Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-environment Interactions

Abstract

Genotype-by-environment interactions (GxE) are important for understanding genotype-phenotype relationships. To date, various statistical models have been proposed to account for GxE effects, especially in genomic selection (GS) studies. Generally, GS does not focus on the detection of each quantitative trait locus (QTL), while the genome-wide association study (GWAS) was designed for QTL detection. GxE modeling methods in GS can be included as covariates in GWAS using unified linear mixed models (LMMs). However, the efficacy of GxE modeling methods in GS studies has not been evaluated for GWAS. In this study, we performed a comprehensive comparison of LMMs that integrate the GxE modeling methods to detect both QTL and QTL-by-environment interaction (QxE) effects. Model efficacy was evaluated using simulation experiments. For the fixed effect terms representing QxE effects, simultaneous scoring of specific and non-specific environmental effects was recommended because of the higher recall and improved genomic inflation factor value. For random effects, it was necessary to account for both GxE and genotype-by-trial (GxT) effects to control genomic inflation factor value. Thus, the recommended LMM includes fixed QTL effect terms that simultaneously score specific and non-specific environmental effects and random effects accounting for both GxE and GxT. The LMM was applied to real tomato phenotype data obtained from two different cropping seasons. We detected not only QTLs with persistent effects across the cropping seasons but also QTLs with QxE effects. The optimal LMM identified in this study successfully detected more QTLs with QxE effects.