Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes.

ABSTRACT: Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a non

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC9308456 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature