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Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits

Nature Genetics (2024)Cite this article

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Abstract

Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.

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Fig. 1: Identification and validation of mQTLs in East Asians.
Fig. 2: The characteristics and potential applications of EA-specific mQTLs.
Fig. 3: Cell-type- and cell-lineage-specific mQTLs and validation.
Fig. 4: Mechanistic interpretation of cis- and lcis-mQTLs.
Fig. 5: mQTL hotspots mediated by TFs and super enhancers.
Fig. 6: A FOSL2-mediated mQTL hotspot influences eosinophil count.
Fig. 7: A NFKB1 trans-mQTL hotspot associated with BMI.

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Data availability

Our mQTL database is available for download at https://www.biosino.org/panmqtl/, which incorporates mQTLs not only in EA (NSPT) but also in published European and South Asian data. The database also supports searching and visualization of genomic, functional and downstream disease/trait hits of mQTLs and mCpGs. The statistics of mQTLs in NSPT and CGZ cohort are available for download at NODE https://www.biosino.org/node under accession number OEP002902, or directly accessed at https://www.biosino.org/node/project/detail/OEP002902. The statistics of mQTLs replicated in CAS is available for download at OMIX https://ngdc.cncb.ac.cn/omix under accession number OMIX004116, or directly accessed at https://ngdc.cncb.ac.cn/omix/release/OMIX004116. The individual-level genotype data is not available because of IRB restrictions due to privacy concerns. The individual-level DNAm data can be requested at https://ngdc.cncb.ac.cn/omix/release/OMIX004363 (NSPT), https://ngdc.cncb.ac.cn/omix/release/OMIX004333 (CAS) and https://www.biosino.org/node/project/detail/OEP002902 (CGZ). Requests are normally processed within 1–3 months. Data usage shall be in full compliance with the Regulations on Management of Human Genetic Resources in China. The DNAm dataset in buccal cells is available by submitting data requests to mrclha.enquiries@ucl.ac.uk; see the full policy at http://www.nshd.mrc.ac.uk/data.aspx. Managed access is in place for this 69-year-old NSHD study to ensure that the use of the data is within the bounds of consent given previously by participants, and to safeguard any potential threat to anonymity because the participants are all born in the same week. The mQTL results of the EUR cohort (GoDMC) were downloaded from http://mqtldb.godmc.org.uk/downloads. The mQTL results of the EUR cohort (FHS) were downloaded from https://ftp.ncbi.nlm.nih.gov/eqtl/original_submissions/FHS_meQTLs/ (date: September 14, 2020). The annotation of CpG probes was downloaded from https://zwdzwd.github.io/InfiniumAnnotation (date: November 25, 2019). Significant GWAS results were downloaded from GWAS Catalog (https://www.ebi.ac.uk/gwas/docs/file-downloads, date: December 25, 2020) and significant EWAS results were downloaded from EWAS Atlas (https://ngdc.cncb.ac.cn/ewas/downloads, date: December 25, 2020). The cis-eQTL results in whole blood were downloaded from GTEx V8 database (https://www.gtexportal.org/home/datasets; date: June 17, 2020) and HGVD (http://www.genome.med.kyoto-u.ac.jp/SnpDB/). The human gene information (Ensembl release v104) was downloaded from GENCODE (https://www.gencodegenes.org/human/release_37lift37.html; date: April 26, 2021), the list of human TFs was from http://humantfs.ccbr.utoronto.ca/download.php (date: April 3, 2020), and the list of House-Keeping genes was downloaded from https://www.tau.ac.il/~elieis/HKG/. Motifs information of TFs was obtained from JASPAR 2020 database (http://jaspar.genereg.net/; date: July 2, 2021) and JASPAR 2022 (date: August 22, 2022). ChIP–seq signals of TFs were downloaded from the ChIP-Atlas database (http://chip-atlas.org/; date: June 2, 2021). Other data sources used in this study include BLUEPRINT mQTLs summary statistics (https://ega-archive.org/datasets/EGAD00001005200); Phenoscanner GWAS summary statistics (http://www.phenoscanner.medschl.cam.ac.uk/); Functional genomic regions from the Functional Annotation of Animal Genomes (FAANG) Project (https://www.faang.org); PCHi-C data (https://osf.io/u8tzp); H3K27ac HiChIP data (https://www.ncbi.nlm.nih.gov/geo/, GSE101498); The DNase-seq data for B cells and T cells and the H3K27ac ChIP–seq data of neutrophil cells (https://www.encodeproject.org); GO terms, KEGG pathways, and Reactome pathways were downloaded from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp); and FANTOM5 (https://fantom.gsc.riken.jp/data/). Experimental Factor Ontology (EFO) (https://www.ebi.ac.uk/ols/ontologies/efo). GWASs in BBJ (https://pheweb.jp/); GWASs in UKBB (https://pan.ukbb.broadinstitute.org/); super enhancer databases (http://www.licpathway.net/sedb/; http://www.asntech.org/dbsuper/; http://www.licpathway.net/SEanalysis/); segmented functional regions from GM12878 cell line (http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeAwgSegmentation); 15 chromatin states (https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointModel/final/).

