Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases

Abstract

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10^-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Figure 1

Variance in eGenes explained by lead e-QLT variants. (A) Variance in eGenes explained by lead cis-eQTLs in relation to the distance in mega base pairs of the lead cis-eQTLs to the transcription start site (TSS) of the cis-gene. (B) Comparison of median variance in the expression levels of eGenes explained by the lead eQTL variants. The median value of variance explained in cis-eGenes was significantly higher than that of trans-eGenes (4.8% versus 2.8%, p < 2e−16). For cis-eGenes, the protein-coding-eGenes had slightly higher median variance explained in the expression level than cis-lncRNA genes (5.1% versus 4.3%, p = 0.0004). No significant difference in variance explained in trans-eGenes of protein-coding genes versus trans-lncRNA genes (2.8% versus 2.7%, p = 0.41).