Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Abstract

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed 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. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

Figure 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.

Figure 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 (Alasoo, 2017) 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 (Morales et al., 2022) reference transcript and all annotated exons of CYP2R1, respectively. The remaining rows show the txrevise (Alasoo et al., 2019) 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.

Figure 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.