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

Abstract

words maximum)The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes), and provide the results as a community resource. We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. In addition, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a common resource for use across the community in research programs. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia SKS, SM, MP, PLDJ, NET, LMM, the AMP-AD Consortium, and the CMC Consortium contributed to the design and generation of the study data.

Genetic control of the human brain proteome

“…We assessed the reproducibility of our eQTL results by comparing them with a previous eQTL meta-analysis of dorsolateral prefrontal cortex (dPFC) data from four cohorts by Sieberts et al (10). We found a high degree of overlap between our eQTL results and the previously published results, which indicates that our methods for eQTL and pQTL identification are valid.…”

“…We tested the reproducibility of our analyses by comparing our eQTLs with those reported previously by (10) in a larger sample of 1,433 cognitively normal and cognitively impaired individuals, and saw a high replication rate of 92.8% (see supplementary material). Furthermore, we assessed the relationship between the minor allele frequencies (MAF) of the tested variants and our declaration of pQTLs and eQTLs (see supplementary material).…”

“…To assess generalizability, we compared our pQTL results to eQTL results from a large meta-analysis using data from the dPFC of 1,433 samples from four cohorts (10). For each SNV that we identified as a pQTL or was identified as an eQTL in the meta-analysis, we compared the effect of each variant on mRNA and protein abundance ( Figure 1B).…”

“…eQTL/pQTLs), the majority of the effects on mRNA and protein abundance are in the same direction (94%, 313/332). To help understand if these results are generalizable, we compared the pQTLs identified at Bonferroni adjusted threshold to the results of eQTL meta-analysis of brain using the dPFC of 1,433 samples from four cohorts (10). For each SNV that we identified as a pQTL (defined by Bonferroni adjusted pvalue threshold) or was identified as an eQTL in the meta-analysis, we compared the effect of each variant on mRNA and protein abundance (Figure S4 B).…”

“…mRNA) in the human brain from the same cohort and among the same individuals with brain proteomes. Finally, we compare the pQTLs identified with eQTLs from a recent meta-analysis of brain expression of over 1,433 brains (10). The results of our analyses are available at http://brainqtl.org and serve as a resource for future investigations.…”