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Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function

Nature Genetics volume 54, pages 18–29 (2022)Cite this article

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Abstract

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP–CpG associations (methylation quantitative trait loci (meQTL), P < 10−14), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961, chromatin immunoprecipitation followed by sequencing (ChIP–seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8+ T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.

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Fig. 1: Summary of results for genome-wide association and replication testing.
Fig. 2: Replication in isolated white cells, isolated adipocytes and adipose tissue.
Fig. 3: Candidate genes for sentinel SNPs that are associated with trans CpG sites that overlap transcription factor-binding sites.
Fig. 4: Regulatory networks and locus colocalization analyses.
Fig. 5: Experimental evaluation of ZNF333 by ChIP–seq.
Fig. 6: White cell iQTL.

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Data availability

Summary statistics for the 11.2 million SNP–CpG pairs reaching genome-wide significance are available at https://zenodo.org/record/5196216#.YRZ3TfJxeUk. ChIP–seq data for ZNF333 are available through the NCBI SRA (accession code SRP284104). Raw genotype, methylation and expression data can be made available upon reasonable request by the authors. Controlled data access to data from the KORA cohort can be obtained through https://epi.helmholtz-muenchen.de. The web links for the publicly available datasets used in the study are as follows: PhenoScanner version 2 (http://www.phenoscanner.medschl.cam.ac.uk), GWAS catalog (https://www.ebi.ac.uk/gwas/docs/file-downloads), meQTL and eQTM data from Bonder et al 2017 (ref. 14). (https://molgenis26.gcc.rug.nl/downloads/biosqtlbrowser/2015_09_02_Primary_cis_meQTLsFDR0.05-ProbeLevel.zip, https://molgenis26.gcc.rug.nl/downloads/biosqtlbrowser/2015_09_02_trans_meQTLsFDR0.05-CpGLevel.txt, https://molgenis26.gcc.rug.nl/downloads/biosqtlbrowser/2015_09_02_cis_eQTMsFDR0.05-CpGLevel.txt), GTEx version 6 eQTL results (https://storage.googleapis.com/gtex_analysis_v6/single_tissue_eqtl_data/GTEx_Analysis_V6_eQTLs.tar.gz), eQTLGen cis eQTL results (https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/cis-eQTLs_full_20180905.txt.gz), TWAS hub (http://twas-hub.org/genes/UBASH3B/), GWAS summary statistics of 114 traits for colocalization analysis (https://zenodo.org/record/3629742), ChIP–seq binding sites (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeRegTfbsClustered/wgEncodeRegTfbsClusteredWithCellsV3.bed.gz, http://tagc.univ-mrs.fr/remap/download/All/filPeaks_public.bed.gz), chromHMM states (http://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/all.mnemonics.bedFiles.tgz), Hi-C data (EGAD00001003106), PPIs (http://string90.embl.de/newstring_download/protein.links.detailed.v9.0.txt.gz). Source data are provided with this paper.

Code availability

Code for the analysis is available at GitHub (https://github.com/heiniglab/hawe2021_meQTL_analyses) and also through Zenodo (https://doi.org/10.5281/zenodo.5529828 (ref. 84)).

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Acknowledgements

The KORA study was initiated and financed by the Helmholtz Zentrum München (German Research Center for Environmental Health), which is funded by the German Federal Ministry of Education and Research (BMBF) and by the state of Bavaria. KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the EU Joint Programming Initiative ‘a Healthy Diet for a Healthy Life’ (DIMENSION grant number 01EA1902A). The work was further supported by the Bavarian State Ministry of Health and Care through the research project DigiMed Bayern (https://www.digimed-bayern.de/). The German Diabetes Center (DDZ) is supported by the Ministry of Culture and Science of the State of North Rhine–Westphalia and the German Federal Ministry of Health. This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD). The LOLIPOP study is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the British Heart Foundation (SP/04/002), the Medical Research Council (G0601966, G0700931), the Wellcome Trust (084723/Z/08/Z), the NIHR (RP-PG-0407-10371), European Union FP7 (EpiMigrant, 279143) and European Union Horizon 2020 (iHealth-T2D, 643774). B.C.L. is supported by the Imperial College Junior Research Fellowship scheme as well as an Academy of Medical Sciences Springboard award. J.C.C. is also supported by the Singapore NMRC (NMRC/STaR/0028/2017). We thank the participants and research staff who made the study possible. For the Northern Finnish Birth Cohort studies, M. Wielscher was supported by the European Union’s Horizon 2020 research and innovation program (grant 633212). NFBC1966 received financial support from the Academy of Finland (grants 104781, 120315, 129269, 1114194 and 24300796, Center of Excellence in Complex Disease Genetics and SALVE), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617), NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01), the NIH–NIMH (5R01MH63706:02), the ENGAGE project and grant agreement HEALTH-F4-2007-201413, EU FP7 EurHEALTHAgeing (277849), the Medical Research Council, UK (G0500539, G0600705, G1002319, PrevMetSyn/SALVE) and an MRC Centenary Early Career Award. NFBC1986 received financial support from EU QLG1-CT-2000-01643 (EUROBLCS) grant E51560, NorFA grant nos. 731, 20056 and 30167 and USA/NIHH 2000 G DF682 grant 50945. The NFBC programs are also funded by the H2020-633595 DynaHEALTH action, the Academy of Finland Exposomic, Genomic and Epigenomic Approach to Prediction of Metabolic and Cardiorespiratory Function and Ill-Health project (285547) and the EU H2020 ALEC project (grant agreement 633212). The MuTHER study was funded by the WT (081917/Z/07/Z). TwinsUK was funded by the WT and the European Community’s Seventh Framework Programme (FP7/2007-2013). The study also received support from the NIHR Clinical Research Facility at Guy’s and St. Thomas’ and King’s College London. Analysis was funded by British Heart Foundation grant RG/14/5/30893 to P.D. and forms part of the research themes contributing to the translational research portfolio of the Barts Cardiovascular Biomedical Research Unit, which is funded by the NIHR. The Saguenay Youth Study has been funded by the Canadian Institutes of Health Research (T.P., Z.P.), the Heart and Stroke Foundation of Canada (Z.P.) and the Canadian Foundation for Innovation (Z.P.). We acknowledge G. Möller and J. Adamski (Helmholtz Center Munich) for their support in the IP–MS transfection experiment. We used data generated by the PCHI-C Consortium31, funded by the UK NIHR, the Medical Research Council (MR/L007150/1) and the Biotechnology and Biological Research Council (BB/J004480/1).

Author information

Author notes

  1. These authors contributed equally: Johann S. Hawe, Rory Wilson, Katharina Schmid.

  2. These authors jointly supervised this work: Jaspal S. Kooner, Marie Loh, Matthias Heinig, Christian Gieger, Melanie Waldenberger, John C. Chambers.

Authors and Affiliations

  1. Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Neuherberg, Germany

    Johann S. Hawe, Katharina T. Schmid, Eudes G. V. Barbosa & Matthias Heinig

  2. Department of Informatics, Technical University of Munich, Garching bei München, Germany

    Johann S. Hawe, Katharina T. Schmid & Matthias Heinig

  3. Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Rory Wilson, Brigitte Kühnel, Clemens Baumbach, Liliane Pfeiffer, Pamela R. Matías-García, Harald Grallert, Annette Peters, Christian Gieger & Melanie Waldenberger

  4. Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany

    Rory Wilson, Brigitte Kühnel, Clemens Baumbach, Liliane Pfeiffer, Pamela R. Matías-García, Harald Grallert, Annette Peters, Christian Gieger & Melanie Waldenberger

  5. Lee Kong Chian School of Medicine, Singapore, Singapore

    Li Zhou, Lakshmi Narayanan Lakshmanan, Yik Weng Yew, Marie Loh & John C. Chambers

