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1. Spatial and seasonal patterns in acoustic detections of sperm whales Physeter macrocephalus along the continental slope in the western North Atlantic Ocean

Author: Stanistreet, JE, Nowacek, DP, Bell, JT, Cholewiak, DM, Hildebrand, JA, Hodge, LEW, Van Parijs, SM, and Read, AJ
Subjects: Zoology, QL1-991, Botany, QK1-989
Abstract: The distribution and seasonal movements of sperm whales Physeter macrocephalus are poorly understood in the western North Atlantic Ocean, despite a long history of human exploitation of the species. Cetacean surveys in this region are typically conducted during the summer, when weather conditions are amenable for visual observation, resulting in a seasonal bias in species occurrence data. In the present study, we conducted multi-year passive acoustic monitoring to assess year-round sperm whale occurrence along the continental slope between Florida and New England, USA. Between 2011 and 2015, we collected 2037 d of recordings using bottom-mounted recorders deployed at 5 sites. We analyzed these recordings for sperm whale echolocation clicks, which were detected commonly between New England and North Carolina, but infrequently off the coast of Florida. In the northern half of the study region, we observed distinct seasonal patterns in the daily prevalence of sperm whale clicks, with a winter peak in occurrence off Cape Hatteras, North Carolina, followed by an increase later in the spring at sites further north. South of Cape Hatteras, seasonal patterns were less apparent. We detected sperm whale clicks during all hours of the day throughout the study area, and did not observe strong diel patterns. Overall, our results provide a comprehensive year-round baseline on the occurrence of sperm whales across multiple recording sites, demonstrating the utility of passive acoustic monitoring to assess patterns in sperm whale occurrence across broad spatial and temporal scales.
Published: 2018
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2. Meta-analysis of epigenome-wide association studies of cognitive abilities

Author: Marioni, RE, McRae, AF, Bressler, J, Colicino, E, Hannon, E, Li, S, Prada, D, Smith, JA, Trevisi, L, Tsai, P-C, Vojinovic, D, Simino, J, Levy, D, Liu, C, Mendelson, M, Satizabal, CL, Yang, Q, Jhun, MA, Kardia, SLR, Zhao, W, Bandinelli, S, Ferrucci, L, Hernandez, DG, Singleton, AB, Harris, SE, Starr, JM, Kiel, DP, McLean, RR, Just, AC, Schwartz, J, Spiro, A, Vokonas, P, Amin, N, Ikram, MA, Uitterlinden, AG, Van Meurs, JBJ, Spector, TD, Steves, C, Baccarelli, AA, Bell, JT, Van Duijn, CM, Fornage, M, Hsu, Y-H, Mill, J, Mosley, TH, Seshadri, S, Deary, IJ, Epidemiology, Gastroenterology & Hepatology, Neurology, Radiology & Nuclear Medicine, and Internal Medicine
Subjects: Adult, Aged, 80 and over, Male, Genomics, DNA Methylation, Middle Aged, Epigenesis, Genetic, Cohort Studies, Cognition, Humans, CpG Islands, Female, Immediate Communication, Aged, Genome-Wide Association Study
Abstract: Cognitive functions are important correlates of health outcomes across the life-course. Individual differences in cognitive functions are partly heritable. Epigenetic modifications, such as DNA methylation, are susceptible to both genetic and environmental factors and may provide insights into individual differences in cognitive functions. Epigenome-wide meta-analyses for blood-based DNA methylation levels at ~420,000 CpG sites were performed for seven measures of cognitive functioning using data from 11 cohorts. CpGs that passed a Bonferroni correction, adjusting for the number of CpGs and cognitive tests, were assessed for: longitudinal change; being under genetic control (methylation QTLs); and associations with brain health (structural MRI), brain methylation and Alzheimer's disease pathology. Across the seven measures of cognitive functioning (meta-analysis n range: 2557-6809), there were epigenome-wide significant (P
Published: 2018

3. Genome-wide association scan allowing for epistasis in type 2 diabetes

Author: Bell, JT, Timpson, NJ, Rayner, NW, Zeggini, E, Frayling, TM, Hattersley, AT, Morris, AP, and McCarthy, MI
Abstract: In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10⁻¹¹). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals (P-value = 5 × 10⁻⁴). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings.
Published: 2016

4. First-generation scan of genome-wide association data allowing for epistasis prioritises multiple interacting candidate loci in T2D

Author: Bell, JT, Timpson, N, Rayner, NW, Zeggini, E, Morris, AP, McCarthy, MI, and Consor, WTCC
Published: 2016

5. Genome-wide search for multiple loci in type 2 diabetes

Author: Bell, JT, Elliot, K, Morris, A, Groves, CJ, Fiddy, SE, Frayling, TM, Hattersley, AT, Walker, M, Hitman, GA, McCarthy, MI, and Wiltshire, S
Published: 2016

6. Differential methylation of TRPA1 promoter is associated with pain sensitivity

Author: Bell JT, Loomis AK, Butcher LM, Gao F, Zhang B, Hyde CL, Sun J, Wu H, Ward K, Harris J, Scollen S, Davies MN, Schalkwyk LC, Mill J, The MuTHER Consortium, Williams FMK, Li N, Deloukas P, Beck S, McMahon SB, Wang J, John SL, and Spector TD
Published: 2014

7. DNA methylation profiling in breast cancer discordant identical twins identifies DOK7 as novel epigenetic biomarker

Author: Heyn H, Carmona FJ, Gomez A, Ferreira HJ, Bell JT, Sayols S, Ward K, Stefansson OA, Moran S, Sandoval J, Eyfjord JE, Spector TD, and Esteller M
Abstract: Using whole blood from 15 twin pairs discordant for breast cancer and high-resolution (450K) DNA methylation analysis, we identified 403 differentially methylated CpG sites including known and novel potential breast cancer genes. Confirming the results in an independent validation cohort of 21 twin pairs determined the docking protein DOK7 as a candidate for blood-based cancer diagnosis. DNA hypermethylation of the promoter region was also seen in primary breast cancer tissues and cancer cell lines. Hypermethylation of DOK7 occurs years before tumor diagnosis, suggesting a role as a powerful epigenetic blood-based biomarker as well as providing insights into breast cancer pathogenesis.
Published: 2013

8. EIGHT COMMON GENETIC VARIANTS ASSOCIATED WITH SERUM DHEAS LEVELS SUGGESTS A KEY ROLE IN AGEING MECHANISMS

Author: Zhai, G, Teumer, A, Stolk, L, Perry, JRB, Vandenput, L, Coviello, AD, Koster, A, Bandinelli, S, Bell, JT, Bhasin, S, Eriksson, J, Eriksson, A, Ernst, F, Ferrucci, L, Frayling, TM, Glass, D, Grundberg, E, Haring, R, Hedman, AK, Hofman, A, Kiel, DP, Kroemer, HK, Liu, Y, Lunetta, L, Lorentzon, M, Mangino, M, Melzer, D, Miljkovic, I, Nica, A, Vasan, RS, Small, KS, Soranzo, N, Uitterlinden, AG, Voelzke, H, Wilson, S, Zhuang, WV, Harris, T, Murabito, JM, Ohlsson, C, Murray, A, Jong, F, Spector, TD, and Wallaschofski, H
Published: 2011

9. Biomarkers of skin ageing using genome wide transcriptomics

Author: Glass, D, Parts, L, Knowles, D, Nica, AC, Nisbet, J, Barrett, A, Sekowska, M, Travers, M, Potter, S, Grundberg, E, Small, K, Hedman, A, Bell, JT, Surdulescu, G, Dimas, AS, Ingle, C, Nestle, FO, di Meglio, P, Min, JL, Wilk, A, Hammond, CJ, Hassanali, N, Yang, T-P, Montgomery, SB, Lindgren, CM, Zondervan, KT, Soranzo, N, Barroso, I, Durbin, R, Ahmadi, K, Winn, J, Bataille, V, Deloukas, P, McCarthy, MI, Spector, TD, Dermitzakis, ET, and Consortium, M
Published: 2011

10. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes

Author: Strawbridge, Rj, Dupuis, J, Prokopenko, I, Barker, A, Ahlqvist, E, Rybin, D, Petrie, Jr, Travers, Me, Bouatia Naji, N, Dimas, As, Nica, A, Wheeler, E, Chen, H, Voight, Bf, Taneera, J, Kanoni, S, Peden, Jf, Turrini, F, Gustafsson, S, Zabena, C, Almgren, P, Barker, Dj, Barnes, D, Dennison, Em, Eriksson, Jg, Eriksson, P, Eury, E, Folkersen, L, Fox, Cs, Frayling, Tm, Goel, A, Gu, Hf, Horikoshi, M, Isomaa, B, Jackson, Au, Jameson, Ka, Kajantie, E, Kerr Conte, J, Kuulasmaa, T, Kuusisto, J, Loos, Rj, Luan, J, Makrilakis, K, Manning, Ak, Martínez Larrad MT, Narisu, N, Nastase Mannila, M, Ohrvik, J, Osmond, C, Pascoe, L, Payne, F, Sayer, Aa, Sennblad, B, Silveira, A, Stancáková, A, Stirrups, K, Swift, Aj, Syvänen, Ac, Tuomi, T, van 't Hooft FM, Walker, M, Weedon, Mn, Xie, W, Zethelius, B, Diagram, Consortium, Giant, Consortium, Muther, Consortium, Cardiogram, Consortium, C4d, Consortium, Ongen, H, Mälarstig, A, Hopewell, Jc, Saleheen, D, Chambers, J, Parish, S, Danesh, J, Kooner, J, Ostenson, Cg, Lind, L, Cooper, Cc, Serrano Ríos, M, Ferrannini, E, Forsen, Tj, Clarke, R, Franzosi, Mg, Seedorf, U, Watkins, H, Froguel, P, Johnson, P, Deloukas, P, Collins, Fs, Laakso, M, Dermitzakis, Et, Boehnke, M, Mccarthy, Mi, Wareham, Nj, Groop, L, Pattou, F, Gloyn, Al, Dedoussis, Gv, Lyssenko, V, Meigs, Jb, Barroso, I, Watanabe, Rm, Ingelsson, E, Langenberg, C, Hamsten, A, Voight BF, Florez J. C., Scott, Lj, Steinthorsdottir, V, Morris, Ap, Dina, C, Welch, Rp, Zeggini, E, Huth, C, Aulchenko, Ys, Thorleifsson, G, Mcculloch, Lj, Ferreira, T, Grallert, H, Amin, N, Wu, G, Willer, Cj, Raychaudhuri, S, Mccarroll, Sa, Hofmann, Om, Qi, L, Segrè, Av, van Hoek, M, Navarro, P, Ardlie, K, Balkau, B, Benediktsson, R, Bennett, Aj, Blagieva, R, Boerwinkle, E, Bonnycastle, Ll, Bengtsson Boström, K, Bravenboer, B, Bumpstead, S, Burtt, P, Charpentier, G, Chines, Ps, Cornelis, M, Couper, Dj, Crawford, G, Doney, As, Elliott, Ks, Elliott, Al, Erdos, Mr, Franklin, Cs, Gieger, C, Grarup, N, Green, T, Griffin, S, Groves, Cj, Guiducci, C, Hadjadj, S, Hassanali, N, Herder, C, Johnson, Pr, Jørgensen, T, Kao, Wh, Klopp, N, Kong, A, Kraft, P, Lauritzen, T, Li, M, Lieverse, A, Lindgren, Cm, Marre, M, Meitinger, T, Midthjell, K, Morken, Ma, Nilsson, P, Owen, Kr, Perry, Jr, Petersen, K, Platou, C, Proença, C, Rathmann, W, William Rayner, N, Robertson, Nr, Rocheleau, G, Roden, M, Sampson, Mj, Saxena, R, Shields, Bm, Shrader, P, Sigurdsson, G, Sparsø, T, Strassburger, K, Stringham, Hm, Sun, Q, Thorand, B, Tichet, J, van Dam RM, van Haeften TW, van Herpt, T, van Vliet JV, Bragi Walters, G, Wijmenga, C, Witteman, J, Bergman, Rn, Cauchi, S, Gyllensten, U, Hansen, T, Hide, Wa, Hitman, Ga, Hofman, A, Hunter, Dj, Hveem, K, Mohlke, Kl, Morris, Ad, Palmer, Cn, Pramstaller, Pp, Rudan, I, Sijbrands, E, Stein, Ld, Tuomilehto, J, Uitterlinden, A, Abecasis, Gr, Boehm, Bo, Campbell, H, Daly, Mj, Hattersley, At, Hu, Fb, Pankow, Js, Pedersen, O, Wichmann, E, Florez, Jc, Sladek, R, Thorsteinsdottir, U, Wilson, Jf, Illig, T, Stefansson, K, Altshuler, D, Speliotes, Ek, Berndt, Si, Monda, Kl, Allen, Hl, Mägi, R, Randall, Jc, Vedantam, S, Winkler, Tw, Workalemahu, T, Heid, Im, Wood, Ar, Weyant, Rj, Estrada, K, Liang, L, Nemesh, J, Park, Jh, Kilpeläinen, To, Yang, J, Esko, T, Feitosa, Mf, Kutalik, Z, Mangino, M, Scherag, A, Smith, Av, Welch, R, Zhao, Jh, Aben, Kk, Absher, Dm, Dixon, Al, Fisher, E, Glazer, Nl, Goddard, Me, Heard Costa NL, Hoesel, V, Hottenga, Jj, Johansson, Å, Johnson, T, Ketkar, S, Lamina, C, Li, S, Moffatt, Mf, Myers, Rh, Peters, Mj, Preuss, M, Ripatti, S, Rivadeneira, F, Sandholt, C, Timpson, Nj, Tyrer, Jp, van Wingerden, S, White, Cc, Wiklund, F, Barlassina, C, Chasman, Di, Cooper, Mn, Jansson, Jo, Lawrence, Rw, Pellikka, N, Shi, J, Thiering, E, Alavere, H, Alibrandi, Mt, Arnold, Am, Aspelund, T, Atwood, Ld, Balmforth, Aj, Ben Shlomo, Y, Bergmann, S, Biebermann, H, Blakemore, Ai, Boes, T, Bornstein, Sr, Brown, Mj, Buchanan, Ta, Busonero, F, Cappuccio, Fp, Cavalcanti Proença, C, Ida Chen YD, Chen, Cm, Coin, L, Connell, J, Day, In, den Heijer, M, Duan, J, Ebrahim, S, Elliott, P, Elosua, R, Eiriksdottir, G, Facheris, Mf, Felix, Sb, Fischer Posovszky, P, Folsom, Ar, Friedrich, N, Freimer, Nb, Fu, M, Gaget, S, Gejman, Pv, Geus, Ej, Gjesing, Ap, Goyette, P, Grässler, J, Greenawalt, Dm, Gudnason, V, Hartikainen, Al, Hall, As, Havulinna, As, Hayward, C, Heath, Ac, Hengstenberg, C, Hicks, Aa, Hinney, A, Homuth, G, Hui, J, Igl, W, Iribarren, C, Jacobs, Kb, Jarick, I, Jewell, E, John, U, Jousilahti, P, Jula, A, Kaakinen, M, Kaplan, Lm, Kathiresan, S, Kettunen, J, Kinnunen, L, Knowles, Jw, Kolcic, I, König, Ir, Koskinen, S, Kovacs, P, Kvaløy, K, Laitinen, J, Lantieri, O, Lanzani, C, Launer, Lj, Lecoeur, C, Terho, L, Lettre, G, Liu, J, Lokki, Ml, Lorentzon, M, Luben, Rn, Ludwig, B, Magic, Manunta, P, Marek, D, Martin, Ng, Mcardle, Wl, Mccarthy, A, Mcknight, B, Melander, O, Meyre, D, Montgomery, Gw, Mulic, R, Ngwa, Js, Nelis, M, Neville, Mj, Nyholt, Dr, O'Donnell, Cj, O'Rahilly, S, Ong, Kk, Oostra, B, Paré, G, Parker, An, Perola, M, Pichler, I, Pietiläinen, Kh, Platou, Cg, Polasek, O, Pouta, A, Rafelt, S, Raitakari, O, Rayner, Nw, Ridderstråle, M, Rief, W, Ruokonen, A, Rzehak, P, Salomaa, V, Sanders, Ar, Sandhu, Ms, Sanna, S, Saramies, J, Savolainen, Mj, Scherag, S, Schipf, S, Schreiber, S, Schunkert, H, Silander, K, Sinisalo, J, Siscovick, Ds, Smit, Jh, Soranzo, N, Sovio, U, Stephens, J, Surakka, I, Tammesoo, Ml, Tardif, Jc, Teder Laving, M, Teslovich, Tm, Thompson, Jr, Thomson, B, Tönjes, A, van Meurs JB, van Ommen GJ, Vatin, V, Viikari, J, Visvikis Siest, S, Vitart, V, Vogel, Ci, Waite, Ll, Wallaschofski, H, Walters, Gb, Widen, E, Wiegand, S, Wild, Sh, Willemsen, G, Witte, Dr, Witteman, Jc, Xu, J, Zhang, Q, Zgaga, L, Ziegler, A, Zitting, P, Beilby, Jp, Farooqi, Is, Hebebrand, J, Huikuri, Hv, James, Al, Kähönen, M, Levinson, Df, Macciardi, F, Nieminen, Ms, Ohlsson, C, Palmer, Lj, Ridker, Pm, Stumvoll, M, Beckmann, Js, Boeing, H, Dorret, I. B., Caulfield, Mj, Chanock, Sj, Cupples, La, Smith, Gd, Erdmann, J, Grönberg, H, Hall, P, Harris, Tb, Hayes, Rb, Heinrich, J, Jarvelin, Mr, Kaprio, J, Karpe, F, Khaw, Kt, Kiemeney, La, Krude, H, Lawlor, Da, Metspalu, A, Munroe, Pb, Ouwehand, Wh, Penninx, Bw, Peters, A, Quertermous, T, Reinehr, T, Rissanen, A, Samani, Nj, Schwarz, Pe, Shuldiner, Ar, Spector, Td, Uda, M, Valle, Tt, Wabitsch, M, Waeber, G, Shaun, P, Eric, E. S., Peter, M. V., Assimes, Tl, Borecki, Ib, Groop, Lc, Haritunians, T, Kaplan, Rc, O'Connell, Jr, Peltonen, L, Schlessinger, D, Strachan, Dp, van Duijn CM, Barroso, H, North, Ke, Hirschhorn, Jn, Nica, Ac, Parts, L, Glass, D, Nisbet, J, Barrett, A, Sekowska, M, Travers, M, Potter, S, Grundberg, E, Small, K, Hedman, Åk, Bataille, V, Bell, Jt, Surdulescu, G, Ingle, C, Nestle, Fo, di Meglio, P, Min, Jl, Wilk, A, Hammond, Cj, Yang, Tp, Montgomery, Sb, Zondervan, Kt, Durbin, R, Ahmadi, K, Reilly, Mp, Holm, H, Stewart, Af, Barbalic, M, Absher, D, Aherrahrou, Z, Allayee, H, Anand, Ss, Andersen, K, Anderson, Jl, Ardissino, D, Ball, Sg, Barnes, Ta, Becker, Dm, Becker, Lc, Berger, K, Bis, Jc, Boekholdt, Sm, Braund, Ps, Burnett, Ms, Buysschaert, I, Cardiogenics, Carlquist, Jf, Chen, L, Cichon, S, Codd, V, Davies, Rw, Dedoussis, G, Dehghan, A, Demissie, S, Devaney, Jm, Diemert, P, Do, R, Doering, A, Eifert, S, El Mokhtari NE, Ellis, Sg, Engert, Jc, Epstein, Se, de Faire, U, Fischer, M, Freyer, J, Gigante, B, Girelli, Domenico, Gretarsdottir, S, Gulcher, Jr, Halperin, E, Hammond, N, Hazen, Sl, Horne, Bd, Jones, Gt, Jukema, Jw, Kaiser, Ma, Kastelein, Jj, Kolovou, G, Laaksonen, R, Lambrechts, D, Leander, K, Lieb, W, Loley, C, Lotery, Aj, Mannucci, Pm, Maouche, S, Martinelli, Nicola, Mckeown, Pp, Meisinger, C, Merlini, Pa, Mooser, V, Morgan, T, Mühleisen, Tw, Muhlestein, Jb, Münzel, T, Musunuru, K, Nahrstaedt, J, Nelson, Cp, Nöthen, Mm, Olivieri, Oliviero, Patel, Rs, Patterson, Cc, Peyvandi, F, Qu, L, Quyyumi, Aa, Rader, Dj, Rallidis, Ls, Rice, C, Rosendaal, Fr, Rubin, D, Sampietro, Ml, Schadt, E, Schäfer, A, Schillert, A, Schrezenmeir, J, Schwartz, Sm, Sivananthan, M, Sivapalaratnam, S, Smith, A, Smith, Tb, Snoep, Jd, Spertus, Ja, Stark, K, Stoll, M, Tang, Wh, Tennstedt, S, Thorgeirsson, G, Tomaszewski, M, Uitterlinden, Ag, van Rij AM, Wells, Ga, Wichmann, He, Wild, Ps, Willenborg, C, Wright, Bj, Ye, S, Zeller, T, Cambien, F, Goodall, Ah, März, W, Blankenberg, S, Roberts, R, Mcpherson, R, Nilesh, J. S., Medical Research Council (MRC), Nica, Alexandra, Ongen, Halit, Dermitzakis, Emmanouil, ACS - Amsterdam Cardiovascular Sciences, Cardiology, Vascular Medicine, Biological Psychology, EMGO+ - Lifestyle, Overweight and Diabetes, Scherag, Andre (Beitragende*r), Hinney, Anke (Beitragende*r), Scherag, S. (Beitragende*r), Vogel, C (Beitragende*r), Hebebrand, Johannes (Beitragende*r), University of Groningen, Wheeler, Eleanor [0000-0002-8616-6444], Barnes, Daniel [0000-0002-3781-7570], Luan, Jian'an [0000-0003-3137-6337], Johnson, Kathleen [0000-0002-6823-3252], Danesh, John [0000-0003-1158-6791], Wareham, Nicholas [0000-0003-1422-2993], Barroso, Ines [0000-0001-5800-4520], Langenberg, Claudia [0000-0002-5017-7344], and Apollo - University of Cambridge Repository
Subjects: Male, Netherlands Twin Register (NTR), endocrine system diseases, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Medizin, Genome-wide association study, Type 2 diabetes, CORONARY HEART-DISEASE, Fasting/blood, 0302 clinical medicine, Insulin, Glucose homeostasis, ddc:576.5, Genome-wide, Diabetes Mellitus, Type 2/blood/genetics/metabolism, CARDIoGRAM Consortium, POPULATION, Proinsulin, RISK, Genetics, 0303 health sciences, INSULIN SENSITIVITY, 11 Medical And Health Sciences, Fasting, Polymorphism, Single Nucleotide/genetics, OBESITY, Female, type 2 diabetes, Life Sciences & Biomedicine, hormones, hormone substitutes, and hormone antagonists, Insulin processing, Adult, medicine.medical_specialty, endocrine system, ENDOCRINOLOGY & METABOLISM, SUSCEPTIBILITY LOCI, Genotype, 030209 endocrinology & metabolism, DIAGRAM Consortium, Biology, C4D Consortium, Polymorphism, Single Nucleotide, Molecular epidemiology [NCEBP 1], 03 medical and health sciences, Insulin resistance, BETA-CELL FUNCTION, SDG 3 - Good Health and Well-being, Internal medicine, GIANT Consortium, Internal Medicine, medicine, Humans, METAANALYSIS, 030304 developmental biology, Science & Technology, Genome, Human, Hormonal regulation [IGMD 6], Genetic Variation, nutritional and metabolic diseases, proinsulin, medicine.disease, Proinsulin/blood, TCF7L2, Endocrinology, Diabetes Mellitus, Type 2, MuTHER Consortium, GLUCOSE-HOMEOSTASIS, Insulin/blood
Abstract: OBJECTIVE Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.
Published: 2011

