An application of vGWAS to differences in flowering time in maize across mega‐environments

Abstract

Genomic regions containing loci with effect sizes that interact with environmental factors are desirable targets for selection because of increasingly unpredictable growing seasons. Although selecting upon such gene‐by‐environment (G × E) loci is vital, identifying significantly associated loci is challenging due to the multiple testing correction. Consequently, G × E loci of small‐ to moderate effect sizes may never be identified via traditional genome‐wide association studies (GWAS). Variance GWAS (vGWAS) have been previously shown to identify G × E loci. Combined with its inherent reduction in the severity of multiple testing, we hypothesized that vGWAS could be successfully used to identify genomic regions likely to contain G × E effects. We used publicly available genotypic and phenotypic data in maize (Zea mays L.) to test the ability of two vGWAS approaches to identify G × E loci controlling two flowering traits. We observed high inflation of from both approaches. This suggests that these two vGWAS approaches are not suitable to the task of identifying G × E loci. We advocate that similar future applications of vGWAS use more sophisticated models that can adequately control the inflation of . Otherwise, the application of vGWAS to search for G × E effects that are critical for combating the effects of climate change will not reach its full potential.