Cotton Genomics and Genetics 2024, Vol.15, No.2, 112-126 http://cropscipublisher.com/index.php/cgg 119 4 NGS and Genetic Diversity in Cotton 4.1 Population genomics and phylogenetics Next-Generation Sequencing (NGS) technologies have greatly enhanced our understanding of genetic diversity in cotton. By enabling comprehensive analysis of population genomics and phylogenetics, NGS provides insights into the genetic variations and evolutionary relationships among different cotton species. 4.1.1 SNP discovery and genotyping Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation and are crucial markers for studying genetic diversity. Next-generation sequencing (NGS) technologies have significantly advanced the discovery and genotyping of single nucleotide polymorphisms (SNPs) in cotton. SNPs are crucial for understanding genetic diversity and population structure. NGS allows for high-throughput sequencing, enabling the identification of numerous SNPs across the cotton genome. This has facilitated the development of high-density genetic maps and the identification of genetic variations associated with important agronomic traits (Šarhanová et al., 2018; Sahu et al., 2020). The ability to sequence large populations of cotton plants has also improved the resolution of quantitative trait loci (QTL) mapping, aiding in the precise localization of genes responsible for desirable traits (Le Nguyen et al., 2019). Wang et al. (2022) investigated the genetic structure and diversity of upland cotton populations across different cultivation regions, analyzing 273 selected upland cotton varieties globally, with a particular focus on China. Using 1 313 331 SNP markers, the study constructed a phylogenetic tree for each sample and performed population structure and principal component analysis (PCA) using ADMIXTURE and EIGENSOFT software, respectively. Kinship estimates were calculated using SPAGeDi software (Figure 4). The results showed that upland cotton varieties could be roughly clustered into 16 subgroups based on their origins, although there was some overlap among the samples. The populations were divided into six groups to calculate genetic diversity indices, revealing that Cluster 4 exhibited relatively high genetic diversity (0.390). The study also found that the genetic differentiation within the experimental cotton population was low (with a population differentiation index ranging from 0.023 68 to 0.106 64), indicating a degree of genetic connectivity among different groups. This research provides a valuable data foundation for mining superior alleles and conducting subsequent association analyses. It also offers significant insights into the origin and evolution of upland cotton and its biodiversity. These findings have practical applications for breeding and conserving genetic resources of upland cotton. 4.1.2 Phylogenetic relationships NGS has revolutionized the study of phylogenetic relationships in cotton by providing comprehensive genomic data. This technology enables the sequencing of entire genomes or large genomic regions, allowing for detailed comparisons between different cotton species and cultivars. Phylogenetic analyses using NGS data have provided insights into the evolutionary history and genetic relationships of cotton species, helping to clarify the origins and domestication processes of cultivated cotton (Šarhanová et al., 2018). The high resolution of NGS data has also allowed for the identification of introgression events and hybridization between different cotton species, which are important for breeding programs aimed at improving cotton varieties (Kushanov et al., 2021). 4.2 Marker-Assisted selection (MAS) Marker-Assisted Selection (MAS) leverages genetic markers identified through NGS to accelerate the breeding of improved cotton varieties. By integrating genomic information into breeding programs, MAS enhances the efficiency and precision of selecting desirable traits. 4.2.1 Identification of QTLs The identification of QTLs associated with important traits in cotton has been greatly enhanced by NGS technologies. By providing high-density genetic maps and enabling genome-wide association studies (GWAS),
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==