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

PLoS Genet. 2025 Jan 10;21(1):e1011553. doi: 10.1371/journal.pgen.1011553. eCollection 2025 Jan.

Abstract

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.

MeSH terms

  • Arabidopsis* / genetics
  • Droughts
  • Flowers / genetics
  • Flowers / growth & development
  • Gene-Environment Interaction*
  • Genome-Wide Association Study*
  • Genotype*
  • Models, Genetic
  • Phenotype
  • Quantitative Trait Loci* / genetics
  • Stress, Physiological / genetics
  • Zea mays / genetics

Grants and funding

This work was partially supported by INRAE’s metaprogram DIGIT-BI0 and by KWS to ADW, and partially funded by the Horizon2020 Project INCREASE, grant agreement number 862862. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.