Evidence map›Paper›PMID 40409268›Full record

ArticleAmerican journal of human genetics2025

Mapping chromatin interactions at melanoma susceptibility loci uncovers distant cis-regulatory gene targets.

Rohit Thakur, Mai Xu, Hayley Sowards, Joshuah Yon, Lea Jessop, Timothy Myers, Tongwu Zhang, Raj Chari, Erping Long, Thomas Rehling and 14 more

Abstract read
In one paragraph

Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Article
  2. Disruption of CTCF binding by germline non-coding variants inmedRxiv : the preprint server for health sciences · 2026
    Article
  3. Article
  4. Functional characterization of the 9q34.13 locus identifiesbioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors.

Rohit ThakurLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Mai XuLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Hayley SowardsLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Joshuah YonLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Lea JessopLaboratory of Genetic Susceptibility, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Timothy MyersLaboratory of Genetic Susceptibility, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Tongwu ZhangIntegrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Raj ChariGenome Modification Core, Frederick National Lab for Cancer Research, Frederick, MD, USA.
Erping LongLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA; Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.
Thomas RehlingLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Rebecca HennesseyLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Karen FunderburkLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Jinhu YinLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Mitchell J MachielaIntegrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Matthew E JohnsonDivision of Human Genetics, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA.
Andrew D WellsCenter for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Alessandra ChesiCenter for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Struan F A GrantCenter for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Mark M IlesLeeds Institute for Data Analytics, University of Leeds, Leeds, UK; NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Maria Teresa LandiIntegrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Matthew H LawPopulation Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia; School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia.
Melanoma Meta-Analysis Consortium
Jiyeon ChoiLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Kevin M BrownLaboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. Electronic address: kevin.brown3@nih.gov.

Funding

NIHR NIHR203331
6 · The paper itself

Abstract

Genome-wide association studies (GWASs) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma-susceptibility genes. Overall, chromatin-interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with epigenomic (chromatin state, accessibility), gene expression (expression quantitative trait locus [eQTL]/transcriptome-wide association study [TWAS]), DNA methylation (methylation QTL [meQTL]/methylome-wide association study [MWAS]), and massively parallel reporter assay (MPRA) data generated from melanoma-relevant cell types to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized credible causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals, we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Indexed as

ChromatinGenetic Predisposition to DiseaseMelanomaChromosome MappingDNA MethylationGene Expression Regulation, NeoplasticGenome-Wide Association StudyHumansMelanocytesPolymorphism, Single NucleotideQuantitative Trait LociChromatincapture-HiCCBLGWASMDM4melanomaSOX4

Identifiers

PMID40409268
PMCPMC12256899

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Registered trials

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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.