Legume Genomics and Genetics 2024, Vol.15, No.4, 176-186 http://cropscipublisher.com/index.php/lgg 177 This study aims to explore the role of genomics in advancing pulse crop productivity. Specifically, it will review the current state of pulse crop genomics and the key technological advancements in this field. Additionally, it will discuss the challenges and limitations associated with traditional breeding methods for pulse crops. The potential of genomics-assisted breeding to overcome these challenges and improve pulse crop yields and resilience will be highlighted. The study will present case studies and success stories of genomics applications in pulse crop breeding. Furthermore, it will provide insights into future directions and opportunities for integrating genomics into sustainable pulse crop production. By addressing these objectives, this study seeks to underscore the transformative potential of genomics in enhancing the productivity and sustainability of pulse crops, ultimately contributing to global food security and agricultural resilience. 2 Genomic Tools and Technologies in Pulse Crop Research 2.1 Next-generation sequencing (NGS) Next-Generation Sequencing (NGS) technologies have revolutionized the field of genomics by enabling rapid and high-throughput sequencing of entire genomes. These technologies have significantly reduced the cost and time required for sequencing, making it feasible to conduct large-scale genomic studies. NGS encompasses various methods, including whole-genome re-sequencing (WGRS), single nucleotide polymorphism (SNP) arrays, and reduced-representation sequencing (RRS) such as genotyping-by-sequencing (GBS) (Abebe, 2019; Torkamaneh et al., 2021). These methods allow for the comprehensive analysis of genetic variation within and between species, facilitating the identification of genetic markers associated with important traits (Pavlopoulos et al., 2013). NGS technologies have been extensively applied in pulse crop genomics to enhance our understanding of genetic diversity, population structure, and the genetic basis of key agronomic traits. For instance, genotyping-by-sequencing (GBS) has been used to discover and genotype SNPs in large populations, aiding in genome-wide association studies (GWAS) and genomic selection (GS) (He et al., 2014; Abebe, 2019). These applications have led to the identification of genetic loci associated with disease resistance, yield, and other important traits, thereby accelerating the breeding of improved pulse crop varieties (Lee et al., 2015; Liu and Yan, 2018). 2.2 Genome-wide association studies (GWAS) Genome-Wide Association Studies (GWAS) are a powerful tool for identifying genetic variants associated with phenotypic traits. GWAS involves scanning the genomes of many individuals to find genetic markers that occur more frequently in individuals with a particular trait. This approach leverages the natural genetic variation within a population to link specific genetic loci to phenotypic traits (Xiao et al., 2017; Gupta, 2021). The success of GWAS depends on the availability of high-density genotyping data and appropriate statistical models to detect significant associations (Pavan et al., 2020). GWAS has been instrumental in identifying genetic loci associated with key traits in pulse crops, such as disease resistance, drought tolerance, and yield. By analyzing large populations with diverse genetic backgrounds, researchers have uncovered numerous genotype-phenotype associations that provide insights into the genetic architecture of complex traits (Liu and Yan, 2018; Torkamaneh et al., 2021). These findings have important implications for breeding programs, as they enable the development of molecular markers for marker-assisted selection (MAS) and the identification of candidate genes for functional studies (Lee et al., 2015; Xiao et al., 2017). 2.3 Genomic selection (GS) Genomic Selection (GS) is a breeding method that uses genome-wide genetic information to predict the breeding value of individuals. Unlike traditional selection methods that rely on phenotypic data alone, GS incorporates genotypic data to enhance the accuracy of selection. The methodology involves genotyping a training population, estimating the effects of genetic markers on the trait of interest, and using these estimates to predict the performance of selection candidates (He et al., 2014; Abebe, 2019). This approach allows for the selection of superior individuals at an early stage, thereby reducing the breeding cycle time.
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