Abstract
Here, we investigated the genetics of weighted functional brain network graph theory measures from 18,445 participants of the UK Biobank (44–80 years). The eighteen measures studied showed low heritability (mean h2SNP = 0.12) and were highly genetically correlated. One genome-wide significant locus was associated with strength of somatomotor and limbic networks. These intergenic variants were located near the PAX8 gene on chromosome 2. Gene-based analyses identified five significantly associated genes for five of the network measures, which have been implicated in sleep duration, neuronal differentiation/development, cancer, and susceptibility to neurodegenerative diseases. Further analysis found that somatomotor network strength was phenotypically associated with sleep duration and insomnia. Single nucleotide polymorphism (SNP) and gene level associations with functional network measures were identified, which may help uncover novel biological pathways relevant to human brain functional network integrity and related disorders that affect it.
Highlights
During aging, the human brain undergoes functional changes which affect the integration of information within and between functional brain networks, and these have been shown to be associated with behavioral changes[1]
We present h 2SNP estimates and results from genome-wide association studies (GWAS) of graph theory measures using resting-state fMRI data from 18,445 UK Biobank participants
We identified significant single nucleotide polymorphism (SNP) and gene associations that survived multiple correction testing with six of the 18 graph theory measures including global efficiency, characteristic path length, and strength of default, dorsal attention, limbic, and somatomotor networks
Summary
The human brain undergoes functional changes which affect the integration of information within and between functional brain networks, and these have been shown to be associated with behavioral changes[1]. While a previous study[10] has looked at genetics of functional connectivity from resting-state fMRI images, to date, there has not been any populationbased study investigating the genetic contribution to functional connectivity using graph theory measures. Studying the genetic architecture of graph theory brain functional measures has important implications11—firstly, it can identify genes associated with network topology; and secondly, it may provide insights into the underlying biological mechanisms of macroscopic network topology in aging and how it alters during disease states. We address the question of how genetics is associated with the integrity of functional connectivity as measured by graph theory measures in resting-state fMRI (rs-fMRI) data. Analysis was carried out to uncover gene-level associations, and the functional consequences of the significant genetic variants were explored
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