Using biological knowledge to discover higher order interactions in genetic association studies
Article first published online: 22 NOV 2010
© 2010 Wiley-Liss, Inc.
Volume 34, Issue 8, pages 863–878, December 2010
How to Cite
Chen, G. K. and Thomas, D. C. (2010), Using biological knowledge to discover higher order interactions in genetic association studies. Genet. Epidemiol., 34: 863–878. doi: 10.1002/gepi.20542
- Issue published online: 22 NOV 2010
- Article first published online: 22 NOV 2010
- Manuscript Accepted: 9 SEP 2010
- Manuscript Revised: 11 AUG 2010
- Manuscript Received: 20 MAY 2010
- NIH. Grant Numbers: R01 ES016813, R01 ES015090
- Bayesian analysis;
- variable selection;
The recent successes of genome-wide association studies (GWAS) have revealed that many of the replicated findings have explained only a small fraction of the heritability of common diseases. One hypothesis that investigators have suggested is that higher order interactions between SNPs or SNPs and environmental risk factors may account for some of this missing heritability. Searching for these interactions poses great statistical and computational challenges. In this article, we propose a novel method that addresses these challenges by incorporating external biological knowledge into a fully Bayesian analysis. The method is designed to be scalable for high-dimensional search spaces (where it supports interactions of any order) because priors that use such knowledge focus the search in regions that are more biologically plausible and avoid having to enumerate all possible interactions. We provide several examples based on simulated data demonstrating how external information can enhance power, specificity, and effect estimates in comparison to conventional approaches based on maximum likelihood estimates. We also apply the method to data from a GWAS for breast cancer, revealing a set of interactions enriched for the Gene Ontology terms growth, metabolic process, and biological regulation. Genet. Epidemiol. 34:863–878, 2010. © 2010 Wiley-Liss, Inc.