TY - JOUR
T1 - A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits
AU - Bhattacharjee, Samsiddhi
AU - Rajaraman, Preetha
AU - Jacobs, Kevin B.
AU - Wheeler, William A.
AU - Melin, Beatrice S.
AU - Hartge, Patricia
AU - Yeager, Meredith
AU - Chung, Charles C.
AU - Chanock, Stephen J.
AU - Chatterjee, Nilanjan
N1 - Funding Information:
This work was supported by the intramural program of the National Cancer Institute, National Institutes of Health, USA. The authors would like to thank the GliomaScan Consortium investigators (Demetrius Albanes, Ulrika Andersson, Laura Beane-Freeman, Christine D. Berg, Julie E. Buring, Mary Ann Butler, Tania Carreon, Helle Collatz Christensen, Maria Feychting, Susan M. Gapstur, J. Michael Gaziano, Graham G. Giles, Goran Hallmans, Susan E. Hankinson, Roger Henriksson, Jane Hoppin, Ann W. Hsing, Peter D. Inskip, Christoffer Johansen, Laurence N. Kolonel, Roberta McKean-Cowdin, Dominique Michaud, Ulrike Peters, Mark P. Purdue, Avima M. Ruder, Howard D. Sesso, Gianluca Severi, Victoria L. Stevens, Kala Visvanathan, Zhaoming Wang, Emily White, Walter C. Willett, and Anne Zeleniuch-Jacquotte) for providing data for the glioma example. The simulation experiments utilized the high-performance computational capabilities of the StatPro Linux cluster at the National Cancer Institute and the Biowulf Linux cluster at the National Institutes of Health, USA. We would also like to thank Alan Genz and Fabio Iwamoto for helpful discussions and two anonymous reviewers for comments and suggestions that helped improve the manuscript.
PY - 2012/5/4
Y1 - 2012/5/4
N2 - 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.
AB - 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.
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U2 - 10.1016/j.ajhg.2012.03.015
DO - 10.1016/j.ajhg.2012.03.015
M3 - Article
C2 - 22560090
AN - SCOPUS:84860771128
SN - 0002-9297
VL - 90
SP - 821
EP - 835
JO - American journal of human genetics
JF - American journal of human genetics
IS - 5
ER -