Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection

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.

Keywords: Barley; GWAS; epistasis; flowering time; gene-set association analysis; heritability; next-generation sequencing; phenology; target capture; target enrichment.

Fig. 1.

Mean temperature influencing flowering time (days to Z49) in barley in seven experimental environmental sets across four locations in two years. r and p represent correlation coefficient and probability, respectively, assuming a linear relationship between flowering and temperature. Whiskers are standard deviations.

Fig. 2.

Phenology of barley accessions with contrasting growth habits, row types, and geographic origin of accessions. Asterisk indicates significant difference in ANOVA. Numbers in parentheses indicate the number of samples, and only the samples positively identified were included.

Fig. 3.

Manhattan plot of single SNP GWAS showing significant SNPs that are associated with flowering time in barley accessions. Significant SNPs are shown with larger symbols above the red dashed line as the significance threshold. Significance was determined by sequential Bonferroni correction at P<0.05.

Fig. 4.

Manhattan plot of gene-set GWAS showing significant genes that are associated with phenology in the barley accessions. Significant genes are shown above the red dashed line as the significance threshold as determined by sequential Bonferroni correction at P<0.05.

Fig. 5.

Significant flowering genes and their regulatory connections in barley (Hordeum vulgare L.). Putative gene name and gene IDs were from Ensembl Plants Hordeum vulgare Genome assembly 082214v1 that was archived in STRING (Szklarczyk et al., 2017). The interactions, including type and effects, were based direct (physical) and indirect (functional) associations from computational prediction and knowledge transfer between organisms, as implemented in STRING (Szklarczyk et al., 2017).