Supporting Information is available in the online issue at wileyonlinelibrary.com.
Pathway-Based Approaches for Sequencing-Based Genome-Wide Association Studies
Article first published online: 5 MAY 2013
© 2013 Wiley Periodicals, Inc.
Volume 37, Issue 5, pages 478–494, July 2013
How to Cite
Wu, G. and Zhi, D. (2013), Pathway-Based Approaches for Sequencing-Based Genome-Wide Association Studies. Genet. Epidemiol., 37: 478–494. doi: 10.1002/gepi.21728
- Issue published online: 14 JUN 2013
- Article first published online: 5 MAY 2013
- Manuscript Accepted: 29 MAR 2013
- Manuscript Revised: 4 MAR 2013
- Manuscript Received: 29 DEC 2012
- National Institute of Health (NIH). Grant Number: R00 RR024163
- National Science Foundation EPSCoR
- UAB IT Research Computing
- pathway analysis;
- sequencing data;
- genome-wide association studies;
- simulation framework;
- chronic obstructive pulmonary disease
For analyzing complex trait association with sequencing data, most current studies test aggregated effects of variants in a gene or genomic region. Although gene-based tests have insufficient power even for moderately sized samples, pathway-based analyses combine information across multiple genes in biological pathways and may offer additional insight. However, most existing pathway association methods are originally designed for genome-wide association studies, and are not comprehensively evaluated for sequencing data. Moreover, region-based rare variant association methods, although potentially applicable to pathway-based analysis by extending their region definition to gene sets, have never been rigorously tested. In the context of exome-based studies, we use simulated and real datasets to evaluate pathway-based association tests. Our simulation strategy adopts a genome-wide genetic model that distributes total genetic effects hierarchically into pathways, genes, and individual variants, allowing the evaluation of pathway-based methods with realistic quantifiable assumptions on the underlying genetic architectures. The results show that, although no single pathway-based association method offers superior performance in all simulated scenarios, a modification of Gene Set Enrichment Analysis approach using statistics from single-marker tests without gene-level collapsing (weighted Kolmogrov-Smirnov [WKS]-Variant method) is consistently powerful. Interestingly, directly applying rare variant association tests (e.g., sequence kernel association test) to pathway analysis offers a similar power, but its results are sensitive to assumptions of genetic architecture. We applied pathway association analysis to an exome-sequencing data of the chronic obstructive pulmonary disease, and found that the WKS-Variant method confirms associated genes previously published.