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Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
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Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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“In males, among the 126 CpGs mapped to the AD-associated CpGs or located in AD-associated DMRs (Supplementary Tables 2, 5), 67 CpGs had cis mQTLs, and 3 CpGs had both cis [58] and trans mQTLs.”
From Distinct sex-specific DNA methylation differences in Alzheimer’s disease (2022) · cites this paper as [58]
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“These results are consistent with the previous observation that a substantial proportion (about 45%) of the DNA methylation sites targeted by the Illumina 450k array are influenced by genetic variants in the blood [58].”
From Distinct sex-specific DNA methylation differences in Alzheimer’s disease (2022) · cites this paper as [58]
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“EWAS addressing the joint effect of genotype and environment are still in its infancy (Min et al., 2021).”
From Can epigenetics shine a light on the biological pathways underlying major mental disorders? (2022) · cites this paper as Min et al., 2021
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“the G allele of the lead variant rs911196 increases methylation of cg01799681, cg16867657, cg21572722, and cg23642061 in whole blood (62, 63)”
From Retinal polyunsaturated fatty acid supplementation reverses aging-related vision decline in mice (2025) · cites this paper as [63]
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“Given the increasingly widespread assessment of DNAm in cohort studies (McCartney et al., 2020; Min et al., 2020), EpiScores offer an affordable and consistent (i.e. array-based) way to utilise these signatures.”
BackgroundSelf-citationGenetic impacts on DNA methylation help elucidate regulatory genomic processes
Villicaña, S., Fernandez, J. C., Hannon, E. J., Christiansen, C., Tsai, P.-C., Maddock, J., Kuh, D. J., Suderman, M. J., Power, C., Relton, C. L., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03011-x · Cited by 76
“A recent large-scale study in 27,750 European samples estimated that DNA methylation levels at up to 45% of CpGs in the blood 450K methylome are associated with meQTL SNPs [10], which are in turn more likely to be GWAS signals than expected by chance.”
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“The GoDMC mQTL resource represents the largest mQTL study to date in terms of sample size but only focused on 450k array sites [31].”
InfluentialSelf-citationEpigenetics of prenatal stress in humans: the current research landscape
Dieckmann, L., & Czamara, D. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01635-9 · Cited by 92
“First of all, DNAm can vary by ancestry [41] and also the genome plays an important role—allele-specific DNAm patterns [58, 92] as well as meQTLs (methylation quantitative trait loci, [59]) have been reported.”
BackgroundSelf-citationThe potential of DNA methylation as a biomarker for obesity and smoking
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Can epigenetics shine a light on the biological pathways underlying major mental disorders?
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“…some studies presented in this review have found evidence that some genotypic variation in some risk alleles can influence DNAm (Klengel et al., 2013; Melas et al., 2013; Perroud et al., 2016), EWAS addressing the joint effect of genotype and environment are still in its infancy (Min et al., 2021).”
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Self-citationThe correlation between CpG methylation and gene expression is driven by sequence variants
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Identical twins carry a persistent epigenetic signature of early genome programming
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“These results suggested that approximately half of the CSF biomarker-associated CpGs are impacted by genetic variation, consistent with a recent large mQTL meta-analysis of blood samples, which estimated that genetic variants influence about 45% of CpGs on the Illumina array [53].”
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Genetic control of DNA methylation is largely shared across European and East Asian populations
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“We sought to quantify this by comparing mQTLs identi fi ed in EAS and not replicated in EUR with the largest mQTL study to date (GoDMC), a meta-analysis of 27,750 EUR participants 22 .”
Self-citationGadd, D. A., Hillary, R. F., McCartney, D. L., Shi, L., Stolicyn, A., Robertson, N., Walker, R. M., McGeachan, R. I., Campbell, A., Shen, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32319-8 · Cited by 45
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Self-citationEvidence for the role of selection for reproductively advantageous alleles in human aging
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Teruel, M., Barturen, G., Martínez‐Bueno, M., Castellini-Pérez, O., Barroso‐Gil, M., Povedano, E., Kerick, M., Català‐Moll, F., Makowska, Z., Buttgereit, A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-01324-0 · Cited by 37
Self-citationDistinct sex-specific DNA methylation differences in Alzheimer’s disease
C. Silva, T., Zhang, W., Young, J. I., Gomez, L., Schmidt, M. A., Varma, A., Chen, X. S., Martin, E. R., & Wang, L. (2022) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-01070-z · Cited by 33
“These results are consistent with the previous observation that a substantial proportion (about 45%) of the DNA methylation sites targeted by the Illumina 450k array are influenced by genetic variants in the blood [58].”
MethodResultInfluentialEpigenome-wide association studies of allergic disease and the environment
Cárdenas, A., Fadadu, R. P., & Koppelman, G. H. (2023) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.05.020 · Cited by 44
Self-citationBakulski, K. M., Blostein, F., & London, S. J. (2023) · Environmental Health Perspectives · doi:10.1289/ehp12956 · Cited by 39
“184 Further, local genetic correlation can lead to noncausal associations between genetic variants and methylation in cis , 185 obscuring inferences.”
BackgroundEpigenetics applied to child and adolescent mental health: Progress, challenges and opportunities
Cecil, C. A. M., Neumann, A., & Walton, E. (2022) · JCPP Advances · doi:10.1002/jcv2.12133 · Cited by 37
“Soberingly, a recent large-scale study applying Mendelian randomization found limited evidence for a causal role of blood-based DNAm in neuropsychiatric disorders (Min et al., 2021).”
BackgroundMethodInfluentialLocal CpG density affects the trajectory and variance of age-associated DNA methylation changes
Higham, J., Kerr, L., Zhang, Q., Walker, R. M., Harris, S. E., Howard, D. M., Hawkins, E. L., Sandu, A., Steele, J. D., Waiter, G. D., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02787-8 · Cited by 32
“These have been characterized as allele-specific methylation or methylation quantitative trait loci (meth-QTLs) [24, 25].”
BackgroundSelf-citationAbrishamcar, S., Chen, J., Feil, D., Kilanowski, A., Koen, N., Vanker, A., Wedderburn, C. J., Donald, K. A., Zar, H. J., Stein, D. J., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02195-3 · Cited by 26
“Lastly, many of our significant CpG sites were associated with at least one known mQTL, which is an indicator of the genetic influence on DNAm levels [56].”
BackgroundMethodEpigenomic insights into common human disease pathology
Bell, C. G. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05206-2 · Cited by 36
“A meta-analysis for mQTLs in blood derived DNAm with the 450 k DNAm array, incorporating 36 studies, including the above Dutch cohort, was performed in 2020 [185].”
BackgroundMethodEpigenetic scores for the circulating proteome as tools for disease prediction
Gadd, D. A., Hillary, R. F., McCartney, D. L., Zaghlool, S. B., Stevenson, A. J., Cheng, Y., Fawns‐Ritchie, C., Nangle, C., Campbell, A., Flaig, R., et al. (2022) · eLife · doi:10.7554/elife.71802 · Cited by 116
“Given the increasingly widespread assessment of DNAm in cohort studies ( McCartney et al., 2020 ; Min et al., 2021 ), EpiScores offer an affordable and consistent (i.e., array-based) way to utilise these signatures.”
BackgroundSelf-citationChen, J., Zhang, H., Fu, T., Zhao, J., Nowak, J., Kalla, R., Wellens, J., Yuan, S., Noble, A., Ventham, N., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105443 · Cited by 27
“44 We extracted information on mQTLs including location, effect allele and other allele, and the corresponding effect estimates (including beta, se, and P value).”
Self-citationTin, A., Schlosser, P., Matías‐García, P. R., Thio, C. H. L., Joehanes, R., Liu, H., Yu, Z., Weihs, A., Hoppmann, A., Grundner-Culemann, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27198-4 · Cited by 29
Self-citationMarttila, S., Viiri, L. E., Mishra, P. P., Kühnel, B., Matías‐García, P. R., Lyytikäinen, L., Ceder, T., Mononen, N., Rathmann, W., Winkelmann, J., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01132-3 · Cited by 29
“php) [73], and an association analysis in the YFS data was performed for these 18 CpG sites and genetic variations located ± 1 Mb of nc886 loci.”
MethodExploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations
Auwerx, C., Sadler, M., Woh, T., Reymond, A., Kutalik, Z., & Porcu, E. (2022) · bioRxiv · doi:10.7554/elife.81097 · Cited by 26
“, 2021), DNA methylation (Min et al., 2021), protein (Sun et al.”
