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Cited by 440Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsRNA Research and Splicing

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

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Kerimov, Nurlan, Hayhurst, James D., Peikova, Kateryna, Manning, Jonathan R., Walter, Peter, Kolberg, Liis, Samoviča, Marija, Sakthivel, Manoj Pandian +9 more·2021·Nature Genetics
DOI 10.1038/s41588-021-00924-wPMC PMC8423625

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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.

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  • In the pQTL-eQTL colocalisation analysis, significant pQTL loci were compared to eQTL Catalogue datasets, excluding those of Kasela et al. and Lepik et al. due to sample overlap.

    “In an pQTL–eQTL colocalisation analysis, we compared our significant pQTL loci to all eQTL Catalogue datasets18, excluding those of Kasela et al.57 and Lepik et al.29 due to sample overlap, containing gene expression, exon expression, transcript usage and txrevise event usage data, and GTEx (version 8)58 datasets containing gene expression data”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [18]

  • The study used the eQTL Catalogue to conduct eQTL-pQTL colocalisation analyses to support findings with orthogonal data.

    “To support our findings with orthogonal data, we used the most comprehensive publicly available eQTL resource, the eQTL Catalogue18, to conduct eQTL–pQTL colocalisation analyses.”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [18]

  • Of the cis associations, 25.5% (2021/7936) colocalised with eQTLs for the corresponding protein-encoding gene from the full eQTL Catalogue, while for the GTEx dataset alone it was 54.3% (273/503).

    “Of the cis associations, 25.5% (2021/7936) colocalised with the eQTLs for the corresponding protein-encoding gene from the full eQTL Catalogue, while for the GTEx dataset alone it was 54.3% (273/503).”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [18]

    The Methods (Co-localization; page unavailable) describe analyses using the eQTL Catalogue and GTEx, but not co-localization of cis associations with eQTLs for corresponding protein-encoding genes. The Results (Detection of additional co-localizations missed in GTEx; page unavailable) report different counts for GWAS loci. Searches for 2021/7936, 273/503, and protein-encoding-gene cis associations found no passage supporting the quoted percentages. (LLM, conf=0.76)

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Authors
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., Burdett, T., Jupp, S., Parkinson, H., Papatheodorou, I., Yates, A. D., Zerbino, D. R., & Alasoo, K.
Affiliations
  • Open Targets (GB)
  • European Bioinformatics Institute (GB)
  • University of Tartu (EE)
Corresponding author
  • Zerbino, Daniel R.
  • Alasoo, Kaur
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2021-09
Volume
53
Issue
9
Pages
1290-1299
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-021-00924-w
PMC
PMC8423625
OpenAlex
W3196426274
Cited by
440
References
20
Citations by year
2026: 98 · 2025: 91 · 2024: 86 · 2023: 72 · 2022: 46 · 2021: 36 · 2020: 9 · 2013: 1
FWCI
35.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • EC | Horizon 2020 Framework Programme (825775)
  • Eesti Teadusagentuur (MOBJD67, PSG415, IUT34-4, PSG59)
  • Open Targets
  • European Molecular Biology Laboratory
  • Wellcome Trust (108749/Z/15/Z)
  • European Bioinformatics Institute
  • EC | European Regional Development Fund (2014-2020.4.01.16-0271)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
expression quantitative trait loci, splicing quantitative trait loci, summary statistics, data harmonization, fine mapping
MeSH terms
Gene Expression Regulation, Humans, CD4-Positive T-Lymphocytes, Quantitative Trait, Heritable, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Databases, Genetic, Quantitative Trait Loci, Genome-Wide Association Study, Datasets as Topic

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