Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.

ABSTRACT: High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretic

SUBMITTER: Tao R 

PROVIDER: S-EPMC4565625 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature