Genetic inputs that influence human cortical structure are governed by pervasive pleiotropy as evidenced by recent genome wide association studies (GWAS). This pleiotropy may be harnessed to realize unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, and other cortical parcellation schemes. We investigated the genetic pleiotropy of the human cortex by applying genomic structural equation modeling (gSEM) to jointly model the genetic architecture of cortical thickness (CT) and surface area (SA) for 34 brain regions reported in the ENIGMA-3 GWAS. First, the empirical genetic covariance matrix and sampling covariance matrix were estimated from GWAS summary statistics. We specified a SEM and estimated parameters by minimizing the discrepancy between model-implied covariance and observed covariance. Standardized root-mean square residual, model Chi-square, Akaike Information Criteria, and the Comparative Fit Index were used to evaluate model fit. The gSEM package in R was used for LD score regression (LDSC) and fit to factor models for SA and CT. The best-fitting model included 6 latent factors for SA and 4 latent factors for CT, where each latent factor was composed of multiple highly pleiotropic regions. Subsequent GWAS of these latent factors found 74 genome-wide significant (GWS) genetic variants (p<5 × 10-8). LDSC results of CT and SA factors were positively correlated with Obsessive Compulsive Disorder (OCD) and Bipolar disorder, but negatively correlated with Alcohol Dependence, Attention Deficit Hyperactivity Disorder (ADHD), Major Depressive Disorder (MDD), and Insomnia. Applying gSEM to model the joint genetic architecture of complex traits and investigate multivariate genetic links among phenotypes offers a new vantage point for understanding genetically informed cortical networks. Keywords: genomic structure equation modeling (gSEM), human cortex, GWAS, genetics, structural covariance networks (SCN), genetically informed brain networks (GIBN)


Rajendra A. Morey MD 1, 2, 3, Yuanchao Zheng MS 4, 5, Delin Sun PhD 2, 3, Melanie E. Garrett MS 6, Marianna Gasperi PhD, Courtney C. Haswell MS 4, 5, C. Lexi Baird BS 4, 5, Adam X. Maihofer MS 6, Paul M. Thompson PhD 30, Sarah Medland PhD 31, Katrina Grasby PhD 31, Daniel L. Gustafson PhD 31, Mark Panizzon PhD 8, 10, William Kremen 31, Caroline M. Nievergelt, PhD 8, 10, Allison E. Ashley-Koch PhD 3, 5, Mark W. Logue PhD 1, 2, 32

Corresponding Author:

Rajendra Morey MD

Duke Brain Imaging and Analysis Center

Duke University School of Medicine

40 Medicine Circle Drive

Durham, NC 27710


Genomic Structural Equation Modeling, Cortex, Cortical Thickness, Cortical Surface Area,


1. National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA

2. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

3. Department of Medicine, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA

4. Brain Imaging and Analysis Center, Duke University, Durham, NC, USA

5. VISN 6 MIRECC, Durham VA Health Care System, Durham, NC, USA

8. Department of Psychiatry, School of Medicine, University of California, San Diego, La Jolla, CA, USA

10. Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA

22. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA

30. Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

31. Queensland Institute for Medical Research, Berghofer Medical Research Institute, Brisbane Australia

32. Departments of Psychiatry and Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA

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