Meta-GWAS for quantitative trait loci identification in soybean

Johnathon M. Shook, Jiaoping Zhang, Sarah E. Jones, Arti Singh, Brian W. Diers, Asheesh K. Singh

Research output: Contribution to journalArticlepeer-review

Abstract

We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Original languageEnglish (US)
Article numberjkab117
JournalG3: Genes, Genomes, Genetics
Volume11
Issue number7
DOIs
StatePublished - Jul 2021

Keywords

  • Agronomic traits
  • Disease resistance
  • GWAS
  • Meta-analysis
  • Seed composition traits

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

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