Events and Seminars : 2012 Seminars

Functional Annotation Signatures as Prior Information in Genetic Association Studies

Edwin S. Iversen, Ph.D.
Associate Research Professor
Department of Statistical Science
Duke University

November 27

We describe the development and application of a model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available annotation data. The model takes the form of a binary regression of the indicator of association on a set of annotation variables; we construct an informative prior distribution on the coefficients in this model that is informed by an analysis of SNPs that have been found previously to be associated and are housed in the GWAS Catalog (GC). To this end, we constructed a matched case—control study of SNPs in which the cases are drawn from the GC and controls are identified from the HapMap database, Release 27, Phases II and III merged genotypes. The set of functional predictors we examined includes measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative. As a result, we expect that only a fraction of the annotation variables will contribute to predicting association. We employed shrinkage priors to reflect this belief and used the normal-exponential-gamma (NEG) distribution for its ability to heavily penalize weakly determined coefficients and to weakly penalize those that are well determined. We evaluate the out-of-sample performance of the model and demonstrate its scalability as a prior distribution for GWAS scale association testing.