89 results on '"Uh HW"'
Search Results
2. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting
- Author
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Chauhan, G, Adams, HHH, Satizabal, CL, Bis, JC, Teumer, A, Sargurupremraj, M, Hofer, E, Trompet, S, Hilal, S, Smith, AV, Jian, X, Malik, R, Traylor, M, Pulit, SL, Amouyel, P, Mazoyer, B, Zhu, YC, Kaffashian, S, Schilling, S, Beecham, GW, Montine, TJ, Schellenberg, GD, Kjartansson, O, Guðnason, V, Knopman, DS, Griswold, ME, Windham, BG, Gottesman, RF, Mosley, TH, Schmidt, R, Saba, Y, Schmidt, H, Takeuchi, F, Yamaguchi, S, Nabika, T, Kato, N, Rajan, KB, Aggarwal, NT, De Jager, PL, Evans, DA, Psaty, BM, Rotter, JI, Rice, K, Lopez, OL, Liao, J, Chen, C, Cheng, CY, Wong, TY, Ikram, MK, van der Lee, SJ, Amin, N, Chouraki, V, Destefano, AL, Aparicio, HJ, Romero, JR, Maillard, P, Decarli, C, Wardlaw, JM, Del C. Valdés Hernández, M, Luciano, M, Liewald, D, Deary, IJ, Starr, JM, Bastin, ME, Maniega, SM, Slagboom, PE, Beekman, M, Deelen, J, Uh, HW, Lemmens, R, Brodaty, H, Wright, MJ, Ames, D, Boncoraglio, GB, Hopewell, JC, Beecham, AH, Blanton, SH, Wright, CB, Sacco, RL, Wen, W, Thalamuthu, A, Armstrong, NJ, Chong, E, Schofield, PR, Kwok, JB, van der Grond, J, Stott, DJ, Ford, I, Jukema, JW, Vernooij, MW, Hofman, A, Uitterlinden, AG, van der Lugt, A, Wittfeld, K, Grabe, HJ, Hosten, N, von Sarnowski, B, Völker, U, Levi, C, Jimenez-Conde, J, Chauhan, G, Adams, HHH, Satizabal, CL, Bis, JC, Teumer, A, Sargurupremraj, M, Hofer, E, Trompet, S, Hilal, S, Smith, AV, Jian, X, Malik, R, Traylor, M, Pulit, SL, Amouyel, P, Mazoyer, B, Zhu, YC, Kaffashian, S, Schilling, S, Beecham, GW, Montine, TJ, Schellenberg, GD, Kjartansson, O, Guðnason, V, Knopman, DS, Griswold, ME, Windham, BG, Gottesman, RF, Mosley, TH, Schmidt, R, Saba, Y, Schmidt, H, Takeuchi, F, Yamaguchi, S, Nabika, T, Kato, N, Rajan, KB, Aggarwal, NT, De Jager, PL, Evans, DA, Psaty, BM, Rotter, JI, Rice, K, Lopez, OL, Liao, J, Chen, C, Cheng, CY, Wong, TY, Ikram, MK, van der Lee, SJ, Amin, N, Chouraki, V, Destefano, AL, Aparicio, HJ, Romero, JR, Maillard, P, Decarli, C, Wardlaw, JM, Del C. Valdés Hernández, M, Luciano, M, Liewald, D, Deary, IJ, Starr, JM, Bastin, ME, Maniega, SM, Slagboom, PE, Beekman, M, Deelen, J, Uh, HW, Lemmens, R, Brodaty, H, Wright, MJ, Ames, D, Boncoraglio, GB, Hopewell, JC, Beecham, AH, Blanton, SH, Wright, CB, Sacco, RL, Wen, W, Thalamuthu, A, Armstrong, NJ, Chong, E, Schofield, PR, Kwok, JB, van der Grond, J, Stott, DJ, Ford, I, Jukema, JW, Vernooij, MW, Hofman, A, Uitterlinden, AG, van der Lugt, A, Wittfeld, K, Grabe, HJ, Hosten, N, von Sarnowski, B, Völker, U, Levi, C, and Jimenez-Conde, J
- Abstract
Objective To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n=20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI]= 4.4 × 10-10; p [SSBI] = 1.2 × 10 -4), diabetes (p[BI] = 1.7 × 10 -8; p [SSBI] = 2.8 × 10 -3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10 -24), and MRI-defined white matter hyperintensity burden (p [BI]=1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significan
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- 2019
3. Facial Wrinkles in Europeans: A Genome-Wide Association Study
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Hamer, Merel, Pardo Cortes, Luba, Jacobs, Leonie, Deelen, J, Uitterlinden, André, Slagboom, E, van Heemst, D, Uh, HW, Beekman, M, Kayser, Manfred, Liu, Fan, Gunn, DA, Nijsten, Tamar, Hamer, Merel, Pardo Cortes, Luba, Jacobs, Leonie, Deelen, J, Uitterlinden, André, Slagboom, E, van Heemst, D, Uh, HW, Beekman, M, Kayser, Manfred, Liu, Fan, Gunn, DA, and Nijsten, Tamar
- Published
- 2018
4. Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels
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Van Leeuwen, EM, Karssen, LC, Deelen, J, Isaacs, A, Medina-Gomez, C, Mbarek, H, Kanterakis, A, Trompet, S, Postmus, I, Verweij, N, Van Enckevort, DJ, Huffman, JE, White, CC, Feitosa, MF, Bartz, TM, Manichaikul, A, Joshi, PK, Peloso, GM, Deelen, P, Van Dijk, F, Willemsen, G, De Geus, EJ, Milaneschi, Y, Penninx, BWJH, Francioli, LC, Menelaou, A, Pulit, SL, Rivadeneira, F, Hofman, A, Oostra, BA, Franco, OH, Leach, IM, Beekman, M, De Craen, AJM, Uh, HW, Trochet, H, Hocking, LJ, Porteous, DJ, Sattar, N, Packard, CJ, Buckley, BM, Brody, JA, Bis, JC, Rotter, JI, Mychaleckyj, JC, Campbell, H, Duan, Q, Lange, LA, Wilson, JF, Hayward, C, Polasek, O, Vitart, V, Rudan, I, Wright, AF, Rich, SS, Psaty, BM, Borecki, IB, Kearney, PM, Stott, DJ, Cupples, LA, Jukema, JW, Van Der Harst, P, Sijbrands, EJ, Hottenga, JJ, Uitterlinden, AG, Swertz, MA, Van Ommen, GJB, De Bakker, PIW, Eline Slagboom, P, Boomsma, DI, Wijmenga, C, Van Duijn, CM, Neerincx, PBT, Elbers, CC, Palamara, PF, Peer, I, Abdellaoui, A, Kloosterman, WP, Van Oven, M, Vermaat, M, Li, M, Laros, JFJ, Stoneking, M, De Knijff, P, Kayser, M, Veldink, JH, Van Den Berg, LH, Byelas, H, Den Dunnen, JT, Dijkstra, M, Amin, N, Van Der Velde, KJ, and Van Setten, J
- Abstract
© 2015 Macmillan Publishers Limited. All rights reserved. Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (∼35,000 samples) with the population-specific reference panel created by the Genome of the Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value
- Published
- 2015
5. Multiethnic Genome-Wide Association Study of Cerebral White Matter Hyperintensities on MRI
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Verhaaren, BFJ, Debette, S, Bis, JC, Smith, JA, Ikram, MK, Adams, HH, Beecham, AH, Rajan, KB, Lopez, LM, Barral, S, Van Buchem, MA, Van Der Grond, J, Smith, AV, Hegenscheid, K, Aggarwal, NT, De Andrade, M, Atkinson, EJ, Beekman, M, Beiser, AS, Blanton, SH, Boerwinkle, E, Brickman, AM, Bryan, RN, Chauhan, G, Chen, CPLH, Chouraki, V, De Craen, AJM, Crivello, F, Deary, IJ, Deelen, J, De Jager, PL, Dufouil, C, Elkind, MSV, Evans, DA, Freudenberger, P, Gottesman, RF, Gunason, V, Habes, M, Heckbert, SR, Heiss, G, Hilal, S, Hofer, E, Hofman, A, Ibrahim-Verbaas, CA, Knopman, DS, Lewis, CE, Liao, J, Liewald, DCM, Luciano, M, Van Der Lugt, A, Martinez, OO, Mayeux, R, Mazoyer, B, Nalls, M, Nauck, M, Niessen, WJ, Oostra, BA, Psaty, BM, Rice, KM, Rotter, JI, Von Sarnowski, B, Schmidt, H, Schreiner, PJ, Schuur, M, Sidney, SS, Sigurdsson, S, Slagboom, PE, Stott, DJM, Van Swieten, JC, Teumer, A, Töglhofer, AM, Traylor, M, Trompet, S, Turner, ST, Tzourio, C, Uh, HW, Uitterlinden, AG, Vernooij, MW, Wang, JJ, Wong, TY, Wardlaw, JM, Windham, BG, Wittfeld, K, Wolf, C, Wright, CB, Yang, Q, Zhao, W, Zijdenbos, A, Jukema, JW, Sacco, RL, Kardia, SLR, Amouyel, P, Mosley, TH, Longstreth, WT, DeCarli, CC, Van Duijn, CM, Schmidt, R, Launer, LJ, and Grabe, HJ
- Subjects
genome-wide association study ,hypertension ,leukoencephalopathies ,polymorphisms, single nucleotide ,cerebrovascular disorders ,cerebral small vessel diseases - Abstract
© 2015 American Heart Association, Inc. Background-The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies. Methods and Results-We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10-19) and identified novel loci on chr10q24 (P=1.6×10-9) and chr2p21 (P=4.4×10-8). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10-8) and chr2p16 (P=1.5×10-8). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16). Conclusions-We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.
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- 2015
6. Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits
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Teumer, A, Qi, QB, Nethander, M, Aschard, H, Bandinelli, S, Beekman, M, Berndt, SI, Bidlingmaier, M, Broer, Linda, Cappola, A, Ceda, G P, Chanock, S, Chen, MH, Chen, T C, Chen, YDI, Chung, JW, Miglianico, F D, Eriksson, J, Ferrucci, L, Friedrich, N, Gnewuch, C, Goodarzi, MO, Grarup, N, Guo, T W, Hammer, E, Hayes, RB, Hicks, AA, Hofman, Bert, Houwing-Duistermaat, JJ, Hu, F, Hunter, DJ, Husemoen, LL, Isaacs, Aaron, Jacobs, KB, Janssen, J.A.M.J.L., Jansson, JO, Jehmlich, N, Johnson, S, Juul, A, Karlsson, M, Kilpelainen, TO, Kovacs, P, Kraft, P, Li, C, Linneberg, A, Liu, YM, Loos, RJF, Lorentzon, M, Lu, YC, Maggio, M, Magi, R, Meigs, J, Mellstrom, D, Nauck, M, Newman, AB, Pollak, MN, Pramstaller, PP, Prokopenko, I, Psaty, BM, Reincke, M, Rimm, EB, Rotter, JI, Saint Pierre, A, Schurmann, C, Seshadri, S, Sjogren, K, Slagboom, PE (Eline), Strickler, H D, Stumvoll, M, Suh, Y S, Sun, Q, Zhang, CL, Svensson, J, Tanaka, T, Tare, A, Tonjes, A, Uh, HW, Duijn, Cornelia, van Heemst, D, Vandenput, L, Vasan, RS, Volker, U, Willems, SM, Ohlsson, C, Wallaschofski, H, Kaplan, RC, Teumer, A, Qi, QB, Nethander, M, Aschard, H, Bandinelli, S, Beekman, M, Berndt, SI, Bidlingmaier, M, Broer, Linda, Cappola, A, Ceda, G P, Chanock, S, Chen, MH, Chen, T C, Chen, YDI, Chung, JW, Miglianico, F D, Eriksson, J, Ferrucci, L, Friedrich, N, Gnewuch, C, Goodarzi, MO, Grarup, N, Guo, T W, Hammer, E, Hayes, RB, Hicks, AA, Hofman, Bert, Houwing-Duistermaat, JJ, Hu, F, Hunter, DJ, Husemoen, LL, Isaacs, Aaron, Jacobs, KB, Janssen, J.A.M.J.L., Jansson, JO, Jehmlich, N, Johnson, S, Juul, A, Karlsson, M, Kilpelainen, TO, Kovacs, P, Kraft, P, Li, C, Linneberg, A, Liu, YM, Loos, RJF, Lorentzon, M, Lu, YC, Maggio, M, Magi, R, Meigs, J, Mellstrom, D, Nauck, M, Newman, AB, Pollak, MN, Pramstaller, PP, Prokopenko, I, Psaty, BM, Reincke, M, Rimm, EB, Rotter, JI, Saint Pierre, A, Schurmann, C, Seshadri, S, Sjogren, K, Slagboom, PE (Eline), Strickler, H D, Stumvoll, M, Suh, Y S, Sun, Q, Zhang, CL, Svensson, J, Tanaka, T, Tare, A, Tonjes, A, Uh, HW, Duijn, Cornelia, van Heemst, D, Vandenput, L, Vasan, RS, Volker, U, Willems, SM, Ohlsson, C, Wallaschofski, H, and Kaplan, RC
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- 2016
7. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels
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Kilpelainen, TO, Carli, J F M, Skowronski, A A, Sun, Q, Kriebel, J, Feitosa, MF, Hedman, AK, Drong, AW, Hayes, J E, Zhao, JH, Pers, TH, Schick, U, Grarup, N, Kutalik, Z, Trompet, S, Mangino, M, Kristiansson, K, Beekman, M, Lyytikainen, LP, Eriksson, J, Henneman, Peter, Lahti, J, Tanaka, T, Luan, JA, Del Greco, F, Pasko, D, Renstrom, F, Willems, SM, Mahajan, A, Rose, LM, Guo, XQ, Liu, YM, Kleber, ME, Perusse, L, Gaunt, T, Ahluwalia, TS, Sung, YJ, Ramos, YF, Amin, Najaf, Amuzu, A, Barroso, I, Bellis, C, Blangero, J, Buckley, BM, Bohringer, S, Chen, YDI, de Craen, A J N, Crosslin, DR, Dale, CE, Dastani, Z, Day, FR, Deelen, J, Delgado, GE, Demirkan, Ayse, Finucane, FM, Ford, I, Garcia, ME, Gieger, C, Gustafsson, S, Hallmans, G, Hankinson, SE, Havulinna, AS, Herder, Cindy, Hernandez, D, Hicks, AA, Hunter, DJ, Illig, T, Ingelsson, E, Ioan-Facsinay, A, Jansson, JO, Jenny, NS, Jorgensen, ME, Jorgensen, T, Karlsson, M, Koenig, W, Kraft, P, Kwekkeboom, J, Laatikainen, T, Ladwig, KH, LeDuc, C A, Lowe, G, Lu, YC, Marques-Vidal, P, Meisinger, C, Menni, C, Morris, AP, Myers, RH, Mannisto, S, Nalls, MA, Paternoster, L, Peters, A, Pradhan, A D, Rankinen, T, Rasmussen-Torvik, LJ, Rathmann, W, Rice, TK, Richards, JB, Ridker, PM, Sattar, N, Savage, DB, Soderberg, S, Timpson, NJ, Vandenput, L, van Heemst, D, Uh, HW, Vohl, MC, Walker, M, Wichmann, HE, Widen, E, Wood, AR, Yao, J, Zeller, T, Zhang, YY, Meulenbelt, I, Kloppenburg, M, Astrup, A, Sorensen, TIA, Sarzynski, MA, Rao, DC, Jousilahti, P, Vartiainen, E, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Kajantie, E, Osmond, C, Palotie, A, Eriksson, JG, Heliovaara, M, Knekt, PB, Koskinen, S, Jula, A, Perola, M, Huupponen, R K, Viikari, JS, Kahonen, M, Lehtimaki, T, Raitakari, OT, Mellstrom, D, Lorentzon, M, Casas, JP, Bandinelli, S, Marz, W, Isaacs, Aaron, van Dijk, KW, Duijn, Cornelia, Harris, TB, Bouchard, C, Allison, MA, Chasman, DI, Ohlsson, C, Lind, L, Scott, RA, Langenberg, C, Wareham, NJ, Ferrucci, L, Frayling, TM, Pramstaller, PP, Borecki, IB, Waterworth, DM, Bergmann, S, Waeber, G, Vollenweider, P, Vestergaard, H, Hansen, T, Pedersen, O, Hu, FB, Slagboom, PE (Eline), Grallert, H, Spector, TD, Jukema, JW, Klein, RJ, Schadt, EE, Franks, PW, Lindgren, CM, Leibel, R L, Loos, RJF, Kilpelainen, TO, Carli, J F M, Skowronski, A A, Sun, Q, Kriebel, J, Feitosa, MF, Hedman, AK, Drong, AW, Hayes, J E, Zhao, JH, Pers, TH, Schick, U, Grarup, N, Kutalik, Z, Trompet, S, Mangino, M, Kristiansson, K, Beekman, M, Lyytikainen, LP, Eriksson, J, Henneman, Peter, Lahti, J, Tanaka, T, Luan, JA, Del Greco, F, Pasko, D, Renstrom, F, Willems, SM, Mahajan, A, Rose, LM, Guo, XQ, Liu, YM, Kleber, ME, Perusse, L, Gaunt, T, Ahluwalia, TS, Sung, YJ, Ramos, YF, Amin, Najaf, Amuzu, A, Barroso, I, Bellis, C, Blangero, J, Buckley, BM, Bohringer, S, Chen, YDI, de Craen, A J N, Crosslin, DR, Dale, CE, Dastani, Z, Day, FR, Deelen, J, Delgado, GE, Demirkan, Ayse, Finucane, FM, Ford, I, Garcia, ME, Gieger, C, Gustafsson, S, Hallmans, G, Hankinson, SE, Havulinna, AS, Herder, Cindy, Hernandez, D, Hicks, AA, Hunter, DJ, Illig, T, Ingelsson, E, Ioan-Facsinay, A, Jansson, JO, Jenny, NS, Jorgensen, ME, Jorgensen, T, Karlsson, M, Koenig, W, Kraft, P, Kwekkeboom, J, Laatikainen, T, Ladwig, KH, LeDuc, C A, Lowe, G, Lu, YC, Marques-Vidal, P, Meisinger, C, Menni, C, Morris, AP, Myers, RH, Mannisto, S, Nalls, MA, Paternoster, L, Peters, A, Pradhan, A D, Rankinen, T, Rasmussen-Torvik, LJ, Rathmann, W, Rice, TK, Richards, JB, Ridker, PM, Sattar, N, Savage, DB, Soderberg, S, Timpson, NJ, Vandenput, L, van Heemst, D, Uh, HW, Vohl, MC, Walker, M, Wichmann, HE, Widen, E, Wood, AR, Yao, J, Zeller, T, Zhang, YY, Meulenbelt, I, Kloppenburg, M, Astrup, A, Sorensen, TIA, Sarzynski, MA, Rao, DC, Jousilahti, P, Vartiainen, E, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Kajantie, E, Osmond, C, Palotie, A, Eriksson, JG, Heliovaara, M, Knekt, PB, Koskinen, S, Jula, A, Perola, M, Huupponen, R K, Viikari, JS, Kahonen, M, Lehtimaki, T, Raitakari, OT, Mellstrom, D, Lorentzon, M, Casas, JP, Bandinelli, S, Marz, W, Isaacs, Aaron, van Dijk, KW, Duijn, Cornelia, Harris, TB, Bouchard, C, Allison, MA, Chasman, DI, Ohlsson, C, Lind, L, Scott, RA, Langenberg, C, Wareham, NJ, Ferrucci, L, Frayling, TM, Pramstaller, PP, Borecki, IB, Waterworth, DM, Bergmann, S, Waeber, G, Vollenweider, P, Vestergaard, H, Hansen, T, Pedersen, O, Hu, FB, Slagboom, PE (Eline), Grallert, H, Spector, TD, Jukema, JW, Klein, RJ, Schadt, EE, Franks, PW, Lindgren, CM, Leibel, R L, and Loos, RJF
- Published
- 2016
8. New genetic loci link adipose and insulin biology to body fat distribution.
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ADIPOGen Consortium, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GEFOS Consortium, GENIE Consortium, International Endogene Consortium, LifeLines Cohort Study, MAGIC Investigators, MuTHER Consortium, PAGE Consortium, ReproGen Consortium, GLGC, ICBP, Dastani, Z., Hivert, MF., Timpson, N., Perry, JR., Yuan, X., Scott, RA., Henneman, P., Heid, IM., Kizer, JR., Lyytikainen, LP., Fuchsberger, C., Tanaka, T., Morris, AP., Small, K., Isaacs, A., Beekman, M., Coassin, S., Lohman, K., Qi, L., Kanoni, S., Pankow, JS., Uh, HW., Wu, Y., Bidulescu, A., Rasmussen-Torvik, LJ., Greenwood, CM., Ladouceur, M., Grimsby, J., Manning, AK., Liu, CT., Kooner, J., Mooser, VE., Vollenweider, P., Kapur, KA., Chambers, J., Wareham, NJ., Langenberg, C., Frants, R., Willemsvan-vanDijk, K., Oostra, BA., Willems, SM., Lamina, C., Winkler, T., Psaty, BM., Tracy, RP., Brody, J., Chen, I., Viikari, J., Kähönen, M., Pramstaller, PP., Evans, DM., St Pourcain, B., Sattar, N., Wood, A., Bandinelli, S., Carlson, OD., Egan, JM., Böhringer, S., van Heemst, D., Kedenko, L., Kristiansson, K., Nuotio, ML., Loo, BM., Harris, T., Garcia, M., Kanaya, A., Haun, M., Klopp, N., Wichmann, HE., Deloukas, P., Katsareli, E., Couper, DJ., Duncan, BB., Kloppenburg, M., Adair, LS., Borja, JB., Wilson, JG., Musani, S., Guo, X., Johnson, T., Semple, R., Teslovich, TM., Allison, MA., Redline, S., Buxbaum, SG., Mohlke, KL., Meulenbelt, I., Ballantyne, CM., Dedoussis, GV., Hu, FB., Liu, Y., Paulweber, B., Spector, TD., Slagboom, P., Ferrucci, L., Jula, A., Perola, M., Raitakari, O., Florez, JC., Salomaa, V., Eriksson, JG., Frayling, TM., Hicks, AA., Lehtimäki, T., Smith, GD., Siscovick, DS., Kronenberg, F., van Duijn, C., Loos, RJ., Waterworth, DM., Meigs, JB., Dupuis, J., Richards, JB., Willenborg, C., Farrall, M., Assimes, TL., Thompson, JR., Ingelsson, E., Saleheen, D., Erdmann, J., Goldstein, BA., Stirrups, K., König, IR., Cazier, JB., Johansson£££Åsa£££ Å., Hall, AS., Lee, JY., Willer, CJ., Chambers, JC., Esko£££Tõnu£££ T., Folkersen, L., Goel, A., Grundberg, E., Havulinna, AS., Ho, WK., Hopewell, JC., Eriksson, N., Kleber, ME., Lundmark, P., Lyytikäinen, LP., Rafelt, S., Shungin, D., Strawbridge, RJ., Thorleifsson, G., Tikkanen, E., Van Zuydam, N., Voight, BF., Waite, LL., Zhang, W., Ziegler, A., Absher, D., Altshuler, D., Balmforth, AJ., Barroso£££Inês£££ I., Braund, PS., Burgdorf, C., Claudi-Boehm, S., Cox, D., Dimitriou, M., Do, R., Doney, AS., El Mokhtari, N., Eriksson, P., Fischer, K., Fontanillas, P., Franco-Cereceda, A., Gigante, B., Groop, L., Gustafsson, S., Hager, J., Hallmans, G., Han, BG., Hunt, SE., Kang, HM., Illig, T., Kessler, T., Knowles, JW., Kolovou, G., Kuusisto, J., Langford, C., Leander, K., Lokki, ML., Lundmark, A., McCarthy, MI., Meisinger, C., Melander, O., Mihailov, E., Maouche, S., Morris, AD., Müller-Nurasyid, M., Nikus, K., Peden, JF., Rayner, NW., Rasheed, A., Rosinger, S., Rubin, D., Rumpf, MP., Schäfer, A., Sivananthan, M., Song, C., Stewart, AF., Tan, 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Murabito, J.M., Nicholson, G., Nyholt, D.R., Olsson, C., Perry, J.R., Reinmaa, E., Salem, R.M., Schadt, E.E., Scott, R.A., Vallejo, E.E., Westra, H.J., Zondervan, K.T., Bakker, S.J., Beilby, J., Bergman, R.N., Blangero, J., Brown, M.J., Chines, P.S., Collins, F.S., Crawford, D.C., de Geus, E.J., Erbel, R., Eriksson, J.G., Ferrannini, E., Forouhi, N.G., Franco, O.H., Gansevoort, R.T., Haiman, C.A., Harris, T.B., Hattersley, A.T., Heliövaara, M., Hicks, A.A., Hingorani, A.D., Hoffmann, W., Humphries, S.E., Jarvelin, M.R., Johansen, B., Jousilahti, P., Jula, A.M., Keinanen-Kiukaanniemi, S.M., Kooner, J.S., Kraja, A.T., Lakka, T.A., Le Marchand, L., Lyssenko, V., Männistö, S., Marette, A., Matise, T.C., McKenzie, C.A., Musk, A.W., Möhlenkamp, S., Morris, A.D., Oldehinkel, A.J., Ong, K.K., Palmer, L.J., Penninx, B.W., Pramstaller, P.P., Raitakari, O.T., Rankinen, T., Rao, D.C., Rice, T.K., Ridker, P.M., Ritchie, M.D., Samani, N.J., Sarzynski, M.A., Schwarz, P.E., Shuldiner, A.R., Staessen, J.A., Steinthorsdottir, V., Stolk, R.P., Strauch, K., Tremblay, A., Vohl, M.C., Wilson, J.F., Witteman, J.C., Adair, L.S., Boehm, B.O., Bornstein, S.R., Bouchard, C., Cauchi, S., Caulfield, M.J., Chambers, J.C., Chasman, D.I., Cooper, R.S., Grabe, H.J., Jöckel, K.H., Munroe, P.B., Oostra, B.A., Palmer, C.N., Pedersen, N.L., Pérusse, L., Saaristo, T.E., Slagboom, P.E., Spector, T.D., Uitterlinden, A.G., Veronesi, G., Walker, M., Wareham, N.J., Wichmann, H.E., Abecasis, G.R., Assimes, T.L., Berndt, S.I., Borecki, I.B., Frayling, T.M., Groop, L.C., Hunter, D.J., Kaplan, R.C., O'Connell, J.R., Strachan, D.P., van Duijn, C.M., Willer, C.J., Visscher, P.M., Yang, J., Hirschhorn, J.N., Zillikens, M.C., McCarthy, M.I., Speliotes, E.K., North, K.E., Fox, C.S., Franks, P.W., Heid, I.M., Loos, R.J., Cupples, L.A., Morris, A.P., Lindgren, C.M., and Mohlke, K.L.
- 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 < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
- Published
- 2015
9. Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels
- Author
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Leeuwen, Elisa, Karssen, Lennart, Deelen, J, Isaacs, Aaron, Medina-Gomez, C, Mbarek, H, Kanterakis, A, Trompet, S, Postmus, I, Verweij, N (Niek), van Enckevort, DJ, Huffman, JE, White, CC, Feitosa, MF, Bartz, TM, Manichaikul, A, Joshi, PK, Peloso, GM, Deelen, P, Dijk, Femke, Willemsen, G, de Geus, EJ, Milaneschi, Y, Penninx, BWJH, Francioli, LC, Menelaou, A, Pulit, SL, Rivadeneira, Fernando, Hofman, Bert, Oostra, Ben, Franco Duran, OH, Leach, IM, Beekman, M, de Craen, AJM, Uh, HW, Trochet, H, Hocking, LJ, Porteous, DJ, Sattar, N, Packard, CJ, Buckley, BM, Brody, JA, Bis, JC, Rotter, JI, Mychaleckyj, JC, Campbell, H, Duan, Q, Lange, LA, Wilson, JF, Hayward, C, Polasek, O, Vitart, V, Rudan, I, Wright, AF, Rich, SS, Psaty, BM, Borecki, IB, Kearney, PM, Stott, DJ, Cupples, LA, Jukema, JW, van der Harst, P, Sijbrands, E.J.G., Hottenga, JJ (Jouke Jan), Uitterlinden, André, Swertz, MA, van Ommen, GJB, de Bakker, PIW, Slagboom, PE (Eline), Boomsma, DI, Wijmenga, C, Duijn, Cornelia, Leeuwen, Elisa, Karssen, Lennart, Deelen, J, Isaacs, Aaron, Medina-Gomez, C, Mbarek, H, Kanterakis, A, Trompet, S, Postmus, I, Verweij, N (Niek), van Enckevort, DJ, Huffman, JE, White, CC, Feitosa, MF, Bartz, TM, Manichaikul, A, Joshi, PK, Peloso, GM, Deelen, P, Dijk, Femke, Willemsen, G, de Geus, EJ, Milaneschi, Y, Penninx, BWJH, Francioli, LC, Menelaou, A, Pulit, SL, Rivadeneira, Fernando, Hofman, Bert, Oostra, Ben, Franco Duran, OH, Leach, IM, Beekman, M, de Craen, AJM, Uh, HW, Trochet, H, Hocking, LJ, Porteous, DJ, Sattar, N, Packard, CJ, Buckley, BM, Brody, JA, Bis, JC, Rotter, JI, Mychaleckyj, JC, Campbell, H, Duan, Q, Lange, LA, Wilson, JF, Hayward, C, Polasek, O, Vitart, V, Rudan, I, Wright, AF, Rich, SS, Psaty, BM, Borecki, IB, Kearney, PM, Stott, DJ, Cupples, LA, Jukema, JW, van der Harst, P, Sijbrands, E.J.G., Hottenga, JJ (Jouke Jan), Uitterlinden, André, Swertz, MA, van Ommen, GJB, de Bakker, PIW, Slagboom, PE (Eline), Boomsma, DI, Wijmenga, C, and Duijn, Cornelia
- Abstract
Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (similar to 35,000 samples) with the population-specific reference panel created by the Genome of the Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value < 6.61 x 10(-4)), including a rare missense variant in ABCA6 (rs77542162, p.Cys1359Arg, frequency 0.034), which is predicted to be deleterious. The frequency of this ABCA6 variant is 3.65-fold increased in the Dutch and its effect (beta(LDL-C) = 0.135, beta(TC) = 0.140) is estimated to be very similar to those observed for single variants in well-known lipid genes, such as LDLR.
- Published
- 2015
10. New genetic loci link adipose and insulin biology to body fat distribution
- Author
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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, 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Dehghan A, Diemert P, Epstein SE, Evans A, Ferrario MM, Ferrières J, Gauguier D, Go AS, Goodall AH, Gudnason V, Hazen S, Holm H, Iribarren C, Jang Y, Kee F, Kim HS, Koenig W, Kratzer W, Kuulasmaa K, Laakso M, Laaksonen R, Lind L, Ouwehand WH, Parish S, Park JE, Pedersen NL, Peters A, Quertermous T, Rader DJ, Schadt E, Shah SH, Sinisalo J, Stark K, Stefansson K, Trégouët DA, Virtamo J, Wallentin L, Wareham N, Zimmermann ME, Nieminen MS, Hengstenberg C, Sandhu MS, Pastinen T, Syvänen AC, Hovingh GK, Dedoussis G, Franks PW, Metspalu A, Zalloua PA, Siegbahn A, Schreiber S, Ripatti S, Blankenberg SS, Clarke R, Boehm BO, O'Donnell C, Reilly MP, März W, Collins R, Kathiresan S, Hamsten A, Kooner JS, Thorsteinsdottir U, Danesh J, Palmer CN, Roberts R, Watkins H, Schunkert H, Samani NJ, Pattaro C, Köttgen A, Teumer A, Garnaas M, Böger CA, Olden M, Chen MH, Tin A, Taliun D, Li M, Gao X, Gorski M, Yang Q, Hundertmark C, Foster MC, O'Seaghdha CM, Glazer N, Smith AV, O'Connell JR, Struchalin M, Li 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P, Pirastu M, Jukema JW, Probst Hensch NM, Toniolo D, Shuldiner AR, Coresh J, Schmidt R, van Duijn CM, Borecki I, Kardia SL, Curhan GC, Rudan I, Gyllensten U, Wilson JF, Franke A, Rettig R, Prokopenko I, Witteman JC, Hayward C, Ridker P, Parsa A, Bochud M, Goessling W, Chasman DI, Kao W, Fox CS, de Boer IH, Glazer NL, Peralta CA, Luan J, Zhao JH, Akylbekova E, Kramer H, van der Harst P, Arking DE, Franceschini N, Egan J, Hernandez D, Reilly M, Townsend RR, Lumley T, Kestenbaum B, Haritunians T, Waeber G, Mooser V, Waterworth D, Lu X, Leak TS, Aasarød K, Skorpen F, Baumert J, Devuyst O, Mychaleckyj JC, Bakker SJ, Hastie ND, Curhan G, Ärnlöv J, Hallan S, Navis G, Shlipak MG, Bull SB, Paterson AD, Rotter JI, Cupples L, Beckmann JS, Dreisbach AW, Kao WH, Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, Oei L, Albagha OM, Amin N, Kemp JP, Koller DL, Minster RL, Moayyeri A, Vandenput L, Willner D, Xiao SM, Yerges Armstrong LM, Zheng HF, Alonso N, Eriksson J, Kammerer CM, Kaptoge SK, Leo PJ, Wilson SG, Aalto V, Alen M, Aragaki AK, Center JR, Dailiana Z, Duggan DJ, Garcia Giralt N, Giroux S, Hocking LJ, Husted LB, Jameson KA, Khusainova R, Kim GS, Kooperberg C, Koromila T, Kruk M, Laaksonen M, Lacroix AZ, Lee SH, Leung PC, Lewis JR, Masi L, Mencej Bedrac S, Nguyen TV, Nogues X, Patel MS, Prezelj J, Rose LM, Scollen S, Siggeirsdottir K, Svensson O, Trummer O, van Schoor NM, Woo J, Zhu K, Balcells S, Brandi ML, Cheng S, Christiansen C, Cooper C, Frost M, Goltzman D, González Macías J, Karlsson M, Khusnutdinova E, Koh JM, Kollia P, Langdahl BL, Leslie WD, Lips P, Ljunggren Ö, Lorenc RS, Marc J, Mellström D, Obermayer Pietsch B, Olmos JM, Pettersson Kymmer U, Reid DM, Riancho JA, Ridker PM, Rousseau F, Slagboom PE, Tang NL, Urreizti R, Van Hul W, Zarrabeitia MT, Castano Betancourt M, Herrera L, Ingvarsson T, Johannsdottir H, Kwan T, Li R, Luben R, Medina Gómez C, Palsson ST, Reppe S, Sigurdsson G, van Meurs JB, Verlaan D, Williams FM, Wood AR, Zhou Y, Gautvik KM, Raychaudhuri S, Cauley JA, Clark GR, Cummings SR, Danoy P, Dennison EM, Eastell R, Eisman JA, Jackson RD, Jones G, Khaw KT, Lorentzon M, McCloskey E, Nandakumar K, Nicholson GC, Peacock M, Pols H, Prince RL, Reid IR, Robbins J, Sambrook PN, Sham PC, Tylavsky FA, Cupples LA, Econs MJ, Kung AW, Reeve J, Streeten EA, Zillikens MC, Ohlsson C, Karasik D, Brown MA, Ralston SH, Ioannidis JP, Kiel DP, Sandholm N, Salem RM, McKnight AJ, Brennan EP, Forsblom C, Isakova T, McKay GJ, Williams WW, Sadlier DM, Mäkinen VP, Swan EJ, Boright AP, Ahlqvist E, Deshmukh HA, Keller BJ, Huang H, Ahola A, Fagerholm E, Gordin D, Harjutsalo V, He B, Heikkilä O, Hietala K, Kytö J, Lahermo P, Lehto M, Österholm AM, Parkkonen M, Pitkäniemi J, Rosengård Bärlund M, Saraheimo M, Sarti C, Söderlund J, Soro Paavonen A, Syreeni A, Thorn LM, Tikkanen H, Tolonen N, Tryggvason K, Tuomilehto J, Wadén J, Gill GV, Prior S, Guiducci C, Mirel DB, Taylor A, Hosseini M, Parving HH, Rossing P, Tarnow L, Ladenvall C, Alhenc Gelas F, Lefebvre P, Rigalleau V, Roussel R, Tregouet DA, Maestroni A, Maestroni S, Falhammar H, Gu T, Möllsten A, Cimponeriu D, Mihai I, Mota M, Mota E, Serafinceanu C, Stavarachi M, Hanson RL, Nelson RG, Kretzler M, Colhoun HM, Panduru NM, Gu H, Brismar K, Zerbini G, Hadjadj S, Marre M, Lajer M, Waggott D, Savage DA, Bain SC, Martin F, Hirschhorn JN, Godson C, Groop PH, Maxwell AP, Schmidt EM, Sengupta S, Peloso GM, Ganna A, Chen J, Buchkovich ML, Mora S, Bragg Gresham JL, Chang HY, Den Hertog HM, Donnelly LA, Ehret GB, Feitosa MF, Ferreira T, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Jackson AU, Kaakinen M, Kettunen J, Li X, Magnusson PK, Mangino M, Montasser ME, Nolte IM, Palmer CD, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Sidore C, Surakka I, Van den Herik EG, Volcik KA, Wong A, Asiki G, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, CESANA, GIANCARLO, Elliott P, Eyjolfsson GI, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hartikainen AL, Hung YJ, Jones MR, Kaleebu P, Kastelein JJ, Kim E, Komulainen P, Kumari M, Lin SY, Lindström J, Mach F, McArdle WL, Müller G, Nagaraja R, Narisu N, Nieminen TV, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stančáková A, Swift AJ, Tiret L, van Pelt L, Vedantam S, Wainwright N, Wijmenga C, Willemsen G, Wilsgaard T, Young EH, Bennett F, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Chakravarti A, Chen YD, Collins FS, Cooper RS, Feranil AB, Freimer NB, Gieger C, Groop LC, Hingorani A, Hovingh G, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Järvelin MR, Kaprio J, Kesäniemi A, Kivimaki M, Koudstaal PJ, Krauss RM, Kuh D, Kyvik KO, Lakka TA, Lindgren CM, Martin NG, McKenzie CA, Meneton P, Moilanen L, Munroe PB, Njølstad I, Power C, Price JF, Rauramaa R, Sanghera DK, Saramies J, Schwarz PE, Sheu WH, Strachan DP, Tayo BO, Tremoli E, Uusitupa M, Whitfield JB, Wolffenbuttel BH, Ordovas JM, Rich SS, Abecasis GR, Abecasis G, Caulfield M, Chasman D, Ehret G, Johnson A, Johnson L, Larson M, Levy D, Munroe P, Newton Cheh C, O'Reilly P, Palmas W, Psaty B, Rice K, Smith A, Snider H, Tobin M, Van Duijn C, Verwoert G, Rice KM, Tobin MD, Verwoert GC, Pihur V, O'Reilly PF, Launer L, Aulchenko Y, Heath S, Sõber S, Arora P, Zhang F, Lucas G, Milaneschi Y, Parker AN, Fava C, Fox ER, Go MJ, Sjögren M, Vinay D, Alexander M, Tabara Y, Shaw Hawkins S, Whincup PH, Shi G, Tayo B, Seielstad M, Sim X, Nguyen KD, Matullo G, Gaunt TR, Onland Moret NC, Cooper MN, Platou CG, Org E, Hardy R, Dahgam S, Palmen J, Kuznetsova T, Uiterwaal CS, Adeyemo A, Ludwig B, Tomaszewski M, Tzoulaki I, Palmer ND, Chang YP, Steinle NI, Grobbee DE, Morrison AC, Najjar S, Hadley D, Brown MJ, Connell JM, Hingorani AD, Day IN, Lawlor DA, Beilby JP, Lawrence RW, Ongen H, Li Y, Young JH, Bis JC, Lee NR, Bolton JA, Chaturvedi N, Islam M, Jafar TH, Kulkarni SR, Grässler J, Howard P, Guarrera S, Ricceri F, Emilsson V, Plump A, Weder AB, Sun YV, Bergman RN, Scott LJ, Stringham HM, Peltonen L, Vartiainen E, Brand SM, Staessen JA, Wang TJ, Burton PR, Artigas MS, Dong Y, Snieder H, Wang X, Zhu H, Lohman KK, Rudock ME, Heckbert SR, Smith NL, Wiggins KL, Doumatey A, Shriner D, Veldre G, Viigimaa M, Kinra S, Prabhakaran D, Tripathy V, Langefeld CD, Rosengren A, Thelle DS, Corsi AM, Singleton A, Forrester T, Hilton G, Salako T, Iwai N, Kita Y, Ogihara T, Ohkubo T, Okamura T, Ueshima H, Umemura S, Eyheramendy S, Meitinger T, Cho YS, Kim HL, Scott J, Sehmi JS, Hedblad B, Nilsson P, Stanèáková A, Raffel LJ, Yao J, Schwartz SM, Ikram M, Longstreth W. Jr, Mosley TH, Seshadri S, Shrine NR, Wain LV, Morken MA, Laitinen J, Zitting P, Cooper JA, van Gilst WH, Janipalli CS, Mani K, Yajnik CS, Mattace Raso FU, Lakatta EG, Orru M, Scuteri A, Ala Korpela M, Kangas AJ, Soininen P, Tukiainen T, Würtz P, Ong RT, Dörr M, Galan P, Hercberg S, Lathrop M, Zelenika D, Zhai G, Meschia JF, Nalls MA, Sharma P, Terzic J, Kumar M, Denniff M, Zukowska Szczechowska E, Wagenknecht LE, Fowkes F, Charchar FJ, Rotimi C, Bots ML, Brand E, Talmud PJ, Nyberg F, Laan M, Palmer LJ, van der Schouw YT, Casas JP, Vineis P, Ganesh SK, Wong TY, Tai ES, Rao DC, Morris RW, Dominiczak AF, Marmot MG, Miki T, Chandak GR, Zhu X, Gyllensten UB, Elosua R, Soranzo N, Sijbrands EJ, Uda M, Vasan RS, Larson MG, Caulfield MJ, Anderson CA, Gordon S, Guo Q, Henders A, Lambert A, Kraft P, Kennedy SH, Macgregor S, Missmer SA, Montgomery GW, Nyholt DR, Painter JN, Roseman F, Treloar SA, Visscher PM, Wallace L, Zondervan KT, Alizadeh B, de Boer RA, Boezen HM, Bruinenberg M, Franke L, Hillege HL, van der Klauw MM, Ormel J, Postma DS, Rosmalen JG, Slaets JP, Stolk RP, Lagou V, Welch RP, Wheeler E, Rehnberg E, Yengo L, Lecoeur C, Johnson PC, Mahajan A, Verweij N, Hottenga JJ, Sennblad B, Salo P, Timpson NJ, Hui J, Bielak LF, Zhao W, Horikoshi M, Navarro P, 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 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, Surdulescu G, Travers ME, Tsaprouni L, Tsoka S, Wilk A, Matise T, Buyske S, Higashio J, Williams R, Nato A, Ambite JL, Deelman E, 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, Pankow J, Gross M, Fornage M, Durda P, Jenny N, Patsy B, Arnold A, Buzkova P, Crawford D, Haines J, Murdock D, Glenn K, Brown Gentry K, Thornton Wells T, Dumitrescu L, Jeff J, Bush WS, Mitchell SL, Goodloe R, Wilson S, Boston J, Malinowski J, Restrepo N, Oetjens M, Fowke J, Zheng W, Spencer K, Ritchie M, Pendergrass S, Le Marchand L, Wilkens L, Park L, Tiirikainen M, Kolonel L, Lim U, Cheng I, Wang H, Shohet R, Haiman C, Stram D, Henderson B, Monroe K, Schumacher F, Peters U, Anderson G, Carlson C, Prentice R, LaCroix A, Wu C, Carty C, Gong J, Rosse S, Young A, Haessler J, Kocarnik J, Lin Y, Jackson R, Duggan D, Kuller L, Stolk L, He C, Sulem P, Barbalic M, Broer L, Byrne EM, Gudbjartsson DF, McArdle PF, Porcu E, van Wingerden S, Zhuang W, Albrecht E, Alizadeh BZ, Lauc LB, Broekmans FJ, Burri A, Chanock SJ, Chen C, Corre T, Coviello AD, d'Adamo P, Davies G, Deary IJ, Ebrahim S, Fauser BC, Ferreli L, Folsom AR, Garcia ME, Hall P, Haller T, Hankinson SE, Hass M, Heath AC, Janssens AC, Keyzer J, Lahti J, Lai S, Laisk T, Laven JS, Liu J, Lopez LM, Louwers YV, Marongiu M, Klaric IM, Masciullo C, McKnight B, Medland SE, Melzer D, Newman AB, Paré G, Peeters PH, Plump AS, Pop VJ, Räikkönen K, Salumets A, Smith JA, Stacey SN, Starr JM, Stathopoulou MG, Tenesa A, Thorand B, Tryggvadottir L, Tsui K, van Dam RM, van Gils CH, van Nierop P, Vink JM, Voorhuis M, Wallaschofski H, Widen E, Wijnands van Gent CJ, 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, 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, and Murray A.
