**4. Importance of SNPs for crop improvement**

Single nucleotide polymorphism (SNP) causes genetic diversity among individuals of the species and can occur at different frequencies in different species throughout the entire genome. SNPs can cause phenotypic diversity among individuals such as the color of different plants or fruits, fruit size, ripening, flowering time adaptation, crop quality, grain yield, or tolerance to various abiotic and biotic factors [42]. While SNPs can cause changes in amino acids in the exon of a gene, it can also be silent. In addition, it can occur in noncoding regions. SNPs can influence promoter activity for gene expression and finally produce a functional protein by transcription. Therefore, identifying functional SNPs in genes and determining their effects on the phenotype can lead to a better understanding of the effects on gene function for product development [43].

Conventional breeding and marker-assisted breeding are two approaches used to perform plant breeding [42, 44]. However, in plant breeding, publications on the application of molecular markers compared to conventional breeding have increased significantly over the past 15 years. Plant breeding forms and will

continue to be the basis for increasing scientific efficiency in the field of food, feed, and industry. The reason why conventional breeding is increasingly preferred is that it requires hybridization between various parents and then selection over a long time (5–15 years) generation based on phenotypic selection to obtain the advanced product [45].

Rapid progress in sequence technologies, including SNP genotyping and genome sequencing, has given new and powerful approaches to mapping complex features and then identifying genes that cause this complexity. Although these methods are first applied in human genetics, their applications in plant genetics and product development are becoming popular [46]. With these new techniques, it is an important advantage to create experimental populations of germplasm collections and homozygous individuals in plants in a short time.

The most obvious advantages of SNP markers are that they are flexible and fast and provide data management convenience. For example, biallelic SNP markers are easy to combine data between groups and create large databases of this data because there are only two alleles for each location. This will also ensure the same allele detection on different genotyping platforms after appropriate quality assessment of these data. Using bioinformatic tools to transform SNP markers from different studies into the same DNA chain can contribute to improvement efforts [47]. With the help of a high-quality reference genome, the fusion sequence and SNPs also provide a stronger analysis of the entire SNP catalog for each species. As the most common type of DNA polymorphism, SNPs can also be specific at the genome-wide locus, which can reveal the selection of SNP variants at the target locus as well as informative marker sets for specific germplasm pools.

Due to the availability of technologies that provide validation and detection of SNPs, the development of SNP markers has become a routine process, especially in products with a reference genome. Appearance of whole-genome sequencing (GWAS) and de novo sequencing of unknown genomes has emerged. However, since 2006, it has been observed that SNPs have been used in publications derived from academic researches that have resulted in the development of crops such as water-tolerant rice, rust-resistant wheat varieties "Patwin," and low phytic acid corn and, briefly, in order to correct the problems in agriculture. Although it does not normally disclose the details of reproductive methodologies to the public, it is known that SNP tokens have been applied in several articles published by companies such as Monsanto, Pioneer Hi Bred International, and Syngenta [48].

The effects on plant protein function and gene expression against a changing condition may result from SNPs occurring in coding regions and regulatory sequences, respectively. Therefore, SNPs have a great potential in genetic, reproductive, and economic importance [49].

Thus, the targeted product can be developed using only the data of the databanks before the fieldwork to efficiently cultivate a crop on logical and evolutionary studies. For example, in plant genetics, SNPs are widely used to identify the cis-regulatory variation within a species based on allele-specific analysis and to discover genes linked to complex genetic features by revealing its distribution, and it gives information about the adaptation of the species in that region. This situation also allows the investigation of the effects of changing conditions, especially by determining SNPs at the transcriptome level with RNA-seq technology. Using the RNA-seq data against two different conditions of the phenotype, you can create a SNP catalog and evaluate the effects on the protein sequence and which SNPs have a significant change in allele frequency. In addition, de novo and reference-based SNP discovery is carried out in many organisms, including many plants with little or no genetic information [50]. The availability of NGS provides a convenient approach to discover all SNPs and learn about genomic position and genotyping in

**61**

**Author details**

Hande Morgil, Yusuf Can Gercek and Isil Tulum\*

\*Address all correspondence to: iciltlm@gmail.com

provided the original work is properly cited.

Department of Biology, Istanbul University, Istanbul, Turkey

© 2020 The Author(s). Licensee IntechOpen. 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,

an increased knowledge and application of SNPs in the future.

*Single Nucleotide Polymorphisms (SNPs) in Plant Genetics and Breeding*

one step. There are many advantages in performing SNP analysis using RNA-seq data. First, thousands of SNPs can be discovered, and expression levels of millions of functional genes with sequence variations can be observed simultaneously. Second, the location of variations in coding regions associated with biological and agronomic properties of plants can be identified, and phenotypes can be estimated by genotypes [51]. It also provides information for relevant studies such as gene characterization, gene expression measurement, and posttranslational process analysis. In addition, emerging technologies have allowed de novo scanning of SNPs computationally even in the absence of a reference genome sequence of any plant variety. Thus, the targeted product can be developed using only the data from data banks before the fieldwork to efficiently cultivate a crop [52]. The development and advancement of SNP technology are extensive for both evolutionary and molecular geneticists, plant breeders, and industry and will be valuable for us to understand

In order to understand evolutionary and genetic relationships between/within species, elucidate traits of agronomic interest in crops, and clarify prone to diseases, SNPs are the first approach. Especially in plant genetic research and breeding, identifying the genetic loci that are responsible for trait variation is fundamental. With the advantages of stability, budget friendly improvements, and high-throughput assays, SNP has become increasingly important in crop genetic studies. The development of genotyping tools for model and non-model crops allows the detection of the variations, and it has been successfully applied in plant science for many years. The shift to the high-throughput genotyping assays and development of next-generation sequencing technologies accelerated the discovery of polymorphisms. However, the error-prone fashion of the NGS analysis tools is still a big concern which can lead to false-positive SNPs. There is a need for the development of a tool for extracting bulk of data, support for the data analyses, and intelligent decision on the accuracy. To fulfill this need, instead of using binary composition of nucleotides, machine learning approaches are being developed. Integrated SNP Mining and Utilization (ISMU) Pipeline is one of the first trials to develop a machine learning approach to SNP discovery. The integrated approach alongside the recent innovations will allow

*DOI: http://dx.doi.org/10.5772/intechopen.91886*

and develop crop species.

**5. Conclusion**

### *Single Nucleotide Polymorphisms (SNPs) in Plant Genetics and Breeding DOI: http://dx.doi.org/10.5772/intechopen.91886*

one step. There are many advantages in performing SNP analysis using RNA-seq data. First, thousands of SNPs can be discovered, and expression levels of millions of functional genes with sequence variations can be observed simultaneously. Second, the location of variations in coding regions associated with biological and agronomic properties of plants can be identified, and phenotypes can be estimated by genotypes [51]. It also provides information for relevant studies such as gene characterization, gene expression measurement, and posttranslational process analysis. In addition, emerging technologies have allowed de novo scanning of SNPs computationally even in the absence of a reference genome sequence of any plant variety. Thus, the targeted product can be developed using only the data from data banks before the fieldwork to efficiently cultivate a crop [52]. The development and advancement of SNP technology are extensive for both evolutionary and molecular geneticists, plant breeders, and industry and will be valuable for us to understand and develop crop species.
