Genetic determinants of blood gene expression and splicing and their contribution to molecular phenotypes and health outcomes

Abstract

SummaryThe biological mechanisms through which most non-protein-coding genetic variants affect disease risk are unknown. To investigate the gene-regulatory cascades that ensue from these variants, we mapped blood gene expression and splicing quantitative trait loci (QTLs) through bulk RNA-sequencing in 4,732 participants, and integrated these data with protein, metabolite and lipid QTLs in the same individuals. We identifiedcis-QTLs for the expression of 17,233 genes and 29,514 splicing events (in 6,853 genes). Using colocalization analysis, we identified 3,430 proteomic and metabolomic traits with a shared association signal with either gene expression or splicing. We quantified the relative contribution of the genetic effects at loci with shared etiology through statistical mediation, observing 222 molecular phenotypes significantly mediated by gene expression or splicing. We uncovered gene-regulatory mechanisms at GWAS disease loci with therapeutic implications, such asWARS1in hypertension,IL7Rin dermatitis andIFNAR2in COVID-19. Our study provides an open-access and interactive resource of the shared genetic etiology across transcriptional phenotypes, molecular traits and health outcomes in humans (https://IntervalRNA.org.uk).

Variant-to-function dissection of rare non-coding GWAS loci with high impact on blood traits

“…Eight of the 20 ATU genes harbour splicing QTLs (sQTL) signals in whole blood in the GTEx Project 32 , but implicating common variants (MAF > 1%) that were conditionally independent from the ones assayed here (r 2 >0.7, EUR population, window size 0.5 Mb, Methods). In the INTERVAL QTL repository 29 we found that three of the ATU variants were sQTLs in the correspondent genes (5 genes) and for an additional 11 ATU genes we found independent common sQTLs. This suggests complex modes of regulation at the transcript level for ATU genes (Extended Figure 3A).…”

“…Further, 12 were MPRA positive and 2 were genie positive. Finally, five of the 22 variants had been previously described as eQTLs for the same regulated genes that we detect in our analyses by the eQTLGen 30 and/or the BLUEPRINT 13 consortia and 1 as an sQTL in the INTERVAL RNA-seq initiative 29 , Table 1.…”

“…To assess regulatory impact of the index variants in a native chromatin context we used two bulk RNA-seq datasets (Table S5). First, the INTERVAL RNA-seq study 29 , that includes gene and transcript quantification from whole blood samples of 2,971 samples with matched WGS data (15x). Overall, we identified heterozygous carriers for 88 of the 94 MPRA screened variants (median of 40 carriers per variant).…”

“…The INTERVAL RNA-sequencing data were generated and processed as previously described 29 . We mapped the RNA-sequencing data to the GRCh38 reference and quantified read counts as described previously 79 with the difference of using GENCODE v31 across 4,731 samples passing quality control.…”