Identification of Loci Governing Agronomic Traits and Mutation Hotspots via a GBS-Based Genome-Wide Association Study in a Soybean Mutant Diversity Pool

Abstract

In this study, we performed a genotyping-by-sequencing analysis and a genome-wide association study of a soybean mutant diversity pool previously constructed by gamma irradiation. A GWAS was conducted to detect significant associations between 37,249 SNPs, 11 agronomic traits, and 6 phytochemical traits. In the merged data set, 66 SNPs on 13 chromosomes were highly associated (FDR p < 0.05) with the following 4 agronomic traits: days of flowering (33 SNPs), flower color (16 SNPs), node number (6 SNPs), and seed coat color (11 SNPs). These results are consistent with the findings of earlier studies on other genetic features (e.g., natural accessions and recombinant inbred lines). Therefore, our observations suggest that the genomic changes in the mutants generated by gamma irradiation occurred at the same loci as the mutations in the natural soybean population. These findings are indicative of the existence of mutation hotspots, or the acceleration of genome evolution in response to high doses of radiation. Moreover, this study demonstrated that the integration of GBS and GWAS to investigate a mutant population derived from gamma irradiation is suitable for dissecting the molecular basis of complex traits in soybeans.

Genome-Wide Association Study (GWAS) of the Agronomic Traits and Phenolic Content in Sorghum (Sorghum bicolor L.) Genotypes

“…The basic principle of GWAS is to integrate genotype and phenotype data through general linear models or mixed linear models (MLM) [27]. Currently, GWAS have been successfully used with mutants in various crops of interest, including for causal SNPs for agronomic and phytochemical traits in Sorghum bicolor, Brassica napus, and Glycine max [28][29][30].…”

Association Study of Agronomic and Oil Traits in Rapeseed (Brassica napus L.) Mutant Lines Using Genotyping-by-Sequencing

“…Nowadays, advances in next-generation sequencing (NGS) technology have enabled effective sequencing of plant genomes and the direct detection of SNPs, which promotes the development of cultivars of interest in plant breeding [4,16]. GBS reduces the genomic complexity by digesting the genome into small fragments (with restriction enzymes) that are read on a short-read sequencing platform [16,17]. As the availability of entire genome sequences and SNP arrays has increased, association mapping has become a robust approach for determining genetic characteristics, which can significantly increase the precision of quantitative trait locus (QTL)-based position estimates [17,18].…”

“…GBS reduces the genomic complexity by digesting the genome into small fragments (with restriction enzymes) that are read on a short-read sequencing platform [16,17]. As the availability of entire genome sequences and SNP arrays has increased, association mapping has become a robust approach for determining genetic characteristics, which can significantly increase the precision of quantitative trait locus (QTL)-based position estimates [17,18]. Moreover, association mapping has been used to overcome the limitations of QTL mapping, particularly because of the large number of SNP markers identified by NGS [16,18].…”

“…The identification of SNPs through GBS not only enables the analysis of genetic diversity but also facilitates the integration of GWAS into a unified research project [16,18,19]. GWAS are a powerful tool for identifying QTLs and genes in crops, such as rapeseed, soybean, and rice [6,17,19,20].…”

Genome-wide association studies for phenological and agronomic traits in mungbean (Vigna radiata L. Wilczek)

“…Locus-specificity, relative abundance, co-dominant nature, and amenability to high-throughput genotyping make single nucleotide polymorphism (SNP) markers highly desirable for marker-assisted breeding ( Terakami et al., 2011 ). The SNP markers significantly associated with agronomic traits have been reported in rice ( Begum et al., 2015 ; Reig-Valiente et al., 2018 ), wheat ( Rahimi et al., 2019 ), maize ( Sadessa et al., 2022 ), and soybean ( Kim et al., 2022 ). To our knowledge, limited studies have been reported for phenological and yield-associated agronomic traits in mungbean using SNP markers.…”