title: A Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minn., USA.
authors.forename: Debashree
authors.initials: D
authors.lastname: Ray
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.forename: Wei
authors.initials: W
authors.lastname: Pan
authors.forename: James S
authors.initials: JS
authors.lastname: Pankow
authors.forename: Saonli
authors.initials: S
authors.lastname: Basu
mesh_terms: D050197:Atherosclerosis / Q000235:genetics; D001499:Bayes Theorem; D016022:Case-Control Studies; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide