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3 results on '"Siemelink, MA"'

1. Smoking is Associated to DNA Methylation in Atherosclerotic Carotid Lesions.

Author

Siemelink MA, van der Laan SW, Haitjema S, van Koeverden ID, Schaap J, Wesseling M, de Jager SCA, Mokry M, van Iterson M, Dekkers KF, Luijk R, Foroughi Asl H, Michoel T, Björkegren JLM, Aavik E, Ylä-Herttuala S, de Borst GJ, Asselbergs FW, El Azzouzi H, den Ruijter HM, Heijmans BT, and Pasterkamp G

Subjects
Aged, Atherosclerosis etiology, Carotid Artery Diseases etiology, CpG Islands genetics, Endarterectomy, Carotid methods, Endarterectomy, Carotid statistics & numerical data, Epigenesis, Genetic, Female, Gene Expression Regulation, Humans, Male, Middle Aged, Plaque, Atherosclerotic etiology, Plaque, Atherosclerotic genetics, Atherosclerosis genetics, Carotid Artery Diseases genetics, DNA Methylation, Epigenomics methods, Genome-Wide Association Study methods, Smoking adverse effects
Abstract

Background: Tobacco smoking is a major risk factor for atherosclerotic disease and has been associated with DNA methylation (DNAm) changes in blood cells. However, whether smoking influences DNAm in the diseased vascular wall is unknown but may prove crucial in understanding the pathophysiology of atherosclerosis. In this study, we associated current tobacco smoking to epigenome-wide DNAm in atherosclerotic plaques from patients undergoing carotid endarterectomy., Methods: DNAm at commonly methylated sites (cytosine-guanine nucleotide pairs separated by a phospho-group [CpGs]) was assessed in atherosclerotic plaque samples and peripheral blood samples from 485 carotid endarterectomy patients. We tested the association of current tobacco smoking with DNAm corrected for age and sex. To control for bias and inflation because of cellular heterogeneity, we applied a Bayesian method to estimate an empirical null distribution as implemented by the R package bacon. Replication of the smoking-associated methylated CpGs in atherosclerotic plaques was executed in the second sample of 190 carotid endarterectomy patients, and results were meta-analyzed using a fixed-effects model., Results: Tobacco smoking was significantly associated to differential DNAm in atherosclerotic lesions of 4 CpGs (false discovery rate <0.05) mapped to 2 different genes ( AHRR, ITPK1) and 17 CpGs mapped to 8 genes and RNAs in blood. The strongest associations were found for CpGs mapped to the gene AHRR, a repressor of the aryl hydrocarbon receptor transcription factor involved in xenobiotic detoxification. One of these methylated CpGs were found to be regulated by local genetic variation., Conclusions: The risk factor tobacco smoking associates with DNAm at multiple loci in carotid atherosclerotic lesions. These observations support further investigation of the relationship between risk factors and epigenetic regulation in atherosclerotic disease.

Published
2018
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2. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Author