11. Multiple sclerosis susceptibility and the X chromosome

Author: Herrera, BM, primary, Cader, MZ, additional, Dyment, DA, additional, Bell, JT, additional, DeLuca, GC, additional, Willer, CJ, additional, Lincoln, MR, additional, Ramagopalan, SV, additional, Chao, M., additional, Orton, S-M., additional, Sadovnick, AD, additional, and Ebers, GC, additional
Published: 2007
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12. Genetic studies of body mass index yield new insights for obesity biology

Author: Locke, Adam E, Kahali, Bratati, Croteau-Chonka, Damien C, Doney, Alex S F, He, Bing, Heikkilä, Outi, Hietala, Kustaa, Kytö, Janne, Lahermo, Päivi, Lehto, Markku, Österholm, Anne-May, Parkkonen, Maija, Pitkäniemi, Janne, Rosengård-Bärlund, Milla, Eklund, Niina, Saraheimo, Markku, Sarti, Cinzia, Söderlund, Jenny, Soro-Paavonen, Aino, Syreeni, Anna, Thorn, Lena M, Tikkanen, Heikki, Tolonen, Nina, Tryggvason, Karl, Tuomilehto, Jaakko, Estrada, Karol, Wadén, Johan, Gill, Geoffrey V, Prior, Sarah, Guiducci, Candace, Mirel, Daniel B, Taylor, Andrew, Hosseini, Mohsen, Parving, Hans-Henrik, Rossing, Peter, Tarnow, Lise, Eury, Elodie, Ladenvall, Claes, Alhenc-Gelas, François, Lefebvre, Pierre, Rigalleau, Vincent, Roussel, Ronan, Tregouet, David-Alexandre, Maestroni, Anna, Maestroni, Silvia, Falhammar, Henrik, Gu, Tianwei, Folkersen, Lasse, Möllsten, Anna, Cimponeriu, Dan, Mihai, Ioana, Mota, Maria, Mota, Eugen, Serafinceanu, Cristian, Stavarachi, Monica, Hanson, Robert L, Nelson, Robert G, Kretzler, Matthias, Fraser, Ross M, Colhoun, Helen M, Panduru, Nicolae Mircea, Gu, Harvest F, Brismar, Kerstin, Zerbini, Gianpaolo, Hadjadj, Samy, Marre, Michel, Groop, Leif, Lajer, Maria, Bull, Shelley B, Garcia, Melissa E, Waggott, Daryl, Paterson, Andrew D, Savage, David A, Bain, Stephen C, Martin, Finian, Hirschhorn, Joel N, Godson, Catherine, Florez, Jose C, Groop, Per-Henrik, Maxwell, Alexander P, Geller, Frank, Willer, Cristen J, Schmidt, Ellen M, Sengupta, Sebanti, Peloso, Gina M, Gustafsson, Stefan, Kanoni, Stavroula, Ganna, Andrea, Chen, Jin, Buchkovich, Martin L, Mora, Samia, Giedraitis, Vilmantas, Beckmann, Jacques S, Bragg-Gresham, Jennifer L, Chang, Hsing-Y, Demirkan, Ayşe, Den Hertog, Heleen M, Do, Ron, Donnelly, Louise A, Ehret, Georg B, Esko, Tõnu, Feitosa, Mary F, Gigante, Bruna, Ferreira, Teresa, Fischer, Krista, Fontanillas, Pierre, Freitag, Daniel F, Gurdasani, Deepti, Heikkilä, Kauko, Hyppönen, Elina, Isaacs, Aaron, Jackson, Anne U, Esko, Tonu, Go, Alan S, Johansson, Åsa, Johnson, Toby, Kaakinen, Marika, Kettunen, Johannes, Kleber, Marcus E, Li, Xiaohui, Luan, Jian'an, Lyytikäinen, Leo-Pekka, Magnusson, Patrik K E, Mangino, Massimo, Golay, Alain, Mihailov, Evelin, Montasser, May E, Müller-Nurasyid, Martina, Nolte, Ilja M, O'Connell, Jeffrey R, Palmer, Cameron D, Perola, Markus, Petersen, Ann-Kristin, Sanna, Serena, Saxena, Richa, Goodall, Alison H, Service, Susan K, Shah, Sonia, Shungin, Dmitry, Sidore, Carlo, Song, Ci, Strawbridge, Rona J, Surakka, Ida, Tanaka, Toshiko, Teslovich, Tanya M, Thorleifsson, Gudmar, Gordon, Scott D, Van den Herik, Evita G, Voight, Benjamin F, Volcik, Kelly A, Waite, Lindsay L, Wong, Andrew, Wu, Ying, Zhang, Weihua, Absher, Devin, Asiki, Gershim, Barroso, Inês, Gorski, Mathias, Been, Latonya F, Bolton, Jennifer L, Bonnycastle, Lori L, Brambilla, Paolo, Burnett, Mary S, Cesana, Giancarlo, Dimitriou, Maria, Doring, Angela, Elliott, Paul, Grabe, Hans-Jörgen, Epstein, Stephen E, Eyjolfsson, Gudmundur Ingi, 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EN., Warrington, NM., Alavere, H., Barroso, I., Berenson, GS., Blackburn, H., Busonero, F., Chen, W., Couper, D., Easton, DF., Eriksson, J., Foroud, T., Geller, F., Hernandez, DG., Kilpeläinen, TO., Li, S., Melbye, M., Murray, JC., Murray, SS., Nelis, M., Ness, AR., Northstone, K., Peacock, M., Pennell, CE., Pharoah, P., Rafnar, T., Rice, JP., Ring, SM., Schork, NJ., Segrè, AV., Sovio, U., Srinivasan, SR., Tammesoo, ML., Tyrer, J., van Meurs JB., Weedon, MN., Wichmann, H., Young, L., Bierut, LJ., Boyd, HA., Econs, MJ., Van T'Hooft, Ferdinand M., Njølstad, Inger, Abecasis, Gonçalo R., Barroso, Inɥ, The MIGEN Consortium, Investigator, Casari, GIORGIO NEVIO, Other departments, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, Landsteiner Laboratory, Clinical Haematology, Damage and Repair in Cancer Development and Cancer Treatment (DARE), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Life 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Mangino, M, Leach, I, Medina-Gomez, C, Medland, S, Nalls, M, Palmer, C, Pasko, D, Pechlivanis, S, Peters, M, Prokopenko, I, Shungin, D, Stancakova, A, Strawbridge, R, Sung, Y, Tanaka, T, Teumer, A, Trompet, S, van der Laan, S, van Setten, J, Van Vliet-Ostaptchouk, J, Wang, Z, Yengo, L, Zhang, W, Isaacs, A, Albrecht, E, Arnlov, J, Arscott, G, Attwood, A, Bandinelli, S, Barrett, A, Bas, I, Bellis, C, Bennett, A, Berne, C, Blagieva, R, Bluher, M, Bohringer, S, Bonnycastle, L, Bottcher, Y, Boyd, H, Bruinenberg, M, Caspersen, I, Chen, Y, Clarke, R, Daw, E, de Craen, A, Delgado, G, Dimitriou, M, Doney, A, Eklund, N, Estrada, K, Eury, E, Folkersen, L, Fraser, R, Garcia, M, Geller, F, Giedraitis, V, Gigante, B, Go, A, Golay, A, Goodall, A, Gordon, S, Gorski, M, Grabe, H, Grallert, H, Grammer, T, Grassler, J, Gronberg, H, Groves, C, Gusto, G, Haessler, J, Hall, P, Haller, T, Hallmans, G, Hartman, C, Hassinen, M, Hayward, C, Heard-Costa, N, Helmer, Q, Hengstenberg, C, Holmen, O, Hottenga, J, James, A, Jeff, J, Johansson, A, Jolley, J, Juliusdottir, T, Kinnunen, L, Koenig, W, Koskenvuo, M, Kratzer, W, Laitinen, J, Lamina, C, Leander, K, Lee, N, Lichtner, P, Lind, L, Lindstrom, J, Lo, K, Lobbens, S, Lorbeer, R, Lu, Y, Mach, F, Magnusson, P, Mahajan, A, Mcardle, W, Mclachlan, S, Menni, C, Merger, S, Mihailov, E, Milani, L, Moayyeri, A, Monda, K, Morken, M, Mulas, A, Muller, G, Muller-Nurasyid, M, Musk, A, Nagaraja, R, Nothen, M, Nolte, I, Pilz, S, Rayner, N, Renstrom, F, Rettig, R, Ried, J, Ripke, S, Robertson, N, Rose, L, Sanna, S, Scharnagl, H, Scholtens, S, Schumacher, F, Scott, W, Seufferlein, T, Shi, J, Smith, A, Smolonska, J, Stanton, A, Steinthorsdottir, V, Stirrups, K, Stringham, H, Sundstrom, J, Swertz, M, Swift, A, Syvanen, A, Tan, S, Tayo, B, Thorand, B, Thorleifsson, G, Tyrer, J, Uh, H, Vandenput, L, Verhulst, F, Vermeulen, S, Verweij, N, Vonk, J, Waite, L, Warren, H, Waterworth, D, Weedon, M, Wilkens, L, Willenborg, C, Wilsgaard, T, Wojczynski, M, Wong, A, Wright, A, Zhang, Q, Brennan, E, Choi, M, Dastani, Z, Drong, A, Eriksson, P, Franco-Cereceda, A, Gadin, J, Gharavi, A, Goddard, M, Handsaker, R, Huang, J, Karpe, F, Kathiresan, S, Keildson, S, Kiryluk, K, Kubo, M, Lee, J, Liang, L, Lifton, R, Ma, B, Mccarroll, S, Mcknight, A, Min, J, Moffatt, M, Montgomery, G, Murabito, J, Nicholson, G, Nyholt, D, Okada, Y, Perry, J, Dorajoo, R, Reinmaa, E, Salem, R, Sandholm, N, Scott, R, Stolk, L, Takahashi, A, Van't Hooft, F, Vinkhuyzen, A, Westra, H, Zheng, W, Zondervan, K, Heath, A, Arveiler, D, Bakker, S, Beilby, J, Bergman, R, Blangero, J, Bovet, P, Campbell, H, Caulfield, M, Cesana, G, Chakravarti, A, Chasman, D, Chines, P, Collins, F, Crawford, D, Cupples, L, Cusi, D, Danesh, J, de Faire, U, Den Ruijter, H, Dominiczak, A, Erbel, R, Erdmann, J, Eriksson, J, Farrall, M, Felix, S, Ferrannini, E, Ferrieres, J, Ford, I, Forouhi, N, Forrester, T, Franco, O, Gansevoort, R, Gejman, P, Gieger, C, Gottesman, O, Gudnason, V, Gyllensten, U, Hall, A, Harris, T, Hattersley, A, Hicks, A, Hindorff, L, Hingorani, A, Hofman, A, Homuth, G, Hovingh, G, Humphries, S, Hunt, S, Hypponen, E, Illig, T, Jacobs, K, Jarvelin, M, Jockel, K, Johansen, B, Jousilahti, P, Jukema, J, Jula, A, Kaprio, J, Kastelein, J, Keinanen-Kiukaanniemi, S, Kiemeney, L, Knekt, P, Kooner, J, Kooperberg, C, Kovacs, P, Kraja, A, Kumari, M, Kuusisto, J, Lakka, T, Langenberg, C, Marchand, L, Lehtimaki, T, Lyssenko, V, Mannisto, S, Marette, A, Matise, T, Mckenzie, C, Mcknight, B, Moll, F, Morris, A, Murray, J, Nelis, M, Ohlsson, C, Oldehinkel, A, Ong, K, Madden, P, Pasterkamp, G, Peden, J, Peters, A, Postma, D, Pramstaller, P, Price, J, Qi, L, Raitakari, O, Rankinen, T, Rao, D, Rice, T, Ridker, P, Rioux, J, Ritchie, M, Rudan, I, Salomaa, V, Samani, N, Saramies, J, Sarzynski, M, Schunkert, H, Schwarz, P, Sever, P, Shuldiner, A, Sinisalo, J, Stolk, R, Strauch, K, Tonjes, A, Tregouet, D, Tremblay, A, Tremoli, E, Virtamo, J, Vohl, M, Volker, U, Waeber, G, Willemsen, G, 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Mccarthy, M, Visscher, P, Scherag, A, Willer, C, Boehnke, M, Mohlke, K, Lindgren, C, Beckmann, J, Barroso, I, North, K, Ingelsson, E, Hirschhorn, J, Loos, R, Speliotes, E, Thompson, J, Goldstein, B, Konig, I, Cazier, J, Grundberg, E, Havulinna, A, Ho, W, Hopewell, J, Eriksson, N, Lundmark, P, Lyytikainen, L, Rafelt, S, Tikkanen, E, Van Zuydam, N, Voight, B, Ziegler, A, Altshuler, D, Balmforth, A, Braund, P, Burgdorf, C, Claudi-Boehm, S, Cox, D, Do, R, El Mokhtari, N, Fontanillas, P, Hager, J, Han, B, Kang, H, Kessler, T, Knowles, J, Kolovou, G, Langford, C, Lokki, M, Lundmark, A, Meisinger, C, Melander, O, Maouche, S, Nikus, K, Rasheed, A, Rosinger, S, Rubin, D, Rumpf, M, Schafer, A, Sivananthan, M, Song, C, Stewart, A, Thorgeirsson, G, van der Schoot, C, Wagner, P, Wells, G, Wild, P, Tsun-Po, Y, Basart, H, Brambilla, P, Cambien, F, Cupples, A, Dehghan, A, Diemert, P, Epstein, S, Evans, A, Ferrario, M, Gauguier, D, Hazen, S, Holm, H, Iribarren, C, Jang, Y, Kahonen, M, Kee, F, Kim, H, 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Kroemer, H, Nauck, M, Stracke, S, Volzke, H, Aulchenko, Y, Polasek, O, Hastie, N, Vitart, V, Helmer, C, Wang, J, Ruggiero, D, Bergmann, S, Viikari, J, Nikopensius, T, Province, M, Ketkar, S, Colhoun, H, Robino, A, Giulianini, F, Kramer, B, Portas, L, Buckley, B, Adam, M, Thun, G, Paulweber, B, Haun, M, Sala, C, Metzger, M, Mitchell, P, Ciullo, M, Kim, S, Vollenweider, P, Gasparini, P, Pirastu, M, Probst-Hensch, N, Kronenberg, F, Toniolo, D, Coresh, J, Schmidt, R, Siscovick, D, Kardia, S, Curhan, G, Franke, A, Parsa, A, Goessling, W, Kao, W, de Boer, I, Peralta, C, Akylbekova, E, Kramer, H, Arking, D, Franceschini, N, Egan, J, Hernandez, D, Townsend, R, Lumley, T, Psaty, B, Kestenbaum, B, Haritunians, T, Mooser, V, Florez, J, Meigs, J, Lu, X, Leak, T, Aasarod, K, Skorpen, F, Baumert, J, Devuyst, O, Mychaleckyj, J, Kedenko, L, Coassin, S, Hallan, S, Navis, G, Shlipak, M, Bull, S, Paterson, A, Rotter, J, Dreisbach, A, Anderson, C, Guo, Q, Henders, A, Lambert, A, Lee, S, Kraft, P, Kennedy, S, Macgregor, S, Missmer, S, Painter, J, Roseman, F, Treloar, S, Wallace, L, Forsblom, C, Isakova, T, Mckay, G, Williams, W, Sadlier, D, Makinen, V, Swan, E, Boright, A, Ahlqvist, E, Keller, B, Huang, H, Ahola, A, Fagerholm, E, Gordin, D, Harjutsalo, V, He, B, Heikkila, O, Hietala, K, Kyto, J, Lahermo, P, Lehto, M, Osterholm, A, Parkkonen, M, Pitkaniemi, J, Rosengard-Barlund, M, Saraheimo, M, Sarti, C, Soderlund, J, Soro-Paavonen, A, Syreeni, A, Thorn, L, Tikkanen, H, Tolonen, N, Tryggvason, K, Waden, J, Gill, G, Prior, S, Guiducci, C, Mirel, D, Taylor, A, Hosseini, M, Parving, H, Rossing, P, Tarnow, L, Ladenvall, C, Alhenc-Gelas, F, Lefebvre, P, Rigalleau, V, Roussel, R, Maestroni, A, Maestroni, S, Falhammar, H, Gu, T, Mollsten, A, Cimponeriu, D, Mihai, I, Mota, M, Mota, E, Serafinceanu, C, Stavarachi, M, Hanson, R, Nelson, R, Kretzler, M, Panduru, N, Gu, H, Brismar, K, Zerbini, G, Hadjadj, S, Marre, M, Lajer, M, Waggott, D, Savage, D, Bain, S, Martin, F, Godson, C, Groop, P, Maxwell, A, Sengupta, S, Peloso, G, Ganna, A, Mora, S, Chang, H, Den Hertog, H, Donnelly, L, Freitag, D, Gurdasani, D, Heikkila, K, Johnson, T, Kaakinen, M, Kettunen, J, Li, X, Montasser, M, Petersen, A, Saxena, R, Service, S, Sidore, C, Surakka, I, Teslovich, T, Van den Herik, E, Volcik, K, Wu, Y, Asiki, G, Been, L, Burnett, M, Elliott, P, Eyjolfsson, G, Goodarzi, M, Gravito, M, Hartikainen, A, Hung, Y, Jones, M, Kaleebu, P, Khaw, K, Kim, E, Komulainen, P, Lin, S, Narisu, N, Nieminen, T, Nsubuga, R, Olafsson, I, Palotie, A, Papamarkou, T, Pomilla, C, Pouta, A, Ruokonen, A, Seeley, J, Silander, K, Tiret, L, van Pelt, L, Wainwright, N, Wijmenga, C, Young, E, Bennett, F, Boomsma, D, Burnier, M, Feranil, A, Freimer, N, Hsiung, C, Kesaniemi, A, Koudstaal, P, Krauss, R, Kyvik, K, Meneton, P, Moilanen, L, Sanghera, D, Sheu, W, Whitfield, J, Wolffenbuttel, B, Ordovas, J, Rich, S, Johnson, L, Larson, M, Levy, D, Newton-Cheh, C, O'Reilly, P, Palmas, W, Rice, K, Snider, H, Tobin, M, Verwoert, G, Pihur, V, Heath, S, Sober, S, Arora, P, Zhang, F, Lucas, G, Milaneschi, Y, Parker, A, Fava, C, Fox, E, Go, M, Sjogren, M, Vinay, D, Alexander, M, Tabara, Y, Shaw-Hawkins, S, Whincup, P, Shi, G, Seielstad, M, Sim, X, Nguyen, K, Matullo, G, Gaunt, T, Onland-Moret, N, Cooper, M, Platou, C, Org, E, Hardy, R, Dahgam, S, Palmen, J, Kuznetsova, T, Uiterwaal, C, Adeyemo, A, Ludwig, B, Tomaszewski, M, Tzoulaki, I, Palmer, N, Chang, Y, Steinle, N, Grobbee, D, Morrison, A, Najjar, S, Hadley, D, Brown, M, Connell, J, Day, I, Lawlor, D, Lawrence, R, Ongen, H, Li, Y, Young, J, Bis, J, Chaturvedi, N, Islam, M, Jafar, T, Kulkarni, S, Howard, P, Guarrera, S, Ricceri, F, Emilsson, V, Plump, A, Weder, A, Sun, Y, Scott, L, Peltonen, L, Vartiainen, E, Brand, S, Staessen, J, Wang, T, Burton, P, Artigas, M, Dong, Y, Wang, X, Zhu, H, Rudock, M, Heckbert, S, Smith, N, Wiggins, K, Doumatey, A, Shriner, D, Veldre, G, Viigimaa, M, Kinra, S, Prabhakaran, D, Tripathy, V, Langefeld, C, Rosengren, A, Thelle, D, Corsi, A, Singleton, A, Hilton, G, Salako, T, Iwai, N, Kita, Y, Ogihara, T, Ohkubo, T, Okamura, T, Ueshima, H, Umemura, S, Eyheramendy, S, Meitinger, T, Cho, Y, Scott, J, Sehmi, J, Hedblad, B, Nilsson, P, Smith, G, Raffel, L, Yao, J, Schwartz, S, Ikram, M, W, L, Mosley, T, Seshadri, S, Shrine, N, Wain, L, Zitting, P, Cooper, J, van Gilst, W, Janipalli, C, Mani, K, Yajnik, C, Mattace-Raso, F, Lakatta, E, Orru, M, Scuteri, A, Ala-Korpela, M, Kangas, A, Soininen, P, Tukiainen, T, Wurtz, P, Ong, R, Dorr, M, Galan, P, Hercberg, S, Lathrop, M, Zelenika, D, Zhai, G, Meschia, J, Sharma, P, Terzic, J, Kumar, M, Denniff, M, Zukowska-Szczechowska, E, Wagenknecht, L, Fowkes, F, Charchar, F, Guo, X, Rotimi, C, Bots, M, Brand, E, Talmud, P, Nyberg, F, Laan, M, Palmer, L, van der Schouw, Y, Casas, J, Vineis, P, Ganesh, S, Wong, T, Tai, E, Morris, R, Marmot, M, Miki, T, Chandak, G, Zhu, X, Elosua, R, Soranzo, N, Sijbrands, E, Uda, M, Vasan, R, Alizadeh, B, de Boer, R, Boezen, H, Hillege, H, van der Klauw, M, Ormel, J, Rosmalen, J, Slaets, J, Lagou, V, Welch, R, Wheeler, E, Rehnberg, E, Rasmussen-Torvik, L, Lecoeur, C, Johnson, P, Sennblad, B, Salo, P, Timpson, N, Evans, D, St Pourcain, B, Bielak, L, Horikoshi, M, Navarro, P, Raychaudhuri, S, Chen, H, Rybin, D, Willems, S, Song, K, An, P, Marullo, L, Jansen, H, Pankow, J, Edkins, S, Varga, T, Oksa, H, Antonella, M, Kong, A, Herder, C, Antti, J, Small, K, Miljkovic, I, Atalay, M, Kiess, W, Smit, J, Campbell, S, Fowkes, G, Rathmann, W, Maerz, W, Watanabe, R, de Geus, E, Penninx, B, Toenjes, A, Peyser, P, Korner, A, Dupuis, J, Cucca, F, Balkau, B, Bouatia-Naji, N, Purcell, S, Musunuru, K, Ardissino, D, Mannucci, P, Anand, S, Engert, J, Morgan, T, Spertus, J, Stoll, M, Girelli, D, Mckeown, P, Patterson, C, Merlini, P, Berzuini, C, Bernardinelli, L, Peyvandi, F, Tubaro, M, Celli, P, Fetiveau, R, Marziliano, N, Casari, G, Galli, M, Ribichini, F, Rossi, M, Bernardi, F, Zonzin, P, Piazza, A, Yee, J, Friedlander, Y, Marrugat, J, Subirana, I, Sala, J, Ramos, R, Williams, G, Nathan, D, Macrae, C, Berglund, G, Asselta, R, Duga, S, Spreafico, M, Daly, M, Nemesh, J, Korn, J, Surti, A, Gianniny, L, Parkin, M, Burtt, N, Gabriel, S, Wright, B, Ball, S, Schunkert, I, Linsel-Nitschke, P, Lieb, W, Fischer, M, Grosshennig, A, Preuss, M, Scholz, M, Chen, Z, Wilensky, R, Matthai, W, Qasim, A, Hakonarson, H, Devaney, J, Pichard, A, Kent, K, Satler, L, Lindsay, J, Waksman, R, Knouff, C, Scheffold, T, Berger, K, Huge, A, Martinelli, N, Olivieri, O, Corrocher, R, Xie, C, Ahmadi, K, Ainali, C, Bataille, V, Bell, J, Buil, A, Dermitzakis, E, Dimas, A, Durbin, R, Glass, D, Hassanali, N, Ingle, C, Knowles, D, Krestyaninova, M, Lowe, C, Meduri, E, Di Meglio, P, Montgomery, S, Nestle, F, Nica, A, Nisbet, J, O'Rahilly, S, Parts, L, Potter, S, Sekowska, M, Shin, S, Surdulescu, G, Travers, M, Tsaprouni, L, Tsoka, S, Wilk, A, Yang, T, Higashio, J, Williams, R, Nato, A, Ambite, J, Deelman, E, Manolio, T, Heiss, G, Taylor, K, Avery, C, Graff, M, Lin, D, Quibrera, M, Cochran, B, Kao, L, Umans, J, Cole, S, Maccluer, J, Person, S, Gross, M, Fornage, M, Durda, P, Jenny, N, Patsy, B, Arnold, A, Buzkova, P, Haines, J, Murdock, D, Glenn, K, Brown-Gentry, K, Thornton-Wells, T, Dumitrescu, L, Bush, W, Mitchell, S, Goodloe, R, Wilson, S, Boston, J, Malinowski, J, Restrepo, N, Oetjens, M, Fowke, J, Spencer, K, Pendergrass, S, Le Marchand, L, Park, L, Tiirikainen, M, Kolonel, L, Cheng, I, Wang, H, Shohet, R, Stram, D, Henderson, B, Monroe, K, Anderson, G, Carlson, C, Prentice, R, Lacroix, A, Wu, C, Carty, C, Rosse, S, Young, A, Kocarnik, J, Lin, Y, Jackson, R, Duggan, D, Kuller, L, He, C, Sulem, P, Barbalic, M, Broer, L, Byrne, E, Gudbjartsson, D, Mcardle, P, Porcu, E, van Wingerden, S, Zhuang, W, Lauc, L, Broekmans, F, Burri, A, Chen, C, Corre, T, Coviello, A, D'Adamo, P, Davies, G, Deary, I, Ebrahim, S, Fauser, B, Ferreli, L, Folsom, A, Hankinson, S, Hass, M, Janssens, A, Karasik, D, Keyzer, J, Kiel, D, Lahti, J, Lai, S, Laisk, T, Laven, J, Liu, J, Lopez, L, Louwers, Y, Marongiu, M, Klaric, I, Masciullo, C, Melzer, D, Newman, A, Pare, G, Peeters, P, Pop, V, Raikkonen, K, Salumets, A, Stacey, S, Starr, J, Stathopoulou, M, Styrkarsdottir, U, Tenesa, A, Tryggvadottir, L, Tsui, K, van Dam, R, van Gils, C, van Nierop, P, Vink, J, Voorhuis, M, Widen, E, Wijnands-Van Gent, C, Yerges-Armstrong, L, Zgaga, L, Zygmunt, M, Buring, J, Crisponi, L, Demerath, E, Streeten, E, Murray, A, Visser, J, Lunetta, K, Elks, C, Cousminer, D, Koller, D, Lin, P, Smith, E, Warrington, N, Alavere, H, Berenson, G, Blackburn, H, Busonero, F, Chen, W, Couper, D, Easton, D, Foroud, T, Kilpelainen, T, Li, S, Murray, S, Ness, A, Northstone, K, Peacock, M, Pennell, C, Pharoah, P, Rafnar, T, Rice, J, Ring, S, Schork, N, Segre, A, Sovio, U, Srinivasan, S, Tammesoo, M, van Meurs, J, Young, L, Bierut, L, Econs, M, The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GLGC, The ICBP, The MAGIC Investigators, The MuTHER Consortium, The MIGen Consortium, The PAGE Consortium, The ReproGen Consortium, The GENIE Consortium, The International Endogene Consortium, Berndt, Sonja I, Justice, Anne E, Hyppönen, Elina Tuulikki, Epidemiology and Data Science, NCA - Neurobiology of mental health, and EMGO - Lifestyle, overweight and diabetes
Subjects: Male, LOCI, Genome-wide association study, Continental Population Groups/genetics, VARIANTS, Body Mass Index, Insulin Secretion, Insulin, Age Factor, Adiposity, ddc:616, Adipogenesis, Genetic Predisposition to Disease/genetics, Synapse, 3. Good health, Continental Population Group, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], GENOME-WIDE ASSOCIATIONPROVIDES INSIGHTSGLYCEMIC TRAITSLOCIMETAANALYSISVARIANTSINDIVIDUALSHIPPOCAMPALARCHITECTURETOPIRAMATE, ddc:500, Adipogenesis/genetics, Single Nucleotide/genetics, Age Factors, Continental Population Groups, Energy Metabolism, Europe, Female, Genetic Predisposition to Disease, Glutamic Acid, Humans, Obesity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Synapses, Genome-Wide Association Study, Multidisciplinary, genetics [Adiposity], Human, Socio-culturale, genetics [Energy Metabolism], ta3111, genetic, body mass index, obesity, SDG 3 - Good Health and Well-being, GLYCEMIC TRAITS, genetics [Continental Population Groups], Genetic variability, Polymorphism, GENOME-WIDE ASSOCIATION, genetics [Adipogenesis], METAANALYSIS, Genetic association, Adipogenesi, genetics [Quantitative Trait Loci], ta1184, metabolism [Glutamic Acid], ta1182, PATHWAYS, metabolism [Synapses], ta3121, medicine.disease, metabolism [Insulin], Adiposity/genetics, Clinical Medicine, Quantitative Trait Loci/genetics, Body mass index, HUMAN HEIGHT, BIO/12 - BIOCHIMICA CLINICA E BIOLOGIA MOLECOLARE CLINICA, Synapses/metabolism, Medizin, Obesity/genetics, Bioinformatics, genetic basis, Obesity/metabolism, genetics [Obesity], body mass index (BMI), genetics [Genetic Predisposition to Disease], ethnology [Europe], 2. Zero hunger, Genetics, ARCHITECTURE, Genetics of obesity, Medicine (all), Single Nucleotide, Polymorphism, Single Nucleotide/genetics, Insulin/metabolism/secretion, Glutamic Acid/metabolism, genetics [Polymorphism, Single Nucleotide], EXPRESSION, Insulin/metabolism, PROVIDES INSIGHTS, genetics [Racial Groups], Biology, Obesity/genetics/metabolism, Europe/ethnology, metabolism [Obesity], Mendelian randomization, medicine, Energy Metabolism/genetics, body mass, genetic analysis, obesity, Klinisk medicin
Abstract: Item does not contain fulltext Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 x 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for approximately 2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Published: 2015

13. New genetic loci link adipose and insulin biology to body fat distribution

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Laura, Mencej-Bedrac, Simona, Helmer, Quinta, Nguyen, Tuan V, Nogues, Xavier, Patel, Millan S, Prezelj, Janez, Rose, Lynda M, Scollen, Serena, Siggeirsdottir, Kristin, Smith, Albert V, Svensson, Olle, Trompet, Stella, Hillege, Hans L, Trummer, Olivia, van Schoor, Natasja M, Woo, Jean, Zhu, Kun, Balcells, Susana, Brandi, Maria Luisa, Buckley, Brendan M, Cheng, Sulin, Christiansen, Claus, Cooper, Cyrus, Holmen, Oddgeir, Dedoussis, George, Ford, Ian, Frost, Morten, Goltzman, David, González-Macías, Jesús, Kähönen, Mika, Karlsson, Magnus, Khusnutdinova, Elza, Koh, Jung-Min, Kollia, Panagoula, Hunt, Steven C, Langdahl, Bente Lomholt, Leslie, William D, Lips, Paul, Ljunggren, Östen, Lorenc, Roman S, Marc, Janja, Mellström, Dan, Obermayer-Pietsch, Barbara, Olmos, José M, Pettersson-Kymmer, Ulrika, Isaacs, Aaron, Reid, David M, Riancho, José A, Ridker, Paul M, Rousseau, François, Slagboom, P Eline, Tang, Nelson L S, Urreizti, Roser, Van Hul, Wim, Viikari, Jorma, Zarrabeitia, María T, Wu, 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S, North, Kari E, Forouhi, Nita G, Edkins, Sarah, Varga, Tibor V, Oksa, Heikki, Antonella, Mulas, Moffatt, Miriam F, Kong, Augustine, Herder, Christian, Antti, Jula, Small, Kerrin, Miljkovic, Iva, Atalay, Mustafa, Kiess, Wieland, Murabito, Joanne M, Smit, Johannes H, Campbell, Susan, Fowkes, Gerard R, Nicholson, George, Kovacs, Peter, Zemunik, Tatijana, Basart, Hanneke V, Rathmann, Wolfgang, Maerz, Winfried, Peters, Annette, Province, Michael A, Hastie, Nicholas D, Olsson, Christian, Stumvoll, Michael, Waterworth, Dawn M, Watanabe, Richard M, de Geus, Eco J C, Penninx, Brenda W, Perry, John Rb, Reinmaa, Eva, Dedoussis, George V, Kutalik, Zoltán, Toenjes, Anke, Peyser, Patricia A, Körner, Antje, Keinanen-Kiukaanniemi, Sirkka M, Schadt, Eric E, Saaristo, Timo E, Dupuis, Josée, Sattar, Naveed, Cucca, Francesco, Balkau, Beverley, Froguel, Philippe, Jarvelin, Marjo-Riitta, Bouatia-Naji, Nabila, Stolk, Lisette, Meigs, James B, Ahmadi, Kourosh R, Ainali, Chrysanthi, Barrett, Amy, Vallejo, Edgar E, Bataille, Veronique, Bell, Jordana T, Buil, Alfonso, Dermitzakis, Emmanouil T, Dimas, Antigone S, Durbin, Richard, Glass, Daniel, Hassanali, Neelam, Westra, Harm-Jan, Ingle, Catherine, Knowles, David, Krestyaninova, Maria, Lowe, Christopher E, Meduri, Eshwar, di Meglio, Paola, Montgomery, Stephen B, Nestle, Frank O, Nica, Alexandra C, Nisbet, James, O'Rahilly, Stephen, Parts, Leopold, Potter, Simon, Sekowska, Magdalena, Shin, So-Youn, Consortium, ADIPOGen, Small, Kerrin S, Surdulescu, Gabriela, Travers, Mary E, Tsaprouni, Loukia, Tsoka, Sophia, Wilk, Alicja, Yang, Tsun-Po, Consortium, CARDIOGRAMplusC4D, Matise, Tara, Buyske, Steve, Higashio, Julia, Williams, Rasheeda, Nato, Andrew, Ambite, Jose Luis, Deelman, Ewa, Manolio, Teri, Hindorff, Lucia, Consortium, CKDGen, Heiss, Gerardo, Taylor, Kira, Franceschini, Nora, Avery, Christy, Graff, Misa, Lin, Danyu, Quibrera, Miguel, Cochran, Barbara, Kao, Linda, Umans, Jason, Consortium, GEFOS, Cole, Shelley, MacCluer, Jean, Person, 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P., Esko, T., Fall, T., Chen, H., Robertson, N., Rybin, D., Chines, PS., Song, K., An, P., Marullo, L., Jansen, H., Oldehinkel, AJ., North, KE., Forouhi, NG., Edkins, S., Varga, TV., Oksa, H., Antonella, M., Kong, A., Herder, C., Antti, J., Miljkovic, I., Atalay, M., Kiess, W., James, AL., Smit, JH., Campbell, S., Fowkes, GR., Basart, HV., Rathmann, W., Maerz, W., Province, MA., Watanabe, RM., de Geus EJ., Penninx, BW., Oostra, B., Toenjes, A., Peyser, PA., Körner, A., Keinanen-Kiukaanniemi, SM., Saaristo, TE., Boomsma, D., Cucca, F., Balkau, B., Froguel, P., Jarvelin, MR., Bouatia-Naji, N., Ahmadi, KR., Ainali, C., Barrett, A., Bataille, V., Bell, JT., Buil, A., Dermitzakis, ET., Dimas, AS., Durbin, R., Glass, D., Hassanali, N., Hedman£££Åsa K£££ ÅK., Ingle, C., Keildson, S., Knowles, D., Krestyaninova, M., Lowe, CE., Meduri, E., di Meglio, P., Min, JL., Montgomery, SB., Nestle, FO., Nica, AC., Nisbet, J., O'Rahilly, S., Parts, L., Potter, S., Sekowska, M., Shin, SY., Small, KS., 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Zgaga, L., Zygmunt, M., Arnold, AM., Buring, JE., Crisponi, L., Demerath, EW., Hunter, DJ., Schlessinger, D., Murray, A., Murabito, JM., Visser, JA., Lunetta, KL., Elks, CE., Cousminer, DL., Feenstra, B., Lin, P., van Wingerden SW., Smith, EN., Warrington, NM., Alavere, H., Barroso, I., Berenson, GS., Blackburn, H., Busonero, F., Chen, W., Couper, D., Easton, DF., Foroud, T., Geller, F., Hernandez, DG., Kilpeläinen, TO., Li, S., Melbye, M., Murray, JC., Murray, SS., Nelis, M., Ness, AR., Northstone, K., Pennell, CE., Pharoah, P., Rafnar, T., Rice, JP., Ring, SM., Schork, NJ., Segrè, AV., Sovio, U., Srinivasan, SR., Tammesoo, ML., Tyrer, J., Weedon, MN., Wichmann, H., Young, L., Zhuang, WV., Bierut, LJ., Boyd, HA., Department of Clinical Sciences, Lund University [Lund], Genetic Epidemiology and Clinical Research Group, Umea University Hospital, Department of Odontology, Umeå University, Signalisation et Transports Ioniques Membranaires (STIM), Université de Poitiers-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Department of Medical Sciences, Center for Biological Sequence Analysis [Lyngby], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Laboratory of Image Science and Technology [Nanjing] (LIST), Southeast University [Jiangsu]-School of Computer Science and Engineering, Limnology, Ecology, Estonian Genome and Medicine, University of Tartu, Institute of Molecular and Cell Biology, Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], Department of Medical Genetics, Université de Lausanne = University of Lausanne (UNIL), Institute of Medical Informatics, Biometry and Epidemiology, Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Centre d'Immunologie de Marseille - Luminy (CIML), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Genetic Epidemiology Unit, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Space Sciences Laboratory [Berkeley] (SSL), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Division of Statistical Genomics, Washington University School of Medicine, King‘s College London, Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Molecular Genetics Section, University of Groningen [Groningen]-University Medical Centre Groningen, Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (EGENODIA (GI3M)), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Department of Pharmacy Sciences, Creighton University Medical Center, Medical Department III, Universität Leipzig, Foie, métabolismes et cancer, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Department of Epidemiology, Erasmus Medical Centre, Netherlands Genomics Initiative (NGI), Netherlands Genomics Initiative, Institute of Epidemiology [Neuherberg] (EPI), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Public Health and Clinical Medicine, Medstar Research Institute, Genetics and Pathology, Finnish Institute of Occupational Health, Epidemiology, University Medical Centre Groningen, Departments of Microbiology & Molecular Genetics and Molecular Biology & Biochemistry, University of California [Irvine] (UC Irvine), Department of Odontology, Cariology, Institute of Human Genetics, Helmholtz Zentrum München = German Research Center for Environmental Health, Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Division of Cardiology, Geneva University Hospital (HUG), Department of Psychiatry and Psychotherapy, Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Department of Genomics, Life and Brain Center, Universität Bonn = University of Bonn, Anaesthesia and Intensive care, Royal Aberdeen Childrens Hospital, UCL Institute of neurology, UCL Institute of Neurology, Human Genetics, The Wellcome Trust Sanger Institute [Cambridge], 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(BU)-National Heart, Lung, and Blood Institute [Bethesda] (NHLBI), Endocrinology and Metabolism, The Churchill Hospital-Oxford Centre for Diabetes, Landsteiner Laboratory, Clinical Haematology, Other departments, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, Lund University Diabetes Centre-Lund University [Lund], Université de Tours-Centre National de la Recherche Scientifique (CNRS)-Université de Poitiers, Technical University of Denmark [Lyngby] (DTU), Université de Lausanne (UNIL), Universität Duisburg-Essen [Essen], Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), University of California [Berkeley], University of California-University of California, Génomique Intégrative et Modélisation des Maladies Métaboliques (EGID), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Centre National de la Recherche Scientifique (CNRS), Universität Leipzig [Leipzig], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), University of California [Irvine] (UCI), German Research Center for Environmental Health, University of Bonn, Czech Academy of Sciences [Prague] (ASCR), Yale University School of Medicine, University of Oxford [Oxford], German Research Center for Environmental Health-Helmholtz-Zentrum München (HZM), Laval University, Laval University [Québec], Turku University Hospital, Lausanne university hospital, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Institut de biologie de Lille - IBL (IBLI), Université de Lille, Sciences et Technologies-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), University of Helsinki-University of Helsinki, Helmholtz-Zentrum München (HZM), National Heart, Lung, and Blood Institute [Bethesda] (NHLBI)-Boston University [Boston] (BU), Internal Medicine, Child and Adolescent Psychiatry / Psychology, Clinical Genetics, Medical Informatics, Obstetrics & Gynecology, Lund University [Lund]-Lund University Diabetes Centre, Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (GI3M), Institute of Medicine-University of Gothenburg (GU), Signalisation et Transports Ioniques Membranaires ( STIM ), Université de Poitiers-Centre National de la Recherche Scientifique ( CNRS ), Technical University of Denmark [Lyngby] ( DTU ), Laboratory of Image Science and Technology [Nanjing] ( LIST ), Department of Medical Epidemiology and Biostatistics ( MEB ), University of Lausanne, Centre d'Immunologie de Marseille - Luminy ( CIML ), Aix Marseille Université ( AMU ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Erasmus MC, Space Sciences Laboratory [Berkeley] ( SSL ), Génomique Intégrative et Modélisation des Maladies Métaboliques ( EGID ), Université de Lille-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Institut Pasteur de Lille, Réseau International des Instituts Pasteur ( RIIP ) -Réseau International des Instituts Pasteur ( RIIP ) -Centre National de la Recherche Scientifique ( CNRS ) -Centre Hospitalier Régional Universitaire [Lille] ( CHRU Lille ), University of Leipzig, Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institute of Epidemiology [Neuherberg] ( EPI ), University of California [Irvine] ( UCI ), Génétique des maladies multifactorielles ( GMM ), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique ( CNRS ), Geneva University Hospital ( HUG ), Bonn Universität [Bonn], Indian Institute of Technology Kanpur ( IIT Kanpur ), The University of North Carolina at Chapel Hill, Université de Bonn, Wellcome Trust Sanger Institute, Harvard University School of Public Health, Czech Academy of Sciences [Prague] ( ASCR ), deCODE genetics, University of Groningen [Groningen]-University Medical Center Groningen-Beatrix Children's Hospital-Groningen Research Institute for Asthma and COPD, Yale School of Medicine, National Heart and Lung Institute ( NHLI ), Réseau International des Instituts Pasteur ( RIIP ) -Réseau International des Instituts Pasteur ( RIIP ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université de Lille, Droit et Santé, University Medical Center Groningen, University of Cambridge [UK] ( CAM ), Wellcome Trust Centre for Human Genetics, University of Pisa [Pisa], University of Cambridge [UK] ( CAM ) -Institute of Metabolic Science, German Research Center for Environmental Health-Helmholtz-Zentrum München ( HZM ), University of Otago, University of Greifswald, University College of London [London] ( UCL ), National Institute for Health and Welfare, Queen's University [Belfast] ( QUB ), University of Hawaii at Manoa ( UHM ), University of Gothenburg ( GU ) -Institute of Medicine, Recherches en Psychopathologie, nouveaux symptômes et lien social ( EA 4050 ), Université de Poitiers-Université de Brest ( UBO ) -Université Catholique de l'Ouest-Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ), Institut de biologie de Lille - IBL ( IBLI ), Réseau International des Instituts Pasteur ( RIIP ) -Réseau International des Instituts Pasteur ( RIIP ) -Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique ( CNRS ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), University Medicine Greifswald,-HELIOS Hospital Stralsund, Finland Institute for Molecular Medicine ( FIMM ), Georgia Prevention Institute, Netherlands Consortium for Healthy Aging, Helmholtz-Zentrum München ( HZM ), National Institutes of Health ( NIH ) -National Cancer Institute ( NIH ), Massachusetts General Hospital, Children's Hospital, Boston, Broad Institute, Cambridge, MA, The University of North Carolina at Chapel Hill-UNC Gillings School of Global Public Health-Carolina Center for Genome Sciences, Shungin D, Winkler TW, Adipogen, Consortium, Cardiogramplusc4d, Consortium, Ckdgen, Consortium, Gefos, Consortium, Genie, Consortium, Glgc, Icbp, International, Endogene Consortium, Lifelines, Cohort Study, Magic, Investigator, Muther, Consortium, Consortium, Page, ReproGen Consortium, Amouyel P, D'Adamo, ADAMO PIO, Gasparini, Paolo, Shungin, Dmitry, Winkler, Thomas W, Croteau-Chonka, Damien C, Ferreira, Teresa, Hypponen, Elina, Mohlke, Karen L, ADIPOGEN Consortium, Int Endogene Consortium, Lee Kong Chian School of Medicine (LKCMedicine), Epidemiologie, RS: CARIM - R3.02 - Hypertension and target organ damage, Université de Tours-Université de Poitiers-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, EMGO+ - Lifestyle, Overweight and Diabetes, Damage and Repair in Cancer Development and Cancer Treatment (DARE), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Life Course Epidemiology (LCE), Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Research Institute for Asthma and COPD (GRIAC), Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Center for Liver, Digestive and Metabolic Diseases (CLDM), Stem Cell Aging Leukemia and Lymphoma (SALL), Shungin, D, Winkler, T, Croteau Chonka, D, Ferreira, T, Locke, A, Mägi, R, Strawbridge, R, Pers, T, Fischer, K, Justice, A, Workalemahu, T, Wu, J, Buchkovich, M, Heard Costa, N, Roman, T, Drong, A, Song, C, Gustafsson, S, Day, F, Esko, T, Fall, T, Kutalik, Z, Luan, J, Randall, J, Scherag, A, Vedantam, S, Wood, A, Chen, J, Fehrmann, R, Karjalainen, J, Kahali, B, Liu, C, Schmidt, E, Absher, D, Amin, N, Anderson, D, Beekman, M, Bragg Gresham, J, Buyske, S, Demirkan, A, Ehret, G, Feitosa, M, Goel, A, Jackson, A, Johnson, T, Kleber, M, Kristiansson, K, Mangino, M, Leach, I, Medina Gomez, C, Palmer, C, Pasko, D, Pechlivanis, S, Peters, M, Prokopenko, I, Stanca'Kova', A, Sung, Y, Tanaka, T, Teumer, A, Van Vliet Ostaptchouk, J, Yengo, L, Zhang, W, Albrecht, E, Ärnlöv, J, Arscott, G, Bandinelli, S, Barrett, A, Bellis, C, Bennett, A, Berne, C, Blüher, M, Böhringer, S, Bonnet, F, Böttcher, Y, Bruinenberg, M, Carba, D, Caspersen, I, Clarke, R, Daw, E, Deelen, J, Deelman, E, Delgado, G, Doney, A, Eklund, N, Erdos, M, Estrada, K, Eury, E, Friedrich, N, Garcia, M, Giedraitis, V, Gigante, B, Go, A, Golay, A, Grallert, H, Grammer, T, Gräsler, J, Grewal, J, Groves, C, Haller, T, Hallmans, G, Hartman, C, Hassinen, M, Hayward, C, Heikkilä, K, Herzig, K, Helmer, Q, Hillege, H, Holmen, O, Hunt, S, Isaacs, A, Ittermann, T, James, A, Johansson, I, Juliusdottir, T, Kalafati, I, Kinnunen, L, Koenig, W, Kooner, I, Kratzer, W, Lamina, C, Leander, K, Lee, N, Lichtner, P, Lind, L, Lindström, J, Lobbens, S, Lorentzon, M, Mach, F, Magnusson, P, Mahajan, A, Mcardle, W, Menni, C, Merger, S, Mihailov, E, Milani, L, Mills, R, Moayyeri, A, Monda, K, Mooijaart, S, Mühleisen, T, Mulas, A, Müller, G, Müller Nurasyid, M, Nagaraja, R, Nalls, M, Narisu, N, Glorioso, N, Nolte, I, Olden, M, Rayner, N, Renstrom, F, Ried, J, Robertson, N, Rose, L, Sanna, S, Scharnagl, H, Scholtens, S, Sennblad, B, Seufferlein, T, Sitlani, C, Smith, A, Stirrups, K, Stringham, H, Sundström, J, Swertz, M, Swift, A, Syvänen, A, Tayo, B, Thorand, B, Thorleifsson, G, Tomaschitz, A, Troffa, C, Van Oort, F, Verweij, N, Vonk, J, Waite, L, Wennauer, R, Wilsgaard, T, Wojczynski, M, Wong, A, Zhang, Q, Zhao, J, Brennan, E, Choi, M, Eriksson, P, Folkersen, L, Franco Cereceda, A, Gharavi, A, Hedman, A, Hivert, M, Huang, J, Kanoni, S, Karpe, F, Keildson, S, Kiryluk, K, Liang, L, Lifton, R, Ma, B, Mcknight, A, Mcpherson, R, Metspalu, A, Min, J, Moffatt, M, Montgomery, G, Murabito, J, Nicholson, G, Nyholt, D, Olsson, C, Perry, J, Reinmaa, E, Salem, R, Sandholm, N, Schadt, E, Scott, R, Stolk, L, Vallejo, E, Westra, H, Zondervan, K, Amouyel, P, Arveiler, D, Bakker, S, Beilby, J, Bergman, R, Blangero, J, Brown, M, Burnier, M, Campbell, H, Chakravarti, A, Chines, P, Claudi Boehm, S, Collins, F, Crawford, D, Danesh, J, De Faire, U, De Geus, E, Dörr, M, Erbel, R, Eriksson, J, Farrall, M, Ferrannini, E, Ferrières, J, Forouhi, N, Forrester, T, Franco, O, Gansevoort, R, Gieger, C, Gudnason, V, Haiman, C, Harris, T, Hattersley, A, Heliövaara, M, Hicks, A, Hingorani, A, Hoffmann, W, Hofman, A, Homuth, G, Humphries, S, Hyppönen, E, Illig, T, Jarvelin, M, Johansen, B, Jousilahti, P, Jula, A, Kaprio, J, Kee, F, Keinanen Kiukaanniemi, S, Kooner, J, Kooperberg, C, Kovacs, P, Kraja, A, Kumari, M, Kuulasmaa, K, Kuusisto, J, Lakka, T, Langenberg, C, Le