metaGE: Investigating genotype x environment interactions through GWAS meta-analysis

Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package.Author summary: In multi-environment trial experiments, the same panel of plants is evaluated in different well-characterized sites and years to describe the genotypic response to environmental stresses. Such experiments require dedicated statistical approaches to allow the identification of quantitative trait locus (QTLs) whose allelic effects are modulated by the stress conditions. We consider an original approach based on the joint analysis of summary results from genome-wide association studies conducted separately in individual environments. Application of the method to Arabidopsis identified QTLs involved in flowering whose effects are strongly modulated by competition. Application to maize identified yield QTLs whose effects strongly correlated with the heat stress level. The method called metaGE drastically reduced the computational burden of the analysis and is distributed as an R package.