Linkage Disequilibrium-Based Subset Selection Approach for Rare Variants Analysis in Sequence Data

• Activity Number: 663
• Type: Invited
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Sanjay Shete*+ and Rajesh Talluri
• Companies: The University of Texas, M. D. Anderson Cancer Center and UT M.D. Anderson Cancer Center
• Keywords: rare variants ; linkage disequilibrium ; Sequence Data ; statistical genetics

Abstract

Rare variants have increasingly been cited as major contributors to a variety of diseases. Recently, several approaches have been proposed to analyze rare variants association with disease. Some approaches sum or group rare variants near a particular locus to improve power while others select the best group of rare variants for testing association such as the step-up approach. We propose a new approach based on the difference in Linkage Disequilibrium (LD) pattern between cases and controls to select the best subset of variants to include in the model. The LD based approach is robust to deleterious and protective effects of rare variants unlike the step-up type methods. The simulations were performed based on HapMap3 dataset of DRD2 gene to compare the performance of LD based subset selection approach. 1000 replicates each with 1000 cases and 1000 controls were simulated based on linkage disequilibrium pattern in DRD2 Gene. Permutations were used to control the type 1 error. The power comparisons after controlling the type 1 error show that the LD based approach is an attractive alternate method that can be considered for subset selection of rare variants.