Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.
Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.
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DOI：
10.1534/g3.114.016097
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发表时间：
2015-02-06
期刊：
影响因子：
--
通讯作者：
de los Campos G
中科院分区：
文献类型：
--
作者：
Lopez-Cruz M;Crossa J;Bonnett D;Dreisigacker S;Poland J;Jannink JL;Singh RP;Autrique E;de los Campos G
基因组选择（GS）模型使用全基因组遗传信息来预测选择候选者的遗传值。最初，这些模型是在不考虑基因型×环境互作（G×E）的情况下开发的。一些作者提出了单环境GS模型的扩展，使用协方差函数或环境协变量来适应G×E。在这项研究中，我们使用标记×环境相互作用（M×E）GS模型对G×E进行建模;该方法在概念上很简单，可以用现有的GS软件实现。我们讨论了如何使用显式回归标记的表型或使用协方差结构（基因组最佳线性无偏预测型模型）的模型可以实现。我们使用M×E模型分析了三个CIMMYT小麦数据集（W1，W2和W3），其中1000多个品系使用基因分型测序进行基因分型，并在CIMMYT位于墨西哥奥巴桑乔城的研究站进行评估，模拟环境条件涵盖不同的灌溉水平，播种日期和种植制度。我们将M×E模型与分层（即，环境内）分析并使用标准（跨环境）GS模型，该模型假设影响在不同环境中是恒定的（即，忽略G×E）。M×E模型的预测准确性大大高于忽略G×E的跨环境分析。根据不同的预测问题，M×E模型的预测准确度与分层分析相似或更高。M×E模型将标记效应和基因组值分解为跨环境稳定的组分（主效应）和环境特异性的组分（相互作用）。因此，原则上，相互作用模型可以揭示哪些变体在不同环境中具有稳定的影响，哪些变体负责G×E。重现分析所需的数据集和脚本作为支持信息公开提供。
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT’s research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.
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影响因子：
3.3
作者：
de los Campos, Gustavo;Naya, Hugo;Cotes, Jose Miguel
通讯作者：
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影响因子：
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影响因子：
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