Quantifying the polygenic architecture of the human cerebral cortex: Extensive genetic overlap between cortical thickness and surface area

Abstract

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.

The genetic architecture of human cortical folding

“…We further found large genetic overlap between all three morphological brain traits, extending our previous findings that surface area and thickness share the majority of their genetic determinants ( 24 , 41 ). We found that the genetics of sulcal depth overlaps more with surface area than with cortical thickness, indicating a closer relation between the neurobiological mechanisms determining the degree of cortical folding and surface area.…”

The role of liver fat in cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition

“…brain MRI measures, characterized by high polygenicity. 45,46 However, the notable exception is liver fat, with substantially lower polygenicity and higher discoverability than the other measures, in line with the relatively few highly significant associations we identified through the GWAS.…”

“…However, it should be noted that global correlations underestimate overlap when a mixture of genetic effects in the same and opposing directions cancels each other out. 46 Indeed, adipose and muscle tissue are known to have complex regulatory cross-talk, both releasing metabolism-regulating molecules to maintain a balanced weight-to-muscle ratio. 71 The increased yield from the multivariate GWAS analysis, nearly doubling the number of unique loci discovered, is in line with the hypothesis of strong biological interplay and shared molecular mechanisms.…”

A morphometric double dissociation: cortical thickness is more related to aging; surface area is more related to cognition

“…Indeed, the number of papers that include both the term “aging” and “brain volume” (N=2715 in a PubMed search as of 01/06/2021) or “cortical thickness” (N=597) far exceeds those investigating other aspects of morphology, such as “aging” combined with “surface area” (N=125) or “curvature” (N=23). Even though several authors have pointed out that volume is a product of cortical thickness and surface area (Norbom et al, 2021; Storsve et al, 2014; Walhovd et al, 2016; Winkler et al, 2018), which in turn are two genetically independent aspects of brain structure (Hofer et al, 2020; McKay et al, 2014; Panizzon et al, 2009; van der Meer et al, 2020), the implication that thickness and area may have dissociable causes (e.g., in ageing) and consequences (e.g., for cognition) have rarely been discussed, especially in adult samples. Moreover, additional detailed morphometric shape measures (such as curvature or sulcal depth) may provide further insight into brain development across the adult lifespan and its relationship with cognitive performance.…”