Abstract
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.