Integrative Analysis of Sequencing and Array Genotype Data for Discovering Disease Associations with Rare Mutations

• Activity Number: 644
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Yijuan Hu* and Yun Li and Paul Auer and Danyu Lin
• Companies: Emory University and The University of North Carolina at Chapel Hill and University of Wisconsin - Milwaukee and The University of North Carolina at Chapel Hill
• Keywords: data integration ; gene-level association tests ; genotype imputation ; linkage disequilibrium ; whole-exome sequencing

Abstract

In the large cohorts that have been used for genome-wide association studies (GWAS), it is prohibitively expensive to sequence all cohort members. A cost-effective strategy is to sequence subjects with extreme values of quantitative traits or those with specific diseases. By imputing the sequencing data from the GWAS data for the cohort members who are not selected for sequencing, one can dramatically increase the number of subjects with information on rare variants. However, ignoring the uncertainties of imputed rare variants in downstream association analysis will inflate the type I error when sequenced subjects are not a random subset of the GWAS subjects. In this article, we provide a valid and efficient approach to combining observed and imputed data on rare variants. We demonstrate through extensive simulation studies that the proposed tests always have correct type I error and are substantially more powerful than the use of accurately imputed variants only and the use of sequencing data alone. We provide an application to the Women's Health Initiative. The relevant software is freely available.