Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases

Abstract

Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis- and trans-expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis-eQTLs (for 12,307 unique genes at FDR<0.05) with a large blood cis-eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed. We inferred the brain cell type for 1,515 cis-eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis-eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis-eQTL for CYP24A1 and a macrophage specific cis-eQTL for CLECL1. To further interpret GWAS hits, we performed trans-eQTL analysis. We identified 2,589 trans-eQTLs (at FDR<0.05) for 373 unique SNPs, affecting 1,263 unique genes, and 21 replicated significantly using single-nucleus RNA-seq data from excitatory neurons. We also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinson's disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.

Common and rare variant association analyses in Amyotrophic Lateral Sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

“…To assess whether rare variant associations could drive the common variant signals at the 15 genome-wide significant loci, we combined the common and rare variants analyses to prioritize genes within these loci. The SNP effects on gene expression were assessed through summary-based Mendelian Randomization (SMR) in blood (eQTLGen 19 ) and a new brain cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain 20 ). Similarly, we analyzed methylation-QTL (mQTL) through SMR in blood and brain-derived mQTL datasets 21–23 .…”

Common and rare variant association analyses in Amyotrophic Lateral Sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

“…The exome-wide association analysis included transcript-level rare-variant burden testing for different models of allele-frequency thresholds and variant annotations (Online methods). This identified NEK1 as the strongest associated gene (minimal P = 4.9 × 10 -8 for disruptive and damaging variants at minor allele frequency < 0.005), which was the only gene to pass the exome-wide significance thresholds (0.05/17,994 = 2.8 × 10 -6 and 0.05/58,058 = 8.6 × 10 -7 for number of genes and protein-coding transcripts, respectively, Supplementary figures [19][20][21][22][23][24][25][26][27][28][29][30][31][32]. This association is independent from the previously reported increased rare variant burden in familial ALS patients 17 that were not included in this study.…”

“…RNA-seq samples from 7 regions of the central nervous system from 15 datasets, and we selected eQTLs derived from the cortex region of the brain in samples of European ancestry (MetaBrain Cortex-EUR eQTLs) as our instrument variable 20 . The European-only ALS summary statistics were used as the outcome.…”

“…First, gene-based association statistics are obtained through the PASCAL method 87 are then combined in a generalized least squares regression model to obtain enrichment statistics. 20 Enrichment analyses were performed for Reactome, Gene Ontology and Human Phenotype Ontology (HPO) terms using the multi-tissue or brain-specific transcriptome datasets to calculate the coexpression matrix.…”

“…We used multi-SNP SMR 76,77 to infer the effect of gene expression variation on ALS using eQTLs (the association of a SNP with expression of a gene) on ALS risk. MetaBrain is a harmonized set of 8,727 RNA-seq samples from 7 regions of the central nervous system from 15 datasets, and we selected eQTLs derived from the cortex region of the brain in samples of European ancestry (MetaBrain Cortex-EUR eQTLs) as our instrument variable 20 . The European-only ALS summary statistics were used as the outcome.…”