Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin
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Abstract
Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer-promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer-promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.
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Assigning well-annotated genomic regions to specific genes is not as simple as choosing the closest gene or genes containing variation highly correlated with associated SNPs.
“assigning even well-annotated genomic regions to specific genes is not as simple as choosing the closest gene or genes containing variation that is highly correlated with the associated SNPs which is usually the default approach (Whalen et al., 2016; Won et al., 2016)”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Whalen et al., 2016
Whalen and colleagues showed TargetFinder results measured only by F1 score, making it impossible to detect the actual true positive rate and true negative rate of their tests.
“For example, when Whalen and colleagues released TargetFinder, a tool to predict enhancer-promoters interactions in genomics, they showed its results measured only by F1 score [13], making it impossible to detect the actual true positive rate and true negative rate of their tests [69].”
From The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) · cites this paper as [13]
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BackgroundMachine learning and integrative analysis of biomedical big data
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- Authors
- Whalen, S., Truty, R. M., & Pollard, K. S.
- Affiliations
- Gladstone Institutes (US)
- Invitae (United States) (US)
- Corresponding author
- Whalen, Sean
- Pollard, Katherine S
- Venue
- Nature Genetics
- Published
- 2016-04-04
- Volume
- 48
- Issue
- 5
- Pages
- 488-496
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4910881
- OpenAlex
- W2333286569
- Cited by
- 456
- References
- 122
- Citations by year
- 2026: 28 · 2025: 33 · 2024: 38 · 2023: 45 · 2022: 40 · 2021: 47 · 2020: 60 · 2019: 58 · 2018: 40 · 2017: 43 · 2016: 20 · 2015: 2 · 2012: 1
- FWCI
- 27.90
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Gladstone Institutes
- National Institutes of Health (HL089707, U01-HL098179)
- National Heart, Lung, and Blood Institute (UM1HL098179, P01HL089707, HL089707, U01HL098179)
- National Institute of Mental Health (R01MH109907)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- enhancer-promoter interactions, chromatin looping, 3D genome organization, distal regulatory elements, epigenetic modifications, transcription factors, structural proteins, cell line variation
- MeSH terms
- Algorithms, Cell Line, Chromatin, DNA, Enhancer Elements, Genetic, Gene Expression Regulation, Humans, Models, Genetic, Nucleic Acid Conformation, Promoter Regions, Genetic, Protein Conformation, Genome
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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.
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