Control of confounding of genetic associations in stratified populations
Control of confounding of genetic associations in stratified populations
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DOI：
10.1086/375613
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发表时间：
2003-06-01
影响因子：
9.8
通讯作者：
McKeigue, PM
中科院分区：
文献类型：
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作者：
Hoggart, CJ;Parra, EJ;McKeigue, PM
为了控制遗传关联研究中隐藏的种群分层，人们提出使用标记基因型数据来推断种群结构的统计方法作为基于家族的设计的可能替代方案。原则上，即使没有关于种群人口统计背景的信息，也可以从标记位点之间的关联以及标记与性状的关联来推断种群结构。在总群体由两个或多个亚群体混合形成的模型中，可以估计和控制混杂。这种方法目前的实施存在局限性，其中最严重的是它们不允许个体混合比例估计的不确定性或模型中亚群缺乏可识别性。我们描述了通过贝叶斯和经典方法相结合来克服这些局限性的方法，并通过使用来自三个混合人群（非裔美国人、非洲加勒比人和西班牙裔美国人）的数据来证明这些方法，其中性状-基因型关联存在极端混杂，因为所研究的性状（皮肤色素沉着）随混合比例的变化而变化。在这些数据集中，多达三分之一的标记基因座显示出与性状的粗略关联。通过群体分层控制混杂因素可以消除这些关联，但与性状候选基因相关的基因座除外。由于只有 32 个提供祖先信息的标记，分析效率约为 70%。这些方法可以处理遗传关联研究中的混杂和选择偏差，使得基于家族的设计变得不必要。
To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations-African American, African Caribbean, and Hispanic American-in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is similar to70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.