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, 3 CpGs had both cis and trans mQTLs.
“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]
The Results section, “Cis- and trans-mQTLs operate through distinct mechanisms” (page unavailable), reports that 11,902 DNAm sites had both cis and trans associations. It does not identify three such CpGs among male AD-associated sites, so it supports the type of association but not the claim’s count or population. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThese results are consistent with the previous observation that about 45% of DNA methylation sites targeted by the Illumina 450k array are influenced by genetic variants in the blood.
“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]
EWAS addressing the joint effect of genotype and environment are still in their infancy.
“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
The Discussion (page unavailable) discusses genetic effects on DNA methylation and their implications for EWAS, but does not assess whether EWAS examining joint genotype–environment effects are in their infancy. Searches for that characterization and for genotype–environment interaction did not find a passage addressing it. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe G allele of the lead variant rs911196 increases methylation of cg01799681, cg16867657, cg21572722, and cg23642061 in whole blood.
“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]
The Results section ('Genetic variants influence 45% of tested DNAm sites'; page not provided) describes a whole-blood study of genetic effects on methylation, but does not identify the claimed variant–site associations. Searches for rs911196 and the four CpG identifiers found no passage stating that its G allele increases methylation at those sites. (LLM, conf=0.75)
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Self-citationHigh resolution genetic mapping of putative causal interactions between regions of open chromatin
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Self-citationEdge‐group sparse PCA for network‐guided high dimensional data analysis
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Self-citationCHAPTER 9 – Random Effects Models for Effect Sizes
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Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives
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Causality-enriched epigenetic age uncouples damage and adaptation
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“MR has been applied to molecular traits using molecular quantitative trait loci, including DNA methylation quantitative trait loci (meQTLs) 12 .”
Epigenome-wide association studies: current knowledge, strategies and recommendations
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“uk), published in 2021, indexes over 270,000 methQTLs identified across 30,000 samples from over 30 cohorts [101].”
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The Rotterdam Study. Design update and major findings between 2020 and 2024
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“Major publications on the relationship between genetics and the transcriptome and methylome included the eQTLgen-consortium paper show - ing results of over 30 thousand individuals [160] and the GO-DMC consortium who published an atlas of genetic effects on DNA methylation [ 161].”
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“Additionally, the relationship between epigenomics and genomics should be better studied since epigenetic markers also present significant heritability estimates [153].”
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Self-citationA genome-wide association analysis reveals new pathogenic pathways in gout
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Molecular quantitative trait loci
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Asthma Inception: Epidemiologic Risk Factors and Natural History Across the Life Course
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“…there is evidence of cord blood DNA-methylation sites being predictive of asthma (92, 93), recent investigations using causal methods suggest that most DNA methylation is the consequence of asthma, suggesting potential future biomarker applications of epigenetics in childhood asthma (88, 94).”
Self-citationMulti-Omics Approaches in Immunological Research
Chu, X., Zhang, B., Koeken, V. A. C. M., Gupta, M. K., & Li, Y. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.668045 · Cited by 86 · see citing works
From genetic associations to genes: methods, applications, and challenges
Qi, T., Song, L., Guo, Y., Chen, C., & Yang, J. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.04.008 · Cited by 84 · see citing works
The immune factors driving DNA methylation variation in human blood
Bergstedt, J., Azzou, S. A. K., Tsuo, K., Jaquaniello, A., Urrutia, A., Rotival, M., Lin, D. T. S., MacIsaac, J. L., Kobor, M. S., Albert, M. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33511-6 · Cited by 81 · see citing works
“…between DNA methylation and DNA sequence variation, attested by the numerous DNA methylation quantitative trait loci (meQTLs) detected so far 20 – 23 , suggest that environmental effects on the epigenome may operate through gene-by-environment interactions, but evidence for such interactions…”
BackgroundEpigenetic scores for the circulating proteome as tools for disease predictionPreprint
Gadd, D. A., Hillary, R. F., McCartney, D. L., Zaghlool, S. B., Stevenson, A. J., Nangle, C., Campbell, A., Flaig, R., Harris, S. E., Walker, R. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.01.404681 · Cited by 15 · see citing works
“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 · see citing works
“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.”
