A Subset-Based Approach Improves Power and Interpretation for the Combined Analysis of Genetic Association Studies of Heterogeneous Traits
A Subset-Based Approach Improves Power and Interpretation for the Combined Analysis of Genetic Association Studies of Heterogeneous Traits
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DOI：
10.1016/j.ajhg.2012.03.015
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发表时间：
2012-05-04
影响因子：
9.8
通讯作者：
Chatterjee, Nilanjan
中科院分区：
文献类型：
--
作者：
Bhattacharjee, Samsiddhi;Rajaraman, Preetha;Chatterjee, Nilanjan
汇集全基因组关联研究（GWAS）增加了力量，但也带来了方法上的挑战，因为研究往往是异质性的。例如，结合相关但不同性状的GWAS可以为发现具有小但常见多效性效应的基因座提供有希望的方向。然而，荟萃分析或汇总分析的经典方法可能不适合这种分析，因为个体变异可能仅与性状的一个子集相关，或者可能在不同方向上表现出影响。我们提出了一种方法，详尽地探讨了研究的子集的存在下，真正的关联信号是在相同的方向或可能相反的方向。一个有效的近似用于快速评估的p值。我们提出了两个说明性的应用程序，一个是对六种不同癌症的单独病例对照研究的荟萃分析，另一个是对神经胶质瘤（一类包含异质亚型的脑肿瘤）的病例对照研究的汇总分析。应用程序和额外的模拟研究表明，所提出的方法提供了改进的电源和更可解释的结果相比，传统的方法进行异质性状的分析。所提出的框架具有遗传关联研究以外的应用。
Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.