Code availability

Code for the analysis is available at GitHub (https://github.com/Fun-Gene/fastQTLmapping) and Zenodo (https://doi.org/10.5281/zenodo.8084877)75. Most operations are carried out by R (https://cran.r-project.org/), and the plots are mainly made by ggplot2 v3.4.2 R package (https://cran.r-project.org/web/packages/ggplot2/index.html). mQTL mapping is performed by fastQTLmapping (https://github.com/Fun-Gene/fastQTLmapping) and R package MatrixEQTL v2.3 (https://cran.r-project.org/web/packages/MatrixEQTL/index.html). Heritability is estimated by GCTA (https://yanglab.westlake.edu.cn/software/gcta/). MKL is available at https://software.intel.com/tools/onemkl. GSL is available at http://www.gnu.org/software/gsl/. Annotation of SNP is based on ANNOVAR (https://annovar.openbioinformatics.org/en/latest/, date: 2020.11.2) and annotation of CpG is based on the manufacturer’s manifest files (date: 2020.10.21). Genotype calling is based on GenomeStudio (https://support.illumina.com/array/array_software/genomestudio/downloads.html). Imputation of SNP chip is based on SHAPEIT2 (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html) and IMPUTE2 (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html). Enrichment analysis of mQTLs is performed by R package clusterProfiler v4.8.1 (https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html). DNAm processing is based on R package minfi Bioconductor package v1.46.0 (https://bioconductor.org/packages/release/bioc/html/minfi.html) and CHAMP Bioconductor package v2.30.0 (https://bioconductor.org/packages/release/bioc/html/ChAMP.html). Cell-type mQTLs are estimated by CellDMC, which is available as part of the EpiDISH v2.8 Bioconductor R package (http://bioconductor.org/packages/devel/EpiDISH. eFORGE is run with the web server at eFORGE2.0 (https://eforge.altiusinstitute.org/). Sharing Effect of cell-type mQTLs is estimated by R package mashr (https://cran.r-project.org/web/packages/mashr/index.html). The GO and KEGG pathway enrichment analyses of mCpGs are conducted using R package missMethyl v1.34.0 (https://bioconductor.org/packages/3.13/bioc/html/missMethyl.html). Genes enrichment for diseases/traits analysis is performed by the R package disgenet2r v0.99.3 (https://www.disgenet.org/disgenet2r) based on the DisGeNET knowledgebase (date: 2021.6.9). The two-sample MR analysis is conducted using the R package TwoSampleMR v0.4.26 (https://mrcieu.github.io/TwoSampleMR/). The HiChIP loops are processed by HiCCUPS and implemented in the Juicer Tools (v0.7.5) with default parameter settings. The influence of SNPs on REs is calculated using the tool OpenCausal (https://github.com/liwenran/OpenCausal). Colocalization is performed by SMR v1.3.1 (https://yanglab.westlake.edu.cn/software/smr/#Download). Enrichment of mQTL CpGs for TF motifs is performed by TFmotifView (http://bardet.u-strasbg.fr/tfmotifview/) and R package PWMEnrich v4.30.0 (https://bioconductor.org/packages/release/bioc/html/PWMEnrich.html). Phenome-wide association analysis is carried out by PheWAS (https://gwas.mrcieu.ac.uk/phewas).

