eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

Abstract

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

Unraveling a novel dual-function regulatory element showing epistatic interaction with a variant that escapes genome-wide association studies

“…To identify such regulatory SNPs, we used various databases and tools to prioritize the most promising candidates. Hence, we first searched for expression Quantitative Trait Loci (eQTLs) using the Elixir eQTL catalogue 32 , revealing several SNPs with a PIP (Posterior Inclusion Probability) score > 0.2 (Figure 4A), indicating the probability of the variant being functional. Among them, rs11240391, located in the LAX1 promoter, exhibited a high PIP score of 0.76 (p = 1.09 x 10 - 10 ) in whole blood tissue (Figure 4A).…”

Extensive co-regulation of neighbouring genes complicates the use of eQTLs in target gene prioritisation

“…To compare different colocalization methods on real-world data, we integrated cis-eQTL data from 131 fine-mapped tissue-specific datasets from the eQTL Catalogue (n = 65-702 individuals, Table S1) [9] with fine-mapped plasma protein QTL data from the INTERVAL cohort (n = 3,301) [5]. We used three Bayesian colocalisation methods: coloc.abf [6], which assumes a single causal variant per locus; coloc.susie [7], which supports multiple fine-mapped causal variants; and colocalization posterior probability (CLPP) defined at the variant level [8].…”

“…To avoid these issues, we extracted genotyped variant positions of Affymetrix Axiom UK Biobank array from the INTERVAL imputed genotype files and re-imputed genotypes to the 1000 Genomes 30x on GRCh38 reference panel with the eQTL-Catalogue/genimpute v23.07.1 workflow. The same workflow was previously used to impute genotypes for all eQTL Catalogue datasets that used genotyping microarrays [9].…”

“…The metadata for SomaLogic aptamers and their mapping to Ensembl gene ids can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.7808390). The cis-pQTL analysis and fine mapping were conducted using the eQTL-Catalogue/qtlmap v23.02.1 workflow as described previously [9]. In all downstream analyses, we used 793 proteins that had at least one purity-filtered SuSiE credible set.…”

“…We downloaded fine-mapped cis-eQTL summary statistics for 131 datasets of the eQTL Catalogue release 6 [9] from eQTL Catalogue FTP server. We ran colocalization analyses pairwise between all eQTL datasets and pQTL data from the INTERVAL study.…”