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A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits.

Abstract：

: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.

journal_name

Am J Hum Genet

journal_title

American journal of human genetics

authors

Bhattacharjee S,Rajaraman P,Jacobs KB,Wheeler WA,Melin BS,Hartge P,GliomaScan Consortium.,Yeager M,Chung CC,Chanock SJ,Chatterjee N

doi

10.1016/j.ajhg.2012.03.015

subject

Has Abstract

pub_date

2012-05-04 00:00:00

pages

821-35

issue

5

eissn

0002-9297

issn

1537-6605

pii

S0002-9297(12)00159-0

journal_volume

90

pub_type

杂志文章,meta分析

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