- 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, we conducted genome-wide association meta-analyses of waist and hip circumference-related traits in up to 224,459 individuals. We identified 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (WHRadjBMI) and an additional 19 loci newly associated with related waist and hip circumference measures (P<5×10-8). Twenty of the 49 WHRadjBMI loci showed significant sexual dimorphism, 19 of which displayed a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation, and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
- Published
- 2015
11. LOCI ASSOCIATED WITH N-GLYCOSYLATION OF HUMAN IGG SHOW PLEIOTROPY WITH AUTOIMMUNE DISEASES AND HAEMATOLOGICAL CANCERS
- Author
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Pučić, Maja, Lauc, Gordan, Huffman, Jennifer E, Zgaga, Lina, Adamczyk, Barbara, Mužinić, Ana, Novokmet, Mislav, Polašek, Ozren, Gornik, Olga, Krištić, Jasminka, Keser, Toma, Vitart, Veronique, Scheijen, B, Uh, HW, Molokhia, M, Patrick, AL, McKeigue, P, Kolčić, Ivana, Lukić, IK, Swann, O, van Leeuwen, FN, Ruhaak, LR, Houwing-Duistermaat, JJ, Slagboom, PE, Beekman, M, de Craen, AJM, Deelder, AM, Zeng, Q, Wang, W, Hastie, ND, Gyllensten, U, Wilson, JF, Wuhrer, Manfred, Wright, AF, Rudd, Pauline M, Hayward, Caroline, Aulchenko, Yurii, Campbell, Harry, and Rudan, Igor.
- Subjects
IgG ,GWAS ,glycomics ,autoimmune diseases - Abstract
Glycosylation of immunoglobulin G (IgG) influences IgG effector function by modulating binding to Fc receptors. To identify genetic loci associated with IgG glycosylation, we quantitated N-linked IgG glycans using two approaches. After isolating IgG from human plasma, we performed 77 quantitative measurements of N-glycosylation using ultra-performance liquid chromatography (UPLC) in 2, 247 individuals from four European discovery populations. In parallel, we measured IgG N-glycans using MALDI-TOF mass spectrometry (MS) in a replication cohort of 1, 848 Europeans. Meta-analysis of genome-wide association study (GWAS) results identified 9 genome-wide significant loci in the discovery analysis and two of the same loci(B4GALT1 and MGAT3)in the replication cohort. Four loci contained genes encoding glycosyltransferases (ST6GAL1, B4GALT1, FUT8, and MGAT3), while the remaining 5 contained genes that have not been previously implicated in protein glycosylation (IKZF1, IL6ST-ANKRD55, ABCF2-SMARCD3, SUV420H1, and SMARCB1-DERL3). However, most of them have been strongly associated with autoimmune and inflammatory conditions (e.g., systemic lupus erythematosus, rheumatoid arthritis, ulcerative colitis, Crohn’s disease, diabetes type 1, multiple sclerosis, Graves’ disease, celiac disease, nodular sclerosis) and/or haematological cancers (acute lymphoblastic leukaemia, Hodgkin lymphoma, and multiple myeloma). Follow-up functional experiments in haplodeficientIkzf1 knock-out mice showed the same general pattern of changes in IgG glycosylation as identified in the meta-analysis. AsIKZF1 was associated with multiple IgGNglycan traits, we explored biomarker potential of affectedN-glycans in 101 cases with SLE and 183 matched controls and demonstrated substantial discriminative power in a ROC-curveanalysis (area under the curve = 0.842). Our study shows that it is possible to identify new loci that control glycosylation of a single plasma protein using GWAS. The results may also provide an explanation for the reported pleiotropy andantagonistic effects of loci involved in autoimmune diseases and haematological cancer.
- Published
- 2013
12. Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals
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Dastani, Z, Hivert, MF, Timpson, N, Perry, JRB, Yuan, X, Scott, RA, Henneman, Peter, Heid, IM, Kizer, JR, Lyytikainen, LP, Fuchsberger, C, Tanaka, T, Morris, AP, Small, K, Isaacs, Aaron, Beekman, M, Coassin, S, Lohman, K, Qi, L, Kanoni, S, Pankow, JS, Uh, HW, Wu, Fenny, Bidulescu, A, Rasmussen-Torvik, LJ, Greenwood, CMT, Ladouceur, M, Grimsby, J, Manning, AK, Liu, CT, Kooner, J, Mooser, VE, Vollenweider, P, Kapur, KA, Chambers, J, Wareham, NJ, Langenberg, C, Frants, R, Willems-vanDijk, K, Oostra, Ben, Willems, SM, Lamina, C, Winkler, TW, Psaty, BM, Tracy, RP, Brody, J, Chen, I, Viikari, J, Kahonen, M, Pramstaller, PP, Evans, DM, St Pourcain, B, Sattar, N, Wood, AR, Bandinelli, S, Carlson, OD, Egan, JM, Bohringer, S, van Heemst, D, Kedenko, L, Kristiansson, K, Nuotio, ML, Loo, BM, Harris, T, Garcia, M, Kanaya, A, Haun, M, Klopp, N, Wichmann, HE, Deloukas, P, Katsareli, E, Couper, DJ, Duncan, BB, Kloppenburg, M, Adair, LS, Borja, JB, Wilson, JG, Musani, S, Guo, XQ, Johnson, T, Semple, R, Teslovich, TM, Allison, MA, Redline, S, Buxbaum, SG, Mohlke, KL, Meulenbelt, I, Ballantyne, CM, Dedoussis, GV, Hu, FB, Liu, YM, Paulweber, B, Spector, TD, Slagboom, PE (Eline), Ferrucci, L, Jula, A, Perola, M, Raitakari, O, Florez, JC, Salomaa, V, Eriksson, JG, Frayling, TM, Hicks, AA, Lehtimaki, T, Smith, GD, Siscovick, DS, Kronenberg, F, Duijn, Cornelia, Loos, RJF, Waterworth, DM, Meigs, JB, Dupuis, J, Richards, JB, Dastani, Z, Hivert, MF, Timpson, N, Perry, JRB, Yuan, X, Scott, RA, Henneman, Peter, Heid, IM, Kizer, JR, Lyytikainen, LP, Fuchsberger, C, Tanaka, T, Morris, AP, Small, K, Isaacs, Aaron, Beekman, M, Coassin, S, Lohman, K, Qi, L, Kanoni, S, Pankow, JS, Uh, HW, Wu, Fenny, Bidulescu, A, Rasmussen-Torvik, LJ, Greenwood, CMT, Ladouceur, M, Grimsby, J, Manning, AK, Liu, CT, Kooner, J, Mooser, VE, Vollenweider, P, Kapur, KA, Chambers, J, Wareham, NJ, Langenberg, C, Frants, R, Willems-vanDijk, K, Oostra, Ben, Willems, SM, Lamina, C, Winkler, TW, Psaty, BM, Tracy, RP, Brody, J, Chen, I, Viikari, J, Kahonen, M, Pramstaller, PP, Evans, DM, St Pourcain, B, Sattar, N, Wood, AR, Bandinelli, S, Carlson, OD, Egan, JM, Bohringer, S, van Heemst, D, Kedenko, L, Kristiansson, K, Nuotio, ML, Loo, BM, Harris, T, Garcia, M, Kanaya, A, Haun, M, Klopp, N, Wichmann, HE, Deloukas, P, Katsareli, E, Couper, DJ, Duncan, BB, Kloppenburg, M, Adair, LS, Borja, JB, Wilson, JG, Musani, S, Guo, XQ, Johnson, T, Semple, R, Teslovich, TM, Allison, MA, Redline, S, Buxbaum, SG, Mohlke, KL, Meulenbelt, I, Ballantyne, CM, Dedoussis, GV, Hu, FB, Liu, YM, Paulweber, B, Spector, TD, Slagboom, PE (Eline), Ferrucci, L, Jula, A, Perola, M, Raitakari, O, Florez, JC, Salomaa, V, Eriksson, JG, Frayling, TM, Hicks, AA, Lehtimaki, T, Smith, GD, Siscovick, DS, Kronenberg, F, Duijn, Cornelia, Loos, RJF, Waterworth, DM, Meigs, JB, Dupuis, J, and Richards, JB
- Abstract
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P=4.5 x 10(-8)-1.2 x 10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3 x 10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p=4.3 x 10(-3), n = 22,044), increased triglycerides (p=2.6 x 10(-14), n = 93,440), increased waist-to-hip ratio (p=1.8 x 10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p=4.4 x 10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p=4.5x10(-13), n = 96,748) and decreased BMI (p= 1.4 x 10(-14), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
- Published
- 2012
13. How to deal with the early GWAS data when imputing and combining different arrays is necessary
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Uh, HW, Deelen, J, Beekman, M, Helmer, Q, Rivadeneira, Fernando, Hottenga, JJ (Jouke Jan), Boomsma, DI, Hofman, Bert, Uitterlinden, André, Slagboom, PE (Eline), Bohringer, S, Houwing-Duistermaat, JJ, Uh, HW, Deelen, J, Beekman, M, Helmer, Q, Rivadeneira, Fernando, Hottenga, JJ (Jouke Jan), Boomsma, DI, Hofman, Bert, Uitterlinden, André, Slagboom, PE (Eline), Bohringer, S, and Houwing-Duistermaat, JJ
- Abstract
Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case-control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (R-T(2)) for the post-imputation QCs in publications. European Journal of Human Genetics (2012) 20, 572-576; doi:10.1038/ejhg.2011.231; published online 21 December 2011
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- 2012
14. Haplotype Estimation from Fuzzy Genotypes Using Penalized Likelihood
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Uh, HW, Eilers, Paul, Uh, HW, and Eilers, Paul
- Abstract
The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain ("fuzzy") genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores.
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- 2011
15. Does treatment of intestinal helminth infections influence malaria? Background and methodology of a longitudinal study of clinical, parasitological and immunological parameters in Nangapanda, Flores, Indonesia (ImmunoSPIN Study).
