Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6:597–610.
Article
PubMed
CAS
Google Scholar
Suzuki MM, Bird A. DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet. 2008;9:465–76.
Article
PubMed
CAS
Google Scholar
Bird AP. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.
Article
PubMed
CAS
Google Scholar
Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 2013;14:204–20.
Article
PubMed
CAS
Google Scholar
Trerotola M, Relli V, Simeone P, Alberti S. Epigenetic inheritance and the missing heritability. Hum Genomics. 2015;9:17.
Article
PubMed
PubMed Central
CAS
Google Scholar
Heard E, Martienssen RA. Transgenerational epigenetic inheritance: myths and mechanisms. Cell. 2014;157:95–109.
Article
PubMed
PubMed Central
CAS
Google Scholar
Lim JP, Brunet A. Bridging the transgenerational gap with epigenetic memory. Trends Genet. 2013;29:176–86.
Article
PubMed
PubMed Central
CAS
Google Scholar
Illumina Support. https://support.illumina.com/. Accessed 8 Feb 2018.
Ziller MJ, Hansen KD, Meissner A, Aryee MJ. Coverage recommendations for methylation analysis by whole genome bisulfite sequencing. Nat Methods. 2015;12:230–2.
Article
PubMed
CAS
Google Scholar
Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012;13:R83.
Article
PubMed
PubMed Central
Google Scholar
Das S, Foerer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.
Article
PubMed
PubMed Central
CAS
Google Scholar
Li Y, Willer C, Sanna S, Abecasis G. Genotype imputation. Annu Rev Genomics Hum Genet. 2009;10:387–406.
Article
PubMed
PubMed Central
CAS
Google Scholar
Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 2010;11:499–511.
Article
PubMed
CAS
Google Scholar
Zhang W, Spector T, Deloukas P, Bell JT, Engelhardt BE. Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements. Genome Biol. 2015;16:14.
Article
PubMed
PubMed Central
Google Scholar
Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18:67.
Article
PubMed
PubMed Central
CAS
Google Scholar
Breiman L. Random forests. Mach Learn. 2001;45:5.
Article
Google Scholar
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.
Article
PubMed
CAS
Google Scholar
Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM; 2016. p. 785–794.
Elliott G, Hong C, Xing X, Zhou X, Li D, Coarfa C, et al. Intermediate DNA methylation is a conserved signature of genome regulation. Nat Commun. 2015;6:6363.
Article
PubMed
PubMed Central
CAS
Google Scholar
Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.
Article
PubMed
PubMed Central
CAS
Google Scholar
Lövkvist C, Dodd IB, Sneppen K, Haerter JO. DNA methylation in human epigenomes depends on local topology of CpG sites. Nucleic Acids Res. 2016;44:5123–32.
Article
PubMed
PubMed Central
CAS
Google Scholar
Zeng H, Gifford DK. Predicting the impact of non-coding variants on DNA methylation. Nucleic Acids Res. 2017;45:e99.
Article
PubMed
PubMed Central
CAS
Google Scholar
Ma B, Wilker EH, Willis-Owen SAG, Byun H, Wong KCC, Motta V, et al. Predicting DNA methylation level across human tissues. Nucleic Acids Res. 2014;42:3515–28.
Article
PubMed
PubMed Central
CAS
Google Scholar
Zhang G, Huang K, Xu Z, Tzeng Y, Conneely KN, Guan W, et al. Across-platform imputation of DNA methylation levels incorporating nonlocal information using penalized functional regression. Genet Epidemiol. 2016;40:333–40.
Article
PubMed
PubMed Central
Google Scholar
Fan S, Huang K, Ai R, Wang M, Wang W. Predicting CpG methylation levels by integrating Infinium HumanMethylation450 BeadChip array data. Genomics. 2016;107:132–7.
Article
PubMed
CAS
Google Scholar
Wang Y, Liu T, Xu D, Shi H, Zhang C, Mo YY, Wang Z. Predicting DNA methylation state of CpG dinucleotide using genome topological features and deep networks. Sci Rep. 2016;6:19598.
Article
PubMed
PubMed Central
CAS
Google Scholar
Ernst J, Kellis M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotech. 2015;33:364–76.
Article
CAS
Google Scholar
Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536:41–7.
Article
PubMed
PubMed Central
CAS
Google Scholar
McCarthy MI, Zeggini E. Genome-wide association studies in type 2 diabetes. Curr Diab Rep. 2009;9:164–71.
Article
PubMed
PubMed Central
CAS
Google Scholar
Saxena R, Saleheen D, Been LF, Garavito ML, Braun T, Bjonnes A, et al. Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in Sikhs of Punjabi origin from India. Diabetes. 2013;62:1746–55.
Article
PubMed
PubMed Central
CAS
Google Scholar
Varshney A, Scott LJ, Welch RP, Erdos MR, Chines PS, Narisu N, et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci. 2017;114:2301–6.
Article
PubMed
PubMed Central
CAS
Google Scholar
Ziller MJ, Gu H, Müller F, Donaghey J, Tsai LT, Kohlbacher O, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013;500:477–81.
Article
PubMed
PubMed Central
CAS
Google Scholar
Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17:208.
Article
PubMed
PubMed Central
CAS
Google Scholar
Kim J, Kollhoff A, Bergmann A, Stubbs L. Methylation-sensitive binding of transcription factor YY1 to an insulator sequence within the paternally expressed imprinted gene, Peg3. Hum Mol Genet. 2003;12:233–45.
Article
PubMed
CAS
Google Scholar
Sekimata M, Murakami-Sekimata A, Homma Y. CpG methylation prevents YY1-mediated transcriptional activation of the vimentin promoter. Biochem Biophys Res Commun. 2011;414:767–72.
Article
PubMed
CAS
Google Scholar
Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Schöler A, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480:490–5.
PubMed
CAS
Google Scholar
Marchal C, Miotto B. Emerging concept in DNA methylation: role of transcription factors in shaping DNA methylation patterns. J Cell Physiol. 2015;230:743–51.
Article
PubMed
CAS
Google Scholar
Varley KE, Gertz J, Bowling KM, Parker SL, Reddy TE, Pauli-Behn F, et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res. 2013;23:555–67.
Article
PubMed
PubMed Central
CAS
Google Scholar
Gao H, Mejhert N, Fretz JA, Arner E, Lorente-Cebrián S, Ehrlund A, et al. Early B cell factor 1 regulates adipocyte morphology and lipolysis in white adipose tissue. Cell Metab. 2014;19:981–92.
Article
PubMed
PubMed Central
CAS
Google Scholar
Petrus P, Mejhert N, Gao H, Bäckdahl J, Arner E, Arner P, Rydén M. Low early B-cell factor 1 (EBF1) activity in human subcutaneous adipose tissue is linked to a pernicious metabolic profile. Diabetes Metab. 2015;41:509–12.
Article
PubMed
CAS
Google Scholar
Wang C, Wang M, Arrington J, Shan T, Yue F, Nie Y, et al. Ascl2 inhibits myogenesis by antagonizing the transcriptional activity of myogenic regulatory factors. Development. 2017;144:235–47.
Article
PubMed
PubMed Central
CAS
Google Scholar
Gao N, Le Lay J, Qin W, Doliba N, Schug J, Fox AJ, et al. Foxa1 and Foxa2 maintain the metabolic and secretory features of the mature beta-cell. Mol Endocrinol. 2010;24:1594–604.
Article
PubMed
PubMed Central
CAS
Google Scholar
Vatamaniuk MZ, Gupta RK, Lantz KA, Doliba NM, Matschinsky FM, Kaestner KH. Foxa1-deficient mice exhibit impaired insulin secretion due to uncoupled oxidative phosphorylation. Diabetes. 2006;10:2730–6.
Article
CAS
Google Scholar
Guo S, Diep D, Plongthongkum N, Fung HL, Zhang K, Zhang K. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet. 2017;49:635–42.
Article
PubMed
PubMed Central
CAS
Google Scholar
Valle T, Tuomilehto J, Bergman RN, Ghosh S, Hauser ER, Eriksson J, et al. Mapping genes for NIDDM. Design of the Finland-United States Investigation of NIDDM genetics (FUSION) study. Diabetes Care. 1998;21:949–58.
Article
PubMed
CAS
Google Scholar
Väätäinen S, Keinänen-Kiukaanniemi S, Saramies J, Uusitalo H, Tuomilehto J, Martikainen J. Quality of life along the diabetes continuum: a cross-sectional view of health-related quality of life and general health status in middle-aged and older Finns. Qual Life Res. 2014;23:1935–44.
Article
PubMed
Google Scholar
Kouki R, Schwab U, Lakka TA, Hassinen M, Savonen K, Komulainen P, et al. Diet, fitness and the metabolic syndrome - the DR’s EXTRA study. Nutr Metab Cardiovasc Dis. 2012;22:553–60.
Article
PubMed
CAS
Google Scholar
Stančáková A, Kuulasmaa T, Paananen J, Jackson AU, Bonnycastle LL, Collins FS. Association of 18 confirmed susceptibility loci for type 2 diabetes with indices of insulin release, proinsulin conversion, and insulin sensitivity in 5,327 nondiabetic Finnish men. Diabetes. 2009;58:2129–36.
Article
PubMed
PubMed Central
CAS
Google Scholar
World Health Organization (WHO), International Diabetes Federation (IDF). Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. Geneva, Switzerland: WHO; 2006.
Google Scholar
Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010; available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
Didion JP, Martin M, Collins FS. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ. 2017;5:e3720.
Article
PubMed
PubMed Central
Google Scholar
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013. Preprint at arXiv:1303.3997v2 [q-bio.GN].
Faust GG, Hall IM. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics. 2014;30:2503–5.
Article
PubMed
PubMed Central
CAS
Google Scholar
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.
Article
PubMed
PubMed Central
CAS
Google Scholar
Pedersen BS, Eyring K, De S, Yang IV, Schwartz DA. Fast and accurate alignment of long bisulfite-seq reads. 2014. Preprint at arXiv:1401.1129 [q.bio.GN].
Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–9.
Article
PubMed
PubMed Central
CAS
Google Scholar
Fortin JP, Fertig E, Hansen K. shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R. F1000Res. 2014;3:175.
PubMed
PubMed Central
Google Scholar
McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.
Article
PubMed
PubMed Central
CAS
Google Scholar
Chen Y, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–9.
Article
PubMed
PubMed Central
CAS
Google Scholar
Price ME, Cotton AM, Lam LL, Farré P, Emberly E, Brown CJ, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin. 2013;6:4.
Article
PubMed
PubMed Central
CAS
Google Scholar
Zhang X, Mu W, Zhang W. On the analysis of the Illumina 450k array data: probes ambiguously mapped to the human genome. Front Genet. 2012;3:73.
PubMed
PubMed Central
Google Scholar
McCartney DL, Walker RM, Morris SW, McIntosh AM, Porteous DJ, Evans KL. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genom Data. 2016;9:22–4.
Article
PubMed
PubMed Central
Google Scholar
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74.
Article
CAS
Google Scholar
Golden path track of the University of Santa Cruz Genome Browser. http://hgdownload.cse.ucsc.edu/goldenPath/hg19/gc5Base/.
Meyer LR, Zweig AS, Hinrichs AS, Karolchik D, Kuhn RM, Wong M, et al. The UCSC genome browser database: extensions and updates 2013. Nucleic Acids Res. 2013;41:D64–9.
Article
PubMed
CAS
Google Scholar
Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011;473:43–9.
Article
PubMed
PubMed Central
CAS
Google Scholar
Mikkelsen TS, Xu Z, Zhang X, Wang L, Gimble JM, Lander ES, Rosen ED. Comparative epigenomic analysis of murine and human adipogenesis. Cell. 2010;143:156–69.
Article
PubMed
PubMed Central
CAS
Google Scholar
Parker SCJ, Stitzel ML, Taylor DL, Orozco JM, Erdos MR, Akiyama JA, et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc Natl Acad Sci. 2013;110:17921–6.
Article
PubMed
PubMed Central
Google Scholar
Scott LJ, Erdos MR, Huyghe JR, Welch RP, Beck AT, Wolford BN, et al. The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nat Commun. 2016;7:11764.
Article
PubMed
PubMed Central
CAS
Google Scholar
Allum F, Shao X, Guénard F, Simon MM, Busche S, Caron M, et al. Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants. Nat Commun. 2015;6:7211.
Article
PubMed
PubMed Central
Google Scholar
Jolma A, Yin Y, Nitta KR, Dave K, Popov A, Taipale M, et al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature. 2015;527:384–8.
Article
PubMed
CAS
Google Scholar
R project. http://www.r-project.org/.
Wright M, Ziegler A. Ranger: a fast implementation of random forests for high dimension data in C++ and R. J. Stat Softw. 2017;77:1–17.
Google Scholar
Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, et al. Mlr: machine learning in R. Journal J Mach Learn Res. 2016;17:1–5.
Google Scholar
Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 2016;26:990–9.
Article
PubMed
PubMed Central
CAS
Google Scholar