Legume Genomics and Genetics 2024, Vol.15, No.4, 187-198 http://cropscipublisher.com/index.php/lgg 193 domestication-related genetic sequences being shared between them. This dual domestication has provided a rich framework for studying the evolutionary history and genetic diversity of the species. 5.3 Analysis of genetic diversity in domesticated and wild common bean Genetic diversity in common bean has been extensively studied using various molecular markers, including SSRs and SNPs. Studies have shown a reduction in genetic diversity from wild to domesticated populations, with a more pronounced bottleneck effect observed in the Mesoamerican gene pool compared to the Andean gene pool (Mamidi et al., 2011; Bitocchi et al., 2013; Gioia et al., 2019). In regions like Southern Italy and Brazil, significant phenotypic and genetic diversity has been observed among local landraces, indicating the presence of a wide-ranging variation that is crucial for breeding programs (Burle et al., 2010; Scarano et al., 2014). Additionally, the use of SNP genotyping platforms has revealed genetic similarities and differences among germplasm entries, aiding in the identification of duplicated lines and ensuring germplasm purity (Raatz et al., 2019). 5.4 Implications for breeding and conservation The genetic diversity observed in common bean landraces and wild populations has important implications for breeding and conservation. The presence of significant intra-varietal differences and the identification of genes linked with desirable traits such as increased leaf and seed size provide valuable resources for genomics-enabled crop improvement (Scarano et al., 2014; Schmutz et al., 2014; Gioia et al., 2019). Conservation efforts, such as on-farm management and the establishment of core collections, are essential to preserve this genetic variability and ensure the sustainability of common bean cultivation (Rivera et al., 2018). Moreover, the development of marker-assisted breeding methods and the use of genomic resources can enhance the efficiency and effectiveness of breeding programs, addressing both biotic and abiotic stresses (Assefa et al., 2019; Raatz et al., 2019). 6 Molecular Tools and Techniques for Studying Phylogenetics and Genetic Diversity 6.1 High-throughput sequencing technologies Next-generation sequencing (NGS) has revolutionized the field of legume genomics by enabling the rapid and cost-effective sequencing of entire genomes and transcriptomes. This technology has been instrumental in uncovering the genetic basis of important traits and understanding the evolutionary history of legumes. For instance, NGS has facilitated the analysis of the soybean transcriptome, revealing insights into seed development and nutrient utilization (O’Rourke et al., 2014). Additionally, NGS has been used to generate large-scale genomic datasets that resolve deep phylogenetic relationships within the legume family, despite challenges such as incomplete lineage sorting (Koenen et al., 2019). The integration of NGS data from different germplasm collections has also provided valuable information on the genetic diversity and evolutionary history of crops like peas (Pavan et al., 2022). Whole-genome sequencing (WGS) provides a comprehensive view of the genetic makeup of an organism, allowing for detailed studies of genome structure, function, and evolution. In legumes, WGS has been used to assemble high-quality reference genomes for species such as common bean and soybean, which are essential for comparative genomics and breeding programs (Schmutz et al., 2014). The availability of these reference genomes has enabled researchers to identify genomic regions associated with domestication and important agronomic traits, thereby facilitating crop improvement efforts (Kumar et al., 2014). Furthermore, WGS has revealed the extent of genome conservation and structural variation among different legume species, highlighting the evolutionary processes that have shaped their genomes (Choi et al., 2004). 6.2 Bioinformatics tools for data analysis Phylogenetic tree construction is a critical step in understanding the evolutionary relationships among legume species. Software tools such as maximum likelihood and Bayesian inference methods have been employed to analyze large genomic datasets and construct robust phylogenetic trees. These tools have been used to resolve the deepest divergences in the legume phylogeny and to test alternative evolutionary hypotheses (Koenen et al., 2019). Additionally, multispecies coalescent methods have been applied to account for gene tree discordance and incomplete lineage sorting, providing a more accurate picture of legume evolution (Crameri et al., 2021).
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