Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. (27th August 2019) - He, Tianhua; Hill, Camilla Beate; Angessa, Tefera Tolera; Zhang, Xiao-Qi; Chen, Kefei; Moody, David; Telfer, Paul; Westcott, Sharon; Li, Chengdao
Abstract : Using gene-set association test and epistasis analysis, this research achieved higher statistical power with potentially high accuracy, and detected significant genes and gene networks that influence flowering time in barley. Abstract: Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene–phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene–phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.