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Wiria AE, Prasetyani MA, Hamid F, Wammes LJ, Lell B, Ariawan I, Uh HW, Wibowo H, Djuardi Y, Wahyuni S, Sutanto I, May L, Luty AJ, Verweij JJ, Sartono E, Yazdanbakhsh M, Supali T, Wiria, Aprilianto E, Prasetyani, Margaretta A, and Hamid, Firdaus
- Abstract
Background: Given that helminth infections are thought to have strong immunomodulatory activity, the question whether helminth infections might affect responses to malaria antigens needs to be addressed. Different cross-sectional studies using diverse methodologies have reported that helminth infections might either exacerbate or reduce the severity of malaria attacks. The same discrepancies have been reported for parasitemia.Methods/design: To determine the effect of geohelminth infections and their treatment on malaria infection and disease outcome, as well as on immunological parameters, the area of Nangapanda on Flores Island, Indonesia, where malaria and helminth parasites are co-endemic was selected for a longitudinal study. Here a Double-blind randomized trial will be performed, incorporating repeated treatment with albendazole (400 mg) or placebo at three monthly intervals. Household characteristic data, anthropometry, the presence of intestinal helminth and Plasmodium spp infections, and the incidence of malaria episodes are recorded. In vitro cultures of whole blood, stimulated with a number of antigens, mitogens and toll like receptor ligands provide relevant immunological parameters at baseline and following 1 and 2 years of treatment rounds. The primary outcome of the study is the prevalence of Plasmodium falciparum and P. vivax infection. The secondary outcome will be incidence and severity of malaria episodes detected via both passive and active follow-up. The tertiary outcome is the inflammatory cytokine profile in response to parasite antigens. The project also facilitates the transfer of state of the art methodologies and technologies, molecular diagnosis of parasitic diseases, immunology and epidemiology from Europe to Indonesia.Discussion: The study will provide data on the effect of helminth infections on malaria. It will also give information on anthelminthic treatment efficacy and effectiveness and could help develop evidence-based policymaking.Trial Registration: This study was approved by The Ethical Committee of Faculty of Medicine, University of Indonesia, ref:194/PT02.FK/Etik/2006 and has been filed by ethics committee of the Leiden University Medical Center.Clinical Trial Number: ISRCTN83830814. The study is reported in accordance with the CONSORT guidelines for cluster-randomized studies. [ABSTRACT FROM AUTHOR]- Published
- 2010
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16. New genetic loci link adipose and insulin biology to body fat distribution
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Shungin, Dmitry, Winkler, Thomas W, Workalemahu, Tsegaselassie, Hartman, Catharina A, Duncan, Emma L, Ntzani, Evangelia E, Oei, Ling, Albagha, Omar M E, Amin, Najaf, Kemp, John P, Koller, Daniel L, Li, Guo, Liu, Ching-Ti, Minster, Ryan L, Hassinen, Maija, Moayyeri, Alireza, Vandenput, Liesbeth, Willner, Dana, Xiao, Su-Mei, Yerges-Armstrong, Laura M, Zheng, Hou-Feng, Alonso, Nerea, Eriksson, Joel, Kammerer, Candace M, Kaptoge, Stephen K, Hayward, Caroline, Leo, Paul J, Thorleifsson, Gudmar, Wilson, Scott G, Wilson, James F, Aalto, Ville, Alen, Markku, Aragaki, Aaron K, Aspelund, Thor, Center, Jacqueline R, Dailiana, Zoe, Heikkilä, Kauko, Duggan, David J, Garcia, Melissa, Garcia-Giralt, Natàlia, Giroux, Sylvie, Hallmans, Göran, Hocking, Lynne J, Husted, Lise Bjerre, Jameson, Karen A, Khusainova, Rita, Kim, Ghi Su, Herzig, Karl-Heinz, Kooperberg, Charles, Koromila, Theodora, Kruk, Marcin, Laaksonen, Marika, Lacroix, Andrea Z, Lee, Seung Hun, Leung, Ping C, Lewis, Joshua R, Masi, 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, Joseph M W, Ittermann, Till, Aulchenko, Yurii S, Castano-Betancourt, Martha, Grundberg, Elin, Herrera, Lizbeth, Ingvarsson, Thorvaldur, Johannsdottir, Hrefna, Kwan, Tony, Li, Rui, Luben, Robert, Medina-Gómez, Carolina, James, Alan L, Palsson, Stefan Th, Reppe, Sjur, Rotter, Jerome I, Sigurdsson, Gunnar, van Meurs, Joyce B J, Verlaan, Dominique, Williams, Frances M K, Wood, Andrew R, Zhou, Yanhua, Gautvik, Kaare M, Johansson, Ingegerd, Pastinen, Tomi, Raychaudhuri, Soumya, Cauley, Jane A, Chasman, Daniel I, Clark, Graeme R, Cummings, Steven R, Danoy, Patrick, Dennison, Elaine M, Eastell, Richard, Eisman, John A, Juliusdottir, Thorhildur, Gudnason, Vilmundur, Hofman, Albert, Jackson, Rebecca D, Jones, Graeme, Jukema, J Wouter, Khaw, Kay-Tee, Lehtimäki, Terho, Liu, Yongmei, Lorentzon, Mattias, McCloskey, Eugene, Kalafati, Ioanna-Panagiota, Mitchell, Braxton D, Nandakumar, Kannabiran, Nicholson, Geoffrey C, Oostra, Ben A, Peacock, Munro, Pols, Huibert A P, Prince, Richard L, Raitakari, Olli, Reid, Ian 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Harjutsalo, Valma, He, Bing, Heikkilä, Outi, Buchkovich, Martin L, Leander, Karin, Hietala, Kustaa, Kytö, Janne, Lahermo, Päivi, Lehto, Markku, Österholm, Anne-May, Parkkonen, Maija, Pitkäniemi, Janne, Rosengård-Bärlund, Milla, Saraheimo, Markku, Sarti, Cinzia, Lee, Nanette R, Söderlund, Jenny, Soro-Paavonen, Aino, Syreeni, Anna, Thorn, Lena M, Tikkanen, Heikki, Tolonen, Nina, Tryggvason, Karl, Tuomilehto, Jaakko, Wadén, Johan, Gill, Geoffrey V, Lichtner, Peter, Prior, Sarah, Guiducci, Candace, Mirel, Daniel B, Taylor, Andrew, Hosseini, Mohsen, Parving, Hans-Henrik, Rossing, Peter, Tarnow, Lise, Ladenvall, Claes, Alhenc-Gelas, François, Lind, Lars, Lefebvre, Pierre, Rigalleau, Vincent, Roussel, Ronan, Tregouet, David-Alexandre, Maestroni, Anna, Maestroni, Silvia, Falhammar, Henrik, Gu, Tianwei, Möllsten, Anna, Cimponeriu, Dan, Lindström, Jaana, Mihai, Ioana, Mota, Maria, Mota, Eugen, Serafinceanu, Cristian, Stavarachi, Monica, Hanson, Robert L, Nelson, Robert G, Kretzler, Matthias, 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Anna-Liisa, Hernandez, Dena, Hicks, Andrew A, Holm, Hilma, Hung, Yi-Jen, Illig, Thomas, Jones, Michelle R, Kaleebu, Pontiano, Kastelein, John J P, Kim, Eric, Klopp, Norman, Komulainen, Pirjo, Monda, Keri L, Kumari, Meena, Langenberg, Claudia, Lin, Shih-Yi, Loos, Ruth J F, Meisinger, Christa, Mooijaart, Simon P, Müller, Gabrielle, Nagaraja, Ramaiah, Narisu, Narisu, Nieminen, Tuomo V M, Nsubuga, Rebecca N, Olafsson, Isleifur, Ong, Ken K, Palotie, Aarno, Papamarkou, Theodore, Pomilla, Cristina, Mühleisen, Thomas W, Pouta, Anneli, Rader, Daniel J, Reilly, Muredach P, Rudan, Igor, Ruokonen, Aimo, Samani, Nilesh, Scharnagl, Hubert, Seeley, Janet, Roman, Tamara S, Mulas, Antonella, Silander, Kaisa, Stančáková, Alena, Stirrups, Kathleen, Swift, Amy J, Tiret, Laurence, Uitterlinden, Andre G, van Pelt, L Joost, Vedantam, Sailaja, Wainwright, Nicholas, Wijmenga, Cisca, Müller, Gabriele, Wild, Sarah H, Willemsen, Gonneke, Wilsgaard, Tom, Young, Elizabeth H, Zhao, Jing Hua, Adair, Linda S, Arveiler, Dominique, Assimes, Themistocles L, Bandinelli, Stefania, Bennett, Franklyn, Bochud, Murielle, Boehm, Bernhard O, Boomsma, Dorret I, Borecki, Ingrid B, Bornstein, Stefan R, Bovet, Pascal, Burnier, Michel, Campbell, Harry, Chakravarti, Aravinda, Chambers, John C, Chen, Yii-Der Ida, Collins, Francis S, Cooper, Richard S, Danesh, John, de Faire, Ulf, Feranil, Alan B, Ferrières, Jean, Ferrucci, Luigi, Nalls, Michael A, Freimer, Nelson B, Gieger, Christian, Groop, Leif C, Gyllensten, Ulf, Hamsten, Anders, Hingorani, Aroon, Hovingh, G Kees, Hsiung, Chao Agnes, Humphries, Steve E, Hveem, Kristian, Iribarren, Carlos, Järvelin, Marjo-Riitta, Jula, Antti, Kaprio, Jaakko, Glorioso, Nicola, Kesäniemi, Antero, Kivimaki, Mika, Kooner, Jaspal S, Koudstaal, Peter J, Krauss, Ronald M, Kuh, Diana, Kuusisto, Johanna, Kyvik, Kirsten O, Laakso, Markku, Lakka, Timo A, Lindgren, Cecilia M, Martin, Nicholas G, März, Winfried, McCarthy, Mark I, McKenzie, Colin A, Meneton, Pierre, Metspalu, Andres, Moilanen, Leena, Morris, Andrew D, Olden, Matthias, Munroe, Patricia B, Njølstad, Inger, Pedersen, Nancy L, Power, Chris, Pramstaller, Peter P, Price, Jackie F, Quertermous, Thomas, Rauramaa, Rainer, Saleheen, Danish, Rayner, Nigel W, Salomaa, Veikko, Sanghera, Dharambir K, Saramies, Jouko, Schwarz, Peter E H, Sheu, Wayne H-H, Siegbahn, Agneta, Spector, Tim D, Strachan, David P, Drong, Alexander W, Renstrom, Frida, Tayo, Bamidele O, Tremoli, Elena, Uusitupa, Matti, Vollenweider, Peter, Wallentin, Lars, Wareham, Nicholas J, Whitfield, John B, Wolffenbuttel, Bruce H R, Ried, Janina S, Ordovas, Jose M, Boerwinkle, Eric, Palmer, Colin N A, Franks, Paul W, Ripatti, Samuli, Sandhu, Manjinder S, Robertson, Neil R, Rich, Stephen S, Boehnke, Michael, Deloukas, Panos, Kathiresan, Sekar, Mohlke, Karen L, Ingelsson, Erik, Abecasis, Gonçalo R, Abecasis, Gonçalo, Caulfield, Mark, Chasman, Dan, Ehret, Georg, Johnson, Andrew, Johnson, Louise, Larson, Martin, Levy, Daniel, Munroe, Patricia, Newton-Cheh, Christopher, O'Reilly, Paul, Palmas, Walter, Psaty, Bruce, Rice, Kenneth, Smith, Albert, Snider, Harold, Tobin, Martin, Van Duijn, Cornelia, Verwoert, Germaine, Rice, Kenneth M, Johnson, Andrew D, Tobin, Martin D, Verwoert, Germaine C, Hwang, Shih-Jen, Pihur, Vasyl, Scholtens, Salome, O'Reilly, Paul F, Teumer, Alexander, Glazer, Nicole L, Launer, Lenore, Aulchenko, Yurii, Heath, Simon, Sennblad, Bengt, Sõber, Siim, Parsa, Afshin, Arora, Pankaj, Dehghan, Abbas, Zhang, Feng, Lucas, Gavin, Peden, John F, Seufferlein, Thomas, Igl, Wilmar, Milaneschi, Yuri, Parker, Alex N, Fava, Cristiano, Fox, Ervin R, Sitlani, Colleen M, Go, Min Jin, van der Harst, Pim, Kao, Wen Hong Linda, Sjögren, Marketa, Vinay, D. G., Alexander, Myriam, Tabara, Yasuharu, Shaw-Hawkins, Sue, Whincup, Peter H, Smith, Albert Vernon, Shi, Gang, Tayo, Bamidele, Seielstad, Mark, Sim, Xueling, Nguyen, Khanh-Dung Hoang, Matullo, Giuseppe, Gaunt, Tom R, Onland-Moret, N Charlotte, Cooper, Matthew N, Platou, Carl G P, Org, Elin, Hardy, Rebecca, Dahgam, Santosh, Palmen, Jutta, Vitart, Veronique, Braund, Peter S, Kuznetsova, Tatiana, Stringham, Heather M, Uiterwaal, Cuno S P M, Adeyemo, Adebowale, Ludwig, Barbara, Tomaszewski, Maciej, Tzoulaki, Ioanna, Palmer, Nicholette D, Sundström, Johan, Chang, Yen-Pei C, Steinle, Nanette I, Grobbee, Diederick E, Arking, Dan E, Kardia, Sharon L, Morrison, Alanna C, Najjar, Samer, Swertz, Morris A, Hadley, David, Brown, Morris J, Connell, John M, Hingorani, Aroon D, Day, Ian N M, Lawlor, Debbie A, Beilby, John P, Lawrence, Robert W, Clarke, Robert, Collins, Rory, Hopewell, Jemma C, Ongen, Halit, Dreisbach, Albert W, Li, Yali, Young, J. H., Bis, Joshua C, Syvänen, Ann-Christine, Chen, Ming-Huei, Pattaro, Cristian, Bolton, Judith A Hoffman, Köttgen, Anna, Bergmann, Sven, Mooser, Vincent, Chaturvedi, Nish, Frayling, Timothy M, Islam, Muhammad, Jafar, Tazeen H, Erdmann, Jeanette, Kulkarni, Smita R, Grässler, Jürgen, Howard, Philip, Thorand, Barbara, Guarrera, Simonetta, Ricceri, Fulvio, Emilsson, Valur, Plump, Andrew, Weder, Alan B, Sun, Yan V, Bergman, Richard N, Scott, Laura J, Peltonen, Leena, Vartiainen, Erkki, Brand, Stefan-Martin, Staessen, Jan A, Wang, Thomas J, Tomaschitz, Andreas, Burton, Paul R, Artigas, Maria Soler, Dong, Yanbin, Snieder, Harold, Wang, Xiaoling, Zhu, Haidong, Lohman, Kurt K, Rudock, Megan E, Heckbert, Susan R, Smith, Nicholas L, Troffa, Chiara, Wiggins, Kerri L, Doumatey, Ayo, Shriner, Daniel, Veldre, Gudrun, Viigimaa, Margus, Kinra, Sanjay, Prabhakaran, Dorairajan, Tripathy, Vikal, Langefeld, Carl D, Rosengren, Annika, van Oort, Floor Va, Thelle, Dag S, Corsi, Anna Maria, Singleton, Andrew, Forrester, Terrence, Hilton, Gina, Salako, Tunde, Iwai, Naoharu, Kita, Yoshikuni, Ogihara, Toshio, Verweij, Niek, Ohkubo, Takayoshi, Okamura, Tomonori, Ueshima, Hirotsugu, Umemura, Satoshi, Eyheramendy, Susana, Meitinger, Thomas, Wichmann, H-Erich, Cho, Yoon Shin, Kim, Hyung-Lae, Lee, Jong-Young, Vonk, Judith M, Scott, James, Sehmi, Joban S, Hedblad, Bo, Nilsson, Peter, Smith, George Davey, Stanèáková, Alena, Raffel, Leslie J, Yao, Jie, O'Donnell, Chris, Schwartz, Stephen M, Ikram, M Arfan, Longstreth, W. 