Patterns of Reliability: Assessing the Reproducibility and Integrity of DNA Methylation Measurement
Open accessAbstract
DNA methylation plays an important role in both normal human development and risk of disease. The most utilized method of assessing DNA methylation uses BeadChips, generating an epigenome-wide "snapshot" of >450,000 observations (probe measurements) per assay. However, the reliability of each of these measurements is not equal, and little consideration is paid to consequences for research. We correlated repeat measurements of the same DNA samples using the Illumina HumanMethylation450K and the Infinium MethylationEPIC BeadChips in 350 blood DNA samples. Probes that were reliably measured were more heritable and showed consistent associations with environmental exposures, gene expression, and greater cross-tissue concordance. Unreliable probes were less replicable and generated an unknown volume of false negatives. This serves as a lesson for working with DNA methylation data, but the lessons are equally applicable to working with other data: as we advance toward generating increasingly greater volumes of data, failure to document reliability risks harming reproducibility.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The mild correlation among first-generation epigenetic clock age gaps could be due to large technical noise in DNA methylation arrays, which limits robustness across datasets.
“This could be due to the large degree of technical noise in DNA methylation arrays152–154, which limits the robustness of applying these models across datasets.”
From Measuring biological age using omics data (2022) · cites this paper as [154]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A guideline of selecting and reporting intraclass correlation coefficients for reliability research
Koo, T. K., & Li, M. Y. (2016) · Journal of Chiropractic Medicine · doi:10.1016/j.jcm.2016.02.012 · Cited by 29,639 · see citing works
DNA methylation age of human tissues and cell types
Horvath, S. (2013) · Genome Biology · doi:10.1186/gb-2013-14-10-r115 · Cited by 7,075 · see citing works
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270 · see citing works
Fast gene set enrichment analysisPreprint
Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M. N., & Sergushichev, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060012 · Cited by 3,742 · see citing works
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719 · see citing works
An epigenetic biomarker of aging for lifespan and healthspan
Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., et al. (2018) · Aging · doi:10.18632/aging.101414 · Cited by 3,675 · see citing works
DNA methylation-based biomarkers and the epigenetic clock theory of ageing
Horvath, S., & Raj, K. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0004-3 · Cited by 3,095 · see citing works
DNA methylation and human disease
Robertson, K. D. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1655 · Cited by 2,864 · see citing works
(1981) · PubMed · Cited by 2,253 · see citing works
Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W. A., Hou, L., & Lin, S. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-587 · Cited by 2,136 · see citing works
Function and information content of DNA methylation
Schübeler, D. (2015) · Nature · doi:10.1038/nature14192 · Cited by 2,060 · see citing works
Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., & Clark, S. J. (2016) · Genome biology · doi:10.1186/s13059-016-1066-1 · Cited by 1,480 · see citing works
OpenMx 2.0: Extended Structural Equation and Statistical Modeling
Neale, M., Hunter, M. D., Pritkin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H., & Boker, S. (2015) · Psychometrika · doi:10.1007/s11336-014-9435-8 · Cited by 1,064 · see citing works
Reproducibility and Replicability in Science
Committee on Reproducibility and Replicability in Science, Board on Behavioral, C., Committee on National Statistics, Education, D. O. B. A. S. S. A., Nuclear and Radiation Studies Board, Studies, D. O. E. A. L., Board on Mathematical Sciences and Analytics, Committee on Applied and Theoretical Statistics, Sciences, D. O. E. A. P., Board on Research Data and Information, et al. (2019) · doi:10.17226/25303 · Cited by 924 · see citing works
Epigenetic Signatures of Cigarette Smoking
Joehanes, R., Just, A. C., Marioni, R. E., Pilling, L. C., Reynolds, L. M., Mandaviya, P. R., Guan, W., Xu, T., Elks, C. E., Aslibekyan, S. W., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001506 · Cited by 915 · see citing works
DNA methylation and healthy human aging
Jones, M. J., Goodman, S., & Kobor, M. (2015) · Aging Cell · doi:10.1111/acel.12349 · Cited by 867 · see citing works
Tobacco smoking leads to extensive genome-wide changes in DNA methylation
Zeilinger, S., Kühnel, B., Klopp, N., Baurecht, H., Kleinschmidt, A., Gieger, C., Weidinger, S., Lattka, E., Adamski, J., Peters, A., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063812 · Cited by 824 · see citing works
DNA Methylation in Cancer and Aging
Klutstein, M., Nejman, D., Greenfield, R., & Cedar, H. (2016) · Cancer Research · doi:10.1158/0008-5472.can-15-3278 · Cited by 810 · see citing works
Morán, S., Arribas, C., & Esteller, M. (2015) · Epigenomics · doi:10.2217/epi.15.114 · Cited by 701 · see citing works
Burt, S. A. (2009) · Psychological Bulletin · doi:10.1037/a0015702 · Cited by 467 · see citing works
Hannon, E., Lunnon, K., Schalkwyk, L., & Mill, J. (2015) · Epigenetics · doi:10.1080/15592294.2015.1100786 · Cited by 455 · see citing works
Self-citationTeen‐aged mothers in contemporary Britain
Moffitt, T. E., & the E‐Risk Study Team (2002) · Journal of Child Psychology and Psychiatry · doi:10.1111/1469-7610.00082 · Cited by 439 · see citing works
Self-citationPoulton, R., Moffitt, T. E., & Silva, P. A. (2015) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-015-1048-8 · Cited by 394 · see citing works
Self-citationLehne, B., Drong, A. W., Loh, M., Zhang, W., Scott, W. R., Tan, S.-T., Afzal, U., Scott, J., Jarvelin, M.-R., Elliott, P., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0600-x · Cited by 380 · see citing works
Identification of 55,000 Replicated DNA Methylation QTL
McRae, A. F., Marioni, R. E., Shah, S. H., Yang, J., Powell, J. E., Harris, S. E., Gibson, J., Henders, A. K., Bowdler, L. M., Painter, J. N., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-35871-w · Cited by 355 · see citing works
Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation
Guida, F., Sandanger, T. M., Castagné, R., Campanella, G., Polidoro, S., Palli, D., Krogh, V., Tumino, R., Sacerdote, C., Panico, S., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddu751 · Cited by 332 · see citing works
Statistical and integrative system-level analysis of DNA methylation data
Teschendorff, A. E., & Relton, C. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.86 · Cited by 292 · see citing works
Lehne, B. C., Drong, A., Loh, M. C. S., Zhang, W., Scott, W. R., Tan, S.-T., Afzal, U., Schulz, R., Scott, J., Järvelin, M., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0934-z · Cited by 284 · see citing works
Dogan, M. V., Shields, B. E., Cutrona, C. E., Gao, L., Gibbons, F. X., Simons, R. L., Monick, M. M., Brody, G. H., Tan, K., Beach, S. R. H., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-151 · Cited by 201 · see citing works
Hannon, E., Knox, O., Sugden, K., Burrage, J., Wong, C., Belsky, D. W., Corcoran, D., Arseneault, L., Moffitt, T., Caspi, A., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007544 · Cited by 187 · see citing works
Self-citationSmoke-related DNA methylation changes in the etiology of human disease
Besingi, W., & Johansson, Å. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt621 · Cited by 170 · see citing works
Marzi, S. J., Sugden, K., Arseneault, L., Belsky, D. W., Burrage, J., Corcoran, D. L., Danese, A., Fisher, H. L., Hannon, E., Moffitt, T. E., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17060693 · Cited by 163 · see citing works
Self-citationNaeem, H., Wong, N., Chatterton, Z., Hong, M., Pedersen, J., Corcoran, N., Hovens, C., & Macintyre, G. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-51 · Cited by 144 · see citing works
Logue, M. W., Smith, A. K., Wolf, E. J., Maniates, H., Stone, A., Schichman, S. A., McGlinchey, R. E., Milberg, W., & Miller, M. W. (2017) · Epigenomics · doi:10.2217/epi-2017-0078 · Cited by 139 · see citing works
Neglecting Major Health Problems and Broadcasting Minor, Uncertain Issues in Lifestyle Science
Ioannidis, J. P. A. (2019) · JAMA · doi:10.1001/jama.2019.17576 · Cited by 113 · see citing works
Solomon, O., MacIsaac, J. L., Quach, H., Tindula, G. N., Kobor, M. S., Huen, K., Meaney, M. J., Eskenazi, B., Barcellos, L. F., & Holland, N. (2018) · Epigenetics · doi:10.1080/15592294.2018.1497386 · Cited by 106 · see citing works
Wilson, R., Wahl, S., Pfeiffer, L., Ward-Caviness, C. K., Kunze, S., Kretschmer, A., Reischl, E., Peters, A., Gieger, C., & Waldenberger, M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4198-0 · Cited by 98 · see citing works
Bose, M., Wu, C., Pankow, J. S., Demerath, E. W., Bressler, J., Fornage, M., Grove, M. L., Mosley, T. H., Hicks, C., North, K., et al. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-312 · Cited by 86 · see citing works
Dynamic DNA methylation in the brain: a new epigenetic mark for experience-dependent plasticity
Tognini, P., Napoli, D., & Pizzorusso, T. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00331 · Cited by 85 · see citing works
Dugué, P., English, D. R., MacInnis, R. J., Jung, C., Bassett, J. K., FitzGerald, L. M., Wong, E. M., Joo, J. E., Hopper, J. L., Southey, M. C., et al. (2016) · Scientific Reports · doi:10.1038/srep30317 · Cited by 85 · see citing works
Epigenetics in comparative biology: why we should pay attention
Burggren, W. W., & Crews, D. (2014) · Integrative and Comparative Biology · doi:10.1093/icb/icu013 · Cited by 72 · see citing works
Epigenetic Variation in Monozygotic Twins: A Genome-Wide Analysis of DNA Methylation in Buccal Cells
van Dongen, J., Ehli, E. A., Slieker, R. C., Bartels, M., Weber, Z. M., Davies, G. E., Slagboom, P. E., Heijmans, B. T., & Boomsma, D. I. (2014) · Genes · doi:10.3390/genes5020347 · Cited by 61 · see citing works
Edgar, R. D., Jones, M. J., Robinson, W. P., & Kobor, M. S. (2017) · Clinical Epigenetics · doi:10.1186/s13148-017-0320-z · Cited by 58 · see citing works
Epigenetic Research in Cancer Epidemiology: Trends, Opportunities, and Challenges
Verma, M., Rogers, S. D., Divi, R. L., Schully, S. D., Nelson, S. A., Su, L. J., Ross, S. A., Pilch, S., Winn, D. M., & Khoury, M. J. (2013) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-13-0573 · Cited by 54 · see citing works
An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells
Kennedy, E., Goehring, G. N., Nichols, M. H., Robins, C., Mehta, D., Klengel, T., Eskin, E., Smith, A. K., & Conneely, K. (2018) · BMC Genomics · doi:10.1186/s12864-018-4842-3 · Cited by 45 · see citing works
Social Epigenetics: A Science of Social Science?
Chung, E. M. L., Cromby, J., Papadopoulos, D., & Tufarelli, C. (2016) · The Sociological Review · doi:10.1111/2059-7932.12019 · Cited by 40 · see citing works
Genetically contextual effects of smoking on genome wide DNA methylation
Dogan, M. V., Beach, S. R. H., & Philibert, R. A. (2017) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32565 · Cited by 38 · see citing works
Genetics meets DNA methylation in rare diseases
Velasco, G., & Francastel, C. (2018) · Clinical Genetics · doi:10.1111/cge.13480 · Cited by 36 · see citing works
Social epigenetics: a science of social science?
Chung, E. M. L., Cromby, J., Papadopoulos, D., & Tufarelli, C. (2016) · The Sociological Review Monographs · doi:10.1002/2059-7932.12019 · Cited by 34 · see citing works
Genome-Wide Epigenetic Studies in Human Disease: A Primer on -Omic Technologies
Yan, H., Tian, S., Slager, S. L., Sun, Z., & Ördög, T. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv187 · Cited by 26 · see citing works
“Philosophers and biologists exploring epigenetics”
Speybroeck, L. V. (2002) · Biology & Philosophy · doi:10.1023/a:1022590131272 · Cited by 17 · see citing works
Recent advances in computational epigenetics
Ruskin, H. J., & Barat, A. (2017) · Advances in Genomics and Genetics · doi:10.2147/agg.s115524 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Measuring biological age using omics data
Rutledge, J., Oh, H., & Wyss-Coray, T. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00511-7 · Cited by 570 · see citing works
Bischoff-Ferrari, H. A., Gängler, S., Wieczorek, M., Belsky, D. W., Ryan, J., Kressig, R. W., Stähelin, H. B., Theiler, R., Dawson-Hughes, B., Rizzoli, R., et al. (2025) · Nature Aging · doi:10.1038/s43587-024-00793-y · Cited by 140 · see citing works
Self-citation
- Authors
- Sugden, K., Hannon, E. J., Arseneault, L., Belsky, D. W., Corcoran, D. L., Fisher, H. L., Houts, R. M., Kandaswamy, R., Moffitt, T. E., Poulton, R., Prinz, J. A., Rasmussen, L. J. H., Williams, B. S., Wong, C. C. Y., Mill, J., & Caspi, A.
- Affiliations
- Duke University (US)
- University of Exeter (GB)
- King's College London (GB)
- Columbia University (US)
- University of Otago (NZ)
- Corresponding author
- Sugden, Karen
- ORCID
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 1
- Issue
- 2
- Pages
- 100014
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC7467214
- OpenAlex
- W3011777860
- Cited by
- 151
- References
- 52
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 30 · 2023: 27 · 2022: 33 · 2021: 12 · 2020: 7
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Medical Research Council (MR/P005918/1, G1002190)
- National Institute on Aging (MR/P005918/1, AG049789, AG032282)
- Biotechnology and Biological Sciences Research Council (MQ14F40)
- British Academy (MD\\170005)
- Health Research Council of New Zealand
- Economic and Social Research Council (ES/N000277/1)
- North Carolina Biotechnology Center (2016-IDG-1013)
- American Asthma Foundation
- National Institute of Child Health and Human Development (HD077482)
- Charles Lafitte Foundation
- Jacobs Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- DNA methylation, measurement reliability, reproducibility, epigenome-wide association study, heritability, environmental exposure, gene expression, blood DNA, false-negative results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
