Skip to Main Content

An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit  cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

About this Publication
Title
A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits.
Pubmed ID
22560090 (View this publication on the PubMed website)
Digital Object Identifier
Publication
Am. J. Hum. Genet. 2012 May 4; Volume 90 (Issue 5): Pages 821-35
Authors
Bhattacharjee S, Rajaraman P, Jacobs KB, Wheeler WA, Melin BS, Hartge P, GliomaScan Consortium, Yeager M, Chung CC, Chanock SJ, Chatterjee N
Affiliations
  • Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 6120 Executive Boulevard, Rockville, MD 20852, USA.
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.

Related CDAS Studies
Related CDAS Projects