Abstract
Background: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. Methods: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. Results: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. Conclusion: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.
Original language | English (US) |
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Pages (from-to) | 69-79 |
Number of pages | 11 |
Journal | Human heredity |
Volume | 79 |
Issue number | 2 |
DOIs | |
State | Published - Jul 22 2015 |
Bibliographical note
Publisher Copyright:© 2015 S. Karger AG, Basel.
Keywords
- Dimension reduction
- Multilocus interaction
- Reversible jump Markov chain Monte Carlo