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.

Fig 1. Uniform re-processing of all datasets.

(A), The number of studies, datasets, donors and samples in the previous publication (R3) and current version of the eQTL Catalogue (R6). (B), Number of genes with at least one significant eQTL (‘eGenes’) on the X chromosome as a function of dataset sample size. Red points indicate datasets for which the X chromosome genotypes were unavailable. (C), The number of eGenes identified in each dataset for the five molecular traits (gene expression, exon expression, transcript usage, txrevise event usage, and Leafcutter splice-junction usage). Datasets newly added since release 3 have been highlighted.

Fig 2. Visualisation of a splicing QTL detected in the CYP2R1 gene.

(A) RNA-seq read coverage across the CYP2R1 gene in GTEx transverse colon tissue stratified by the genotype of the lead sQTL variant (chr11_14855172_G_A). All introns have been shortened to 50 nt with wiggleplotr [29] to make variation in exonic read coverage easier to see. (B) Effect sizes and 95% confidence intervals of the lead sQTL variant on the expression level of individual exons (or exonic parts) of CYP2R1. Associations significant at FDR < = 1% are shown in dark blue. (C) The top two rows show the MANE Select [30] reference transcript and all annotated exons of CYP2R1, respectively. The remaining rows show the txrevise [5] event annotations used for sQTL mapping. The short version of exon 4 (between dashed lines) is only present in annotated nonsense-mediated decay (NMD) transcripts.

Fig 3. Sharing of significantly colocalised signals with vItamin D.

(A) Number of colocalised signals detected by the different molecular QTL quantification methods and sharing between them. (B) Number of colocalised signals assigned to empirical functional consequence (eQTL, sQTL, puQTL, apaQTL or ambiguous) and sharing structure between them. (C) Number of independent colocalised signals associated with either a single target gene or multiple target genes in each functional consequences group. eQTL—expression QTL, sQTL—splicing QTL, puQTL—promoter usage QTL, apaQTL—alternative polyadenylation QTL.