Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/126617
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Type: Journal article
Title: The genetic architecture of the human cerebral cortex
Author: Grasby, K.L.
Jahanshad, N.
Painter, J.N.
Colodro-Conde, L.
Bralten, J.
Hibar, D.P.
Lind, P.A.
Pizzagalli, F.
Ching, C.R.K.
McMahon, M.A.B.
Shatokhina, N.
Zsembik, L.C.P.
Thomopoulos, S.I.
Zhu, A.H.
Strike, L.T.
Agartz, I.
Alhusaini, S.
Almeida, M.A.A.
Alnæs, D.
Amlien, I.K.
et al.
Citation: Science, 2020; 367(6484):1-14
Publisher: American Association for the Advancement of Science
Issue Date: 2020
ISSN: 0036-8075
1095-9203
Statement of
Responsibility: 
Katrina L. Grasby, Neda Jahanshad, Jodie N. Painter, Lucía Colodro-Conde, Janita Bralten
Abstract: INTRODUCTION: The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE: To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS: We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION: This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function.
Keywords: Alzheimer’s Disease Neuroimaging Initiative
CHARGE Consortium
EPIGEN Consortium
IMAGEN Consortium
SYS Consortium
Parkinson’s Progression Markers Initiative
Enhancing NeuroImaging Genetics through Meta-Analysis Consortium (ENIGMA)—Genetics working group
Cerebral Cortex
Humans
Parkinson Disease
Magnetic Resonance Imaging
Organ Size
Brain Mapping
Cognition
Attention Deficit Disorder with Hyperactivity
Genetic Variation
Genome-Wide Association Study
Genetic Loci
Rights: © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
DOI: 10.1126/science.aay6690
Grant ID: http://purl.org/au-research/grants/nhmrc/1158127
http://purl.org/au-research/grants/nhmrc/1103623
http://purl.org/au-research/grants/nhmrc/1173025
http://purl.org/au-research/grants/nhmrc/350833
http://purl.org/au-research/grants/nhmrc/568969
http://purl.org/au-research/grants/nhmrc/1067137
http://purl.org/au-research/grants/nhmrc/1147644
http://purl.org/au-research/grants/nhmrc/1051672
http://purl.org/au-research/grants/nhmrc/1121474
http://purl.org/au-research/grants/nhmrc/1105825
http://purl.org/au-research/grants/nhmrc/401184
http://purl.org/au-research/grants/nhmrc/496682
http://purl.org/au-research/grants/nhmrc/1009064
http://purl.org/au-research/grants/nhmrc/401162
http://purl.org/au-research/grants/nhmrc/350833
http://purl.org/au-research/grants/nhmrc/401184
http://purl.org/au-research/grants/nhmrc/568969
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