BackgroundPrenatal Maternal Smoke, DNA Methylation, and Multi-omics of Tissues and Child Health
Cosín‐Tomás, M., Cilleros‐Portet, A., Aguilar-Lacasaña, S., Fernández‐Jiménez, N., & Bustamante, M. (2022) · Current Environmental Health Reports · doi:10.1007/s40572-022-00361-9 · Cited by 30
Self-citationShen, X., Caramaschi, D., Adams, M. J., Walker, R. M., Min, J. L., Kwong, A. S. F., Hemani, G., Whalley, H. C., Whalley, H. C., Harris, S. E., et al. (2021) · Genome Medicine · doi:10.1016/j.euroneuro.2021.07.038 · Cited by 34
Self-citationLussier, A. A., Schuurmans, I. K., Großbach, A., Maclsaac, J. L., Dever, K., Koen, N., Zar, H. J., Stein, D. J., Kobor, M. S., & Dunn, E. C. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01761-4 · Cited by 25
“The information provided through this web portal includes CpG-level DNAm distributions, correlations across arrays, mQTL annotation from GoDMC [21], as well as detailed statistics of the array-based bias estimations.”
Zhou, X., Xiao, Q., Jiang, F., Sun, J., Wang, L., Yu, L., Zhou, Y., Zhao, J., Zhang, H., Yuan, S., et al. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02397-6 · Cited by 29
“Sex, age at measurement, batch variables, smoking and recorded cell counts were used to adjust for possible confounding and to reduce residual variation [17].”
MethodInfluentialSelf-citationLi, S., Ye, Z.-F., Mather, K., Nguyen, T., Dite, G., Armstrong, N., Wong, E., Thalamuthu, A., Giles, G., Craig, J., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.103927 · Cited by 24
Epigenetic Regulation of F2RL3 Associates With Myocardial Infarction and Platelet Function
Corbin, L. J., Taylor, A. E., White, S. J., Williams, C. M., Taylor, K., van den Bosch, M. T. J., Teasdale, J. E., Jones, M., Bond, M., Harper, M. T., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.318836 · Cited by 40
Self-citationA machine learning case–control classifier for schizophrenia based on DNA methylation in blood
Gunasekara, C., Hannon, E. J., MacKay, H., Coarfa, C., McQuillin, A., Clair, D. S., Mill, J. S., & Waterland, R. A. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01496-3 · Cited by 33
“79-fold enriched for mQTL [54], respectively (see probe-level tabulation in Supplementary Table S4).”
BackgroundSelf-citationLinares-Pineda, T., Peña-Montero, N., Fragoso-Bargas, N., Gutiérrez-Repiso, C., Lima-Rubio, F., Suárez-Arana, M., Sánchez-Pozo, A., Tinahones, F., Molina-Vega, M., Picón-César, M. J., et al. (2023) · Clinical Epigenetics · doi:10.1186/s13148-023-01523-8 · Cited by 23
“mQTL We performed look-ups in Genetics of DNA Methylation Consortium (GoDMC) [52] of the CpGs replicated in EPIPREG to identify methylation quantitative trait loci (mQTL).”
MethodIntegrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk
Yang, Y., Chen, Y., Xu, S., Guo, X., Jia, G., Ping, J., Shu, X., Zhao, T., Yuan, F., Wang, G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50404-y · Cited by 27
“In the largest GWAS of blood DNA methylation conducted to date ( n = 32,851), methylation QTLs (mQTLs) were identi fi ed for ~45.2% (190,102) of CpGs on the Illumina HumanMethylation450 BeadChip 14 .”
InfluentialNeumann, A., Sammallahti, S., Cosín-Tomás, M., Reese, S. E., Suderman, M., Alemany, S., Almqvist, C., Andrušaitytė, S., Arshad, S. H., Bakermans-Kranenburg, M., et al. (2024) · Genome Medicine · doi:10.1186/s13073-025-01451-7 · Cited by 22
“DNAm is influenced by genetic [1, 2] and environmental factors, beginning in utero (e.g., maternal smoking [3], stressful life events [4], air pollution [5], or physical activity [6]).”
Self-citationJuvinao-Quintero, D. L., Sharp, G. C., Sanderson, E. C. M., Relton, C. L., & Elliott, H. R. (2023) · Diabetologia · doi:10.1007/s00125-023-05914-7 · Cited by 23
“for type 2 diabetes Using GoDMC summary data [29], we”
MethodInfluentialSelf-citationSadler, M. C., Auwerx, C., Lepik, K., Porcu, E., & Kutalik, Z. (2022) · Nature Communications · doi:10.1038/s41467-022-35196-3 · Cited by 35
“(6)) allowed to obtain the estimated d MP.”
BackgroundSobczyk, M., Richardson, T., Zuber, V., Min, J. L., Godmc, Gaunt, T. R., & Paternoster, L. (2020) · medRxiv · doi:10.1101/2020.11.30.20240838 · Cited by 22
Self-citationLi, Z., Liang, D., Ebelt, S., Gearing, M., Kobor, M. S., Konwar, C., Maclsaac, J. L., Dever, K., Wingo, A. P., Levey, A. I., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13650 · Cited by 33
“57 All analyses were completed in R (v4.”
MethodZhu, Z., Li, Y., Freishtat, R. J., Celedon, J. C., Espinola, J. A., Harmon, B. T., Hahn, A., Camargo, C. A., Liang, L., & Hasegawa, K. (2023) · Nature Communications · doi:10.1038/s41467-023-41300-y · Cited by 27
“Finally,weperformedMendelianrandomizationanalysistoinvestigate the potential causal relationships of severity-related DMCs (meQTL data from the Genetics of DNA Methylation Consortium) with four respiratory traits (GWAS data from the UK Biobank), including asthma 50,68 – 71 , FEV1 72 , FVC 72 , and FEV1/FVC 72 .”
InfluentialVTRNA2-1: Genetic Variation, Heritable Methylation and Disease Association
Dugué, P., Yu, C., McKay, T., Wong, E. M., Joo, J. E., Tsimiklis, H., Hammet, F., Mahmoodi, M. S., Theys, D., kConFab, et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22052535 · Cited by 24
“[18], but these studies carried out more tests because they investigated >400,000 CpGs.”
BackgroundSarnowski, C., Huan, T., Ma, Y., Joehanes, R., Beiser, A., DeCarli, C. S., Heard‐Costa, N. L., Levy, D. A., Lin, H., Liu, C., et al. (2023) · Clinical Epigenetics · doi:10.1186/s13148-023-01589-4 · Cited by 22
“Using several publicly available resources for mQTLs in the blood (BIOS QTL browser [37], mQTLdb [38], GoDMC browser [39] , and FHS mQTLs results[40]) or in the brain (xQTLServe browser [41]), we identify cis-methylation quantitative trait loci (mQTLs) at cg17901584– DHCR24 , cg15150970– DNMT3A ,…”
BackgroundMiller, R. G., Mychaleckyj, J. C., Önengüt-Gümüşcü, S., Orchard, T. J., & Costacou, T. (2023) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2022-003068 · Cited by 26
“SNPs with p 5×10 −8 were identified as meQTLs for the corresponding CpG. Results were compared with the Genetics of DNA Methylation Consortium (GoDMC) database of meQTLs 33 for external validation.”
Lee, M. K., Huan, T., McCartney, D. L., Chittoor, G., de Vries, M., Lahousse, L., Nguyen, J. N., Brody, J. A., Fernandez, J. C., Terzikhan, N., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202108-1907oc · Cited by 33
“mQTL, colocalization, and MR analyses Using GoDMC (29), 798 of our 1,103 significant CpGs had at least one mQTL.”
MethodInfluentialSelf-citationZheng, X., Tian, Z., Che, X., Zhang, X., Xiang, Y., Ge, Z., Zhai, Z., Ma, Q., & Pan, J. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae853 · Cited by 22
“Additionally , the summary data of CpG-associated meQTLs are sourced from the Genetics of DNA Methylation Consortium (GoDMC), a meQTL database containing genetic and methylation data from over 30 000 participants ( 25 ).”
