Abstract
Epigenetic modifications influence gene expression levels, impact organismal traits, and play a role in the development of diseases. Therefore, variants in genes involved in epigenetic processes are likely to be important in disease susceptibility, and the frequency of variants may vary between populations with African and European ancestries. Here, we analyse an integrated dataset to define the frequencies, associated traits, and functional impact of epigenetic gene variants among individuals of African and European ancestry represented in the UK Biobank. We find that the frequencies of 88.4% of epigenetic gene variants significantly differ between these groups. Furthermore, we find that these variants map to many reported traits and diseases, and we show that allele-frequency differences can alter statistical power and the likelihood of detecting associations across ancestry groups, particularly given the substantial sample-size imbalance between the UK Biobank European-ancestry and African-ancestry subsets. Additionally, we observe that variants associated with traits are significantly enriched for quantitative trait loci that affect DNA methylation, chromatin accessibility, and gene expression. We find that methylation quantitative trait loci account for 71.2% of the variants influencing gene expression. Moreover, variants linked to biomarker traits exhibit high correlation. We therefore conclude that epigenetic gene variants associated with traits tend to differ in their allele frequencies among African and European populations and are enriched for QTLs.
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Data availability
The raw datasets that support the findings of this manuscript are available from the following sources. The pre-processed datasets are accessible via Zenodo (https://doi.org/10.5281/zenodo.12789774)90 under the Creative Commons Attribution 4.0 license. List of Resources and Datasets: UK Biobank: Biomarker measurements, allele frequencies, and GWAS summary statistics of various biomarkers. https://www.ukbiobank.ac.uk ; https://pan-ukb-us-east-1.s3.amazonaws.com. dbSNP: SNP data and allele frequencies. https://www.ncbi.nlm.nih.gov/snp/. GWAS Catalog: Associations between SNPs and various traits. https://www.ebi.ac.uk/gwas/. GTEx: eQTL, hQTL, and sQTL data. https://gtexportal.org/. mQTLdb: mQTLs. http://www.mqtldb.org. OncoBase Databases: mQTLs. https://ngdc.cncb.ac.cn/databasecommons/database/id/6069. EUR Genome-phenome Archive: H3Africa datasets. https://ega-archive.org/about/ega/ and EGAD00001008577. Reactome Pathways: Epigenetic gene annotations. https://reactome.org/. Ensembl BioMart: Gene annotations and chromosomal positions. http://mart.ensembl.org/info/data/biomart/index.html. Ensembl Variant Effect Predictor: Functional impact of SNPs: Functional impact of SNPs. http://mart.ensembl.org/info/docs/tools/vep/index.html.
Code availability
Code to reproduce most of the results and plots is available from the following GitHub repository: https://github.com/smsinks/epigenetic-gene-variant-dynamics-analysis.
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Acknowledgements
We would like to acknowledge the contributions of the following individuals for their valuable support and collaboration in this research: Enock Matovu (matovue04@yahoo.com), Busisiwe Mlotshwa (mlotshwab@ub.ac.bw), Simo Gustaveand (gsimoca@yahoo.fr), and Martin Simuunza (martin.simuunza@unza.zm).
Funding
This research has been conducted using the UK Biobank Resource under Application Number 53163. The funding for this project was provided by H3ABioNet, supported by the National Institutes of Health Common Fund under grant number U24HG006941. Clement A. Adebamowo and Sally N. Adebamowo were supported by the African Collaborative Center for Microbiome and Genomics Research (ACCME) Grant (1U54HG006947), funds through the Maryland Department of Health’s Cigarette Restitution Fund Program (CH-649-CRF), and the University of Maryland Greenebaum Cancer Center Support Grant (P30CA134274). The content of this publication is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health.
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The study was conceptualised by Musalula Sinkala (M.S.), Gaone Retshabile (G.R.), Phelelani T. Mpangase (P.T.M.), Nicola Mulder (N.M.), Salia Bamba (S.B.), Modibo K Goita (M.K.G), Victoria Nembaware (V.N.), Samar S. M. Elsheikh (S.S.M.E.), Jeannine Heckmann (J.H.), Kevin Esoh (K.E.), Mogomotsi Matshaba (M.M.), Guida Landoure (G.L.) Ambroise Wonkam (A.W.), Michele Ramsay (M.R.), Clement A. Adebamowo (C.A.A.), Sally N. Adebamowo (S.N.A.), and Ofon Elvis Amih (O.E.A.). The methodology was designed by M.S., G.R., P.T.M., and N.M. Data collection and provision were carried out by M.S., G.R., P.T.M., S.B., M.K.G., V.N., S.S.M.E., J.H., K.E., O.E.A., G.L., A.W., M.M., M.R., C.A.A., and S.N.A. Formal analysis of the data was performed by M.S., G.R., S.B., and M.K.G. The manuscript was drafted by M.S., G.R., P.T.M., and N.M. Editing and reviewing of the manuscript were carried out by M.S., G.R., P.T.M., S.B., M.K.G., G.L., V.N., S.S.M.E., J.H., K.E., M.M., A.W., M.R., C.A.A., S.N.A., O.E.A., and N.M. M.S produced data visualisations. N.M. supervised the study.
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Sinkala, M., Retshabile, G., Mpangase, P.T. et al. Mapping epigenetic gene variant dynamics: comparative analysis of frequency, functional impact and trait associations in African and European populations. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41871-y
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DOI: https://doi.org/10.1038/s41598-026-41871-y
