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Methylation quantitative trait loci analysis in Korean exposome study

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Abstract

Background

Environmental exposure and genotype variation influence DNA methylation. Studies on the effects of genotype variation were performed mainly on European ancestries. We analyzed the genetic effects on cord blood methylation of Koreans.

Methods

As part of the Korean Exposome study project, DNA was extracted from 192 cord blood samples for analysis. Cord blood samples were genotyped via Asian Precision Medicine Research Array analysis and methylation was measured using the Methylation EPIC Beadchip kits. The associations between genotypes and CpG methylation were analyzed with matrix eQTL.

Results

Conditional analysis revealed 34,425 methylation quantitative trait loci (mQTLs), and trans-mQTLs constituted 7.2% of all the associated CpG sites. About 80% of the total trans-associations were trans-chromosomal and the related SNPs were concentrated on chromosome 19. According to the results of DAVID, cis-mQTL-related SNPs resulting in amino acid substitutions were related to signal peptides or glycosylation.

Conclusion

We identified genotype variations associated with DNA methylation in the cord blood obtained from Koreans.

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References

  • Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363–1369

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, McKenney SL, Witter F, Walston J, Feinberg AP, Fallin MD (2016) DNA methylation of cord blood cell types: applications for mixed cell birth studies. Epigenetics 11:354–362

    PubMed  PubMed Central  Google Scholar 

  • Banovich NE, Lan X, McVicker G, van de Geijn B, Degner JF, Blischak JD, Roux J, Pritchard JK, Gilad Y (2014) Methylation qtls are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet 10:e1004663

    PubMed  PubMed Central  Google Scholar 

  • Bell JT, Pai AA, Pickrell JK, Gaffney DJ, Pique-Regi R, Degner JF, Gilad Y, Pritchard JK (2011) DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol 12:R10

    CAS  PubMed  PubMed Central  Google Scholar 

  • Boks MP, Derks EM, Weisenberger DJ, Strengman E, Janson E, Sommer IE, Kahn RS, Ophoff RA (2009) The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS ONE 4:e6767

    PubMed  PubMed Central  Google Scholar 

  • Carlson CS, Matise TC, North KE, Haiman CA, Fesinmeyer MD, Buyske S, Schumacher FR, Peters U, Franceschini N, Ritchie MD et al (2013) Generalization and dilution of association results from European GWAS in populations of Non-European ancestry: the PAGE study. PLoS Biol 11:e1001661

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7

    PubMed  PubMed Central  Google Scholar 

  • Chen W, Liu J, Yuan D, Zuo Y, Liu Z, Liu S, Zhu Q, Qiu G, Huang S, Giampietro PF (2016) Progress and perspective of TBX6 gene in congenital vertebral malformations. Oncotarget 7:57430

    PubMed  PubMed Central  Google Scholar 

  • Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57

    Google Scholar 

  • Consortium TMs (1999) Complete sequence and gene map of a human major histocompatibility complex. Nature 401:921

    Google Scholar 

  • Delaneau O, Marchini J, Zagury J-F (2012) A linear complexity phasing method for thousands of genomes. Nat Methods 9:179

    CAS  Google Scholar 

  • den Dekker HT, Burrows K, Felix JF, Salas LA, Nedeljkovic I, Yao J, Rifas-Shiman SL, Ruiz-Arenas C, Amin N, Bustamante M et al (2019) Newborn DNA-methylation, childhood lung function, and the risks of asthma and COPD across the life course. Eur Respir J 53:1801795

    Google Scholar 

  • Feinberg AP (2018) The key role of epigenetics in human disease prevention and mitigation. New Engl J Med 378:1323–1334

    CAS  PubMed  Google Scholar 

  • Ferguson-Smith AC (2011) Genomic imprinting: the emergence of an epigenetic paradigm. Nat Rev Genet 12:565

    CAS  PubMed  Google Scholar 

  • Gaunt TR, Shihab HA, Hemani G, Min JL, Woodward G, Lyttleton O, Zheng J, Duggirala A, McArdle WL, Ho K et al (2016) Systematic identification of genetic influences on methylation across the human life course. Genome Biol 17:61

    PubMed  PubMed Central  Google Scholar 

  • Gordon L, Joo JE, Powell JE, Ollikainen M, Novakovic B, Li X, Andronikos R, Cruickshank MN, Conneely KN, Smith AK et al (2012) Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence. Genome Res 22:1395–1406

    CAS  PubMed  PubMed Central  Google Scholar 

  • Gu Z, Gu L, Eils R, Schlesner M, Brors B (2014) circlize implements and enhances circular visualization in R. Bioinformatics 30:2811–2812

    CAS  PubMed  Google Scholar 

  • Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, Troakes C, Turecki G, O’Donovan MC, Schalkwyk LC et al (2015) Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci 19:48

    PubMed  PubMed Central  Google Scholar 

  • Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13:86

    PubMed  PubMed Central  Google Scholar 

  • Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5:e1000529

    PubMed  PubMed Central  Google Scholar 

  • Howie B, Marchini J, Stephens M (2011) Genotype imputation with thousands of genomes. G3 Genes Genomes Genet 1:457–470

    Google Scholar 

  • Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44:955

    CAS  PubMed  PubMed Central  Google Scholar 

  • Huang DW, Sherman BT, Lempicki RA (2008) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13

    PubMed Central  Google Scholar 

  • Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols 4:44

    CAS  Google Scholar 

  • Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127

    PubMed  Google Scholar 

  • Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC, Breton C, Reese SE, Markunas CA, Richmond RC, Xu C-J et al (2016) DNA methylation in newborns and maternal smoking in pregnancy: genome-wide Consortium Meta-analysis. Am J Hum Genet 98:680–696

    CAS  PubMed  PubMed Central  Google Scholar 

  • Koh EJ, Hwang SY (2019) Multi-omics approaches for understanding environmental exposure and human health. Mol Cell Toxicol 15:1–7

    Google Scholar 

  • Küpers LK, Monnereau C, Sharp GC, Yousefi P, Salas LA, Ghantous A, Page CM, Reese SE, Wilcox AJ, Czamara D et al (2019) Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight. Nat commun 10:1893

    PubMed  PubMed Central  Google Scholar 

  • Mackay DJ, Callaway JL, Marks SM, White HE, Acerini CL, Boonen SE, Dayanikli P, Firth HV, Goodship JA, Haemers AP (2008) Hypomethylation of multiple imprinted loci in individuals with transient neonatal diabetes is associated with mutations in ZFP57. Nat Genet 40:949

    CAS  PubMed  Google Scholar 

  • McRae AF, Powell JE, Henders AK, Bowdler L, Hemani G, Shah S, Painter JN, Martin NG, Visscher PM, Montgomery GW (2014) Contribution of genetic variation to transgenerational inheritance of DNA methylation. Genome Biol 15:R73

    PubMed  PubMed Central  Google Scholar 

  • McRae AF, Marioni RE, Shah S, Yang J, Powell JE, Harris SE, Gibson J, Henders AK, Bowdler L, Painter JN et al (2018) Identification of 55,000 replicated DNA methylation QTL. Sci Rep 8:17605

    PubMed  PubMed Central  Google Scholar 

  • Min JL, Hemani G, Davey Smith G, Relton C, Suderman M (2018) Meffil: efficient normalization and analysis of very large DNA methylation datasets. Bioinformatics 34:3983–3989

    CAS  PubMed  PubMed Central  Google Scholar 

  • Moon DH, Kwon SO, Kim WJ, Hong Y (2019) Identification of serial DNA methylation changes in the blood samples of patients with lung cancer. Tuberc Respir Dis 82:126–132

    Google Scholar 

  • Richardson TG, Shihab HA, Hemani G, Zheng J, Hannon E, Mill J, Carnero-Montoro E, Bell JT, Lyttleton O, McArdle WL et al (2016) Collapsed methylation quantitative trait loci analysis for low frequency and rare variants. Hum Mol Genet 25:4339–4349

    CAS  PubMed  PubMed Central  Google Scholar 

  • Shabalin AA (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28:1353–1358

    CAS  PubMed  PubMed Central  Google Scholar 

  • Shi J, Marconett CN, Duan J, Hyland PL, Li P, Wang Z, Wheeler W, Zhou B, Campan M, Lee DS et al (2014) Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue. Nat commun 5:3365

    PubMed  Google Scholar 

  • Stricker SH, Köferle A, Beck S (2016) From profiles to function in epigenomics. Nat Rev Genet 18:51

    PubMed  Google Scholar 

  • Takahashi N, Coluccio A, Thorball CW, Planet E, Shi H, Offner S, Turelli P, Imbeault M, Ferguson-Smith AC, Trono D (2019) ZNF445 is a primary regulator of genomic imprinting. Genes Dev 33:49–54

    CAS  PubMed  PubMed Central  Google Scholar 

  • Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S (2012) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29:189–196

    PubMed  PubMed Central  Google Scholar 

  • Turnbull J, Girard J-M, Lohi H, Chan EM, Wang P, Tiberia E, Omer S, Ahmed M, Bennett C, Chakrabarty A (2012) Early-onset Lafora body disease. Brain 135:2684–2698

    PubMed  PubMed Central  Google Scholar 

  • Turner SD (2018) qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. J Open Source Softw 3:731. https://doi.org/10.21105/joss.00731

    Article  Google Scholar 

  • Wu N, Ming X, Xiao J, Wu Z, Chen X, Shinawi M, Shen Y, Yu G, Liu J, Xie H (2015) TBX6 null variants and a common hypomorphic allele in congenital scoliosis. New Engl J Med 372:341–350

    CAS  PubMed  Google Scholar 

  • Xu Z, Niu L, Li L, Taylor JA (2015) ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res 44:e20–e20

    PubMed  PubMed Central  Google Scholar 

  • Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yang J, Ferreira T, Morris AP, Medland SE, Madden PA, Heath AC, Martin NG, Montgomery GW, Weedon MN, Loos RJ (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44:369

    CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the Korean Environment Industry & Technology Institute (KEITI) through “the Environmental Health Action Program”, funded by Korea Ministry of Environment (2017001360005).

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Author notes

  1. Woo Jin Kim and Seung Yong Hwang have contributed equally to this work.

Authors and Affiliations

  1. Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea

    Jaehyun Park & Sungho Won

  2. Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, Republic of Korea

    Sung Ok Kwon

  3. Department of Internal Medicine, Kangwon National University, Chuncheon, Republic of Korea

    San-Ha Kim & Woo Jin Kim

  4. BioCore Co., Ltd., Seoul, Republic of Korea

    Seung Jun Kim & Seung Yong Hwang

  5. Department of Bio-Nanotechnology, Hanyang University, Ansan, Republic of Korea

    Eun Jung Koh & Seung Yong Hwang

  6. Department of Public Health Sciences, Seoul National University, Seoul, Republic of Korea

    Sungho Won

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  1. Jaehyun Park
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Correspondence to Woo Jin Kim or Seung Yong Hwang.

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This study received ethical approval of the Kangwon National University Hospital IRB (B-2017-11-006).

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Written informed consent was provided by each participant.

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Park, J., Kwon, S.O., Kim, SH. et al. Methylation quantitative trait loci analysis in Korean exposome study. Mol. Cell. Toxicol. 16, 175–183 (2020). https://doi.org/10.1007/s13273-019-00068-3

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