The genetic architecture of the human cerebral cortex

Abstract

12The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants, including structural variants, impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 51,662 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 255 nominally significant loci (P ≤ 5 x 10 -8 ); 199 survived multiple testing correction (P ≤ 8.3 x 10 -10 ; 187 surface area; 12 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression and ADHD.One Sentence Summary: Common genetic variation is associated with inter-individual variation in the structure of the human cortex, both globally and within specific regions, and is shared with genetic risk factors for some neuropsychiatric disorders.The human cerebral cortex is the outer grey matter layer of the brain, which is implicated in multiple aspects of higher cognitive function. Its distinct folding pattern is characterised by convex (gyral) and concave (sulcal) regions. Computational brain mapping approaches use the consistent folding patterns across individual cortices to label brain regions(1). During fetal development excitatory neurons, the predominant neuronal cell-type in the cortex, are generated from neural progenitor cells in the developing germinal zone(2). The radial unit hypothesis(3) posits that the expansion of cortical surface area (SA) is driven by the proliferation of these neural progenitor cells, whereas thickness (TH) is determined by the number of neurogenic divisions. Variation in global and regional measures of cortical SA and TH are associated with neuropsychiatric disorders and psychological traits(4) ( Table S1). Twin and family-based brain imaging studies show that SA and TH measurements are highly heritable and are largely influenced by independent genetic factors(5). Despite extensive studies of genes impacting cortical structure in model organisms (6), our current understanding of genetic variation impacting human cortical size and patterning is limited to rare, highly penetrant variants (7,8). These variants often disrupt cortical development, leading to altered post-natal structure. However, little is known about how common genetic variants impact human cortical SA and TH.To address this, we conducted genome-wide association meta-analyses of cortical SA and TH measures in 51,662 individuals from 60 cohorts from around the world (Tables S2-S4). Cortical measures were extracted from structural brain MRI scan...

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

“…Arguing against this point, a recent genetic correlation study measured whether genetic effects are shared between two genome‐wide association analyses: the meta‐analytic combination of many sites via the ENIGMA consortium and one UK Biobank site for global surface area and thickness . The genetic correlations approach one, indicating an almost complete shared genetic basis between meta‐analysis and one‐site analysis . A lack of strict statistical thresholds or requirements for replication have led to many publications without reproducible associations .…”

“…This resistance stems from an assumption that heterogeneity induced from meta‐analysis can dilute or alter effects. Arguing against this point, a recent genetic correlation study measured whether genetic effects are shared between two genome‐wide association analyses: the meta‐analytic combination of many sites via the ENIGMA consortium and one UK Biobank site for global surface area and thickness . The genetic correlations approach one, indicating an almost complete shared genetic basis between meta‐analysis and one‐site analysis .…”

“…Effect size relationships across traits. Each bar corresponds to a common genetic variant, a single‐nucleotide polymorphism (SNP), with an association to cortical structure, subcortical structure or intracranial volume (ICV), height, schizophrenia, and educational attainment . The highest effect size SNP for each trait are shown.…”

“…Genetic association studies continue to identify both common and rare variants that impact brain structures at the gross anatomical level . MRI is suited to measure gross structural traits of intracranial volume and regional phenotypes like hippocampal volume or cortical surface area and thickness . These phenotypes can be measured in large numbers of living humans at a relatively affordable cost.…”

“…Arguing against this point, a recent genetic correlation study measured whether genetic effects are shared between two genome-wide association analyses: the meta-analytic combination of many sites via the ENIGMA consortium and one UK Biobank site for global surface area and thickness. 74 The genetic correlations approach one, indicating an almost complete shared genetic basis between meta-analysis Table 1. Good practices for imaging genetics studies of gross brain structure Assess reliability of phenotype measurements prior to conducting association studies to understand noise levels Conduct unbiased genome-wide association rather than biased selection of candidate genes Appropriately power an association test based on effect sizes from similar phenotypes (current gross brain structure genome-wide association studies suggest~10 000 samples are needed to detect significant common variant associations) Apply rigorous statistical thresholds for association across the whole genome (P < 5 × 10 −8 for common variant associations with more stringent thresholds if multiple phenotypes are tested) When conducting a genome-wide association meta-analysis to achieve sufficient sample sizes, extensive quality checking of individual site level data is necessary prior to meta-analysis Attempt replication of significant associations in independent datasets…”