Genetic contributions to multivariate data-driven brain networks constructed via source-based morphometry

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)4899-4913
Number of pages15
JournalCerebral Cortex
Issue number9
StatePublished - Sep 1 2020


  • Genome-wide association analysis (GWAS)
  • Genome-wide complex trait analysis (GCTA)
  • Linkage disequilibrium score regression (LDSC)
  • Source-based morphometry (SBM)
  • Structural magnetic resonance imaging (sMRI)

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience


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