Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

• Activity Number: 168
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Ran Tao* and Donglin Zeng and Nora Franceschini and Kari E. North and Eric Boerwinkle and Danyu Lin
• Companies: and The University of North Carolina and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of Texas Health Science Center and The University of North Carolina
• Keywords: Association studies ; Gene-level tests ; Linear regression ; Quantitative traits ; Rare variants ; Sequencing studies

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.