  6. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    Benjamin C. Lehne, William R. Scott, Matthias Wielscher, Ville Karhunen, Weihua Zhang, Marjo-Riitta Jarvelin, Marie Loh & John C. Chambers

  7. Genome Institute of Singapore, Singapore, Singapore

    Dominic P. Lee, Matias I. Autio, Wilson L. W. Tan & Roger S. Y. Foo

  8. Centre for Genomic Health, Queen Mary University of London, London, UK

    Eirini Marouli & Panos Deloukas

  9. William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    Eirini Marouli, Stephane Bourgeois, Josine L. Min & Panos Deloukas

  10. Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada

    Manon Bernard, Jean Shin & Zdenka Pausova

  11. The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada

    Manon Bernard, Jean Shin & Zdenka Pausova

  12. Cardiovascular Research Institute, National University Health Systems, National University of Singapore, Singapore, Singapore

    Matias I. Autio & Roger S. Y. Foo

  13. German Center for Diabetes Research (DZD), partner site Düsseldorf, Düsseldorf, Germany

    Christian Herder, Wolfgang Rathmann & Michael Roden

  14. Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany

    Christian Herder & Michael Roden

  15. Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany

    Christian Herder & Michael Roden

  16. Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland

    Ville Karhunen, Sylvain Sebert & Marjo-Riitta Jarvelin

  17. Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Thomas Meitinger

  18. Institute of Human Genetics, Technical University Munich, Munich, Germany

    Thomas Meitinger & Holger Prokisch

  19. Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Holger Prokisch

  20. Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Wolfgang Rathmann

  21. Biocenter Oulu, University of Oulu, Oulu, Finland

    Sylvain Sebert & Marjo-Riitta Jarvelin

  22. Department for Genomics of Common Diseases, School of Public Health, Imperial College London, London, UK

    Sylvain Sebert

  23. Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany

    Konstantin Strauch

  24. Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Konstantin Strauch

  25. Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany

    Konstantin Strauch

  26. Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Southall, UK

    Weihua Zhang

  27. Research Unit Protein Science, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich, Germany

    Stefanie M. Hauck & Juliane Merl-Pham

  28. German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany

    Harald Grallert

  29. Hannover Unified Biobank, Hannover Medical School, Hannover, Germany

    Thomas Illig

  30. Institute for Human Genetics, Hannover Medical School, Hannover, Germany

    Thomas Illig

  31. German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Hannover, Germany

    Annette Peters & Melanie Waldenberger

  32. Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Canada

    Tomas Paus

  33. Unit of Primary Care, Oulu University Hospital, Oulu, Finland

    Marjo-Riitta Jarvelin

  34. National Heart and Lung Institute, Imperial College London, London, UK

    Jaspal S. Kooner

  35. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK

    Kourosh R. Ahmadi, Veronique Bataille, Jordana T. Bell, Daniel Glass, Kerrin S. Small, Tim D. Spector & Gabriela Surdulescu

  36. Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, Strand, London, UK

    Chrysanthi Ainali & Sophia Tsoka

  37. Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, UK

    Amy Barrett, Neelam Hassanali, Mark I. McCarthy & Mary E. Travers

  38. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland

    Alfonso Buil, Emmanouil T. Dermitzakis, Antigone S. Dimas, Stephen B. Montgomery & Alexandra C. Nica

  39. Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK

    Richard Durbin, Catherine Ingle, Eshwar Meduri, James Nisbet, Leopold Parts, Simon Potter, Johanna Sandling, Magdalena Sekowska, So-Youn Shin, Nicole Soranzo, Loukia Tsaprouni, Alicja Wilk & Tsun-Po Yang

  40. Children’s Mercy Hospitals and Clinics, Kansas City, MO, USA

    Elin Grundberg

  41. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    Åsa K. Hedman, Cecilia M. Lindgren, Mark I. McCarthy & Krina T. Zondervan