BackgroundMethodResultInfluentialSelf-citationHillary, R., McCartney, D., Smith, H. M., Bernabéu, E., Gadd, D., Chybowska, A., Cheng, Y.-P., Murphy, L., Wrobel, N., Campbell, A., et al. (2023) · PLoS Medicine · doi:10.1371/journal.pmed.1004247 · Cited by 68 · see citing works
“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 84 · see citing works
“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
Heikkinen, A., Bollepalli, S., & Ollikainen, M. (2022) · Journal of Internal Medicine · doi:10.1111/joim.13496 · Cited by 68 · see citing works
Self-citationJiang, F., Zhao, J., Sun, J., Chen, W.-X., Zhao, Y., Zhou, S., Yuan, S., Timofeeva, M., Law, P., Larsson, S., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105126 · Cited by 61 · see citing works
Can epigenetics shine a light on the biological pathways underlying major mental disorders?
Alameda, L., Trotta, G., Quigley, H., Rodriguez, V., Gadelrab, R., Dwir, D., Dempster, E., Wong, C. C. Y., & Forti, M. D. (2022) · Psychological Medicine · doi:10.1017/s0033291721005559 · Cited by 60 · see citing works
“…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).”
BackgroundMeta-analyses identify DNA methylation associated with kidney function and damage
Schlosser, P., Tin, A., Matías‐García, P. R., Thio, C. H. L., Joehanes, R., Liu, H., Weihs, A., Yu, Z., Hoppmann, A., Grundner-Culemann, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27234-3 · Cited by 75 · see citing works
Self-citationThe correlation between CpG methylation and gene expression is driven by sequence variants
Stefánsson, Ó. A., Sigurpalsdottir, B. D., Rögnvaldsson, S., Halldorsson, G. H., Juliusson, K., Sveinbjörnsson, G., Gunnarsson, B., Beyter, D., Jónsson, H., Gudjonsson, S. A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01851-2 · Cited by 67 · see citing works
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“containing methylation-QTLs (meQTLs) [23], and the second containing expressionQTLs (eQTLs) [24].”
BackgroundSelf-citationSilva, T. C., Young, J. I., Zhang, L., Gomez, L., Schmidt, M. A., Varma, A., Chen, X. S., Martin, E. R., & Wang, L. (2022) · doi:10.1101/2022.04.11.22273748 · Cited by 57
Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer’s disease
Silva, T. C., Young, J. I., Zhang, L., Gomez, L., Schmidt, M. A., Varma, A., Chen, X. S., Martin, E. R., & Wang, L. (2022) · Nature Communications · doi:10.1038/s41467-022-32475-x · Cited by 55 · see citing works
Juvinao-Quintero, D., Marioni, R., Ochoa-Rosales, C., Russ, T. C., Deary, I., van Meurs, J. V., Voortman, T., Hivert, M., Sharp, G., Relton, C., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01027-3 · Cited by 54 · see citing works
Self-citationCecil, C. A. M., & Nigg, J. T. (2022) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-022-00609-y · Cited by 71 · see citing works
“may in part reflect (and potentially mediate) genetic liability for ADHD, an hypothesis supported by recent evidence of a functional relationship between genetic and epigenetic variation at this specific locus [66], although environmental influences are also at play [34].”
BackgroundAn overview of DNA methylation-derived trait score methods and applications
Nabais, M. F., Gadd, D. A., Hannon, E. J., Mill, J. S., McRae, A. F., & Wray, N. R. (2023) · Genome biology · doi:10.1186/s13059-023-02855-7 · Cited by 73 · see citing works
“While most SNPs only confer a small effect on DNAm variation [4], some associations are strong (up to 2 standard deviation units/allele [4]) and likely imply a”
BackgroundSelf-citationSmyth, L., Dahlström, E., Syreeni, A., Kerr, K., Kilner, J., Doyle, R., Brennan, E., Nair, V., Fermin, D., Nelson, R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34963-6 · Cited by 50 · see citing works
Elliott, H. R., Burrows, K., Min, J. L., Tillin, T., Mason, D., Wright, J., Santorelli, G., Smith, G. D., Lawlor, D., Hughes, A. D., et al. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01351-2 · Cited by 50 · see citing works
“For example, mQTLs from GoDMC were identified in individuals of European ancestry [53].”
BackgroundMethodResultInfluentialSelf-citationThe missing diversity in human epigenomic studies
Breeze, C. E., Beck, S., Berndt, S. I., & Franceschini, N. (2022) · Nature Genetics · doi:10.1038/s41588-022-01081-4 · Cited by 55 · see citing works
Identical twins carry a persistent epigenetic signature of early genome programming
van Dongen, J., Gordon, S., McRae, A., Odintsova, V., Mbarek, H., Breeze, C., Sugden, K., Lundgren, S., Castillo-Fernandez, J., Hannon, E., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25583-7 · Cited by 49 · see citing works
Self-citationArtificial intelligence for dementia drug discovery and trials optimization
Doherty, T., Yao, Z., Khleifat, A. A. L., Tantiangco, H., Tamburin, S., Albertyn, C., Thakur, L., Llewellyn, D. J., Oxtoby, N. P., Lourida, I., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13428 · Cited by 48 · see citing works
“43,44 Although 10%–20% of patients have a relevant family history, in most cases the condition appears sporadic.”
Bogan, S. N., & Yi, S. V. (2024) · Genome Biology and Evolution · doi:10.1093/gbe/evae022 · Cited by 57 · see citing works
“For example, 15% to 17% of the additive genetic variance of DNA methylation in blood could be explained by mQTLs in a study of 32,851 participants (Min et al. 2021).”
BackgroundZhang, H., Kalla, R., Chen, J., Zhao, J., Zhou, X., Adams, A., Noble, A., Ventham, N. T., Wellens, J., Ho, G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44841-y · Cited by 44 · see citing works
“To minimize potential confounding and residual variability, the model was adjusted for covariates including sex, age at measurement, batch variables, smoking status, and recorded cell counts 14 .”
InfluentialSelf-citationSomatic mutation as an explanation for epigenetic aging
Koch, Z., Li, A., Evans, D. S., Cummings, S., & Ideker, T. (2023) · doi:10.1101/2023.12.08.569638 · Cited by 44 · see citing works
Physical Activity, Sedentary Behavior, and Type 2 Diabetes: Mendelian Randomization Analysis
Yuan, S., Li, X., Liu, Q., Wang, Z., Jiang, X., Burgess, S., & Larsson, S. C. (2023) · Journal of the Endocrine Society · doi:10.1210/jendso/bvad090 · Cited by 49 · see citing works
“We further identified methylation quantitative trait loci (mQTL) that regulate the methylation levels of identified MVPA-related CpG sites based on the Accessible Resource for Integrative Epigenomic Studies Genetics of DNA Methylation Consortium (GoDMC) [32].”
BackgroundMethodAn epigenome-wide view of osteoarthritis in primary tissues
Kreitmaier, P., Suderman, M., Southam, L., de Almeida, R. C., Hatzikotoulas, K., Meulenbelt, I., Steinberg, J., Relton, C., Wilkinson, J., & Zeggini, E. (2022) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.05.010 · Cited by 43 · see citing works
Self-citationTobi, E. W., Juvinao-Quintero, D. L., Ronkainen, J., Ott, R., Alfano, R., Canouil, M., Geurtsen, M. L., Khamis, A., Küpers, L. K., Lim, I. Y., et al. (2022) · Diabetes Care · doi:10.2337/dc21-1701 · Cited by 58 · see citing works
Self-citationNew insights into the role of immunity and inflammation in diabetic kidney disease in the omics era
Hu, X., Chen, S., Ye, S., Chen, W., & Zhou, Y. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1342837 · Cited by 34 · see citing works
Shang, L., Zhao, W., Wang, Y.-Z., Li, Z., Choi, J. J., Kho, M., Mosley, T. H., Kardia, S. L. R., Smith, J. A., & Zhou, X. (2023) · Nature Communications · doi:10.1038/s41467-023-37961-4 · Cited by 47 · see citing works
“The over-representation and under-representation results of the identi fi ed cis-meCpG sites in distinct genomic regions in African Americans in the present studies are consistent with the results on individualsfrom African ancestry 32 aswell as European ancestry 24,33 – 36 in prior studies.”
InfluentialGunasekara, C., MacKay, H., Scott, C. A., Li, S., Laritsky, E., Baker, M. S., Grimm, S. L., Jun, G., Li, Y., Chen, R., et al. (2023) · Genome biology · doi:10.1186/s13059-022-02827-3 · Cited by 44 · see citing works
“(17)) computed mQTL using 33,000 individuals with DNA methylation data generated in the HM450 platform.”
MethodCheung, W. A., Johnson, A. F., Rowell, W., Farrow, E., Hall, R. P., Cohen, A. S. A., Means, J. C., Zion, T., Portik, D. M., Saunders, C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38782-1 · Cited by 39 · see citing works
Epigenome-Wide Meta-Analysis of Prenatal Maternal Stressful Life Events and Newborn DNA Methylation
Ruehlmann, A. K., Sammallahti, S., Hidalgo, A. P. C., Bakulski, K., Binder, E., Campbell, M., Caramaschi, D., Cecil, C., Colicino, E., Cruceanu, C., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02010-5 · Cited by 39 · see citing works
“The catalogue was down-loaded from http://www.helixomics.isglobal.org/.”
Self-citationZhang, W., Young, J. I., Gomez, L., Schmidt, M. A., Lukacsovich, D., Varma, A., Chen, X. S., Martin, E. R., & Wang, L. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01216-7 · Cited by 38 · see citing works
“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].”
BackgroundMethodResultInfluentialMélen, E., Koppelman, G., Vicedo-Cabrera, A., Andersen, Z. J., & Bunyavanich, S. (2022) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(22)00215-2 · Cited by 37 · see citing works
Self-citationIntegrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes
McAllan, L., Baranas̆ić, D., Villicaña, S., Brown, S., Zhang, W., Lehne, B. C., Adamo, M., Jenkinson, A. D., Elkalaawy, M., Mohammadi, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38439-z · Cited by 45 · see citing works
Self-citationPeng, Q., Liu, X., Li, W., Jing, H., Li, J., Gao, X., Luo, Q., Breeze, C. E., Pan, S., Zheng, Q., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01494-9 · Cited by 42 · see citing works
“7 and 8).”
Genetic control of DNA methylation is largely shared across European and East Asian populations
Hatton, A. A., Cheng, F., Lin, T., Shen, R., Chen, J., Zheng, Z., Qu, J., Lyu, F., Harris, S. E., Cox, S. R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47005-0 · Cited by 35 · see citing works
“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 · see citing works
Self-citationMortlock, S., Houshdaran, S., Kosti, I., Rahmioğlu, N., NEZHAT, C. R., Vitonis, A. F., Andrews, S. V., Grosjean, P., Paranjpe, M. D., Horne, A. W., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05070-z · Cited by 38 · see citing works
Self-citationEvidence for the role of selection for reproductively advantageous alleles in human aging
Long, E., & Zhang, J. (2023) · Science Advances · doi:10.1126/sciadv.adh4990 · Cited by 42 · see citing works
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 34 · see citing works
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 · see citing works
“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 42 · see citing works
Self-citationBakulski, K. M., Blostein, F., & London, S. J. (2023) · Environmental Health Perspectives · doi:10.1289/ehp12956 · Cited by 38 · see citing works
“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 33 · see citing works
“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 · see citing works
“These have been characterized as allele-specific methylation or methylation quantitative trait loci (meth-QTLs) [24, 25].”
