Open AccessShared genetic influences on resting‐state functional networks of the brain
Author(s) -
P.O.F.T. Guimarães João,
Sprooten E.,
Beckmann C. F.,
Franke B.,
Bralten J.
Publication year - 2022
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.25712
Subject(s) - genome wide association study , pleiotropy , imaging genetics , resting state fmri , single nucleotide polymorphism , univariate , multivariate statistics , genetic architecture , functional magnetic resonance imaging , biology , genetic association , genetics , neuroimaging , biobank , computational biology , neuroscience , quantitative trait locus , gene , computer science , machine learning , genotype , phenotype
The amplitude of activation in brain resting state networks (RSNs), measured with resting‐state functional magnetic resonance imaging, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome‐wide association studies (GWASs) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study, we used a novel multivariate method called genomic structural equation modeling to model latent factors that capture the shared genomic influence on RSNs and to identify single nucleotide polymorphisms (SNPs) and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank ( N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample ( N = 21,081), and then tested in an independent sample from the same cohort ( N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modeling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.