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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A database of our results is available as a resource to the community at http://mqtldb.godmc.org.uk. The individual-level genotype and DNAm data are available by request from each individual study or can be downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo), European Genome–Phenome Archive (EGA, https://ega-archive.org) or Array Express (https://www.ebi.ac.uk/arrayexpress). As the consent for most studies requires the data to be under managed access, the individual-level genotype and DNAm data are not available from a public repository unless stated.
ALS BATCH1 and -2 data are available to researchers by request as outlined in the Project MinE access policy. ARIES data are available to researchers by request from the Avon Longitudinal Study of Parents and Children Executive Committee (http://www.bristol.ac.uk/alspac/researchers/access) as outlined in the study’s access policy http://www.bristol.ac.uk/media-library/sites/alspac/documents/researchers/data-access/ALSPAC_Access_Policy.pdf. BAMSE data are available from the GABRIEL consortium as well as on request in EGA, under accession no. EGAC00001000786. BASICMAR DNAm data are available under accession no. GSE69138. Born-in-Bradford data are available to researchers who submit an expression of interest to the Born-in-Bradford Executive Group (https://borninbradford.nhs.uk/research). BSGS DNAm data are available under accession no. GSE56105. GOYA data are available by request from DNBC: https://www.dnbc.dk. Dunedin data are available via a managed access system (contact: email@example.com). E-Risk DNAm data are available under accession no. GSE105018. Estonian biobank (ECGUT) data can be accessed on ethical approval by submitting a data release request to the Estonian Genome Center, University of Tartu (http://www.geenivaramu.ee/en/access-biopank/data-access). EPIC-Norfolk data can be accessed by contacting the study management committee: http://www.srl.cam.ac.uk/epic/contact. Requests for EPICOR data accession may be sent to Professor Giuseppe Matullo (firstname.lastname@example.org). FTC data can be accessed on approval from the Data Access Committee of the Institute for Molecular Medicine Finland FIMM (email@example.com). Requests for Generation R data access are evaluated by the Generation R Management Team. Researchers can obtain a de-identified GLAKU dataset after having obtained an approval from the GLAKU Study Board. GSK DNAm data are available under accession no. GSE125105. INMA data are available by request from the INfancia y Medio Ambiente Executive Committee for researchers who meet the criteria for access to confidential data. IOW F2 data are available by request from Isle of Wight Third Generation Study. Please contact Mr Stephen Potter (firstname.lastname@example.org). LLS DNAm data were submitted to the EGA under accession no. EGAS00001001077. LBC1921 and LBC1936 data are available on request from the Lothian Birth Cohort Study, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh (I.Deary@ed.ac.uk). DNAm from MARTHA participants are available under accession no. E-MTAB-3127. NTR DNAm data are available on request in EGA, under the accession no. EGAD00010000887. PIAMA data are available on request. Requests can be submitted to the PIAMA Principal Investigators (https://piama.iras.uu.nl/english). PRECISESADS data are available through ELIXIR at https://doi.org/10.17881/th9v-xt85. Collaboration in data analysis of PREDO is possible through specific research proposals sent to the PREDO Study Board (email@example.com) or primary investigators Katri Räikkönen (firstname.lastname@example.org) or Hannele Laivuori (email@example.com). Data are available on request at Project MinE (https://www.projectmine.com). Raine data are available on request (https://ross.rainestudy.org.au). Requests for the data accession of the Rotterdam Study may be sent to Frank van Rooij (firstname.lastname@example.org). SABRE data are available by request from SABRE (https://www.sabrestudy.org). SCZ1 DNAm data are available under accession no. GSE80417. SCZ2 DNAm data are available under accession no. GSE84727. SYS data are available on request addressed to Dr. Zdenka Pausova (email@example.com) and Dr. Tomas Paus (firstname.lastname@example.org). Further details about the protocol can be found at http://www.saguenay-youth-study.org. TwinsUK DNAm data are available in the GEO under accession nos. GSE62992 and GSE121633. TwinsUK adipose DNAm data are stored in EGA under the accession no. E-MTAB-1866. Access to additional individual-level genotype and phenotype data can be applied for through the TwinsUK data access committee: http://twinsuk.ac.uk/resources-for-researchers/access-our-data. Individual-level DNAm and genetic data from the UK Household Longitudinal Study are available on application through the EGA under accession no. EGAS00001001232. Nonidentifiable Generation Scotland data will be made available to researchers through the GS:SFHS Access Committee. MESA DNAm data are available under accession nos. GSE56046 and GSE56581. Tissue DNAm data are available from accession no. GSE78743. Brain DNAm data can be found under accession no. GSE58885.
Cohort descriptions and further contact details can be found in the Supplementary Note.
For the enrichments, we used chromatin states from the Epigenome Roadmap (https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/imputed12marks/jointModel/final), TFBSs from the ENCODE project (http://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeAwgTfbsUniform) downloaded from the LOLA core database (http://databio.org/regiondb), and gene annotations from https://zwdzwd.github.io/InfiniumAnnotation or GARFIELD (https://www.ebi.ac.uk/birney-srv/GARFIELD). To extract GWA signals for co-localization, we used the MRBase database (https://www.mrbase.org).
Datasets were processed using https://github.com/perishky/meffil unless stated otherwise. Individual study analysts used a github pipeline https://github.com/MRCIEU/godmc to conduct the mQTL analysis. We used https://github.com/MRCIEU/godmc_phase1_analysis for the phase 1 analysis, https://github.com/explodecomputer/random-metal for the meta-analyses and https://github.com/MRCIEU/godmc_phase2_analysis for the follow-up analyses.
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C.L.R., G.D.S., G.S., J.L.M., K.B., M. Suderman, T.G.R. and T.R.G. are supported by the UK Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/1, MC_UU_00011/4, MC_UU_00011/5). C.L.R. receives support from a Cancer Research UK Programme grant (no. C18281/A191169). G.H. is funded by the Wellcome Trust and the Royal Society (208806/Z/17/Z). E.H. and J.M. were supported by MRC project grants (nos. MR/K013807/1 and MR/R005176/1 to J.M.) and an MRC Clinical Infrastructure award (no. MR/M008924/1 to J.M.). B.T.H. is supported by the Netherlands CardioVascular Research Initiative (the Dutch Heart Foundation, Dutch Federation of University Medical Centres, the Netherlands Organisation for Health Research and Development, and the Royal Netherlands Academy of Sciences) for the GENIUS project ‘Generating the best evidence-based pharmaceutical targets for atherosclerosis’ (CVON2011-19, CVON2017-20). J.T.B. was supported by the Economic and Social Research Council (grant no. ES/N000404/1). The present study was also supported by JPI HDHL-funded DIMENSION project (administered by the BBSRC UK, grant no. BB/S020845/1 to J.T.B., and by ZonMW the Netherlands, grant no. 529051021 to B.T.H). A.D.B. has been supported by a Wellcome Trust PhD Training Fellowship for Clinicians and the Edinburgh Clinical Academic Track programme (204979/Z/16/Z). J. Klughammer was supported by a DOC fellowship of the Austrian Academy of Sciences. Cohort-specific acknowledgements and funding are presented in the Supplementary Note.
T.R.G. receives funding from GlaxoSmithKline and Biogen for unrelated research. The other authors declare no competing interests.
Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer review reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We used 337 independent SNPs on chromosome 20 with a p-value<1e-14. The number of SNPs used for each study are indicated in the bottom plot. a, Mstatistic (Magosi et al., PLoS Genet., 13, e1006755 (2017)) for each of the 36 cohorts. b, Boxplot of mQTL effect sizes for each of the 36 studies. The center line of a boxplot corresponds to the median value. The lower and upper box limits indicate the first and third quartiles (the 25th and 75th percentiles). The length of the whiskers corresponds to values up to 1.5 times the IQR in either direction.
a, Density plot of the distance of SNP from DNAm site against the -log10 p-value of 4,533 intrachromosomal trans-mQTL associations (>1Mb). b, Density plot of the distance of SNP from DNAm site against the -log10 p-value of 248,607 cis-mQTL associations (<1Mb).
a, For each DNAm site, the strongest absolute effect size (the maximum absolute additive change in DNAm level measured in SD per allele) was selected. The kernel density estimations of the effect sizes were shown for all sites with a mQTL (n=190,102), sites with cis only effects (n=170,986), cis effects for sites with cis and trans effects (n=11,902), trans effects for sites with cis and trans effects (n=11,902) and sites with trans only effects (n=7,214). Comparing the strongest effect size for each site in a two-sided linear regression model showed that cis+trans sites had larger cis effect sizes (per allele SD change = 0.05 (s.e.= 0.002), p<2e-16) as compared to cis only sites and weaker trans effect sizes (per allele SD change = −0.06 (s.e.= 0.002), p<2e-16) as compared to trans only sites. To detect these small trans effect sizes at sites with both a cis and a trans association, it is crucial to regress out the cis effect to decrease the residual variance and improve power to detect a trans effect. b, The violin plots represent kernel density estimates of the weighted SD across 36 cohorts for each DNAm site. The center line of the boxplot in the violin plots corresponds to the median value. The lower and upper box limits indicate the first and third quartiles (the 25th and 75th percentiles). The length of the whiskers corresponds to values up to 1.5 times the IQR in either direction.
a, Loss in power in twostage design. We calculated the power of detecting a cis association in at least one of the 22 studies at p<1e-5 or a trans association in at least two of 22 studies at p<1e-5. b, Expected number of mQTLs. Using the number of mQTLs with a particular r2 value, and the power of detecting mQTLs with that r2 value, we calculated how many mQTLs would expect to exist with that value.
For each mQTL category, the correlation of genetic effects between tissues (rb) were estimated using the rb method25 where we used the blood mQTLs as reference. DNAm levels are categorized as low (<0.2), intermediate (0.2–0.8) or high (>0.8).
a, To test if the annotations of the SNPs involved in trans-mQTLs were specific to the annotations of the DNAm sites that they influence, we compared the real SNP-DNAm site pairs against permuted SNP-DNAm site pairs, where the biological link between SNP and site is severed whilst maintaining the distribution of annotations for the SNPs and sites. We constructed 100 such permuted datasets b, SNP and site positions were annotated against genomic features, and we quantified how frequently mQTLs were found for each pair of SNP-DNAm site annotations. This enabled the construction of 2D-annotation matrices for both the real trans-mQTL list and the permuted trans-mQTL lists. c, Distribution of two-dimensional enrichment values of trans-mQTLs. There was substantial departure from the null in the real dataset for all tissues indicating that the TFBS of a site depended on the TFBS of the SNP that influenced it. d, A bipartite graph of the two-dimensional enrichment for trans-mQTLs, SNPs annotations (blue) with pemp< 0.01 after multiple testing correction co-occur with particular site annotations (red).
a, To evaluate if a site having a shared causal variant with a trait was potentially due to the site being on the causal pathway to the trait, we reasoned that independent instruments for the site should exhibit consistent effects on the outcome consistent with the original co-localizing variant. b, Amongst the putative co-localizing signals, 440 involved a DNAm site that had at least one other independent mQTL. The plot shows the causal effect estimate estimated from the original co-localizing signal against the causal effect estimates obtained from the independent variants (n=440). Grey regions represent the 95% confidence of the slope. c, Correspondence of MR estimates amongst multiple independent instruments on 36 blood traits. To evaluate if a site having a shared causal variant with a blood trait was potentially due to the site being on the causal pathway to the trait, we reasoned that independent instruments for the site should exhibit consistent effects on the outcome consistent with the original co-localizing variant. Amongst the putative co-localizing signals, 30% involved a DNAm site that had at least one other independent mQTL. The plot shows the causal effect estimate estimated from the original co-localizing signal against the causal effect estimates obtained from the independent variants. The HLA region has been removed and betas are plotted.
Extended Data Fig. 8 Genomic inflation factors for genome-wide scans of causal effects of traits on DNAm sites.
Each trait (x axis) was tested for causal effects against (on average) 317,659 DNAm sites, excluding sites in the MHC region. The p-values from IVW MR analysis were used to estimate the genomic inflation for each trait (y-axis). Traits are ordered by genomic inflation factor.
Supplementary Methods and Results, Acknowledgements, Supplementary Figs. 1–40, Supplementary References.
Supplementary Tables 1–20.
Discovery and replication of 169,656 mQTL associations in GoDMC (n = 27,750) and Generation Scotland (n = 5,101).
The relationship between the variance in DNA methylation explained by mQTL effects in GoDMC, and the estimated contribution of additive genetic effects.
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Min, J.L., Hemani, G., Hannon, E. et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 53, 1311–1321 (2021). https://doi.org/10.1038/s41588-021-00923-x
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