Additive Genetic Variability and the Bayesian Alphabet
Additive Genetic Variability and the Bayesian Alphabet
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DOI：
10.1534/genetics.109.103952
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发表时间：
2009-09-01
期刊：
影响因子：
3.3
通讯作者：
Fernando, Rohan
中科院分区：
文献类型：
--
作者：
Gianola, Daniel;de los Campos, Gustavo;Fernando, Rohan
在数量性状预测的统计模型中使用所有可用的分子标记导致了在动物和植物育种中可以被称为基因组辅助选择范例的现象。本文对基因组辅助动物和作物遗传评价中的一些理论和统计概念进行了批判性的回顾。首先，在标准假设下，研究了一些回归模型中标记效应的贝叶斯方差与加性遗传方差之间的关系。其次，标记基因型和亲属之间的相似性之间的联系进行了探讨，并基于标记的模型和无穷小模型之间的联系进行了审查。第三，与使用贝叶斯模型的标记辅助选择，重点对先验的作用，相关的问题，从理论的角度进行检查。通过模拟说明了已提出的贝叶斯规范（称为“Bayer A”）相对于先验知识的敏感性。方法，可以解决这些贝叶斯回归过程中的一些潜在的缺点进行了简要的讨论。
The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what: could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called "Bayer A") with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly.