Incorporating Model Uncertainty in Detecting Rare Variants: The Bayesian Risk Index

• Activity Number: 570
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Melanie Quintana*+ and Duncan Thomas and Jonine Berstein and David V. Conti
• Companies: University of Southern California and University of Southern California and Memorial Sloan-Kettering Cancer Center and University of Southern California
• Keywords: rare variants ; risk index ; genetic association studies ; Bayesian model uncertainty ; multiplicity correction ; sequence analysis

Abstract

We are interested in investigating the involvement of multiple rare variants within a given region by conducting analyses of individual regions with two goals: (1) to first determine if regional rare variation in aggregate is associated with risk; and (2) conditional upon the region being associated we wish to identify specific genetic variants within the region that are driving the association. In particular, we seek a formal integrated analysis that achieves both of our goals. Our framework aims at constructing a risk index based on multiple rare variants within a region and then detecting associated regions based on the indices. Our analytical strategy is novel in that we use a Bayesian approach to incorporate model uncertainty in the selection of which variants to include in the index as well as whether the variant is involved as a protective or risk contributing factor. Using a set of study-based simulations, we show that our methodology has added power over the more commonly used methods. In addition, we apply the approach to sequence data within BRCA1 for samples from the WECARE Study of second primary breast cancers.