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Visscher, Peter M, Wallace, Leanne, Zondervan, Krina T, Alizadeh, Behrooz Z, de Boer, Rudolf A, Boezen, H Marike, Bruinenberg, Marcel, Karpe, Fredrik, Franke, Lude, van der Klauw, Melanie M, Ormel, Johan, Postma, Dirkje S, Rosmalen, Judith G M, Slaets, Joris P, Keildson, Sarah, Stolk, Ronald P, Scott, Robert A, Lagou, Vasiliki, Welch, Ryan P, Wheeler, Eleanor, Mägi, Reedik, Kiryluk, Krzysztof, Rehnberg, Emil, Rasmussen-Torvik, Laura J, Yengo, Loïc, Lecoeur, Cecile, Liang, Liming, Johnson, Paul C D, Hottenga, Jouke-Jan, Lifton, Richard P, Salo, Perttu, Timpson, Nicholas J, St Pourcain, Beate, Andrews, Jeanette S, Hui, Jennie, Bielak, Lawrence F, Ma, Baoshan, Zhao, Wei, Horikoshi, Momoko, Navarro, Pau, Esko, Tönu, McKnight, Amy J, Fall, Tove, Chen, Han, Robertson, Neil, Rybin, Denis, McPherson, Ruth, Willems, Sara M, Chines, Peter S, Kang, Hyun Min, Song, Kijoung, Croteau-Chonka, Damien C, An, Ping, Marullo, Letizia, Jansen, Hanneke, Oldehinkel, Albertine J, Min, Josine L, Pankow, James 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, 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Haessler, J., Kocarnik, J., Lin, Y., Jackson, R., Duggan, D., Kuller, L., Stolk, L., He, C., Sulem, P., Barbalic, M., Broer, L., Byrne, EM., Gudbjartsson, DF., McArdle, PF., Porcu, E., van Wingerden, S., Zhuang, W., Albrecht, E., Alizadeh, BZ., Lauc, LB., Broekmans, FJ., Burri, A., Chanock, SJ., Chen, C., Corre, T., Coviello, AD., d'Adamo, P., Davies, G., Deary, IJ., Ebrahim, S., Fauser, BC., Ferreli, L., Folsom, AR., Garcia, ME., Hall, P., Haller, T., Hankinson, SE., Hass, M., Heath, AC., Janssens, AC., Keyzer, J., Lahti, J., Lai, S., Laisk, T., Laven, JS., Liu, J., Lopez, LM., Louwers, YV., Marongiu, M., Klaric, IM., Masciullo, C., McKnight, B., Medland, SE., Melzer, D., Newman, AB., Paré, G., Peeters, PH., Plump, AS., Pop, VJ., Räikkönen, K., Salumets, A., Smith, JA., Stacey, SN., Starr, JM., Stathopoulou, MG., Tenesa, A., Thorand, B., Tryggvadottir, L., Tsui, K., van Dam RM., van Gils CH., van Nierop, P., Vink, JM., Voorhuis, M., Wallaschofski, H., Widen, E., Wijnands-van Gent CJ., 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], Departments of Epidemiology and Nutrition, Harvard School of Public Health, Institute of Experimental Medicine, Czech Academy of Sciences [Prague] (CAS), Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University Hospital, Department of Genetics, University of Groningen [Groningen], deCODE Genetics, deCODE genetics [Reykjavik], Pediatric Pulmonology, Allergology & Epidemiology, University of Groningen [Groningen]-University Medical Center Groningen [Groningen] (UMCG)-Beatrix Children's Hospital-Groningen Research Institute for Asthma and COPD, Department of Nutrition-Dietetics, Harokopio University of Athens, Yale School of Medicine [New Haven, Connecticut] (YSM), National Heart and Lung Institute (NHLI), Imperial College London, Queensland Institute of Medical Research, Concord Hospital, Epidémiologie des maladies chroniques : impact des interactions gène environnement sur la santé des populations, 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, Dept. 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Greifswald - University of Greifswald, Interfaculty Institute for Genetics and Functional Genomics, 5 University Street, Centre for Paediatric Epidemiology and Biostatistics, University College of London [London] (UCL), MRC Centre for Epidemiology of Child Health, UCL Institute of Child Health, Unit for Molecular Epidemiology, Institute of Health Sciences and Biocenter Oulu, University of Oulu, Department of Epidemiology and Biostatistics, Department of Life Course and Services, National Institute for Health and Welfare [Helsinki], Department of Epidemiology and Public Health, Queen's University [Belfast] (QUB), Interdisciplinary Center of Clinical Research, Department of Physiology, University of Eastern Finland-Institute of Biomedicine, University of Hawai‘i [Mānoa] (UHM), Department of Clinical Chemistry, University of Tampere [Finland]-Tampere University Hospital, Chronic Disease Epidemiology and Prevention Unit, Université Laval [Québec] (ULaval), Centre for Bone and Arthritis 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= University of Helsinki, Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, Department of Pediatrics, Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Department of Public Health, South Ostrobothnia Central Hospital, Department of Clinical and Preventive Medicine, Danube-University Krems, Netherlands Consortium for Healthy Aging [Leiden, Netherlands] (NCHA), Institute of Public Health and Clinical Nutrition, University of Eastern Finland, MRC epidemiology Unit, Institute of Epidemiology, Clinical Genetics Branch, Division of Cancer Epidemiology & Genetics, National Institutes of Health [Bethesda] (NIH)-National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH), Department of Oncology, Queensland Brain Institute, University of Queensland [Brisbane], Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital [Boston], Divisions of Genetics and Endocrinology and Program in Genomics, Boston Children's Hospital, Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], University of North Carolina System (UNC)-University of North Carolina System (UNC)-UNC Gillings School of Global Public Health-Carolina Center for Genome Sciences, Metabolic Disease Group, University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Epidemiology Unit, Addenbrooke's Hospital-Medical Research Council (MRC), Framingham Heart Study, Boston University [Boston] (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, Smith, J, Stacey, S, Starr, J, Stathopoulou, M, Tenesa, A, Tryggvadottir, L, Tsui, K, van Dam, R, van Gils, C, van Nierop, P, Vink, J, Voorhuis, M, Wallaschofski, H, Widen, E, Wijnands van Gent, C, Zgaga, L, Zygmunt, M, Buring, J, Crisponi, L, Demerath, E, Murray, A, Visser, J, Lunetta, K, Elks, C, Cousminer, D, Feenstra, B, Lin, P, Smith, E, Warrington, N, Alavere, H, Berenson, G, Blackburn, H, Busonero, F, Chen, W, Easton, D, Foroud, T, Geller, F, Kilpeläinen, T, Li, S, Melbye, M, Murray, J, Murray, S, Ness, A, Northstone, K, Pennell, C, Pharoah, P, Rafnar, T, Rice, J, Ring, S, Schork, N, Segrè, A, Sovio, U, Srinivasan, S, Tammesoo, M, Tyrer, J, Weedon, M, Young, L, Bierut, L, Boyd, H, Psychiatry, NCA - Neurobiology of mental health, and EMGO - Lifestyle, overweight and diabetes
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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
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- 2015
17. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age
- Author
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Doris Lechner, Miriam Capri, Stefan Böhringer, Stefan Schreiber, Gonneke Willemsen, Paolo Garagnani, Irene Maeve Rea, Andres Metspalu, Palmi V. Jonsson, Thomas B. L. Kirkwood, Lene Christiansen, Fernando Rivadeneira, Giuseppina Rose, J. Wouter Jukema, Serena Dato, Owen A. Ross, Almut Nebel, Cornelia M. van Duijn, Gary Saunders, Bernard Jeune, David J. Stott, Jeanine J. Houwing-Duistermaat, A. Murphy, Anton J. M. de Craen, Friederike Flachsbart, Karen Andersen-Ranberg, Albert Hofman, Ian Ford, Ellen A. Nohr, Giuseppe Passarino, Krista Fischer, Elisa Cevenini, Carmen Martin-Ruiz, Jutta Gampe, Iris Postmus, Christopher P. Nelson, Stefano Salvioli, Alberto Montesanto, Mark Lathrop, Marianne Nygaard, Marie E. Breen, Jennifer Harrow, Hae-Won Uh, Erik B. van den Akker, Thorkild I. A. Sørensen, André G. Uitterlinden, Alexander Viktorin, Bastiaan T. Heijmans, Susan E. McNerlan, Quinta Helmer, Naveed Sattar, Claudio Franceschi, Eco J. C. de Geus, E. Mihailov, Jouke-Jan Hottenga, Qihua Tan, Kari Stefansson, Yoichiro Kamatani, Paolina Crocco, Henning Tiemeier, Stella Trompet, Patrik K. E. Magnusson, Marian Beekman, Riin Tamm, Amke Caliebe, Maris Alver, Femke-Anouska Heinsen, Pilar Galan, Daníel F. Guðbjartsson, Joris Deelen, Linda Broer, Ruud van der Breggen, Kristin L. Ayers, Anna M. Bennet, Dorret I. Boomsma, P. Eline Slagboom, Kaare Christensen, Diana van Heemst, Joanna Collerton, Karen Davies, Rudi G. J. Westendorp, Hélène Blanché, Lavinia Paternoster, Nilesh J. Samani, Hreinn Stefansson, Simon P. Mooijaart, Heather J. Cordell, Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Netherlands Consortium for Healthy Aging [Leiden, Netherlands] (NCHA), LeidenUniversity Medical Centre, Department of Epidemiology, The Netherlands Cancer Institute, Institute of Genetic Medicine, Newcastle University [Newcastle], National Institute of Public Health, University of Southern Denmark (SDU), Department of Clinical Genetics, Odense University Hospital, Fondation Jean Dausset - Centre d’Étude du Polymorphisme Humain, Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], University of Tartu, Institute of Molecular and Cell Biology, Department of Gerontology and Geriatrics, Leiden University Medical Center (LUMC), deCODE genetics [Reykjavik], Christian-Albrechts University of Kiel, University of Calabria, Delft University of Technology (TU Delft), Department of Cardiovascular Sciences, Université Catholique de Louvain = Catholic University of Louvain (UCL), University Hospitals Leicester, Genome Campus, The Wellcome Trust Sanger Institute [Cambridge], School of Medicine, Dentistry and Biomedical Sciences [Belfast], Queen's University [Belfast] (QUB), University of Iowa [Iowa City], DIMES: Department of Experimental, Diagnostic and Specialty Medicine, Bologna University Hospital, Institute for Ageing and Health, University of Glasgow, Max Planck Institute for Demographic Research (MPIDR), Max-Planck-Gesellschaft, Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), EMGO Institute for Health and Care Research, VU University Amsterdam Medical Center, Landspitali National University Hospital of Iceland, University of Iceland, McGill University = Université McGill [Montréal, Canada], Genome Quebec Innovation Centre, Institut de Génomique, Belfast Health and Social Care Trust, Estonian Biocentre, Partenaires INRAE, Aarhus University [Aarhus], School of Social and Community Medicine, Erasmus University Rotterdam, Mayo Clinic, BHF Glasgow Cardiovascular Research Centre, University Medical Center Schleswig-Holstein, Institute of Cardiovascular and Medical Sciences, Sophia Children's Hospital, Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA), Augustinus Foundation, Avera Institute for Human Genetics (AIHG), AXA Research Fund, Belfast City Hospital Trust Fund, Biobanking and Biomolecular Resources Research Infrastructure (BBMRI -NL) [184.021.007], Biotechnology and Biological Sciences Research Council (BBSRC), Bristol-Myers Squibb, Center for Inherited Disease Research (CIDR), Centre for Medical Systems Biology (CMSB), CERA Foundation, Commissariat a L'Energie Atomique (CEA)-Centre National de Genotypage (CNG), Danish Agency for Science, Technology and Innovation (DASTI)/The Danish Council for Independent Research (DCIR) [11-107308], Danish National Research Foundation (DNRF), Department of Health and Social Services (Northern Ireland), DFG-Cluster of Excellence 'Inflammation at Interfaces', Dunhill Medical Trust [R124/0509], Egmont Foundation, Estonian Science Foundation [7859], Estonian Government [SF0180142s08], European Research Council (ERC) [230374], European Science Foundation (ESF) [EU/QLRT-2001-01254], European Union [FP5-QLK6-CY-2001-00128, FP6-LIFESCIHEALTH-36894, FP6-LSH M-CT-2004-503270, FP7-HEALTH-2007-B-223004, FP7-HEALTH-F4-2007-201413, FP7-HEALTH-F4-2008-202047, FP7-HEALTH-2009-single-stage-242244, FP7-HEALTH-2010-two-stage-259679], Fondation Caisse d'Epargne Rhone-Alpes Lyon CERAL, Genetic Association Information Network (GAIN) of the Foundation for the US National Institutes of Health (NIMH) [MH081802], GenomEUtwin [EU/QLRT-2001-01254, QLG2-CT-2002-01254], Guy's & St Thomas' NHS Foundation Trust, Health Foundation, Heart and Lung foundation [20070481], Innovation-Oriented Research Program on Genomics (SenterNovem) [IGE05007], Institut National de la Recherche Agronomique (INRA), Institut National de la Sante et de la Recherche Medicale (INSERM), King's College London, Medical Research Council (MRC) [G0500997, G0601333], Ministere de l'Enseignement superieur et de la Recherche (MESR), National Institutes of Health (NIH)/National Institute of Aging (NIA) [P01AG08761, R01D0042157-01A, U01DK066134], National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre, NBIC BioAssist [NWO-NBIC/BioAssist/RK/2008.024], Netherlands Consortium for Healthy Ageing (NCHA) [050-060-810], Netherlands Genomics Initiative (NGI), Netherlands Heart Foundation (NHF) [2001 D 032], Netherlands Organization for Scientific Research (NWO, MagW/ZonMW) [904-61-090, 904-61-193, 480-04-004, 400-05-717, Spinozapremie 56-464-14192, 