Epiallelic variation of non-coding RNA genes and their phenotypic consequences
Liu, J., & Zhong, X. (2024) · Nature Communications · doi:10.1038/s41467-024-45771-5 · Cited by 20
Blood‐based multivariate methylation risk score for cognitive impairment and dementia
Koetsier, J., Cavill, R., Reijnders, R. A., Harvey, J., Homann, J., Kouhsar, M. P., Deckers, K., Köhler, S., Eijssen, L., van den Hove, D., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14061 · Cited by 17
“48 We obtained the genetic determinants of the model’s CpGs from the Genetics of DNA Methylation Consortium (goDMC) using the following parameters: clumped cis-and trans-mQTLs, P 1e-5 8 and the joint variation quantified using the r.jive package (v2.”
Self-citationGenome‐wide association studies: utility and limitations for research in physiology
Ciochetti, N. P., Lugli‐Moraes, B., da Silva, B. S., & Rovaris, D. L. (2023) · The Journal of Physiology · doi:10.1113/jp284241 · Cited by 25
“Also, GWAS on major depression (Levey et al., 2021) and suicide behaviours (Docherty et al., 2022; Mullins et al., 2022) can contribute to our understanding of the biology underlying subjective well-being, while the GWAS on migraines (Hautakangas et al., 2022) has the potential to unravel…”
Küpers, L. K., Fernández‐Barrés, S., Nounu, A., Friedman, C., Fore, R., Mancano, G., Dabelea, D., Rifas‐Shiman, S. L., Mulder, R. H., Oken, E., et al. (2022) · Epigenetics · doi:10.1080/15592294.2022.2038412 · Cited by 24
“[31] and those listed as methylation quantitative trait loci (mQTLs) [32].”
BackgroundSelf-citationSmith, R. G., Pishva, E., Kouhsar, M. P., Imm, J. L., Dobričić, V., Johannsen, P., Wittig, M., Franke, A., Vandenberghe, R. R., Schaeverbeke, J. M., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14098 · Cited by 15
“Taking forward eight DMPs corresponding to cis -mQTLs ( P 1 × 10 − 5 ) in this region in the goDMC database, 48 we tested for possible pleiotropic effects between DNA methylation and CSF protein levels.”
BackgroundHuang, Z.-D., Peng, S., Cen, T., Wang, X., Ma, L., & Cao, Z. (2024) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.14040 · Cited by 20
Hubers, N., Hagenbeek, F. A., Pool, R., Dejean, S., Harms, A. C., Roetman, P. J., van Beijsterveldt, C. E. M., Fanos, V., Ehli, E. A., Vermeiren, R. R. J. M., et al. (2023) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32955 · Cited by 26
“Associations and correlations have been observed between the genome and epigenome (Min et al., 2021; van Dongen et al., 2016), the genome and the metabolome (Hagenbeek et al., 2020; Hagenbeek et al., 2023), and the epigenome and the metabolome (Gomez-Alonso et al., 2021).”
BackgroundSelf-citationA comparison of the genes and genesets identified by EWAS and GWAS of fourteen complex traitsPreprint
Battram, T., Gaunt, T. R., Relton, C., Timpson, N., & Hemani, G. (2022) · medRxiv · doi:10.1101/2022.03.25.22272928 · Cited by 18
“In this paper we do not attempt to infer the causal structure of DNA methylation-trait associations directly 74 as has been attempted through methods such as Mendelian randomization, but is often difficult due to being unable 75 to prove the vertical pleiotropy assumption (16,22).”
BackgroundMethodSelf-citationKatrinli, Ş., Wani, A. H., Maihofer, A. X., Ratanatharathorn, A. D., Daskalakis, N. P., Montalvo‐Ortiz, J. L., Núñez-Ríos, D. L., Zannas, A. S., Zhao, X., Aiello, A. E., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01417-1 · Cited by 39
Self-citationQin, Q., Zhou, Y., Guo, J., Chen, Q., Tang, W., Li, Y., You, J., & Li, Q. (2024) · Genome Medicine · doi:10.1186/s13073-024-01318-3 · Cited by 16
Low-input and single-cell methods for Infinium DNA methylation BeadChips
Lee, S. M., Loo, C. E., Prasasya, R. D., Bartolomei, M. S., Kohli, R. M., & Zhou, W. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae127 · Cited by 27
“…the adoption of sequencing-based methods is catching up in case and mechanistic studies, the Infinium technology remains the most used assay platform for population-level studies such as meQTL studies ( 4 ,5 ), epigenetic risk scores ( 6 ,7 ), and other epigenome-wide association studies ( 8 ,9 ).”
MethodCorradi, C., Lencioni, G., Gentiluomo, M., Felici, A., Latiano, A., Kiudelis, G., van Eijck, C. H. J., Marta, K., Lawlor, R. T., Tavano, F., et al. (2023) · Journal of Medical Genetics · doi:10.1136/jmg-2022-108910 · Cited by 21
Admixture mapping of severe asthma exacerbations in Hispanic/Latino children and youth
Luis, E. H., Mak, A. C. Y., Pérez-García, J., Martín-González, E., Eng, C. S., Beckman, K. B., Huntsman, S., Hu, D., González‐Pérez, R., Pérez, J. M. H., et al. (2022) · Thorax · doi:10.1136/thorax-2022-218755 · Cited by 17
Metadata
- Authors
- Min, J. L., Hemani, G., Hannon, E., Dekkers, K. F., Castillo-Fernandez, J., Luijk, R., Carnero-Montoro, E., Lawson, D. J., Burrows, K., Suderman, M., Bretherick, A. D., Richardson, T. G., Klughammer, J., Iotchkova, V., Sharp, G., Al Khleifat, A., Shatunov, A., Iacoangeli, A., McArdle, W. L., Ho, K. M., Kumar, A., Söderhäll, C., Soriano-Tárraga, C., Giralt-Steinhauer, E., Kazmi, N., Mason, D., McRae, A. F., Corcoran, D. L., Sugden, K., Kasela, S., Cardona, A., Day, F. R., Cugliari, G., Viberti, C., Guarrera, S., Lerro, M., Gupta, R., Bollepalli, S., Mandaviya, P., Zeng, Y., Clarke, T.-K., Walker, R. M., Schmoll, V., Czamara, D., Ruiz-Arenas, C., Rezwan, F. I., Marioni, R. E., Lin, T., Awaloff, Y., Germain, M., Aïssi, D., Zwamborn, R., van Eijk, K., Dekker, A., van Dongen, J., Hottenga, J.-J., Willemsen, G., Xu, C.-J., Barturen, G., Català-Moll, F., Kerick, M., Wang, C., Melton, P., Elliott, H. R., Shin, J., Bernard, M., Yet, I., Smart, M., Gorrie-Stone, T., BIOS Consortium, Shaw, C., Al Chalabi, A., Ring, S. M., Pershagen, G., Melén, E., Jiménez-Conde, J., Roquer, J., Lawlor, D. A., Wright, J., Martin, N. G., Montgomery, G. W., Moffitt, T. E., Poulton, R., Esko, T., Milani, L., Metspalu, A., Perry, J. R. B., Ong, K. K., Wareham, N. J., Matullo, G., Sacerdote, C., Panico, S., Caspi, A., Arseneault, L., Gagnon, F., Ollikainen, M., Kaprio, J., Felix, J. F., Rivadeneira, F., Tiemeier, H., van IJzendoorn, M. H., Uitterlinden, A. G., Jaddoe, V. W. V., Haley, C., McIntosh, A. M., Evans, K. L., Murray, A., Räikkönen, K., Lahti, J., Nohr, E. A., Sørensen, T. I. A., Hansen, T., Morgen, C. S., Binder, E. B., Lucae, S., Gonzalez, J. R., Bustamante, M., Sunyer, J., Holloway, J. W., Karmaus, W., Zhang, H., Deary, I. J., Wray, N. R., Starr, J. M., Beekman, M., van Heemst, D., Slagboom, P. E., Morange, P.-E., Trégouët, D.-A., Veldink, J. H., Davies, G. E., de Geus, E. J. C., Boomsma, D. I., Vonk, J. M., Brunekreef, B., Koppelman, G. H., Alarcón-Riquelme, M. E., Huang, R.-C., Pennell, C. E., van Meurs, J., Ikram, M. A., Hughes, A. D., Tillin, T., Chaturvedi, N., Pausova, Z., Paus, T., Spector, T. D., Kumari, M., Schalkwyk, L. C., Visscher, P. M., Davey Smith, G., Bock, C., Gaunt, T. R., Bell, J. T., Heijmans, B. T., Mill, J., & Relton, C. L.
- Affiliations
- University of Bristol (GB)
- University of Exeter (GB)
- Leiden University Medical Center (NL)
- Institute of Genetics and Cancer (GB)
- Austrian Academy of Sciences (AT)
- MRC Weatherall Institute of Molecular Medicine (GB)
- King's College London (GB)
- Swiss Tropical and Public Health Institute (CH)
- Karolinska Institutet (SE)
- Bradford Institute for Health Research (GB)
- The University of Queensland (AU)
- Duke University (US)
- University of Tartu (EE)
- University of Cambridge (GB)
- Italian institute for Genomic Medicine (IT)
- University of Toronto (CA)
- University of Helsinki (FI)
- Erasmus MC (NL)
- Sun Yat-sen University (CN)
- Royal Edinburgh Hospital (GB)
- Western General Hospital (GB)
- Max Planck Institute of Psychiatry (DE)
- Universitat Pompeu Fabra (ES)
- Aberystwyth University (GB)
- Université de Bordeaux (FR)
- Universität Hamburg (DE)
- University Medical Center Utrecht (NL)
- Amsterdam Public Health (NL)
- University Medical Center Groningen (NL)
- Universidad de Granada (ES)
- Consejo Superior de Investigaciones Científicas (ES)
- University of Newcastle Australia (AU)
- University of Tasmania (AU)
- University of Essex (GB)
- King's College Hospital (GB)
- Hospital Del Mar (ES)
- QIMR Berghofer Medical Research Institute (AU)
- University of Otago (NZ)
- Broad Institute (US)
- Federico II University Hospital (IT)
- Harvard University (US)
- University College London (GB)
- University of Aberdeen (GB)
- University of Southern Denmark (DK)
- University of Copenhagen (DK)
- Emory University (US)
- University of Southampton (GB)
- University of Memphis (US)
- University of Edinburgh (GB)
- Alzheimer Scotland (GB)
- Inserm (FR)
- Avera Institute for Human Genetics (US)
- Utrecht University (NL)
- The Kids Research Institute Australia (AU)
- Corresponding author
- Min, Josine L.
- ORCID
- Min, Josine L.
- Hemani, Gibran
- Hannon, Eilis
- Dekkers, Koen F.
- Castillo-Fernandez, Juan
- Lawson, Daniel J.
- Burrows, Kimberley
- Suderman, Matthew
- Bretherick, Andrew D.
- Richardson, Tom G.
- Klughammer, Johanna
- Iotchkova, Valentina
- Sharp, Gemma
- Al Khleifat, Ahmad
- Shatunov, Aleksey
- Iacoangeli, Alfredo
- McArdle, Wendy L.
- Ho, Karen M.
- Kumar, Ashish
- Söderhäll, Cilla
- Soriano-Tárraga, Carolina
- Kazmi, Nabila
- Mason, Dan
- McRae, Allan F.
- Corcoran, David L.
- Sugden, Karen
- Kasela, Silva
- Cardona, Alexia
- Day, Felix R.
- Cugliari, Giovanni
- Guarrera, Simonetta
- Gupta, Richa
- Bollepalli, Sailalitha
- Mandaviya, Pooja
- Zeng, Yanni
- Clarke, Toni-Kim
- Walker, Rosie M.
- Schmoll, Vanessa
- Czamara, Darina
- Ruiz-Arenas, Carlos
- Rezwan, Faisal I.
- Marioni, Riccardo E.
- Lin, Tian
- Germain, Marine
- Aïssi, Dylan
- Zwamborn, Ramona
- van Eijk, Kristel
- van Dongen, Jenny
- Hottenga, Jouke-Jan
- Willemsen, Gonneke
- Xu, Cheng-Jian
- Barturen, Guillermo
- Kerick, Martin
- Wang, Carol
- Melton, Phillip
- Elliott, Hannah R.
- Shin, Jean
- Bernard, Manon
- Yet, Idil
- Smart, Melissa
- Gorrie-Stone, Tyler
- Al Chalabi, Ammar
- Ring, Susan M.
- Pershagen, Göran
- Melén, Erik
- Jiménez-Conde, Jordi
- Roquer, Jaume
- Lawlor, Deborah A.
