Genomic Prediction of Breeding Values when Modeling Genotype x Environment Interaction using Pedigree and Dense Molecular Markers
Genomic Prediction of Breeding Values when Modeling Genotype x Environment Interaction using Pedigree and Dense Molecular Markers
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DOI：
10.2135/cropsci2011.06.0299
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发表时间：
2012-03-01
期刊：
影响因子：
2.3
通讯作者：
Crossa, Jose
中科院分区：
文献类型：
--
作者：
Burgueno, Juan;de los Campos, Gustavo;Crossa, Jose
基因组选择（GS）已成为动植物育种的重要辅助手段。多环境（多特征）模型允许跨环境（特征）借用信息，这可以提高预测准确性。本研究提出了GS的多环境（多性状）模型，并比较了这些模型的预测精度：（一）多环境分析没有系谱和标记信息，和（二）多环境系谱或/和标记为基础的模型。一个统计框架，将系谱和分子标记信息的多环境数据模型的描述和应用于数据，起源于小麦（小麦L。）多环境试验考虑了与植物育种者相关的两个预测问题：（CV1）预测未测试的基因型（“新”开发的品系）的性能，以及（CV 2）预测在某些环境中已评估但在其他环境中未评估的基因型的性能。结果证实了使用标记和系谱信息的模型优于仅基于系谱信息的模型。具有谱系和/或标记的模型比不包括这两个信息来源中的任一个的简单线性混合模型具有更好的预测准确性。我们的结论是，这些试验的评估可以大大受益于使用多环境GS模型。
Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker-based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes ("newly" developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confirmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefit greatly from using multienvironment GS models.