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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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Methylation risk scores for childhood aeroallergen sensitization: Results from the LISA birth cohort
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Self-citationDNA methylation in peripheral tissues and left-handedness
Odintsova, V., Suderman, M., Hagenbeek, F., Caramaschi, D., Hottenga, J., Pool, R., Heijmans, B., Hoen, P. '., van Meurs, J. V., Isaacs, A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-022-08998-0 · Cited by 17
Self-citationA blood- and brain-based EWAS of smoking
Chybowska, A. D., Bernabeu, E., Yousefi, P., Suderman, M. J., Hillary, R. F., Clark, R. C., MacGillivray, L., Murphy, L., Harris, S. E., Corley, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58357-6 · Cited by 17
Self-citationAbrishamcar, S., Zhuang, B. C., Thomas, M., Gladish, N., MacIsaac, J. L., Jones, M. J., Simons, E., Moraes, T. J., Mandhane, P. J., Brook, J. R., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03148-8 · Cited by 30
A narrative on the neurobiological roots of attachment-system functioning
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“DNA methylation is thought to be in fl uenced by prenatal 133 and postnatal life events 134 and genetic background 135,136 , and its state is reversible 137 .”
BackgroundEvaluating the association between placenta DNA methylation and cognitive functions in the offspring
Cosín‐Tomás, M., Díez-Ahijado, L., Cilleros‐Portet, A., Fernández, M. F., Fernández, M. F., Guxens, M., Júlvez, J., Llop, S., López-Espinosa, M.-J., Subiza, M., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03094-5 · Cited by 20
Self-citationShen, X., Barbu, M. C., Caramaschi, D., Arathimos, R., Czamara, D., David, F. S., Dearman, A., Dilkes, E., Herrera-Rivero, M., Huider, F., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00486-4 · Cited by 19
“Covariates for the simplest model were age, sex, batch, the first 20 methylation principal components (PCs) or surrogate variables 56 based on the study protocol for each individual cohort, and white-blood-cell proportions estimated from DNAm data of CD8 + T cells, CD4 + T cells, natural killer…”
Self-citationGenetic Regulation of Transcription in the Endometrium in Health and Disease
Mortlock, S., McKinnon, B. D., & Montgomery, G. W. (2022) · Frontiers in Reproductive Health · doi:10.3389/frph.2021.795464 · Cited by 24
“In addition to the cycle changes observed in most individuals, epigenetic signatures (8, 9) and the expression of many genes is under genetic control in endometrium (Figure 1) and other tissues contributing to variation between individuals (6, 7, 10).”
BackgroundSelf-citationCharacterising developmental dynamics of adult epigenetic clock sites
Mulder, R. H., Neumann, A., Felix, J. F., Suderman, M. J., & Cecil, C. A. M. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105425 · Cited by 18
“1,27,29 Consistent with this, meQTLs related to clock sites were enriched for GWAS hits for epigenetic ageing.”
Self-citationScalable screening of ternary-code DNA methylation dynamics associated with human traits
Goldberg, D. C., Cloud, C., Lee, S. M., Barnes, B., Gruber, S., Kim, E., Pottekat, A., Westphal, M. S., McAuliffe, L., Majounie, E., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100929 · Cited by 16
Genome-wide association study in Alzheimer’s disease: a bibliometric and visualization analysis
Zhang, J., Wang, Y., Zhang, Y., & Yao, J. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1290657 · Cited by 22
Zhang, W., Young, J. I., Gomez, L., Schmidt, M. A., Lukacsovich, D., Varma, A., Chen, X. S., Kunkle, B. W., Martin, E. R., & Wang, L. (2024) · Epigenetics · doi:10.1080/15592294.2024.2333660 · Cited by 15
“We searched mQTLs for CpG probes using the GoDMC database [26], which was downloaded from http://mqtldb.godmc.org.uk/downloads.”
Epigenetics insights from perceived facial aging
Vladimir, K., Perišić, M. M., Štorga, M., Mostashari, A., & Khanin, R. (2023) · Clinical Epigenetics · doi:10.1186/s13148-023-01590-x · Cited by 19
“Large-scale studies reported that methylation at gene promoters is negatively correlated with gene expression, while methylation within gene bodies can be positively or negatively correlated with gene expression [22].”
BackgroundGao, X., Wang, Y., Hou, W., Liu, Z.-Y., & Ma, X.-K. (2022) · IEEE/ACM Transactions on Computational Biology & Bioinformatics · doi:10.1109/tcbb.2022.3229678 · Cited by 15
“And, great evidence demonstrates that aberrant DNA methylation is closely associated with a variety of human diseases [1], [2], [3], [4], [5].”
BackgroundKasela, S., Aguet, F., Kim-Hellmuth, S., Brown, B. C., Nachun, D., Tracy, R., Durda, P., Liu, Y.-M., Taylor, K., Johnson, W., et al. (2023) · bioRxiv · doi:10.1101/2023.06.26.546528 · Cited by 14
“Bulk tissue molecular quantitative trait loci (molQTLs) have been valuable in highlighting potential target genes and gene regulatory mechanisms of disease-associated genetic variants 1–3 .”
Self-citationA roadmap to ALS prevention: strategies and priorities
Benatar, M. G., Goutman, S. A., Staats, K. A., Feldman, E. L., Weisskopf, M. G., Talbott, E. O., Dave, K. D., Thakur, N. M., & Al‐Chalabi, A. (2023) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2022-330473 · Cited by 18
Self-citationA genome-wide association analysis of 2,622,830 individuals reveals new pathogenic pathways in gout.Preprint
Major, T. J., Takei, R., Matsuo, H., Leask, M., Topless, R., Shirai, Y., Li, Z., Ji, A., Cadzow, M., Sumpter, N., et al. (2022) · medRxiv · doi:10.1101/2022.11.26.22281768 · Cited by 14
Why epigenetics is (not) a biosocial science and why that matters
Chiapperino, L., & Paneni, F. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01366-9 · Cited by 18
“On the other hand, this demands reconsidering notions of causality in these epigenetic associations which may be less linear than expected, and blur the boundary between genetic and environmental determinants of disease [13, 14].”
BackgroundMarttila, S., Tamminen, H., Rajić, S., Mishra, P. P., Lehtimäki, T. J., Raitakari, O. T., Kähönen, M. A. P., Kananen, L., Jylhävä, J., Hägg, S., et al. (2022) · Epigenomics · doi:10.2217/epi-2022-0228 · Cited by 16
Self-citationLecorguillé, M., Mcauliffe, F., Twomey, P., Viljoen, K., Mehegan, J., Kelleher, C., Suderman, M., & Phillips, C. (2022) · Journal of Clinical Endocrinology and Metabolism · doi:10.1210/clinem/dgac553 · Cited by 14
Self-citationEpigenetics of early-life adversity in youth: cross-sectional and longitudinal associations
Sumner, J. A., Gambazza, S., Gao, X., Baccarelli, A., Uddin, M., & McLaughlin, K. A. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01269-9 · Cited by 22
Lauby, S. C., Fleming, A. S., & McGowan, P. O. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.04.021 · Cited by 14
Blood DNA methylation signature for incident dementia: Evidence from longitudinal cohorts
Zhang, W., Young, J. I., Gomez, L., Schmidt, M. A., Lukacsovich, D., Kunkle, B. W., Chen, X. S., Martin, E. R., & Wang, L. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14496 · Cited by 14
“42 Consistent with previous observations that genetic influences on DNAm in the blood are widespread, 61 we found that about 40% (99 out of 271 Compared to gene expression and proteins, methylated DNA is relatively stable and can be easily detected, thus serving as an excellent source of biomarkers.”
Ji, A., Sui, Y., Xue, X., Ji, X.-P., Shi, W., Shi, Y.-Y., Terkeltaub, R., Dalbeth, N., Takei, R., Yan, F., et al. (2024) · Arthritis & Rheumatology · doi:10.1002/art.42969 · Cited by 13
“32 Of the 14 DNAm sites, cg10501036 (511 bp from rs8009475) was located in the RCOR1 promoter 4 ).”
Self-citationKasela, S., Aguet, F., Kim-Hellmuth, S., Brown, B. C., Nachun, D. C., Tracy, R. P., Durda, P., Liu, Y., Taylor, K. D., Johnson, W. C., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.11.013 · Cited by 29
Self-citationConcepts, mechanisms and implications of long-term epigenetic inheritance.
Hollwey, E., Briffa, A., Howard, M., & Zilberman, D. (2023) · Current Opinion in Genetics and Development · doi:10.1016/j.gde.2023.102087 · Cited by 13
Fang, F., Quach, B. C., Lawrence, K. G., van Dongen, J., Marks, J. A., Lundgren, S., Lin, M., Odintsova, V. V., Costeira, R., Xu, Z., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02310-w · Cited by 12
“We used an R software tool, ipDMR [46], to identify DMRs in which a cluster of correlated CpGs showed evidence for association with lifetime cannabis use. ipDMR calculates an overall p -value for small intervals bordered by two adjacent CpGs based on the association p -values from an EWAS analysis.”
BackgroundMethodSelf-citationDerakhshan, M., Kessler, N. J., Ishida, M., Demetriou, C., Brucato, N., Moore, G. E., Fall, C. H. D., Chandak, G. R., Ricaut, F., Prentice, A. M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac503 · Cited by 15
“To determine the degree to which hypervariability at hvCpGs is explained by mQTL effects, each hvCpG was matched to a CpG amongst those reported in the GoDMC meta-analysis (51).”
InfluentialEvolution of genome-wide methylation profiling technologiesPreprint
Montaño, C., & Timp, W. (2025) · Genome Research · doi:10.1101/gr.278407.123 · Cited by 12
“For instance, the largest population study to date using epigenomic profiling using Illumina Infinium microarrays involved 32,851 participants (Min et al. 2021), compared to the UK Biobank ’ s exome sequencing of 454,787 individuals (Backman et al. 2021) and whole-genome sequencing of 150,119…”
Cilleros‐Portet, A., Lesseur, C., Marí, S., Cosín‐Tomás, M., Lozano, M., Irizar, A., Burt, A., García‐Santisteban, I., Garrido-Martín, D., Escaramís, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57760-3 · Cited by 20
“Following Min et al., we kept all DNAm PCs that cumulatively explained 80% of the variance with a maximum number of 20 PCs for subsequent steps 28 .”
InfluentialSelf-citationGenome-Wide Methylation Analysis Reveals a KCNK3 -Prominent Causal Cascade on Hypertension
Huang, D., Shang, W., Xu, M., Wan, Q., Zhang, J., Tang, X., Shen, Y., Wang, Y., & Yu, Y. (2024) · Circulation Research · doi:10.1161/circresaha.124.324455 · Cited by 20
Czamara, D., Dieckmann, L., Lahti‐Pulkkinen, M., Cruceanu, C., Henrich, W., Plagemann, A., Räikkönen, K., Braun, T., Binder, E. B., Lahti, J. M. T., et al. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05208-0 · Cited by 15
“Our results suggested that meQTLs were quite consistent across males and females: we identified 3,039,847 sex-consistent meQTLs (formed by 1,087,627 unique SNPs and 70,848 unique CpGs) at cis - significance level of 1 × 10 −08 [58] and showing consistent effect directions in males and females.”
MethodResultSelf-citationLukacsovich, D., O’Shea, D. M., Huang, H., Zhang, W., Young, J. I., Chen, X. S., Dietrich, S., Kunkle, B. W., Martin, E. R., & Wang, L. (2024) · Database · doi:10.1093/database/baae061 · Cited by 9
“First, to examine whether there was any overlap between significant CpGs in the analysis of female samples and those in male sample analysis, we selected the “SIF” and “SIM” datasets, corresponding to female and male summary statistics datasets in Silva et al .”
Šehović, E., Zellers, S. M., Youssef, M. K., Heikkinen, A., Kaprio, J. A., & Ollikainen, M. (2023) · Clinical Epigenetics · doi:10.1186/s13148-023-01594-7 · Cited by 14
“We used the Genetics of DNA Methylation Consortium (GoDMC) meQTL database [40, 41] to explore whether the CpG sites associated with PDS or PA are established meQTL.”
BackgroundMethodSelf-citationHillary, R., McCartney, D., Bernabéu, E., Gadd, D., Cheng, Y., Chybowska, A., Smith, H., Murphy, L., Wrobel, N., Campbell, A., et al. (2023) · medRxiv · doi:10.1101/2023.01.10.23284387 · Cited by 12
“The GoDMC mQTL resource represents the largest mQTL study to date in terms of sample size but only focused on 450k array sites (30).”
MethodSelf-citationBingen, J. M., Clark, L. V., Band, M. R., Munzir, S. I., & Carrithers, M. D. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1058817 · Cited by 17
“Although known SNP regions were filtered prior to analysis, many probes remain in the data set that are associated with genetic variation at CpG loci (Min et al., 2021).”
BackgroundDynamic patterns of blood lipids and DNA methylation in response to statin therapy
Qin, X.-Y., Wang, Y.-Z., Pedersen, N., Tang, B.-W., & Hägg, S. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01375-8 · Cited by 12
“A recent large-scale genome-wide DNA methylation quantitative trait locus analysis may give some explanations [27].”
BackgroundLifestyle and Genetic Factors Modify Parent-of-Origin Effects on the Human Methylome
Zeng, Y., Amador, C., Gao, C., Walker, R. M., Morris, S. W., Campbell, A., Frkatović-Hodžić, A., Madden, R. A., Adams, M. J., He, S., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103730 · Cited by 14
Self-citationSun, J., Liu, M., Zhang, X., Li, X., Ye, J., Zhao, J., Zhou, S., Ji, H., Tan, Y., Bian, Z., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.106005 · Cited by 12
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“The prediction accuracy was measured by the coef fi cient of determination ( R 2 ).”
InfluentialDNA methylation signature of a lifestyle-based resilience index for cognitive health
Zhang, W. Y., Lukacsovich, D., Young, J. I., Gomez, L., Schmidt, M. A., Martin, E. R., Kunkle, B. W., Chen, X. S., O’Shea, D. M., Galvin, J. E., et al. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01733-7 · Cited by 11
“For correlation and overlap with genetic susceptibility loci, we searched for mQTLs in the blood using the GoDMC database (http:// mqtldb. godmc. org. uk/ downl oads) [57].”
InfluentialBozack, A., & Trasande, L. (2024) · Epigenomics · doi:10.1080/17501911.2024.2426441 · Cited by 11
McDonnell, E., Orr, S. E., Barter, M. J., Rux, D., Brumwell, A., Wrobel, N., Murphy, L., Overman, L. M., Sorial, A. K., Young, D. A., et al. (2024) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.10.017 · Cited by 11
“This supports existing evidence that trait-linked mQTLs primarily impact gene expression through modulation of DNAm within coding (transcribed) 61 regions and distal regulatory elements (as opposed to promoters).”
Green space exposure and blood DNA methylation at birth and in childhood - A multi-cohort study.
Aguilar-Lacasaña, S., Marques, I. F., de Castro, M., Dadvand, P., Escribà, X., Fossati, S., González, J. R., Nieuwenhuijsen, M., Alfano, R., Annesi-Maesano, I., et al. (2024) · Environment International · doi:10.1016/j.envint.2024.108684 · Cited by 11
Self-citationShi, X., Li, M., Yao, J., Li, M. D., & Yang, Z. (2024) · Addiction · doi:10.1111/add.16465 · Cited by 11
Opioid medication use and blood DNA methylation: epigenome-wide association meta-analysis
Lee, M., Joehanes, R., McCartney, D., Kho, M., Hüls, A., Wyss, A., Liu, C.-Y., Walker, R., Kardia, S., Wingo, T. S., et al. (2022) · Epigenomics · doi:10.2217/epi-2022-0353 · Cited by 11
Self-citationTemporal associations between leukocytes DNA methylation and blood lipids: a longitudinal study
Wu, Z.-Y., Chen, L., Hong, X., Si, J., Cao, W., Yu, C.-Q., Huang, T., Sun, D., Liao, C., Pang, Y., et al. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01356-x · Cited by 11
“Previous studies have explored the causal relationship between ABCG1 methylation and TG and HDL-C using MR and gene expression analysis methods and obtained opposite directions [17, 45, 46].”
MethodMolecular Quantitative Trait Locus Mapping in Human Complex Diseases
Olayinka, O. A., O’Neill, N. K., Farrer, L. A., Wang, G., & Zhang, X. (2022) · Current Protocols · doi:10.1002/cpz1.426 · Cited by 11
“%) of the additive genetic variance can be explained by mQTL and often influence disease phenotypes in complex ways (Min et al., 2021).”
BackgroundRetinal polyunsaturated fatty acid supplementation reverses aging-related vision decline in mice
Gao, F., Tom, E., Rydz, C., Cho, W., Kolesnikov, A. V., Sha, Y., Papadam, A., Jafari, S., Joseph, A., Ahanchi, A., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.ads5769 · Cited by 10
Leveraging epigenetic alterations in pancreatic ductal adenocarcinoma for clinical applications
Tost, J., Aksoy, S. A., Campa, D., Corradi, C., Farinella, R., Ibáñez‐Costa, A., Dubrot, J., Earl, J., Melian, E. B., Kataki, A., et al. (2025) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2025.01.003 · Cited by 11
Zhan, Z.-Q., Li, J.-X., Chen, Y.-X., & Fang, J.-Y. (2024) · Science of the Total Environment · doi:10.1016/j.scitotenv.2024.175633 · Cited by 10
Liu, Y., Liu, S., Zhen, D.-S., Huang, J., & He, F.-J. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c00484 · Cited by 10
Data Resource Profile: Whole-Blood DNA Methylation Resource in Generation Scotland (MeGS)
Walker, R., McCartney, D., Carr, K., Barber, M., Shen, X.-Y., Campbell, A., Bernabeu, E., Aitken, E., Fawkes, A., Wrobel, N., et al. (2024) · medRxiv · doi:10.1093/ije/dyaf091 · Cited by 10
“The resource has also contributed to large-scale methylation quantitative trait loci (meQTL) consortia efforts [31], as well as multiple methodological papers.”
