Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

ABSTRACT: Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic c

SUBMITTER: Tian X 

PROVIDER: S-EPMC11639157 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature