CoChat
Sign Up
Cited by 669Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyBioinformatics and Genomic Networks

Genome-wide enhancer maps link risk variants to disease genes

Open access
Nasser, Joseph, Bergman, Drew T., Fulco, Charles P., Guckelberger, Philine, Doughty, Benjamin R., Patwardhan, Tejal A., Jones, Thouis R., Nguyen, Tung H. +24 more·2021·Nature
DOI 10.1038/s41586-021-03446-xPMC PMC9153265
Open this paper in CoChat — free account

Abstract

Genome-wide association studies (GWAS) have identified thousands of noncoding loci that are associated with human diseases and complex traits, each of which could reveal insights into the mechanisms of disease 1. Many of the underlying causal variants may affect enhancers 2,3, but we lack accurate maps of enhancers and their target genes to interpret such variants. We recently developed the activity-by-contact (ABC) model to predict which enhancers regulate which genes and validated the model using CRISPR perturbations in several cell types 4. Here we apply this ABC model to create enhancer-gene maps in 131 human cell types and tissues, and use these maps to interpret the functions of GWAS variants. Across 72 diseases and complex traits, ABC links 5,036 GWAS signals to 2,249 unique genes, including a class of 577 genes that appear to influence multiple phenotypes through variants in enhancers that act in different cell types. In inflammatory bowel disease (IBD), causal variants are enriched in predicted enhancers by more than 20-fold in particular cell types such as dendritic cells, and ABC achieves higher precision than other regulatory methods at connecting noncoding variants to target genes. These variant-to-function maps reveal an enhancer that contains an IBD risk variant and that regulates the expression of PPIF to alter the membrane potential of mitochondria in macrophages. Our study reveals principles of genome regulation, identifies genes that affect IBD and provides a resource and generalizable strategy to connect risk variants of common diseases to their molecular and cellular functions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41586-021-03446-x

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Among the prioritized CpGs, 8 CpGs were located in enhancer regions.

    “Among them, 8 CpGs were located in enhancer regions [53].”

    From Distinct sex-specific DNA methylation differences in Alzheimer’s disease (2022) · cites this paper as [53]

    The section “ABC enhancer-gene maps in 131 biosamples” describes genome-wide enhancer maps that could be used to identify enhancer regions. It does not report the citing authors’ prioritized CpGs or establish that eight of them fall in those regions; the citation supports the enhancer annotation, not the count. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • A total of 10 CpGs (all in females) overlapped with enhancer regions.

    “A total of 10 CpGs (all of them in females) overlapped with enhancer regions [53]”

    From Distinct sex-specific DNA methylation differences in Alzheimer’s disease (2022) · cites this paper as [53]

    The ‘ABC enhancer-gene maps in 131 biosamples’ section describes the paper’s enhancer regions, so it supports citing this work as a source for enhancer maps. Neither that section nor the searched passages reports 10 overlapping CpGs or a female-only finding; the ‘10 variants’ in Methods refers instead to IBD variants with allelic effects on H3K27ac. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • GWAS-detected genetic variants associated with complex traits and diseases are typically non-coding, non-promoter associated and highly enriched in enhancers.

    “The importance of enhancer variation in human pathology has been further highlighted by genome-wide association studies (GWAS), since GWAS-detected genetic variants associated with complex traits and diseases are typically non-coding, non-promoter associated and highly enriched in enhancers [184, 208, 209].”

    From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [184]

    The Abstract says most causal variants do not alter protein-coding sequences, and ‘Enrichment of GWAS variants in enhancers’ reports strong enhancer enrichment among fine-mapped variants for 72 traits. The copy does not establish that GWAS-detected variants are typically non-promoter-associated; the enrichment analysis also focuses on credible sets without coding or splice-site variants. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The ABC model showed good performance in predicting transcriptional effects of CRISPRi-mediated enhancer perturbations in dozens of loci in human cell lines, particularly for more proximal enhancers.

    “The ABC model showed a good performance in predicting the transcriptional effects of CRISPRi-mediated enhancer perturbations (one-by-one) in dozens of loci in human cell lines, particularly for the more proximal enhancers [183, 184].”

    From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [184]

    In “ABC enhancer-gene maps in 131 biosamples” (page unavailable), the paper reports that ABC performed well at classifying regulatory connections against 5,755 CRISPR-tested element–gene pairs across 11 cell types and states. It does not establish that performance was particularly good for proximal enhancers: Extended Data Fig. 4 reports different positive rates by distance and cautions against comparing precision–recall curves across distance groups. The reported evaluation classifies regulatory effects rather than predicting their magnitude. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • When comparing Borzoi, Enformer and a distance-to-TSS baseline on classifying measured positive from negative enhancer-gene interactions, Borzoi has superior AUPRC and AUROC at all distances.

    “When comparing Borzoi, Enformer and a distance-to-TSS baseline on their ability to classify measured positive from negative enhancer–gene interactions in data from previous works60–65, we find that Borzoi has superior average precision (AUPRC) and area under the receiver operating characteristic curve (AUROC) at all distances (Fig. 4c and Extended Data Fig. 5a).”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [65]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 57

  1. [1]Claussnitzer M A brief history of human disease genetics. Nature 577, 179– 189, doi: 10.1038/s41586-019-1879-7 (2020). 31915397 PMC7405896 · DOI · Paper page
  2. [2]Farh KK-H Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337– 343, doi: 10.1038/nature13835 (2015). 25363779 PMC4336207 · DOI · Paper page
  3. [4]Fulco CP Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat Genet 51, 1664– 1669, doi: 10.1038/s41588-019-0538-0 (2019). 31784727 PMC6886585 · DOI · Paper page
  4. [5]Westra H-J & Franke L From genome to function by studying eQTLs. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1842, 1896– 1902, doi: 10.1016/j.bbadis.2014.04.024 (2014). 24798236 · DOI
  5. [6]Gasperini M, Tome JM & Shendure J Towards a comprehensive catalogue of validated and target-linked human enhancers. Nat Rev Genet, doi: 10.1038/s41576-019-0209-0 (2020). PMC7845138 31988385 · DOI · Paper page
  6. [7]van Arensbergen J, van Steensel B & Bussemaker HJ In search of the determinants of enhancer-promoter interaction specificity. Trends Cell Biol 24, 695– 702, doi: 10.1016/j.tcb.2014.07.004 (2014). 25160912 PMC4252644 · DOI
  7. [8]Consortium EP An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57– 74, doi: 10.1038/nature11247 (2012). 22955616 PMC3439153 · DOI · Paper page
  8. [10]Consortium WTCC Bayesian refinement of association signals for 14 loci in 3 common diseases. Nature genetics 44, 1294– 1301, doi: 10.1038/ng.2435 (2012). 23104008 PMC3791416 · DOI
  9. [11]Ulirsch JC Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat Genet 51, 683– 693, doi: 10.1038/s41588-019-0362-6 (2019). 30858613 PMC6441389 · DOI · Paper page
  10. [13]Rescigno M & Di Sabatino A Dendritic cells in intestinal homeostasis and disease. J Clin Invest 119, 2441– 2450, doi: 10.1172/JCI39134 (2009). 19729841 PMC2735931 · DOI
  11. [14]Graham DB & Xavier RJ Pathway paradigms revealed from the genetics of inflammatory bowel disease. Nature 578, 527– 539, doi: 10.1038/s41586-020-2025-2 (2020). 32103191 PMC7871366 · DOI
  12. [15]Mountjoy E Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. bioRxiv (preprint), doi: 10.1101/2020.09.16.299271 (2020). PMC7611956 34711957 · DOI
  13. [16]Buniello A The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47, D1005– D1012, doi: 10.1093/nar/gky1120 (2019). 30445434 PMC6323933 · DOI · Paper page
  14. [17]Stacey D ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res 47, e3, doi: 10.1093/nar/gky837 (2019). 30239796 PMC6326795 · DOI
  15. [18]Chun S Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nature genetics 49, 600– 605, doi: 10.1038/ng.3795 (2017). 28218759 PMC5374036 · DOI · Paper page
  16. [19]Carvalho-Silva D Open Targets Platform: new developments and updates two years on. Nucleic Acids Research 47, D1056– D1065, doi: 10.1093/nar/gky1133 (2019). 30462303 PMC6324073 · DOI
  17. [20]Barbeira AN Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genet 15, e1007889, doi: 10.1371/journal.pgen.1007889 (2019). 30668570 PMC6358100 · DOI
  18. [21]Hauberg ME Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression. Am J Hum Genet 101, 157, doi: 10.1016/j.ajhg.2017.06.003 (2017). 28686855 PMC5501865 · DOI
  19. [23]Liu Y, Sarkar A, Kheradpour P, Ernst J & Kellis M Evidence of reduced recombination rate in human regulatory domains. Genome Biol 18, 193, doi: 10.1186/s13059-017-1308-x (2017). 29058599 PMC5651596 · DOI
  20. [24]Granja JM Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat Biotechnol 37, 1458– 1465, doi: 10.1038/s41587-019-0332-7 (2019). 31792411 PMC7258684 · DOI · Paper page
  21. [26]Sheffield NC Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome research 23, 777– 788, doi: 10.1101/gr.152140.112 (2013). 23482648 PMC3638134 · DOI
  22. [27]Cao Q Reconstruction of enhancer-target networks in 935 samples of human primary cells, tissues and cell lines. Nat Genet 49, 1428– 1436, doi: 10.1038/ng.3950 (2017). 28869592 · DOI
  23. [28]Thurman RE The accessible chromatin landscape of the human genome. Nature 489, 75– 82, doi: 10.1038/nature11232 (2012). 22955617 PMC3721348 · DOI · Paper page
  24. [29]Gao T & Qian J EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species. Nucleic Acids Res 48, D58– D64, doi: 10.1093/nar/gkz980 (2020). 31740966 PMC7145677 · DOI
  25. [30]Whalen S, Truty RM & Pollard KS Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin. Nature genetics, doi: 10.1038/ng.3539 (2016). PMC4910881 27064255 · DOI · Paper page
  26. [31]Consortium G Genetic effects on gene expression across human tissues. Nature 550, 204– 213, doi: 10.1038/nature24277 (2017). 29022597 PMC5776756 · DOI · Paper page
  27. [32]Engreitz JM Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature, doi: 10.1038/nature20149 (2016). PMC6853796 27783602 · DOI · Paper page
  28. [33]Wainberg M Opportunities and challenges for transcriptome-wide association studies. Nat Genet 51, 592– 599, doi: 10.1038/s41588-019-0385-z (2019). 30926968 PMC6777347 · DOI
  29. [34]Franke A Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nature Genetics 42, 1118– 1125, doi: 10.1038/ng.717 (2010). 21102463 PMC3299551 · DOI · Paper page
  30. [36]Linares PM & Gisbert JP Role of growth factors in the development of lymphangiogenesis driven by inflammatory bowel disease: a review. Inflamm Bowel Dis 17, 1814– 1821, doi: 10.1002/ibd.21554 (2011). 21744436 · DOI
  31. [37]Wang X & Goldstein DB Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease. Am J Hum Genet 106, 215– 233, doi: 10.1016/j.ajhg.2020.01.012 (2020). 32032514 PMC7010980 · DOI
  32. [38]Imielinski M Common variants at five new loci associated with early-onset inflammatory bowel disease. Nature Genetics 41, 1335– 1340, doi: 10.1038/ng.489 (2009). 19915574 PMC3267927 · DOI
  33. [39]Elrod JW & Molkentin JD Physiologic functions of cyclophilin D and the mitochondrial permeability transition pore. Circ J 77, 1111– 1122, doi: 10.1253/circj.cj-13-0321 (2013). 23538482 PMC6397958 · DOI
  34. [40]Ip WKE, Hoshi N, Shouval DS, Snapper S & Medzhitov R Anti-inflammatory effect of IL-10 mediated by metabolic reprogramming of macrophages. Science (New York, N.Y) 356, 513– 519, doi: 10.1126/science.aal3535 (2017). PMC6260791 28473584 · DOI
  35. [42]Buenrostro JD, Wu B, Chang HY & Greenleaf WJ ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol 109, 21 29 21– 21 29 29, doi: 10.1002/0471142727.mb2129s109 (2015). PMC4374986 25559105 · DOI · Paper page
