Multiple Test Procedure for Detecting Disease-Associated Rare Variants

• Activity Number: 34
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Jianping Sun*+ and Li Hsu and Yingye Zheng
• Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
• Keywords: rare variants ; hierarchical model

Abstract

Genome wide association studies (GWAS) have successfully identified hundreds of common genetic variants which are associated with many complex human diseases. However, these common variants only explain a small proportion of variation in disease risk. One hypothesis is that rare variants may play a role in complex diseases. Extending association studies to rare variants is therefore important.

Standard single variant-based methods used in GWAS have low powers when directly applied to rare variants due to low frequency in the sample. To increase power, rare variants are often grouped and analyzed jointly. These methods can be generally classified into two categories: testing the mean effect of the grouped rare variants, such as burden test (Li and Leal, 2008), and testing the variance of the grouped rare variants, such as SKAT (Wu et. al., 2011). We develop a novel testing procedure based on a hierarchical model, in which both the mean effect as well as the heterogeneous effects are incorporated. Extensive simulations show that the proposed method is more powerful than either the burden or SKAT test across a wide range of underlying disease models.