and 0.37 cM, respectively. By expanding the population and developing CAPS marker, GW5 was narrowed down to OJ1097-A12 and between CAPS markers Cw5 and Cw6. Within this region, compared with wide-type rice with slender grains,

The already cloned GS3, GW2 and GW5 grain shape-related genes are all in negative correlation with grain shape. That is, a high gene expression level corresponds to the decrease in seed size. The cloned GS5 is a positive regulator for grain width, seed setting percentage and thousand seed weight. High GS5 expression level can help accelerate cell cycle and facilitate the transverse cell division of spikelet, thus increasing husk width and speeding up the filling of rice grains and the development of endosperm. This will finally lead to larger and heavier seeds and higher single plant yield [62]. A lot of researches show that besides the difference in grain size, in the two genetic materials with identical genetic background, large-grain materials have higher GS5 expression than small-grain ones. The grain width, thousand seed weight and single plant yield also increase by 8.7%, 7.0% and 7.4% respectively. GS5 is located between RM593 and RM574 on the short arm of Chromo‐ some 5, encoding serine carboxypeptidase. The sequencing of recombinant single plant and the transformation test of GS5 show that GS5's influence on grain size comes from promoter variation. The comparative sequencing of GS5 promoter for 51 rice varieties from differ‐ ent parts of Asia shows that GS5 has three different combinations in nature: GS5 largegrain haplotype, GS5 medium-grain haplotype and GS5 small-grain haplotype. They perfectly correspond to the three grain widths of different varieties: wide, medium and narrow shape. Of the above three types, GS5 small-grain haplotype is wild type, while GS5 large-grain haplotype is the mutant with acquired functions in rice domestication and breeding. The mosaic transformation of promoter further shows that the forming of these mutants relies on the natural variation of GS5 promoter. Therefore, GS5 plays an impor‐ tant role in the artificial domestication and breeding of rice and contributes greatly to the diversity of genes controlling the grain size of rice. These results indicate that the muta‐ tion of GS5 gene is related to the grain size of rice. The discovery of this mutation can help boost rice yield and may also help increase the yield of other crops [62].

#### **4.5. GW8**

Chinese researchers discover a key functional gene GW8 which can simultaneously affect rice quality and yield. By making use of QTL mapping and advanced backcross population, it is located at the distance of about 7.5 Kb on Chromosome 8 between marker RM502 and PSM711. The sequencing shows that a SBP transcription factor OsSPL16 encoded by this gene can simultaneously control grain size, grain shape and rice quality [63]. In the Pakistan's Basmati rice including the most delicious varieties in the world, the variation of OsSPL16 promoter is observed, which results in decreased expression. Moreover, the gene overexpression can promote cell division, broaden the grain, improve grain filling rate and increase thousandgrain weight. All these will contribute to rice yield increase. The research also finds that GW8 gene is present in high-yield rice which is grown over large areas in China at present. GW8 gene has been discovered to play an important role in the China's rice yield increase. Later in the experimental fields in Beijing, Guangzhou and Hainan, the researchers discover a variant type of GW8 gene in high-yield rice. The key site mutation not only improves rice quality, but increases grain number per spike. If the new variation site of GW8 gene is introduced into Basmati rice, the yield will increase by 14% with high quality; if it is introduced into China's high-yield rice variety, the rice quality will be remarkably enhanced with unchanged yield. In the meantime, GW8 gene has been used in molecular breeding program, and new variety Huabiao No. 1 containing excellent genes such as GW8 was successfully bred in 2009. Huabiao No. 1 has passed variety certification in Guangdong [63]. Therefore, successful cloning of GW8 gene and expounding of molecular mechanism provide new genes with important application value for high-yield and good-quality molecular breeding of hybrid rice. These achievements help reveal molecular mechanism of product synergy for rice quality.

## **4.6. Utilization of grains shape genes**

Those cloned genes controlling rice grain shape related to yield trait are not only favorable to reveal the complex genetic mechanism of rice yield-related trait, but also provide theoretical and technical basis for molecular marker-assisted selection in rice. Through researches on primary core collection of 170 rice varieties and 10 oversea rice varieties, Fan et al [64] found that C-A single base mutation (SNP) on the second exon of GS3 is highly associated with grain length. On this basis, they developed a functional marker SF28, which can be applied to molecular marker-assisted selection of GS3 gene to improve rice appearance and yield. Song et al [60] also make in-depth researches on yield and quality in rice breeding, and discovered that NIL (*GW2*) increased single plant grain yield by 19.7% compared to FAZ1. However, the grain number per spike decreased by 29.9%, and *GW2* alleles from WY3 had no influence on leaf morphology, grain filling and edible quality of FAZ1.

In the breeding practice, cloning of the important genes controlling rice grain shape gives some revelation to its molecular mechanism, but its application in breeding is still difficult. For example, some gene resources may originate from natural selection before Indica-Japonica differentiation or artificial selection after Indica-Japonica differentiation. The fulfillment of some gene functions requires specific genetic factors under different genetic backgrounds. Quality declining due to enlarging grain size and increasing grain weight is another problem that needs to be solved. Therefore, in addition to making use of single gene, the genetic improvement of important agronomic characters of rice is also necessary. Mining key genes with pleiotropism or gene pyramiding is an effective and quick means to breed super-highyield rice varieties.

## **5. Genes with pleiotropic effect to yield related traits**

High and stable yield has always been considered as one of the most important objectives in crop research. The genes related to rice yield are the key object of the research on rice breeding and molecular biology. Rice yield per unit area depends on grain number per spike, effective panicle number per plant, thousand-grain weight and seed setting percentage. Meanwhile, plant height and growth period exert huge influences on rice plant morphology and adapta‐ bility. The research also finds that many pleiotropic genes are present in rice and involved in regulating multiple growth and development processes, as well as rice vegetative growth and reproduction. Pleiotropic genes are crucial in regulating rice morphogenesis and flower organ development, directly associated with rice yield. Yield and heading date are the basic prop‐ erties to evaluate practical value of rice. The former reflects income, while the latter decides rice adaption area and season. At present, some pleiotropic genes affecting yield, composition factors and heading date have been cloned.

## **5.1. Ghd7**

simultaneously control grain size, grain shape and rice quality [63]. In the Pakistan's Basmati rice including the most delicious varieties in the world, the variation of OsSPL16 promoter is observed, which results in decreased expression. Moreover, the gene overexpression can promote cell division, broaden the grain, improve grain filling rate and increase thousandgrain weight. All these will contribute to rice yield increase. The research also finds that GW8 gene is present in high-yield rice which is grown over large areas in China at present. GW8 gene has been discovered to play an important role in the China's rice yield increase. Later in the experimental fields in Beijing, Guangzhou and Hainan, the researchers discover a variant type of GW8 gene in high-yield rice. The key site mutation not only improves rice quality, but increases grain number per spike. If the new variation site of GW8 gene is introduced into Basmati rice, the yield will increase by 14% with high quality; if it is introduced into China's high-yield rice variety, the rice quality will be remarkably enhanced with unchanged yield. In the meantime, GW8 gene has been used in molecular breeding program, and new variety Huabiao No. 1 containing excellent genes such as GW8 was successfully bred in 2009. Huabiao No. 1 has passed variety certification in Guangdong [63]. Therefore, successful cloning of GW8 gene and expounding of molecular mechanism provide new genes with important application value for high-yield and good-quality molecular breeding of hybrid rice. These achievements

Those cloned genes controlling rice grain shape related to yield trait are not only favorable to reveal the complex genetic mechanism of rice yield-related trait, but also provide theoretical and technical basis for molecular marker-assisted selection in rice. Through researches on primary core collection of 170 rice varieties and 10 oversea rice varieties, Fan et al [64] found that C-A single base mutation (SNP) on the second exon of GS3 is highly associated with grain length. On this basis, they developed a functional marker SF28, which can be applied to molecular marker-assisted selection of GS3 gene to improve rice appearance and yield. Song et al [60] also make in-depth researches on yield and quality in rice breeding, and discovered that NIL (*GW2*) increased single plant grain yield by 19.7% compared to FAZ1. However, the grain number per spike decreased by 29.9%, and *GW2* alleles from WY3 had no influence on

In the breeding practice, cloning of the important genes controlling rice grain shape gives some revelation to its molecular mechanism, but its application in breeding is still difficult. For example, some gene resources may originate from natural selection before Indica-Japonica differentiation or artificial selection after Indica-Japonica differentiation. The fulfillment of some gene functions requires specific genetic factors under different genetic backgrounds. Quality declining due to enlarging grain size and increasing grain weight is another problem that needs to be solved. Therefore, in addition to making use of single gene, the genetic improvement of important agronomic characters of rice is also necessary. Mining key genes with pleiotropism or gene pyramiding is an effective and quick means to breed super-high-

help reveal molecular mechanism of product synergy for rice quality.

leaf morphology, grain filling and edible quality of FAZ1.

**4.6. Utilization of grains shape genes**

164 Rice - Germplasm, Genetics and Improvement

yield rice varieties.

