Informative Variable Selection for Rare Variant Association Studies

• Activity Number: 17
• Type: Topic Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Melanie Quintana*+ and David V Conti
• Companies: University of Southern California and University of Southern California
• Keywords: Bayesian ; Risk Index ; Rare Variants ; Model Uncertainty ; Bayesian Model Averaging ; Genetic Risk Factors

Abstract

We are interested in investigating the involvement of multiple rare variants within a given region by conducting a formal integrated analysis with two goals: (1) to determine if rare variation in aggregate is associated with risk; and (2) conditional upon a region being associated, to identify specific variants driving the association. In particular, our framework constructs a risk index based on multiple rare variants within a region. Our analytical strategy is novel in that we use a Bayesian approach to incorporate uncertainty in the selection of variants to include in the index as well as the direction of the effects. Additionally, our approach allows for inference at both the region and variant specific levels. We also extend our approach by introducing a novel informative prior on the marginal inclusion probabilities that incorporates variant specific biological information (such as conservation, genomic region, mutation type, etc.). Using a set of simulations, we show that our methodology has added power over other popular rare variant methods to detect associations. Finally, we apply the approach to sequence data from the WECARE Study of second primary breast cancers.