  36. [43]Zhu J Genome-wide chromatin state transitions associated with developmental and environmental cues. Cell 152, 642– 654, doi: 10.1016/j.cell.2012.12.033 (2013). 23333102 PMC3563935 · DOI
  37. [44]Consortium EP An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57– 74, doi: 10.1038/nature11247 (2012). 22955616 PMC3439153 · DOI · Paper page
  38. [48]Amemiya HM, Kundaje A & Boyle AP The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep 9, 9354, doi: 10.1038/s41598-019-45839-z (2019). 31249361 PMC6597582 · DOI · Paper page
  39. [51]Langmead B & Salzberg SL Fast gapped-read alignment with Bowtie 2. Nature methods 9, 357– 359, doi: 10.1038/nmeth.1923 (2012). 22388286 PMC3322381 · DOI · Paper page
  40. [52]Liu JZ Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nature genetics 47, 979– 986, doi: 10.1038/ng.3359 (2015). 26192919 PMC4881818 · DOI · Paper page
  41. [53]de Lange KM Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nature genetics 49, 256– 261, doi: 10.1038/ng.3760 (2017). 28067908 PMC5289481 · DOI · Paper page
  42. [54]Loh PR, Kichaev G, Gazal S, Schoech AP & Price AL Mixed-model association for biobank-scale datasets. Nat Genet 50, 906– 908, doi: 10.1038/s41588-018-0144-6 (2018). 29892013 PMC6309610 · DOI · Paper page
  43. [55]Zhou W Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet 50, 1335– 1341, doi: 10.1038/s41588-018-0184-y (2018). 30104761 PMC6119127 · DOI
  44. [56]Benner C Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies. Am J Hum Genet 101, 539– 551, doi: 10.1016/j.ajhg.2017.08.012 (2017). 28942963 PMC5630179 · DOI
  45. [57]Wang G, Sarkar A, Carbonetto P & Stephens M A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society: Series B (Statistical Methodology), doi: 10.1111/rssb.12388 (2020). PMC10201948 37220626 · DOI · Paper page
  46. [58]McLaren W The Ensembl Variant Effect Predictor. Genome Biol 17, 122, doi: 10.1186/s13059-016-0974-4 (2016). 27268795 PMC4893825 · DOI · Paper page
  47. [59]Fujita PA The UCSC Genome Browser database: update 2011. Nucleic acids research 39, D876– 882, doi: 10.1093/nar/gkq963 (2011). 20959295 PMC3242726 · DOI
  48. [60]Carrillo-de-Santa-Pau E Automatic identification of informative regions with epigenomic changes associated to hematopoiesis. Nucleic Acids Research 45, 9244– 9259, doi: 10.1093/nar/gkx618 (2017). 28934481 PMC5716146 · DOI
  49. [61]Astle WJ The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 167, 1415– 1429.e1419, doi: 10.1016/j.cell.2016.10.042 (2016). 27863252 PMC5300907 · DOI · Paper page
  50. [62]Finucane HK Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature genetics 47, 1228– 1235, doi: 10.1038/ng.3404 (2015). 26414678 PMC4626285 · DOI · Paper page
  51. [63]Chen L Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells. Cell 167, 1398– 1414.e1324, doi: 10.1016/j.cell.2016.10.026 (2016). 27863251 PMC5119954 · DOI · Paper page
  52. [64]Liberzon A Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739– 1740, doi: 10.1093/bioinformatics/btr260 (2011). 21546393 PMC3106198 · DOI · Paper page
  53. [65]Kerimov N eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs. bioRxiv (preprint), doi: 10.1101/2020.01.29.924266 (2021). PMC8423625 34493866 · DOI
  54. [67]Kerpedjiev P HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol 19, 125, doi: 10.1186/s13059-018-1486-1 (2018). 30143029 PMC6109259 · DOI
  55. [68]Rao SS A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665– 1680, doi: 10.1016/j.cell.2014.11.021 (2014). 25497547 PMC5635824 · DOI · Paper page
  56. [69]Novakovic B β-Glucan Reverses the Epigenetic State of LPS-Induced Immunological Tolerance. Cell 167, 1354– 1368.e1314, doi: 10.1016/j.cell.2016.09.034 (2016). 27863248 PMC5927328 · DOI
  57. [70]Donnard E Comparative Analysis of Immune Cells Reveals a Conserved Regulatory Lexicon. Cell Syst 6, 381– 394 e387, doi: 10.1016/j.cels.2018.01.002 (2018). 29454939 PMC5876141 · DOI

