Genome-wide association study for root-related traits at vegetative growth stages of soybean (Glycine max L. Merrill)

Enhancing plant adaptation to challenging climates through breeding techniques requires studying plant systems with diverse genetic architectures. Comprehensive understanding of the genetic architecture of root traits is crucial for analyzing overall plant development and incorporating these insights into crop breeding programs on challenging climate adaptation. To dissect genetic architecture of root traits in soybean, we applied genome-wide association study (GWAS) in soybean germplasm population. Phenotyping of six root-related traits was performed at two plant growth stages, V 1 (two-weeks growth stage) and V 2 (three-weeks growth stage), under hydroponic culture and GWAS was performed to identify key SNPs and genes associated with root traits. Total 58 single nucleotide polymorphisms (SNPs) associated with six root-related traits were detected for two growth stages using three GWAS models, Mixed Linear Model (MLM), the Fixed and Random Model Circulating Probability Unification (FarmCPU) and 3 V multi-locus random-SNP-effect Mixed Linear Model (3VmrMLM). A total of 35 SNPs were detected for six root traits at V 1 stage, while 23 SNPs were detected for the same traits at V 2 stage. Quantitative trait locus (QTL) qRoot10.1 represented by three significant SNPs, was identified for primary root length (PRL) at V 1 and V 2 stage, and for root tips (RT) at V 2 stage. Further, QTL qRoot10.1 was validated for PRL and total root length (TRL) in a separate set of soybean population. Candidate gene analysis in genomic regions of 58 SNPs identified 63 candidate genes, with annotations associated with various pathways of root development. Differential gene expression analysis of the candidate gene Glyma.10g273000 at qRoot10.1 revealed a significant difference in expression between long-rooting and short-rooting genotypes. In this study, we offer new insights into the root architecture of soybean, identifying key SNPs and genes that could be instrumental in future breeding programs aimed at developing highly efficient root systems in soybean.