Identification of loci governing eight agronomic traits using a <scp>GBS</scp>‐<scp>GWAS</scp> approach and validation by <scp>QTL</scp> mapping in soya bean

TLDR

Soybean is a major source of edible oil and protein for human consumption and animal feed. The study aims to uncover the genetic basis of soybean traits to inform breeding strategies. A GWAS was performed using a GBS‑derived panel of 304 short‑season lines, generating >47,000 SNPs, and 139 diverse lines were phenotyped for eight agronomic traits across six environments. The GWAS uncovered highly significant loci for simple traits and 1–8 loci for complex traits, most overlapping known QTL regions, and several were validated by biparental QTL mapping, showing that GBS‑based GWAS complements classical mapping for dissecting soybean agronomic traits.

Abstract

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome-wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping-by-sequencing (GBS) approach was used to provide dense genome-wide marker coverage (>47,000 SNPs) for a panel of 304 short-season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.