Identification of QTL-by-Environment Interaction by Controlling Polygenic Background Effect

The QTL by environment interaction (QxE) effect is hard to detect because there are no effective ways to control the genomic background. In this study, we propose a novel linear mixed model that simultaneously analyzes data from multiple environments to detect QxE interactions. This model incorporates two different kinship matrices derived from the genome-wide markers to control both main and interaction polygenic background effects. Simulation studies demonstrated that our approach was more powerful than the meta-analysis and inclusive composite interval mapping methods. We further analyzed four agronomic traits of rice across four environments. A main effect QTL was identified for 1000-grain weight (KGW), while no QTLs were found for tiller number. Additionally, a large QTL with a significant QxE interaction was detected on chromosome 7 affecting grain number, yield and KGW. This region harbored two important genes, PROG1 and Ghd7. Furthermore, we applied our mixed model to analyze lodging in barley across six environments. The six regions exhibiting QxE interaction effects identified by our approach overlapped with the SNPs previously identified using EM and MCMC-based Bayesian methods, further validating the robustness of our approach. Both simulation studies and empirical data analyses showed that our method outperformed all other methods compared.