Exploring efficient linear mixed models to detect quantitative trait locus-by-environment interactions

Abstract

Genotype-by-environment interactions (G×E) are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G×E 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. G×E modeling methods in GS can be included as covariates in GWAS using unified linear mixed models (LMMs). However, the efficacy of G×E modeling methods in GS studies has not been evaluated for GWAS. In this study, we performed a comprehensive comparison of LMMs that integrate the G×E modeling methods to detect both QTL and QTL-by-environment interaction (Q×E) effects. Model efficacy was evaluated using simulation experiments. For the fixed effect terms representing Q×E 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 G×E and genotype-by-trial (G×T) 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 G×E and G×T. 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 Q×E effects. The optimal LMM identified in this study successfully detected more QTLs with Q×E effects.

Cytonuclear diversity underlying clock adaptation to warming climate in wild barley ( Hordeum vulgare ssp. spontaneum )

“…The rationale was to try to include single representative from each of the 255 micro-site of this hierarchical collection (51 sites including each 5 micro-sites with 4 representatives). We performed a GWAS using the traits values per se and by applying a QTL by Environment (Q x E) model for the OT and HT environments (Yamamoto & Matsunaga, 2021). Moreover, since the analysis of RH (this study) and the ASHER populations (Bdolach et al,2019) indicated a significant effect of the plasmotype diversity on the photosynthetic rhythm plasticity, we included sequencing information from isolated chloroplast DNA for a portion of this panel (see Methods; Table S4 ).…”

“…The selection process led to a final set of 13,786 informative markers for GWAS analysis (TableS5 ). We wished to detect QTLs with persistent effects across the two environments (OT and HT) but also with Q × E effects, i.e., loci with specific effects to a certain environment ( Malosetti et al2013;Yamamoto & Matsunaga, 2021).…”

Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS

“…This method demonstrated enhanced power, effectively controlled FDR, and simultaneously adapted for environmental factors to enlarge the effectiveness of GWAS. A study evaluated the overall G × E interaction using LMMs [ 109 ]. Authors considered instantaneous scoring of particular and general environmental effects for fixed effect terms demonstrating G × E effects in this study.…”

“…Authors considered instantaneous scoring of particular and general environmental effects for fixed effect terms demonstrating G × E effects in this study. The genomic inflation factor is controlled by considering both G × E and G × T (genotype by trial) effect for random effects terms [ 109 ]. The LMM approach was applied to tomato phenotype data collected in two different seasons.…”

“…Results showed that this method identified both QTLs with consistent effects throughout the cultivating seasons and G × E effects. Moreover, this study discovered more QTLs with G × E effects than other LMM methods [ 109 ]. Recently, Li, et al [ 110 ] established a compressed variance component mixed model framework, namely 3VmrMLM (three-variance-component mixed model), to detect QTNs and QTN × E and QTN × QTN interaction and estimate all their possible effects by controlling all the possibly polygenic backgrounds.…”