Quantifying the polygenic architecture of the human cerebral cortex: Extensive genetic overlap between cortical thickness and surface area
Dennis van der Meer,
Wesley K Thompson,
Kevin S. O’Connell,
Jennifer Monereo Sanchez,
David EJ Linden,
Lars T. Westlye,
Anders M Dale,
Ole A. Andreassen
Posted 06 Dec 2019
bioRxiv DOI: 10.1101/868307 (published DOI: 10.1093/cercor/bhaa146)
Posted 06 Dec 2019
Introduction: 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 remains unknown. Our ability to identify causal genetic variants can be improved by employing better delineated, less noisy brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, i.e. lower polygenicity and higher discoverability. Methods: Using Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area. We further determined the polygenicity and discoverability of regional cortical measures from five often-employed parcellation schemes. We made use of UK Biobank data from 31,312 healthy White European individuals (mean age 55.5, standard deviation (SD) 7.4, 52.1% female). Results: Contrary to previous reports, we found large genetic overlap between total surface area and mean thickness, sharing 4427 out of 7150 causal variants. Regional surface area was more discoverable (p=4.1x10-6) and less polygenic (p=.007) than regional thickness measures. We further found that genetically-informed and less granular parcellation schemes had highest discoverability, with no differences in polygenicity. Conclusions: These findings may serve as a roadmap for improved future GWAS studies; Knowledge of which measures or parcellations are most discoverable, as well as the genetic overlap between these measures, may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.
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