175.010.2005.011, 911-03-012, 985-10-002, Addiction-31160008, Middelg-root-911-09-032], Netspar - Living longer for a good health, NHS North of Tyne (Newcastle Primary Care Trust), Pharmacy Foundation, Regione Autonoma della Sardegna, Rutgers University Cell and DNA Repository [NIMH U24 MH068457-06], Swedish Research Council [M-2005-1112], Tampere University Hospital and Academy of Finland, Danish Interdisciplinary Research Council, Health Foundation (Helsefonden), Ministry for Higher Education, National Program for Research Infrastructure [09-063256], March of Dimes Birth Defects Foundation, Swedish Foundation for Strategic Research (SSF), Unilever Discover Colworth, Universite Paris 13, University of Tartu [SP1GVAR-ENG], Velux Foundation, VU University's Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam (NCA), Wellcome Trust [084762, 085475, 087436], IDEAL [FP7-HEALTH-2010-two-stage-259679], Research and Education into Ageing-0153, European Regional Development Fund, Deelen J, Beekman M, Uh HW, Broer L, Ayers KL, Tan Q, Kamatani Y, Bennet AM, Tamm R, Trompet S, Guðbjartsson DF, Flachsbart F, Rose G, Viktorin A, Fischer K, Nygaard M, Cordell HJ, Crocco P, van den Akker EB, Böhringer S, Helmer Q, Nelson CP, Saunders GI, Alver M, Andersen-Ranberg K, Breen ME, van der Breggen R, Caliebe A, Capri M, Cevenini E, Collerton JC, Dato S, Davies K, Ford I, Gampe J, Garagnani P, de Geus EJ, Harrow J, van Heemst D, Heijmans BT, Heinsen FA, Hottenga JJ, Hofman A, Jeune B, Jonsson PV, Lathrop M, Lechner D, Martin-Ruiz C, McNerlan SE, Mihailov E, Montesanto A, Mooijaart SP, Murphy A, Nohr EA, Paternoster L, Postmus I, Rivadeneira F, Ross OA, Salvioli S, Sattar N, Schreiber S, Stefánsson H, Stott DJ, Tiemeier H, Uitterlinden AG, Westendorp RG, Willemsen G, Samani NJ, Galan P, Sørensen TI, Boomsma DI, Jukema JW, Rea IM, Passarino G, de Craen AJ, Christensen K, Nebel A, Stefánsson K, Metspalu A, Magnusson P, Blanché H, Christiansen L, Kirkwood TB, van Duijn CM, Franceschi C, Houwing-Duistermaat JJ, Slagboom PE., Leiden Univ, Dept Mol Epidemiol, NL-2300 RC Leiden, Netherlands [ 2 ] Leiden Univ, Netherlands Consortium Healthy Ageing, NL-2300 RC Leiden, Netherlands [ 3 ] Leiden Univ, Dept Med Stat & Bioinformat, NL-2300 RC Leiden, Netherlands [ 4 ] Leiden Univ, Dept Cardiol, NL-2300 RC Leiden, Netherlands [ 5 ] Leiden Univ, Dept Gerontol & Geriatr, NL-2300 RC Leiden, Netherlands [ 6 ] Erasmus MC, Dept Epidemiol, NL-3000 CA Rotterdam, Netherlands [ 7 ] Erasmus MC, Dept Internal Med, NL-3000 CA Rotterdam, Netherlands [ 8 ] Newcastle Univ, Int Ctr Life, Inst Med Genet, Newcastle Upon Tyne NE1 3BZ, Tyne & Wear, England [ 9 ] Univ So Denmark, Inst Publ Hlth, DK-5000 Odense C, Denmark [ 10 ] Univ So Denmark, Inst Clin Res, Dept Gynecol & Obstet, DK-5000 Odense C, Denmark [ 11 ] Odense Univ Hosp, Dept Clin Genet, DK-5000 Odense C, Denmark [ 12 ] Odense Univ Hosp, Clin Biochem & Pharmacol, DK-5000 Odense C, Denmark [ 13 ] Fdn Jean Dausset CEPH, F-75010 Paris, France [ 14 ] Karolinska Inst, Dept Med Epidemiol & Biostat, SE-17177 Stockholm, Sweden [ 15 ] Univ Tartu, Estonian Genome Ctr, Tartu 51010, Estonia [ 16 ] Univ Tartu, Inst Mol & Cell Biol, EE-51010 Tartu, Estonia [ 17 ] deCODE Genet, Populat Gen, IS-101 Reykjavik, Iceland [ 18 ] Univ Kiel, Inst Clin Mol Biol, D-24105 Kiel, Germany [ 19 ] Univ Kiel, Inst Med Informat & Stat, D-24105 Kiel, Germany [ 20 ] Univ Calabria, Dept Biol Ecol & Earth Sci, I-87036 Arcavacata Di Rende, Italy [ 21 ] Delft Univ Technol, Delft Bioinformat Lab, NL-2600 GA Delft, Netherlands [ 22 ] Univ Leicester, Dept Cardiovasc Sci, Leicester LE3 9QP, Leics, England [ 23 ] Glenfield Hosp, Cardiovasc Biomed Res Unit, Natl Inst Hlth Res, Leicester LE3 9QP, Leics, England [ 24 ] Wellcome Trust Sanger Inst, Wellcome Trust Genome Campus, Cambridge CB10 1SA, England [ 25 ] Queens Univ Belfast, Sch Med Dent & Biomed Sci, Belfast BT9 7BL, Antrim, North Ireland [ 26 ] Univ Iowa, Dept Psychiat, Iowa City, IA 52242 USA [ 27 ] Univ Bologna, Dept Expt Diagnost & Specialty Med, I-40126 Bologna, Italy [ 28 ] Univ Bologna, Interdepartmental Ctr L Galvani, I-40126 Bologna, Italy [ 29 ] Newcastle Univ, Inst Ageing & Hlth, Newcastle Upon Tyne NE4 5PL, Tyne & Wear, England [ 30 ] Univ Glasgow, Robertson Ctr Biostat, Glasgow G12 8QQ, Lanark, Scotland [ 31 ] Univ Glasgow, Inst Cardiovasc & Med Sci, Glasgow G12 8QQ, Lanark, Scotland [ 32 ] Max Planck Inst Demograf Forsch, Lab Stat Demog, D-18057 Rostock, Germany [ 33 ] Vrije Univ Amsterdam, Dept Biol Psychol, NL-1081 BT Amsterdam, Netherlands [ 34 ] Vrije Univ Amsterdam Med Ctr, EMGO Inst Hlth & Care Res, NL-1081 BT Amsterdam, Netherlands [ 35 ] Landspitali Univ Hosp, IS-101 Reykjavik, Iceland [ 36 ] Univ Iceland, Fac Med, IS-101 Reykjavik, Iceland [ 37 ] CEA, Inst Genom, F-91057 Evry, France [ 38 ] McGill Univ, Montreal, PQ H3G 1A4, Canada [ 39 ] Genome Quebec Innovat Ctr, Montreal, PQ H3G 1A4, Canada [ 40 ] Belfast Hlth & Social Care Trust, Cytogenet Lab, Belfast BT8 8BH, Antrim, North Ireland [ 41 ] Estonian Bioctr, EE-51010 Tartu, Estonia [ 42 ] Aarhus Univ, Dept Publ Hlth, Epidemiol Sect, DK-8000 Aarhus C, Denmark [ 43 ] Univ Bristol, Sch Social & Community Med, MRC Ctr Causal Anal Translat Epidemiol, Bristol BS8 2BN, Avon, England [ 44 ] Mayo Clin, Dept Neurosci, Jacksonville, FL 32224 USA [ 45 ] Univ Glasgow, Fac Med, BHF Glasgow Cardiovasc Res Ctr, Glasgow G12 8TA, Lanark, Scotland [ 46 ] Univ Kiel, PopGen Biobank, D-24105 Kiel, Germany [ 47 ] Univ Hosp Schleswig Holstein, D-24105 Kiel, Germany [ 48 ] Sophia Childrens Univ Hosp, Erasmus Med Ctr, Dept Child & Adolescent Psychiat, NL-3000 CA Rotterdam, Netherlands [ 49 ] Univ Paris 04, UREN, U557, INSERM, F-93017 Bobigny, France [ 50 ] U1125 Inra, F-93017 Bobigny, France [ 51 ] Cnam, F-93017 Bobigny, France [ 52 ] Univ Paris 13, CRNH IdF, F-93017 Bobigny, France [ 53 ] Univ Copenhagen, Fac Hlth & Med Sci, Sect Metab Genet, Novo Nordisk Fdn Ctr, DK-2200 Copenhagen N, Denmark [ 54 ] Inst Prevent Med, DK-2000 Copenhagen, Denmark [ 55 ] Frederiksberg Univ Hosp, DK-2000 Copenhagen, Denmark [ 56 ] Interuniv Cardiol Inst Netherlands, NL-3501 DG Utrecht, Netherlands [ 57 ] Bellaria Hosp, IRCCS Inst Neurol Sci, I-40139 Bologna, Italy [ 58 ] CNR, ISOF, I-40129 Bologna, Italy, Epidemiology, Surgery, Internal Medicine, Child and Adolescent Psychiatry / Psychology, ProdInra, Migration, Vrije Universiteit Amsterdam [Amsterdam] (VU), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), VU University Amsterdam, Biological Psychology, and EMGO+ - Lifestyle, Overweight and Diabetes
- Subjects
Male ,Netherlands Twin Register (NTR) ,Disease/genetics ,Lífslíkur ,Longevity/genetics ,[SDV.MHEP.PHY] Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO] ,Genetic Linkage ,Genome-wide association study ,0302 clinical medicine ,Prospective Studies ,Genetics (clinical) ,Genetics ,Aged, 80 and over ,0303 health sciences ,education.field_of_study ,[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology ,Association Studies Articles ,Age Factors ,Chromosome Mapping ,Genetic Loci/physiology ,General Medicine ,3. Good health ,Europe ,Phenotype ,Cardiovascular Diseases ,Hypertension ,Chromosomes, Human, Pair 5 ,Female ,Human Longevity, genetics, meta-analysis ,Aging/genetics ,Cardiology and Cardiovascular Medicine ,HUMAN AGING ,Longevity ,European Continental Ancestry Group ,Population ,HUMAN GENETICS ,Single-nucleotide polymorphism ,Locus (genetics) ,Biology ,FAMILIAL LONGEVITY ,White People ,03 medical and health sciences ,Gene mapping ,SDG 3 - Good Health and Well-being ,Cardiovascular Diseases/genetics ,[SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO] ,Humans ,Allele ,education ,Molecular Biology ,Aged ,030304 developmental biology ,Genetic association ,Öldrun ,Genome, Human ,Arfgengi ,Minor allele frequency ,Ageing ,Genetic Loci ,Chromosomes, Human, Pair 19 ,Hypertension/genetics ,030217 neurology & neurosurgery ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology ,Genome-Wide Association Study - Abstract
To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. The genetic contribution to the variation in human lifespan is ∼ 25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥ 85 years) and 16 121 younger controls (
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- 2014
18. Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals
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Seppo Koskinen, Christian Herder, Daniel I. Chasman, Andrew R. Wood, Jonna L. Grimsby, J.F. Wilson, Day Inm., Massimo Mangino, Gonneke Willemsen, Robert W. Mahley, Cristian Pattaro, Nicole L. Glazer, T.B. Harris, Irene Pichler, M S Sandhu, D. van Heemst, Christine Proença, Martha Ganser, Robert A. Hegele, Richa Saxena, Eleftheria Zeggini, Markku Laakso, Peter Kraft, Judith B. Borja, Karen L. Mohlke, J B Richards, de Geus Ejc., Robert Sladek, Cristen J. Willer, Samy Hadjadj, S.M. Boekholdt, Gina M. Peloso, Kijoung Song, Sutapa Mukherjee, Gudmar Thorleifsson, Winston Hide, Mark I. McCarthy, Ruth E. Pakyz, Marian Beekman, Ayellet V. Segrè, Inga Prokopenko, Ping An, George Dedoussis, Danielle Posthuma, Jeanette Erdmann, Simon J. Griffin, Nilesh J. Samani, Inke R. König, Frank B. Hu, Lokki M-L., David M. Evans, Xiaohui Li, Valgerdur Steinthorsdottir, Aimo Ruokonen, A Pouta, Kerrin S. Small, Cecilia M. Lindgren, O Le Bacquer, Xijing Han, Florian Kronenberg, E Katsareli, Christian Dina, S. Gabriel, Jochen Spranger, James S. Pankow, M. Kloppenburg, Penninx Bwjh., Torben Hansen, Josh Smith, Jennie Hui, Gordon H. Williams, Mark Seielstad, Ingrid B. Borecki, Weihua Zhang, Peter P. Pramstaller, Stephen J. Sharp, Neil R. Robertson, Zee Ryl., Mike Sampson, Angela Silveira, C.M. van Duijn, Anders Hamsten, Peter Shrader, Denis Rybin, Chen Y-Di., Gunnar Sigurdsson, Michael Stumvoll, Russel Tracy, Mark O. Goodarzi, Göran Hallmans, Michael R. Erdos, Valeriya Lyssenko, Juha Saharinen, Sven Bergmann, Jeffrey R. O'Connell, Debbie A Lawlor, Thomas Meitinger, Yvonne Böttcher, Jérôme Delplanque, Sarah G. Buxbaum, Silvia Naitza, Shah Ebrahim, Graham A. Hitman, Angelo Scuteri, Aroon D. Hingorani, Heribert Schunkert, François Pattou, Claudia Lamina, A L Elliott, Sekar Kathiresan, Dawn M. Waterworth, Jennifer A. Brody, Thomas Quertermous, Leena Peltonen, Josephine M. Egan, Daniel J. Rader, J F Peden, Yarnell Jwg., Daniel S. 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Voight, Wolfgang Rathmann, Mark Walker, Markus Perola, M. A. Province, Veikko Salomaa, James B. Meigs, George Davey Smith, Robert Clarke, Gerard Waeber, Stefania Bandinelli, Sally L. Ricketts, Kaisa Silander, Loos Rjf., Amanda J. Bennett, John C. Chambers, Marilyn C. Cornelis, L A Cupples, Andrew T. Hattersley, M Sandhu, Marju Orho-Melander, C M van Duijn, Olli T. Raitakari, David Meyre, Ida Surakka, Jouke-Jan Hottenga, Uh H-W., Kari Stefansson, David Melzer, P E Slagboom, Kristian Midthjell, Robert K. Semple, James P. Pirruccello, Aloysius G Lieverse, Åsa Johansson, Michael Roden, Felicity Payne, Eric J.G. Sijbrands, N P Burtt, David R. Hillman, Michael Marmot, Todd Green, Eric E. Schadt, Sijbrands Ejg., Tien Yin Wong, Coin Ljm., K B Boström, Olov Rolandsson, A D Morris, David Altshuler, Harald Grallert, L C Groop, Alan F. Wright, Karen Kapur, Xueling Sim, Philippe Froguel, K O Kyvik, T. Lauritzen, Linda S. 