Quantifying the Polygenic Architecture of the Human Cerebral Cortex: Extensive Genetic Overlap between Cortical Thickness and Surface Area

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 30; no. 10; pp. 5597 - 5603
• Main Authors: van der Meer, Dennis, Frei, Oleksandr, Kaufmann, Tobias, Chen, Chi-Hua, Thompson, Wesley K, O’Connell, Kevin S, Monereo Sánchez, Jennifer, Linden, David E J, Westlye, Lars T, Dale, Anders M, Andreassen, Ole A
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 03.09.2020
• Subjects: Aged; Cerebral Cortex - anatomy & histology; Female; Genome-Wide Association Study; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; Male; Middle Aged; Multifactorial Inheritance; Organ Size - genetics; Original; surface area; parcellation; pleiotropy; cortical thickness; polygenicity
• ISSN: 1047-3211; 1460-2199
• DOI: 10.1093/cercor/bhaa146

Abstract The thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remain unknown. Our ability to identify causal genetic variants can be improved by employing brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, that is, lower polygenicity and higher discoverability. Using Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area, as well as the polygenicity and discoverability of regional measures. We made use of UK Biobank data from 30 880 healthy White European individuals (mean age 64.3, standard deviation 7.5, 52.1% female). We found large genetic overlap between total surface area and mean thickness, sharing 4016 out of 7941 causal variants. Regional surface area was more discoverable (P = 2.6 × 10−6) and less polygenic (P = 0.004) than regional thickness measures. These findings may serve as a roadmap for improved future GWAS studies; knowledge of which measures are most discoverable may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.