Polygenic Architecture of Human Neuroanatomical Diversity

Abstract

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.

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

“…The vectors of 3403 phenotypic and 3403 genetic interregional correlations were strongly positively associated ( r = 0.84; b = 0.60; SE = 0.007, p < 2 × 10 −16 , R 2 = 70%) (Figure 4a ), indicating that volumes that were strongly phenotypically correlated were also strongly genetically correlated. Magnitudes of genetic correlations tended to be slightly larger than phenotypic correlations (intercept = 0.06) which is consistent with previous reports (Biton et al, 2020 ) (Figure 5a ).…”

23. General Dimensions of Human Brain Morphometry Inferred From Genome-Wide Association Data

“…The vectors of 3403 phenotypic and 3403 genetic interregional correlations were strongly positively associated (r = 0.84; b = 0.60; 4a), indicating that volumes that were strongly phenotypically correlated were also strongly genetically correlated. Magnitudes of genetic correlations tended to be slightly larger than phenotypic correlations (intercept = 0.06) which is consistent with previous reports (Biton et al, 2020) (Figure 5a).…”

“…The close phenotypic and genetic correspondence in interregional covariance means that inferences from genetic to phenotypic dimensions are viable. This is in line with previous studies comparing phenotypic and genetic correlations between morphometric traits (Biton et al, 2020;Sodini et al, 2018). According to Cheverud's Conjecture, this indicates that genetics of brain organisation operate on the same dimensions as are evident phenotypically, and likely index the same developmental processes.…”

Shared genetic etiology between cortical brain morphology and tobacco, alcohol, and cannabis use

“…It is established that genetic variants associated with a given trait can also be related to other attributes as well. 22 A notable potential mediator is that of cognitive ability and educational attainment. Cognitive ability has been robustly linked to increased total surface area and ICV.…”