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

Author:

Rodrigue Amanda L1,Alexander-Bloch Aaron F2,Knowles Emma E M1,Mathias Samuel R1,Mollon Josephine1,Koenis Marinka M G23,Perrone-Bizzozero Nora I45,Almasy Laura6,Turner Jessica A78,Calhoun Vince D2789,Glahn David C13

Affiliation:

1. Department of Psychiatry, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA

2. Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA

3. Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA

4. Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA

5. Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA

6. Department of Genetics, Perelman School of Medicine, and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA

7. Psychology Department, Neurosciences Institute, Georgia State University, Atlanta, GA 30303, USA

8. The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA

9. Mind Research Network, Department of Psychiatry and Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA

Abstract

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.

Funder

National Institute of Mental Health

Publisher

Oxford University Press (OUP)

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience

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