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Acknowledgements

This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (grant XDB38000000 to S.W., F.L. and P.J.), the National Natural Science Foundation of China (NSFC; 92249302 to S.W. and T.N., 32325013 to S.W., 32370699 and 32170652 to A.E.T., 81930056 to F.L., 32170657 to Y.Z. and L.S., 32200472 to W.L.), CAS Young Team Program for Stable Support of Basic Research (YSBR-077 to S.W.), CAS Interdisciplinary Innovation Team to S.W., CAS Youth Innovation Promotion Association (2020276 to Q.P.), Shanghai Science and Technology Commission Excellent Academic Leaders Program (22XD1424700 to S.W.), the Strategic Priority Research Program of Chinese Academy of Sciences (grant XDC01000000 to F.L.), the National Key Research and Development Project (2018YFC0910403 to S.W. and 2018YFE0201603 to Y.Z. and L.S.), Ministry of Science and Technology of the People’s Republic of China (2015FY111700 to L.J.), Science and Technology Commission of Shanghai Municipality Major Project (2017SHZDZX01 to L.J., S.W., F.L., Y.Z. and L.S.), 111 Project (B13016 to L.J.), CAMS Innovation Fund for Medical Science (2019-I2M-5-066 to L.J. and J.W.), Shanghai Science and Technology Commission Excellent Academic Leaders Program (22XD1424700 to S.W.), Science and Technology Service Network Initiative of Chinese Academy of Sciences (KFJ-STS-QYZD-2021-08-001 and KFJ-STS-ZDTP-079 to F.L.), Naif Arab University for Security Sciences (NAUSS-23-R18 and NAUSS-23-R19 to F.L.), CAS Young Team Program for Stable Support of Basic Research (YSBR-077 to S.W.), CAS Interdisciplinary Innovation Team to S.W., CAS Youth Innovation Promotion Association (2020276 to Q.P.), China Postdoctoral Science Foundation (2021M693274 and BX2021336 to W.L.). We are grateful to S. Beck from University College London, W. H. Wong from Stanford University and C. Wang from Huazhong University of Science and Technology for helpful discussion, C. Relton and J. Min from the University of Bristol for sharing information about SNPs, CpGs and mQTLs in GoDMC, X. Chen from Taizhou Institute of Health Sciences of Fudan University, Y. Fan from Human Phenome Institute of Fudan University and Y. Hu from CAS for providing materials and samples in this study, and X. Cai and Q. Qian from the University of Chinese Academy of Sciences for helping in data preparation.

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  1. These authors contributed equally: Qianqian Peng, Xinxuan Liu, Wenran Li, Han Jing.

Authors and Affiliations

  1. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Qianqian Peng, Wenran Li, Han Jing, Jiarui Li, Qi Luo, Jiaqiang Qian, Liyun Yuan, Chenglong You, Siyuan Du, Guoqing Zhang, Andrew E. Teschendorff & Sijia Wang

  2. CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China

    Xinxuan Liu, Xingjian Gao, Siyu Pan, Qiwen Zheng, Guochao Li, Na Yuan, Peilin Jia, Changqing Zeng & Fan Liu

  3. School of Future Technology, University of Chinese Academy of Sciences, Beijing, China

    Xinxuan Liu & Fan Liu

  4. Altius Institute for Biomedical Sciences, Seattle, WA, USA

    Charles E. Breeze

  5. State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, and Human Phenome Institute, Fudan University, Shanghai, China

    Yuanting Zheng, Jiucun Wang, Leming Shi, Bo Wen & Li Jin

  6. Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China

    Yuanting Zheng, Jingze Tan & Leming Shi

  7. Taizhou Institute of Health Sciences, Fudan University, Taizhou, China

    Ziyu Yuan, Jiucun Wang, Guoqing Zhang, Leming Shi, Li Jin & Sijia Wang

  8. Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China

    Jiucun Wang & Li Jin

  9. Shenzhen Chipscreen Biosciences Co. Ltd., Shenzhen, China

    Xianping Lu

  10. Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, USA

    Shicheng Guo

  11. Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA

    Shicheng Guo

  12. MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, China

    Yun Liu

  13. State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, China

    Ting Ni

  14. The Fifth People’s Hospital of Shanghai and Institutes of Biomedical Sciences, Fudan University, Shanghai, China

    Bo Wen

  15. Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia

    Fan Liu

  16. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Sijia Wang

Authors
  1. Qianqian Peng
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  2. Xinxuan Liu
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  3. Wenran Li
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  4. Han Jing
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  5. Jiarui Li
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  6. Xingjian Gao
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  7. Qi Luo
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  8. Charles E. Breeze
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  9. Siyu Pan
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  10. Qiwen Zheng
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  11. Guochao Li
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  12. Jiaqiang Qian
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  13. Liyun Yuan
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  14. Na Yuan
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  15. Chenglong You
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  16. Siyuan Du
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  17. Yuanting Zheng
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  18. Ziyu Yuan
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  19. Jingze Tan
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  20. Peilin Jia
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  21. Jiucun Wang
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  22. Guoqing Zhang
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  23. Xianping Lu
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  24. Leming Shi
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  25. Shicheng Guo
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  26. Yun Liu
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  27. Ting Ni
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  28. Bo Wen
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  29. Changqing Zeng
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  30. Li Jin
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  31. Andrew E. Teschendorff
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  32. Fan Liu
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  33. Sijia Wang
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Contributions

