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
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
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DOI：
10.1534/genetics.107.071068
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发表时间：
2007-11-01
期刊：
影响因子：
3.3
通讯作者：
van Eeuwijk, Fred A.
中科院分区：
文献类型：
--
作者：
Boer, Martin P.;Wright, Deanne;van Eeuwijk, Fred A.
植物的复杂数量性状是在多个环境中对基因型集合进行测量的结果，这些过程以复杂的方式同时依赖于基因型和环境。为了更好地理解在不同环境中观察到的这些性状的遗传结构，应该使用关于基因型和环境的明确信息的统计模型来建模基因型与环境的相互作用。我们提出的建模方法解释了基因型与环境的相互作用的差异数量性状基因座（QTL）的表达与环境变量。我们分析了1994年和1995年期间在美国玉米带12个环境中评估的976个F-5玉米测交后代的实验数据集的谷物产量和谷物水分。我们使用的策略是基于混合模型，并开始与多环境数据的表型分析，建模基因型环境之间的相互作用和相关的遗传相关性的环境，同时考虑到环境内的错误结构。通过引入标记信息作为基因型协变量，将表型混合模型扩展为QTL模型。大多数QTL表现出明显的QTL-环境互作（QEI）。通过将环境协变量纳入混合模型，进一步分析QEI。大多数QTL 1可以被理解为有条件的经度或年份的差异QTL表达，这两个结果的温度差异在关键阶段的增长。
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 multienvironment 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 QE1 could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.