Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology

A.A. Shadrin*, T. Kaufmann, D. van der Meer, C.E. Palmer, C. Makowski, R. Loughnan, T.L. Jernigan, T.M. Seibert, D.J. Hagler, O.B. Smeland, E. Motazedi, Y.H. Chu, A.H. Lin, W.Q. Cheng, G. Hindley, W.K. Thompson, C.C. Fan, D. Holland, L.T. Westlye, O. FreiO.A. Andreassen, A.M. Dale

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N = 35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study (R). This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.
Original languageEnglish
Article number118603
Number of pages8
Publication statusPublished - 1 Dec 2021


  • Multivariate vertex-wise analysis
  • Cortical surface area
  • Cortical thickness
  • Genome-wide association study
  • Distributed polygenic architecture


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