*Ghd7* is the first reported pleiotropic gene which exerts major influence on rice heading date and yield-related trait [65]. It also can control grain number per spike, plant height and heading date simultaneously. Among the F2:3 and recombinant inbred line population constructed by Zhenshan 97 and Minghui 63, *Ghd7* is located between marker R1440 and C1023 on Chromo‐ some 7, and further accurately located on 79kb between RM5436 and RM2256. Through backcrossing, the fragment containing *Ghd7* in Minghui 63 is introduced into Zhenshan 97, and near isogenic line is obtained based on Zhenshan 97. Compared to the recurrent parent Zhenshan 97, NIL heading date is late by 21.2 days, and plant height higher by 33cm, main stem spikelets increased from 130 to 216, and yield per plant also increased by 50%. Through classical map-based cloning, three NIL-F2 macro-populations are adopted to locate Ghd7 to the range of 0.28 cM. In comparison with Zhenshan 97, Minghui 63 allele at the genetic locus delays heading, and enhances plant height, grain number per panicle and yield. In fact, the pleiotropism of Ghd7 region has been generally revealed in the preliminary mapping. Among Zhenshan 97/Minghui 63RIL populations, Minghui 63 allele delays heading, and enhances plant height, grain number per panicle or thousand-grain weight [66]; while in F2:3 population, Minghui 63 allele delays heading, and enhances plant height, grain number per panicle, thousand-grain weight and rice yield [67]. Through map-based cloning, this Minghui 63 *Ghd7* cDNA has a total length of 1013bp, and encodes a nucleoprotein composed of 257 amino acids; the product is CCT (CO, CO-LIKE and TIMING OF CAB1) structural protein, which is similar to CCT domain of Arabidopsis thaliana CO protein, but is obviously different from the latter. Ghd7 protein has no obvious zinc finger, and no homology relation with the Arabidopsis thaliana CO protein. Ghd7 expression is mainly in tender leaves, apical meristem, secondary branch differentiation primordium and phloem of vascular bundle in mature leaves. In view of microscopic structures of plants with Ghd7 expression, cell number obviously increases, so it is supposed that Ghd7 accelerates cell division. More secondary branches are differentiated in the development of immature spike, which become an anatomical base of improving grain number per spike. At the same time, thickening of stems also is in favor of keeping good plant shape to facilitate stable yield. Ghd7 expression is controlled by photoperiod, and mRNA expression is characterized by day and night rhythm. The expression is inhibited in short days; while in long days, Ghd7 inhibits Hd3a and Ehd1 expression, and this mechanism of flowering control may be unique to rice. As this gene significantly prolong the plant growth period, the panicles have a longer time for development which results in higher grain number and spikes become larger. Moreover, this gene also affects flowering time, plant height and other traits, showing obvious pleiotropic feature. Under the long-day conditions, *Ghd7* over-expression can delay heading, increase plant height and grain number per panicle. The natural mutants with weakened functions can be grown in temperate or even colder regions. Therefore, *Ghd7* plays an important role in yield potential and adaptability on global scale of rice [65]. The subsequent researches discover that single phyA or combination of phyB and phyC can induce accumulation of Ghd7 mRNA, and phyB can independently reduce Ghd7m RNA to a certain extent. Furthermore, phyB and phyA can separately affect the activity of Ghd7 and Hd1 [68]. However, Shibaya et al. [69] indicated genetic interaction between *Hd2* and H*d16* or *Hd2* and *Ghd7*. *Hd2* and the related genetic interaction play an important role in controlling heading date under the long-day conditions.

Researches prove that most of high-yield rice varieties contain *Ghd7* gene. Wild-type *Ghd7* gene can delay heading, and improve plant height and grain number per panicle. Comparative sequencing of *Ghd7* alleles among 19 rice cultivers over Asia found that five genotypes had the proteins encoded by various *Ghd7* alleles. Minghui 63 is the representative of the first genotype, and alleles for this genotype have stronger functions. The varieties with this genotype are mainly distributed in rice production areas in the south of China as well as tropical and sub-tropical region, with longer growth time. Cultivar Nipponbare represents the second genotype, and alleles for this genotype have weakened functions. The varieties with this genotype are mainly distributed in North China and the same latitude regions. Hejiang 19 and Mudanjiang 8 are the representatives of the third genotype, and the alleles totally lose the functions due to terminator mutation. The varieties with this genotype are mainly distributed in Heilongjiang province in the north of China, and rice growth period is adaptable to shorter-summer condition. The fourth genotype is only discovered in Teqing varieties, and this genotype also has stronger functions. The distributed geographical regions are similar to the first one. The last genotype is deficiency of *Ghd7*, and the varieties with this genotype are mainly distributed in China's double cropping rice areas. Based on the above-mentioned researches, it is known that different genotypes of *Ghd7* are associated with rice variety distribution. These genes are not only involved in development regulation, but also are related to plant geographical distribution. The successful cloning of rice *Ghd7* gene greatly deepens the understanding of genetic and molecular basis of complex quantitative traits. This also constitutes a good illustration of pleiotropism rarely discovered in traditional genetics. The isolation of *Ghd7* gene demonstrates that complex quantitative traits such as yield can be improved through biotechnology like qualitative traits. Relevant information of this gene can be directly used in the mining of genes that are significant to improving yield and ecological adaptability to realize genetic improvement. *Ghd7* gene, which has been isolated with con‐ firmed function in yield increase, can greatly shorten the screening time of high-yielding rice variety. This is a key step taken towards improving crop yield in the global range.

## **5.2. DTH8 (Ghd8)**

shape to facilitate stable yield. Ghd7 expression is controlled by photoperiod, and mRNA expression is characterized by day and night rhythm. The expression is inhibited in short days; while in long days, Ghd7 inhibits Hd3a and Ehd1 expression, and this mechanism of flowering control may be unique to rice. As this gene significantly prolong the plant growth period, the panicles have a longer time for development which results in higher grain number and spikes become larger. Moreover, this gene also affects flowering time, plant height and other traits, showing obvious pleiotropic feature. Under the long-day conditions, *Ghd7* over-expression can delay heading, increase plant height and grain number per panicle. The natural mutants with weakened functions can be grown in temperate or even colder regions. Therefore, *Ghd7* plays an important role in yield potential and adaptability on global scale of rice [65]. The subsequent researches discover that single phyA or combination of phyB and phyC can induce accumulation of Ghd7 mRNA, and phyB can independently reduce Ghd7m RNA to a certain extent. Furthermore, phyB and phyA can separately affect the activity of Ghd7 and Hd1 [68]. However, Shibaya et al. [69] indicated genetic interaction between *Hd2* and H*d16* or *Hd2* and *Ghd7*. *Hd2* and the related genetic interaction play an important role in controlling heading

Researches prove that most of high-yield rice varieties contain *Ghd7* gene. Wild-type *Ghd7* gene can delay heading, and improve plant height and grain number per panicle. Comparative sequencing of *Ghd7* alleles among 19 rice cultivers over Asia found that five genotypes had the proteins encoded by various *Ghd7* alleles. Minghui 63 is the representative of the first genotype, and alleles for this genotype have stronger functions. The varieties with this genotype are mainly distributed in rice production areas in the south of China as well as tropical and sub-tropical region, with longer growth time. Cultivar Nipponbare represents the second genotype, and alleles for this genotype have weakened functions. The varieties with this genotype are mainly distributed in North China and the same latitude regions. Hejiang 19 and Mudanjiang 8 are the representatives of the third genotype, and the alleles totally lose the functions due to terminator mutation. The varieties with this genotype are mainly distributed in Heilongjiang province in the north of China, and rice growth period is adaptable to shorter-summer condition. The fourth genotype is only discovered in Teqing varieties, and this genotype also has stronger functions. The distributed geographical regions are similar to the first one. The last genotype is deficiency of *Ghd7*, and the varieties with this genotype are mainly distributed in China's double cropping rice areas. Based on the above-mentioned researches, it is known that different genotypes of *Ghd7* are associated with rice variety distribution. These genes are not only involved in development regulation, but also are related to plant geographical distribution. The successful cloning of rice *Ghd7* gene greatly deepens the understanding of genetic and molecular basis of complex quantitative traits. This also constitutes a good illustration of pleiotropism rarely discovered in traditional genetics. The isolation of *Ghd7* gene demonstrates that complex quantitative traits such as yield can be improved through biotechnology like qualitative traits. Relevant information of this gene can be directly used in the mining of genes that are significant to improving yield and ecological adaptability to realize genetic improvement. *Ghd7* gene, which has been isolated with con‐ firmed function in yield increase, can greatly shorten the screening time of high-yielding rice

variety. This is a key step taken towards improving crop yield in the global range.

date under the long-day conditions.

166 Rice - Germplasm, Genetics and Improvement

The effect of *DTH8 (Ghd8)* is similar to that of *Ghd7* in delaying rice heading and improving yield-related traits. Other functions include enhancing plant height, grain number per panicle, total grain number per panicle and yield [70,71]. The pleiotropism is also observed in prelimi‐ nary mapping. In the Zhenshan 97/HR5 RIL population, HR5 allele delays heading, and improves plant height and total grain number per spike [72]. Through construction of near isogenic lines containing target genes, *Ghd8* is isolated and cloned by virtue of preliminary mapping, comparative sequencing and genetic transformation. It encodes a HAP3 subunit containing CCAAT-box-binding transcription factor [70, 71]. HAP complex consists of three subunits, HAP2/NF-YA/CBF-B, HAP3/NF-YB/CBF-A and HAP5/NF-YC/CBF-C. Moreover, HAP complex can bind to CCAAT sequence in the promoter, and regulate expression of target genes. OsHAP3H is a HAP3 subunit of HAP complex [73]*. DTH8/Ghd8/LHD1* is proved to be the HAP3H subunit encoding "CCAAT box binding protein" of the transcription factor. It can simultaneously regulate rice yield, plant height and heading date [70, 71, 74]. It is reported that *DTH8* can be expressed in the multiple tissues, down-regulate the transcription of *Ehd1* and *Hd3a* under long-day conditions, and is independent of *Ghd7* and *Hd1.* Under long-day condition, the introduction of *DTH8* allele in Asominori can obviously prolong heading date, and increase plant height and grain number per panicle of *CSSL61 [72]. Ghd8* can delay rice flowering by regulating Ehd1, RFT1 and Hd3a under long-day condition, but promote rice flowering in short-day condition. Also Ghd 8 can up-regulate the expression of MOC1 gene controlling rice tillering and lateral branches to increase tillering number, primary and secondary branch numbers [71]. However, some variations in LHD1 (Ghd8) coding area are related to late panicles. LHD1 can down-regulate the expression of some flowering transcrip‐ tion activators such as Ehd1, Hd3a and RFT1 in long-day condition, but not inhibit these genes in short-day condition. This indicates that LHD1 can delay flowering through inhibiting their expression in long-day condition [74]. By main variation sites and character association analysis of Ghd8 and also cluster analysis of monoploidy of different protein sequences, nearisogenic lines of different allelotypes are constructed to obtain four favorable Ghd8 allelotypes. Among them, Ghd8-9311 and Ghd8-ruf allelotypes can increase yield but not delay flowering, so these alleles are suitable for varieties grown in areas with good sunlight and temperature. Ghd8-MH63 and Ghd8-Nip allelotypes are photostable, so they are applicable to increase yield of varieties in short-day areas.

