Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms.


Journal

American journal of human genetics
ISSN: 1537-6605
Titre abrégé: Am J Hum Genet
Pays: United States
ID NLM: 0370475

Informations de publication

Date de publication:
02 09 2021
Historique:
received: 20 03 2021
accepted: 23 06 2021
pubmed: 23 7 2021
medline: 16 9 2021
entrez: 22 7 2021
Statut: ppublish

Résumé

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.

Identifiants

pubmed: 34293285
pii: S0002-9297(21)00266-4
doi: 10.1016/j.ajhg.2021.06.018
pmc: PMC8456160
pii:
doi:

Substances chimiques

Chromatin 0
Interferon Regulatory Factors 0
interferon regulatory factor-4 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, N.I.H., Intramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1631-1646

Subventions

Organisme : NCI NIH HHS
ID : R01 CA083115
Pays : United States
Organisme : Cancer Research UK
ID : C588/A19167
Pays : United Kingdom

Informations de copyright

Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no competing interests.

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Auteurs

Tongwu Zhang (T)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Jiyeon Choi (J)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Ramile Dilshat (R)

Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland.

Berglind Ósk Einarsdóttir (BÓ)

Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland.

Michael A Kovacs (MA)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Mai Xu (M)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Michael Malasky (M)

Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Salma Chowdhury (S)

Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Kristine Jones (K)

Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

D Timothy Bishop (DT)

Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS9 7TF, UK.

Alisa M Goldstein (AM)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Mark M Iles (MM)

Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS9 7TF, UK.

Maria Teresa Landi (MT)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Matthew H Law (MH)

Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia.

Jianxin Shi (J)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.

Eiríkur Steingrímsson (E)

Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland.

Kevin M Brown (KM)

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA. Electronic address: kevin.brown3@nih.gov.

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