A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize

Abstract

Complex quantitative traits of plants as measured on collections of genotypes across multiple environments are the outcome of processes that depend in intricate ways on genotype and environment simultaneously. For a better understanding of the genetic architecture of such traits as observed across environments, genotype-by-environment interaction should be modeled with statistical models that use explicit information on genotypes and environments. The modeling approach we propose explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables. We analyzed grain yield and grain moisture for an experimental data set composed of 976 F(5) maize testcross progenies evaluated across 12 environments in the U.S. corn belt during 1994 and 1995. The strategy we used was based on mixed models and started with a phenotypic analysis of multi-environment data, modeling genotype-by-environment interactions and associated genetic correlations between environments, while taking into account intraenvironmental error structures. The phenotypic mixed models were then extended to QTL models via the incorporation of marker information as genotypic covariables. A majority of the detected QTL showed significant QTL-by-environment interactions (QEI). The QEI were further analyzed by including environmental covariates into the mixed model. Most QEI could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.

Figure 1.—

Genome scan for moisture. (Top) The P-values for the test for main effects (blue) and the test for environment-specific effects (green) are shown. The red horizontal line is the 5% genomewide significance threshold. The green horizontal lines in the bottom section indicate significant environment-specific effects. (Bottom) The environment-specific QTL effects are shown. Blue (red) indicates that parent A (B) has significantly higher moisture values. For the VCOV structure we used the second-order factor analytic model.

Figure 2.—

Genome scan for yield. (Top) The P-values for the test for main effects (blue) and the test for environment-specific effects (green) are shown. The red horizontal line is the 5% genomewide significance threshold. The green horizontal lines in the bottom section indicate significant environment-specific effects. (Bottom) The environment-specific QTL effects are shown. Blue (red) indicates that parent A (B) has significantly higher yield values. For the VCOV structure we used the first-order factor analytic model.

Figure 3.—

Biplot for environmental classification data. The circles are the environments, with 1994 in blue and 1995 in light green. The environmental covariates are indicated by squares. For a further description of the environments and the environmental covariates see materials and methods and Table 1.