BackgroundSelf-citationAbrishamcar, S., Chen, J.-Y., Feil, D., Kilanowski, A., Koen, N., Vanker, A., Wedderburn, C., Donald, K., Zar, H., Stein, D. J., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02195-3 · Cited by 29 · see citing works
“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 32 · see citing works
“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 114 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“, 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 26 · see citing works
Self-citationShen, X.-Y., Caramaschi, D., Adams, M., Walker, R., Min, J. L., Kwong, A., Hemani, G., Barbu, M., Whalley, H., Harris, S., et al. (2021) · Genome Medicine · doi:10.1186/s13073-022-01039-5 · Cited by 26 · see citing works
Self-citationLussier, A., Schuurmans, I., Großbach, A., Maclsaac, J., Dever, K., Koen, N., Zar, H., Stein, D., Kobor, M., & Dunn, E. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01761-4 · Cited by 25 · see citing works
“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.-J., Yu, L.-L., Zhou, Y.-J., Zhao, J., Zhang, H., Yuan, S., et al. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02397-6 · Cited by 24 · see citing works
“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 · see citing works
Epigenetic Regulation of F2RL3 Associates With Myocardial Infarction and Platelet Function
Corbin, L. J., White, S. J., Taylor, A. E., 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 39 · see citing works
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 32 · see citing works
“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 · see citing works
“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.-X., Xu, S., Guo, X.-Y., Jia, G., Ping, J., Shu, X., Zhao, T.-Y., Yuan, F., Wang, G., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50404-y · Cited by 22 · see citing works
“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 · see citing works
“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., Sharp, G., Sanderson, E., Relton, C., & Elliott, H. (2023) · Diabetologia · doi:10.1007/s00125-023-05914-7 · Cited by 22 · see citing works
“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 34 · see citing works
“(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 32 · see citing works
“57 All analyses were completed in R (v4.”
MethodZhu, Z.-Z., Li, Y., Freishtat, R., Celedón, J., Espinola, J., Harmon, B., Hahn, A., Camargo, C. A., Liang, L.-M., & Hasegawa, K. (2023) · Nature Communications · doi:10.1038/s41467-023-41300-y · Cited by 21
“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 · see citing works
“[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 21 · see citing works
“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 · see citing works
“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., Huan, T., McCartney, D., Chittoor, G., de Vries, M., Lahousse, L., Nguyen, J. N., Brody, J., Castillo-Fernandez, J., Terzikhan, N., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202108-1907oc · Cited by 20 · see citing works
“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.-J., Zhai, Z., Ma, Q.-F., & Pan, J.-B. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae853 · Cited by 19 · see citing works
“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.-H. (2024) · Nature Communications · doi:10.1038/s41467-024-45771-5 · Cited by 19 · see citing works
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 · see citing works
“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., Lugli-Moraes, B., da Silva, B. D., & Rovaris, D. (2023) · Journal of Physiology · doi:10.1113/jp284241 · Cited by 19 · see citing works
“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 23 · see citing works
“[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 · see citing works
“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., Peng, S., Cen, T., Wang, X.-X., Ma, L., & Cao, Z.-G. (2024) · Journal of Clinical Periodontology · doi:10.1111/jcpe.14040 · Cited by 18 · see citing works
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 24 · see citing works
“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 · see citing works
“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 35 · see citing works
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 · see citing works
Low-input and single-cell methods for Infinium DNA methylation BeadChips
Lee, S. M., Loo, C. E., Prasasya, R., Bartolomei, M., Kohli, R. M., & Zhou, W.-D. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae127 · Cited by 17 · see citing works
“…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. V., Márta, K., Lawlor, R., Tavano, F., et al. (2023) · Journal of Medical Genetics · doi:10.1136/jmg-2022-108910 · Cited by 17
Admixture mapping of severe asthma exacerbations in Hispanic/Latino children and youth
Herrera-Luis, E., Mak, A. C. Y., Pérez-García, J., Martín-González, E., Eng, C., Beckman, K., Huntsman, S., Hu, D., González-Pérez, R., Hernández-Pérez, J. M., et al. (2022) · Thorax · doi:10.1136/thorax-2022-218755 · Cited by 17 · see citing works
- 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 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)
- University College London (GB)
- 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 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
- Slagboom, P. Eline
- Morange, Pierre-Emmanuel
- Trégouët, David-Alexandre
- Veldink, Jan H.
- Boomsma, Dorret I.
- Vonk, Judith M.
- Brunekreef, Bert
- Koppelman, Gerard H.
- Alarcón-Riquelme, Marta E.
- Huang, Rae-Chi
- Pennell, Craig E.
- 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 · 2020: 2
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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