A mixed model QTL analysis for multiple environment trial data using environmental covariables for QTLxE, with an example in maize

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 F5 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 intra-environmental 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 QTLs showed significant QTL-by-environment interactions (QEI). The QEI was further analysed 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.

Key Words: Environmental covariates, Genotype by environment interactions, QTL by environment interaction, QTL mapping, multi-environment trial

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