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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 contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.
17 Keywords
Adult
Aged
Bipolar Disorder
Brain
Brain Mapping
Depressive Disorder, Major
Female
Genetic Association Studies
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
Multivariate Analysis
Nerve Net
Principal Component Analysis
Schizophrenia
11 Authors
Amanda L Rodrigue
Aaron F Alexander-Bloch
Emma E M Knowles
Samuel R Mathias
Josephine Mollon
Marinka M G Koenis
Nora I Perrone-Bizzozero
Laura Almasy
Jessica A Turner
Vince D Calhoun
David C Glahn
Enabling scientific discoveries that improve human health