  42. University of Cambridge, Cambridge, UK

    David Knowles

  43. European Bioinformatics Institute, Hinxton, UK

    Maria Krestyaninova

  44. University of Cambridge Metabolic Research Labs, Institute of Metabolic Science, Addenbrooke’s Hospital Cambridge, Cambridge, UK

    Christopher E. Lowe & Stephen O’Rahilly

  45. Cambridge NIHR Biomedical Research Centre, Addenbrooke’s Hospital, Cambridge, UK

    Christopher E. Lowe & Stephen O’Rahilly

  46. St. John’s Institute of Dermatology, King’s College London, London, UK

    Paola di Meglio & Frank O. Nestle

Authors
  1. Johann S. Hawe
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  2. Rory Wilson
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  3. Katharina T. Schmid
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  4. Li Zhou
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  5. Lakshmi Narayanan Lakshmanan
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  6. Benjamin C. Lehne
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  7. Brigitte Kühnel
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  8. William R. Scott
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  9. Matthias Wielscher
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  10. Yik Weng Yew
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  11. Clemens Baumbach
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  12. Dominic P. Lee
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  13. Eirini Marouli
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  14. Manon Bernard
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  15. Liliane Pfeiffer
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  16. Pamela R. Matías-García
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  17. Matias I. Autio
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  18. Stephane Bourgeois
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  19. Christian Herder
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  20. Ville Karhunen
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  21. Thomas Meitinger
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  22. Holger Prokisch
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  23. Wolfgang Rathmann
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  24. Michael Roden
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  25. Sylvain Sebert
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  26. Jean Shin
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  27. Konstantin Strauch
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  28. Weihua Zhang
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  29. Wilson L. W. Tan
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  30. Stefanie M. Hauck
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  31. Juliane Merl-Pham
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  32. Harald Grallert
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  33. Eudes G. V. Barbosa
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  34. Thomas Illig
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  35. Annette Peters
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  36. Tomas Paus
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  37. Zdenka Pausova
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  38. Panos Deloukas
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  39. Roger S. Y. Foo
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  40. Marjo-Riitta Jarvelin
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  41. Jaspal S. Kooner
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  42. Marie Loh
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  43. Matthias Heinig
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  44. Christian Gieger
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  45. Melanie Waldenberger
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  46. John C. Chambers
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Consortia

MuTHER Consortium

  • Kourosh R. Ahmadi
  • , Chrysanthi Ainali
  • , Amy Barrett
  • , Veronique Bataille
  • , Jordana T. Bell
  • , Alfonso Buil
  • , Emmanouil T. Dermitzakis
  • , Antigone S. Dimas
  • , Richard Durbin
  • , Daniel Glass
  • , Elin Grundberg
  • , Neelam Hassanali
  • , Åsa K. Hedman
  • , Catherine Ingle
  • , David Knowles
  • , Maria Krestyaninova
  • , Cecilia M. Lindgren
  • , Christopher E. Lowe
  • , Mark I. McCarthy
  • , Eshwar Meduri
  • , Paola di Meglio
  • , Josine L. Min
  • , Stephen B. Montgomery
  • , Frank O. Nestle
  • , Alexandra C. Nica
  • , James Nisbet
  • , Stephen O’Rahilly
  • , Leopold Parts
  • , Simon Potter
  • , Johanna Sandling
  • , Magdalena Sekowska
  • , So-Youn Shin
  • , Kerrin S. Small
  • , Nicole Soranzo
  • , Tim D. Spector
  • , Gabriela Surdulescu
  • , Mary E. Travers
  • , Loukia Tsaprouni
  • , Sophia Tsoka
  • , Alicja Wilk
  • , Tsun-Po Yang
  •  & Krina T. Zondervan

Contributions

Data collection and analysis in the contributing population studies: KORA, A.P., B.K., C.G., C.H., C.B., H.P., K. Strauch, L. Pfeiffer, M. Waldenberger, M.R., R.W., T.I., T.M. and W.R.; LOLIPOP, B.C.L., J.S.K., J.C.C., W.Z. and W.R.S.; MuTHER, E. Marouli; MuTHER Consortium, P.D. and S.B.; NFBC, M.-R.J., M. Wielscher, S.S. and V.K.; SYS, J. Shin, M.B., T.P. and Z.P. Data collection and molecular follow-up analyses: ChIP–seq, D.P.L., M.I.A., R.S.Y.F. and W.L.W.T.; ChIP–MS, S.M.H., J.M.-P. and P.R.M.-G. Data analysis and writing group (alphabetical order): J.C.C., J.S.H., M.H., C.G., B.C.L., M.L., K. Schmid, M. Waldenberger and R.W.

Corresponding authors

Correspondence to Jaspal S. Kooner, Marie Loh, Matthias Heinig, Christian Gieger, Melanie Waldenberger or John C. Chambers.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Charles Danko and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Study design.

Overview of study design.

Extended Data Fig. 2 Replication testing of meQTLs within and across ancestries.

a: Ancestry-specific replication of SNP-CpG pairs identified by genome-wide association. Effect size: change in methylation (0-1 scale) per allele copy of the SNP. Axes set to [-0.5,0.5]. b: Ancestry-specific replication, by pair proximity and MAF. Bars: no. of pairs identified in discovery in given category. Blue: replicated; yellow: not replicated. c: Cross-ancestry replication, by pair proximity and MAF. Top: discovery in EU, replication in SA; bottom: discovery in SA, replication in EU. Bars: no. of pairs identified in discovery in given category. Blue: replicated; yellow: not replicated. d: Cross-platform: replication in KORA F4 (N<=1731) of published MeDIP-seq meQTLs, by significance threshold. Blue lines: no. of replicated results (of 328); histograms: no. of replicated results over 100 randomly selected matched datasets. P-values: one-sided, no adjustment for multiple testing. See Methods for test description. EU: European; SA: South Asian; MAF: Minor allele frequency.

Extended Data Fig. 3 Variance in DNA methylation explained by meQTL SNPs.

Histograms showing the proportions of variance of DNA methylation explained by genetic variants in both populations when variants are located in cis (left), long-range cis (middle) or trans (right) of the associated CpG site. EU: European; SA: South Asian.

Extended Data Fig. 4 Analysis of proximity between meQTL SNPs and CpGs.

Panel a: Histogram showing for each CpG site the genomic distance between CpG and the closest associated SNP from the cosmopolitan set of 10,346,172 SNP-CpG pairs identified in cis (association confirmed in both Europeans and South Asians). Panel b: Boxplots showing the proportion of SNP-CpG pairs that reach genome-wide significance for different distance categories (x-axis), compared to SNP-CpG pairs on different chromosomes (trans).1,000 random samples of 10,000 SNPs were taken. P-values above each box are based on a comparison (one-sided t-test) between the proportion of SNP-CpG pairs in trans that reach significance, and the proportion that reach significance in the respective same-chromosome distance window. Boxplots show medians (center lines), first and third quartiles (lower and upper box limits, respectively), 1.5-fold interquartile ranges (whisker extents) and outliers (black circles).

Extended Data Fig. 5 Functional genomic context of meQTL SNPs and CpGs.

Panel a: Genomic overlap between chromatin state annotations (15- state model; Roadmap Epigenomics Project and SNPs/CpGs identified by genome-wide association and cross-ancestry replication testing. Results are presented as a heatmap showing the P-values for enrichment (blue) or depletion (yellow) in the respective chromatin state (two-sided t-test). P-values have been Bonferroni-adjusted for the total number of tests (see Methods for details). Panel b: Colocalisation of SNPs and CpG sites in promoter and enhancer chromatin states. The histograms show the frequency at which CpG sites that localise in promoter or enhancer chromatin states have at least one cis-meQTL SNP that localises to the same chromatin state. Observed (turquoise) cis-meQTL pairs colocalise to the same chromatin state more frequently than matched background SNP-CpG pairs (grey). Panel c: Distance distributions for cis SNP-CpG pairs 1) localising to the same state (left), 2) where one entity localises to a promoter/enhancer state and the other to neither promoter nor enhancer state (center) and 3) one entity localises to a promoter and the other to an enhancer state. Panel d: Overlap of SNP-CpG associations with chromatin contacts in primary cells. The x-axis shows the fraction of SNP-CpG pairs that localise within the same topologically associated domain (TAD, left panel) or that overlap with Hi-C contacts (center and right panels). The left panel shows localisation of long-range cis-meQTLs within the same TAD. The center panel shows the overlap of long range cis-meQTLs (same chromosome, distance SNP - CpG > 1Mb) with contacts from promoter capture Hi-C (PCHi-C). The right panel shows overlap of trans-meQTL with Hi-C contacts. The blue vertical arrows indicate the overlap observed in the data. The grey histograms show the distribution of the fraction of randomly sampled SNP-CpG pairs overlapping contact regions for each category.

Extended Data Fig. 6 Enrichment of meQTL SNPs and CPGs for association with gene expression.

Sentinel meQTL SNPs and CpGs are enriched for association with gene expression in cis and trans (SNPs) and only in cis (CpGs). Panel a: Results are presented as the proportion of SNPs that are observed to be associated with gene expression in cis (top row) or in trans (bottom row), stratified by proximity between SNP and CpG for the respective SNP-CpG pair (cis, long-range cis and trans from left to right). Panel b: Similarly, results are presented as the proportion of CpGs that are observed to be associated with gene expression in cis (top row) or in trans (bottom row), stratified by proximity between SNP and CpG for the respective SNP-CpG pair (cis, long-range cis and trans from left to right). Both panels: In each plot, the observed proportion (yellow boxplots) is compared to the proportion expected under the null hypothesis based on permutation testing (blue boxplots, see Methods). Inset in each figure is the P-value for comparison between observed and expected proportions (t-test). Boxplots show medians (center lines), first and third quartiles (lower and upper box limits, respectively), 1.5-fold interquartile ranges (whisker extents) and outliers (black circles). Proportions were calculated based on 100 sets of permutations with 1,000 SNPs (Panel A) or 1,000 CpGs (Panel B) in each permutation.

Extended Data Fig. 7 Enrichment of meQTL SNPs and CpGs for associations with phenotypic traits.

(A) SNPs influencing DNA methylation (left panel) and SNPs identified to be population interacting meQTL based on our cosmopolitan discovery analysis (right panel) are both enriched for association with phenotypic traits, Analysis carried out using using QTLEnrich and 114 uniformly processed GWAS summary statistics. The volcano plot shows the log2 fold enrichment of significant GWAS hits among iQTL on the x-axis and the -log10 of the P-value of the enrichment test on the y-axis. Each point represents one of 114 GWAS studies. The transparency of the fill colour indicates the false discovery rate (FDR < 5%: no transparency). (B) Sentinel CpGs are enriched for clinical and metabolic traits. We tested the Sentinel CpGs for association with 277 available clinical and metabolic traits (NMR metabolomics). We used permutation testing to generate expectations under the null hypothesis, and to determine both the magnitude and probability for enrichment. Results show strong evidence that our genetically regulated Sentinel CpGs are enriched for association with traits (enriched at P<0.05/277 for 252 phenotypes) with median enrichment 1.10 (IQR: 1.06-1.15).

Extended Data Fig. 8 CpG sites associated with trans-acting sentinel SNPs are enriched for location in transcription factor binding sites.

Heatmap showing the enrichment (or depletion) of CpG sites for trans-acting sentinel SNPs (x-axis) with the DNA binding sites of known transcription factors (y-axis). Log2 odds ratios compare the frequency of overlap for the CpGs associated with the respective SNP, compared to the background frequency of overlap for all tested CpG sites. Results are shown for the 45 sentinel SNPs that show evidence for overlap with known transcription factor binding sites (out of the 115 tested trans-acting sentinel SNPs with at least five associated CpG sites).

Extended Data Fig. 9 Trans-acting regulatory networks at the CTCF, NFKB1, REST, NFE2, MAD1L1 and ENRICH1 loci.

(a) Circos plots summarising i. genomic distribution of CpGs associated in trans [inner connections], and ii. known DNA binding sites of transcription factor encoded in cis [outer ring], for sentinel SNPs at CTCF, NFKB1, REST and NFE2 loci. Inset are observed and expected proportions of CpG sites that overlap respective DNA binding sites as available for different cell lines (see Methods). FDR < 1.17 × 10−2 for all cell lines and transcription factors. (b) Regulatory network of ERICH1 locus illustrating the connection between SNP rs10103269 (yellow rectangle) and expression of identified candidate gene ERICH1 (yellow ellipse), which is connected through protein-protein and protein-DNA interactions to methylation at trans-associated CpG sites (beige rectangles). Ellipses represent genes encoded at the genetic locus identified by the sentinel or that are part of the protein-protein interaction network. Genes marked with an asterisk (*) show co-expression with the candidate gene. Bold gene names indicate a strong genetic effect of the sentinel on the expression of that gene (eQTL). Fill colour of ellipses represent the random walk score (colour bar legend). The colour of edges connecting genes and CpG sites represent: i. protein-protein interactions (purple), ii. protein-DNA interactions identified by TFBS overlap (green), and iii. proximity (distance < 1 Mb) between genes and SNPs or CpG sites (blue). The thickness of edges represents correlation with gene expression (thick) or no correlation of/with gene expression (thin). Boxplot shows the effect of sentinel SNP (rs10103269) in cis on expression of ERICH1 with the p-value from linear regression of expression ~ genotype (n=1,546 biologically independent samples combined from both cohorts). Center line indicates median, lower and upper box limits correspond to the first and third quartiles, respectively; whisker extent indicates 1.5-fold interquartile range; outliers not shown. (c) MAD1L1 locus pathway analysis. Annotations and symbols are as described in (b).

Source data

Extended Data Fig. 10 Experimental validation at the ZNF333 locus.

Panel a. Regulatory network of the ZNF333 locus. Annotations and symbols are as described in Extended Figure 9. The boxplot shows the effect of sentinel SNP (rs6511961) in cis on expression of the candidate gene ZNF333 with the p-value from the linear regression of expression ~ genotype (n=1,546 biologically independent samples combined from both cohorts). Panels b-d. HCT116 cells were transfected with ZNF333-FLAG/Myc tagged or GFP-control plasmids in biological replicates. Panel b. Protein lysates were Western blotted for ZNF333 expression using FLAG or MYC antibodies as validations. GAPDH was used as loading control (n=2). Source data: Membranes were cut into three pieces for optimisation of exposure. Top left panel: Original uncropped and unprocessed scans. Top right panel: Scan exposure optimized for molecular ladder. Bottom left panel: Scan exposure optimized for GAPDH. Bottom right panel: Final overlay figure. Panel c. Heatmap showing the Pearson correlation between ChIP-seq performed for ZNF333 using either FLAG or MYC antibodies. Panel d. Motifs of known TFs enriched in ZNF333 binding sites showing perfect overlap between ChIP with FLAG and MYC antibodies.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–9

Reporting Summary

Supplementary Tables

Supplementary Tables 1–41.

Source data

Source Data Extended Data Fig. 9

Unprocessed data for ZNF333 ChIP–seq experiment.

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Hawe, J.S., Wilson, R., Schmid, K.T. et al. Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function. Nat Genet 54, 18–29 (2022). https://doi.org/10.1038/s41588-021-00969-x

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