- Wright, John
- Martin, Nicholas G.
- Montgomery, Grant W.
- Moffitt, Terrie E.
- Poulton, Richie
- Esko, Tõnu
- Milani, Lili
- Metspalu, Andres
- Perry, John R. B.
- Ong, Ken K.
- Wareham, Nicholas J.
- Matullo, Giuseppe
- Sacerdote, Carlotta
- Panico, Salvatore
- Caspi, Avshalom
- Arseneault, Louise
- Ollikainen, Miina
- Kaprio, Jaakko
- Felix, Janine F.
- Rivadeneira, Fernando
- Tiemeier, Henning
- van IJzendoorn, Marinus H.
- Uitterlinden, André G.
- Jaddoe, Vincent W. V.
- Haley, Chris
- McIntosh, Andrew M.
- Evans, Kathryn L.
- Murray, Alison
- Räikkönen, Katri
- Lahti, Jari
- Nohr, Ellen A.
- Sørensen, Thorkild I. A.
- Hansen, Torben
- Morgen, Camilla S.
- Binder, Elisabeth B.
- Lucae, Susanne
- Gonzalez, Juan Ramon
- Bustamante, Mariona
- Sunyer, Jordi
- Holloway, John W.
- Karmaus, Wilfried
- Zhang, Hongmei
- Deary, Ian J.
- Wray, Naomi R.
- Starr, John M.
- Beekman, Marian
- van Heemst, Diana
- Slagboom, P. Eline
- Morange, Pierre-Emmanuel
- Trégouët, David-Alexandre
- Veldink, Jan H.
- de Geus, Eco J. C.
- Boomsma, Dorret I.
- Vonk, Judith M.
- Brunekreef, Bert
- Koppelman, Gerard H.
- Alarcón-Riquelme, Marta E.
- Huang, Rae-Chi
- Pennell, Craig E.
- van Meurs, Joyce
- Ikram, M. Arfan
- Hughes, Alun D.
- Tillin, Therese
- Chaturvedi, Nish
- Pausova, Zdenka
- Paus, Tomas
- Spector, Timothy D.
- Schalkwyk, Leonard C.
- Visscher, Peter M.
- Davey Smith, George
- Bock, Christoph
- Gaunt, Tom R.
- Bell, Jordana T.
- Heijmans, Bastiaan T.
- Mill, Jonathan
- Relton, Caroline L.
- Venue
- Nature Genetics
- Published
- 2021-09
- Volume
- 53
- Issue
- 9
- Pages
- 1311-1321
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC7612069
- OpenAlex
- W3113754461
- Cited by
- 506
- References
- 88
- Citations by year
- 2026: 77 · 2025: 107 · 2024: 117 · 2023: 86 · 2022: 88 · 2021: 29
- FWCI
- 26.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- Bristol Research (University of Bristol) (publishedVersion, other-oa)
- VU Research Portal (publishedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Open Access at Essex (University of Essex) (acceptedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- UTAS Research Repository (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- HAL (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- edoc (University of Basel) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Research Portal (King's College London) (submittedVersion)
- Oskar-Bordeaux (Universite de Bordeaux) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (208806/Z/17/Z, 204979/Z/16/Z)
- Royal Society (208806/Z/17/Z)
- RCUK | Medical Research Council (MR/R005176/1, MR/K013807/1, MR/M008924/1, MC_UU_00011/5, MC_UU_00011/1, MC_UU_00011/4)
- Österreichischen Akademie der Wissenschaften (doc fellowship)
- RCUK | Biotechnology and Biological Sciences Research Council (BB/S020845/1)
- RCUK | Economic and Social Research Council (ES/N000404/1)
- ZonMw (529051021)
- Netherlands CardioVascular Research Initiative
- Cancer Research UK (C18281/A191169)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- DNA methylation, DNA methylation quantitative trait loci, genetic variants, blood DNA methylation, polygenic architecture, trans-eQTL, gene networks, complex diseases, genotype-phenotype correlation, gene expression regulation
- MeSH terms
- Chromosome Mapping, DNA, Gene Expression Regulation, Humans, DNA Methylation, Quantitative Trait, Heritable, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Epigenesis, Genetic, Genome-Wide Association Study, Transcriptome
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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.
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