A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Nurlan Kerimov,Liis Kolberg,Stephen J Trevanion,Peter Walter,Andrew D Yates,Simon Jupp,James D Hayhurst,Irene Papatheodorou,Manoj Pandian Sakthivel,Kateryna Peikova,Tony Burdett,Ivan Kuzmin,Marija Samoviča,Kaur Alasoo,Helen Parkinson,Daniel R Zerbino,Jonathan R Manning

doi:10.1038/s41588-021-00924-w

Abstract

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

Highlights

Gene expression and splicing QTLs are a powerful tool to link disease-associated genetic variants to putative target genes
Using both expression quantitative trait locus (eQTL) sharing and matrix factorization approaches on fine-mapped eQTL signals, we found that differences in eQTL effect sizes between datasets are dominated by biological differences between cell types and tissues rather than technical differences in sample processing
160.75 Mb addition to gene expression QTLs, we identified QTLs at the levels of exon expression, transcript usage and splicing, which were often absent from the original studies

Summary

Introduction

Gene expression and splicing QTLs are a powerful tool to link disease-associated genetic variants to putative target genes. Systematic co-localization efforts based on GTEx data have identified putative target genes for 47% of the GWAS loci[3] Still, these genetic effects mediate only 11% of disease heritability[4], suggesting that many regulatory effects cannot be detected in bulk tissues at a steady state[5]. Reliable fine mapping requires precise information about in-sample linkage disequilibrium (LD) between genetic variants which is usually not available[26,27] To overcome these limitations, we have uniformly re-processed (Fig. 1a) individual-level eQTL data from 112 datasets across 21 independent studies (see Fig. 2). We found that a much smaller proportion of eQTLs is shared between purified cell types and bulk tissues, and between different cell types This eQTL diversity manifests itself at the level of disease co-localization, where we detect many novel co-localizations that are missed when analyzing GTEx data alone. In NatUre Genetics | VOL 53 | September 2021 | 1290–1299 | www.nature.com/naturegenetics

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