Cited by

Sorted by citations

Authors
Nasser, J., Bergman, D. T., Fulco, C. P., Guckelberger, P., Doughty, B. R., Patwardhan, T. A., Jones, T. R., Nguyen, T. H., Ulirsch, J. C., Lekschas, F., Mualim, K., Natri, H. M., Weeks, E. M., Munson, G., Kane, M., Kang, H. Y., Cui, A., Ray, J. P., Eisenhaure, T. M., Collins, R. L., Dey, K., Pfister, H., Price, A. L., Epstein, C. B., Kundaje, A., Xavier, R. J., Daly, M. J., Huang, H., Finucane, H. K., Hacohen, N., Lander, E. S., & Engreitz, J. M.
Affiliations
  • Broad Institute (US)
  • Harvard University (US)
  • Stanford Medicine (US)
  • Lucile Packard Children's Hospital (US)
Corresponding author
  • Lander, Eric S.
  • Engreitz, Jesse M.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2021-04-07
Volume
593
Issue
7858
Pages
238-243
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-021-03446-x
PMC
PMC9153265
OpenAlex
W3151678771
Cited by
669
References
13
Citations by year
2026: 98 · 2025: 125 · 2024: 162 · 2023: 136 · 2022: 108 · 2021: 36 · 2020: 2 · 2012: 1
FWCI
38.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Työväentutkimus Vuosikirja (submittedVersion, other-oa)
Funders
  • National Institute of Mental Health (R37MH107649, R01MH101244)
  • National Human Genome Research Institute (K99HG009917, R35HG011324, P50HG006193, R00HG009917, U01HG009379, U24HG009446)
  • National Cancer Institute (U01CA200059)
  • National Institute of Allergy and Infectious Diseases (K22AI153648)
  • National Institute of Diabetes and Digestive and Kidney Diseases (K01DK114379, P30DK043351)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
inflammatory bowel disease, cyclophilin D, mitochondrial membrane potential, macrophages, dendritic cells, complex traits
MeSH terms
Peptidyl-Prolyl Isomerase D, Cell Line, Chromosomes, Human, Pair 10, Dendritic Cells, Enhancer Elements, Genetic, Female, Humans, Macrophages, Male, Mitochondria, Organ Specificity, Phenotype, Genetic Variation, Inflammatory Bowel Diseases, Genome, Human, Genetic Predisposition to Disease, Cyclophilins, 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 ›

Are you an author, or do you see a problem with this page? Request a review