#### **5.3. APO1**

APO1 is also a pleiotropic gene, and can simultaneously affect vegetative growth and repro‐ ductive development. During vegetative growth stage, apo1 mutant can promote blade growth and blade number more than wild type. During reproductive growth stage, apo1 mutant can lead to smaller panicle, and smaller primary branch and spikelet numbers than wild type. apo1 mutant transforms flower stamens into lodicules, causing carpel to abnormally stretch and carpellody in glumes. Thus, lodicule number increases and stamen reduces. APO1 encodes an F-box protein, which is mainly expressed in apical meristem and lateral organ primordium. APO1 plays an important role in regulation of meristem destiny, and positively regulates the primary branch and spikelet numbers. Spikelet meristems of apo1 mutant form early and the formation period of lodicules and carpel is also prolonged. This inferred infer that APO1 participates in time regulation of meristem attributes. APO1 positively regulates the expres‐ sion of C-class gene related to homoetic transformation, and affects flower organ attributes [75]. In 2010, Japanese scholars made use of chromosomal segment substitution lines (CSSL) to identify a QTL effectively controlling stalk thickness, which is named as SCM2. SCM2 is equivalent to previously reported APO1 gene controlling panicle structure through map-based cloning. The near isogenic lines containing SCM2 have phenotypes of reinforced stalk strength and increased spike number, showing that this gene has pleiotropic effects. Although SCM2 is the functionally acquired mutant of APO1, it has no negative effects associated with overexpression of APO1 mutant as have been previously, including decreasing spike number and abnormal spikelets. SCM2 can prevent yield reduction from lodging due to application of chemical fertilizer in high-plant varieties [76].

## **6. Perspective**

Discovery of semi-dwarf gene sd1 from DGWG and Aizizhan triggers a revolution in global rice production. Cytoplasmic male sterile gene found in wild rice enables the rice heterosis to be fully shown and realized through three line system, followed by another leap in rice yield. The application of excellent germplasm resources and their genes plays a key role in enhancing rice yield. With the development of new technology, germplasm will find more applications. In face of new requirements for current world's production development, the following respects should be considered in the researches on rice yield increase: (1) select and breed high yield, high quality new rice variety with endurable storage and stress tolerance; (2) select and breed new rice variety for special purposes; (3) trans-breed a series of cytoplasmic male sterile lines and restorer lines with stable, high quality and combining fertility; (4) clone and isolate genes controlling important agronomic characters. In general, core collection, backbone parents, excellent medium material and excellent variety are mainly considered as the basis to systematically create and construct saturated mutant library and genotype-phenotype database. Gene cloning, association analysis and gene regulatory network analysis should be adopted to study the molecular mechanism of good character formation, and explore allelism difference and genetic effect of relevant characters such as high yield, high quality and stress resistance of super rice. High-throughput genotyping, favorable gene pyramiding and improvement, conventional and molecular breeding are proper techniques to breed the new lines and variety of super rice with advantages in yield, quality and stress resistance. To breed the above-mentioned varieties, the key is to further develop and utilize new gene resources based on the existing varieties, including gene resources contained in indica and japonica subspecies, *Oryza* species and mutant species. At the same time, we need to improve breeding method and identification technology, including combined adoption of transgene technology, composite hybridization and rice molecular marker-assisted selection. In particular, the conventional breeding method should be combined with biotechnology, which will be the important way to breed super rice of super-high yield.

## **Author details**

primary branch and spikelet numbers. Spikelet meristems of apo1 mutant form early and the formation period of lodicules and carpel is also prolonged. This inferred infer that APO1 participates in time regulation of meristem attributes. APO1 positively regulates the expres‐ sion of C-class gene related to homoetic transformation, and affects flower organ attributes [75]. In 2010, Japanese scholars made use of chromosomal segment substitution lines (CSSL) to identify a QTL effectively controlling stalk thickness, which is named as SCM2. SCM2 is equivalent to previously reported APO1 gene controlling panicle structure through map-based cloning. The near isogenic lines containing SCM2 have phenotypes of reinforced stalk strength and increased spike number, showing that this gene has pleiotropic effects. Although SCM2 is the functionally acquired mutant of APO1, it has no negative effects associated with overexpression of APO1 mutant as have been previously, including decreasing spike number and abnormal spikelets. SCM2 can prevent yield reduction from lodging due to application of

Discovery of semi-dwarf gene sd1 from DGWG and Aizizhan triggers a revolution in global rice production. Cytoplasmic male sterile gene found in wild rice enables the rice heterosis to be fully shown and realized through three line system, followed by another leap in rice yield. The application of excellent germplasm resources and their genes plays a key role in enhancing rice yield. With the development of new technology, germplasm will find more applications. In face of new requirements for current world's production development, the following respects should be considered in the researches on rice yield increase: (1) select and breed high yield, high quality new rice variety with endurable storage and stress tolerance; (2) select and breed new rice variety for special purposes; (3) trans-breed a series of cytoplasmic male sterile lines and restorer lines with stable, high quality and combining fertility; (4) clone and isolate genes controlling important agronomic characters. In general, core collection, backbone parents, excellent medium material and excellent variety are mainly considered as the basis to systematically create and construct saturated mutant library and genotype-phenotype database. Gene cloning, association analysis and gene regulatory network analysis should be adopted to study the molecular mechanism of good character formation, and explore allelism difference and genetic effect of relevant characters such as high yield, high quality and stress resistance of super rice. High-throughput genotyping, favorable gene pyramiding and improvement, conventional and molecular breeding are proper techniques to breed the new lines and variety of super rice with advantages in yield, quality and stress resistance. To breed the above-mentioned varieties, the key is to further develop and utilize new gene resources based on the existing varieties, including gene resources contained in indica and japonica subspecies, *Oryza* species and mutant species. At the same time, we need to improve breeding method and identification technology, including combined adoption of transgene technology, composite hybridization and rice molecular marker-assisted selection. In particular, the conventional breeding method should be combined with biotechnology, which will be the

chemical fertilizer in high-plant varieties [76].

168 Rice - Germplasm, Genetics and Improvement

important way to breed super rice of super-high yield.

**6. Perspective**

Dawei Xue1 , Qian Qian2 and Sheng Teng3\*

\*Address all correspondence to: steng@sibs.ac.cn

1 College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R.China

2 State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, P.R.China

3 Shanghai Institute of Plant Physiology and Ecology, Shanghai Institute for Biological Sci‐ ences, The Chinese Academy of Sciences, Shanghai, P.R.China

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## **Functional Characterization of Genes/QTLs for Increasing Rice Yield Potential**

Jie-Zheng Ying, Yu-Yu Chen and Hong-Wei Zhang

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56820

## **1. Introduction**

Rice (*Oryza sativa* L.) as the monocot model plant and an important food crop is cultivated worldwide. Due to the rapid growth of the world's population, rice yield is urgently required to increase to meet world food demand. In the last century, rice yield experienced rapid growth twice in China, which is mainly attributed to the exploitations of *semi-dwarf 1*(*sd1*) gene and heterosis of F1 hybrid. Before the green revolution, rice varieties were tall and had a low harvest index. Introgression of *sd1* into the varieties significantly reduced the plant height and increased the harvest index, which resulted in a dramatic increase of rice productivity [1]. Heterosis breeding has been widely used to improve rice yield potential. Hybrid rice varieties usually have a yield advantage of 10-20% over the conventional inbred varieties, thus cover more than half of the total rice area in China at present [2, 3]. However, rice yield per unit area has not been much elevated and the arable land for rice cultivation has kept decreasing during the past two decades. New genetic improvement strategies are urgently required to break the bottleneck of yield potential of current varieties, which largely rely on the elucidation and exploitation of genetic and molecular basis for rice yield traits [4].

Rice yield traits are complex agronomic traits governed by multiple genes called as quantita‐ tive traits loci (QTLs), which usually show a continuous phenotypic distribution in a segre‐ gating population derived from a cross of a pair of inbred lines. Most QTLs for yield traits show small genetic effect and are difficult to be identified. These minor QTLs play a vital role in regulating yield trait and are widely utilized in commercial rice varieties, so that finemapping and map-based cloning of these QTLs will be beneficial for breeding. Number of panicles per plant, number of grains per panicle, and grain weight are three component traits which are determined by tiller, panicle and grain development. Dissecting the genetic basis of these traits by QTL mapping can facilitate breeding high yield varieties. However, it is rather

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

difficult to isolate these QTLs because each contributes little effect to yield traits, and the effect is strongly affected by the environment. In recent years, tremendous progress has been attained and many QTLs for rice yield traits have been isolated and functionally analyzed in detail, which provides new sights into the molecular mechanisms of the formation of rice yield traits. Meanwhile, mutant analysis has also functionally characterized many genes involved in yield traits because of the availability of rice genome and rice mutant collections. These studies greatly strengthen our understanding of regulatory mechanisms of these traits. In this chapter, we summarize the recent progress in the genetic and molecular mechanisms underlying rice yield traits and illustrate a strategy to develop varieties with higher yield potential.

## **2. Identification and validation of QTLs for rice yield traits**

## **2.1. Identification of QTLs**

QTL mapping of a target trait is defined as the chromosomal location and genetic characteri‐ zation of QTLs for the trait through the association between genetic markers and phenotypic variations. To facilitate this mapping, development of mapping population, construction of linkage map and phenotypic evaluation are essential for QTL analysis.

Typically, mapping population includes F2 plants, doubled haploid lines (DHLs) and recombi‐ nant inbred lines (RILs). F2 population that carries the complete genetic information from the parents can be easily developed, but its phenotypic evaluation cannot be replicated [5]. Due to the inherent homozygosity in the lines, both DHL and RIL populations can be planted repeatedly in different planting seasons and environment conditions as many times as necessary. DHL population is limitedly used in QTL mapping due to the difficulties in the plant regeneration from cultured anthers, especially for *indica* rice varieties [6, 7]. RIL popu‐ lation is widely used in QTL mapping although it is time-consuming and labor-intensive to prepare the population. Many RIL populations have been developed from inter-subspecies crosses [8, 9], intra-subspecies crosses [10-12], or crosses between commercial cultivars and wild rice [13].

Linkage map is composed of many linkage groups according to different chromosomes, which are constructed by genotyping using genome-wide polymorphic markers. DNA based molecular markers, such as restriction fragment length polymorphism (RFLP), simple sequence repeat (SSR), cleaved amplified polymorphic sequence (CAPS) and single nucleotide polymorphism (SNP), are widely applied in the construction of linkage map. Based on the complete genome-wide sequence of rice, it becomes easier to design genome-wide polymor‐ phic markers and construct high density molecular linkage map [14].

Yield trait conditioned by QTLs usually varies continuously in a mapping population. Phenotypic values are difficult to be accurately measured due to environmental influences, especially for F2 population without replication. The precision of phenotypic data greatly affects the resolution of QTL mapping [15].

Thousands of QTLs for rice yield traits have been detected and are distributed throughout the whole genome while many of them are co-localized (http://www.gramene.org). We use one of our studies to illustrate the general process for the classical characteristics of QTLs (Figure 1). A RIL mapping population is developed from an *indica*-*indica* cross between Zhenshan 97B and Milyang 46 using single seed descent method. QTL analysis shows that each yield trait is controlled by several QTLs. These QTLs are dispersedly distributed on chromosomes, and function on yield productivity not only by their own effects, but also by within-locus and interlocus interactions [11].

difficult to isolate these QTLs because each contributes little effect to yield traits, and the effect is strongly affected by the environment. In recent years, tremendous progress has been attained and many QTLs for rice yield traits have been isolated and functionally analyzed in detail, which provides new sights into the molecular mechanisms of the formation of rice yield traits. Meanwhile, mutant analysis has also functionally characterized many genes involved in yield traits because of the availability of rice genome and rice mutant collections. These studies greatly strengthen our understanding of regulatory mechanisms of these traits. In this chapter, we summarize the recent progress in the genetic and molecular mechanisms underlying rice

yield traits and illustrate a strategy to develop varieties with higher yield potential.

QTL mapping of a target trait is defined as the chromosomal location and genetic characteri‐ zation of QTLs for the trait through the association between genetic markers and phenotypic variations. To facilitate this mapping, development of mapping population, construction of

Typically, mapping population includes F2 plants, doubled haploid lines (DHLs) and recombi‐ nant inbred lines (RILs). F2 population that carries the complete genetic information from the parents can be easily developed, but its phenotypic evaluation cannot be replicated [5]. Due to the inherent homozygosity in the lines, both DHL and RIL populations can be planted repeatedly in different planting seasons and environment conditions as many times as necessary. DHL population is limitedly used in QTL mapping due to the difficulties in the plant regeneration from cultured anthers, especially for *indica* rice varieties [6, 7]. RIL popu‐ lation is widely used in QTL mapping although it is time-consuming and labor-intensive to prepare the population. Many RIL populations have been developed from inter-subspecies crosses [8, 9], intra-subspecies crosses [10-12], or crosses between commercial cultivars and

Linkage map is composed of many linkage groups according to different chromosomes, which are constructed by genotyping using genome-wide polymorphic markers. DNA based molecular markers, such as restriction fragment length polymorphism (RFLP), simple sequence repeat (SSR), cleaved amplified polymorphic sequence (CAPS) and single nucleotide polymorphism (SNP), are widely applied in the construction of linkage map. Based on the complete genome-wide sequence of rice, it becomes easier to design genome-wide polymor‐

Yield trait conditioned by QTLs usually varies continuously in a mapping population. Phenotypic values are difficult to be accurately measured due to environmental influences, especially for F2 population without replication. The precision of phenotypic data greatly

**2. Identification and validation of QTLs for rice yield traits**

linkage map and phenotypic evaluation are essential for QTL analysis.

phic markers and construct high density molecular linkage map [14].

affects the resolution of QTL mapping [15].

**2.1. Identification of QTLs**

178 Rice - Germplasm, Genetics and Improvement

wild rice [13].

RIL, recombinant inbred line; MAS, marker-assisted selection; RHL, residual heterozygous line; NIL, near isogenic line; QTL, quantitative trait locus

**Figure 1.** Flowchart for developing NILs through screening RHLs in a Zhenshan 97B/Miyang 46 RIL population.

#### **2.2. Validation of QTLs**

Primary mapping cannot delimit an individual QTL in a precise location, so that further experiments are necessary to validate the biological function of target QTLs individually. Development of near isogenic lines (NILs) is an efficient strategy for QTL validation. NILs contain the segregated target QTL region in a homogeneous genetic background. In general, NILs are produced by consecutive backcrosses with a recurrent parent aided by molecular marker assisted selection (MAS). Only the plants carrying the target QTL in the recurrent parent background will be selected to further develop NILs. Many QTLs controlling rice yield traits have been validated by this classical method. However, successful backcross combining with MAS is laborious and time-consuming. During the process of developing Zhenshan 97B / Miyang 46 RIL population, we form a new method for developing NILs by screening residual heterozygous lines (RHLs). The RHLs should contain a heterozygous chromosomal segment at the target QTL region in a nearly homozygous genetic background [2] (Figure 1). Following MAS in a high density marker linkage map, a series of RHLs with overlapping segregated segments for the target QTL are selected from F7 RILs. This method has proven to be efficient, and several yield trait QTLs, such as *qGY6* and *qGL7-2*, have been successfully fine-mapped and validated [2, 16].

Introgression lines (ILs) and chromosome segment substitution lines (CSSLs), which are developed by backcrossing repeatedly with the recurrent parent, can also be used for QTL validation, fine-mapping and breeding superior rice varieties[17,18].

## **3. QTLs/genes for rice yield traits**

#### **3.1. QTLs/genes for tillering**

Rice tillers are mainly composed of primary, secondary and tertiary tillers, which are shoot branches arising from the unelongated basal internodes. Tillering starts with the appearance of the fourth leaf from the main culm. Usually, the duration of tillering will last about 25-30 days. The number of panicles and yield potential are determined by panicle-bearing tillers, and grain yield are mainly contributed by primary and secondary tillers. Therefore, tiller number is considered a key component in determining rice yield. Some key genetic factors responsible for rice tillering have been molecularly characterized (Figure 2 and Table 1).

Rice tillering undergoes two major processes, the formation and outgrowth of tiller bud. Isolation and characterization of *MONOCULM* (*MOC1*) provide a new sight for the formation of tiller bud. The *moc1* mutant phenotypically exhibits only one main culm without any tillers due to the deficiency to form axillary bud. *MOC1* encodes a member of *GAI*, *RGA* and *SCR* (GRAS) family nuclear proteins to function on the formation of axillary buds [19].

Functional Characterization of Genes/QTLs for Increasing Rice Yield Potential http://dx.doi.org/10.5772/56820 181

**2.2. Validation of QTLs**

180 Rice - Germplasm, Genetics and Improvement

and validated [2, 16].

**3. QTLs/genes for rice yield traits**

**3.1. QTLs/genes for tillering**

Primary mapping cannot delimit an individual QTL in a precise location, so that further experiments are necessary to validate the biological function of target QTLs individually. Development of near isogenic lines (NILs) is an efficient strategy for QTL validation. NILs contain the segregated target QTL region in a homogeneous genetic background. In general, NILs are produced by consecutive backcrosses with a recurrent parent aided by molecular marker assisted selection (MAS). Only the plants carrying the target QTL in the recurrent parent background will be selected to further develop NILs. Many QTLs controlling rice yield traits have been validated by this classical method. However, successful backcross combining with MAS is laborious and time-consuming. During the process of developing Zhenshan 97B / Miyang 46 RIL population, we form a new method for developing NILs by screening residual heterozygous lines (RHLs). The RHLs should contain a heterozygous chromosomal segment at the target QTL region in a nearly homozygous genetic background [2] (Figure 1). Following MAS in a high density marker linkage map, a series of RHLs with overlapping segregated segments for the target QTL are selected from F7 RILs. This method has proven to be efficient, and several yield trait QTLs, such as *qGY6* and *qGL7-2*, have been successfully fine-mapped

Introgression lines (ILs) and chromosome segment substitution lines (CSSLs), which are developed by backcrossing repeatedly with the recurrent parent, can also be used for QTL

Rice tillers are mainly composed of primary, secondary and tertiary tillers, which are shoot branches arising from the unelongated basal internodes. Tillering starts with the appearance of the fourth leaf from the main culm. Usually, the duration of tillering will last about 25-30 days. The number of panicles and yield potential are determined by panicle-bearing tillers, and grain yield are mainly contributed by primary and secondary tillers. Therefore, tiller number is considered a key component in determining rice yield. Some key genetic factors responsible for rice tillering have been molecularly characterized (Figure 2 and Table 1).

Rice tillering undergoes two major processes, the formation and outgrowth of tiller bud. Isolation and characterization of *MONOCULM* (*MOC1*) provide a new sight for the formation of tiller bud. The *moc1* mutant phenotypically exhibits only one main culm without any tillers due to the deficiency to form axillary bud. *MOC1* encodes a member of *GAI*, *RGA* and *SCR*

(GRAS) family nuclear proteins to function on the formation of axillary buds [19].

validation, fine-mapping and breeding superior rice varieties[17,18].

**Figure 2.** Distribution of the cloned genes/QTLs for rice yield traits in a physical map based on Nipponbare genome sequence; Red ellipses indicate the centromere region according to the data from the International Rice Genome Se‐ quencing Project (http://rgp.dna.affrc.go.jp/IRGSP/); the color horizontal lines represent the locations of each gene/QTL on the chromosome for each trait; blue for number of panicles per plant (NPP); crimson for number of grains per panicle (NGP); green for grain weight (GW).



NPP, number of panicles per plant; NGP, number of grains per panicle; GW, grain weight; GS, grain size; GF, grain filling; M/QTL, mutant/QTL; Hom\*, Homolog.

