Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• High-quality RNA-sequencing and genotyping data from post-mortem brain collections are available from consortia like CommonMind Consortium (CMC) and AMP-AD.
• Previous eQTL studies have provided valuable insights but can be limited by sample size and cohort-specific analyses.

Purpose of the Study:

• To perform a large-scale brain cis-eQTL meta-analysis using combined data from multiple cohorts.
• To identify significant eQTLs in both cerebral cortex and cerebellum.
• To provide a comprehensive brain eQTL resource for the research community and demonstrate its utility in disease gene discovery.

Main Methods:

• Meta-analysis of cis-eQTL data from 1433 cerebral cortical samples and 261 cerebellar samples across four cohorts.
• Comparison of meta-analysis power against individual cohort analyses and the Genotype-Tissue Expression (GTEx) Project.
• Application of colocalization analysis to identify genes underlying schizophrenia GWAS association peaks.

Main Results:

• >4.1 million significant cis-eQTLs for >18,000 genes in the cerebral cortex.
• 874,836 significant cis-eQTLs for >10,000 genes in the cerebellum.
• Substantially improved power in the meta-analysis compared to individual cohorts and GTEx; observed distinct eQTL patterns between brain regions.
• Identified a potential novel gene colocalization with lncRNA RP11-677M14.2 for schizophrenia GWAS peaks (posterior probability 0.975).

Conclusions:

• The large-scale brain cis-eQTL meta-analysis significantly enhances the power to detect gene-regulatory relationships in the brain.
• The generated eQTL data serves as a crucial resource for genetic studies of brain function and neurological disorders.
• Colocalization analysis demonstrates the utility of this resource for pinpointing causal genes at GWAS loci, as exemplified by the schizophrenia association study.