Self-citationCamerota, M., Lester, B. M., Castellanos, F. X., Carter, B. S., Check, J. F., Helderman, J., Hofheimer, J. A., McGowan, E. C., Neal, C. R., Pastyrnak, S. L., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02841-y · Cited by 14
Low-input and single-cell methods for Infinium DNA methylation BeadChips
Lee, S. M., Loo, C. E., Prasasya, R., Bartolomei, M., Kohli, R., & Zhou, W.-D. (2023) · bioRxiv · doi:10.1101/2023.09.18.558252 · Cited by 10
“While 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).”
MethodLuo, M., Walton, E., Neumann, A., Thio, C. H. L., Felix, J. F., van IJzendoorn, M. H., Pappa, I., & Cecil, C. A. M. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13866 · Cited by 9
“When such mQTLs were identified, we extracted all available SNPs associations within a 1 Mb radius from these index mQTLs from GoDMC, and intersected with SNPs from a GWAS of schizophrenia (Lam et al., 2019).”
InfluentialSelf-citationWang, S., Fu, J., & Fang, X. (2023) · Journal of Ovarian Research · doi:10.1186/s13048-023-01142-0 · Cited by 11
RABC: Rheumatoid Arthritis Bioinformatics Center
Chen, H., Xu, J., Wei, S., Jia, Z., Sun, C., Kang, J., Guo, X., Zhang, N., Tao, J., Dong, Y., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac850 · Cited by 16
“To further explore the regulatory mechanisms of these risk SNPs, they were integrated with GTEx (27), RMVar (28), and GoDMC (29) database to identify risk eQTLs, risk m6A-SNPs and risk mQTL. Finally, the results of these analyses were also included in the RABC.”
MethodComplex trait methylation scores in the prediction of major depressive disorder
Barbu, M., Amador, C., Kwong, A., Shen, X.-Y., Adams, M., Howard, D. M., Walker, R., Morris, S., Min, J. L., Liu, C.-Y., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104000 · Cited by 10
Self-citationKoller, D., He, J., Løkhammer, S., Aranda, S., Qiu, D., Davtian, D., Chen, Q.-Y., Xu, Z., Mao, Z.-Z., Friligkou, E., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02582-2 · Cited by 9
“For EUR, blood mQTL datasets included the GoDMC catalog 84 In EUR, we also investigated an endometrium-specific mQTL dataset including data from 984 female participants with a median age of 34.3 (range = 18–69) years 28 .”
InfluentialUnified high-resolution immune cell fraction estimation in blood tissue from birth to old age
Guo, X.-L., Sulaiman, M., Neumann, A., Zheng, S. C., Cecil, C., Teschendorff, A. E., & Heijmans, B. (2025) · bioRxiv · doi:10.1186/s13073-025-01489-7 · Cited by 9
“Around 30% of human CpGs constitute mQTLs [107, 129].”
Self-citationWang, J., Hu, J.-X., Qin, D., Han, D., & Hu, J.-P. (2024) · The World Allergy Organization Journal · doi:10.1016/j.waojou.2024.101008 · Cited by 9
Giordano, A., Pignolet, B., Mascia, E., Clarelli, F., Sorosina, M., Misra, K., Bucciarelli, F., Ferrè, L., Moiola, L., Liblau, R., et al. (2024) · Annals of Neurology · doi:10.1002/ana.26959 · Cited by 9
Lee, H.-S., Kim, B., & Park, T. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01671-5 · Cited by 9
mLiftOver: harmonizing data across Infinium DNA methylation platforms
Chen, B. H., & Zhou, W.-D. (2024) · bioRxiv · doi:10.1093/bioinformatics/btae423 · Cited by 9
“…United States) (Bibikova et al. 2006) are widely used assay technologies for population-scale DNA methylation profiling, including meQTL studies (Min et al. 2021, Hawe et al. 2022), epigenetic risk scoring (Aref-Eshghi et al. 2020, Thompson et al. 2022), and epigenome-wide association studies…”
MethodNew insights into the (epi)genetics of twinning
van Dongen, J., Hubers, N., & Boomsma, D. (2023) · Human Reproduction · doi:10.1093/humrep/dead131 · Cited by 9
“The published results from the Genetics of DNA Methylation Consortium (Min et al. , 2021) contain methylation Quantitative Trait Loci (mQTL); these are loci harbouring genetic variants associated with methylation levels of nearby CpGs (i.e. cis mQTLs) or with methylation levels of distant CpGs (i.e. trans mQTLs).”
BackgroundSelf-citationZhou, Y.-J., Zhou, X., Sun, J., Wang, L.-J., Zhao, J., Chen, J., Yuan, S., He, Y.-Z., Timofeeva, M., Spiliopoulou, A., et al. (2023) · International Journal of Cancer · doi:10.1002/ijc.34656 · Cited by 9
“uk/), a mQTL database containing genetic and methylation data from over 30 000 participants.(16) DNA methylation data were quantified from bisulfite-converted genomic blood DNA using Illumina Infinium HumanMethylation450 (HM450) BeadChip.”
MethodCilleros-Portet, A., Lesseur, C., Marí, S., Cosín-Tomás, M., Lozano, M., Irizar, A., Burt, A., García-Santisteban, I., Martin, D., Escaramís, G., et al. (2023) · medRxiv · doi:10.1101/2023.03.07.23286905 · Cited by 9
Self-citationLarge-scale integration of the non-coding RNAs with DNA methylation in human cancers
Shen, S., Chen, J., Li, H., Jiang, Y., Wei, Y., Zhang, R., Zhao, Y., & Chen, F. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112261 · Cited by 10
Fu, J., Qin, W.-J., Tong, Q., Li, Z., Shao, Y., Liu, Z.-Q., Liu, C., Wang, Z.-C., & Xu, X.-D. (2022) · Cancer Medicine · doi:10.1002/cam4.4838 · Cited by 9
Mitochondrial genome-wide analysis of nuclear DNA methylation quantitative trait loci
Laaksonen, J., Mishra, P. P., Seppälä, I., Raitoharju, E., Marttila, S., Mononen, N., Lyytikäinen, L., Kleber, M. E., Delgado, G. E., Lepistö, M., et al. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab339 · Cited by 8
“Genome-wide association studies have identified numerous DNAm quantitative trait loci (9–11), but these studies have not examined the mitochondrial genome.”
Hop, P. J., Zwamborn, R., Hannon, E., Shireby, G., Nabais, M. F., Walker, E., van Rheenen, W., van Vugt, J. V., Dekker, A. M., Westeneng, H., et al. (2021) · medRxiv · doi:10.1101/2021.03.12.21253115 · Cited by 9
Self-citationCommon DNA sequence variation influences epigenetic aging in African populationsPreprint
Meeks, G. L., Scelza, B., Asnake, H. M., Prall, S., Patin, E., Froment, A., Fagny, M., Quintana-Murci, L., Henn, B., & Gopalan, S. (2024) · bioRxiv · doi:10.1101/2024.08.26.608843 · Cited by 8
Perini, S., Filosi, M., The Italian Autism Network, Allibrio, G., Basadonne, I., Benvenuto, A., Buono, S., Bravaccio, C., Casonato, C., Ceppi, E., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02407-4 · Cited by 14
“However, it must be taken into account that epigenetic signatures tend to be tissue-specific: interindividual variation in whole blood is not a strong predictor of interindividual variation in the brain, although DNA methylation in whole blood significantly co-varies with that in the brain at some genomic loci, includ…”
BackgroundMethodIssarapu, P., Arumalla, M., Elliott, H., Nongmaithem, S., Sankareswaran, A., Betts, M., Sajjadi, S., Kessler, N. J., Bayyana, S., Mansuri, S. R., et al. (2022) · bioRxiv · doi:10.1038/s41467-023-40607-0 · Cited by 8
“…link between SOCS3m and height, we conducted a two-sample Mendelian randomization (MR) analysis using the previously identi fi ed European trans-mQTL (rs4383852; proxied by rs2884013) as an instrumental variable 26 and publicly available summary statistics from a large European height GWAS 27 .”
Self-citationAsthma exacerbations: the genes behind the scenes
Herrera-Luis, E., Forno, E., Celedon, J., & Pino-Yanes, M. (2022) · Journal of investigational allergology & clinical immunology · doi:10.18176/jiaci.0878 · Cited by 8
“In this context, investigating the effect of variants on different -omics layers (Figure, E) has become easier, given the availability of multiple free tools online (see [46-49]).”
BackgroundRevisiting genetic artifacts on DNA methylation microarrays exposes novel biological implications
Jiménez, B. P., Kayser, M., & Vidaki, A. (2021) · Genome Biology · doi:10.1186/s13059-021-02484-y · Cited by 8
“covery of methylation quantitative trait loci (meQTL) [13, 14].”
BackgroundGenetic regulation of spermine oxidase activity and cancer risk: a Mendelian randomization study
Fadista, J., Yakimov, V., Võsa, U., Hansen, C., Kasela, S., Skotte, L., Geller, F., Courraud, J., Esko, T., Kukuskina, V., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-97069-x · Cited by 8
Self-citationGenetic regulation of sperm DNA methylation in cattle through meQTL mapping
Fouéré, C., Costes, V., Hozé, C., Shankar, A. R. R., Besnard, F., Moreira, G. C. M., Sorin, V., Danvic, C. L., Chaulot‐Talmon, A., Ali, F., et al. (2025) · BMC Genomics · doi:10.1186/s12864-025-11934-x · Cited by 6
“Min et al. [8] underpinned that variants associated with meQTLs are enriched in chromatin states, although the enrichments depended on the type of association (active chromatin domains for cis and cis-+ trans-meQTLs, heterochromatin for trans-meQTLs).”
InfluentialWang, L., Lan, Z., He, Y., Cheng, Y., He, R., & Jiang, Z. (2025) · The Journal of Headache and Pain · doi:10.1186/s10194-025-02075-3 · Cited by 8
“…including array type, sex, age, smoking status, cell counts, genetic principal components, and non-genetic principal components of DNA methylation [29]. eQTL data were acquired from the eQTLGen Consortium, encompassing cis-eQTL analyses based on transcriptomic data from peripheral blood samples…”
Kitaba, N. T., Østergaard, T. M., Lønnebotn, M., Accordini, S., Real, F. G., Malinovschi, A., Oudin, A., Benediktsdóttir, B., González, F. J. C., Gómez, L. P., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08121-9 · Cited by 8
“(mQTL)wascarriedoutusingthegoDMC 57 and the MeQTL EPIC 58 databases.”
Self-citationCao, S., Zeng, Y., Pang, K., Chen, M., Guo, R., Wu, N., Fang, C., Deng, H., Zhang, X., Xie, X., et al. (2025) · Clinical Epigenetics · doi:10.1186/s13148-024-01808-6 · Cited by 6
“Covariate adjustments were made for gender, measured age, batch variables, smoking, and recorded cell counts to reduce confounding effects and residual variation [18].”
Self-citationde Oliveira, N. F. P., Persuhn, D., & Santos, M. D. D. (2024) · Genes · doi:10.3390/genes15121504 · Cited by 7
Ischemic Postconditioning Regulates New Cell Death Mechanisms in Stroke: Disulfidptosis
Liu, S., Wu, Q., Xu, C., Wang, L., Wang, J., Liu, C., & Zhao, H. (2024) · Biomolecules · doi:10.3390/biom14111390 · Cited by 10
“…an international collaboration of human epidemiological studies with over 30,000 participants providing genetic and DNA methylation data. mQTLs data were obtained from the publicly available resource at http://fileserve.mrcieu.ac.uk/mqtl/assoc_meta_all.csv.gz (accessed on 30 June 2024) [38].”
Self-citationPolyunsaturated Fatty Acid - mediated Cellular Rejuvenation for Reversing Age-related Vision DeclinePreprint
Gao, F.-Y., Tom, E., Rydz, C., Cho, W., Kolesnikov, A. V., Sha, Y.-T., Papadam, A., Jafari, S., Joseph, A., Ahanchi, A., et al. (2024) · bioRxiv · doi:10.1101/2024.07.01.601592 · Cited by 7
“While the associated variants were not found to influence gene expression of ELOVL2 in retina or brain, the G allele of the lead variant rs911196 increases methylation of cg01799681, cg16867657, cg21572722 and cg23642061 in whole blood 64,65 .”
DNA methylation models of protein abundance across the lifecourse
Waterfield, S., Yousefi, P., & Suderman, M. J. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01802-y · Cited by 6
Self-citationYeung, E., Biedrzycki, R. J., Gómez-Herrera, L., Issarapu, P., Dou, J., Marques, I., Mansuri, S. R., Page, C., Harbs, J., Khodasevich, D., et al. (2024) · Aging Cell · doi:10.1111/acel.14194 · Cited by 7
“Using the GoDMC catalog (Min et al., 2021) of over 30,000 adults, we compiled the numbers of potential mQTLs associated with each of the 33 CpGs (Table S15).”
Self-citationLu, Y., Oliva, M., Pierce, B. L., Liu, J., & Chen, L. (2024) · Nature Communications · doi:10.1038/s41467-024-46675-0 · Cited by 7
“Similar replication results were observed in GoDMC data (Table 2).”
InfluentialHowell, A., Relton, C. L., Martin, R. M., Zheng, J., & Kurian, K. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-33621-1 · Cited by 8
Self-citationGenome‐wide DNA methylation analysis of aggressive behaviour: a longitudinal population‐based study
Pishva, E., van den Hove, D., Laroche, V., Lvovs, A., Roy, A., Ortega, G., Burrage, J., Veidebaum, T., Kanarik, M., Mill, J. S., et al. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13782 · Cited by 11
Self-citationHatton, A., Hillary, R., Bernabéu, E., McCartney, D., Marioni, R., & McRae, A. (2023) · bioRxiv · doi:10.1101/2023.03.05.530890 · Cited by 7
Self-citationWeihs, A., Chaker, L., Martin, T. C., Braun, K. V., Campbell, P. J., Cox, S. R., Fornage, M., Gieger, C., Grabe, H., Grallert, H., et al. (2023) · Thyroid · doi:10.1089/thy.2022.0373 · Cited by 7
“Using mQTL results identified in GoDMC, 19 several independent cis and trans genetic variants impact the blood DNAm levels of all significant CpG sites except DOT1L (Supplementary Table S5).”
Self-citationPromoter-Adjacent DNA Hypermethylation Can Downmodulate Gene Expression: TBX15 in the Muscle Lineage
Ehrlich, K. C., Lacey, M. R., Baribault, C., Sen, S., Esteve, P. O., Pradhan, S., & Ehrlich, M. (2022) · Epigenomes · doi:10.3390/epigenomes6040043 · Cited by 13
“Our findings have implications for better understanding differentiation within the SkM lineage and the transcriptional and epigenetic changes in SkM that occur with exercise and aging [20].”
BackgroundDNA methylation and aeroallergen sensitization: The chicken or the egg?
Kilanowski, A., Merid, S. K., Abrishamcar, S., Feil, D., Thiering, E., Waldenberger, M., Melén, E., Peters, A., Standl, M., & Hüls, A. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01332-5 · Cited by 8
“Publications investigating mQTLs found that DNAm changes are often seen as a consequence of diseases rather than their cause [46] and this is supported by our findings on the allergy-related MRS.”
ResultGenome-wide discovery for diabetes-dependent triglycerides-associated loci
Selvaraj, M. S., Paruchuri, K., Haidermota, S., Bernardo, R. C., Rich, S. S., Peloso, G. M., & Natarajan, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275934 · Cited by 11
Hillary, R. F., Gadd, D. A., McCartney, D. L., Shi, L., Campbell, A., Walker, R. M., Ritchie, C. W., Deary, I. J., Evans, K. L., Nevado‐Holgado, A. J., et al. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12280 · Cited by 10
“We also performed look-up analyses of mQTL databases including GoDMC and phenoscanner (62-64).”
MethodSelf-citationMa, J., Lai, C.-Q., Li, X. S., Wang, M., Wang, Z., Yao, J., Guo, X., Taylor, K. D., Lee, S., Tracy, R. P., et al. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02060-w · Cited by 6
Sinke, L., Delerue, T., Wilson, R., Lu, X., Xia, Y.-J., Costeira, R., Nasr, M. K., Beekman, M., Franke, L., Zhernakova, A., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06549-6 · Cited by 6
“Linkage disequilibrium (LD) proxies with R 2 >0.8 for the remaining SNPs were downloaded from the NIH’s LDlink tool version 5.6.7_20240620 [38], and GoDMC data [35] were extracted for these where available.”
InfluentialSelf-citationZhang, Y. E., Du, X., Yang, Y., Ren, Y., Zhou, L., Hua, J., & Wang, H. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-01908-x · Cited by 7
“No pre-performed or third-party analyses were used ancestry, using Illumina 450K and EPIC BeadChip platforms to conduct a meta-GWAS for investigating the genetic basis of DNA methylation variation [26].”
Epigenome-wide association study of pregnancy exposure to green space and placental DNA methylation
Aguilar-Lacasaña, S., Cosín-Tomás, M., Raimbault, B., Gómez-Herrera, L., Sánchez, O., Zanini, M. J., Capdevila, R. P., Foraster, M., Gascón, M., Rivas, I., et al. (2024) · medRxiv · doi:10.1101/2024.07.21.24310780 · Cited by 6
Self-citation
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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