Evidence map›Paper›PMID 36423637›Full record

ArticleAmerican journal of human genetics2022

Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity.

Erping Long, Jinhu Yin, Karen M Funderburk, Mai Xu, James Feng, Alexander Kane, Tongwu Zhang, Timothy Myers, Alyxandra Golden, Rohit Thakur and 14 more

Open access · bronzeAbstract read
In one paragraph

Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
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

34 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
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  6. Review
  7. Functional characterization of the 9q34.13 locus identifiesbioRxiv : the preprint server for biology · 2026
    Article
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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

24 authors at 5 institutions in 4 countries.

Erping LongDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Jinhu YinDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Karen M FunderburkDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Mai XuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
James FengDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Alexander KaneDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Tongwu ZhangDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Timothy MyersDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Alyxandra GoldenDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Rohit ThakurDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Hyunkyung KongDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Lea JessopDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Eun Young KimDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Kristine JonesDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Raj ChariGenome Modification Core, Frederick National Lab for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Mitchell J MachielaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Kai YuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Melanoma Meta-Analysis Consortium
Mark M IlesLeeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS2 9NL, UK.
Maria Teresa LandiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Matthew H LawStatistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia; School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia.
Stephen J ChanockDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Kevin M BrownDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Jiyeon ChoiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. Electronic address: jiyeon.choi2@nih.gov.
National Cancer Institute · USFrederick National Laboratory for Cancer Research · USQueensland University of Technology · AUUniversity of Leeds · GBYonsei University · KR

Funding

Genetic Epidermiology of MelanomaR01CA083115 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BISHOP, DAVID TIMOTHY, ELDER, DAVID ERIC · 2001 to 2017
$13.0M
NCI NIH HHS R01 CA083115
6 · The paper itself

Abstract

The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR < 1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.

Indexed as

MelanomaSkin NeoplasmsBiological AssayCutaneous Malignant MelanomaGenome-Wide Association StudyHumansReceptors, G-Protein-CoupledTranscription FactorsGPRC5A protein, humanReceptors, G-Protein-CoupledTranscription Factorscell of tumor origincell-type specificityCRISPRieQTLGWASGWAS follow-upmelanomaMPRAtranscription factorsvariant scoring

Identifiers

PMID36423637
PMCPMC9748337
OpenAlexW4309825982

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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.