Evidence map›Paper›PMID 34996498›Full record

ArticleGenome biology2022

Integrative analysis of 3604 GWAS reveals multiple novel cell type-specific regulatory associations.

Charles E Breeze, Eric Haugen, Alex Reynolds, Andrew Teschendorff, Jenny van Dongen, Qing Lan, Nathaniel Rothman, Guillaume Bourque, Ian Dunham, Stephan Beck and 3 more

Abstract read
In one paragraph

Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. EpiATLAS - a reference for human epigenomic research.bioRxiv : the preprint server for biology · 2026
    Article
  6. Smoking drives an epigenetic memory of aberrant hematopoiesis.medRxiv : the preprint server for health sciences · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Epigenomic insights into common human disease pathology.Cellular and molecular life sciences : CMLS · 2024
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Charles E BreezeNational Cancer Institute, NIH, Bethesda, MD, 20892, USA. c.breeze@ucl.ac.uk.ORCID 0000-0002-5294-915X
Eric HaugenAltius Institute for Biomedical Sciences, Seattle, WA, 98121, USA.
Alex ReynoldsAltius Institute for Biomedical Sciences, Seattle, WA, 98121, USA.
Andrew TeschendorffCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
Jenny van DongenDepartment of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, 1081BT, The Netherlands.
Qing LanNational Cancer Institute, NIH, Bethesda, MD, 20892, USA.
Nathaniel RothmanNational Cancer Institute, NIH, Bethesda, MD, 20892, USA.
Guillaume BourqueDepartment of Human Genetics, McGill University and Génome Québec Innovation Center, Montréal, H3A 0G1, Canada.
Ian DunhamEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Stephan BeckUCL Cancer Institute, University College London, WC1E 6BT, London, UK.
John StamatoyannopoulosAltius Institute for Biomedical Sciences, Seattle, WA, 98121, USA.
Nora FranceschiniDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
Sonja I BerndtNational Cancer Institute, NIH, Bethesda, MD, 20892, USA.

Funding

Leveraging ancestry to map kidney lociR01MD012765 · NIMHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRANCESCHINI, NORA · 2017 to 2021
$3.6M
Genetics of kidney disease in diverse populationsR01DK117445 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRANCESCHINI, NORA · 2018 to 2022
$2.7M
NIDDK NIH HHS R01 DK117445NIMHD NIH HHS R01 MD012765Wellcome TrustWellcome Trust 218274/Z/19/Z
6 · The paper itself

Abstract

backgroundGenome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are known to preferentially co-locate to active regulatory elements in tissues and cell types relevant to disease aetiology. Further characterisation of associated cell type-specific regulation can broaden our understanding of how GWAS signals may contribute to disease risk.

resultsTo gain insight into potential functional mechanisms underlying GWAS associations, we developed FORGE2 ( https://forge2.altiusinstitute.org/ ), which is an updated version of the FORGE web tool. FORGE2 uses an expanded atlas of cell type-specific regulatory element annotations, including DNase I hotspots, five histone mark categories and 15 hidden Markov model (HMM) chromatin states, to identify tissue- and cell type-specific signals. An analysis of 3,604 GWAS from the NHGRI-EBI GWAS catalogue yielded at least one significant disease/trait-tissue association for 2,057 GWAS, including > 400 associations specific to epigenomic marks in immune tissues and cell types, > 30 associations specific to heart tissue, and > 60 associations specific to brain tissue, highlighting the key potential of tissue- and cell type-specific regulatory elements. Importantly, we demonstrate that FORGE2 analysis can separate previously observed accessible chromatin enrichments into different chromatin states, such as enhancers or active transcription start sites, providing a greater understanding of underlying regulatory mechanisms. Interestingly, tissue-specific enrichments for repressive chromatin states and histone marks were also detected, suggesting a role for tissue-specific repressed regions in GWAS-mediated disease aetiology.

conclusionIn summary, we demonstrate that FORGE2 has the potential to uncover previously unreported disease-tissue associations and identify new candidate mechanisms. FORGE2 is a transparent, user-friendly web tool for the integrative analysis of loci discovered from GWAS.

Indexed as

Genome-Wide Association StudyPolymorphism, Single NucleotideEpigenomicsGenetic Predisposition to DiseaseHumansPhenotypeRegulatory Sequences, Nucleic Acid

Identifiers

PMID34996498
PMCPMC8742386

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.