» Articles » PMID: 30401456

Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship Between Methylomic Variation, Gene Expression, and Complex Traits

Overview
Journal Am J Hum Genet
Publisher Cell Press
Specialty Genetics
Date 2018 Nov 8
PMID 30401456
Citations 88
Authors
Affiliations
Soon will be listed here.
Abstract

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.

Citing Articles

Genetic-epigenetic interactions (meQTLs) in orofacial clefts etiology.

Machado-Paula L, Romanowska J, Lie R, Hovey L, Doolittle B, Awotoye W medRxiv. 2025; .

PMID: 39990564 PMC: 11844571. DOI: 10.1101/2025.02.09.25321494.


Genome-wide interaction association analysis identifies interactive effects of childhood maltreatment and kynurenine pathway on depression.

Sun Y, Liao Y, Zhang Y, Lu Z, Ma Y, Kang Z Nat Commun. 2025; 16(1):1748.

PMID: 39966400 PMC: 11836188. DOI: 10.1038/s41467-025-57066-4.


Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

Zhou J, Weinberger D, Han S Sci Adv. 2025; 11(1):eadn1870.

PMID: 39742481 PMC: 11691643. DOI: 10.1126/sciadv.adn1870.


Multiomics identification of ALDH9A1 as a crucial immunoregulatory molecule involved in calcific aortic valve disease.

Chen L, Zheng P, Shi X Sci Rep. 2024; 14(1):23577.

PMID: 39384885 PMC: 11464510. DOI: 10.1038/s41598-024-75115-8.


Schizophrenia is associated with altered DNA methylation variance.

Kiltschewskij D, Reay W, Cairns M Mol Psychiatry. 2024; .

PMID: 39271751 DOI: 10.1038/s41380-024-02749-5.


References
1.
Westra H, Peters M, Esko T, Yaghootkar H, Schurmann C, Kettunen J . Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013; 45(10):1238-1243. PMC: 3991562. DOI: 10.1038/ng.2756. View

2.
Liu J, van Sommeren S, Huang H, Ng S, Alberts R, Takahashi A . Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015; 47(9):979-986. PMC: 4881818. DOI: 10.1038/ng.3359. View

3.
Liu Y, Li X, Aryee M, Ekstrom T, Padyukov L, Klareskog L . GeMes, clusters of DNA methylation under genetic control, can inform genetic and epigenetic analysis of disease. Am J Hum Genet. 2014; 94(4):485-95. PMC: 3980524. DOI: 10.1016/j.ajhg.2014.02.011. View

4.
Bonder M, Luijk R, Zhernakova D, Moed M, Deelen P, Vermaat M . Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2016; 49(1):131-138. DOI: 10.1038/ng.3721. View

5.
Pidsley R, Zotenko E, Peters T, Lawrence M, Risbridger G, Molloy P . Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016; 17(1):208. PMC: 5055731. DOI: 10.1186/s13059-016-1066-1. View