A unified semiparametric framework for quantitative trait loci analyses, with application to spike phenotypes
A unified semiparametric framework for quantitative trait loci analyses, with application to spike phenotypes
复制标题
DOI：
10.1198/016214506000000834
复制
发表时间：
2007-03-01
影响因子：
3.7
通讯作者：
Yandell, Brian S.
中科院分区：
文献类型：
--
作者：
Jin, Chunfang;Fine, Jason P.;Yandell, Brian S.
本文提出了一个适用于回交和互交设计中复杂表型的多数量性状基因座（QTL）分析的通用半参数模型。该模型提供了关于遗传假设的检验，如加性、显性和上位性，这些假设不需要指定表型分布的形式。这与以前的方法的基础上转换为常态和广义线性模型，需要仔细考虑的表型分布。推论涉及扩展的部分和条件似然开发的单QTL回交模型。我们证明了条件似然法对未观察到的选择性基因分型是稳健的，而部分似然法和其他标准方法则不然。为了便于基因组筛选，提出了一种新的重排方法，其在精神上类似于流行的排列测试。该方法的主要优点是可广泛应用于非正态表型的多个QTL，并大大减少了计算量。一个彻底的个案研究穗数据的遗传影响恢复李斯特菌感染的小鼠杂交实验。应用表明，所提出的方法可能会得到实质性不同的结论比现有的区间映射方法从参数模型中存在未观察到的选择。
This article proposes a general semiparametric model for multiple quantitative trait loci (QTL) analyses of complex phenotypes in backcross and intercross designs. The model provides tests about genetic hypotheses, such as additivity, dominance, and epistasis, that do not require specifying the form of the phenotypic distribution. This contrasts with previous approaches based on transformations to normality and generalized linear models, which require careful consideration of the phenotypic distribution. Inferences involve extensions of partial and conditional likelihoods developed for single-QTL backcross models. We demonstrate that conditional likelihood is robust to unobserved selective genotyping, whereas partial likelihood and other standard methods are not. To facilitate genome screens, a novel resampling method is proposed that is similar in spirit to the popular permutation tests. Its main advantages are that it is broadly applicable to multiple QTLs with nonnormal phenotypes and achieves a substantial reduction in computational burden. A thorough case study of spike data on the genetic influences to recovery from Listeria infection in a mouse intercross experiment is presented. The application reveals that the proposed methods may give substantively different conclusions than those obtained with existing interval mapping methods from parametric models in the presence of unobserved selection.