Justice AE, Winkler TW, Feitosa MF, Graff M, Fisher VA, Young K, Barata L, Deng X, Czajkowski J, Hadley D, Ngwa JS, Ahluwalia TS, Chu AY, Heard-Costa NL, Lim E, Perez J, Eicher JD, Kutalik Z, Xue L, Mahajan A, Renström F, Wu J, Qi Q, Ahmad S, Alfred T, Amin N, Bielak LF, Bonnefond A, Bragg J, Cadby G, Chittani M, Coggeshall S, Corre T, Direk N, Eriksson J, Fischer K, Gorski M, Neergaard Harder M, Horikoshi M, Huang T, Huffman JE, Jackson AU, Justesen JM, Kanoni S, Kinnunen L, Kleber ME, Komulainen P, Kumari M, Lim U, Luan J, Lyytikäinen LP, Mangino M, Manichaikul A, Marten J, Middelberg RPS, Müller-Nurasyid M, Navarro P, Pérusse L, Pervjakova N, Sarti C, Smith AV, Smith JA, Stančáková A, Strawbridge RJ, Stringham HM, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van der Most PJ, Van Vliet-Ostaptchouk JV, Vedantam SL, Verweij N, Vink JM, Vitart V, Wu Y, Yengo L, Zhang W, Hua Zhao J, Zimmermann ME, Zubair N, Abecasis GR, Adair LS, Afaq S, Afzal U, Bakker SJL, Bartz TM, Beilby J, Bergman RN, Bergmann S, Biffar R, Blangero J, Boerwinkle E, Bonnycastle LL, Bottinger E, Braga D, Buckley BM, Buyske S, Campbell H, Chambers JC, Collins FS, Curran JE, de Borst GJ, de Craen AJM, de Geus EJC, Dedoussis G, Delgado GE, den Ruijter HM, Eiriksdottir G, Eriksson AL, Esko T, Faul JD, Ford I, Forrester T, Gertow K, Gigante B, Glorioso N, Gong J, Grallert H, Grammer TB, Grarup N, Haitjema S, Hallmans G, Hamsten A, Hansen T, Harris TB, Hartman CA, Hassinen M, Hastie ND, Heath AC, Hernandez D, Hindorff L, Hocking LJ, Hollensted M, Holmen OL, Homuth G, Jan Hottenga J, Huang J, Hung J, Hutri-Kähönen N, Ingelsson E, James AL, Jansson JO, Jarvelin MR, Jhun MA, Jørgensen ME, Juonala M, Kähönen M, Karlsson M, Koistinen HA, Kolcic I, Kolovou G, Kooperberg C, Krämer BK, Kuusisto J, Kvaløy K, Lakka TA, Langenberg C, Launer LJ, Leander K, Lee NR, Lind L, Lindgren CM, Linneberg A, Lobbens S, Loh M, Lorentzon M, Luben R, Lubke G, Ludolph-Donislawski A, Lupoli S, Madden PAF, Männikkö R, Marques-Vidal P, Martin NG, McKenzie CA, McKnight B, Mellström D, Menni C, Montgomery GW, Musk AB, Narisu N, Nauck M, Nolte IM, Oldehinkel AJ, Olden M, Ong KK, Padmanabhan S, Peyser PA, Pisinger C, Porteous DJ, Raitakari OT, Rankinen T, Rao DC, Rasmussen-Torvik LJ, Rawal R, Rice T, Ridker PM, Rose LM, Bien SA, Rudan I, Sanna S, Sarzynski MA, Sattar N, Savonen K, Schlessinger D, Scholtens S, Schurmann C, Scott RA, Sennblad B, Siemelink MA, Silbernagel G, Slagboom PE, Snieder H, Staessen JA, Stott DJ, Swertz MA, Swift AJ, Taylor KD, Tayo BO, Thorand B, Thuillier D, Tuomilehto J, Uitterlinden AG, Vandenput L, Vohl MC, Völzke H, Vonk JM, Waeber G, Waldenberger M, Westendorp RGJ, Wild S, Willemsen G, Wolffenbuttel BHR, Wong A, Wright AF, Zhao W, Zillikens MC, Baldassarre D, Balkau B, Bandinelli S, Böger CA, Boomsma DI, Bouchard C, Bruinenberg M, Chasman DI, Chen YD, Chines PS, Cooper RS, Cucca F, Cusi D, Faire U, Ferrucci L, Franks PW, Froguel P, Gordon-Larsen P, Grabe HJ, Gudnason V, Haiman CA, Hayward C, Hveem K, Johnson AD, Wouter Jukema J, Kardia SLR, Kivimaki M, Kooner JS, Kuh D, Laakso M, Lehtimäki T, Marchand LL, März W, McCarthy MI, Metspalu A, Morris AP, Ohlsson C, Palmer LJ, Pasterkamp G, Pedersen O, Peters A, Peters U, Polasek O, Psaty BM, Qi L, Rauramaa R, Smith BH, Sørensen TIA, Strauch K, Tiemeier H, Tremoli E, van der Harst P, Vestergaard H, Vollenweider P, Wareham NJ, Weir DR, Whitfield JB, Wilson JF, Tyrrell J, Frayling TM, Barroso I, Boehnke M, Deloukas P, Fox CS, Hirschhorn JN, Hunter DJ, Spector TD, Strachan DP, van Duijn CM, Heid IM, Mohlke KL, Marchini J, Loos RJF, Kilpeläinen TO, Liu CT, Borecki IB, North KE, and Cupples LA

