Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions
Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions
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DOI：
10.1007/s13253-015-0222-5
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发表时间：
2015-12-01
影响因子：
1.4
通讯作者：
Perez-Rodriguez, Paulino
中科院分区：
文献类型：
--
作者：
de los Campos, Gustavo;Veturi, Yogasudha;Perez-Rodriguez, Paulino
自然和人工选择的种群通常表现出某种程度的分层。在全基因组关联研究和全基因组回归（WGR）分析中，群体分层要么被忽略，要么被视为潜在的混杂因素。然而，等位基因频率和连锁不平衡模式的系统差异可能会引起亚群特异性效应。从这个角度来看，结构充当效果调节剂而不是混杂因素。在本文中，我们扩展了植物和动物育种中常用的 WGR 模型，以考虑亚种群特定的影响。这是通过将标记效应分解为主要效应和描述特定群体偏差的交互成分来实现的。该模型可以与变量选择和收缩方法一起使用，并且可以使用现有的基因组选择软件来实现。使用小麦和猪育种数据集，我们将交互 WGR 模型的参数估计和预测准确性与忽略种群分层的 WGR 分析（跨组分析）和分层（即子种群内）WGR 分析进行比较。交互模型对亚群体之间的平均效应相关性进行特定性状的估计；我们发现这种相关性不仅取决于群体之间等位基因频率的遗传分化程度，而且还取决于性状之间的差异。预测准确性的评估表明交互模型相对于其他两种方法具有一定的优越性。这种优势是由于跨数据集和特征的交互模型的性能具有更好的稳定性的结果；事实上，在几乎所有情况下，交互模型要么是性能最佳的模型，要么性能接近最佳性能的模型。
Naturally and artificially selected populations usually exhibit some degree of stratification. In Genome-Wide Association Studies and in Whole-Genome Regressions (WGR) analyses, population stratification has been either ignored or dealt with as a potential confounder. However, systematic differences in allele frequency and in patterns of linkage disequilibrium can induce sub-population-specific effects. From this perspective, structure acts as an effect modifier rather than as a confounder. In this article, we extend WGR models commonly used in plant and animal breeding to allow for sub-population-specific effects. This is achieved by decomposing marker effects into main effects and interaction components that describe group-specific deviations. The model can be used both with variable selection and shrinkage methods and can be implemented using existing software for genomic selection. Using a wheat and a pig breeding data set, we compare parameter estimates and the prediction accuracy of the interaction WGR model with WGR analysis ignoring population stratification (across-group analysis) and with a stratified (i.e., within-sub-population) WGR analysis. The interaction model renders trait-specific estimates of the average correlation of effects between sub-populations; we find that such correlation not only depends on the extent of genetic differentiation in allele frequencies between groups but also varies among traits. The evaluation of prediction accuracy shows a modest superiority of the interaction model relative to the other two approaches. This superiority is the result of better stability in performance of the interaction models across data sets and traits; indeed, in almost all cases, the interaction model was either the best performing model or it performed close to the best performing model.