Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry.

ABSTRACT: Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that con

SUBMITTER: Rodrigue AL 

PROVIDER: S-EPMC7391270 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature