**2. Protein content in rice**

*Agronomy - Climate Change and Food Security*

ment of the bioavailability of the same.

requirement of humans in developing countries [6].

20–30% prolamin [5]. Interestingly, rice supplies about 40% of the total protein

Mineral elements are critical for several cellular and metabolic activities [10]. Biofortification of staple crops provides a sustainable methodology to triumph over the mineral deficiency. Attempts were made for the development, release, and distribution of biofortified crops with the help of agronomic practices and biotechnological techniques and also by using plant breeding methods. Various old rice varieties with high grain iron and zinc content were screened, and breeding methods with improved agronomic characteristics combined the higher mineral characteristics. In 2013, the Bangladesh Rice Research Institute released zinc-enriched rice varieties (BRRIdhan 62, BRRIdhan 72, and BRRIdhan 64), claiming to contain 20–22 ppm of zinc in brown rice. An improved line (IR68144-3B-2-2-3) has been identified in India and Philippines in a cross between a high-yielding variety (IR72) and a large, traditional variety (ZawaBonday) with a top grain iron concentration about 21ppm in brown rice [11]. Similarly, Jalmagna, a traditional variety with almost double the iron and zinc concentration of common rice variety, has been identified for further breeding programs to improve iron and zinc concentration by nearly 40 percent more than that of conventional rice variety [11]. ICAR-Indian Institute of Rice Research, Hyderabad, Telangana, developed biofortified pure line variety, DRR Dhan 45. It possesses high zinc (22.6 ppm) in polished grain. It has been released and notified in 2016 for Karnataka, Tamil Nadu, Andhra Pradesh, and Telangana. Its average grain yield is 50.0 q/ha. It matures in 125–130 days [12, 13]. Another pure line variety DRR Dhan 49 with high zinc (25.2 ppm) in polished grain is released and notified in 2018 for Gujarat, Maharashtra, and Kerala. Its average grain yield is 50.0 q/ha and matures in 125–130 days [13]. Mineral element accumulation in the grain is a complex process and is highly influenced by environmental factors. This resulted in less effective early-generation phenotypic selections for mineral grain elements and slowed progress in the breeding of biofortified rice varieties [14]. In-depth understanding of the genetic basis of mineral elements at the molecular level and the identification of significant effects of QTLs can help to speed up the development of biofortified rice varieties through marker-assisted breeding [15]. Rice is a model for cereal crops. Vast genomic resources are available, including genome-wide single nucleotide polymorphic (SNP) molecular markers and various advanced genomic platforms, to enable complex traits to be dissected at the molecular level [16]. Several studies to chart QTLs for biofortified traits include the use of introgression lines (ILs) [17] and double haploids (DHs) to uncover QTLs [18]. However, the stability of released genotypes is an important consideration to hope for a meticulous performance of released genotypes for stable produce for the farmers [19, 20]. Hence, molecular breeding approach for biofortification of crop offers a sustainable and long-term solution. Also, biofortified crops with increased bioavailability of essential protein, vitamins, and micronutrients are deployed to consumers through traditional farming and food trading practices, thus providing a feasible way to reach undernourished and low-income families with limited access to various diets, supplements, and fortified foods [21]. The common processes involved in the development of the biofortified rice variety (**Figure 1**).

Phytate is a crucial mineral storage compound in seed, with a mixed cation salt of phytic acid accounting for approximately 75% of total seed phosphorus content [7]. The significant portion of the phosphorus taken from the soil by plants is ultimately translocated to the seed and further synthesised into phytic acid. Phytate is vital for the development of seeds and also as an antioxidant, anticancer agent, lowering chronic disease rates, and preventing coronary disease [8]. Phytic acid is known as an anti-nutritional factor because it forms complexes in seeds with proteins and essential minerals such as Fe, Zn, and Ca [9] and leads to the impair-

**36**

Grain protein content (GPC) in rice is one of the major factors which decides the nutritional value of rice food and influences the palatability of cooked rice [22]. Rice's seed protein content consists of 60–80% glutelin and 20–30% prolamin, regulated by 15 and 34 genes, respectively [5]. It supplies about 40% of the protein to humans through diet in developing nations, and rice GPC quality is high, owing to lysine richness (3.8%) [6]. Improving GPC in rice grain is, therefore, a significant goal for plant breeders and biotechnologists. More than 20 QTL mapping studies have been conducted in the last two decades to explore the genetic base of the protein content in rice. Moreover, to our knowledge, more than 80 stable and consistent QTLs for GPC have been identified and mapped on all 12 chromosomes of rice, although most of them were mapped on chromosomes 1, 2, 6, 7, 10, and 11 (**Table 1**). For the first time, Tan et al. [28] mapped two QTLs, one in the interval of markers C952-Wx on chromosome 6, with the phenotypic variance explain (PVE) 13.0%, and the other one in the interval markers R1245-RM234 on chromosome 7 with PVE 6.0%. In another study, Aluko et al. [29] identified and mapped four QTLs among 312 DH lines derived from the BC3F1 of an interspecific cross of *O. sativa* × *O. glaberrima* explaining the phenotypic variance of 4.8–15.0%. Among the four QTLs, one QTL, pro6, was closely associated with Wx gene influencing rice quality. Thereafter, several studies have been conducted to map the QTLs regulating GPC in rice [26, 40–43].

Zheng et al. [39] employed unconditional and conditional QTL mapping methods to analyse the developmental behaviour of protein content and protein


**39**

**Cross** *O. sativa* (V20A) × *O. glaberrima*

(accession 103,544)

Japonica rice

RILs (92)

3

2.3–16.3

2, 6, 9

(Moritawase) × Japonica rice

(Koshihikari)

Koshihikari/Indica rice (Kasalath)//

BILs (92)

2

14.3–14.8

6, 10

R1952 (14.3), R2447 (14.8)

[33]

Japonica rice (Koshihikari)

Indica rice (Chuan) × Japonica rice

RILs (286)

2

2.69–4.50

6, 7

(Nanyangzhan)

Indica rice (Xieqingzao B) × Indica

RILs (209)

5

3.9–19.3

3, 4, 5, 6, 10

RM251–RM282 (10.5), RM190–RZ516 (19.3)

[35]

[36]

rice (Milyang 46)

Indica rice (Zhenshan 97) × Indica

RILs (241) DH lines

3

6.92–22.98

1, 11

RM287-RM26755 (21.21), 11,025-RM287 (22.98)

[37]

(120)

9

1.60–9.26

2, 3, 5, 6, 7, 10,

11, 12

rice (Minghui 63)

Tongil variety

(Samgang) × Japonica variety

(Nagdong)

Japonica rice (Asominori) × Indica

CSSLs (66)

9

3.0–53.7

1, 2, 3, 6, 8, 11

R1982 (10.4–14.2), XNpb113 (12.0–13.8), C1350 (23.6),

[38]

[39]

G1149 (13.0–53.7)

R265B-XNpb36 (10.50), C1003-C688 (12.67),

XNpb212-G1318 (13.86), C606-XNpb238 (14.63),

R1854-R2373 (15.65), XNpb24-C562 (17.60), XNpb338-C796

(19.59), R758-XNpb15 (19.74), XNpb268-R411 (23.70)

RM445–RM418 (25.9)

[26]

rice (IR24)

Japonica rice (Asominori) × Indica

RILs (71)

10

8.53–23.70

1, 3, 4, 6, 7, 8, 9,

10, 12

rice (IR24)

Indica rice (Zhenshan 97B) × Indica

RILs (188)

2

7.2–25.9

1, 7

rice (Delong 208)

BC3(TC) F1 families (308)

1

9.0–10.0

8

**Population type and size**

**No. of total QTLs**

**PVE range (additive effect QTLs)**

**Chromosomes/**

**chromosome arms**

**Marker intervals/nearest markers for major QTL (PVE)**

**References**

*Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger*

[34]

*DOI: http://dx.doi.org/10.5772/intechopen.91144*

[32]

[31]


#### *Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger DOI: http://dx.doi.org/10.5772/intechopen.91144*

*Agronomy - Climate Change and Food Security*

[24]

[23]

**38**

**Cross**

**Population** 

**No. of** 

**PVE range** 

**Chromosomes/**

**Marker intervals/nearest markers for major QTL (PVE)**

**References**

**chromosome arms**

**(additive** 

**effect** 

**QTLs)**

**total** 

**QTLs**

**type and** 

**size**

**Amino acid content**

Indica rice (Zhenshan 97) × Indica

RILs (190)

2 QTL

4.05–33.3

1, 7

RM472-RM104 (Asp/Thr/Gly/Ala/Tyr/Pro/Lys/Ser/Glu/

Asp/Val/Met/Ile/Leu/Phe/His/Arg/Cys) (5.7–33.3)

R321–RM55 (12), RZ398–RM204 (12), RG101–G393 (15),

C1003B–RG103 (15), RG118–C794 (20), RM53–RZ599 (22),

RM258–RG561 (22), RG424–R2549 (23), RG528–RG128

(24), RM20b–C732 (35) [His]; C734b–RZ649 (16),

R321–RM55 (18), RG424–R2549 (21),RM258–RG561 (21),

R3203–RM20A (22), RM53–RZ599 (23), RG528–RG128

(23), RM20b–C732 (33)

R2632–C39 (Ser) (13.5), RG173–RM81A (Val) (14.5),

[25]

[26]

RZ536–TEL3 (Met) (48.8)

RM328–RM107 (Asp/Thr/Ser/Gly/Val/Ile/Phe/Lys/Taa)

(13.2), MRG186–MRG4499(Asp/Thr/Ser/Glu/Gly/ Ala/

Cys/Val/Met/Ile/Phe/Arg/Pro/Taa) (14.4–27.5), RM493–

RM562 (Asp/Thr/Glu/Gly/Ala/Val/Leu/Phe/ Arg/Pro/Taa)

(24.2 31.7)

id3015453-id3016090 (Ala-10.2, Phe-10.6, Iso-11.2, Val-12.4, Leu-12.4), id3001422 fd10 (Lys-10.8)

[27]

clusters

rice (Nanyangzhan)

Indica rice (Zhenshan 97) × Indica

RILs (241)

10

12–35 (His);

1, 2, 3, 6, 7, 10, 11,

12 (His); 2, 3, 5, 6,

7, 10, 11, 12

(His) + 8

16–33 (Arg)

(Arg)

rice (Minghui 63)

Indica rice (Zhenshan 97) × Indica

RILs (241)

12

3.4–48.8

1, 11

rice (Minghui 63)

Indica rice (Zhenshan 97B) × Indica

RILs (188)

3 QTL

4.2–31.7

1,7,9

clusters

rice (Delong 208)

*O. sativa* (Dasanbyeo) × *O. sativa*

RILs (172)

6

10.2–12.4

3

(TR22183)

**Protein content**

Indica rice (Zhenshan 97) × Indica

RILs (238)

2

6.0–13.0

6, 7

C952-Wx (13) RM226–RM297 (15)

C22-RG449d (16.5), ZG34B-G20 (22.5), RG435-RG172a

(35.0)

[29]

[30]

[28]

rice (Minghui 63)

Indica rice (Caiapo) × *Oryza* 

DH lines

4

4.8–15.0

1, 2, 6, 11

(312)

DH lines

5

6.9–35.0

1, 4, 5, 6, 7

(81)

*glaberrima* (IRGC 103544)

Indica rice (Gui630) × Japonica rice

(02428)


**41**

**Cross** *O. sativa* ssp. Indica (Teqing) × *O. rufipogon* Griff.

Indica rice (Bala) × Japonica rice (Azucena)

RILs (79)

GZn-4; GFe- 4

11.2–14.8; 9.7–21.4

6, 7, 10; 1, 3, 4, 7

G1082 (11.2), G20 (11.4), AB0601 (14.7), C223 (14.8) [GZn]; R1440 (15.5), C949 (16.2), R1618 (21.4) [GFe]

CT206-G177 (10.83), RZ516-G30 (12.38) [GZn]

Indica cultivar (ZYQ8) x Japonica cultivar (JX17)

Indica rice (Madhukar) × Indica rice (Swarna) Indica rice (PAU201) x Indica rice

F2 (247)

GZn-3;

4.7–19.1;

2, 10; 2, 3, 7, 10, 12

2.4–26.8

GFe- 8

(Palman 579)

Indica cultivar (Ce258) x Japonica

BILs (200

GZn-4;

2–24.4;

3, 6, 7, 8; 6, 11

10.2–18.3

GFe-1

and 201)

breeding line (IR75862) and Indica

cultivar (ZGX1) x Japonica breeding

line (IR75862)

Indica cultivar (Swarna) X Japonica

RILs (60)

GFe-1

39

1

RM490-RM5 (39)

RG172-RM340 (11.8) [GZn]; RG123-RG172 (16.7),

RG510-RZ251 (28.2)

RM171-RM590 (15.0), RM573 (15.2), RM6 (17.6),

RM24085-RM566 (21.9)

[52]

[53]

[54]

rice (Moroberekan)

*O. sativa* (XB) × *O. rufipogon*

BILs (202)

GZn-6;

5.3–11.8;

3, 4, 6, 7, 10, 12;

3, 6, 9

6.1–28.2

GFe-3

(accession of DWR)

*O. sativa* (Nipponbare)/*O.* 

BRILs (151)

GZn-4

15.0–21.9

2, 9, 10

*meridionalis* (W1627)//Nipponbare

DH lines (127) RILs (168)

GZn-6; GFe-7

29–35; 69–71

3, 7, 12; 1, 5, 7, 12

RM501–OsZip2 (29), RM7–RM517 (31), RM260–RM7102 (34), RM234–RM248 (35), RM248–RM8007 (35), RM17–

RM260 (35) [GZn]; RM243–RM488 (69), RM488–RM490 (69.2), RM574–RM122 (69.2), RM234–RM248 (69), RM248–

RM8007 (69), RM17–RM260 (71), RM 260–RM7102 (71) [GFe]

8RM474–RM184 (19.1) [Zn]; RM491–RM519 (16.9),

[51]

RM228–RM496 (18.1), RM53–RM521 (21.4), RM221–

RM208 (26.8)

RM293–RM85 (11.1–14.4), RM407–RM152 (11.2–18.0)

[44]

[GZn]; RM3–RM340 (10.2–18.3) [GFe]

GZn-2

10.83–12.38

4, 6

ILs (85)

GZn-2; GFe- 1

5–11; 7

5, 8; 2

RM152 (11) [Zn]

[17]

**Population type and size**

**No. of total QTLs**

**PVE range (additive effect QTLs)**

**Chromosomes/**

**chromosome arms**

**Marker intervals/nearest markers for major QTL (PVE)**

**References**

*Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger*

[50]

*DOI: http://dx.doi.org/10.5772/intechopen.91144*

[49]

[34]

#### *Agronomy - Climate Change and Food Security*


#### *Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger DOI: http://dx.doi.org/10.5772/intechopen.91144*

*Agronomy - Climate Change and Food Security*

[41]

**40**

**Cross**

**Population** 

**No. of** 

**PVE range** 

**Chromosomes/**

**Marker intervals/nearest markers for major QTL (PVE)**

**References**

**chromosome arms**

**(additive** 

**effect** 

**QTLs)**

**total** 

**QTLs**

**type and** 

**size**

Koshihikari/Indica rice (Kasalath)//

BILs (182) DH lines

1

39–41

2

(133)

4

6.26–12.11

2, 3, 7, 10

R250-C746 (10.04), C16-C809 (11.07), C847-C596 (12.11)

RM12532–RM555 (39–41)

[40]

Japonica rice (Koshihikari)

Indica rice

(Cheongcheong) × Indica rice

(Nagdong)

Japonica cultivar (CJ06) × Indica

DH lines

1

12.3–15.8

10

RM216-RM467 (12.3–15.8)

RM506-RM1235 (39), RM24934-RM25128 (40),

RM219-RM23914 (40)

RM423-RM6375 (11.72), GS3-SLAF13430 (13.50)

RM7124 (10.38–15.43)

[44]

[45]

[42]

[43]

(116)

DH lines

3

39–40

8,9,10

(133)

rice cultivar (TN1)

Indica rice

(Cheongcheong) × Indica rice

(Nagdong)

*O. sativa* (M201) × *O. sativa* (JY293)

Japonica variety

(Sasanishiki) × Indica variety

(Habataki)

Indica rice

DH lines

1

14

7

RM8261 (14)

[46]

(120)

(Cheongcheong) × Indica rice

(Nagdong)

Naveen/O. sativa (ARC 10075)//O.

BC3F5 lines

3

6.70–17.35

1, 2, 7

CSCWR\_Os01g02590\_\_61041 (13.85), CSCWR\_

[47]

Os02g10740\_65058 (6.70–17.35)

(200)

sativa (Naveen)

**Iron and Zinc**

Indica variety (IR64) × Japonica

DH lines

GZn-2;

q

1, 12; 2, 8, 12

RM235–RM17 (12.8), RM34–RM237 (15) [GZn]; RM270–

[18]

RM17 (13.8), RM53–RM300 (16.5), RM137–RM325A (18.3)

[GFe]

R3166-RG360 (12.34), C794-RG118 (18.61) [GZn];

[48]

C472-R2638 (11.11), RG236-C112 (25.81) [GFe]

GFe-3

(129)

variety (Azucena)

Indica cultivar (Zhengshan

RILs (241)

GZn-3;

5.3–18.61;

5, 7, 11; 1, 9

11.11–25.81

GFe-2

97) × Indica cultivar (Minghui 63)

RILs (234) CSSLs (39)

1#

10.38–15.43

1

5 \$

6.74–13.50

1, 2, 3, 4


**Table 1.** *List of QTLs identified for biofortification traits in rice.*

**43**

of PVE.

gene method.

**3. Amino acid content**

*Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger*

index in rice. At four stages of grain filling, viz. 7, 14, 21, and 28 DAF, they mapped 10 unconditional QTLs and 6 conditional QTLs, explaining 8.53–19.59% and 8.76–23.70% of PVE for GPC, respectively, and 11 unconditional QTLs and 9 conditional QTLs explaining 7.46–16.97% and 7.46–18.88% of PVE for protein index, respectively. A strategy to detect more QTLs for rice grain quality within subpopulations [44]. Xu et al. [58] detected a total of 29 QTLs in the whole population and 10 QTLs in the two subpopulations for 7 traits, 4 of which (1 qPRO3.1 for protein content) were detected in the entire population but the remaining 6 QTLs were not. These six QTLs with minor effects might have been covered by the Wx locus when mapped in the whole population. In addition to usual biparental populations such as recombinant inbred lines, backcross inbred lines, and doubled haploid lines, advanced population, i.e. chromosome segment substitution line (CSSL) populations, has also been employed [45]. Yang et al. [45] used a CSSL population derived from the cross of a Japonica variety (Sasanishiki) with Indica variety (Habataki) and identified a total of seven QTLs in three environments, although only one QTL (qPC-1) was detected across three environments explaining 10.38–15.43% of PVE. Furthermore, they developed F2 and F3 segregating populations from the cross between a CSSL with low PC, SL402, harbouring qPC-1 and Sasanishiki, and delimited the region of qPC-1 to a 41-kb on chromosome 1. These results may be helpful to introgress the QTL for GPC into rice cultivars using marker-assisted selection. In one study, Bruno et al. [46] observed compromised heritability percentage for protein while higher heritability percentage for the amylose content in a DH population derived from a cross between Cheongcheong and Nagdong. They mapped a QTL for GPC on chromosome 7 linked with the marker RM8261, explaining 14%

As has been shown by previous studies, identification of robust QTLs for GPC in rice grains has been restricted because of lack of appropriate donors, non-utilisation of high-throughput phenotyping and genotyping platforms, and high genotype × environment (G × E) interaction. To overcome these restrictions, recently Chattopadhyay et al. [47] genotyped a BC3F4 mapping population derived from the cross between grain protein donor, ARC10075 and high-yielding cultivar Naveen, using 40 K Affymetrix custom SNP array, and identified three stable QTLs (viz. qGPC1.1, qSGPC2.1, and qSGPC7.1) for GPC explaining 13, 14, and 7.8% of PVE, respectively. QTLs identified in this study can be useful to improve the nutritional quality of rice grain. The closely linked markers that flanked the identified QTLs can be used to aid quality selection in breeding programs. And the results of the coincidence between the QTL detected, and the loci involved in protein biosynthesis pathways, might be helpful for gene cloning by the candidate

In addition to GPC, improvement in the amino acid composition is important to meet the food demands of a growing global population. A major function of proteins in nutrition is to supply adequate amounts of required amino acids [59, 60]. Depending on requirement and availability in animal metabolic processes, essential amino acids cannot be synthesised by animals, but play a crucial role in metabolism [61]. Therefore, improving amino acid content in rice grain is an important objective. Several studies using the linkage mapping approach with various mapping populations have provided useful genetic information for improving the amino acid composition (AAC) in rice grains. Wang et al. [23] identified 18 chromosomal

*DOI: http://dx.doi.org/10.5772/intechopen.91144*

*Agronomy - Climate Change and Food Security*

#### *Breeding for Biofortification Traits in Rice: Means to Eradicate Hidden Hunger DOI: http://dx.doi.org/10.5772/intechopen.91144*

index in rice. At four stages of grain filling, viz. 7, 14, 21, and 28 DAF, they mapped 10 unconditional QTLs and 6 conditional QTLs, explaining 8.53–19.59% and 8.76–23.70% of PVE for GPC, respectively, and 11 unconditional QTLs and 9 conditional QTLs explaining 7.46–16.97% and 7.46–18.88% of PVE for protein index, respectively. A strategy to detect more QTLs for rice grain quality within subpopulations [44]. Xu et al. [58] detected a total of 29 QTLs in the whole population and 10 QTLs in the two subpopulations for 7 traits, 4 of which (1 qPRO3.1 for protein content) were detected in the entire population but the remaining 6 QTLs were not. These six QTLs with minor effects might have been covered by the Wx locus when mapped in the whole population. In addition to usual biparental populations such as recombinant inbred lines, backcross inbred lines, and doubled haploid lines, advanced population, i.e. chromosome segment substitution line (CSSL) populations, has also been employed [45]. Yang et al. [45] used a CSSL population derived from the cross of a Japonica variety (Sasanishiki) with Indica variety (Habataki) and identified a total of seven QTLs in three environments, although only one QTL (qPC-1) was detected across three environments explaining 10.38–15.43% of PVE. Furthermore, they developed F2 and F3 segregating populations from the cross between a CSSL with low PC, SL402, harbouring qPC-1 and Sasanishiki, and delimited the region of qPC-1 to a 41-kb on chromosome 1. These results may be helpful to introgress the QTL for GPC into rice cultivars using marker-assisted selection. In one study, Bruno et al. [46] observed compromised heritability percentage for protein while higher heritability percentage for the amylose content in a DH population derived from a cross between Cheongcheong and Nagdong. They mapped a QTL for GPC on chromosome 7 linked with the marker RM8261, explaining 14% of PVE.

As has been shown by previous studies, identification of robust QTLs for GPC in rice grains has been restricted because of lack of appropriate donors, non-utilisation of high-throughput phenotyping and genotyping platforms, and high genotype × environment (G × E) interaction. To overcome these restrictions, recently Chattopadhyay et al. [47] genotyped a BC3F4 mapping population derived from the cross between grain protein donor, ARC10075 and high-yielding cultivar Naveen, using 40 K Affymetrix custom SNP array, and identified three stable QTLs (viz. qGPC1.1, qSGPC2.1, and qSGPC7.1) for GPC explaining 13, 14, and 7.8% of PVE, respectively. QTLs identified in this study can be useful to improve the nutritional quality of rice grain. The closely linked markers that flanked the identified QTLs can be used to aid quality selection in breeding programs. And the results of the coincidence between the QTL detected, and the loci involved in protein biosynthesis pathways, might be helpful for gene cloning by the candidate gene method.
