Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data

BMC Bioinformatics. 2022 Sep 28;23(Suppl 3):398. doi: 10.1186/s12859-022-04947-w.


Background: Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV).

Results: As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein-protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others.

Conclusions: These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain.

Keywords: Brain imaging; Brain volume; Gene expression; Genetic variation; Imaging genomic association.

Publication types

  • Meta-Analysis

MeSH terms

  • Brain / diagnostic imaging
  • Genome-Wide Association Study* / methods
  • Humans
  • Multifactorial Inheritance
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Transcriptome*