Subjects
Adiposity genetics, Adult, Body Fat Distribution, Body Mass Index, Epistasis, Genetic, Humans, Phenotype, Polymorphism, Single Nucleotide, Waist Circumference genetics, Waist-Hip Ratio, Genetic Predisposition to Disease genetics, Genome-Wide Association Study methods, Obesity genetics, Quantitative Trait Loci genetics, Smoking genetics
Abstract

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

Published
2017
Full Text
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3. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Author

Justice, AE, Winkler, TW, Feitosa, MF, Graff, M, Fisher, VA, Young, K, Barata, L, Deng, X, Czajkowski, J, Hadley, D, Ngwa, JS, Ahluwalia, TS, Chu, AY, Heard-Costa, NL, Lim, E, Perez, J, Eicher, JD, Kutalik, Z, Xue, L, Mahajan, A, Renström, F, Wu, J, Qi, Q, Ahmad, S, Alfred, T, Amin, N, Bielak, LF, Bonnefond, A, Bragg, J, Cadby, G, Chittani, M, Coggeshall, S, Corre, T, Direk, N, Eriksson, J, Fischer, K, Gorski, M, Neergaard Harder, M, Horikoshi, M, Huang, T, Huffman, JE, Jackson, AU, Justesen, JM, Kanoni, S, Kinnunen, L, Kleber, ME, Komulainen, P, Kumari, M, Lim, U, Luan, J, Lyytikäinen, L-P, Mangino, M, Manichaikul, A, Marten, J, Middelberg, RPS, Müller-Nurasyid, M, Navarro, P, Pérusse, L, Pervjakova, N, Sarti, C, Smith, AV, Smith, JA, Stančáková, A, Strawbridge, RJ, Stringham, HM, Sung, YJ, Tanaka, T, Teumer, A, Trompet, S, Van Der Laan, SW, Van Der Most, PJ, Van Vliet-Ostaptchouk, JV, Vedantam, SL, Verweij, N, Vink, JM, Vitart, V, Wu, Y, Yengo, L, Zhang, W, Hua Zhao, J, Zimmermann, ME, Zubair, N, Abecasis, GR, Adair, LS, Afaq, S, Afzal, U, Bakker, SJL, Bartz, TM, Beilby, J, Bergman, RN, Bergmann, S, Biffar, R, Blangero, J, Boerwinkle, E, Bonnycastle, LL, Bottinger, E, Braga, D, Buckley, BM, Buyske, S, Campbell, H, Chambers, JC, Collins, FS, Curran, JE, De Borst, GJ, De Craen, AJM, De Geus, EJC, Dedoussis, G, Delgado, GE, Den Ruijter, HM, Eiriksdottir, G, Eriksson, AL, Esko, T, Faul, JD, Ford, I, Forrester, T, Gertow, K, Gigante, B, Glorioso, N, Gong, J, Grallert, H, Grammer, TB, Grarup, N, Haitjema, S, Hallmans, G, Hamsten, A, Hansen, T, Harris, TB, Hartman, CA, Hassinen, M, Hastie, ND, Heath, AC, Hernandez, D, Hindorff, L, Hocking, LJ, Hollensted, M, Holmen, OL, Homuth, G, Jan