A powerful and robust method for mapping quantitative trait loci in general pedigrees
A powerful and robust method for mapping quantitative trait loci in general pedigrees
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DOI：
10.1086/431683
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发表时间：
2005-07-01
影响因子：
9.8
通讯作者：
Lin, DY
中科院分区：
文献类型：
--
作者：
Diao, G;Lin, DY
方差-分量模型是在一般人类家系中定位数量性状基因座的首选方法。该模型假设性状值呈正态分布，包括主基因效应、随机多基因和环境效应以及协变量效应。违反正态假设会对第一类误差和功率产生不利影响。实现常态的一种可能方式是改变特质价值观。真正的转变在实践中是未知的，不同的转变可能会产生相互矛盾的结果。此外，常用的转换方法在处理离群性状值时效果不佳。我们提出了一种新的扩展的方差分量模型，允许真正的变换函数完全不指定。我们提出了有效的基于似然的方法来估计方差分量和检验遗传连锁。仿真研究表明，当正态假设成立时，新方法与现有的方差分量方法一样有效；当正态假设失效时，新方法仍然能够精确地控制第一类误差，并且比现有方法更有效。我们为酒精中毒遗传学合作研究进行了全基因组单胺氧化酶B扫描。在那项研究中，当三个离群性状值被排除在分析之外时，基于现有的方差分量方法的结果发生了巨大的变化，而我们的方法在有或没有这三个异常值的情况下得到了基本上相同的答案。实现新方法的计算机程序是免费提供的。
The variance- components model is the method of choice for mapping quantitative trait loci in general human pedigrees. This model assumes normally distributed trait values and includes a major gene effect, random polygenic and environmental effects, and covariate effects. Violation of the normality assumption has detrimental effects on the type I error and power. One possible way of achieving normality is to transform trait values. The true transformation is unknown in practice, and different transformations may yield conflicting results. In addition, the commonly used transformations are ineffective in dealing with outlying trait values. We propose a novel extension of the variance- components model that allows the true transformation function to be completely unspecified. We present efficient likelihood- based procedures to estimate variance components and to test for genetic linkage. Simulation studies demonstrated that the new method is as powerful as the existing variance- components methods when the normality assumption holds; when the normality assumption fails, the new method still provides accurate control of type I error and is substantially more powerful than the existing methods. We performed a genomewide scan of monoamine oxidase B for the Collaborative Study on the Genetics of Alcoholism. In that study, the results that are based on the existing variance- components method changed dramatically when three outlying trait values were excluded from the analysis, whereas our method yielded essentially the same answers with or without those three outliers. The computer program that implements the new method is freely available.