**Table 1.** Genes/QTLs for rice yield traits are reviewed in this chapter

**Traits Gene/QTL MSU-ID Encoded protein M/QTL Refs** NPP *D27* LOC\_Os11g37660 Iron-containing protein M [24] NPP *D10* LOC\_Os01g54270 Carotenoid cleavage dioxygenase 8 M [23]

NPP *D17*/*HTD1* LOC\_Os04g46470 carotenoid cleavage dioxygenase M [22] NPP *D3* LOC\_Os06g06050 F-box leucine-trich repeat protein M [25] NPP *MOC1* LOC\_Os06g40780 GRAS family nuclear protein M [19]

NPP, NGP *PROG1* LOC\_Os07g05900 Zinc-finger nuclear transcription factor QTL [29, 30]

NGP *Gn1a* LOC\_Os01g10110 Cytokinin oxidase/dehydrogenase QTL [39] NGP *LOG* LOC\_Os01g40630 Cytokinin-activating enzyme M [40] NGP, NPP *LAX1* LOC\_Os01g61480 A bHLH transcription factor M [32] NGP *LP* LOC\_Os02g15950 Kelch repeat-containing F-box protein M [41] NGP, NPP *APO2* LOC\_Os04g51000 Plant-specific transcription factor M [35] NGP *APO1* LOC\_Os06g45460 F-box protein M [34] NGP, NPP *DEP2* LOC\_Os07g42410 Plant-specific protein M [38]

factor

family

OsHAP3 subunit of a CCAAT-box

binding protein QTL [63, 64]

hydrolase QTL [45]

NGP *Hd1* LOC\_Os06g16370 Protein with a zinc finger domain QTL [67] NGP *Ghd7* LOC\_Os07g15770 CCT domain protein QTL [62]

NGP *EHD1* LOC\_Os10g32600 B-type response regulator QTL [65, 66] NGP *DEP1* LOC\_Os09g26999 PEBP-like domain protein QTL [37]

GW, GS *GW2* LOC\_Os02g14720 RING-type E3 ubiquitin ligase QTL [53] GW, GS *PGL2* LOC\_Os02g51320 Atypical bHLH protein Hom\* [49] GW, GS *PGL1* LOC\_Os03g07510 Atypical bHLH protein Hom\* [48] GW, GS *GS3* LOC\_Os03g29380 Trans-membrane protein QTL [42] GW, GS *BRD1* LOC\_Os03g40540 Brassinosteroid-6-oxidase M [51]

protein

M [26]

M [33]

M [36]

like 14 QTL [27, 28]

kinase QTL [31]

NPP *D14* LOC\_Os03g10620 Alpha/beta-fold hydrolase superfamily

NPP, NGP *OsSPL14* LOC\_Os08g39890 Souamosa promoter binding protein-

NPP, NGP *qGY2-1* LOC\_Os02g05980 Leucine-rich repeat receptor-like

NGP, NPP *FZP* LOC\_Os07g47330 Ethylene-responsive element-binding

NGP *SP1* LOC\_Os11g12740 Transporter of the peptide transporter

GW, GS *TGW6* LOC\_Os06g41850 Iindole-3-acetic acid (IAA)-glucose

NGP *Ghd8/DTH8* LOC\_Os08g07740

182 Rice - Germplasm, Genetics and Improvement

Phytohormone pathways play a crucial role in controlling the outgrowth of tiller bud from leaf sheath. Plant hormones interact to regulate axillary bud outgrowth. It is well known that auxin maintains shoot apical dominance and inhibit axillary bud outgrowth, whereas cytoki‐ nins promote branches development [4]. Strigolactones, as a new kind of terpenoid plant hormones, might act as the downstream of auxin to inhibit axillary bud outgrowth. Several genes involved in the synthesis and signaling pathway of strigolactones are isolated and functionally characterized through analyzing a serious of tillering dwarf mutants [20, 21]. *DWARF17* (*D17*)/ *HIGH-TILLERING DWARF1* (*HTD1*), *DWARF10* (*D10*) and *DWARF27* (*D27*) are involved in the biosynthesis of strgolactones, while *DWARF3* (*D3*) and *DWARF14* (*D14*) act in the signaling pathway in rice [22-26]. Their loss-of-function causes similar phenotype of enhanced shoot branches accompanying with reduced plant height. Although the relationship among phytohormones in regulating axillary bud outgrowth is complex and requires more proof to substantiate, recent advances in the regulatory mechanisms involved in phytohor‐ mones help further understand rice tillering.

Tiller number and angle are major determinants of rice plant architecture. New plant type known as ideal plant architecture (IPA) is proposed with reduced tiller number with almost no unproductive tillers to improve cultivar yield potential. A major QTL for IPA encoding SOUAMOSA PROMOTER BINDING PROTEIN-LIKE 14 (OsSPL14) has been cloned. *OsSPL14* is regulated by microRNA OsmiR156, and increasing level of the *OsSPL14* transcript and protein results in an IPA phenotype and higher grain productivity [27, 28]. *PROSTRATE* *GROWTH 1*(*PROG1*) controlling wide tiller angle and great number of tillers in wild rice species encodes a zinc-finger nuclear transcription factor and is highly expressed in the axillary meristems. An amino acid substitution caused by a SNP in *PROG1* leads to the transition from prostate growth of the wild rice *O. rufipogon* to erect growth of the domesticated rice *O. sativa* [29, 30]. In addition, *qGY2-1*, a major QTL for grain yield per plant, encodes leucine-rich repeat receptor-like kinase (LRK), and over-express of *LRK1* causes more tillers and greater grain yield than the wild type [31].

## **3.2. QTLs/genes regulating number of grains per panicle**

Number of grains per panicle is an important agronomic trait for grain productivity, which is determined by the panicle formation. During the past two decades, many genes/QTLs controlling panicle development have been characterized (Figure 2 and Table 1). Rice panicle developed from a terminal inflorescence at the top of a stem contains panicle axis, primary and secondary branches, pedicel and spikelets. Pedicels arise from the primary and secondary branches and bear spikelets on the top. Panicles and the bearing spikelets on them directly determine the rice yield.

Inflorescence development determines the formation of rice panicle. Inflorescence meristem generates primary and secondary branches meristems, and subsequent spikelet meristems. Several genes involved in the formation of inflorescence branch and spikelet meristems are identified through mutant analysis. *LAX1* encodes a basic helix-loop-helix (bHLH) transcrip‐ tion factor and is required for the initiation/maintenance of inflorescence branch meristems. The *lax1* mutant produces severely reduced primary and secondary branches and spikelets [32]. *FRIZZY PANICLE* (*FZP*), which encodes an ethylene-responsive element-binding factor (ERF), is responsible for the establishment of floral meristem identity through suppressing the formation of axillary meristems within the spikelet meristem. The *fzp* mutant is deficient in spikelet development and exhibits sequential rounds of branching instead of the formation of florets [33]. *ABERRANT PANICLE ORGNIZATION* (*APO1*), which encodes an F-box protein, functions in preventing the precocious transition from branch meristems to spikelet meristems. The *apo1* loss-of-function mutants produce small panicles with greatly reduced branches and spikelets [34]. In addition, *APO2* interacts with *APO1* to regulate panicle development [35].

Rice panicle size is largely determined by the number and length of primary and secondary branches. *SHORT PANICLE 1* (*SP1*) encodes a putative transporter of the peptide transporter family and participates in the elongation of rice panicle. The mutation of *SP1* causes a shortpanicle phenotype due to the defect in the elongation of inflorescence branches in the *sp1* mutant [36]. *OsSPL14* not only controls tillering, but also promotes panicle branching and produces larger panicles with more spikelets [27, 28].

Rice panicle architecture is mainly determined by the arrangement of primary and secondary branches and grain density. Erect panicle is an important agronomic trait closely related to grain yield. *DENSE AND ERECT PANICLE1* (*DEP1*) encodes a phosphatidylethanolaminebinding (PEBP) protein-like domain protein and controls panicle branches, grain density and erect panicle. The gain-of-function mutation in *DEP1* resulted in the phenotype of increased primary and secondary branches and number of grains per panicle, and decreased panicle length [37]. *DEP2*, which encodes a plant-specific protein and is strongly expressed in young panicles, is responsible for panicle outgrowth and elongation. The *dep2* mutant displays a dense and erect panicle phenotype [38].

Cytokinins regulate number of spikelets per panicle. A major QTL, *GRAIN NUMBER1* (*Gn1a*), which encodes cytokinin oxidase/dehydrogenase (OsCKX2), controls number of grains per panicle. Repression of *OsCKX2* leads to cytokinin accumulation, which finally results in the increase of number of grains per panicle and grain yield [39]. *LONELY GUY* (*LOG*) is respon‐ sible for shoot meristem activity and encodes cytokinin-activating enzyme for the conversion from inactive cytokinin nucleotides to the free-base forms. Loss of function of *LOG* results in producing small panicles with reduced panicle branches and grains in the *log* mutant [40]. *LARGER PANICLE* (*LP*) encoding a Kelch repeat-containing F-box protein regulates panicle architecture. Larger panicle with more primary branches and grains is observed in the *LP* lossof-function mutants. *LP* could regulate panicle architecture by modulating cytokinin level due to the significant down-regulation of *OsCKX2* expression level in the mutants [41]. Further‐ more, *DEP1* might control the number of panicle branches through cytokinin pathway because expression level of *OsCKX2* is clearly down-regulated in NIL-dep1 plant [37]. These studies imply that the phytohormone cytokinin plays a vital role in regulating panicle development.

## **3.3. QTLs/genes controlling grain weight**

*GROWTH 1*(*PROG1*) controlling wide tiller angle and great number of tillers in wild rice species encodes a zinc-finger nuclear transcription factor and is highly expressed in the axillary meristems. An amino acid substitution caused by a SNP in *PROG1* leads to the transition from prostate growth of the wild rice *O. rufipogon* to erect growth of the domesticated rice *O. sativa* [29, 30]. In addition, *qGY2-1*, a major QTL for grain yield per plant, encodes leucine-rich repeat receptor-like kinase (LRK), and over-express of *LRK1* causes more tillers and greater

Number of grains per panicle is an important agronomic trait for grain productivity, which is determined by the panicle formation. During the past two decades, many genes/QTLs controlling panicle development have been characterized (Figure 2 and Table 1). Rice panicle developed from a terminal inflorescence at the top of a stem contains panicle axis, primary and secondary branches, pedicel and spikelets. Pedicels arise from the primary and secondary branches and bear spikelets on the top. Panicles and the bearing spikelets on them directly

Inflorescence development determines the formation of rice panicle. Inflorescence meristem generates primary and secondary branches meristems, and subsequent spikelet meristems. Several genes involved in the formation of inflorescence branch and spikelet meristems are identified through mutant analysis. *LAX1* encodes a basic helix-loop-helix (bHLH) transcrip‐ tion factor and is required for the initiation/maintenance of inflorescence branch meristems. The *lax1* mutant produces severely reduced primary and secondary branches and spikelets [32]. *FRIZZY PANICLE* (*FZP*), which encodes an ethylene-responsive element-binding factor (ERF), is responsible for the establishment of floral meristem identity through suppressing the formation of axillary meristems within the spikelet meristem. The *fzp* mutant is deficient in spikelet development and exhibits sequential rounds of branching instead of the formation of florets [33]. *ABERRANT PANICLE ORGNIZATION* (*APO1*), which encodes an F-box protein, functions in preventing the precocious transition from branch meristems to spikelet meristems. The *apo1* loss-of-function mutants produce small panicles with greatly reduced branches and spikelets [34]. In addition, *APO2* interacts with *APO1* to regulate panicle development [35]. Rice panicle size is largely determined by the number and length of primary and secondary branches. *SHORT PANICLE 1* (*SP1*) encodes a putative transporter of the peptide transporter family and participates in the elongation of rice panicle. The mutation of *SP1* causes a shortpanicle phenotype due to the defect in the elongation of inflorescence branches in the *sp1* mutant [36]. *OsSPL14* not only controls tillering, but also promotes panicle branching and

Rice panicle architecture is mainly determined by the arrangement of primary and secondary branches and grain density. Erect panicle is an important agronomic trait closely related to grain yield. *DENSE AND ERECT PANICLE1* (*DEP1*) encodes a phosphatidylethanolaminebinding (PEBP) protein-like domain protein and controls panicle branches, grain density and erect panicle. The gain-of-function mutation in *DEP1* resulted in the phenotype of increased primary and secondary branches and number of grains per panicle, and decreased panicle

grain yield than the wild type [31].

184 Rice - Germplasm, Genetics and Improvement

determine the rice yield.

**3.2. QTLs/genes regulating number of grains per panicle**

produces larger panicles with more spikelets [27, 28].

Rice grain is closely enclosed by a hull which is composed of one palea, lemma, rachilla and two sterile lemmas. A brown rice mainly consists of bran, endosperm and embryo. During the process of grain filling, endosperm cells expand and accumulate a massive amount of nu‐ trients, mainly starch. Rice grain weight is largely determined by the endosperm size. Dozens of genes/QTLs involved in rice grain weight have been isolated and molecularly characterized (Figure 2 and Table 1).

Given that each grain in a rice panicle can be fully filled, grain weight is determined by grain size, which can be measured with grain length, width and thickness. *GS3*, *GL3.1*/*qGL3* and *TGW6*, three major QTLs controlling grain length, are map-based cloned and functionally analyzed [42-45]. *GS3* encodes a putative trans-membrane protein containing four putative domains, a plant-specific organ size regulation (OSR) domain, a trans-membrane domain, a tumor necrosis factor receptor/nerve growth factor receptor (TNFR/NGFR) family cysteinerich domain and a von Willebrand factor type C (VWFC). Loss-of-function or deletion of plantspecific OSR domain results in long grain phenotype [42]. *GL3.1*/*qGL3* encodes Ser/Thr phosphatase of phosphatase kelch family to regulate grain length and yield. *GL3.1/qGL3* directly down-regulates Cyclin-T1;3 to dephosphorylate Cyclin-T1;3 and results in short grain shape [43,44]. *THOUSAND-GRAIN WEIGHT 6* (*TGW6*) controls grain length and weight, which expression is especially high around the endosperm in the pericarp. *TGW6* possesses indole-3-acetic acid (IAA)-glucose hydrolase to decompose IAA-glucose into IAA and glucose, which influences the transition timing from the syncytial to the cellular phase and results in short grain phenotype [45]. *SMALL AND ROUND SEED* (*SRS3*), which encodes a protein of the kinesin 13 subfamily containing a kinesin motor domain and a coiled-coil structure, is strongly expressed in developing organs and regulates rice grain length. The *srs3* mutant shows shorter cells compared to the wild type, which causes the small and round seed phenotype [46]. *Srs5* encodes alpha-tubulin protein and its mutation produces a semidominant mutant exhibiting similar phenotype with the *srs3* mutant [47]. *POSITIVE REGU‐ LATOR OF GRAIN LENGTH 1*(*PGL 1*) and *ANTAGONIST OF PGL1* (*APG*) encode an antagonistic pair of bHLH proteins and interact to regulate rice grain length [48]. *PGL 1* and *PGL 2* redundantly suppress the function of *APG* to form elongated grains [49]. In addition, brassinosteroid (BR) pathway affects rice grain size. A series of mutants related to the synthesis and signaling pathway of BR such as *d61*, *brd1*and *short grain1* (*sg1*) display shorter grain phenotype than their wild types [50-52].

Four QTLs conditioning grain width, *GW2*, *qSW5/GW5*, *GS5* and *GW8*, have been isolated and characterized. *GW2* encodes a RING-type protein with E3 ubiquitin ligase activity to function in the protein degradation through the ubiquitin-proteasome pathway. *GW2 E3* ligase negatively regulates cell division and the mutant allele of *GW2* promotes spikelet hull cell division to result in an increase of grain width and weight [53]. *GW5* and *qSW5* are the same QTL on chromosome 5 in fact, identified by two research groups separately [54, 55]. *qSW5/GW5* encodes a novel nuclear protein, physically interacting with polyubiquitin and acting in the ubiquitin-proteasome pathway to regulate cell division. *qSW5/GW5* is also a negative regulator for grain width and its mutant allele causes an increase of grain width [54]. *GS5* encodes a putative serine carboxypeptidase and positively regulates grain width. Over expression of *GS5* promotes cell division and results in increased grain width [56]. *GW8*, synonym with *OsSPL16*, encodes a positive regulator of cell proliferation and conditioning grain width and yield. Enhanced expression level of *GW8* promotes cell division and grain filling, while its lossof-function forms a slender grain [57].

Grain thickness largely depends on the ability of grain filling. *GRAIN INCOMPLETE FILLING 1*(*GIF1*), which encodes a cell-wall invertase to download sucrose in the ovular and stylar vascular tissues and hydrolyzes sucrose to glucose and fructose for the starch synthesis in the endosperm, is responsible for rice grain-filling and yield. Mutation in the *GIF1* causes slower grain-filling to result in reduced levels of glucose, fructose and sucrose in the *gif1* mutants. Compared to the wild type *GIF1*, the cultivated *GIF1* displays a restricted expression during the filling stage to bring about grain weight increase [58]. Expression level of *GIF1* is substan‐ tially low in the *heading and grain weight* (*hgw*) mutant, which delays the heading date and reduces grain weight. *HGW* encodes a novel plant-specific ubiquitin-associated domain protein and acts through *GIF1* to regulate grain width and weight [59]. *FLOURY ENDO‐ SPERM2* (*FLO2*) encodes a protein harboring a tetratricopeptide repeat motif and preferen‐ tially expresses in developing seeds. *FLO2* positively regulates the expression of genes involved in production of storage starch and proteins in the endosperm, so mutation of *FLO2* causes significantly smaller grain size phenotype [60].

## **4. QTLs/genes for rice yield-related traits**

Plant height and heading date are two important agronomic traits closely related to rice yield. The Green Revolution has made a tremendous contribution to solve the global food crisis, and this mark achievement in rice is caused by the application of *sd1* gene. *SD1* encodes an oxidase enzyme involved in the biosynthesis pathway of gibberellin, which is one of the most impor‐ tant determining factors of plant height. Mutation of *SD1* produces semi-dwarf phenotype and significantly increase rice yield [61].

Genes/QTLs controlling heading date usually prolong the duration of panicle differentiation to produce more spikelets per panicle and enhance grain yield potential. *Ghd7* and *Ghd8*/*DTH8* are key genes regulating heading date to enhance grain yield and plant height under long-day conditions. *Ghd7* encodes a CCT domain protein and pleiotropically controls an array of traits such as number of grains per panicle, plant height and heading date. Increased expression level of *Ghd7* under long-day conditions suppresses the expression of *Hd3a*, which results in delaying heading date and prolongs the duration of panicle differentiation [62]. Similarly, *Ghd8*/*DTH8* encodes the OsHAP3 subunit of a CCAAT-box binding protein and simultane‐ ously regulates grain yield, heading date, and plant height. *Ghd8*/*DTH8* can down-regulate the express level of *Early heading date 1*(*Ehd1*) and *Hd3a* under long-day conditions, which leads to delay heading date and produce 50% more grains per plant [63, 64]. *Ehd1* and *Hd1* can regulate panicle development. Increased expression level of *Hd3a* and *RFT1* reduces number of primary branches per panicle in the line combining *Hd1* and *Ehd1* [65, 66]. In addition, *Hd1* increases number of spikelets per panicle and grain yield by suppressing *Hd3a* expression and delaying heading date [67].

## **5. Future perspectives**

shows shorter cells compared to the wild type, which causes the small and round seed phenotype [46]. *Srs5* encodes alpha-tubulin protein and its mutation produces a semidominant mutant exhibiting similar phenotype with the *srs3* mutant [47]. *POSITIVE REGU‐ LATOR OF GRAIN LENGTH 1*(*PGL 1*) and *ANTAGONIST OF PGL1* (*APG*) encode an antagonistic pair of bHLH proteins and interact to regulate rice grain length [48]. *PGL 1* and *PGL 2* redundantly suppress the function of *APG* to form elongated grains [49]. In addition, brassinosteroid (BR) pathway affects rice grain size. A series of mutants related to the synthesis and signaling pathway of BR such as *d61*, *brd1*and *short grain1* (*sg1*) display shorter grain

Four QTLs conditioning grain width, *GW2*, *qSW5/GW5*, *GS5* and *GW8*, have been isolated and characterized. *GW2* encodes a RING-type protein with E3 ubiquitin ligase activity to function in the protein degradation through the ubiquitin-proteasome pathway. *GW2 E3* ligase negatively regulates cell division and the mutant allele of *GW2* promotes spikelet hull cell division to result in an increase of grain width and weight [53]. *GW5* and *qSW5* are the same QTL on chromosome 5 in fact, identified by two research groups separately [54, 55]. *qSW5/GW5* encodes a novel nuclear protein, physically interacting with polyubiquitin and acting in the ubiquitin-proteasome pathway to regulate cell division. *qSW5/GW5* is also a negative regulator for grain width and its mutant allele causes an increase of grain width [54]. *GS5* encodes a putative serine carboxypeptidase and positively regulates grain width. Over expression of *GS5* promotes cell division and results in increased grain width [56]. *GW8*, synonym with *OsSPL16*, encodes a positive regulator of cell proliferation and conditioning grain width and yield. Enhanced expression level of *GW8* promotes cell division and grain filling, while its loss-

Grain thickness largely depends on the ability of grain filling. *GRAIN INCOMPLETE FILLING 1*(*GIF1*), which encodes a cell-wall invertase to download sucrose in the ovular and stylar vascular tissues and hydrolyzes sucrose to glucose and fructose for the starch synthesis in the endosperm, is responsible for rice grain-filling and yield. Mutation in the *GIF1* causes slower grain-filling to result in reduced levels of glucose, fructose and sucrose in the *gif1* mutants. Compared to the wild type *GIF1*, the cultivated *GIF1* displays a restricted expression during the filling stage to bring about grain weight increase [58]. Expression level of *GIF1* is substan‐ tially low in the *heading and grain weight* (*hgw*) mutant, which delays the heading date and reduces grain weight. *HGW* encodes a novel plant-specific ubiquitin-associated domain protein and acts through *GIF1* to regulate grain width and weight [59]. *FLOURY ENDO‐ SPERM2* (*FLO2*) encodes a protein harboring a tetratricopeptide repeat motif and preferen‐ tially expresses in developing seeds. *FLO2* positively regulates the expression of genes involved in production of storage starch and proteins in the endosperm, so mutation of *FLO2*

Plant height and heading date are two important agronomic traits closely related to rice yield. The Green Revolution has made a tremendous contribution to solve the global food crisis, and

phenotype than their wild types [50-52].

186 Rice - Germplasm, Genetics and Improvement

of-function forms a slender grain [57].

causes significantly smaller grain size phenotype [60].

**4. QTLs/genes for rice yield-related traits**

As mentioned above, cloning and functional characterization of genes/QTLs have greatly strengthened our understanding in the genetic and molecular mechanisms underlying rice yield traits, which has facilitated the breeding efforts for higher yield potential varieties. Pyramiding of favorable genes/QTLs has become an efficient strategy in rice genetic improve‐ ment and is widely adopted by rice breeders. For instance, combination of *Gn1* (*Gn1a+Gn1b*) and *sd1* into Koshihikari has simultaneously improved two traits with increased grain numbers per plant by 23% and reduced plant height by 18% as compared to wild type Koshihikari [39]. The NIL (*GW8*/*gs3*) with a pyramiding of *GW8* and *gs3* produces longer and wider grains than the wild type NIL (*gw8*/*GS3*) [57].

Although tremendous progress has been made in the studies of rice yield trait, there is still a long way to clearly elucidate the molecular mechanisms responsible for the formation of rice yield traits. Almost all the rice yield traits including number of panicles per plant, number of grains per panicle and grain weight exhibit comprehensive and continuous variations in the genetic population or among the commercial varieties, typically due to the function of multiple genes called as QTLs. According to the Gramene database, thousands of QTLs conditioning rice yield traits have been detected by QTL mapping and majorities of them are minor QTLs with small genetic effect, which are difficult to be identified through mutant analysis. How‐ ever, minor QTLs may participate in different molecular pathways to regulate rice yield traits and play a vital role in improving yield potential. During the long domestication process, these minor QTLs have been selected and combined relying on the breeders' experience to develop cultivated varieties. Therefore, more efforts are necessary to isolate minor QTLs and elucidate the functional mechanisms in the future.

Natural variation exists widely in the genes/QTLs, resulting in many alleles for each gene/QTL. For example, *Ghd7* has at least five alleles including *Ghd7-0*, *Ghd7-0a*, *Ghd7-1*, *Ghd7-2* and *Ghd7-3* which enable rice to be cultivated in different ecotype regions. Till now, it is still rather difficult to combine favorable alleles freely in breeding higher yield potential varieties. Mining the alleles is a key base to combine the alleles. Based on the affordable next-generation sequencing technology, association mapping is a promising strategy to mine favorable alleles using a set of diverse germplasm accessions. On the basis of available and favorable alleles, an efficient breeding strategy has been proposed to exploit rice yield potential, involved in identifying the genes/QTLs for rice yield traits, mining alleles of target genes/QTLs through candidate-gene association mapping, developing functional markers and combining favorable alleles in cultivated varieties (Figure 3).

**Figure 3.** Flowchart for depicting a new strategy to breed higher yield varieties

## **Acknowledgements**

This work was supported by the Chinese High-Yielding Transgenic Program (2013ZX08001004-006), the Chinese 863 Program (2012AA101102), and the Zhejiang Provincial Natural Science Foundation (Y3110394). We are grateful to Dr. Jie-Yun Zhuang at China National Rice Research Institute for valuable discussions and suggestions.

## **Author details**

Jie-Zheng Ying\* , Yu-Yu Chen and Hong-Wei Zhang

\*Address all correspondence to: yingjiezheng@caas.cn

State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou, China

## **References**

minor QTLs have been selected and combined relying on the breeders' experience to develop cultivated varieties. Therefore, more efforts are necessary to isolate minor QTLs and elucidate

Natural variation exists widely in the genes/QTLs, resulting in many alleles for each gene/QTL. For example, *Ghd7* has at least five alleles including *Ghd7-0*, *Ghd7-0a*, *Ghd7-1*, *Ghd7-2* and *Ghd7-3* which enable rice to be cultivated in different ecotype regions. Till now, it is still rather difficult to combine favorable alleles freely in breeding higher yield potential varieties. Mining the alleles is a key base to combine the alleles. Based on the affordable next-generation sequencing technology, association mapping is a promising strategy to mine favorable alleles using a set of diverse germplasm accessions. On the basis of available and favorable alleles, an efficient breeding strategy has been proposed to exploit rice yield potential, involved in identifying the genes/QTLs for rice yield traits, mining alleles of target genes/QTLs through candidate-gene association mapping, developing functional markers and combining favorable

**Association mapping**

This work was supported by the Chinese High-Yielding Transgenic Program (2013ZX08001004-006), the Chinese 863 Program (2012AA101102), and the Zhejiang Provincial Natural Science Foundation (Y3110394). We are grateful to Dr. Jie-Yun Zhuang at China

State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement,

**Alleles and functional markers**

**Combination of favorable alleles**

**Higher yield varieties**

**QTLs A set of diverse germplasm Map-based cloning**

**Major varieties**

National Rice Research Institute for valuable discussions and suggestions.

, Yu-Yu Chen and Hong-Wei Zhang

\*Address all correspondence to: yingjiezheng@caas.cn

China National Rice Research Institute, Hangzhou, China

**Figure 3.** Flowchart for depicting a new strategy to breed higher yield varieties

the functional mechanisms in the future.

188 Rice - Germplasm, Genetics and Improvement

alleles in cultivated varieties (Figure 3).

**Mutants Genes Mutant analysis**

**Genetic populations**

**Acknowledgements**

**Author details**

Jie-Zheng Ying\*


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## **Chapter 7**

## **Current Advances on Genetic Resistance to Rice Blast Disease**

Xueyan Wang, Seonghee Lee, Jichun Wang, Jianbing Ma, Tracy Bianco and Yulin Jia

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56824

## **1. Introduction**

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[66] Doi K, Izawa T, Fuse T, Yamanouchi U, Kubo T, Shimatani Z, Yano M, Yoshimura A: Ehd1, a B-type response regulator in rice, confers short-day promotion of flowering and controls FT-like gene expression independently of Hd1. *Genes Dev* 2004,

[67] Zhang ZH, Wang K, Guo L, Zhu YJ, Fan YY, Cheng SH, Zhuang JY: Pleiotropism of the photoperiod-insensitive allele of *Hd1* on heading date, plant height and yield

18:926-936.

194 Rice - Germplasm, Genetics and Improvement

traits in rice. *PLoS One* 2012, 7:e52538.

#### **1.1. The historical and contemporary aspects of rice blast disease**

Rice (*Oryza sativa L*.) is one of the most important staple foods that feed more than half of the world's population, with Asia and Africa as the largest consuming regions [1]. Blast disease caused by *Magnaporthe oryzae* (Hebert) Barr is one of the most damaging diseases of rice. This disease was first known as rice fever disease in China as early as 1637 [2]. Blast disease was first reported in the United States in 1876, and has been identified in 85 rice-producing countries or regions worldwide (Figure 1).

Blast severely affects lowland rice in temperate and subtropical areas of Asia, and is highly destructive to upland rice in tropical areas of Asia, Latin America, and Africa [3]. Although blast is considered the most destructive rice disease due to the favorable environmental conditions for disease occurrence and worldwide distribution, little information about annual yield losses are available. Table 1 summarizes reported blast outbreaks with annual yield losses from five countries. In China, 40-50% yield losses were observed under severe rice blast infection; in some cases, 100% yield losses were found in severely infected fields [4]. Yield losses of 5-10%, 8%, and 14% were reported in India from 1960 to 1961, Korea from the mid-1970s, and China from 1980 to 1981, respectively [3]. The highest yield losses were recorded in the Philippines; ranging from 50% to 85% in 1963 [3]. It was estimated that 1.6 billion dollars were lost from 1975-1990 due to blast disease worldwide [5]. The estimated annual loss of rice was enough to feed 60 million people for one year [6] (Table 1).

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Figure 1.** Worldwide distribution of rice blast disease. Red dots show the countries or regions where blast disease has been reported.


**Table 1.** Yield losses due to blast.

#### **1.2. The biology of** *M. oryzae*

The most common symptoms in commercial rice fields induced by *M. oryzae* can be found on all the above ground parts of the rice plant at all growth stages. Seeds display brown spots after infection, which may have resulted from the infection of the florets as they mature into seeds. Infected roots have also been observed; however, lesions on the sheaths were relatively rare. Infections on young seedlings are initiated when the conidia are deposited on the surface of the leaves. Water is essential for spores to germinate and attach to the leaf surface [7, 8]. Under optimal conditions, spore germination occurs rapidly and the polarized germ tubes are formed within hours after landing on the leaf [9]. The secondary cycles are initiated by the spores produced by the lesions on the young seedlings, which can be repeated many times through the growing season. Thousands of spores can be produced from a single lesion in 15 days after infection. Typically blast lesions are diamond shaped (Figure 2A). Initial lesions appear dark green or grey with brown borders; while, older lesions are light tan with necrotic borders. Under favorable conditions, lesions can merge together and rapidly enlarge to several centimeters in length, eventually killing the leaf, and ultimately resulting in plant death. On resistant cultivars, lesions induced by *M. oryzae* usually remain small in size (1-2mm) and appear brown to dark brown in color. Disease severity of rice blast and the amount of spores produced on single lesion depends on temperature, field conditions, relative humidity, fertilization levels, and genotype of rice cultivars. In general, moderate temperatures (~24°C), high relative humidity (90-92%), and high moisture with at least a 12-hour period are advan‐ tageous for rice blast. The disease severity of the vegetative phase during the growing season highly influences the amount of disease during the reproductive phase. Spores produced at the end of the growing season may result in collar blast and neck blast; neck blast often causes direct crop loss (Figure 2B).

v v v v v

**Figure 1.** Worldwide distribution of rice blast disease. Red dots show the countries or regions where blast disease has

The most common symptoms in commercial rice fields induced by *M. oryzae* can be found on all the above ground parts of the rice plant at all growth stages. Seeds display brown spots after infection, which may have resulted from the infection of the florets as they mature into seeds. Infected roots have also been observed; however, lesions on the sheaths were relatively rare. Infections on young seedlings are initiated when the conidia are deposited on the surface of the leaves. Water is essential for spores to germinate and attach to the leaf surface [7, 8]. Under optimal conditions, spore germination occurs rapidly and the polarized germ tubes are

**Yield loss (%) Country Year** 5-10 India 1960-61 50-60 Philippines 1963 70-85 Philippines 1969-70 8 Korea mid-70s 14 China 1980-81 60 Thailand 1982

Source: http://www.knowledgebank.irri.org/ipm/rice-blast.html

been reported.

196 Rice - Germplasm, Genetics and Improvement

**Table 1.** Yield losses due to blast.

**1.2. The biology of** *M. oryzae*

vv vv vv

vv

v

v

v

Existence of physiological races of *M. oryzae* complicates the identification of resistance *(R)* genes. Physiological races of *M. oryzae* were first reported by Sasaki in Japan as early as 1922 [10]. From 1950s to 60s differential rice lines resistant to races of *M. oryzae* were identified in Japan, the United States, India, the Philippines, and South Korea. In 1961, 18 physiological races of *M. oryzae* were identified with 12 differential rice varieties in Japan. During that time, an international differential system using 8 rice varieties was establish‐ ed [11]. In China, the identification of *M. oryzae* races was initiated in late 1970s. Seven rice varieties, Tebo, Zhenlong13, Sifeng43, Dongnong363, Kanto51, Hejiang18, and Lijiangxin‐ tuanheigu (LTH), and 43 isolates of *M. oryzae* were used. In 1976, Yamada and his colleagues identified 23 races of *M. oryzae* from 2245 isolates with 9 differential rice varieties. Duan et al [12] used Yuyun 1 (with *Pia*), Gaoliangdao (with *Pii*), Kanto51 (with *Pik*), Chugeng1 (with *Pikm*), Dianyu1 × Fook Kam (with *Piz*), Dali782 (with *Pita*), Dan83-3 (with *Pita2*), and Chengbao1 (with*Pizt*) as differential varieties to characterize races of *M. oryzae* in China. These blast *R* genes are described in more details in the part II of this chapter. Nearisogenic lines (NILs) were chosen to better identify races of *M. oryzae* in a gene-for-gene specific manner. The NILs with *indica* high-susceptible variety CO39 background was developed at the International Rice Research Institute (IRRI), the Philippines [13]. In the United States, Marchetti [14] reported that the races IB-54, ID-13, IG-1, and IH-1 of *M. oryzae* were the most common. Most recently, monogenic lines with 24 major blast *R* genes in BC1 of LTH were developed by scientists at IRRI and Japan [15].

Extensive analysis of rice germplasm with physiological races in the past century reveals that complete genetic resistance (vertical resistance) is conferred by major blast *R* genes named as *Piricularia* genes or *Pi*-genes. These genes are often specific in preventing infections by strains of *M. oryzae* that contain the corresponding avirulence genes; whereas, incomplete resistance (slow-blasting components or horizontal resistance, field resistance, or dilatory resistance) is often conditioned by more than one gene on different chromosomal regions. These genes are

**Figure 2.** Symptoms of leaf (A) and neck blast diseases (B) in commercial rice fields.

referred to as quantitative resistant loci (QTLs). Resistant germpalsms carrying both major and minor *R* genes and are extremely important genetic resources that rice breeders can use to improve blast resistance in elite rice varieties.

## **2. Mapped blast** *R* **genes**

Blast *R* genes are predicted to play important roles in the frontier of rice defense responses. During interactions between rice and blast pathogens, products of the *R* gene can specifically recognize the corresponding elicitors of *M. oryzae*. Since the *Pia* gene, indentified in 1967 by Kiyosawa as the first blast *R* gene from the *japonica* variety Aichi Asahi [16], 99 blast *R* genes have been identified; in which 45% were found in *japonica* cultivars, 51% in *indica* cultivars, and the rest 4% in wild rice species (Table 2 to 5). Most deployed *R* genes have often been identified in Asian cultivated rice, specially rice cultivars from Japan and China, with the exception of *Pi9*, *Pi54rh*, *Pi40(t)*, and *Pirf2-1(t)*, which were domesticated from *O. minuta*, *O.* *rhizomatis*, *O. australiensis*, and *O. rufipogon*, respectively. All *R* genes have been mapped on all rice chromosomes except for chromosome 3 (Tables 2 to 4; Fig 3). Host genotypes, chromoso‐ mal loci, and molecular markers that are tightly linked to blast *R* genes are summarized in Figure 2 and Table 2 (60 major *R* gene) and Table 3 (17 minor *R* gene). Among them, three major *R* gene clusters have been well characeterized: the *Piz* locus on Chromosome 6, the *Pik* locus on Chromosome 11, and the *Pita* locus on Chromosome 12 (Figure 3). More detailed imformation of mapped blast *R* genes can be found at http://www.ricedata.cn/gene/, http:// www.shigen.nig.ac.jp/rice/oryzabaseV4/, and http://www.gramene.com.


\* refers to number of the genes on the chromosome.

referred to as quantitative resistant loci (QTLs). Resistant germpalsms carrying both major and minor *R* genes and are extremely important genetic resources that rice breeders can use to

Blast *R* genes are predicted to play important roles in the frontier of rice defense responses. During interactions between rice and blast pathogens, products of the *R* gene can specifically recognize the corresponding elicitors of *M. oryzae*. Since the *Pia* gene, indentified in 1967 by Kiyosawa as the first blast *R* gene from the *japonica* variety Aichi Asahi [16], 99 blast *R* genes have been identified; in which 45% were found in *japonica* cultivars, 51% in *indica* cultivars, and the rest 4% in wild rice species (Table 2 to 5). Most deployed *R* genes have often been identified in Asian cultivated rice, specially rice cultivars from Japan and China, with the exception of *Pi9*, *Pi54rh*, *Pi40(t)*, and *Pirf2-1(t)*, which were domesticated from *O. minuta*, *O.*

improve blast resistance in elite rice varieties.

**Figure 2.** Symptoms of leaf (A) and neck blast diseases (B) in commercial rice fields.

**2. Mapped blast** *R* **genes**

198 Rice - Germplasm, Genetics and Improvement

**Table 2.** Summary of blast *R* (major and minor, mapped and cloned) genes on rice chromosomes.




**Chr.**

**Name of** *R* **gene**

**Name of germplasm**

200 Rice - Germplasm, Genetics and Improvement

2 *Pitq-5* Teqing 150.5-157.5

2 *Piy(t)* Yanxian No.1 153.2-154.1

4 *Pi39(t)\** Chubu 111 107.4-108.2

6 *Pi2-1* Tianjingyeshengdao Allilic to Pi2/9

6 *Pi2-2* Jefferson 58.7

6 *Pi13(t)\** Kasalath 67.7-68.5

IR65482-4-136-2-2 *O. australiensis*

6 *Pi50(t)* Er-Ba-Zhan 46.8

6 *Piz* Zenith 58.7

8 *Pi55(t)* Yuejingsimiao 2 99.1-102.1

54.1-61.6

6 *Pi40(t)*

**Map position**

2 *Pid1(t)* Digu 87.5-89.9 RM262 ZB13 [22]

2 *Pig(t)* Guangchangzhan 142.0-154.1 RM166, RM208 Ken53-33 [24]

RM3284,

RM3743, RM5473

5 *Pi10(t)* Tongil 88.5-102.8 RG13 IB46 [27] 5 *Pi23(t)* Suweon 365 59.3-99.5 [28]

> AP4791, AP4007

RM19817, AP5659-5

6 *Pi8(t)* Kasalath 74.6-78.2 Amp-3 Race 447.1 [31] 6 *Pi13(t)\** Maowangu 74.6-78.2 Amp-3 [19]

> RM2123, RM20155

6 *Pi22(t)* Suweon 365 38.4-41.9 KJ-201 [28] 6 *Pi26(t)\** Gumei 2 51.0-61.6 B10, R674 Ca89 [33]

> RM527, RM3330

GDAP51, GDAP16

6 *Pigm(t)* Gumei 4 65.8 C5483, C0428 CH109 (ZC13), CH147 (ZB25), CH131 (ZA1) [36]

 *Pitq-1* Teqing 103.0-124.4 C236, RG653 IC-17, IB-49, IE-1 [23] *Pi17(t)* DJ123 94.0-104.0 Est9 [38] *Pi42(t)* Zhe733 58.5 RM72 IE1K [39] *Pi33(t)* IR64 45.4 RM72, C483 Guy11 [40]

> RM1345, RM3452

8 *PiGD-1(t)* Sanhuangzhan 2 53.7 RG1034 GD RFDW-I [42] 9 *Pi3(t)* C104PKT, 31.3-33.0 40N23r PO6-6 [43]

z4792, z60510, z5765

RG520, RZ446b

**(cM) ># Markers Name of pathogenic Strains Ref.**

IC-17, IB-49, IE-1, IG-1 [23]

CHL477, CHL473, P06-6, IC-17, 87-4 [29]

HN318-2, CHL438, KJ201, ROR1, PO6-6 [30]

Ken54-04, 95Mu-29, Ina86-137 [32]

KJ105, Ca89, PO6-6, M101-1-29-1, M64-1-3-9 [34]

CHL688 [41]

09-3041a, SC0602, SCRB14, HN0102,

W06-18a

[26]

[35]

[37]

RM208 97-27-2, Zhong10-8-14 [25]


\* This *R* gene shares the same name with another *R* gene.
