title: Bayesian variable selection for post-analytic interrogation of susceptibility loci.
abstract: Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material.
authors.affiliation: Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A.
authors.forename: Siying
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A.
authors.forename: Sara
authors.initials: S
authors.lastname: Nunez
authors.affiliation: Department of Medicine, Division of Cardiology, and the Irving Institute for Clinical and Translational Research at Columbia University, New York City, New York, U.S.A.
authors.forename: Muredach P
authors.initials: MP
authors.lastname: Reilly
authors.affiliation: Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, U.S.A.
authors.forename: Andrea S
authors.initials: AS
authors.lastname: Foulkes
mesh_terms: D001499:Bayes Theorem; D004198:Disease Susceptibility; D056426:Genetic Loci; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D020641:Polymorphism, Single Nucleotide
keywords: Bayesian variable selection (BVS); Genome-wide association studies (GWAS); Long non-coding RNAs (lncRNAs); Protein coding gene-level testing; Regulatory elements; Spike and slab prior