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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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Items 301–400 of 428
Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health
Manu, D. M., Mwinyi, J., & Schiöth, H. B. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23105856 · Cited by 2
Novel discoveries and enhanced genomic prediction from modelling genetic risk of cancer age-at-onsetPreprint
Maksimova, E. S., Ojavee, S. E., Läll, K., Sadler, M. C., Mägi, R., Kutalik, Z., & Robinson, M. R. (2022) · medRxiv · doi:10.1101/2022.03.25.22272955 · Cited by 3
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MethodXl, M., Song, J., Hong, X., & Lin, Z. (2026) · Metabolites · doi:10.3390/metabo16060378 · Cited by 1
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Palmieri, F., Castellano‐Escuder, P., Parra-Vargas, M., Leal-Witt, M. J., Ramon‐Krauel, M., Lerín, C., Perera, A., & Jiménez‐Chillarón, J. C. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02104-1
“The lack of common biomarkers between studies can be attributed to the fact that DNA methylation is dynamic and responds to a complex interplay of factors, including genetic background [54], previous environmental exposures [55], and perhaps most significantly, the age of the participants.”
Li, Y., Liu, Y.-D., Chen, X., Kong, W., Wang, Y., & Jiang, T. (2026) · BMC Biology · doi:10.1186/s12915-026-02634-3 · Cited by 1
“To assess their potential genetic regulation, we cross-referenced pDMR coordinates with the GoDMC database and found that 103 pDMRs harbored an average of two putative meQTL-associated CpG sites [34].”
Liu, J., Song, Z.-Q., Shi, C.-G., Lu, X., Zhang, A.-J., Chen, Y.-H., & Zhang, X.-J. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02145-6 · Cited by 1
“We then extracted the corresponding cis-mQTLs from the GoDMC Consortium database [18].”
Environmental Noise and Cardiovascular Risk: A Causal Inference Study
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“[21] The selection of IVs was conducted separately for each CpG site.”
Self-citationAn atlas of genetic effects on the monocyte methylome across European and African populations
Zhang, W., Qiu, C., Zhang, X., Zhang, Z., Su, K., Luo, Z., Liu, M., Zhao, B., Wu, L., Tian, Q., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01629-7 · Cited by 1
“Incorporating non-genetic PCs derived from DNAm data has been shown to significantly improve the power to detect true associations [11], while posing minimal risk of introducing collider bias.”
InfluentialIdentification of Dynamic Genetic Influences on DNA Methylation from Birth to AdulthoodPreprint
Li, Y.-Y., Staley, J. R., Großbach, A., Cappadona, C., Lussier, A., Dunn, E. C., Felix, J. F., Cecil, C., Stein, D. J., Zar, H., et al. (2026) · bioRxiv · doi:10.64898/2026.03.09.710470 · Cited by 1
“While GoDMC reported an overrepresentation of TF-binding sites for CTCF, SMC3, and RAD21 among trans -mQTL CpGs 6 , our analysis identified enrichment exclusively for CTCF, a key architectural protein involved in 3D genome organization and the regulation of enhancer – promoter interactions 32,33 .”
Self-citationGenetic regulation of methylation across East Asian and European populations
Liu, R.-Z., Chen, T.-T., Xia, Y., Lin, S.-C., Ge, T., Chen, C.-Y., Feng, Y. A., Huang, H.-L., & Lin, Y.-F. (2026) · Nature Communications · doi:10.1038/s41467-026-69372-6 · Cited by 1
“While mQTL studies have successfully identi fi ed numerous cis-acting mQTLs, most have focused on the European (EUR) populations 4,5 .”
InfluentialJeon, S.-M., Cho, S., Lee, Y.-S., Lee, J.-Y., Kang, E., Kim, T. D., Shin, J., Jo, H., & Kang, S.-U. (2026) · Brain Science · doi:10.3390/brainsci16020194 · Cited by 1
“PGC-1 α is important in neuroprotection, including upregulation of antioxidant enzymes, such as superoxide dismutase (SOD) and catalase, which mitigate damage from reactive oxygen species (ROS) [17,18].”
Self-citationEffects of 6-months of SSRI use on DNA-methylation and gene expression in blood
Barker, L. F., McRae, A. F., Yuen, H. P., Henders, A. K., Wallace, L. M., Lin, T., Davyson, E., Phassouliotis, C., Spark, J., Kerr, M., et al. (2026) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2026.106520 · Cited by 1
“Significant probes were queried in GoDMC’s mQTLdb (Min et al., 2021) and in the Framingham heart study eQTM dataset (Keshawarz et al., 2023).”
Self-citationLiu, J., Chen, Z., Yang, Q., Lin, H., Wang, S., Li, M., Wang, T., Zhao, Z., Xu, M., Chen, Y., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01379-y · Cited by 2
“These phenotypes included DNA methylation (N = 32,851) 24 , gene expression (N ranging from 174 to 29,621) 25 – 27 , alternative splicing events (N = 305) 28 and protein levels (N ranging from 35,329 to 35,431) 29 – 38 , which these variants werenotedasmolecularquantitativetraitloci(QTLs).”
Ciantar, J., Rajić, S., Kostiniuk, D., Raulamo, E., Kartiosuo, N., Lai, L., Mishra, P. P., Lyytikäinen, L., Kleber, M., Rovio, S., et al. (2026) · Clinical Epigenetics · doi:10.1186/s13148-025-02037-1 · Cited by 1
“To discover genetic variants, possibly affecting the association between the methylation of discovered CpG sites and liver variables, first methylation quantitative loci (meQTLs) were investigated in the GoDMC database [50].”
Li, W.-P., Kong, W.-Y., Shen, C., Fan, H., Shen, Y.-L., Zhang, Y.-Z., & Zheng, L. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-02016-6 · Cited by 1
“Subsequently, CpG site-associated methylation quantitative trait sites (mQTL) were identified from the Genetics of DNAm Consortium (GoDMC) database, which contains genetic and methylation data from over 30,000 individuals [35].”
Ling, D.-Y., Jiang, Z., Zhou, H.-L., Cui, Z.-B., Chen, Z.-W., Ma, X.-W., Wang, X., Zheng, S., & Wang, Q. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.119457 · Cited by 1
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Genetic determinants of monocyte splicing are enriched for disease susceptibility loci
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Meijer, M., Konwar, C., Asiimwe, R., Maclsaac, J., Ramadori, K., Lin, D. T. S., Garland, E. L., Ostlund, B. D., Kobor, M., Crowell, S. E., et al. (2025) · Environmental epigenetics · doi:10.1093/eep/dvaf021 · Cited by 1
“CMRs and individual CpG sites are both influenced by environmental factors and genetic variants, of which the latter can be measured through methylation quantitative trait loci (mQTLs) [ 24 ].”
Guo, H., Zheng, G., Li, J., Yao, S., Jia, Q., Tan, J., & Meng, Z. (2025) · Discover Oncology · doi:10.1007/s12672-025-03468-1 · Cited by 1
“This dataset includes cis-and trans-acting variants across 36 cohorts ( N = 27,750) and analyzes 420,509 DNA methylation sites using 10 million variants [32] (Tables S1 and S5).”
Tavares, J. F., Liu, D., Talevi, V., Eichelmann, F., Jannasch, F., Schulze, M. B., Aziz, N. A., NoÌthlings, U., & Breteler, M. (2025) · medRxiv · doi:10.1101/2025.05.17.25327830 · Cited by 1
“22 Kim et al. demonstrated a link between a higher DASH score and reduced epigenetic age acceleration.”
Navarro-Delgado, E. I., Czamara, D., Edwards, K., Fu, M. P., Merrill, S., Konwar, C., MacIsaac, J., Lin, D. T. S., Mandhane, P., Simons, E., et al. (2025) · bioRxiv · doi:10.1186/s13059-025-03864-4 · Cited by 1
“…to interrogate specifically the association of genetic variants with DNAme, such as the largest mQTL blood study to date (44% of CpGs under cis genetic control) [52] and the whole blood narrow-sense heritability estimates in twins (41% of CpGs with significant additive genetic effects) [30].”
Self-citationShin, J., Bressler, J., Grove, M. L., Brown, M. R., Selvin, E., Pankow, J. S., Fornage, M., Morrison, A. C., & Sarnowski, C. (2025) · Epigenetics · doi:10.1080/15592294.2025.2498857 · Cited by 2
“We also searched publicly available mQTL databases of blood samples including mQTL Database [39], BIOS QTL browser [40] and GoDMC mQTL [41], and a mQTL database of brain samples, Brain xQTLServe [42], where all four mQTL databases were from European populations. mQTLs associations can differ by…”
InfluentialSelf-citationDerakhshan, M., Issarapu, P., Prentice, A. M., Kuebel, J. A., Moore, S. E., Bernstein, R. M., Sesay, A. K., Jammeh, M., Waterland, R. A., Hellenthal, G., et al. (2025) · medRxiv · doi:10.1101/2025.04.22.25326054 · Cited by 2
“Investigation of the influence of genetic variation on DNAm through methylation quantitative trait loci (mQTL) mapping can help elucidate the effects of transcription factors on gene regulation 14 .”
InfluentialIntergenerational transmission of complex traits and the offspring methylome
Hagenbeek, F. A., Pool, R., Asselt, A. J. V., Ehli, E. A., Smit, A. B., Bartels, M., Hottenga, J., Dolan, C. V., van Dongen, J., & Boomsma, D. I. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-02981-7 · Cited by 6
Self-citationGolub, Y., Wulff, A., & Plösch, T. (2025) · European Child and Adolescent Psychiatry · doi:10.1007/s00787-025-02686-w · Cited by 1
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Zheng, Z., Song, Y., Li, X., Luò, T., & Tan, X. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-01851-x · Cited by 1
“All available mQTLs for the CpG loci of interest were obtained from the Genetics of DNA Methylation Consortium (GoDMC) [44], which includes a diverse range of 36 cohorts from various studies and populations, such as ALSPAC, Brisbane Systems Genetics Study (BSGS), and GSK, providing a valuable…”
Exploration and validation of biomarkers for Alzheimer's disease based on GEO database
Gan, Y., Sun, J., Yang, D., Fang, C.-X., Zhou, Z., & Yin, J.-J. (2025) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2025.03.006 · Cited by 1
Xiong, L., Zhang, H., Guo, Y., & Tao, Y. (2025) · Biomedicines · doi:10.3390/biomedicines13030581 · Cited by 1
“The DNA mQTL data can be accessed through the GoDMC database (http://mqtldb. godmc.org.uk/index, accessed on 15 August 2024), which encompasses 36 cohorts comprising a total of 27,750 European samples derived from whole blood [23].”
Self-citationSingh, M., Dolan, C. V., Lapato, D. M., Hottenga, J., Pool, R., Verhulst, B., Boomsma, D. I., Breeze, C. E., de Geus, E. J. C., Hemani, G., et al. (2025) · European Journal of Epidemiology · doi:10.1007/s10654-024-01187-5 · Cited by 3
“Of the 16,940 smoking-associated CpGs, 12,940 had summary statistics for cis-mQTLs available from the Genetics of DNA Methylation Consortium (GoDMC; excluding NTR) [21] (Fig.”
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“More recently, a meta-analysis (Min et al., 2021) using linkage disequilibrium (LD) clumping identified 248,607 independent cis-mQTL associations accounting for 45% of the 450K array.”
An atlas of genetic effects on the monocyte methylome across European and African populationsPreprint
Zhang, W.-H., Zhang, X., Qiu, C., Zhang, Z.-C., Su, K.-J., Luo, Z., Liu, M., Zhao, B.-X., Wu, L., Tian, Q., et al. (2024) · medRxiv · doi:10.1101/2024.08.12.24311885 · Cited by 1
“After outlier removal, we recalculated genetic PCs for use in subsequent analyses.”
InfluentialBlood- and brain-based genome-wide association studies of smoking
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“Lead and methylation quantitative loci among the results of GrimAge DNAm pack years GWAS were identified using the default settings in FUMA (41) and goDMC (42), respectively.”
MethodSelf-citationHeikkinen, A., Esser, V. F. C., Lundgren, S., Lee, S. H., Hakkarainen, A., Lundbom, J., Kuula, J., Groop, P., Heinonen, S., Pajukanta, P., et al. (2024) · medRxiv · doi:10.1101/2024.04.02.24304959 · Cited by 1
Self-citationWhat makes clocks tick? Characterizing developmental dynamics of adult epigenetic clock sitesPreprint
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Self-citationNg-Kee-Kwong, J., & Bretherick, A. (2024) · medRxiv · doi:10.1101/2024.03.04.24303678 · Cited by 1
“We first selected, from all the meQTL results in GoDMC [11], the maximum absolute z-score per genetic variant.”
Self-citationElliott, H. R., Bennett, C. L., Caramaschi, D., & English, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-65605-0 · Cited by 2
“DNA methylation at cg11453712 has been previously linked to age 40 and is associated with nearby genetic variants 41 .”
BackgroundSelf-citationYang, Y., Chen, Y.-X., Xu, S., Guo, X.-Y., Jia, G., Ping, J., Shu, X., Zhao, T., Yuan, F., Wang, G., et al. (2023) · medRxiv · doi:10.1101/2023.08.09.23293899 · Cited by 1
A Parkinson's disease genetic risk score associates with blood DNAm on chromosome 17Preprint
Casazza, W., Schaffner, S., Artaud, F., Domenighetti, C., Schulze-Hentrich, J., Lesage, S., Brice, A., Corvol, J.-C., Mostafavi, S., Kobor, M., et al. (2023) · medRxiv · doi:10.1101/2023.07.21.23293014 · Cited by 1
Novel genetic loci in adolescent-onset gout derived from whole genome sequencing of a Chinese cohortPreprint
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Self-citationA genome-wide catalog of sites where peripheral DNA methylation reflects the brain
Nishitani, S., Han, S.-Z., Ishii, T., Shimura, A., Phuong, N. J., Kato, A., Chambers, M., Wang, T., Isozaki, M., Higashino, Y., et al. (2026) · bioRxiv · doi:10.64898/2026.10.23.761276
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Genetic and epigenetic factors in obesity
Martinović, L. S., & Pavlić, S. D. (2026) · Best Practice & Research Clinical Gastroenterology · doi:10.1016/j.bpg.2026.102151
Machine learning and statistical methods for molecular quantitative trait loci
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From reference epigenomes to response epigenomes: a public-health case built on the developing brain
Hartung, T. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1916784
“The meQTLs that constrain the epigenome (33) are precisely the instruments this approach needs, so genomic constraint becomes an analytic asset rather than only a confounder.”
GIA: Germline-Informed Aging with AlphaGenome Finds Genetically Regulated CpGs
Lim, S. (2026) · doi:10.48550/arxiv.2609.17801
“Large-scale methylation quantitative trait locus (meQTL) studies, including the Genetics of DNA Methylation Consortium (GoDMC), have mapped extensive genetic associations with blood methylation sites [1].”
Fu, Y., Wang, Z., Liu, S., Liu, L., & Tang, X.-D. (2026) · Genes · doi:10.3390/genes17091125
Associations between diet quality, epigenetic aging and epigenome in two population-based cohorts
Tavares, J. F., Liu, D., Talevi, V., Eichelmann, F., Jannasch, F., Schulze, M. B., Aziz, N. A., Nöthlings, U., & Breteler, M. M. B. (2026) · Nature Communications · doi:10.1038/s41467-026-77064-4 · Cited by 1
The prenatal exposome and genome in predictive modelling of DNA methylationPreprint
Mulder, R. H., Isaevska, E., Cappadona, C., Defina, S., Neumann, A., Felix, J. F., Walton, E., Suderman, M., & Cecil, C. A. M. (2026) · bioRxiv · doi:10.64898/2026.08.12.742972
“First, the pre-selected meQTLs that were included to study the influence of genomics on DNAm, were based on resources that did not rely on ALSPAC and/or Generation R data 15,25 – which meant that these resources were not the largest studies available.”
Self-citationChen, M., Wei, Q., Li, H., Ruan, H., Fu, L., Ma, J., Xia, X., Liao, Y., Hou, C., & Huang, H. (2026) · npj Mental Health Research · doi:10.1038/s44184-026-00232-3
“DNA methylation quantitative trait locus (mQTLs) association data from 27,750 individuals 16 and brain mQTLs data (estimated effective n = 1160) 17 were utilised in this analysis.”
