Cortessis VK, Thomas DC, Levine AJ, Breton CV, Mack TM, Siegmund KD, Haile RW, Laird PW. Environmental epigenetics: prospects for studying epigenetic mediation of exposure-response relationships. Hum Genet. 2012; 131:1565–89.

Article
CAS
PubMed
PubMed Central
Google Scholar

Feinberg AP, Fallin MD. Epigenetics at the crossroads of genes and the environment. J Am Med Assoc. 2015; 314:1129–30.

Article
CAS
Google Scholar

International Diabetes Federation. The IDF consensus worldwide definition of the metabolic syndrome. http://www.idf.org/metabolic-syndrome. Accessed 28 Feb 2017.

Kaur J. A comprehensive review on metabolic syndrome. Cardiol Res Pract. 2014;2014. doi:10.1155/2014/943162.

Fan J, Lv J. Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc Ser B. 2008; 70:849–911.

Article
Google Scholar

Fan J, Samworth R, Wu Y. Ultrahigh dimensional feature selection: Beyond the linear model. J Mach Learn Res. 2009; 10:2013–038.

PubMed
PubMed Central
Google Scholar

Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B. 2005; 67:301–20.

Article
Google Scholar

Bell B, Rose CL, A D. The veterans administration longitudinal study of healthy aging. The Gerontologist. 1966; 6:179–84.

Article
CAS
PubMed
Google Scholar

Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of illumina infinium dna methylation beadarrays. Nucleic Acids Res. 2013; 41:e90. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627582/pdf/gkt090.pdf.

Davis S, Du P, Bilke S, Tim Triche J, Bootwalla M. Methylumi: Handle Illumina Methylation Data. R Package Version 2.16.0. 2015. http://bioconductor.org/packages/release/bioc/html/methylumi.html.

Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S. A beta-mixture quantile normalization method for correcting probe design bias in illumina infinium 450 k dna methylation data. Bioinformatics. 2013; 29:189–96.

Article
CAS
PubMed
Google Scholar

Du P, Zhang X, Huang C, Jafari N, Kibbe W, Hou L, Lin S. Comparison of beta-value and m-value methods for quantifying methylation levels by microarray analysis. BMC Bioinforma. 2010; 11:1–9.

Article
Google Scholar

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for dna microarrays. Bioinformatics. 2001; 17:520–5.

Article
CAS
PubMed
Google Scholar

Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. Dna methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinforma. 2012; 13:86–6.

Article
Google Scholar

Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics. 2007; 8:118–27.

Article
PubMed
Google Scholar

Moen EL, Zhang X, Mu W, Delaney SM, Wing C, McQuade J, Myers J, Godley LA, Dolan ME, Zhang W. Genome-wide variation of cytosine modifications between european and african populations and the implications for complex traits. Genetics. 2013; 194:987–96.

Article
CAS
PubMed
PubMed Central
Google Scholar

Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B. 1994; 58:267–88.

Google Scholar

Hastie T, Tibshirani R, Friedman J. The elements of statistical learning; data mining, inference and prediction. New York: Springer; 2009.

Google Scholar

Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan J, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013; 49:359–67.

Article
CAS
PubMed
Google Scholar

Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013; 14:115–5.

Article
Google Scholar

Breheny P, Huang J. Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann Appl Stat. 2011; 5:232–53.

Article
PubMed
PubMed Central
Google Scholar

Bach F. Bolasso: Model consistent lasso estimation through the bootstrap In: McCallum A, Roweis S, editors. Proceedings of the 25th International Conference on Machine Learning: 5-9, July 2008; Helsinki, Finland. New York: ACM: 2008. p. 33–40.

Google Scholar

Bach F. Model-Consistent Sparse Estimation Through the Bootstrap. working paper or preprint. https://hal.archives-ouvertes.fr/hal-00354771. Accessed 28 Feb 2017.

Hand DJ, Till RJ. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn. 2001; 45:171–86.

Article
Google Scholar

Pfeiffer L, Wahl S, Pilling LC, Reischl E, Sandling JK, Kunze S, Holdt LM, Kretschmer A, Schramm K, Adamski J, Klopp N, Illig T, Hedman ÅK, Roden M, Hernandez DG, Singleton AB, Thasler WE, Grallert H, Gieger C, Herder C, Teupser D, Meisinger C, Spector TD, Kronenberg F, Prokisch H, Melzer D, Peters A, Deloukas P, Ferrucci L, Waldenberger M. Dna methylation of lipid-related genes affects blood lipid levels. Circ Cardiovasc Genet. 2015; 8:334–42.

Article
CAS
PubMed
PubMed Central
Google Scholar

Hidalgo B, Irvin MR, Sha J, Zhi D, Aslibekyan S, Absher D, Tiwari HK, Kabagambe EK, Ordovas JM, Arnett DK. Epigenome-Wide Association Study of Fasting Measures of Glucose, Insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network Study. Diabetes. 2014; 63:801–7.

Article
CAS
PubMed
PubMed Central
Google Scholar

Ding J, Reynolds LM, Zeller T, Müller C, Lohman K, Nicklas BJ, Kritchevsky SB, Huang Z, de la Fuente A, Soranzo N, Settlage RE, Chuang CC, Howard T, Xu N, Goodarzi MO, Chen Y-DI, Rotter JI, Siscovick DS, Parks JS, Murphy S, Jacobs DR, Post W, Tracy RP, Wild PS, Blankenberg S, Hoeschele I, Herrington D, McCall CE, Liu Y. Alterations of a cellular cholesterol metabolism network are a molecular feature of obesity-related type 2 diabetes and cardiovascular disease. Diabetes. 2015; 64:3464–74.

Article
CAS
PubMed
PubMed Central
Google Scholar

Kennedy MA, Barrera GC, Nakamura K, Ángel Baldán, Tarr P, Fishbein MC, Frank J, Francone OL, Edwards PA. ABCG1 has a critical role in mediating cholesterol efflux to HDL and preventing cellular lipid accumulation. Cell Metab. 2005; 1:121–31.

Article
CAS
PubMed
Google Scholar

Frisdal E, Lay SL, Hooton H, Poupel L, Olivier M, Alili R, Plengpanich W, Villard EF, Gilibert S, Lhomme M, Superville A, Miftah-Alkhair L, John Chapman M, Dallinga-Thie GM, Venteclef N, Poitou C, Tordjman J, Lesnik P, Kontush A, Huby T, Dugail I, Clement K, Guerin M, Goff WL. Adipocyte atp-binding cassette g1 promotes triglyceride storage, fat mass growth and human obesity. Diabetes. 2015; 64:840–55.

Drzewinska J, Walczak-Drzewiecka A, Ratajewski M. Identification and analysis of the promoter region of the human DHCR24 gene: involvement of DNA methylation and histone acetylation. Mol Biol Rep. 2011; 38:1091–101.

Article
CAS
PubMed
Google Scholar

Zerenturk EJ, Sharpe LJ, Ikonen E, Brown AJ. Desmosterol and dhcr24: Unexpected new directions for a terminal step in cholesterol synthesis. Prog Lipid Res. 2013; 52:666–80.

Article
CAS
PubMed
Google Scholar

Luu W, Zerenturk EJ, Kristiana I, Bucknall MP, Sharpe LJ, Brown AJ. Signaling regulates activity of dhcr24, the final enzyme in cholesterol synthesis. J Lipid Res. 2014; 55:410–20.

Article
CAS
PubMed
PubMed Central
Google Scholar

Zheng Y, Fei Z, Zhang W, Starren JB, Liu L, Baccarelli AA, Li Y, Hou L. PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures. Bioinformatics. 2014; 30:2802–7.

Article
CAS
PubMed
PubMed Central
Google Scholar

Zhang H, Zheng Y, Zhang Z, Gao T, Joyce B, Yoon G, Zhang W, Schwartz J, Just A, Colicino P Elenaand Vokonas, Zhao L, Lv J, Baccarelli A, Hou L, Liu L. Estimating and testing high-dimensional mediation effects in epigenetic studies. Bioinformatics. 2016; 32:3150–4.

Article
CAS
PubMed
Google Scholar