General dimensions of human brain morphometry inferred from genome‐wide association data

• Published in: Human brain mapping Vol. 44; no. 8; pp. 3311 - 3323
• Main Authors: Fürtjes, Anna E., Arathimos, Ryan, Coleman, Jonathan R. I., Cole, James H., Cox, Simon R., Deary, Ian J., Fuente, Javier, Madole, James W., Tucker‐Drob, Elliot M., Ritchie, Stuart J.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-06-2023
• Subjects: Adults; Age; Aging; Brain; Brain - diagnostic imaging; brain age; Cognition; Cognitive ability; Cognitive Dysfunction; complex traits; Covariance; Genetic diversity; Genetic variance; genetics; Genome-Wide Association Study - methods; Genomes; Genomic analysis; Humans; Linkage disequilibrium; Magnetic resonance imaging; Morphometry; Neurobiology; Polymorphism, Single Nucleotide; Population decline; Principal components analysis; Regions; statistical modelling; structural brain networks; structural neuroimageing; genetics; structural brain networks; structural neuroimageing; complex traits; statistical modelling; brain age; cognitive ability
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.26283

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain‐wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome‐wide association data for 83 brain‐wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain‐wide regions accounted for substantial genetic variance (R2 = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure ‐ specifically frontal and parietal volumes thought to be part of the central executive network ‐ tended to be somewhat more susceptible towards age (r = −0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg = 0.17–0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain‐wide morphometry and cognitive ageing.
Understanding the neurodegenerative mechanisms underlying cognitive declines in the general population may facilitate early detection of adverse health outcomes in late life. By modelling structural brain networks on the basis of their underlying genetic architecture, we explore genetic links between brain organisation, cognitive ability and ageing and demonstrate substantial shared genetic etiology between brain organisation and factors that all have potential social and economic consequences for ageing societies.