S.W., F.L. and A.E.T. designed and drafted the work. Q.P., X.L., W.L., H.J. and A.E.T. performed the statistical analyses and contributed to data interpretation and writing of the paper. J.L., Q.L., C.E.B., G.L. and S.P. contributed to data analysis. X.G. contributed to the program of QTL mapping (fastQTLmapping). J.L., N.Y., J.Q., L.Y. and G.Z. generated the mQTL database. C.Y. and S.D. contributed to preprocessing of methylation and SNP chip data in the discovery cohort. L.J., J.W., J.T. and Z.Y. contributed to the design and acquisition of data in the discovery cohort NSPT. Q.Z., P.J. and C.Z. contributed to the design and acquisition of data in the validation panel CAS. Y.Z., X.L. and L.S. contributed to the design and acquisition of data in the validation panel CGZ. S.G., Y.L., T.N. and B.W. contributed to the design of this work. All the authors revised this work, approved the submitted version, agreed with personal contributions and are responsible for the integrity of the data and the accuracy of the data analysis.

Corresponding authors

Correspondence to Andrew E. Teschendorff, Fan Liu or Sijia Wang.

Ethics declarations

Competing interests

X. Lu is an employee of Shenzhen Chipscreen Biosciences. The other authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Carmen Marsit, Matthew Sudermann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 mQTLs enrichment for different functional elements.

a,b, Enrichment of mQTLs (a) and mCpGs (b) in six functional elements: CTCF-enriched elements (CTCF), enhancers (E), promoters (P), promoter flanking regions (PF), regulatory elements (RE), and TF binding sites (TFBS). The y-axis indicates the fold changes (see Methods) and the significance from the one-tailed hypergeometric test is denoted by different symbols on each bar, that is, *, P < 0.05; **, P < 0.01; ***, P < 0.001. c, Enrichment of cis-mQTL pairs (left), lcis-mQTL pairs (middle), and trans-mQTL pairs (right) in all combinations of the six functional categories (that is, CTCF-E, P-E, RE-E, and etc). A one-tailed hypergeometric test is applied. The fold changes are labeled within each box. d, Proportion of SNP-CpG pairs of mQTL within the same TAD. e, Comparison of distance distributions of mQTLs and that of 3D loops.

Extended Data Fig. 2 The cis-colocalization at chr13q14.11 provides epigenetic evidence for the East Asian-specific height-association (rs7335629-height).

a, The East Asian-specific height signal (rs7335629) is in high-linkage with three SNPs in the colocalization locus at chr13q14.11. b, rs7335629 has potential chromatin interaction with one of the CpGs (cg21067652) that colocalized at chr13q14.11. c, Both rs7335629 and cg21067652 are located in regions of high DNase in several blood cell lines. d, Two-sample MR result indicates that cg21067652 is a causal factor for ELF1 RNA expression in CAGE (N = 2,765). Two-tailed MR egger test is applied. The dot and error bar indicate the beta value and s.e., which is SNP effect on CpG (x-axis) and ELF1 expression (y-axis). The blue dotted line indicates the regression line from MR egger test with beta = -9.19, P = 1.34◊10-4. e, Two-sample MR result indicates that cg21067652 is a causal factor for height in BBJ (N = 165,056). Two-tailed MR egger test is applied. The dot and error bar indicate the beta value and s.e., which is the SNP effect on CpG (x-axis) and body height (y-axis). The blue dotted line indicates the regression line from the MR egger test with beta = -0.31, P = 3.73◊10-9.

Extended Data Fig. 3 The enrichment of cis- and trans-colocalizations in EA-specific colocalizations and functional states.

a, Enrichment of trans- vs cis-mQTLs amongst EA-specific colocalizations vs others in NSPT (left), and amongst EA-specific vs EAS-EUR shared colocalizations (right). Trans-, cis- colocalizations in East Asian is carried out based on mQTLs in NSPT (N = 3,523) and 107 GWASs in BBJ (N = ~170,000). Trans-, cis-colocalization in European is carried out based on mQTLs in GoDMC (N = 27,750) and 107 GWAS traits in UKBB (N = ~500,000) which are overlapped with traits in BBJ. b, Enrichment results of cis- vs trans-colocalization loci in functional elements. Left: enrichment of cis- and trans-colocalization loci in functional elements; Middle, enrichment of East Asian-specific and EAS-EUR shared cis-colocalization loci in functional elements; Right, enrichment of East Asian-specific and EAS-EUR shared trans-colocalization loci in functional elements. c, Enrichment of cis- and trans-colocalization loci in chromatin states. Left, enrichment of cis- and trans-colocalization loci in chromatin states; Middle, enrichment of East Asian-specific and EAS-EUR shared cis-colocalization signals in chromatin states; Right, enrichment of East Asian-specific and EAS-EUR shared trans-colocalization signals in chromatin states. Two-tailed Fisher’s exact test is applied. Each point with an error bar indicates log10-scaled odds ratio and its 95% confidence interval.

Extended Data Fig. 4 The relation between the trans-colocalization at chr21q22.2 and blood cell traits and immune diseases.

a, The geographic distribution of rs80109907 allele frequencies in different populations (1000 Genomes Phase 3) by the Geography of Genetic Variants (GGV) browser (https://popgen.uchicago.edu/ggv). b, The PheWAS result of rs80107709 (https://gwas.mrcieu.ac.uk/phewas). c, The colocalization result of chr21q22.2 with other blood cell count and immune-related diseases. SMR test is applied, and the x-axis indicates the beta estimates from original GWAS while the y-axis shows the -log10(P) of the SMR test. d, Two-sample MR results showing that 39 CpGs are causal for 7 traits (several blood cell count and immune-related diseases) at FDR < 0.05. MR IVW test is applied. Red and blue squares indicate positive or negative causal effect of CpG on trait, while the size of the square indicates -log10(P) of the MR IVW test.

Supplementary information

Supplementary Information

Supplementary Protocols, References and Figs. 1–16.

Reporting Summary

Peer Review File

Supplementary Tables 1–23

Supplementary Table 1: The extent of 3.46 million GoDMC mQTLs being also mQTLs in NSPT for different MAF bins. Supplementary Table 2: The extent of 2.65 million NSPT mQTLs being also mQTLs in GoDMC for different MAF bins. Supplementary Table 3: NSPT-only mQTLs (NSPT P < 10−14) defined with different significance thresholds in GoDMC. Supplementary Table 4: The overlapping of population-specific and nonspecific mQTLs with significant SNPs and associations in GWAS Catalog, UKBB and BBJ. Supplementary Table 5: EA-specific mQTLs met with associations and signals in three groups (BBJ-specific, UKBB-specific and shared). Supplementary Table 6: Colocalization results of EA-specific mQTLs and GWAS signals of 230 traits in BBJ. Supplementary Table 7: A total of 144 locus–trait associations (96 loci and 38 traits) identified by cis-colocalization, especially in EAs based on mQTLs in NSPT and traits in BBJ. Supplementary Table 8: A total of 541 locus–trait associations (36 loci and 15 traits) identified by trans-colocalization, especially in EAs based on mQTLs in NSPT and traits in BBJ. Supplementary Table 9: PheWAS result of rs80109907 from public database (https://gwas.mrcieu.ac.uk/phewas/). Supplementary Table 10: Weaker (compared to basophil count) but significant East Asian-specific trans-colocalizations at the locus on chr21q22.2 involving several blood cell counts and immune-related diseases. Supplementary Table 11: A total of 233 CpGs significantly enriched for motifs of 62 TFs (P < 5.3 × 10−5). Supplementary Table 12: Enrichment of 233 CpGs in the binding sites of 13 TFs was validated by blood cell ChIP–seq data. Supplementary Table 13: Cell-lineage-specific mQTLs calculated for random unrelated SNP–CpG pairs. Supplementary Table 14: mQTL hotspots in NSPT. Supplementary Table 15: The 12 (of 16) hotspots index mQTLs or their high LD (r2 > 0.6) trans-mQTLs in GWAS Catalog (P < 5 × 10−8). Supplementary Table 16: mQTL hotspots on each chromosome in NSPT. Supplementary Table 17: Annotation of 16 trans-mQTL hotspots. Supplementary Table 18: Overlap of nearby TF/DBPs related cis-eQTL and trans-mQTLs in each trans-mQTL hotspot. Supplementary Table 19: Overlap of nearby TF/DBPs related cis-eQTL in GTEX V7 (P < 0.05) and trans-mQTLs in each trans-mQTL hotspot. Supplementary Table 20: Enrichment of SNPs with significant OpenCausal scores in trans-mQTL hotspots. Supplementary Table 21: A total of 21 putative causal mCpGs (rs4666078trans-associated) of blood eosinophil count identified by two-sample MR analysis. Supplementary Table 22: Two-sample MR analysis statistics summary of NFKB1 CpGs. Supplementary Table 23: Two-sample MR analysis statistics summary of BMI EWAS CpGs.

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Peng, Q., Liu, X., Li, W. et al. Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. Nat Genet (2024). https://doi.org/10.1038/s41588-023-01494-9

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