Rethinking evidence for epigenetic inheritance in human research
Løkhammer, S., Cecil, C. A. M., Jugessur, A., Hellard, S. L., & Mansuy, I. M. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.07.007
Constantino-Pettit, A. M., Lussier, A. A., Ruppel, M., Dunn, E. C., Czamara, D., Smyser, T. A., Bogdan, R., Warner, B. B., Smyser, C. D., Barch, D. M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.20.739628
Self-citationDNA methylation as proxy of genetic, prenatal and perinatal psychiatric risk factors
Isaevska, E., Mulder, R. H., Schuurmans, I., Creasey, N., Felix, J., Pingault, J.-B., van Haren, N. V., Cecil, C. A. M., & Neumann, A. (2026) · medRxiv · doi:10.64898/2026.07.22.26358558
“…missMethyl R-package), (2) trait enrichment 32,33 (3) enrichment analyses for CpGs at or near SNPs, methylation quantitative trait loci (mQTLs) 34 , CoRSIVs (“correlated regions of systemic interindividual variation”, known to have a strong genetic influence, high inter-individual variability…”
Self-citationLi, W., Li, Y., Dong, P., Tang, J.-X., Liu, S., Tu, L., & Xu, X.-Z. (2026) · Tobacco Induced Diseases · doi:10.18332/tid/216384
“Methylation levels were quantified using the Illumina Infinium HumanMethylation450 BeadChip platform on bisulfite-converted genomic DNA across 36 cohorts, encompassing 27750 European samples sourced from whole blood 14 .”
Ciantar, J., Raitoharju, E., Marttila, S., Lehtimäki, T., Pohjonen, E., Ahinko, K., & Kaartinen, N. (2026) · Bioscience Reports · doi:10.1042/bsr20260093
“We then investigated whether meQTLs, underlying genetic variants that affect DNA methylation, could play a causal role in the differential methylation at these sites by cross-checking the identified CpG sites with the GoDMC database [35] (available at: http://mqtldb.godmc.org.uk/).”
Cheng, S., & Xu, S. (2026) · Molecular Biology and Evolution · doi:10.1093/molbev/msag160
Tian, Y., Wong, J., McDonnell, S. K., Zhong, H., Wu, L., Larson, N. B., Manley, B. J., & Wang, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.17.732357
“18 One strategy to more effectively pinpoint functional noncoding variants is to evaluate their regulatory potential by examining the local methylation pattern flanking each allele.”
Schellhas, L., Luo, M., Felix, J. F., Page, C. M., Bekkhus, M., Munafo, M. R., Zuccolo, L., Havdahl, A., Cecil, C. A. M., & Sharp, G. C. (2026) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.70186
“Importantly, however, DNAm-phenotype associations do not by themselves establish mediation or causality, and large-scale genetic studies of blood DNAm highlight that causal interpretations can be difficult to support for many DNAm-trait links (Min et al., 2021).”
Self-citationDNAm landscape up to 4 months post SARS-CoV-2 infection: insights from four population-based cohorts
Matías-García, P., Lai, L., Delerue, T., Ohnmacht, J., Stark, K. J., Warmerdam, R., Kolodkin, A., Six-Merker, J., Mangold, N., Günther, K., et al. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02166-1
“The influence of genetic variants in the identified CpGs, namely the overlap with methylation quantitative trait loci (meQTL), was queried in the BIOS QTL database and GoDMC [43, 44].”
Li, X., An, W., Wang, X., Yin, Z.-X., Gao, Y., Lan, Y., Zhang, L.-Y., Zhao, B.-B., Chang, L.-X., Ruan, M., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01599-2
“Summarystatisticsfor DNAm quantitative trait loci (mQTLs) were obtained from the Genetics of DNA Methylation Consortium (GoDMC), which comprises data from 27,750Europeanindividualsandincludes420,509DNAmsitesmeasuredin wholeblood 28 (http://mqtldb.godmc.org.uk/downloads, accessed August 6, 2024).”
