Biological interpretation of genome-wide association studies using predicted gene functions
Open accessAbstract
The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. Identifying which genes and pathways explain genetic associations is challenging. Here, the authors present DEPICT, a tool for gene prioritization, pathway analysis and tissue/cell-type enrichment analysis that can be used to generate testable hypotheses from genetic association studies.
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At the standard false discovery rate threshold of 5%, DEPICT prioritizes 1,838 genes, a tenfold increase relative to the earlier GWAS of EduYears.
“At the standard false discovery rate (FDR) threshold of 5%, the bioinformatics tool DEPICT19 prioritizes 1,838 genes (Supplementary Table 7), a tenfold increase relative to the DEPICT results from an earlier GWAS of EduYears10.”
From Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals (2018) · cites this paper as [19]
Genes near the lead SNPs are overwhelmingly enriched for expression in the central nervous system.
“we applied the bioinformatics tool DEPICT19 and found that, relative to other genes, genes near our lead SNPs are overwhelmingly enriched for expression in the central nervous system (Fig. 3A, Supplementary Table 6).”
From Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals (2018) · cites this paper as [19]
CLECL1 has previously been linked to altered CLECL1 RNA expression in monocytes.
“On the other hand, CLECL1 represents a simpler case of a known susceptibility effect that has previously been linked to altered CLECL1 RNA expression in monocytes (26, 32); its enrichment in microglial cells, which share many molecular pathways with other myeloid cells, is more straightforward to understand.”
From Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility (2019) · cites this paper as [32]
The Methods (Gene prioritization benchmark; page unavailable) discusses genes whose transcription is regulated in whole blood, but does not identify CLECL1 or report altered CLECL1 RNA expression in monocytes. Searches for CLECL1 and monocyte expression found no passage addressing that specific link. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSuggestive evidence for enrichment (FDR≤0.2) was observed for ten tissues, including female genitalia, uterus, endocrine glands, endometrium, ovary and Fallopian tubes.
“Additionally, suggestive evidence for enrichment (FDR≤0.2) was observed for ten tissues, including female genitalia, uterus, endocrine glands, endometrium, ovary and Fallopian tubes (Supplementary Data 16).”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [32]
When using results with evidence of genome-wide suggestive association (P<1 × 10−5) in either all or Grade B meta-analysis, DEPICT provided evidence for significant enrichment (FDR≤0.05) of COPB1 PPI subnetwork gene set.
“However, when using results with evidence of genome-wide suggestive association (P<1 × 10−5) in either all or Grade B meta-analysis, DEPICT provided evidence for significant enrichment (FDR≤0.05) of COPB1 PPI subnetwork gene set (Supplementary Data 16).”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [32]
DEPICT analysis provided little support for potential genes, pathways, tissues or cell types reaching multiple testing threshold (FDR≤0.05).
“DEPICT32 analysis provided little support for potential genes, pathways, tissues or cell types reaching multiple testing threshold (FDR≤0.05).”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [32]
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“1 with any of the 200 effects, (iii) had high scores of regulatory potential byusing a cell-specific network approach, and (iv) had a similar coexpression pattern as identified with DEPICT (33).”
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Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease
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Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals
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Quantifying genetic effects on disease mediated by assayed gene expression levels
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Assessment of network module identification across complex diseases
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Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction
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New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk
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Self-citationThe statistical properties of gene-set analysis
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Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation
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Self-citationGenetic architecture of 11 organ traits derived from abdominal MRI using deep learning
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Self-citationAssociation analyses identify 31 new risk loci for colorectal cancer susceptibility
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Genome-wide association study of medication-use and associated disease in the UK Biobank
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- Authors
- Pers, T. H., Karjalainen, J. M., Chan, Y., Westra, H.-J., Wood, A. R., Yang, J., Lui, J. C., Vedantam, S., Gustafsson, S., Esko, T., Frayling, T., Speliotes, E. K., Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Boehnke, M., Raychaudhuri, S., Fehrmann, R. S. N., Hirschhorn, J. N., & Franke, L.
- Affiliations
- Broad Institute (US)
- University Medical Center Groningen (NL)
- Brigham and Women's Hospital (US)
- University of Exeter (GB)
- The University of Queensland (AU)
- National Institutes of Health (US)
- Uppsala University (SE)
- University of Michigan (US)
- Corresponding author
- Pers, Tune H.
- Hirschhorn, Joel N.
- Franke, Lude
- ORCID
- Venue
- Nature Communications
- Published
- 2015-01-19
- Volume
- 6
- Issue
- 1
- Pages
- 5890-5890
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4420238
- OpenAlex
- W2125972459
- Cited by
- 787
- References
- 40
- Citations by year
- 2026: 32 · 2025: 46 · 2024: 53 · 2023: 69 · 2022: 76 · 2021: 106 · 2020: 77 · 2019: 100 · 2018: 87 · 2017: 66 · 2016: 49 · 2015: 26
- FWCI
- 33.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc-nd)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- SERVAL (Université de Lausanne) (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (acceptedVersion, other-oa)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
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- Pure Amsterdam UMC (submittedVersion, other-oa)
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- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
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- Discovery Research Portal (University of Dundee) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Site cant be reached (submittedVersion)
- Open Research Exeter (University of Exeter) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Open Research (University of Surrey) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Wellcome Trust (098395)
- National Institute for Health and Care Research (NF-SI-0611-10170, NF-SI-0611-10219, NF-SI-0514-10027, NF-SI-0513-10059)
- European Commission (259867)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (FP7 2007-2013, 916·10·135, 14374, 10135)
- Lundbeckfonden (R190 2014-3904)
- Danmarks Frie Forskningsfond
- Alfred Benzon Foundation
- Novo Nordisk Fonden (NNF14OC0010513, NNF13OC0005785)
- National Institutes of Health (5r01ar063759-07, FP7/2007-2013)
- Medical Research Council (MR/K013351/1, MR/K006584/1, MR/N01104X/1)
- National Heart, Lung, and Blood Institute (R01HL117078)
- National Cancer Institute (P30CA071789)
- National Institute of General Medical Sciences (U01GM092691)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK075787, P30DK020572)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (UH2AR067677, R01-AR063759)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- genome-wide association study, gene prioritization, pathway enrichment, causal genes, hypothesis generation
- MeSH terms
- Software, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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