Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree
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DOI：
10.1534/genetics.109.101501
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发表时间：
2009-05-01
期刊：
影响因子：
3.3
通讯作者：
Cotes, Jose Miguel
中科院分区：
文献类型：
--
作者：
de los Campos, Gustavo;Naya, Hugo;Cotes, Jose Miguel
全基因组密集标记的可用性给育种计划带来了机遇和挑战。一个重要的问题是如何利用密集标记和系谱以及表型记录来预测复杂性状的遗传值。如果回归模型中包含大量标记物，则可能需要标记物特异性回归系数收缩。因此，贝叶斯最少。绝对收缩和选择算子（LASSO）（BL）似乎是一种用于在回归模型中拟合标记效应的有趣方法。本文适应BL到达一个回归模型，标记，谱系，和协变量以外的标记被认为是共同的BL和其他标记为基础的回归模型之间的连接进行了讨论，和BL的灵敏度相对于选择分配给关键参数的先验分布进行评估，使用模拟。将该模型拟合到小麦和小鼠种群的两个数据集，并使用交叉验证方法进行评估。结果表明，在回归中包括标记物进一步提高了模型的预测能力。一个实现该模型的R程序是免费提供的。
The availability of genomewide dense markers brings opportunities and challenges to breeding programs. An important question concerns the ways in which dense markers and pedigrees, together with phenotypic records, should be used to arrive at predictions of genetic values for complex traits. If a large number of markers are included in a regression model, marker-specific shrinkage of regression coefficients may be needed. For this reason, the Bayesian least. absolute shrinkage and selection operator (LASSO) (BL) appears to be an interesting approach for fitting marker effects in a regression model. This article adapts the BL to arrive at a regression model where markers, pedigrees, and covariates other than markers are considered jointly Connections between BL and other marker-based regression models are discussed, and the sensitivity of BL with respect to the choice of prior distributions assigned to key parameters is evaluated using simulation. The proposed model was fitted to two data sets from wheat and mouse Populations, and evaluated using cross-validation methods. Results indicate that inclusion of markers in the regression further improved the predictive ability of models. An R program that implements the proposed model is freely available.