Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions.
Solveig K SiebertsThanneer M PerumalMinerva M CarrasquilloMariet AllenJoseph S ReddyGabriel E HoffmanKristen K DangJohn CalleyPhilip J EbertJames EddyXue WangAnna K GreenwoodSara Mostafavinull nullnull nullLarsson OmbergMette A PetersBenjamin A LogsdonPhilip Lawrence De JagerNilüfer Ertekin-TanerLara M MangravitePublished in: Scientific data (2020)
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). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).
Keyphrases
- cerebral ischemia
- resting state
- systematic review
- white matter
- genome wide
- subarachnoid hemorrhage
- functional connectivity
- genome wide identification
- healthcare
- poor prognosis
- multiple sclerosis
- single cell
- brain injury
- randomized controlled trial
- meta analyses
- mental health
- high throughput
- quality improvement
- blood brain barrier
- machine learning
- electronic health record
- copy number
- gene expression
- case control
- binding protein
- genome wide analysis