Legume Genomics and Genetics 2024, Vol.15, No.3, 105-117 http://cropscipublisher.com/index.php/lgg 109 constructing genetic maps and understanding genome organization (Pavan et al., 2022). The GenoPea 13.2 K SNP Array, for instance, has facilitated the identification of ohnologue-rich regions and local duplicates within the pea genome (Tayeh et al., 2015). 4.3 Geographic distribution of genetic variation The geographic distribution of genetic variation in peas reflects their evolutionary history and domestication patterns. Studies have shown that genetic variation in pea populations is structured based on geographic patterns, with distinct populations exhibiting different evolutionary histories. For example, the average decay of linkage disequilibrium (LD) varies significantly among genetically distinct populations, indicating diverse evolutionary trajectories (Pavan et al., 2022). The genetic diversity within pea collections often clusters into subpopulations corresponding to different geographic regions, with frequent genetic exchange between populations (Rana et al., 2017; Rispail et al., 2023). This geographic structuring is crucial for understanding the expansion of pea cultivation from its domestication center to other regions worldwide (Pavan et al., 2022). In summary, the genetic diversity in peas is shaped by contributions from wild relatives, landraces, and cultivated varieties, assessed through various molecular markers, and structured by geographic distribution. These insights are vital for breeding programs aimed at improving pea varieties for future agricultural challenges. 5 Genomic Tools and Resources 5.1 Advances in sequencing technologies for peas Recent advancements in sequencing technologies have significantly enhanced our understanding of the pea genome. The integration of next-generation sequencing (NGS) data from different genotyping-by-sequencing (GBS) libraries has allowed researchers to explore pea biodiversity on an unprecedented scale. For instance, a study combined GBS data from two Pisumgermplasm collections, resulting in a dataset of 652 accessions and 22 127 markers. This comprehensive dataset facilitated the analysis of population structure and genetic variation, revealing geographic patterns and evolutionary histories of pea diversification (Pavan et al., 2022). Additionally, the use of 454 sequencing has enabled a detailed characterization of repetitive DNA in the pea genome, identifying major repeat families and providing insights into the genome’s architecture and function (Macas et al., 2007). 5.2 Genome-wide association studies (GWAS) in peas Genome-wide association studies (GWAS) have become a pivotal tool in identifying genetic variants associated with important traits in peas. Traditional GWAS approaches require high-density genotyping of large numbers of individuals, which can be resource-intensive. However, innovative methods such as extreme-phenotype GWAS (XP-GWAS) have been developed to overcome these challenges. XP-GWAS involves genotyping pools of individuals with extreme phenotypes, allowing for the discovery of trait-associated variants without extensive genotyping resources (Yang et al., 2015). This method has proven effective in other crops and holds promise for application in peas. Furthermore, the integration of GWAS with population genomics has provided a comprehensive view of the genetic basis of fitness, local adaptation, and phenotypic traits in legumes (Cortinovis et al., 2020). 5.3 Pea genomic databases and bioinformatics resources The development of genomic databases and bioinformatics tools has been instrumental in advancing pea research. One notable resource is the translational genomics toolkit, which leverages syntenic relationships between pea and other model legumes such as Medicago truncatula. This toolkit allows researchers to map pea genes onto a consensus genetic map and identify candidate genes for various traits (Bordat et al., 2011). Additionally, the GWAS Atlas is a curated resource that integrates genome-wide variant-trait associations across multiple species, including plants and animals. This database provides a valuable platform for accessing high-quality GWAS data, facilitating genetic research and breeding applications in peas (Tian et al., 2019).
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