A Bayesian partitioning model for the detection of multilocus effects in case-control studies

Debashree Ray, Xiang Li, Wei Pan, Jim Pankow, Saonli Basu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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 languageEnglish (US)
Pages (from-to)69-79
Number of pages11
JournalHuman heredity
Issue number2
StatePublished - Jul 22 2015

Bibliographical note

Publisher Copyright:
© 2015 S. Karger AG, Basel.


  • Dimension reduction
  • Multilocus interaction
  • Reversible jump Markov chain Monte Carlo


Dive into the research topics of 'A Bayesian partitioning model for the detection of multilocus effects in case-control studies'. Together they form a unique fingerprint.

Cite this