Marchand, L, Lehtimäki, T, Lyssenko, V, Männistö, S, Marette, A, Matise, T, Mckenzie, C, Mcknight, B, Musk, A, Möhlenkamp, S, Morris, A, Nelis, M, Ohlsson, C, Oldehinkel, A, Ong, K, Palmer, L, Penninx, B, Peters, A, Pramstaller, P, Raitakari, O, Rankinen, T, Rao, D, Rice, T, Ridker, P, Ritchie, M, Rudan, I, Salomaa, V, Samani, N, Saramies, J, Sarzynski, M, Schwarz, P, Shuldiner, A, Staessen, J, Steinthorsdottir, V, Stolk, R, Strauch, K, Tönjes, A, Tremblay, A, Tremoli, E, Vohl, M, Völker, U, Vollenweider, P, Wilson, J, Witteman, J, Adair, L, Bochud, M, Boehm, B, Bornstein, S, Bouchard, C, Cauchi, S, Caulfield, M, Chambers, J, Chasman, D, Cooper, R, Dedoussis, G, Ferrucci, L, Froguel, P, Grabe, H, Hamsten, A, Hui, J, Hveem, K, Jöckel, K, Kivimaki, M, Kuh, D, Laakso, M, Liu, Y, März, W, Munroe, P, Njolstad, I, Oostra, B, Pedersen, N, Perola, M, Pe'Russe, L, Peters, U, Power, C, Quertermous, T, Rauramaa, R, Rivadeneira, F, Saaristo, T, Saleheen, D, Sinisalo, J, Slagboom, P, Snieder, H, Spector, T, Thorsteinsdottir, U, Stumvoll, M, Tuomilehto, J, Uitterlinden, A, Uusitupa, M, Van Der Harst, P, Veronesi, G, Walker, M, Wareham, N, Watkins, H, Wichmann, H, Abecasis, G, Assimes, T, Berndt, S, Boehnke, M, Borecki, I, Deloukas, P, Franke, L, Frayling, T, Groop, L, Hunter, D, Kaplan, R, O'Connell, J, Qi, L, Schlessinger, D, Strachan, D, Stefansson, K, Van Duijn, C, Willer, C, Visscher, P, Yang, J, Hirschhorn, J, Zillikens, M, Mccarthy, M, Speliotes, E, North, K, Fox, C, Barroso, I, Franks, P, Ingelsson, E, Heid, I, Loos, R, Cupples, L, Lindgren, C, Mohlke, K, Dastani, Z, Timpson, N, Yuan, X, Henneman, P, Kizer, J, Lyytikainen, L, Fuchsberger, C, Small, K, Coassin, S, Lohman, K, Pankow, J, Uh, H, Wu, Y, Bidulescu, A, Rasmussen Torvik, L, Greenwood, C, Ladouceur, M, Grimsby, J, Manning, A, Mooser, V, Kapur, K, Frants, R, Willemsvan vanDijk, K, Willems, S, Psaty, B, Tracy, R, Brody, J, Chen, I, Viikari, J, Kähönen, M, Evans, D, St Pourcain, B, Sattar, N, Carlson, O, Egan, J, van Heemst, D, Kedenko, L, Nuotio, M, Loo, B, Kanaya, A, Haun, M, Klopp, N, Katsareli, E, Couper, D, Duncan, B, Kloppenburg, M, Borja, J, Musani, S, Guo, X, Semple, R, Teslovich, T, Allison, M, Redline, S, Buxbaum, S, Meulenbelt, I, Ballantyne, C, Hu, F, Paulweber, B, Florez, J, Smith, G, Siscovick, D, Kronenberg, F, van Duijn, C, Waterworth, D, Meigs, J, Dupuis, J, Richards, J, Willenborg, C, Thompson, J, Erdmann, J, Goldstein, B, König, I, Cazier, J, Johansson, Å, Hall, A, Lee, J, Grundberg, E, Havulinna, A, Ho, W, Hopewell, J, Eriksson, N, Lundmark, P, Lyytikäinen, L, Rafelt, S, Tikkanen, E, Van Zuydam, N, Voight, B, Ziegler, A, Altshuler, D, Balmforth, A, Braund, P, Burgdorf, C, Cox, D, Dimitriou, M, Do, R, El Mokhtari, N, Fontanillas, P, Hager, J, Han, B, Kang, H, Kessler, T, Knowles, J, Kolovou, G, Langford, C, Lokki, M, Lundmark, A, Meisinger, C, Melander, O, Maouche, S, Nikus, K, Peden, J, Rasheed, A, Rosinger, S, Rubin, D, Rumpf, M, Schäfer, A, Sivananthan, M, Stewart, A, Tan, S, Thorgeirsson, G, van der Schoot, C, Wagner, P, Wells, G, Wild, P, Yang, T, Basart, H, Boerwinkle, E, Brambilla, P, Cambien, F, Cupples, A, de Faire, U, Dehghan, A, Diemert, P, Epstein, S, Evans, A, Ferrario, M, Gauguier, D, Goodall, A, Hazen, S, Holm, H, Iribarren, C, Jang, Y, Kim, H, Laaksonen, R, Ouwehand, W, Parish, S, Park, J, Rader, D, Shah, S, Stark, K, Trégouët, D, Virtamo, J, Wallentin, L, Zimmermann, M, Nieminen, M, Hengstenberg, C, Sandhu, M, Pastinen, T, Hovingh, G, Zalloua, P, Siegbahn, A, Schreiber, S, Ripatti, S, Blankenberg, S, O'Donnell, C, Reilly, M, Collins, R, Kathiresan, S, Roberts, R, Schunkert, H, Pattaro, C, Köttgen, A, Garnaas, M, Böger, C, Chen, M, Tin, A, Taliun, D, Li, M, Gao, X, Gorski, M, Yang, Q, Hundertmark, C, Foster, M, O'Seaghdha, C, Glazer, N, Struchalin, M, Li, G, Johnson, A, Gierman, H, Hwang, S, Atkinson, E, Cornelis, M, Chouraki, V, Holliday, E, Sorice, R, Deshmukh, H, Ulivi, S, Chu, A, Murgia, F, Trompet, S, Imboden, M, Kollerits, B, Pistis, G, Launer, L, Aspelund, T, Eiriksdottir, G, Mitchell, B, Schmidt, H, Cavalieri, M, Rao, M, de Andrade, M, Turner, S, Ding, J, Andrews, J, Freedman, B, Döring, A, Kolcic, I, Zemunik, T, Boban, M, Minelli, C, Wheeler, H, Igl, W, Zaboli, G, Wild, S, Wright, A, Ellinghaus, D, Nöthlings, U, Jacobs, G, Biffar, R, Endlich, K, Ernst, F, Kroemer, H, Nauck, M, Stracke, S, Völzke, H, Aulchenko, Y, Polasek, O, Hastie, N, Vitart, V, Helmer, C, Wang, J, Ruggiero, D, Bergmann, S, Nikopensius, T, Province, M, Ketkar, S, Colhoun, H, Robino, A, Giulianini, F, Krämer, B, Portas, L, Ford, I, Buckley, B, Adam, M, Thun, G, Sala, C, Metzger, M, Mitchell, P, Ciullo, M, Kim, S, Gasparini, P, Pirastu, M, Jukema, J, Probst Hensch, N, Toniolo, D, Coresh, J, Schmidt, R, Kardia, S, Curhan, G, Gyllensten, U, Franke, A, Rettig, R, Parsa, A, Goessling, W, Kao, W, de Boer, I, Peralta, C, Akylbekova, E, Kramer, H, van der Harst, P, Arking, D, Franceschini, N, Hernandez, D, Townsend, R, Lumley, T, Kestenbaum, B, Haritunians, T, Waeber, G, Lu, X, Leak, T, Aasarød, K, Skorpen, F, Baumert, J, Devuyst, O, Mychaleckyj, J, Hallan, S, Navis, G, Shlipak, M, Bull, S, Paterson, A, Rotter, J, Beckmann, J, Dreisbach, A, Styrkarsdottir, U, Evangelou, E, Hsu, Y, Duncan, E, Ntzani, E, Oei, L, Albagha, O, Kemp, J, Koller, D, Minster, R, Vandenput, L, Willner, D, Xiao, S, Yerges Armstrong, L, Zheng, H, Alonso, N, Kammerer, C, Kaptoge, S, Leo, P, Wilson, S, Aalto, V, Alen, M, Aragaki, A, Center, J, Dailiana, Z, Duggan, D, Garcia Giralt, N, Giroux, S, Hocking, L, Husted, L, Jameson, K, Khusainova, R, Kim, G, Koromila, T, Kruk, M, Laaksonen, M, Lacroix, A, Lee, S, Leung, P, Lewis, J, Masi, L, Mencej Bedrac, S, Nguyen, T, Nogues, X, Patel, M, Prezelj, J, Scollen, S, Siggeirsdottir, K, Svensson, O, Trummer, O, van Schoor, N, Woo, J, Zhu, K, Balcells, S, Brandi, M, Cheng, S, Christiansen, C, Cooper, C, Frost, M, Goltzman, D, González Macías, J, Karlsson, M, Khusnutdinova, E, Koh, J, Kollia, P, Langdahl, B, Leslie, W, Lips, P, Ljunggren, Ö, Lorenc, R, Marc, J, Mellström, D, Obermayer Pietsch, B, Olmos, J, Pettersson Kymmer, U, Reid, D, Riancho, J, Rousseau, F, Tang, N, Urreizti, R, Van Hul, W, Zarrabeitia, M, Castano Betancourt, M, Herrera, L, Ingvarsson, T, Johannsdottir, H, Kwan, T, Li, R, Luben, R, Medina Gómez, C, Palsson, S, Reppe, S, Sigurdsson, G, van Meurs, J, Verlaan, D, Williams, F, Zhou, Y, Gautvik, K, Raychaudhuri, S, Cauley, J, Clark, G, Cummings, S, Danoy, P, Dennison, E, Eastell, R, Eisman, J, Jackson, R, Jones, G, Khaw, K, Mccloskey, E, Nandakumar, K, Peacock, M, Pols, H, Prince, R, Reid, I, Robbins, J, Sambrook, P, Sham, P, Tylavsky, F, Econs, M, Kung, A, Reeve, J, Streeten, E, Karasik, D, Ralston, S, Ioannidis, J, Kiel, D, Forsblom, C, Isakova, T, Mckay, G, Williams, W, Sadlier, D, Mäkinen, V, Swan, E, Boright, A, Ahlqvist, E, Keller, B, Huang, H, Ahola, A, Fagerholm, E, Gordin, D, Harjutsalo, V, He, B, Heikkilä, O, Hietala, K, Kytö, J, Lahermo, P, Lehto, M, Österholm, A, Parkkonen, M, Pitkäniemi, J, Rosengård Bärlund, M, Saraheimo, M, Sarti, C, Söderlund, J, Soro Paavonen, A, Syreeni, A, Thorn, L, Tikkanen, H, Tolonen, N, Tryggvason, K, Wadén, J, Gill, G, Prior, S, Guiducci, C, Mirel, D, Taylor, A, Hosseini, M, Parving, H, Rossing, P, Tarnow, L, Ladenvall, C, Alhenc Gelas, F, Lefebvre, P, Rigalleau, V, Roussel, R, Tregouet, D, Maestroni, A, Maestroni, S, Falhammar, H, Gu, T, Möllsten, A, Cimponeriu, D, Mihai, I, Mota, M, Mota, E, Serafinceanu, C, Stavarachi, M, Hanson, R, Nelson, R, Kretzler, M, Panduru, N, Gu, H, Brismar, K, Zerbini, G, Hadjadj, S, Marre, M, Lajer, M, Waggott, D, Savage, D, Bain, S, Martin, F, Godson, C, Groop, P, Maxwell, A, Sengupta, S, Peloso, G, Ganna, A, Mora, S, Chang, H, Den Hertog, H, Donnelly, L, Fraser, R, Freitag, D, Gurdasani, D, Kaakinen, M, Kettunen, J, Li, X, Montasser, M, Petersen, A, Saxena, R, Service, S, Sidore, C, Surakka, I, Van den Herik, E, Volcik, K, Asiki, G, Been, L, Bolton, J, Bonnycastle, L, Burnett, M, Cesana, G, Elliott, P, Eyjolfsson, G, Goodarzi, M, Gravito, M, Hartikainen, A, Hung, Y, Jones, M, Kaleebu, P, Kastelein, J, Kim, E, Komulainen, P, Lin, S, Nieminen, T, Nsubuga, R, Olafsson, I, Palotie, A, Papamarkou, T, Pomilla, C, Pouta, A, Ruokonen, A, Seeley, J, Silander, K, Stančáková, A, Tiret, L, van Pelt, L, Wainwright, N, Wijmenga, C, Willemsen, G, Young, E, Bennett, F, Boomsma, D, Bovet, P, Chen, Y, Feranil, A, Freimer, N, Hsiung, C, Järvelin, M, Kesäniemi, A, Koudstaal, P, Krauss, R, Kyvik, K, Martin, N, Meneton, P, Moilanen, L, Njølstad, I, Price, J, Sanghera, D, Sheu, W, Whitfield, J, Wolffenbuttel, B, Ordovas, J, Rich, S, Johnson, L, Larson, M, Levy, D, Newton Cheh, C, O'Reilly, P, Palmas, W, Rice, K, Snider, H, Tobin, M, Verwoert, G, Pihur, V, Heath, S, Sõber, S, Arora, P, Zhang, F, Lucas, G, Milaneschi, Y, Parker, A, Fava, C, Fox, E, Go, M, Sjögren, M, Vinay, D, Alexander, M, Tabara, Y, Shaw Hawkins, S, Whincup, P, Shi, G, Seielstad, M, Sim, X, Nguyen, K, Matullo, G, Gaunt, T, Onland Moret, N, Cooper, M, Platou, C, Org, E, Hardy, R, Dahgam, S, Palmen, J, Kuznetsova, T, Uiterwaal, C, Adeyemo, A, Ludwig, B, Tomaszewski, M, Tzoulaki, I, Palmer, N, Chang, Y, Steinle, N, Grobbee, D, Morrison, A, Najjar, S, Hadley, D, Connell, J, Day, I, Lawlor, D, Lawrence, R, Ongen, H, Li, Y, Young, J, Bis, J, Chaturvedi, N, Islam, M, Jafar, T, Kulkarni, S, Grässler, J, Howard, P, Guarrera, S, Ricceri, F, Emilsson, V, Plump, A, Weder, A, Sun, Y, Scott, L, Peltonen, L, Vartiainen, E, Brand, S, Wang, T, Burton, P, Artigas, M, Dong, Y, Wang, X, Zhu, H, Rudock, M, Heckbert, S, Smith, N, Wiggins, K, Doumatey, A, Shriner, D, Veldre, G, Viigimaa, M, Kinra, S, Prabhakaran, D, Tripathy, V, Langefeld, C, Rosengren, A, Thelle, D, Corsi, A, Singleton, A, Hilton, G, Salako, T, Iwai, N, Kita, Y, Ogihara, T, Ohkubo, T, Okamura, T, Ueshima, H, Umemura, S, Eyheramendy, S, Meitinger, T, Cho, Y, Scott, J, Sehmi, J, Hedblad, B, Nilsson, P, Stanèáková, A, Raffel, L, Yao, J, Schwartz, S, Ikram, M, Longstreth W., J, Mosley, T, Seshadri, S, Shrine, N, Wain, L, Morken, M, Laitinen, J, Zitting, P, Cooper, J, van Gilst, W, Janipalli, C, Mani, K, Yajnik, C, Mattace Raso, F, Lakatta, E, Orru, M, Scuteri, A, Ala Korpela, M, Kangas, A, Soininen, P, Tukiainen, T, Würtz, P, Ong, R, Galan, P, Hercberg, S, Lathrop, M, Zelenika, D, Zhai, G, Meschia, J, Sharma, P, Terzic, J, Kumar, M, Denniff, M, Zukowska Szczechowska, E, Wagenknecht, L, Fowkes, F, Charchar, F, Rotimi, C, Bots, M, Brand, E, Talmud, P, Nyberg, F, Laan, M, van der Schouw, Y, Casas, J, Vineis, P, Ganesh, S, Wong, T, Tai, E, Morris, R, Dominiczak, A, Marmot, M, Miki, T, Chandak, G, Zhu, X, Elosua, R, Soranzo, N, Sijbrands, E, Uda, M, Vasan, R, Anderson, C, Gordon, S, Guo, Q, Henders, A, Lambert, A, Kraft, P, Kennedy, S, Macgregor, S, Missmer, S, Painter, J, Roseman, F, Treloar, S, Wallace, L, Alizadeh, B, de Boer, R, Boezen, H, van der Klauw, M, Ormel, J, Postma, D, Rosmalen, J, Slaets, J, Lagou, V, Welch, R, Wheeler, E, Rehnberg, E, Lecoeur, C, Johnson, P, Hottenga, J, Salo, P, Bielak, L, Zhao, W, Horikoshi, M, Navarro, P, Chen, H, Rybin, D, Song, K, An, P, Marullo, L, Jansen, H, Edkins, S, Varga, T, Oksa, H, Antonella, M, Kong, A, Herder, C, Antti, J, Miljkovic, I, Atalay, M, Kiess, W, Smit, J, Campbell, S, Fowkes, G, Rathmann, W, Maerz, W, Watanabe, R, de Geus, E, Toenjes, A, Peyser, P, Körner, A, Cucca, F, Balkau, B, Bouatia Naji, N, Ahmadi, K, Ainali, C, Bataille, V, Bell, J, Buil, A, Dermitzakis, E, Dimas, A, Durbin, R, Glass, D, Hassanali, N, Hedman, Å, Ingle, C, Knowles, D, Krestyaninova, M, Lowe, C, Meduri, E, di Meglio, P, Montgomery, S, Nestle, F, Nica, A, Nisbet, J, O'Rahilly, S, Parts, L, Potter, S, Sekowska, M, Shin, S, Surdulescu, G, Travers, M, Tsaprouni, L, Tsoka, S, Wilk, A, Higashio, J, Williams, R, Nato, A, Ambite, J, Manolio, T, Hindorff, L, Heiss, G, Taylor, K, Avery, C, Graff, M, Lin, D, Quibrera, M, Cochran, B, Kao, L, Umans, J, Cole, S, Maccluer, J, Person, S, Gross, M, Fornage, M, Durda, P, Jenny, N, Patsy, B, Arnold, A, Buzkova, P, Haines, J, Murdock, D, Glenn, K, Brown Gentry, K, Thornton Wells, T, Dumitrescu, L, Jeff, J, Bush, W, Mitchell, S, Goodloe, R, Boston, J, Malinowski, J, Restrepo, N, Oetjens, M, Fowke, J, Zheng, W, Spencer, K, Pendergrass, S, Wilkens, L, Park, L, Tiirikainen, M, Kolonel, L, Lim, U, Cheng, I, Wang, H, Shohet, R, Stram, D, Henderson, B, Monroe, K, Schumacher, F, Anderson, G, Carlson, C, Prentice, R, Wu, C, Carty, C, Gong, J, Rosse, S, Young, A, Haessler, J, Kocarnik, J, Lin, Y, Kuller, L, He, C, Sulem, P, Barbalic, M, Broer, L, Byrne, E, Gudbjartsson, D, Mcardle, P, Porcu, E, van Wingerden, S, Zhuang, W, Lauc, L, Broekmans, F, Burri, A, Chanock, S, Chen, C, Corre, T, Coviello, A, D'Adamo, P, Davies, G, Deary, I, Ebrahim, S, Fauser, B, Ferreli, L, Folsom, A, Hall, P, Hankinson, S, Hass, M, Heath, A, Janssens, A, Keyzer, J, Lahti, J, Lai, S, Laisk, T, Laven, J, Liu, J, Lopez, L, Louwers, Y, Marongiu, M, Klaric, I, Masciullo, C, Medland, S, Melzer, D, Newman, A, Paré, G, Peeters, P, Pop, V, Räikkönen, K, Salumets, A, 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Subjects: Adipose Tissue/metabolism, Male, genetic association, subcutaneous fat, Transcription, Genetic, Adipocytes, Adipogenesis, Adipose Tissue, Age Factors, Body Mass Index, Continental Population Groups, Epigenesis, Genetic, Europe, Female, Genome, Human, Humans, Insulin, Insulin Resistance, Models, Biological, Neovascularization, Physiologic, Obesity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Sex Characteristics, Waist-Hip Ratio, Body Fat Distribution, Genome-Wide Association Study, Multidisciplinary, Insulin Resistance/genetics, Genome-wide association study, Continental Population Groups/genetics, genetic analysis, heritability, gene cluster, Science::Biological sciences::Human anatomy and physiology [DRNTU], 0302 clinical medicine, high density lipoprotein cholesterol, Models, genetics [Insulin Resistance], histone modification, Age Factor, insulin receptor, 0303 health sciences, Adipocyte, Human/genetics, CARDIOGRAMplusC4D Consortium, ADIPOGENIC DIFFERENTIATION, genetic correlation, body fat, Continental Population Group, priority journal, 5 trisphosphate 3 phosphatase, GEFOS Consortium, meta analysis (topic), Science & Technology - Other Topics, ddc:500, transcription regulation, Adipogenesis/genetics, Single Nucleotide/genetics, Human, medicine.medical_specialty, Waist, phosphatidylinositol 3, European, ta3111, genetic regulation, Article, developmental biology, 03 medical and health sciences, MAGIC Investigators, transcription initiation site, SDG 3 - Good Health and Well-being, Genetic, genomics, GLYCEMIC TRAITS, genetics [Continental Population Groups], Polymorphism, GENOME-WIDE ASSOCIATION, Physiologic, genetics [Adipogenesis], Adipocytes/metabolism, Europe/ethnology, Genome, Human/genetics, Insulin/metabolism, Neovascularization, Physiologic/genetics, Obesity/genetics, Polymorphism, Single Nucleotide/genetics, Quantitative Trait Loci/genetics, Transcription, Genetic/genetics, Genetic/genetics, Adipogenesi, Science & Technology, adiponectin, [ SDV ] Life Sciences [q-bio], vasculotropin, genetics [Quantitative Trait Loci], ta1184, Racial Groups, ta1182, gene mapping, ta3121, triacylglycerol blood level, medicine.disease, Biological, major clinical study, amino acid sequence, metabolism [Insulin], Endocrinology, metabolism [Adipocytes], genetic loci, insulin, body fat, GLGC, International Endogene Consortium, metabolism [Adipose Tissue], Body mass index, HUMAN HEIGHT, Epigenesis, LifeLines Cohort Study, ReproGen Consortium, BIO/12 - BIOCHIMICA CLINICA E BIOLOGIA MOLECOLARE CLINICA, tissue level, Physiologic/genetics, [SDV]Life Sciences [q-bio], Medizin, Adipose tissue, low density lipoprotein cholesterol, PAGE Consortium, COMMON SNPS, angiogenesis, Waist–hip ratio, genetics [Obesity], MESH: Adipocytes/metabolism Adipogenesis/genetics Adipose Tissue/metabolism* Age Factors Body Fat Distribution* Body Mass Index Continental Population Groups/genetics Epigenesis, Genetic Europe/ethnology Female Genome, Human/genetics Genome-Wide Association Study* Humans Insulin/metabolism* Insulin Resistance/genetics Male Models, Biological Neovascularization, Physiologic/genetics Obesity/genetics Polymorphism, Single Nucleotide/genetics Quantitative Trait Loci/genetics* Sex Characteristics Transcription, Genetic/genetics Waist-Hip Ratio, single nucleotide polymorphism, fat, genetic variability, molecular biology, body mass index (BMI), ethnology [Europe], peroxisome proliferator activated receptor, 2. Zero hunger, Genetics, Genome, Single Nucleotide, waist circumference, insulin, phosphatidylinositol 3,4,5 trisphosphate 3 phosphatase, triacylglycerol, vasculotropin, developmental biology, gene expression, genome, numerical model, adipocyte, adipose tissue, body fat distribution, body mass, female, gene locus, gene structure, hip circumference, human, insulin resistance, lipoprotein blood level, male, obesity, protein protein interaction, sex difference, waist hip ratio, Multidisciplinary Sciences, genetics [Transcription, Genetic], genetics [Polymorphism, Single Nucleotide], ADIPOGen Consortium, genetics [Neovascularization, Physiologic], Transcription, SUSCEPTIBILITY LOCI, General Science & Technology, ICBP, 030209 endocrinology & metabolism, Biology, adipocyte, MESH : Adipocytes/metabolism Adipogenesis/genetics Adipose Tissue/metabolism* Age Factors Body Fat Distribution* Body Mass Index Continental Population Groups/genetics Epigenesis, Genetic Europe/ethnology Female Genome, Human/genetics Genome-Wide Association Study* Humans Insulin/metabolism* Insulin Resistance/genetics Male Models, Biological Neovascularization, Physiologic/genetics Obesity/genetics Polymorphism, Single Nucleotide/genetics Quantitative Trait Loci/genetics* Sex Characteristics Transcription, Genetic/genetics Waist-Hip Ratio, MESENCHYMAL STEM-CELLS, GENIE Consortium, SEXUAL-DIMORPHISM, Insulin resistance, Internal medicine, medicine, genetics [Genome, Human], ABDOMINAL ADIPOSITY, Neovascularization, 030304 developmental biology, FALSE DISCOVERY, CKDGen Consortium, Sex Characteristic, MuTHER Consortium, numerical model
Abstract: Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P
Published: 2015

14. Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index

Author: Hoggart, C, Venturini, G, Mangino, M, Gomez, F, Ascari, G, Zhao, J, Teumer, A, Winkler, T, Tšernikova, N, Luan, J, Mihailov, E, Ehret, G, Zhang, W, Lamparter, D, Esko, T, Macé, A, Rüeger, S, Bochud, P, Barcella, M, Dauvilliers, Y, Benyamin, B, Evans, D, Hayward, C, Lopez, M, Franke, L, Russo, A, Heid, I, Salvi, E, Vendantam, S, Arking, D, Boerwinkle, E, Chambers, J, Fiorito, G, Grallert, H, Guarrera, S, Homuth, G, Huffman, J, Porteous, D, Berg, J, Blackwood, D, Campbell, H, Cavanagh, J, Connell, J, Connor, M, Cunningham Burley, S, Deary, I, Dominiczak, A, Ellis, P, Fitzpatrick, B, Ford, I, Gertz, R, Grau, A, Haddow, G, Jackson, C, Kerr, S, Lindsay, R, Mcgilchrist, M, Mcintyre, D, Morris, A, Morton, R, Muir, W, Murray, G, Palmer, C, Pell, J, Philp, A, Porteous, M, Procter, R, Ralston, S, Reid, D, Sinnott, R, Smith, B, St Clair, D, Sullivan, F, Sweetland, M, Ure, J, Watt, G, Wolf, R, Wright, A, de Bakker, P, Bültmann, U, Geleijnse, M, Harst, P, Koppelman, G, Rosmalen, J, van Rossum, L, Smidt, H, Swertz, M, Stolk, R, Alizadeh, B, de Boer, R, Boezen, H, Bruinenberg, M, van der Harst, P, Hillege, H, van der Klauw, M, Navis, G, Ormel, J, Postma, D, Slaets, J, Snieder, H, Wolffenbuttel, B, Wijmenga, C, Berndt, S, Gustafsson, S, Mägi, R, Ganna, A, Wheeler, E, Feitosa, M, Justice, A, Monda, K, Croteau Chonka, D, Day, F, Fall, T, Ferreira, T, Gentilini, D, Jackson, A, Randall, J, Vedantam, S, Willer, C, Wood, A, Workalemahu, T, Hu, Y, Lee, S, Liang, L, Lin, D, Min, J, Neale, B, Thorleifsson, G, Yang, J, Albrecht, E, Amin, N, Bragg Gresham, J, Cadby, G, den Heijer, M, Eklund, N, Fischer, K, Goel, A, Hottenga, J, Jarick, I, Johansson, A, Johnson, T, Kanoni, S, Kleber, M, König, I, Kristiansson, K, Kutalik, Z, Lamina, C, Lecoeur, C, Li, G, Mcardle, W, Medina Gomez, C, Müller Nurasyid, M, Ngwa, J, Nolte, I, Paternoster, L, Pechlivanis, S, Perola, M, Peters, M, Preuss, M, Rose, L, Shi, J, Shungin, D, Smith, A, Strawbridge, R, Surakka, I, Trip, M, Tyrer, J, Van Vliet 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Peters, A, Polasek, O, Pouta, A, Pramstaller, P, Prokopenko, I, Pütter, C, Radhakrishnan, A, Raitakari, O, Rendon, A, Rivadeneira, F, Rudan, I, Saaristo, T, Sambrook, J, Sanders, A, Sanna, S, Saramies, J, Schipf, S, Schreiber, S, Schunkert, H, Shin, S, Signorini, S, Sinisalo, J, Skrobek, B, Soranzo, N, Stancakova, A, Stark, K, Stephens, J, Stirrups, K, Stumvoll, M, Swift, A, Theodoraki, E, Thorand, B, Tregouet, D, Tremoli, E, Van der Klauw, M, van Meurs, J, Vermeulen, S, Viikari, J, Virtamo, J, Vitart, V, Waeber, G, Wang, Z, Widen, E, Wild, S, Willemsen, G, Winkelmann, B, Witteman, J, Wong, A, Zillikens, M, Amouyel, P, Boehm, B, Boomsma, D, Caulfield, M, Chanock, S, Cupples, L, Cusi, D, Dedoussis, G, Erdmann, J, Eriksson, J, Franks, P, Froguel, P, Gieger, C, Gyllensten, U, Hamsten, A, Harris, T, Hengstenberg, C, Hicks, A, Hingorani, A, Hinney, A, Hofman, A, Hovingh, K, Hveem, K, Illig, T, Jarvelin, M, Jöckel, K, Keinanen Kiukaanniemi, S, Kiemeney, L, Kuh, D, Laakso, M, Lehtimäki, T, 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Teumer, A, Winkler, Tw, Tšernikova, N, Luan, J, Mihailov, E, Ehret, Gb, Zhang, W, Lamparter, D, Esko, T, Macé, A, Rüeger, S, Bochud, Py, Barcella, M, Dauvilliers, Y, Benyamin, B, Evans, Dm, Hayward, C, Lopez, Mf, Franke, L, Russo, A, Heid, Im, Salvi, E, Vendantam, S, Arking, De, Boerwinkle, E, Chambers, Jc, Fiorito, G, Grallert, H, Guarrera, S, Homuth, G, Huffman, Je, Porteous, D, GENERATION SCOTLAND, Consortium, LIFELINES COHORT, Study, Giant, Consortium, Manunta, Paolo, Moradpour, D, Iranzo, A, Hebebrand, J, Kemp, Jp, Lammers, Gj, Aubert, V, Heim, Mh, Martin, Ng, Montgomery, Gw, PERAITA ADRADOS, R, Santamaria, J, Negro, F, Schmidt, Co, Scott, Ra, Spector, Td, Strauch, K, Völzke, H, Wareham, Nj, Yuan, W, Bell, Jt, Chakravarti, A, Kooner, J, Peters, A, Matullo, G, Wallaschofski, H, Whitfield, Jb, Paccaud, F, Vollenweider, P, Bergmann, S, Beckmann, J, Tafti, M, Hastie, Nd, Cusi, D, Bochud, M, Frayling, Tm, Metspalu, A, Jarvelin, Mr, Scherag, A, Smith, Gd, Borecki, Ib, Rousson, V, Hirschhorn, Jn, Rivolta, C, Loos, Rj, Kutalik, Z., Hoggart, C, Zhao, J, Winkler, T, Ehret, G, Bochud, P, Evans, D, Lopez, M, Heid, I, Arking, D, Chambers, J, Huffman, J, Berg, J, Blackwood, D, Campbell, H, Cavanagh, J, Connell, J, Connor, M, Cunningham Burley, S, Deary, I, Dominiczak, A, Ellis, P, Fitzpatrick, B, Ford, I, Gertz, R, Grau, A, Haddow, G, Jackson, C, Kerr, S, Lindsay, R, Mcgilchrist, M, Mcintyre, D, Morris, A, Morton, R, Muir, W, Murray, G, Palmer, C, Pell, J, Philp, A, Porteous, M, Procter, R, Ralston, S, Reid, D, Sinnott, R, Smith, B, St Clair, D, Sullivan, F, Sweetland, M, Ure, J, Watt, G, Wolf, R, Wright, A, de Bakker, P, Bültmann, U, Geleijnse, M, Harst, P, Koppelman, G, Rosmalen, J, van Rossum, L, Smidt, H, Swertz, M, Stolk, R, Alizadeh, B, de Boer, R, Boezen, H, Bruinenberg, M, van der Harst, P, Hillege, H, van der Klauw, M, Navis, G, Ormel, J, Postma, D, Slaets, J, Snieder, H, Wolffenbuttel, B, Wijmenga, C, Berndt, S, Gustafsson, S, Mägi, R, Ganna, A, Wheeler, E, Feitosa, M, Justice, A, Monda, K, Croteau Chonka, D, Day, F, Fall, T, Ferreira, T, Gentilini, D, Jackson, A, Randall, J, Vedantam, S, Willer, C, Wood, A, Workalemahu, T, Hu, Y, Lee, S, Liang, L, Lin, D, Min, J, Neale, B, Thorleifsson, G, Yang, J, Albrecht, E, Amin, N, Bragg Gresham, J, Cadby, G, den Heijer, M, Eklund, N, Fischer, K, Goel, A, Hottenga, J, Jarick, I, Johansson, A, Johnson, T, Kanoni, S, Kleber, M, König, I, Kristiansson, K, Kutalik, Z, Lamina, C, Lecoeur, C, Li, G, Mcardle, W, Medina Gomez, C, Müller Nurasyid, M, Ngwa, J, Nolte, I, Paternoster, L, Pechlivanis, S, Perola, M, Peters, M, Preuss, M, Rose, L, Shi, J, Shungin, D, Smith, A, Strawbridge, R, Surakka, I, Trip, M, Tyrer, J, Van Vliet Ostaptchouk, J, Vandenput, L, Waite, L, Absher, D, Asselbergs, F, Atalay, M, Attwood, A, Balmforth, A, Basart, H, Beilby, J, Bonnycastle, L, Brambilla, P, Chasman, D, Chines, P, Collins, F, Cookson, W, de Faire, U, de Vegt, F, Dei, M, Dimitriou, M, Edkins, S, Estrada, K, Farrall, M, Ferrario, M, Ferrières, J, Frau, F, Gejman, P, Grönberg, H, Gudnason, V, Hall, A, Hall, P, Hartikainen, A, Heard Costa, N, Heath, A, Hu, F, Hunt, S, Hyppönen, E, Iribarren, C, Jacobs, K, Jansson, J, Jula, A, Kähönen, M, Kathiresan, S, Kee, F, Khaw, K, Kivimaki, M, Koenig, W, Kraja, A, Kumari, M, Karikuulasmaa, N, Kuusisto, J, Laitinen, J, Lakka, T, Langenberg, C, Launer, L, Lind, L, Lindström, J, Liu, J, Liuzzi, A, Lokki, M, Lorentzon, M, Madden, P, Magnusson, P, Manunta, P, Marek, D, März, W, Leach, I, Mcknight, B, Medland, S, Milani, L, Montgomery, G, Mooser, V, Mühleisen, T, Munroe, P, Musk, A, Narisu, N, Nicholson, G, Nohr, E, Ong, K, Oostra, B, Palotie, A, Peden, J, Pedersen, N, Polasek, O, Pouta, A, Pramstaller, P, Prokopenko, I, Pütter, C, Radhakrishnan, A, Raitakari, O, Rendon, A, Rivadeneira, F, Rudan, I, Saaristo, T, Sambrook, J, Sanders, A, Sanna, S, Saramies, J, Schipf, S, Schreiber, S, Schunkert, H, Shin, S, Signorini, S, Sinisalo, J, Skrobek, B, Soranzo, N, Stancakova, A, Stark, K, Stephens, J, Stirrups, K, Stumvoll, M, Swift, A, Theodoraki, E, Thorand, B, Tregouet, D, Tremoli, E, Van der Klauw, M, van Meurs, J, Vermeulen, S, Viikari, J, Virtamo, J, Vitart, V, Waeber, G, Wang, Z, Widen, E, Wild, S, Willemsen, G, Winkelmann, B, Witteman, J, Wong, A, Zillikens, M, Amouyel, P, Boehm, B, Boomsma, D, Caulfield, M, Chanock, S, Cupples, L, Dedoussis, G, Erdmann, J, Eriksson, J, Franks, P, Froguel, P, Gieger, C, Gyllensten, U, Hamsten, A, Harris, T, Hengstenberg, C, Hicks, A, Hingorani, A, Hinney, A, Hofman, A, Hovingh, K, Hveem, K, Illig, T, Jarvelin, M, Jöckel, K, Keinanen Kiukaanniemi, S, Kiemeney, L, Kuh, D, Laakso, M, Lehtimäki, T, Levinson, D, Martin, N, Nieminen, M, Njølstad, I, Ohlsson, C, Oldehinkel, A, Ouwehand, W, Palmer, L, Penninx, B, Power, C, Province, M, Psaty, B, Qi, L, Rauramaa, R, Ridker, P, Ripatti, S, Salomaa, V, Samani, N, Sørensen, T, Spector, T, Stefansson, K, Tönjes, A, Tuomilehto, J, Uitterlinden, A, Uusitupa, M, Wareham, N, Watkins, H, Wichmann, H, Wilson, J, Abecasis, G, Assimes, T, Barroso, I, Boehnke, M, Borecki, I, Deloukas, P, Fox, C, Frayling, T, Groop, L, Haritunian, T, Hunter, D, Kaplan, R, Karpe, F, Miriammoffatt, N, Mohlke, K, O'Connell, J, Pawitan, Y, Schadt, E, Schlessinger, D, Steinthorsdottir, V, Strachan, D, Thorsteinsdottir, U, van Duijn, C, Visscher, P, Di Blasio, A, Hirschhorn, J, Lindgren, C, Meyre, D, Mccarthy, M, Speliotes, E, North, K, Loos, R, Ingelsson, E, Kemp, J, Lammers, G, Heim, M, Peraita Adrados, R, Schmidt, C, Scott, R, Bell, J, Whitfield, J, Hastie, N, and Da Smith, G
Subjects: Epigenomics, Male, Netherlands Twin Register (NTR), body mass index, gene, SNP, Cancer Research, BIO/12 - BIOCHIMICA CLINICA E BIOLOGIA MOLECOLARE CLINICA, Potassium Channels, Glucose Transport Proteins, Facilitative, Medizin, Genome-wide association study, CHILDREN, ddc:616.07, FAMILIES, Body Mass Index, 0302 clinical medicine, Polymorphism (computer science), Genotype, LifeLines Cohort study, GENETICS & HEREDITY, Tandem Pore Domain, Genetics (clinical), ASSOCIATIONS, ddc:616, Genetics, 0303 health sciences, QUANTITATIVE TRAIT LOCI, Ecology, Genomics, Single Nucleotide, Generation Scotland Consortium, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], OBESITY, Physical Sciences, KCNK9 protein, Epigenetics, Female, ALCOHOLISM, Glucose Transport Proteins, Life Sciences & Biomedicine, Statistics (Mathematics), Human, Research Article, VARIANCES, Adult, PENETRANCE, GENES, lcsh:QH426-470, Evolution, European Continental Ancestry Group, Single-nucleotide polymorphism, Biostatistics, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, White People, 03 medical and health sciences, Genomic Imprinting, Potassium Channels, Tandem Pore Domain, Genetic, Behavior and Systematics, SDG 3 - Good Health and Well-being, Genetic linkage, GIANT Consortium, Genome-Wide Association Studies, Humans, Genetic Predisposition to Disease, Statistical Methods, Allele, Polymorphism, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Gene Expression Regulation, Genome-Wide Association Study, Obesity, 030304 developmental biology, 0604 Genetics, Science & Technology, LINKAGE ANALYSIS, SLC2A10 protein, Biology and Life Sciences, Computational Biology, Facilitative, Genome Analysis, Ecology, Evolution, Behavior and Systematic, lcsh:Genetics, Inflammatory diseases Radboud Institute for Health Sciences [Radboudumc 5], 3111 Biomedicine, Genomic imprinting, Mathematics, 030217 neurology & neurosurgery, Developmental Biology
Abstract: The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ∼4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P, Author Summary Large genetic association studies have revealed many genetic factors influencing common traits, such as body mass index (BMI). These studies assume that the effect of genetic variants is the same regardless of whether they are inherited from the mother or the father. In our study, we have developed a new approach that allows us to investigate variants whose impact depends on their parental origin (parent-of-origin effects), in unrelated samples when the parental origin cannot be inferred. This is feasible because at genetic markers at which such effects occur there is increased variability of the trait among individuals who inherited different genetic codes from their mother and their father compared to individuals who inherited the same genetic code from both parents. We applied this methodology to discover genetic markers with parent-of-origin effects (POEs) on BMI. This resulted in six candidate markers showing strong POE association. We then attempted to replicate the POE effects of these markers in family studies (where one can infer the parental origin of the inherited variants). Two of our candidates showed significant association in the family studies, the paternal and maternal effects of these markers were in the opposite direction.
Published: 2014

15. Genetic effects on the skin methylome in healthy older twins.

Author: Shore CJ, Villicaña S, El-Sayed Moustafa JS, Roberts AL, Gunn DA, Bataille V, Deloukas P, Spector TD, Small KS, and Bell JT
Subjects: Humans, Female, Aged, Genome-Wide Association Study, Middle Aged, Twins, Monozygotic genetics, Melanoma genetics, United Kingdom, Epigenesis, Genetic, DNA Methylation, Quantitative Trait Loci, Skin metabolism, CpG Islands genetics, Epigenome
Abstract: Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin., Competing Interests: Declaration of interests DG is an employee of Unilever., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2024
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16. Development of a (Poly)phenol Metabolic Signature for Assessing (Poly)phenol-Rich Dietary Patterns.

Author: Li Y, Xu Y, Le Sayec M, Yan X, Spector TD, Steves CJ, Bell JT, Small KS, Menni C, Gibson R, and Rodriguez-Mateos A
Subjects: Humans, Adult, Female, Male, Middle Aged, Young Adult, Metabolomics, Dietary Patterns, Diet, Polyphenols metabolism, Polyphenols urine, Polyphenols administration & dosage
Abstract: The objective assessment of habitual (poly)phenol-rich diets in nutritional epidemiology studies remains challenging. This study developed and evaluated the metabolic signature of a (poly)phenol-rich dietary score (PPS) using a targeted metabolomics method comprising 105 representative (poly)phenol metabolites, analyzed in 24 h of urine samples collected from healthy volunteers. The metabolites that were significantly associated with PPS after adjusting for energy intake were selected to establish a metabolic signature using a combination of linear regression followed by ridge regression to estimate penalized weights for each metabolite. A metabolic signature comprising 51 metabolites was significantly associated with adherence to PPS in 24 h urine samples, as well as with (poly)phenol intake estimated from food frequency questionnaires and diaries. Internal and external data sets were used for validation, and plasma, spot urine, and 24 h urine samples were compared. The metabolic signature proposed here has the potential to accurately reflect adherence to (poly)phenol-rich diets, and may be used as an objective tool for the assessment of (poly)phenol intake.
Published: 2024
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17. Enhanced resolution profiling in twins reveals differential methylation signatures of type 2 diabetes with links to its complications.

Author: Christiansen C, Potier L, Martin TC, Villicaña S, Castillo-Fernandez JE, Mangino M, Menni C, Tsai PC, Campbell PJ, Mullin S, Ordoñana JR, Monteagudo O, Sachdev PS, Mather KA, Trollor JN, Pietilainen KH, Ollikainen M, Dalgård C, Kyvik K, Christensen K, van Dongen J, Willemsen G, Boomsma DI, Magnusson PKE, Pedersen NL, Wilson SG, Grundberg E, Spector TD, and Bell JT
Subjects: Humans, Female, Male, Genome-Wide Association Study, Genetic Predisposition to Disease, Middle Aged, Epigenesis, Genetic, Otx Transcription Factors genetics, Otx Transcription Factors metabolism, Diabetes Complications genetics, Gene Expression Profiling, Diabetes Mellitus, Type 2 genetics, DNA Methylation, CpG Islands
Abstract: Background: Type 2 diabetes (T2D) susceptibility is influenced by genetic and environmental factors. Previous findings suggest DNA methylation as a potential mechanism in T2D pathogenesis and progression., Methods: We profiled DNA methylation in 248 blood samples from participants of European ancestry from 7 twin cohorts using a methylation sequencing platform targeting regulatory genomic regions encompassing 2,048,698 CpG sites., Findings: We find and replicate 3 previously unreported T2D differentially methylated CpG positions (T2D-DMPs) at FDR 5% in RGL3, NGB and OTX2, and 20 signals at FDR 25%, of which 14 replicated. Integrating genetic variation and T2D-discordant monozygotic twin analyses, we identify both genetic-based and genetic-independent T2D-DMPs. The signals annotate to genes with established GWAS and EWAS links to T2D and its complications, including blood pressure (RGL3) and eye disease (OTX2)., Interpretation: The results help to improve our understanding of T2D disease pathogenesis and progression and may provide biomarkers for its complications., Funding: Funding acknowledgements for each cohort can be found in the Supplementary Note., Competing Interests: Declaration of interests The authors have no conflicts to declare., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Published: 2024
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18. Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison.

Author: Zhang X and Bell JT
Subjects: Humans, Phenotype, Gene Frequency, Biological Variation, Population, Models, Genetic, Quantitative Trait Loci, Gene-Environment Interaction
Abstract: Genetically associated phenotypic variability has been widely observed across organisms and traits, including in humans. Both gene-gene and gene-environment interactions can lead to an increase in genetically associated phenotypic variability. Therefore, detecting the underlying genetic variants, or variance Quantitative Trait Loci (vQTLs), can provide novel insights into complex traits. Established approaches to detect vQTLs apply different methodologies from variance-only approaches to mean-variance joint tests, but a comprehensive comparison of these methods is lacking. Here, we review available methods to detect vQTLs in humans, carry out a simulation study to assess their performance under different biological scenarios of gene-environment interactions, and apply the optimal approaches for vQTL identification to gene expression data. Overall, with a minor allele frequency (MAF) of less than 0.2, the squared residual value linear model (SVLM) and the deviation regression model (DRM) are optimal when the data follow normal and non-normal distributions, respectively. In addition, the Brown-Forsythe (BF) test is one of the optimal methods when the MAF is 0.2 or larger, irrespective of phenotype distribution. Additionally, a larger sample size and more balanced sample distribution in different exposure categories increase the power of BF, SVLM, and DRM. Our results highlight vQTL detection methods that perform optimally under realistic simulation settings and show that their relative performance depends on the phenotype distribution, allele frequency, sample size, and the type of exposure in the interaction model underlying the vQTL., Competing Interests: Conflicts of interest. The author(s) declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)
Published: 2024
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19. Epigenome-wide Association Study Shows Differential DNA Methylation of MDC1, KLF9, and CUTA in Autoimmune Thyroid Disease.

Author: Lafontaine N, Shore CJ, Campbell PJ, Mullin BH, Brown SJ, Panicker V, Dudbridge F, Brix TH, Hegedüs L, Wilson SG, Bell JT, and Walsh JP
Subjects: Humans, Adaptor Proteins, Signal Transducing genetics, Australia epidemiology, Cell Cycle Proteins genetics, DNA Methylation, Epigenesis, Genetic, Epigenome, Kruppel-Like Transcription Factors genetics, Graves Disease genetics, Hashimoto Disease genetics
Abstract: Context: Autoimmune thyroid disease (AITD) includes Graves disease (GD) and Hashimoto disease (HD), which often run in the same family. AITD etiology is incompletely understood: Genetic factors may account for up to 75% of phenotypic variance, whereas epigenetic effects (including DNA methylation [DNAm]) may contribute to the remaining variance (eg, why some individuals develop GD and others HD)., Objective: This work aimed to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) comparing GD to HD., Methods: Whole-blood DNAm was measured across the genome using the Infinium MethylationEPIC array in 32 Australian patients with GD and 30 with HD (discovery cohort) and 32 Danish patients with GD and 32 with HD (replication cohort). Linear mixed models were used to test for differences in quantile-normalized β values of DNAm between GD and HD and data were later meta-analyzed. Comb-p software was used to identify DMRs., Results: We identified epigenome-wide significant differences (P < 9E-8) and replicated (P < .05) 2 DMPs between GD and HD (cg06315208 within MDC1 and cg00049440 within KLF9). We identified and replicated a DMR within CUTA (5 CpGs at 6p21.32). We also identified 64 DMPs and 137 DMRs in the meta-analysis., Conclusion: Our study reveals differences in DNAm between GD and HD, which may help explain why some people develop GD and others HD and provide a link to environmental risk factors. Additional research is needed to advance understanding of the role of DNAm in AITD and investigate its prognostic and therapeutic potential., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society.)
Published: 2024
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20. Cross-sectional analyses of metabolites across biological samples mediating dietary acid load and chronic kidney disease.

Author: Attaye I, Beynon-Cobb B, Louca P, Nogal A, Visconti A, Tettamanzi F, Wong K, Michellotti G, Spector TD, Falchi M, Bell JT, and Menni C
Abstract: Chronic kidney disease (CKD) is a major public health burden, with dietary acid load (DAL) and gut microbiota playing crucial roles. As DAL can affect the host metabolome, potentially via the gut microbiota, we cross-sectionally investigated the interplay between DAL, host metabolome, gut microbiota, and early-stage CKD (TwinsUK, n = 1,453). DAL was positively associated with CKD stage G1-G2 (Beta (95% confidence interval) = 0.34 (0.007; 0.7), p = 0.046). After adjusting for covariates and multiple testing, we identified 15 serum, 14 urine, 8 stool, and 7 saliva metabolites, primarily lipids and amino acids, associated with both DAL and CKD progression. Of these, 8 serum, 2 urine, and one stool metabolites were found to mediate the DAL-CKD association. Furthermore, the stool metabolite 5-methylhexanoate (i7:0) correlated with 26 gut microbial species. Our findings emphasize the gut microbiota's therapeutic potential in countering DAL's impact on CKD through the host metabolome. Interventional and longitudinal studies are needed to establish causality., Competing Interests: T.D.S. is a co-founder and shareholder of ZOE Ltd., (© 2024 The Authors.)
Published: 2024
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21. Usual dietary intake and change in DNA methylation over years: EWAS in KORA FF4 and KORA fit.

Author: Hellbach F, Freuer D, Meisinger C, Peters A, Winkelmann J, Costeira R, Hauner H, Baumeister SE, Bell JT, Waldenberger M, and Linseisen J
Abstract: Introduction: Changes in DNA methylation can increase or suppress the expression of health-relevant genes. We investigated for the first time the relationship between habitual food consumption and changes in DNA methylation., Methods: The German KORA FF4 and KORA Fit studies were used to study the change in methylation over a median follow-up of 4 years. Only subjects participating in both surveys and with available dietary and methylation data were included in the analysis (n = 465). DNA methylation was measured using the Infinium MethylationEPIC BeadChip (Illumina), resulting in 735,527 shared CpGs across both studies. Generalized estimating equation models with an interaction term of exposure and time point were used to analyze the association of 34 food groups, folic acid, and two dietary patterns with changes in DNA methylation over time., Results: The results were corrected for genomic inflation. Significant interaction terms indicate different effects between both time points. We observed only a few significant associations between food intake and change in DNA methylation, except for cream and spirit consumption. The annotated genes include CLN3, PROM1, DLEU7, TLL2, and UGT1A10., Discussion: We identified weak associations between food consumption and DNA methylation change. The differential results for cream and spirits, both consumed in low quantities, require replication in independent studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hellbach, Freuer, Meisinger, Peters, Winkelmann, Costeira, Hauner, Baumeister, Bell, Waldenberger and Linseisen.)
Published: 2024
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22. Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use.

Author: Fang F, Quach B, Lawrence KG, van Dongen J, Marks JA, Lundgren S, Lin M, Odintsova VV, Costeira R, Xu Z, Zhou L, Mandal M, Xia Y, Vink JM, Bierut LJ, Ollikainen M, Taylor JA, Bell JT, Kaprio J, Boomsma DI, Xu K, Sandler DP, Hancock DB, and Johnson EO
Subjects: Adult, Female, Humans, Male, Middle Aged, Cannabis genetics, Cigarette Smoking genetics, Epigenesis, Genetic genetics, Epigenomics methods, Marijuana Smoking genetics, Marijuana Use genetics, Black People, CpG Islands genetics, DNA Methylation genetics, Epigenome genetics, Genome-Wide Association Study methods, White People genetics
Abstract: Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9436 participants (7795 European and 1641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 ( p < 5.85 × 10 - 7 ) : cg22572071 near gene ADGRF1, cg15280358 in ADAM12, cg00813162 in ACTN1, and cg01101459 near LINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated to APOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes., (© 2023. The Author(s).)
Published: 2024
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23. Dietary and supplemental intake of vitamins C and E is associated with altered DNA methylation in an epigenome-wide association study meta-analysis.

Author: Keshawarz A, Joehanes R, Ma J, Lee GY, Costeira R, Tsai PC, Masachs OM, Bell JT, Wilson R, Thorand B, Winkelmann J, Peters A, Linseisen J, Waldenberger M, Lehtimäki T, Mishra PP, Kähönen M, Raitakari O, Helminen M, Wang CA, Melton PE, Huang RC, Pennell CE, O'Sullivan TA, Ochoa-Rosales C, Voortman T, van Meurs JBJ, Young KL, Graff M, Wang Y, Kiel DP, Smith CE, Jacques PF, and Levy D
Subjects: Humans, Epigenome, Vitamins pharmacology, Vitamin E, Genome-Wide Association Study methods, CpG Islands, Epigenesis, Genetic, DNA Methylation, Ascorbic Acid
Abstract: Background: Dietary intake of antioxidants such as vitamins C and E protect against oxidative stress, and may also be associated with altered DNA methylation patterns., Methods: We meta-analysed epigenome-wide association study (EWAS) results from 11,866 participants across eight population-based cohorts to evaluate the association between self-reported dietary and supplemental intake of vitamins C and E with DNA methylation. EWAS were adjusted for age, sex, BMI, caloric intake, blood cell type proportion, smoking status, alcohol consumption, and technical covariates. Significant results of the meta-analysis were subsequently evaluated in gene set enrichment analysis (GSEA) and expression quantitative trait methylation (eQTM) analysis., Results: In meta-analysis, methylation at 4,656 CpG sites was significantly associated with vitamin C intake at FDR ≤ 0.05. The most significant CpG sites associated with vitamin C (at FDR ≤ 0.01) were enriched for pathways associated with systems development and cell signalling in GSEA, and were associated with downstream expression of genes enriched in the immune response in eQTM analysis. Furthermore, methylation at 160 CpG sites was significantly associated with vitamin E intake at FDR ≤ 0.05, but GSEA and eQTM analysis of the top most significant CpG sites associated with vitamin E did not identify significant enrichment of any biological pathways investigated., Conclusions: We identified significant associations of many CpG sites with vitamin C and E intake, and our results suggest that vitamin C intake may be associated with systems development and the immune response.
Published: 2023
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24. A Fecal Metabolite Signature of Impaired Fasting Glucose: Results From Two Independent Population-Based Cohorts.

Author: Nogal A, Tettamanzi F, Dong Q, Louca P, Visconti A, Christiansen C, Breuninger T, Linseisen J, Grallert H, Wawro N, Asnicar F, Wong K, Baleanu AF, Michelotti GA, Segata N, Falchi M, Peters A, Franks PW, Bagnardi V, Spector TD, Bell JT, Gieger C, Valdes AM, and Menni C
Subjects: Adult, Humans, Fasting, Glucose, Blood Glucose metabolism, Prediabetic State complications, Diabetes Mellitus, Type 2 complications, Niacin
Abstract: Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We searched for fecal metabolites, a readout of gut microbiome function, associated with impaired fasting glucose (IFG) in 142 individuals with IFG and 1,105 healthy individuals from the UK Adult Twin Registry (TwinsUK). We used the Cooperative Health Research in the Region of Augsburg (KORA) cohort (318 IFG individuals, 689 healthy individuals) to replicate our findings. We linearly combined eight IFG-positively associated metabolites (1-methylxantine, nicotinate, glucuronate, uridine, cholesterol, serine, caffeine, and protoporphyrin IX) into an IFG-metabolite score, which was significantly associated with higher odds ratios (ORs) for IFG (TwinsUK: OR 3.9 [95% CI 3.02-5.02], P < 0.0001, KORA: OR 1.3 [95% CI 1.16-1.52], P < 0.0001) and incident type 2 diabetes (T2D; TwinsUK: hazard ratio 4 [95% CI 1.97-8], P = 0.0002). Although these are host-produced metabolites, we found that the gut microbiome is strongly associated with their fecal levels (area under the curve >70%). Abundances of Faecalibacillus intestinalis, Dorea formicigenerans, Ruminococcus torques, and Dorea sp. AF24-7LB were positively associated with IFG, and such associations were partially mediated by 1-methylxanthine and nicotinate (variance accounted for mean 14.4% [SD 5.1], P < 0.05). Our results suggest that the gut microbiome is linked to prediabetes not only via the production of microbial metabolites but also by affecting intestinal absorption/excretion of host-produced metabolites and xenobiotics, which are correlated with the risk of IFG. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes and T2D onset., Article Highlights: Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We investigated whether there is a fecal metabolite signature of impaired fasting glucose (IFG) and the possible underlying mechanisms of action. We identified a fecal metabolite signature of IFG associated with prevalent IFG in two independent cohorts and incident type 2 diabetes in a subanalysis. Although the signature consists of metabolites of nonmicrobial origin, it is strongly correlated with gut microbiome composition. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes by affecting intestinal absorption or excretion of host compounds and xenobiotics., (© 2023 by the American Diabetes Association.)
Published: 2023
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25. Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome.

Author: Costeira R, Evangelista L, Wilson R, Yan X, Hellbach F, Sinke L, Christiansen C, Villicaña S, Masachs OM, Tsai PC, Mangino M, Menni C, Berry SE, Beekman M, van Heemst D, Slagboom PE, Heijmans BT, Suhre K, Kastenmüller G, Gieger C, Peters A, Small KS, Linseisen J, Waldenberger M, and Bell JT
Subjects: Humans, Vitamin B 12, Epigenome, DNA Methylation, Folic Acid, Vitamin B 6, Biomarkers, Minor Histocompatibility Antigens, Amino Acid Transport System ASC, Vitamin B Complex
Abstract: Background: B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants., Results: Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals., Conclusion: Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome., (© 2023. BioMed Central Ltd., part of Springer Nature.)
Published: 2023
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26. Leveraging osteoclast genetic regulatory data to identify genes with a role in osteoarthritis.

Author: Mullin BH, Zhu K, Brown SJ, Mullin S, Dudbridge F, Pavlos NJ, Richards JB, Grundberg E, Bell JT, Zeggini E, Walsh JP, Xu J, and Wilson SG
Subjects: Humans, Genome-Wide Association Study methods, Genetic Predisposition to Disease, Gene Expression Regulation, Osteoclasts metabolism, Osteoarthritis genetics, Osteoarthritis metabolism
Abstract: There has been a growing interest in the role of the subchondral bone and its resident osteoclasts in the progression of osteoarthritis (OA). A recent genome-wide association study (GWAS) identified 100 independent association signals for OA traits. Most of these signals are led by noncoding variants, suggesting that genetic regulatory effects may drive many of the associations. We have generated a unique human osteoclast-like cell-specific expression quantitative trait locus (eQTL) resource for studying the genetics of bone disease. Considering the potential role of osteoclasts in the pathogenesis of OA, we performed an integrative analysis of this dataset with the recently published OA GWAS results. Summary data-based Mendelian randomization (SMR) and colocalization analyses identified 38 genes with a potential role in OA, including some that have been implicated in Mendelian diseases with joint/skeletal abnormalities, such as BICRA, EIF6, CHST3, and FBN2. Several OA GWAS signals demonstrated colocalization with more than one eQTL peak, including at 19q13.32 (hip OA with BCAM, PRKD2, and BICRA eQTL). We also identified a number of eQTL signals colocalizing with more than one OA trait, including FAM53A, GCAT, HMGN1, MGAT4A, RRP7BP, and TRIOBP. An SMR analysis identified 3 loci with evidence of pleiotropic effects on OA-risk and gene expression: LINC01481, CPNE1, and EIF6. Both CPNE1 and EIF6 are located at 20q11.22, a locus harboring 2 other strong OA candidate genes, GDF5 and UQCC1, suggesting the presence of an OA-risk gene cluster. In summary, we have used our osteoclast-specific eQTL dataset to identify genes potentially involved with the pathogenesis of OA., Competing Interests: Conflicts of interest The author(s) declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)
Published: 2023
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27. Hemin availability induces coordinated DNA methylation and gene expression changes in Porphyromonas gingivalis .

Author: Costeira R, Aduse-Opoku J, Vernon JJ, Rodriguez-Algarra F, Joseph S, Devine DA, Marsh PD, Rakyan V, Curtis MA, and Bell JT
Subjects: Humans, Porphyromonas gingivalis genetics, DNA Methylation genetics, ATP-Binding Cassette Transporters genetics, Gene Expression, Hemin pharmacology, Periodontal Diseases genetics
Abstract: Periodontal disease is a chronic inflammatory disease in which the oral pathogen Porphyromonas gingivalis plays an important role. Porphyromonas gingivalis expresses virulence determinants in response to higher hemin concentrations, but the underlying regulatory processes remain unclear. Bacterial DNA methylation has the potential to fulfil this mechanistic role. We characterized the methylome of P. gingivalis , and compared its variation to transcriptome changes in response to hemin availability. Porphyromonas gingivalis W50 was grown in chemostat continuous culture with excess or limited hemin, prior to whole-methylome and transcriptome profiling using Nanopore and Illumina RNA-Seq. DNA methylation was quantified for Dam/Dcm motifs and all-context N6-methyladenine (6mA) and 5-methylcytosine (5mC). Of all 1,992 genes analyzed, 161 and 268 were respectively over- and under-expressed with excess hemin. Notably, we detected differential DNA methylation signatures for the Dam "GATC" motif and both all-context 6mA and 5mC in response to hemin availability. Joint analyses identified a subset of coordinated changes in gene expression, 6mA, and 5mC methylation that target genes involved in lactate utilization and ABC transporters. The results identify altered methylation and expression responses to hemin availability in P. gingivalis , with insights into mechanisms regulating its virulence in periodontal disease. IMPORTANCE DNA methylation has important roles in bacteria, including in the regulation of transcription. Porphyromonas gingivalis , an oral pathogen in periodontitis, exhibits well-established gene expression changes in response to hemin availability. However, the regulatory processes underlying these effects remain unknown. We profiled the novel P. gingivalis epigenome, and assessed epigenetic and transcriptome variation under limited and excess hemin conditions. As expected, multiple gene expression changes were detected in response to limited and excess hemin that reflect health and disease, respectively. Notably, we also detected differential DNA methylation signatures for the Dam "GATC" motif and both all-context 6mA and 5mC in response to hemin. Joint analyses identified coordinated changes in gene expression, 6mA, and 5mC methylation that target genes involved in lactate utilization and ABC transporters. The results identify novel regulatory processes underlying the mechanism of hemin regulated gene expression in P. gingivalis, with phenotypic impacts on its virulence in periodontal disease., Competing Interests: The authors declare no conflict of interest.
Published: 2023
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28. Stratified genome-wide association analysis of type 2 diabetes reveals subgroups with genetic and environmental heterogeneity.

Author: Christiansen CE, Arathimos R, Pain O, Molokhia M, Bell JT, and Lewis CM
Subjects: Humans, Genome-Wide Association Study, Genetic Predisposition to Disease, Risk Factors, Polymorphism, Single Nucleotide genetics, Diabetes Mellitus, Type 2 genetics
Abstract: Type 2 diabetes (T2D) is a heterogeneous illness caused by genetic and environmental factors. Previous genome-wide association studies (GWAS) have identified many genetic variants associated with T2D and found evidence of differing genetic profiles by age-at-onset. This study seeks to explore further the genetic and environmental drivers of T2D by analyzing subgroups on the basis of age-at-onset of diabetes and body mass index (BMI). In the UK Biobank, 36 494 T2D cases were stratified into three subgroups, and GWAS was performed for all T2D cases and for each subgroup relative to 421 021 controls. Altogether, 18 single nucleotide polymorphisms were significantly associated with T2D genome-wide in one or more subgroups and also showed evidence of heterogeneity between the subgroups (Cochrane's Q P < 0.01), with two SNPs remaining significant after multiple testing (in CDKN2B and CYTIP). Combined risk scores, on the basis of genetic profile, BMI and age, resulted in excellent diabetes prediction [area under the ROC curve (AUC) = 0.92]. A modest improvement in prediction (AUC = 0.93) was seen when the contribution of genetic and environmental factors was evaluated separately for each subgroup. Increasing sample sizes of genetic studies enables us to stratify disease cases into subgroups, which have sufficient power to highlight areas of genetic heterogeneity. Despite some evidence that optimizing combined risk scores by subgroup improves prediction, larger sample sizes are likely needed for prediction when using a stratification approach., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Published: 2023
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29. Genetic impacts on DNA methylation help elucidate regulatory genomic processes.

Author: Villicaña S, Castillo-Fernandez J, Hannon E, Christiansen C, Tsai PC, Maddock J, Kuh D, Suderman M, Power C, Relton C, Ploubidis G, Wong A, Hardy R, Goodman A, Ong KK, and Bell JT
Subjects: Humans, CpG Islands, Quantitative Trait Loci, Gene Expression Regulation, Polymorphism, Single Nucleotide, Genome-Wide Association Study methods, DNA Methylation, Genomics
Abstract: Background: Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk., Results: We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1520 co-localisations across 1325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs (e.g. through disruption of transcription factor binding sites for CTCF and SMC3) and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites)., Conclusions: Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk ., (© 2023. The Author(s).)
Published: 2023
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30. Accelerated epigenetic aging and DNA methylation alterations in Berardinelli-Seip congenital lipodystrophy.

Author: Qannan A, Bejaoui Y, Izadi M, Yousri NA, Razzaq A, Christiansen C, Martin GM, Bell JT, Horvath S, Oshima J, Megarbane A, Ericsson J, Pourkarimi E, and El Hajj N
Subjects: Humans, DNA Methylation genetics, Mutation, Aging genetics, Epigenesis, Genetic, Lipodystrophy, Congenital Generalized genetics, Diabetes Mellitus, Type 2 genetics, GTP-Binding Protein gamma Subunits, Lipodystrophy genetics
Abstract: Berardinelli-Seip congenital lipodystrophy type 2 (CGL2) is a very rare human genetic disorder with potential significance to the understanding of the pathobiology of aging. CGL2 patients display characteristic progeroid features and suffer from type 2 diabetes, insulin resistance and fatty liver. In this study, we profiled genome-wide DNA methylation levels in CGL2 patients with BSCL2 mutations to study epigenetic age acceleration and DNA methylation alterations. This analysis revealed significant age acceleration in blood DNA of CGL2 patients using both first- and second-generation epigenetic clocks. We also observed a shortened lifespan of Caenorhabditis elegans following knockdown of the BSCL2 homolog seip-1 on a daf-16/forkhead box, class O mutant background. DNA methylation analysis revealed significant differentially methylated sites enriched for lyase activity, kinase regulator activity, protein kinase regulator activity and kinase activator activity. We could also observe significant hypomethylation in the promoter of the dual specificity phosphatase 22 gene when comparing CGL2 patients versus controls. We conclude that in line with the observed progeroid features, CGL2 patients exhibit significant epigenetic age acceleration and DNA methylation alterations that might affect pathways/genes of potential relevance to the disease., (© The Author(s) 2023. Published by Oxford University Press.)
Published: 2023
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31. Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes.

Author: McAllan L, Baranasic D, Villicaña S, Brown S, Zhang W, Lehne B, Adamo M, Jenkinson A, Elkalaawy M, Mohammadi B, Hashemi M, Fernandes N, Lambie N, Williams R, Christiansen C, Yang Y, Zudina L, Lagou V, Tan S, Castillo-Fernandez J, King JWD, Soong R, Elliott P, Scott J, Prokopenko I, Cebola I, Loh M, Lenhard B, Batterham RL, Bell JT, Chambers JC, Kooner JS, and Scott WR
Subjects: Humans, Adipocytes metabolism, Obesity metabolism, Genomics, Epigenesis, Genetic, DNA Methylation, Diabetes Mellitus metabolism
Abstract: DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions., (© 2023. The Author(s).)
Published: 2023
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32. Interplay between the (Poly)phenol Metabolome, Gut Microbiome, and Cardiovascular Health in Women: A Cross-Sectional Study from the TwinsUK Cohort.

Author: Li Y, Xu Y, Le Roy C, Hu J, Steves CJ, Bell JT, Spector TD, Gibson R, Menni C, and Rodriguez-Mateos A
Subjects: Humans, Female, Phenol, Cross-Sectional Studies, Propionates, Phenols, Metabolome, Gastrointestinal Microbiome, Cardiovascular Diseases epidemiology, Cardiovascular Diseases etiology
Abstract: Background: Dietary (poly)phenol consumption is inversely associated with cardiovascular disease (CVD) risk in epidemiological studies, but little is known about the role of the gut microbiome in this relationship., Methods: In 200 healthy females, aged 62.0 ± 10.0 years, from the TwinsUK cohort, 114 individual (poly)phenol metabolites were measured from spot urine using ultra-high-performance liquid chromatography-mass spectrometry. The associations between metabolites, the gut microbiome (alpha diversity and genera), and cardiovascular scores were investigated using linear mixed models adjusting age, BMI, fibre, energy intake, family relatedness, and multiple testing (FDR < 0.1)., Results: Significant associations were found between phenolic acid metabolites, CVD risk, and the gut microbiome. A total of 35 phenolic acid metabolites were associated with the Firmicutes phylum, while 5 metabolites were associated with alpha diversity (FDR-adjusted p < 0.05). Negative associations were observed between the atherosclerotic CVD (ASCVD) risk score and five phenolic acid metabolites, two tyrosol metabolites, and daidzein with stdBeta (95% (CI)) ranging from -0.05 (-0.09, -0.01) for 3-(2,4-dihydroxyphenyl)propanoic acid to -0.04 (-0.08, -0.003) for 2-hydroxycinnamic acid (FDR-adjusted p < 0.1). The genus 5-7N15 in the Bacteroidetes phylum was positively associated with the same metabolites, including 3-(3,5-dihydroxyphenyl)propanoic acid, 3-(2,4-dihydroxyphenyl)propanoic acid, 3-(3,4-dihydroxyphenyl)propanoic acid), 3-hydroxyphenylethanol-4-sulfate, and 4-hydroxyphenylethanol-3-sulfate)(stdBeta (95% CI): 0.23 (0.09, 0.36) to 0.28 (0.15, 0.42), FDR-adjusted p < 0.05), and negatively associated with the ASCVD score (stdBeta (95% CI): -0.05 (-0.09, -0.01), FDR-adjusted p = 0.02). Mediation analysis showed that genus 5-7N15 mediated 23.8% of the total effect of 3-(3,4-dihydroxyphenyl)propanoic acid on the ASCVD score., Conclusions: Coffee, tea, red wine, and several vegetables and fruits, especially berries, are the most abundant food sources of phenolic acids that have the strongest associations with CVD risk. We found that the gut microbiome, particularly the genus 5-7N15, partially mediates the negative association between urinary (poly)phenols and cardiovascular risk, supporting a key role of the gut microbiome in the health benefits of dietary (poly)phenols.
Published: 2023
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33. Pooled analysis of epigenome-wide association studies of food consumption in KORA, TwinsUK and LLS.

Author: Hellbach F, Sinke L, Costeira R, Baumeister SE, Beekman M, Louca P, Leeming ER, Mompeo O, Berry S, Wilson R, Wawro N, Freuer D, Hauner H, Peters A, Winkelmann J, Koenig W, Meisinger C, Waldenberger M, Heijmans BT, Slagboom PE, Bell JT, and Linseisen J
Subjects: Male, Middle Aged, Humans, Female, CpG Islands, Epigenesis, Genetic, DNA Methylation, Epigenome genetics, Genome-Wide Association Study
Abstract: Purpose: Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation., Methods: Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group., Results: We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others., Conclusion: This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content., (© 2022. The Author(s).)
Published: 2023
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34. Epigenome-Wide Association Study Reveals CpG Sites Associated with Thyroid Function and Regulatory Effects on KLF9 .

Author: Weihs A, Chaker L, Martin TC, Braun KVE, Campbell PJ, Cox SR, Fornage M, Gieger C, Grabe HJ, Grallert H, Harris SE, Kühnel B, Marioni RE, Martin NG, McCartney DL, McRae AF, Meisinger C, van Meurs JBJ, Nano J, Nauck M, Peters A, Prokisch H, Roden M, Selvin E, Beekman M, van Heemst D, Slagboom EP, Swenson BR, Tin A, Tsai PC, Uitterlinden A, Visser WE, Völzke H, Waldenberger M, Walsh JP, Köttgen A, Wilson SG, Peeters RP, Bell JT, Medici M, and Teumer A
Subjects: Humans, Thyroid Gland, Thyroxine genetics, CpG Islands, Genome-Wide Association Study, Kruppel-Like Transcription Factors genetics, Epigenome, Triiodothyronine
Abstract: Background: Thyroid hormones play a key role in differentiation and metabolism and are known regulators of gene expression through both genomic and epigenetic processes including DNA methylation. The aim of this study was to examine associations between thyroid hormones and DNA methylation. Methods: We carried out a fixed-effect meta-analysis of epigenome-wide association study (EWAS) of blood DNA methylation sites from 8 cohorts from the ThyroidOmics Consortium, incorporating up to 7073 participants of both European and African ancestry, implementing a discovery and replication stage. Statistical analyses were conducted using normalized beta CpG values as dependent and log-transformed thyrotropin (TSH), free thyroxine, and free triiodothyronine levels, respectively, as independent variable in a linear model. The replicated findings were correlated with gene expression levels in whole blood and tested for causal influence of TSH and free thyroxine by two-sample Mendelian randomization (MR). Results: Epigenome-wide significant associations ( p -value <1.1E-7) of three CpGs for free thyroxine, five for free triiodothyronine, and two for TSH concentrations were discovered and replicated (combined p -values = 1.5E-9 to 4.3E-28). The associations included CpG sites annotated to KLF9 (cg00049440) and DOT1L (cg04173586) that overlap with all three traits, consistent with hypothalamic-pituitary-thyroid axis physiology. Significant associations were also found for CpGs in FKBP5 for free thyroxine, and at CSNK1D/LINCO1970 and LRRC8D for free triiodothyronine. MR analyses supported a causal effect of thyroid status on DNA methylation of KLF9 . DNA methylation of cg00049440 in KLF9 was inversely correlated with KLF9 gene expression in blood. The CpG at CSNK1D/LINC01970 overlapped with thyroid hormone receptor alpha binding peaks in liver cells. The total additive heritability of the methylation levels of the six significant CpG sites was between 25% and 57%. Significant methylation QTLs were identified for CpGs at KLF9 , FKBP5 , LRRC8D , and CSNK1D/LINC01970 . Conclusions: We report novel associations between TSH, thyroid hormones, and blood-based DNA methylation. This study advances our understanding of thyroid hormone action particularly related to KLF9 and serves as a proof-of-concept that integrations of EWAS with other -omics data can provide a valuable tool for unraveling thyroid hormone signaling in humans by complementing and feeding classical in vitro and animal studies.
Published: 2023
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35. DNA methylation analysis identifies novel genetic loci associated with circulating fibrinogen levels in blood.

Author: Hahn J, Bressler J, Domingo-Relloso A, Chen MH, McCartney DL, Teumer A, van Dongen J, Kleber ME, Aïssi D, Swenson BR, Yao J, Zhao W, Huang J, Xia Y, Brown MR, Costeira R, de Geus EJC, Delgado GE, Dobson DA, Elliott P, Grabe HJ, Guo X, Harris SE, Huffman JE, Kardia SLR, Liu Y, Lorkowski S, Marioni RE, Nauck M, Ratliff SM, Sabater-Lleal M, Spector TD, Suchon P, Taylor KD, Thibord F, Trégouët DA, Wiggins KL, Willemsen G, Bell JT, Boomsma DI, Cole SA, Cox SR, Dehghan A, Greinacher A, Haack K, März W, Morange PE, Rotter JI, Sotoodehnia N, Tellez-Plaza M, Navas-Acien A, Smith JA, Johnson AD, Fornage M, Smith NL, Wolberg AS, Morrison AC, and de Vries PS
Abstract: Background: Fibrinogen plays an essential role in blood coagulation and inflammation. Circulating fibrinogen levels may be determined by inter-individual differences in DNA methylation at CpG sites, and vice versa., Methods: We performed an epigenome-wide association study (EWAS) of circulating fibrinogen levels in 18,037 White, Black, American Indian, and Hispanic participants representing 14 studies from the CHARGE consortium. Circulating leukocyte DNA methylation was measured in 12,904 participants using the Illumina 450K array, and in 5,133 participants using the EPIC array. Each study performed an EWAS of fibrinogen using linear mixed models adjusted for potential confounders. Study-specific results were combined using array-specific meta-analysis, followed by cross-replication of epigenome-wide significant associations. We compared models with and without C-reactive protein (CRP) adjustment to examine the role of inflammation., Results: We identified 208 and 87 significant CpG sites associated with fibrinogen from the 450K (p-value<1.03×10 -7 ) and EPIC arrays (p-value<5.78×10 -8 ), respectively. There were 78 associations from the 450K array that replicated in the EPIC array and 26 vice versa. After accounting for the overlapping sites, there were 83 replicated CpG sites located in 61 loci, of which only 4 have been previously reported for fibrinogen. Examples of genes located near these CpG sites were SOCS3 and AIM2, which are involved in inflammatory pathways. The associations for all 83 replicated CpG sites were attenuated after CRP adjustment, although many remained significant., Conclusion: We identified 83 CpG sites associated with circulating fibrinogen levels. These associations are partially driven by inflammatory pathways shared by both fibrinogen and CRP., (Copyright © 2023 International Society on Thrombosis and Haemostasis. All rights reserved.)
Published: 2023
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36. Age acquired skewed X chromosome inactivation is associated with adverse health outcomes in humans.

Author: Roberts AL, Morea A, Amar A, Zito A, El-Sayed Moustafa JS, Tomlinson M, Bowyer RCE, Zhang X, Christiansen C, Costeira R, Steves CJ, Mangino M, Bell JT, Wong CCY, Vyse TJ, and Small KS
Subjects: Female, Humans, Male, Cross-Sectional Studies, Follow-Up Studies, Outcome Assessment, Health Care, Prospective Studies, Cardiovascular Diseases genetics, X Chromosome Inactivation
Abstract: Background: Ageing is a heterogenous process characterised by cellular and molecular hallmarks, including changes to haematopoietic stem cells and is a primary risk factor for chronic diseases. X chromosome inactivation (XCI) randomly transcriptionally silences either the maternal or paternal X in each cell of 46, XX females to balance the gene expression with 46, XY males. Age acquired XCI-skew describes the preferential selection of cells across a tissue resulting in an imbalance of XCI, which is particularly prevalent in blood tissues of ageing females, and yet its clinical consequences are unknown., Methods: We assayed XCI in 1575 females from the TwinsUK population cohort using DNA extracted from whole blood. We employed prospective, cross-sectional, and intra-twin study designs to characterise the relationship of XCI-skew with molecular and cellular measures of ageing, cardiovascular disease risk, and cancer diagnosis., Results: We demonstrate that XCI-skew is independent of traditional markers of biological ageing and is associated with a haematopoietic bias towards the myeloid lineage. Using an atherosclerotic cardiovascular disease risk score, which captures traditional risk factors, XCI-skew is associated with an increased cardiovascular disease risk both cross-sectionally and within XCI-skew discordant twin pairs. In a prospective 10 year follow-up study, XCI-skew is predictive of future cancer incidence., Conclusions: Our study demonstrates that age acquired XCI-skew captures changes to the haematopoietic stem cell population and has clinical potential as a unique biomarker of chronic disease risk., Funding: KSS acknowledges funding from the Medical Research Council [MR/M004422/1 and MR/R023131/1]. JTB acknowledges funding from the ESRC [ES/N000404/1]. MM acknowledges funding from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London., Competing Interests: AR, AM, AA, AZ, JE, MT, RB, XZ, CC, RC, MM, JB, CW, TV, KS No competing interests declared, CS Consultant for Zoe Ltd, (© 2022, Roberts et al.)
Published: 2022
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37. Pulmonary Function and Blood DNA Methylation: A Multiancestry Epigenome-Wide Association Meta-analysis.

Author: Lee M, Huan T, McCartney DL, Chittoor G, de Vries M, Lahousse L, Nguyen JN, Brody JA, Castillo-Fernandez J, Terzikhan N, Qi C, Joehanes R, Min JL, Smilnak GJ, Shaw JR, Yang CX, Colicino E, Hoang TT, Bermingham ML, Xu H, Justice AE, Xu CJ, Rich SS, Cox SR, Vonk JM, Prokić I, Sotoodehnia N, Tsai PC, Schwartz JD, Leung JM, Sikdar S, Walker RM, Harris SE, van der Plaat DA, Van Den Berg DJ, Bartz TM, Spector TD, Vokonas PS, Marioni RE, Taylor AM, Liu Y, Barr RG, Lange LA, Baccarelli AA, Obeidat M, Fornage M, Wang T, Ward JM, Motsinger-Reif AA, Hemani G, Koppelman GH, Bell JT, Gharib SA, Brusselle G, Boezen HM, North KE, Levy D, Evans KL, Dupuis J, Breeze CE, Manichaikul A, and London SJ
Subjects: CpG Islands, Epigenesis, Genetic genetics, Epigenomics, Genome-Wide Association Study, Humans, Infant, Newborn, Lung, DNA Methylation genetics, Epigenome
Abstract: Rationale: Methylation integrates factors present at birth and modifiable across the lifespan that can influence pulmonary function. Studies are limited in scope and replication. Objectives: To conduct large-scale epigenome-wide meta-analyses of blood DNA methylation and pulmonary function. Methods: Twelve cohorts analyzed associations of methylation at cytosine-phosphate-guanine probes (CpGs), using Illumina 450K or EPIC/850K arrays, with FEV 1 , FVC, and FEV 1 /FVC. We performed multiancestry epigenome-wide meta-analyses (total of 17,503 individuals; 14,761 European, 2,549 African, and 193 Hispanic/Latino ancestries) and interpreted results using integrative epigenomics. Measurements and Main Results: We identified 1,267 CpGs (1,042 genes) differentially methylated (false discovery rate, <0.025) in relation to FEV 1 , FVC, or FEV 1 /FVC, including 1,240 novel and 73 also related to chronic obstructive pulmonary disease (1,787 cases). We found 294 CpGs unique to European or African ancestry and 395 CpGs unique to never or ever smokers. The majority of significant CpGs correlated with nearby gene expression in blood. Findings were enriched in key regulatory elements for gene function, including accessible chromatin elements, in both blood and lung. Sixty-nine implicated genes are targets of investigational or approved drugs. One example novel gene highlighted by integrative epigenomic and druggable target analysis is TNFRSF4. Mendelian randomization and colocalization analyses suggest that epigenome-wide association study signals capture causal regulatory genomic loci. Conclusions: We identified numerous novel loci differentially methylated in relation to pulmonary function; few were detected in large genome-wide association studies. Integrative analyses highlight functional relevance and potential therapeutic targets. This comprehensive discovery of potentially modifiable, novel lung function loci expands knowledge gained from genetic studies, providing insights into lung pathogenesis.
Published: 2022
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38. ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19.

Author: El-Sayed Moustafa JS, Jackson AU, Brotman SM, Guan L, Villicaña S, Roberts AL, Zito A, Bonnycastle L, Erdos MR, Narisu N, Stringham HM, Welch R, Yan T, Lakka T, Parker S, Tuomilehto J, Seow J, Graham C, Huettner I, Acors S, Kouphou N, Wadge S, Duncan EL, Steves CJ, Doores KJ, Malim MH, Collins FS, Pajukanta P, Boehnke M, Koistinen HA, Laakso M, Falchi M, Bell JT, Scott LJ, Mohlke KL, and Small KS
Subjects: Cardiometabolic Risk Factors, Diabetes Mellitus, Type 2 genetics, Endothelial Cells metabolism, Humans, Obesity, SARS-CoV-2, Adipose Tissue metabolism, Angiotensin-Converting Enzyme 2 genetics, Angiotensin-Converting Enzyme 2 metabolism, COVID-19 complications, COVID-19 genetics
Abstract: Background: COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain., Subjects/methods: In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry., Results: Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10 -6 ), obesity status (P = 4.81 × 10 -5 ), higher serum fasting insulin (P = 5.32 × 10 -4 ), BMI (P = 3.94 × 10 -4 ), and lower serum HDL levels (P = 1.92 × 10 -7 ). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10 -4 ) and higher proportion of macrophages (P = 2.74 × 10 -5 ). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression., Conclusions: Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19., (© 2022. The Author(s).)
Published: 2022
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39. Adipose methylome integrative-omic analyses reveal genetic and dietary metabolic health drivers and insulin resistance classifiers.

Author: Christiansen C, Tomlinson M, Eliot M, Nilsson E, Costeira R, Xia Y, Villicaña S, Mompeo O, Wells P, Castillo-Fernandez J, Potier L, Vohl MC, Tchernof A, Moustafa JE, Menni C, Steves CJ, Kelsey K, Ling C, Grundberg E, Small KS, and Bell JT
Subjects: Body Mass Index, DNA Methylation, Diet, Epigenesis, Genetic, Epigenome, Humans, Insulin Resistance genetics
Abstract: Background: There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, a robust methylation signature has been associated with body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study, we dissect the subcutaneous adipose tissue (SAT) methylome signature relevant to metabolic health by focusing on VF as the major risk factor of metabolic disease. We integrate results with genetic, blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data to assess and quantify genetic and environmental drivers of the identified signals, as well as their potential functional roles., Methods: Epigenome-wide association analyses were carried out to determine visceral fat mass-associated differentially methylated positions (VF-DMPs) in SAT samples from 538 TwinsUK participants. Validation and replication were performed in 333 individuals from 3 independent cohorts. To assess functional impacts of the VF-DMPs, the association between VF and gene expression was determined at the genes annotated to the VF-DMPs and an association analysis was carried out to determine whether methylation at the VF-DMPs is associated with gene expression. Further epigenetic analyses were carried out to compare methylation levels at the VF-DMPs as the response variables and a range of different metabolic health phenotypes including android:gynoid fat ratio (AGR), lipids, blood metabolomic profiles, insulin resistance, T2D and dietary intake variables. The results from all analyses were integrated to identify signals that exhibit altered SAT function and have strong relevance to metabolic health., Results: We identified 1181 CpG positions in 788 genes to be differentially methylated with VF (VF-DMPs) with significant enrichment in the insulin signalling pathway. Follow-up cross-omic analysis of VF-DMPs integrating genetics, gene expression, metabolomics, diet, and metabolic traits highlighted VF-DMPs located in 9 genes with strong relevance to metabolic disease mechanisms, with replication of signals in FASN, SREBF1, TAGLN2, PC and CFAP410. PC methylation showed evidence for mediating effects of diet on VF. FASN DNA methylation exhibited putative causal effects on VF that were also strongly associated with insulin resistance and methylation levels in FASN better classified insulin resistance (AUC=0.91) than BMI or VF alone., Conclusions: Our findings help characterise the adiposity-associated methylation signature of SAT, with insights for metabolic disease risk., (© 2022. The Author(s).)
Published: 2022
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40. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases.

Author: Wielscher M, Mandaviya PR, Kuehnel B, Joehanes R, Mustafa R, Robinson O, Zhang Y, Bodinier B, Walton E, Mishra PP, Schlosser P, Wilson R, Tsai PC, Palaniswamy S, Marioni RE, Fiorito G, Cugliari G, Karhunen V, Ghanbari M, Psaty BM, Loh M, Bis JC, Lehne B, Sotoodehnia N, Deary IJ, Chadeau-Hyam M, Brody JA, Cardona A, Selvin E, Smith AK, Miller AH, Torres MA, Marouli E, Gào X, van Meurs JBJ, Graf-Schindler J, Rathmann W, Koenig W, Peters A, Weninger W, Farlik M, Zhang T, Chen W, Xia Y, Teumer A, Nauck M, Grabe HJ, Doerr M, Lehtimäki T, Guan W, Milani L, Tanaka T, Fisher K, Waite LL, Kasela S, Vineis P, Verweij N, van der Harst P, Iacoviello L, Sacerdote C, Panico S, Krogh V, Tumino R, Tzala E, Matullo G, Hurme MA, Raitakari OT, Colicino E, Baccarelli AA, Kähönen M, Herzig KH, Li S, Conneely KN, Kooner JS, Köttgen A, Heijmans BT, Deloukas P, Relton C, Ong KK, Bell JT, Boerwinkle E, Elliott P, Brenner H, Beekman M, Levy D, Waldenberger M, Chambers JC, Dehghan A, and Järvelin MR
Subjects: C-Reactive Protein genetics, CpG Islands genetics, Humans, Nucleotide Motifs, DNA Methylation genetics, Inflammation genetics
Abstract: We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD., (© 2022. The Author(s).)
Published: 2022
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41. The Care of American Indian Children in North Carolina: Protecting Our Future and Our Heritage.

Author: Bell JT
Subjects: Child, Forecasting, Humans, Minority Groups, North Carolina, Indians, North American, American Indian or Alaska Native
Abstract: Native American children face many different challenges when it comes to their health. They are predisposed to an increased rate of diseases such as diabetes and asthma. Traditional medicine is still practiced in many Native communities. As health care professionals, we must be culturally sensitive to their needs. One way to improve Native health care is to increase the number of Native physicians practicing in their communities., (©2021 by the North Carolina Institute of Medicine and The Duke Endowment. All rights reserved.)
Published: 2021
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42. Meta-analysis of epigenome-wide association studies of carotid intima-media thickness.

Author: Portilla-Fernández E, Hwang SJ, Wilson R, Maddock J, Hill WD, Teumer A, Mishra PP, Brody JA, Joehanes R, Ligthart S, Ghanbari M, Kavousi M, Roks AJM, Danser AHJ, Levy D, Peters A, Ghasemi S, Schminke U, Dörr M, Grabe HJ, Lehtimäki T, Kähönen M, Hurme MA, Bartz TM, Sotoodehnia N, Bis JC, Thiery J, Koenig W, Ong KK, Bell JT, Meisinger C, Wardlaw JM, Starr JM, Seissler J, Then C, Rathmann W, Ikram MA, Psaty BM, Raitakari OT, Völzke H, Deary IJ, Wong A, Waldenberger M, O'Donnell CJ, and Dehghan A
Subjects: Cross-Sectional Studies, Epigenome, Humans, Risk Factors, Carotid Intima-Media Thickness, Coronary Artery Disease diagnostic imaging, Coronary Artery Disease genetics
Abstract: Common carotid intima-media thickness (cIMT) is an index of subclinical atherosclerosis that is associated with ischemic stroke and coronary artery disease (CAD). We undertook a cross-sectional epigenome-wide association study (EWAS) of measures of cIMT in 6400 individuals. Mendelian randomization analysis was applied to investigate the potential causal role of DNA methylation in the link between atherosclerotic cardiovascular risk factors and cIMT or clinical cardiovascular disease. The CpG site cg05575921 was associated with cIMT (beta = -0.0264, p value = 3.5 × 10 -8 ) in the discovery panel and was replicated in replication panel (beta = -0.07, p value = 0.005). This CpG is located at chr5:81649347 in the intron 3 of the aryl hydrocarbon receptor repressor gene (AHRR). Our results indicate that DNA methylation at cg05575921 might be in the pathway between smoking, cIMT and stroke. Moreover, in a region-based analysis, 34 differentially methylated regions (DMRs) were identified of which a DMR upstream of ALOX12 showed the strongest association with cIMT (p value = 1.4 × 10 -13 ). In conclusion, our study suggests that DNA methylation may play a role in the link between cardiovascular risk factors, cIMT and clinical cardiovascular disease., (© 2021. The Author(s).)
Published: 2021
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43. Validating biomarkers and models for epigenetic inference of alcohol consumption from blood.

Author: Maas SCE, Vidaki A, Teumer A, Costeira R, Wilson R, van Dongen J, Beekman M, Völker U, Grabe HJ, Kunze S, Ladwig KH, van Meurs JBJ, Uitterlinden AG, Voortman T, Boomsma DI, Slagboom PE, van Heemst D, van der Kallen CJH, van den Berg LH, Waldenberger M, Völzke H, Peters A, Bell JT, Ikram MA, Ghanbari M, and Kayser M
Subjects: Alcohol Drinking genetics, Area Under Curve, Biomarkers blood, DNA Methylation, Epigenesis, Genetic physiology, Genome-Wide Association Study methods, Genome-Wide Association Study statistics & numerical data, Humans, ROC Curve, Reproducibility of Results, Alcohol Drinking blood, Biomarkers analysis, Epigenesis, Genetic genetics
Abstract: Background: Information on long-term alcohol consumption is relevant for medical and public health research, disease therapy, and other areas. Recently, DNA methylation-based inference of alcohol consumption from blood was reported with high accuracy, but these results were based on employing the same dataset for model training and testing, which can lead to accuracy overestimation. Moreover, only subsets of alcohol consumption categories were used, which makes it impossible to extrapolate such models to the general population. By using data from eight population-based European cohorts (N = 4677), we internally and externally validated the previously reported biomarkers and models for epigenetic inference of alcohol consumption from blood and developed new models comprising all data from all categories., Results: By employing data from six European cohorts (N = 2883), we empirically tested the reproducibility of the previously suggested biomarkers and prediction models via ten-fold internal cross-validation. In contrast to previous findings, all seven models based on 144-CpGs yielded lower mean AUCs compared to the models with less CpGs. For instance, the 144-CpG heavy versus non-drinkers model gave an AUC of 0.78 ± 0.06, while the 5 and 23 CpG models achieved 0.83 ± 0.05, respectively. The transportability of the models was empirically tested via external validation in three independent European cohorts (N = 1794), revealing high AUC variance between datasets within models. For instance, the 144-CpG heavy versus non-drinkers model yielded AUCs ranging from 0.60 to 0.84 between datasets. The newly developed models that considered data from all categories showed low AUCs but gave low AUC variation in the external validation. For instance, the 144-CpG heavy and at-risk versus light and non-drinkers model achieved AUCs of 0.67 ± 0.02 in the internal cross-validation and 0.61-0.66 in the external validation datasets., Conclusions: The outcomes of our internal and external validation demonstrate that the previously reported prediction models suffer from both overfitting and accuracy overestimation. Our results show that the previously proposed biomarkers are not yet sufficient for accurate and robust inference of alcohol consumption from blood. Overall, our findings imply that DNA methylation prediction biomarkers and models need to be improved considerably before epigenetic inference of alcohol consumption from blood can be considered for practical applications., (© 2021. The Author(s).)
Published: 2021
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44. DNA methylation signatures of incident coronary heart disease: findings from epigenome-wide association studies.

Author: Xia Y, Brewer A, and Bell JT
Subjects: Coronary Disease complications, Coronary Disease physiopathology, DNA Methylation physiology, Genome-Wide Association Study methods, Humans, Risk Factors, Coronary Disease genetics, DNA Methylation genetics, Genome-Wide Association Study statistics & numerical data
Abstract: Coronary heart disease (CHD) is a type of cardiovascular disease (CVD) that affects the coronary arteries, which provide oxygenated blood to the heart. It is a major cause of mortality worldwide. Various prediction methods have been developed to assess the likelihood of developing CHD, including those based on clinical features and genetic variation. Recent epigenome-wide studies have identified DNA methylation signatures associated with the development of CHD, indicating that DNA methylation may play a role in predicting future CHD. This narrative review summarises recent findings from DNA methylation studies of incident CHD (iCHD) events from epigenome-wide association studies (EWASs). The results suggest that DNA methylation signatures may identify new mechanisms involved in CHD progression and could prove a useful adjunct for the prediction of future CHD., (© 2021. The Author(s).)
Published: 2021
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45. Identical twins carry a persistent epigenetic signature of early genome programming.

Author: van Dongen J, Gordon SD, McRae AF, Odintsova VV, Mbarek H, Breeze CE, Sugden K, Lundgren S, Castillo-Fernandez JE, Hannon E, Moffitt TE, Hagenbeek FA, van Beijsterveldt CEM, Jan Hottenga J, Tsai PC, Min JL, Hemani G, Ehli EA, Paul F, Stern CD, Heijmans BT, Slagboom PE, Daxinger L, van der Maarel SM, de Geus EJC, Willemsen G, Montgomery GW, Reversade B, Ollikainen M, Kaprio J, Spector TD, Bell JT, Mill J, Caspi A, Martin NG, and Boomsma DI
Subjects: Adult, Finland, Genotype, Humans, Middle Aged, Netherlands, Polymorphism, Single Nucleotide, Registries statistics & numerical data, Retrospective Studies, United Kingdom, Young Adult, DNA Methylation, Epigenesis, Genetic, Epigenomics methods, Quantitative Trait Loci genetics, Twinning, Monozygotic genetics, Twins, Monozygotic genetics
Abstract: Monozygotic (MZ) twins and higher-order multiples arise when a zygote splits during pre-implantation stages of development. The mechanisms underpinning this event have remained a mystery. Because MZ twinning rarely runs in families, the leading hypothesis is that it occurs at random. Here, we show that MZ twinning is strongly associated with a stable DNA methylation signature in adult somatic tissues. This signature spans regions near telomeres and centromeres, Polycomb-repressed regions and heterochromatin, genes involved in cell-adhesion, WNT signaling, cell fate, and putative human metastable epialleles. Our study also demonstrates a never-anticipated corollary: because identical twins keep a lifelong molecular signature, we can retrospectively diagnose if a person was conceived as monozygotic twin., (© 2021. The Author(s).)
Published: 2021
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46. Estrogen and COVID-19 symptoms: Associations in women from the COVID Symptom Study.

Author: Costeira R, Lee KA, Murray B, Christiansen C, Castillo-Fernandez J, Ni Lochlainn M, Capdevila Pujol J, Macfarlane H, Kenny LC, Buchan I, Wolf J, Rymer J, Ourselin S, Steves CJ, Spector TD, Newson LR, and Bell JT
Subjects: Adult, Cohort Studies, Comorbidity, Female, Humans, Middle Aged, Risk Factors, United Kingdom, COVID-19 epidemiology, Estrogen Replacement Therapy, Estrogens metabolism, Menopause metabolism
Abstract: It has been widely observed that adult men of all ages are at higher risk of developing serious complications from COVID-19 when compared with women. This study aimed to investigate the association of COVID-19 positivity and severity with estrogen exposure in women, in a population based matched cohort study of female users of the COVID Symptom Study application in the UK. Analyses included 152,637 women for menopausal status, 295,689 women for exogenous estrogen intake in the form of the combined oral contraceptive pill (COCP), and 151,193 menopausal women for hormone replacement therapy (HRT). Data were collected using the COVID Symptom Study in May-June 2020. Analyses investigated associations between predicted or tested COVID-19 status and menopausal status, COCP use, and HRT use, adjusting for age, smoking and BMI, with follow-up age sensitivity analysis, and validation in a subset of participants from the TwinsUK cohort. Menopausal women had higher rates of predicted COVID-19 (P = 0.003). COCP-users had lower rates of predicted COVID-19 (P = 8.03E-05), with reduction in hospital attendance (P = 0.023). Menopausal women using HRT or hormonal therapies did not exhibit consistent associations, including increased rates of predicted COVID-19 (P = 2.22E-05) for HRT users alone. The findings support a protective effect of estrogen exposure on COVID-19, based on positive association between predicted COVID-19 with menopausal status, and negative association with COCP use. HRT use was positively associated with COVID-19, but the results should be considered with caution due to lack of data on HRT type, route of administration, duration of treatment, and potential unaccounted for confounders and comorbidities., Competing Interests: Zoe Global Limited co-developed the app pro bono for non-commercial purposes. JCP and JW work for Zoe Global, and TDS is a consultant to Zoe Global. IB is Chief Data Scientist Advisor for AstraZeneca. These do not alter our adherence to PLoS ONE policies on sharing data and materials.
Published: 2021
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47. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.

Author: Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, and Relton CL
Subjects: Chromosome Mapping, Epigenesis, Genetic genetics, Genome-Wide Association Study, Humans, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide genetics, Quantitative Trait, Heritable, Transcriptome genetics, DNA metabolism, DNA Methylation genetics, Gene Expression Regulation genetics, Genetic Predisposition to Disease genetics, Quantitative Trait Loci genetics
Abstract: Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2021
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48. Childhood growth and development and DNA methylation age in mid-life.

Author: Maddock J, Castillo-Fernandez J, Wong A, Ploubidis GB, Kuh D, Bell JT, and Hardy R
Subjects: Adolescent, Adult, Age Factors, Body Mass Index, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, United Kingdom, Young Adult, Aging genetics, Biomarkers, Birth Weight genetics, Body Height genetics, Child Development, DNA Methylation genetics, Weight Gain genetics
Abstract: Background: In the first study of its kind, we examine the association between growth and development in early life and DNAm age biomarkers in mid-life., Methods: Participants were from the Medical Research Council National Survey of Health and Development (n = 1376). Four DNAm age acceleration (AgeAccel) biomarkers were measured when participants were aged 53 years: AgeAccelHannum; AgeAccelHorvath; AgeAccelLevine; and AgeAccelGrim. Exposure variables included: relative weight gain (standardised residuals from models of current weight z-score on current height, and previous weight and height z-scores); and linear growth (standardised residuals from models of current height z-score on previous height and weight z-scores) during infancy (0-2 years, weight gain only), early childhood (2-4 years), middle childhood (4-7 years) and late childhood to adolescence (7-15 years); age at menarche; and pubertal stage for men at 14-15 years. The relationship between relative weight gain and linear growth and AgeAccel was investigated using conditional growth models. We replicated analyses from the late childhood to adolescence period and pubertal timing among 240 participants from The National Child and Development Study (NCDS)., Results: A 1SD increase in relative weight gain in late childhood to adolescence was associated with 0.50 years (95% CI 0.20, 0.79) higher AgeAccelGrim. Although the CI includes the null, the estimate was similar in NCDS [0.57 years (95% CI - 0.01, 1.16)] There was no strong evidence that relative weight gain and linear growth in childhood was associated with any other AgeAccel biomarker. There was no relationship between pubertal timing in men and AgeAccel biomarkers. Women who reached menarche ≥ 12 years had 1.20 years (95% CI 0.15, 2.24) higher AgeAccelGrim on average than women who reached menarche < 12 years; however, this was not replicated in NCDS and was not statistically significant after Bonferroni correction., Conclusions: Our findings generally do not support an association between growth and AgeAccel biomarkers in mid-life. However, we found rapid weight gain during pubertal development, previously related to higher cardiovascular disease risk, to be associated with older AgeAccelGrim. Given this is an exploratory study, this finding requires replication., (© 2021. The Author(s).)
Published: 2021
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49. Publisher Correction: A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids.

Author: Jhun MA, Mendelson M, Wilson R, Gondalia R, Joehanes R, Salfati E, Zhao X, Braun KVE, Do AN, Hedman ÅK, Zhang T, Carnero-Montoro E, Shen J, Bartz TM, Brody JA, Montasser ME, O'Connell JR, Yao C, Xia R, Boerwinkle E, Grove M, Guan W, Liliane P, Singmann P, Müller-Nurasyid M, Meitinger T, Gieger C, Peters A, Zhao W, Ware EB, Smith JA, Dhana K, van Meurs J, Uitterlinden A, Ikram MA, Ghanbari M, Zhi D, Gustafsson S, Lind L, Li S, Sun D, Spector TD, Chen YI, Damcott C, Shuldiner AR, Absher DM, Horvath S, Tsao PS, Kardia S, Psaty BM, Sotoodehnia N, Bell JT, Ingelsson E, Chen W, Dehghan A, Arnett DK, Waldenberger M, Hou L, Whitsel EA, Baccarelli A, Levy D, Fornage M, Irvin MR, and Assimes TL
Published: 2021
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50. Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging.

Author: McCartney DL, Min JL, Richmond RC, Lu AT, Sobczyk MK, Davies G, Broer L, Guo X, Jeong A, Jung J, Kasela S, Katrinli S, Kuo PL, Matias-Garcia PR, Mishra PP, Nygaard M, Palviainen T, Patki A, Raffield LM, Ratliff SM, Richardson TG, Robinson O, Soerensen M, Sun D, Tsai PC, van der Zee MD, Walker RM, Wang X, Wang Y, Xia R, Xu Z, Yao J, Zhao W, Correa A, Boerwinkle E, Dugué PA, Durda P, Elliott HR, Gieger C, de Geus EJC, Harris SE, Hemani G, Imboden M, Kähönen M, Kardia SLR, Kresovich JK, Li S, Lunetta KL, Mangino M, Mason D, McIntosh AM, Mengel-From J, Moore AZ, Murabito JM, Ollikainen M, Pankow JS, Pedersen NL, Peters A, Polidoro S, Porteous DJ, Raitakari O, Rich SS, Sandler DP, Sillanpää E, Smith AK, Southey MC, Strauch K, Tiwari H, Tanaka T, Tillin T, Uitterlinden AG, Van Den Berg DJ, van Dongen J, Wilson JG, Wright J, Yet I, Arnett D, Bandinelli S, Bell JT, Binder AM, Boomsma DI, Chen W, Christensen K, Conneely KN, Elliott P, Ferrucci L, Fornage M, Hägg S, Hayward C, Irvin M, Kaprio J, Lawlor DA, Lehtimäki T, Lohoff FW, Milani L, Milne RL, Probst-Hensch N, Reiner AP, Ritz B, Rotter JI, Smith JA, Taylor JA, van Meurs JBJ, Vineis P, Waldenberger M, Deary IJ, Relton CL, Horvath S, and Marioni RE
Subjects: Adiposity genetics, Adiposity immunology, Aging immunology, Biomarkers metabolism, C-Reactive Protein genetics, C-Reactive Protein immunology, CpG Islands, Educational Status, Genetic Markers, Genome, Human, Genome-Wide Association Study, Granulocytes cytology, Granulocytes immunology, Humans, Immunity, Innate, Lipid Metabolism genetics, Lipid Metabolism immunology, Plasminogen Activator Inhibitor 1 genetics, Plasminogen Activator Inhibitor 1 immunology, Aging genetics, DNA Methylation, Epigenesis, Genetic, Genetic Loci, Multifactorial Inheritance
Abstract: Background: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field., Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels., Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.
Published: 2021
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