Sultana, N., Yang, F., Kitaba, N. T., Potter, S., Holloway, J. W., Arshad, S. H., & Zhang, H. (2025) · Epigenomes · doi:10.1016/j.jaci.2024.12.851
“%) were also reported in the GoDMC database [13], supporting the robustness of these associations.”
Self-citationGPX7 marks fibroblast-associated stromal–innate immune crosstalk in ulcerative colitis
He, J., Chen, R., Qiu, S.-F., Li, P., & Gao, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1856998
“For mediation, methylation QTL (mQTL) summary statistics were obtained from public mQTL resources (GoDMC/mQTL portals (23)).”
DNA methylation and gene expression signatures for common childhood infections
Herrera-Luis, E., Sisó, D. P., Vidal, M., Margetaki, K., Pissanidis, A. A. K., Aguilar, R., Vrijheid, M., Dobaño, C., Kogevinas, M., Karachaliou, M., et al. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02130-z
“Fourth, we cannot disregard that DNAm variation directly influences health outcomes, since this interplay is far more complex [59] than what could be captured in this study.”
Self-citationSaed, A., Akbarzadeh, M., Gohari, F., Asadrouh, N., Jahromizadeh, A. M., Sabaie, H., Zarkesh, M., Hedayati, M., Azizi, F., & Daneshpour, M. (2026) · Human Genomics · doi:10.1186/s40246-026-00972-5
“…were accessed via the SMR Portal (yanglab.westlake.edu.cn) on 25 September 2025, including GTEx v8 eQTL/sQTL (liver, brain, pituitary, spleen, kidney) [15], eQTLGen Whole Blood [16], MetaBrain brain eQTL [17], GoDMC blood mQTL [18], and Fenland plasma pQTL [19] and INTERVAL plasma pQTL [20].”
Li, Y., Jiang, T., Qian, L., & Wang, Y. (2026) · bioRxiv · doi:10.64898/2026.04.20.719515
“To this end, we integrated our previously published SNV panel of the Chinese population in the China100K project 54 with public meQTL resources 55 , and found that SNVs affect at most 2-7% of pDMRs and hDMRs identified in this study, although this represents a highly conservative overestimation.”
Hsu, F.-M., Pickering, H., Bosinger, S., Eckalbar, W., Maecker, H., Kim-schulze, S., Ozonoff, A., Diray-Arce, J., Schaenman, J., Reed, E. F., et al. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02138-5
“To be noted, we mapped these TG-specific CpG loci to the GoDMC mQTL database and there is no overlap [41], suggesting that the identified methylation differences are less likely to be driven by known common mQTLs and may instead reflect infection-associated epigenetic changes.”
Liu, L., Zhu, Z., Du, M., Liu, X., Tan, Q. Q., Xiao, L., Zheng, X., Huang, Z., & 廖正步 (2026) · Medicine · doi:10.1097/md.0000000000048310
“[16] We drew genome-wide association studies (GWAS) summary statistics for depression (F5_DEPRESSIO, cases = 47,696, controls = 3,59,290) and PTSD (F5_PTSD, cases = 2615, controls = 3,68,054) from the FinnGen R10 release, [17] a large nationwide biobank with genetic information available for over…”
The aging epigenome: integrative analyses reveal intersection with Alzheimer’s disease
Zhang, W., Lukacsovich, D., Young, J. I., Gomez, L., Schmidt, M. A., Kunkle, B. W., Chen, X., Martin, E. R., Wang, L., & Initiative, A. D. N. (2026) · GeroScience · doi:10.1007/s11357-026-02195-x
“To select significant blood mQTLs of aging-associated CpGs in GoDMC, we used the same criteria as the original study [34], that is, considering a cis P-value smaller than 10 −8 and a trans P-value smaller than 10 −14 as significant.”
InfluentialLi, Z., Hong, L., Lv, X., Yang, S.-Y., Ye, F., Huang, L.-J., Jiang, S., & Zhao, H.-Y. (2026) · Communications Biology · doi:10.1038/s42003-026-10033-1
“With the development of epigenome-wide association studies (EWAS) and genome-wide association studies (GWAS), MR has been applied to molecular traits using methylation quantitative trait loci (mQTLs) to identify causal cytosine-phosphate-guanine (CpG) sites for disease risks 7,15 .”
Shang, K.-L., Luo, C., Tan, X., Xu, M.-L., Li, B., Wang, H.-H., & Cheng, F.-H. (2026) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-026-02237-7
“Genetic and nongenetic principal components, predicted smoking and cell counts were further included as additional confounders in the regression model [36 ].”
Han, H.-H., Huang, E., Liu, D., Zhang, Y., Zhou, S.-Y., Huang, L.-Z., & Du, X.-K. (2026) · Epigenetics & Chromatin · doi:10.1186/s13072-026-00673-2
“Genetic instrumental variables (IVs) associated with this gene set were selected for analysis: Instrumental variables for gene epigenetic regulation (DNA methylation levels, cis-mQTLs) obtained from the GoDMC database ( h t t p : / / m q t l d b . g o d m c . o r g . u k / d o w n l o a d s) [16].”
Timing matters in population epigenomics
Cecil, C. A. M., Felix, J. F., & Neumann, A. (2026) · Epigenomics · doi:10.1080/17501911.2026.2652837
Self-citationHirose, N., Mizuno, S., Niwa, Y., Hara, T., Yamamoto, H., Koyama, E., Ueda, J., Tsuboi, T., & Takata, A. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117151
Hahn, J., Bressler, J., Fornage, M., Boerwinkle, E., Smith, N. L., Morrison, A., & de Vries, P. D. (2026) · Clinical Epigenetics · doi:10.1186/s13148-025-02028-2
“For significantly associated and replicated CpG sites from each phenotype, genetic instruments were identified using a publicly available database of methylation quantitative trait loci (meQTLs) from the Genetics of DNA Methylation Consortium (GoDMC) [42].”
Penner-Goeke, S., & Binder, E. B. (2026) · Neuron · doi:10.1016/j.neuron.2026.02.014 · Cited by 3
Self-citationEstimating population structure using epigenome-wide methylation data
Wang, Z., Taylor, K., Rotter, J., Rich, S. S., Zheng, Y.-N., Hou, L., Guo, X.-Q., Bressler, J., Raffield, L., Liu, Y.-M., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag142
“Similar to the previously proposed methods [8, 10, 11, 35], our approach also relies on the assumption that some DNAm sites are associated with genetics [6, 36].”
Li, W., Zeng, W., & Wong, W. H. (2026) · Cybernetics and Intelligence · doi:10.26599/cai.2024.9390003
Epigenome-wide association study of blood proteome in the Atherosclerosis Risk in Communities study
Li, Y., Surapaneni, A., Rodriguez-Hernandez, Z., Schlosser, P., Rhee, E. P., Boerwinkle, E., Yu, B., Grove, M. L., Ruggles, K. V., Coresh, J., et al. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100585
Caramaschi, D., Fernández-Barrés, S., Casey, E., Cruells, A., Czamara, D., Sharkawy, M. E., Elliott, H. R., Fore, R., Gairola, R., Gruzieva, O., et al. (2026) · Clinical Epigenetics · doi:10.1186/s13148-025-02042-4
“…(meQTLs) were retrieved from the Genetics of DNAm consortium (GoDMC) data-base ( h t t p : / / m q t l d b . g o d m c . o r g . u k / i n d e x) [34], and cis expression quantitative trait DNAm (eQTMs) from the Human Early Life Exposome (HELIX) database ( h t t p s : / / h e l i x o m i c s . i…”
Self-citationLi, S., Zeng, W., Li, W., Liu, C.-C., Zhou, Y., Ni, X., Stackpole, M. L., Yeh, A., Melehy, A., Lu, D. S., et al. (2026) · Genome biology · doi:10.1186/s13059-026-04006-0
“We also curated race-specific meQTLs for European Americans and African Americans from recent studies [31, 32].”
Anguita‐Ruiz, A., Torres‐Martos, Á., Bustos‐Aibar, M., Setó-Llorens, A., Ruiz‐Ojeda, F. J., Moreno, L. A., Gil, Á., Gil‐Campos, M., Bueno, G., Leis, R., et al. (2026) · Cardiovascular Diabetology · doi:10.1186/s12933-026-03101-7 · Cited by 2
“First, we queried the GoDMC (Genetics of DNA Methylation Consortium) catalogue [109], a large-scale reference resource that aggregates harmonized mQTL results from multiple population-based studies and provides robust evidence for cis-and trans-mQTL effects across the epigenome.”
Luo, D.-L., & Lussier, A. (2026) · medRxiv · doi:10.64898/2026.01.30.26345158
“SNPs associated with the PAE-or FASD-related DNAm loci were retrieved from GoDMC database [30].”
Unveiling Shared Genetic Architectures and Causality: Intestinal Diseases and Neurological Diseases
Zhao, N., Tan, S.-H., Fu, Q., Li, Y., Tian, T., Cheng, Z., Zhang, D., Gao, L., Bao, W., Zhang, D.-P., et al. (2026) · Brain and Behavior · doi:10.1002/brb3.71269
“For each of these CpG sites, we derived mQTLs (methylation quantitative trait loci) robustly associated with their methylation level in whole blood from the Genetics of DNA Methylation Consortium (Min et al. 2021 ) as the instrumental variables (IVs).”
Song, Z.-Q., Wang, B.-X., Zhou, Z.-B., Wu, S.-K., Sun, Y.-H., & Chen, Y.-H. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02048-6
“The DNA methylation quantitative trait locus (mQTL) data were obtained from the GoDMC consortium, which includes 32,851 European-ancestry individuals and provides over 270,000 independent mQTLs [24].”
Zhang, H., Zhao, J., Chen, J., Ma, X.-Y., Zhou, S., Noble, A., Kalla, R., Wellens, J., Liu, K., Theodoratou, E., et al. (2026) · Advancement of science · doi:10.1002/advs.202516704
“[ 84 ] We selected cis -mQTLs (the distance between mQTL and CpG site 1 MB) which were associated with each CpG site at a genome-wide significance ( p 5 × 10 − 8 ), and extracted the location, beta, se, and p -value of each cis-mQTL.”
Self-citationZhang, M., Li, T., Tan, L., Yu, J., Yao, Q., & Chen, L. (2026) · International Journal of COPD · doi:10.2147/copd.s563463
Guo, J., Huang, F., Lu, P., & Lu, M.-J. (2026) · Cardiovascular Therapeutics · doi:10.1155/cdr/4885476
Wan, X., Lu, W.-C., Xue, J., Huang, J., Qian, W.-H., Hu, Z.-Y., You, W.-L., & Zhang, Y.-F. (2026) · Mediators of Inflammation · doi:10.1155/mi/3846184
Wei, Y., Lan, Z., Xie, Y., Yi, Z., Du, X., & Jiang, Z. (2026) · Molecular Neurobiology · doi:10.1007/s12035-026-06279-9
“Blood methylation QTLs (mQTLs), blood and tissue-specific expression QTLs (eQTLs), and circulating protein QTLs (pQTLs) interrogate complementary levels of molecular regulation [10, 13].”
Chlorinated solvents in groundwater and human DNA methylation: The Italian case of Bussi sul Tirino.
Bolognesi, G., Iannuzzi, V., Sarno, S., de Sanctis, A., Bacalini, M., Ravaioli, F., Ferraresi, F., Sala, C., Fanti, S. D., Luiselli, D., et al. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.119548
DNA methylation signatures of Life’s Essential 8 and their implications for dementia
Lukacsovich, D., Wang, L.-Y., Young, J., Zhang, W., Gomez, L., Schmidt, M., Gardener, H., Agudelo, C., Dueker, N. D., Elfassy, T., et al. (2025) · Alzheimer's Research & Therapy · doi:10.1186/s13195-025-01903-7
“For correlation and overlap with genetic loci, we searched blood mQTLs using the GoDMC database ( h t t p : / / m q t l d b . g o d m c . o r g . u k / d o w n l o a d s) [42].”
InfluentialWang, P., Feng, R., & Liu, F. (2025) · Epigenetics · doi:10.1080/15592294.2025.2599682 · Cited by 1
“Although recent large-scale genetic studies have demonstrated that certain CpG sites exert causal effects across multiple diseases or traits in both Asian and European populations, PC has not yet been included in these analyses [17,18].”
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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