Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies.

ABSTRACT: Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of t

SUBMITTER: Chung W 

PROVIDER: S-EPMC9001998 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature