A reaction norm model for genomic selection using high-dimensional genomic and environmental data.
A reaction norm model for genomic selection using high-dimensional genomic and environmental data.
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DOI：
10.1007/s00122-013-2243-1
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发表时间：
2014-03
影响因子：
5.4
通讯作者：
de los Campos, Gustavo
中科院分区：
文献类型：
--
作者：
Jarquin, Diego;Crossa, Jose;Lacaze, Xavier;Du Cheyron, Philippe;Daucourt, Joelle;Lorgeou, Josiane;Piraux, Francis;Guerreiro, Laurent;Perez, Paulino;Calus, Mario;Burgueno, Juan;de los Campos, Gustavo
结合高维标记和高维环境协变量的主效应和互作效应的新方法提高了小麦产量跨环境和内环境的预测精度。在大多数农作物中，基因对性状的影响受到环境条件的调节，导致遗传受到环境互作的影响(G×E)。现代基因分型技术可以非常详细地描述基因组的特征，现代信息系统可以产生大量的环境数据。原则上，可以使用标记和环境协变量(ECs)之间的交互作用来解释G×E。然而，当基因类型和环境信息是高维的时，对所有可能的交互作用进行显式建模变得不可行。在这篇文章中，我们展示了如何使用协方差函数对高维标记集和EC之间的交互进行建模。该模型由(随机)反应范数组成，其中遗传梯度和环境梯度分别被描述为标记和ECs的线性函数。我们使用Arvalis的数据对提出的方法进行了评估，该数据包括139个小麦品系，这些品系具有2395个SNPs，并评估了8年来和法国北部不同地点的粮食产量。根据作物物候的五个阶段定义的68个EC被用于分析。交互作用项在环境内产量差异中占有相当大的比例(16%)，考虑交互作用项的模型预测精度显著高于仅基于主效应项的模型(17%-34%)。针对目标环境条件的育种已成为大多数育种计划的中心优先事项。像这里介绍的方法，可以利用丰富的基因组和环境信息，将变得越来越重要。
New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.
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影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
影响因子：
3
作者：
GOLLOB, HF
通讯作者：
GOLLOB, HF
DOI：
10.1198/108571104x4423
发表时间：
2004-09-01
影响因子：
1.4
作者：
Crossa, J;Yang, RC;Cornelius, PL
通讯作者：
Cornelius, PL
影响因子：
3.3
作者：
de los Campos, Gustavo;Naya, Hugo;Cotes, Jose Miguel
通讯作者：
Cotes, Jose Miguel
影响因子：
3.3
作者：
Boer, Martin P.;Wright, Deanne;van Eeuwijk, Fred A.
通讯作者：
van Eeuwijk, Fred A.