期刊
HUMAN HEREDITY
卷 73, 期 2, 页码 63-72出版社
KARGER
DOI: 10.1159/000336196
关键词
GWAS; Pathway association analysis; INTERSNP; Parallel computing
资金
- Deutsche Forschungsgemeinschaft [BE 3828/3-2, BE 3828/4-1]
Objectives: Pathway association analysis (PAA) tests for an excess of moderately significant SNPs in genes from a common pathway. Methods: We present a Monte-Carlo simulation framework that allows to formulate the main ideas of existing PAA approaches using a self-contained rather than a competitive null hypothesis. A stand-alone implementation in INTERSNP makes time-consuming communication with standard GWAS software redundant. By additional parallelization with the OpenMP API, we achieve a reduction in running time for PAA by orders of magnitude, making a power simulation study for PAA feasible. Our approach properly accounts for linkage disequilibrium and is robust with respect to residual X inflation. Results: We demonstrate that under simple, realistic disease models, PAA can actually strongly outperform the GWAS single-marker approach. PAA methods that make use of the strength of the SNP association (GenGen, Fisher's combination test), in general, perform better than ratio-based methods (ALIGATOR, SNP ratio), whereas the relative performance of gene-based scoring (ALIGATOR, GenGen) and pathway-based scoring (SNP ratio, Fisher's combination test) depends on the architecture of the assumed disease model. Finally, we present a new PAA score that models independent signals from the same gene in a regression framework and show that it is a reasonable compromise that combines the advantages of existing ideas. Copyright (C) 2012 S. Karger AG, Basel
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