This article is a US government work and, as such, is in the public domain in the United States of America.
Pathway analysis by adaptive combination of P-values†
Article first published online: 30 MAR 2009
This article is a US Government work and, as such, is in the public domain in the United States of America. Published in 2009 by Wiley-Liss, Inc.
Volume 33, Issue 8, pages 700–709, December 2009
How to Cite
Yu, K., Li, Q., Bergen, A. W., Pfeiffer, R. M., Rosenberg, P. S., Caporaso, N., Kraft, P. and Chatterjee, N. (2009), Pathway analysis by adaptive combination of P-values. Genet. Epidemiol., 33: 700–709. doi: 10.1002/gepi.20422
- Issue published online: 19 NOV 2009
- Article first published online: 30 MAR 2009
- Manuscript Accepted: 22 FEB 2009
- Manuscript Revised: 30 JAN 2009
- Manuscript Received: 4 DEC 2008
- Intramural Research Program of NIH and the National Cancer Institute
- National Science Foundation of China. Grant Number: 10371126
- NIH. Grant Number: U01 DA020830
- pathway analysis;
- genetic association study;
- permutation procedure
It is increasingly recognized that pathway analyses—a joint test of association between the outcome and a group of single nucleotide polymorphisms (SNPs) within a biological pathway—could potentially complement single-SNP analysis and provide additional insights for the genetic architecture of complex diseases. Building upon existing P-value combining methods, we propose a class of highly flexible pathway analysis approaches based on an adaptive rank truncated product statistic that can effectively combine evidence of associations over different SNPs and genes within a pathway. The statistical significance of the pathway-level test statistics is evaluated using a highly efficient permutation algorithm that remains computationally feasible irrespective of the size of the pathway and complexity of the underlying test statistics for summarizing SNP- and gene-level associations. We demonstrate through simulation studies that a gene-based analysis that treats the underlying genes, as opposed to the underlying SNPs, as the basic units for hypothesis testing, is a very robust and powerful approach to pathway-based association testing. We also illustrate the advantage of the proposed methods using a study of the association between the nicotinic receptor pathway and cigarette smoking behaviors. Genet. Epidemiol. 33:700–709, 2009. Published 2009 Wiley-Liss, Inc.