Hottenga, J, Huang, J, Hung, J, Hutri-Kähönen, N, Ingelsson, E, James, AL, Jansson, J-O, Jarvelin, M-R, Jhun, MA, Jørgensen, ME, Juonala, M, Kähönen, M, Karlsson, M, Koistinen, HA, Kolcic, I, Kolovou, G, Kooperberg, C, Krämer, BK, Kuusisto, J, Kvaløy, K, Lakka, TA, Langenberg, C, Launer, LJ, Leander, K, Lee, NR, Lind, L, Lindgren, CM, Linneberg, A, Lobbens, S, Loh, M, Lorentzon, M, Luben, R, Lubke, G, Ludolph-Donislawski, A, Lupoli, S, Madden, PAF, Männikkö, R, Marques-Vidal, P, Martin, NG, McKenzie, CA, McKnight, B, Mellström, D, Menni, C, Montgomery, GW, Musk, AB, Narisu, N, Nauck, M, Nolte, IM, Oldehinkel, AJ, Olden, M, Ong, KK, Padmanabhan, S, Peyser, PA, Pisinger, C, Porteous, DJ, Raitakari, OT, Rankinen, T, Rao, DC, Rasmussen-Torvik, LJ, Rawal, R, Rice, T, Ridker, PM, Rose, LM, Bien, SA, Rudan, I, Sanna, S, Sarzynski, MA, Sattar, N, Savonen, K, Schlessinger, D, Scholtens, S, Schurmann, C, Scott, RA, Sennblad, B, Siemelink, MA, Silbernagel, G, Slagboom, PE, Snieder, H, Staessen, JA, Stott, DJ, Swertz, MA, Swift, AJ, Taylor, KD, Tayo, BO, Thorand, B, Thuillier, D, Tuomilehto, J, Uitterlinden, AG, Vandenput, L, Vohl, M-C, Völzke, H, Vonk, JM, Waeber, G, Waldenberger, M, Westendorp, RGJ, Wild, S, Willemsen, G, Wolffenbuttel, BHR, Wong, A, Wright, AF, Zhao, W, Zillikens, MC, Baldassarre, D, Balkau, B, Bandinelli, S, Böger, CA, Boomsma, DI, Bouchard, C, Bruinenberg, M, Chasman, DI, Chen, Y-D, Chines, PS, Cooper, RS, Cucca, F, Cusi, D, Faire, UD, Ferrucci, L, Franks, PW, Froguel, P, Gordon-Larsen, P, Grabe, H-J, Gudnason, V, Haiman, CA, Hayward, C, Hveem, K, Johnson, AD, Wouter Jukema, J, Kardia, SLR, Kivimaki, M, Kooner, JS, Kuh, D, Laakso, M, Lehtimäki, T, Marchand, LL, März, W, McCarthy, MI, Metspalu, A, Morris, AP, Ohlsson, C, Palmer, LJ, Pasterkamp, G, Pedersen, O, Peters, A, Peters, U, Polasek, O, Psaty, BM, Qi, L, Rauramaa, R, Smith, BH, Sørensen, TIA, Strauch, K, Tiemeier, H, Tremoli, E, Van Der Harst, P, Vestergaard, H, Vollenweider, P, Wareham, NJ, Weir, DR, Whitfield, JB, Wilson, JF, Tyrrell, J, Frayling, TM, Barroso, I, Boehnke, M, Deloukas, P, Fox, CS, Hirschhorn, JN, Hunter, DJ, Spector, TD, Strachan, DP, Van Duijn, CM, Heid, IM, Mohlke, KL, Marchini, J, Loos, RJF, Kilpeläinen, TO, Liu, C-T, Borecki, IB, North, KE, and Cupples, LA

Subjects
Adult, Waist-Hip Ratio, Quantitative Trait Loci, Smoking, Epistasis, Genetic, Polymorphism, Single Nucleotide, 3. Good health, Body Mass Index, Phenotype, Body Fat Distribution, Humans, Genetic Predisposition to Disease, Obesity, Waist Circumference, Adiposity, Genome-Wide Association Study
Abstract

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

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