Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

• Activity Number: 184
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ran Tao*+ and Danyu Lin and Donglin Zeng
• Companies: and University of North Carolina and University of North Carolina at Chapel Hill
• Keywords: Association studies ; Gene-level tests ; Linear regression ; Next-generation sequencing ; Quantitative traits ; Rare variants

Abstract

High-throughput DNA sequencing is a cutting-edge technology for genetic association studies. Currently, it is prohibitively expensive to sequence all subjects in a large cohort. A cost-effective strategy is to preferentially sequence the subjects with the extreme values of a quantitative trait. We consider the situation in which the sampling depends on multiple quantitative traits. Under such outcome-dependent sampling, standard linear regression analysis is invalid and inefficient. We construct a semiparametric likelihood that properly reflects the sampling mechanism. In our formulation, quantitative traits are related to genetic variants and covariates through a multivariate linear regression model while the distributions of genetic variants and covariates are arbitrary. We implement a computationally efficient algorithm and establish the theoretical properties of the resulting estimators. We pay special attention to the gene-level association tests for rare variants. Simulation studies demonstrate the superiority of the proposed methods over standard linear regression methods. Two applications to the CHARGE-TSS data and NHLBI ESP data are provided.