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Hveem, K, Laakso, M, Morris, Ad, Palmer, Cn, Rudan, I, Sijbrands, E, Stein, Ld, Tuomilehto, J, Uitterlinden, A, Walker, M, Watanabe, Rm, Abecasis, Gr, Boehm, Bo, Campbell, H, Daly, Mj, Hattersley, At, Pedersen, O, Barroso, I, Groop, L, Sladek, R, Thorsteinsdottir, U, Wilson, Jf, Illig, T, Froguel, P, van Duijn, Cm, Stefansson, K, Altshuler, D, Boehnke, M, Mccarthy, Mi, Soranzo, N, Wheeler, E, Glazer, Nl, Bouatia Naji, N, Mägi, R, Randall, J, Elliott, P, Rybin, D, Dehghan, A, Hottenga, Jj, Song, K, Goel, A, Lajunen, T, Cavalcanti Proença, C, Kumari, M, Timpson, Nj, Zabena, C, Ingelsson, E, An, P, O'Connell, J, Luan, J, Elliott, A, Roccasecca, Rm, Pattou, F, Sethupathy, P, Ariyurek, Y, Barter, P, Beilby, Jp, Ben Shlomo, Y, Bergmann, S, Bochud, M, Bonnefond, A, Borch Johnsen, K, Böttcher, Y, Brunner, E, Bumpstead, Sj, Chen, Yd, Clarke, R, Coin, Lj, Cooper, Mn, Crisponi, L, Day, In, de Geus, Ej, Delplanque, J, Fedson, Ac, Fischer Rosinsky, A, Forouhi, Ng, Franzosi, Mg, Galan, P, Goodarzi, Mo, Graessler, J, Grundy, S, Gwilliam, R, Hallmans, G, Hammond, N, Han, X, Hartikainen, Al, Hayward, C, Heath, Sc, Hercberg, S, Hillman, Dr, Hingorani, Ad, Hui, J, Hung, J, Kaakinen, M, Kaprio, J, Kesaniemi, Ya, Kivimaki, M, Knight, B, Koskinen, S, Kovacs, P, Kyvik, Ko, Lathrop, Gm, Lawlor, Da, Le Bacquer, O, Lecoeur, C, Li, Y, Mahley, R, Mangino, M, Martínez Larrad, Mt, Mcateer, Jb, Mcpherson, R, Meisinger, C, Melzer, D, Meyre, D, Mitchell, Bd, Mukherjee, S, Naitza, S, Neville, Mj, Orrù, M, Pakyz, R, Paolisso, Giuseppe, Pattaro, C, Pearson, D, Peden, Jf, Pedersen, Nl, Pfeiffer, Af, Pichler, I, Polasek, O, Posthuma, D, Potter, Sc, Pouta, A, Province, Ma, Rice, K, Ripatti, S, Rivadeneira, F, Rolandsson, O, Sandbaek, A, Sandhu, M, Sanna, S, Sayer, Aa, Scheet, P, Seedorf, U, Sharp, Sj, Shields, B, Sigurðsson, G, Sijbrands, Ej, Silveira, A, Simpson, L, Singleton, A, Smith, Nl, Sovio, U, Swift, A, Syddall, H, Syvänen, Ac, Tönjes, A, Uitterlinden, Ag, van Dijk, Kw, Varma, D, Visvikis Siest, S, Vitart, V, Vogelzangs, N, Waeber, G, Wagner, Pj, Walley, A, Ward, Kl, Watkins, H, Wild, Sh, Willemsen, G, Witteman, Jc, Yarnell, Jw, Zelenika, D, Zethelius, B, Zhai, G, Zhao, Jh, Zillikens, Mc, Diagram, Consortium, Giant, Consortium, Global B., Pgen Consortium, Borecki, Ib, Meneton, P, Magnusson, Pk, Nathan, Dm, Williams, Gh, Silander, K, Bornstein, Sr, Schwarz, P, Spranger, J, Karpe, F, Shuldiner, Ar, Cooper, C, Serrano Ríos, M, Lind, L, Palmer, Lj, Hu FB, 1st, Franks, Pw, Ebrahim, S, Marmot, M, Wright, Af, Stumvoll, M, Hamsten, A, Procardis, Consortium, Buchanan, Ta, Valle, Tt, Rotter, Ji, Penninx, Bw, Boomsma, Di, Cao, A, Scuteri, A, Schlessinger, D, Uda, M, Ruokonen, A, Jarvelin, Mr, Peltonen, L, Mooser, V, Magic, Investigator, Glgc, Consortium, Musunuru, K, Smith, Av, Edmondson, Ac, Stylianou, Im, Koseki, M, Pirruccello, Jp, Chasman, Di, Johansen, Ct, Fouchier, Sw, Peloso, Gm, Barbalic, M, Ricketts, Sl, Bis, Jc, Feitosa, Mf, Orho Melander, M, Melander, O, Li, X, Cho, Y, Go, Mj, Kim, Yj, Lee, Jy, Park, T, Kim, K, Sim, X, Ong, Rt, Croteau Chonka, Dc, Lange, La, Smith, Jd, Ziegler, A, Zhang, W, Zee, Ry, Whitfield, Jb, Thompson, Jr, Surakka, I, Smit, Jh, Sinisalo, J, Scott, J, Saharinen, J, Sabatti, C, Rose, Lm, Roberts, R, Rieder, M, Parker, An, Pare, G, O'Donnell, Cj, Nieminen, M, Nickerson, Da, Montgomery, Gw, Mcardle, W, Masson, D, Martin, Ng, Marroni, F, Lucas, G, Luben, R, Lokki, Ml, Lettre, G, Launer, Lj, Lakatta, Eg, Laaksonen, R, König, Ir, Khaw, Kt, Kaplan, Lm, Johansson, Å, Janssens, Ac, Igl, W, Hovingh, Gk, Hengstenberg, C, Havulinna, A, Hastie, Nd, Harris, Tb, Haritunians, T, Hall, A, Groop, Lc, Gonzalez, E, Freimer, Nb, Erdmann, J, Ejebe, Kg, Döring, A, Dominiczak, Af, Demissie, S, de Faire, U, Caulfield, Mj, Boekholdt, Sm, Assimes, Tl, Quertermous, T, Seielstad, M, Wong, Ty, Tai, E, Feranil, Ab, Kuzawa, Cw, Taylor HA, Jr, Gabriel, Sb, Holm, H, Gudnason, V, Krauss, Rm, Ordovas, Jm, Munroe, Pb, Tall, Ar, Hegele, Ra, Kastelein, Jj, Schadt, Ee, Strachan, Dp, Reilly, Mp, Samani, Nj, Schunkert, H, Cupples, La, Ridker, Pm, Rader, Dj, Kathiresan, S., Medical Research Council (MRC), Perry, John [0000-0001-6483-3771], Wareham, Nicholas [0000-0003-1422-2993], Langenberg, Claudia [0000-0002-5017-7344], Semple, Robert [0000-0001-6539-3069], Griffin, Simon [0000-0002-2157-4797], Barroso, Ines [0000-0001-5800-4520], Soranzo, Nicole [0000-0003-1095-3852], Wheeler, Eleanor [0000-0002-8616-6444], Luan, Jian'an [0000-0003-3137-6337], Forouhi, Nita [0000-0002-5041-248X], Sharp, Stephen [0000-0003-2375-1440], Sovio, Ulla [0000-0002-0799-1105], Zhao, Jing Hua [0000-0003-4930-3582], Luben, Robert [0000-0002-5088-6343], Khaw, Kay-Tee [0000-0002-8802-2903], Sandhu, Manjinder [0000-0002-2725-142X], Apollo - University of Cambridge Repository, Biological Psychology, Functional Genomics, Neuroscience Campus Amsterdam - Attention & Cognition, EMGO+ - Lifestyle, Overweight and Diabetes, Other departments, Experimental Vascular Medicine, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, Cardiology, Human genetics, Psychiatry, NCA - Attention & Cognition, EMGO - Lifestyle, overweight and diabetes, Lääketieteen yksikkö - School of Medicine, University of Tampere, Institute for Molecular Medicine Finland, Hjelt Institute (-2014), Clinicum, Department of General Practice and Primary Health Care, Department of Public Health, Haartman Institute (-2014), Transplantation Laboratory, Biostatistics Helsinki, Quantitative Genetics, Complex Disease Genetics, Genetic Epidemiology, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Procardis Consortium, MAGIC investigators, GLGC Consortium, Olson, J., Kronmal, R., Robbins, J., Chaves, PH., Burke, G., Kuller, LH., Tracy, R., Gottdiener, J., Prineas, R., Becker, JT., Enright, P., Klein, R., and O'Leary, DH.
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Netherlands Twin Register (NTR) ,Male ,Insulin Resistance/genetics ,VARIANTS ,0302 clinical medicine ,POPULATION ,African Americans ,blood/genetics ,0303 health sciences ,education.field_of_study ,Adiponectin/blood ,Adiponectin/genetics ,Asian Continental Ancestry Group ,Cholesterol, HDL/genetics ,Diabetes Mellitus, Type 2/genetics ,European Continental Ancestry Group ,Female ,Gene Expression ,Genetic Predisposition to Disease ,Genome-Wide Association Study ,Glucose Tolerance Test ,Humans ,Metabolic Networks and Pathways ,Polymorphism, Single Nucleotide ,Waist-Hip Ratio ,Global B Pgen Consortium ,MAGIC investigators ,3. Good health ,Cholesterol ,Medicine ,Adiponectin ,Type 2 ,medicine.medical_specialty ,HDL ,Biolääketieteet - Biomedicine ,Single-nucleotide polymorphism ,DIAGRAM Consortium ,White People ,Molecular Genetics ,GLGC Consortium ,03 medical and health sciences ,Asian People ,SDG 3 - Good Health and Well-being ,GIANT Consortium ,Diabetes Mellitus ,Genetics ,DIAGRAM+ Consortium ,GENOME-WIDE ASSOCIATION ,Polymorphism ,education ,Biology ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,0604 Genetics ,Science & Technology ,GLGC Investigators ,nutritional and metabolic diseases ,ta3121 ,medicine.disease ,Obesity ,Black or African American ,blood/genetics, African Americans, Asian Continental Ancestry Group, Cholesterol ,genetics, Diabetes Mellitus ,genetics, European Continental Ancestry Group, Female, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Glucose Tolerance Test, Humans, Insulin Resistance ,genetics, Male, Metabolic Networks and Pathways, Polymorphism ,Single Nucleotide, Waist-Hip Ratio ,Endocrinology ,Diabetes Mellitus, Type 2 ,Developmental Biology ,Type 2/genetics ,Cancer Research ,Type 2 diabetes ,QH426-470 ,030204 cardiovascular system & hematology ,LIPID CONCENTRATIONS ,GENETICS & HEREDITY ,Genetics (clinical) ,RISK ,2. Zero hunger ,INSULIN-RESISTANCE ,Glucose tolerance test ,medicine.diagnostic_test ,MAGIC Consortium ,Single Nucleotide ,ADIPOSE-TISSUE ,CORONARY-ARTERY-DISEASE ,Life Sciences & Biomedicine ,Research Article ,Clinical Research Design ,GENETIC-BASIS ,Population ,Insulin resistance ,Internal medicine ,Diabetes mellitus ,medicine ,ddc:610 ,030304 developmental biology ,RECEPTOR ,Cholesterol, HDL ,Human Genetics ,HDL/genetics ,3121 General medicine, internal medicine and other clinical medicine ,MuTHER Consortium ,3111 Biomedicine ,Procardis Consortium ,Insulin Resistance - Abstract
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10−8–1.2×10−43). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p, Author Summary Serum adiponectin levels are highly heritable and are inversely correlated with the risk of type 2 diabetes (T2D), coronary artery disease, stroke, and several metabolic traits. To identify common genetic variants associated with adiponectin levels and risk of T2D and metabolic traits, we conducted a meta-analysis of genome-wide association studies of 45,891 multi-ethnic individuals. In addition to confirming that variants at the ADIPOQ and CDH13 loci influence adiponectin levels, our analyses revealed that 10 new loci also affecting circulating adiponectin levels. We demonstrated that expression levels of several genes in these candidate regions are associated with serum adiponectin levels. Using a powerful novel method to assess the contribution of the identified variants with other traits using summary-level results from large-scale GWAS consortia, we provide evidence that the risk alleles for adiponectin are associated with deleterious changes in T2D risk and metabolic syndrome traits (triglycerides, HDL, post-prandial glucose, insulin, and waist-to-hip ratio), demonstrating that the identified loci, taken together, impact upon metabolic disease.
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- 2012
19. Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS.
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Gu Z, Uh HW, Houwing-Duistermaat J, and El Bouhaddani S
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In many studies of human diseases, multiple omics datasets are measured. Typically, these omics datasets are studied one by one with the disease, thus the relationship between omics is overlooked. Modeling the joint part of multiple omics and its association to the outcome disease will provide insights into the complex molecular base of the disease. Several dimension reduction methods which jointly model multiple omics and two-stage approaches that model the omics and outcome in separate steps are available. Holistic one-stage models for both omics and outcome are lacking. In this article, we propose a novel one-stage method that jointly models an outcome variable with omics. We establish the model identifiability and develop EM algorithms to obtain maximum likelihood estimators of the parameters for normally and Bernoulli distributed outcomes. Test statistics are proposed to infer the association between the outcome and omics, and their asymptotic distributions are derived. Extensive simulation studies are conducted to evaluate the proposed model. The method is illustrated by modeling Down syndrome as outcome and methylation and glycomics as omics datasets. Here we show that our model provides more insight by jointly considering methylation and glycomics., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)
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- 2024
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20. Statistical integration of multi-omics and drug screening data from cell lines.
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El Bouhaddani S, Höllerhage M, Uh HW, Moebius C, Bickle M, Höglinger G, and Houwing-Duistermaat J
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- Humans, Multiomics, Drug Evaluation, Preclinical, Proteomics methods, Computational Biology methods, Synucleinopathies
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Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 el Bouhaddani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2024
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21. Digital Twins in Healthcare: Methodological Challenges and Opportunities.
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Meijer C, Uh HW, and El Bouhaddani S
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One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.
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- 2023
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22. Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis.
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Gill S, Bunting KV, Sartini C, Cardoso VR, Ghoreishi N, Uh HW, Williams JA, Suzart-Woischnik K, Banerjee A, Asselbergs FW, Eijkemans M, Gkoutos GV, and Kotecha D
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- Electrocardiography, Humans, Sensitivity and Specificity, Smartphone, Atrial Fibrillation diagnosis, Photoplethysmography
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Objectives: Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection., Methods: This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool., Results: 28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias., Conclusion: PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection., Competing Interests: Competing interests: Dr Gill reports funding through the BigData@Heart Innovative Medicines Initiative, grant no.116074. Dr Bunting reports a grant from the University of Birmingham’s British Heart Foundation Accelerator Award (BHF AA/18/2/34218). Dr Sartini reports that Bayer AG is one of the partners that have funded the IMI framework and is employed by Bayer AG in this IMI collaboration. Mr Roth Cardoso has nothing to disclose. Ms Narges Ghoreishi has nothing to disclose. Dr Uh reports grants and personal fees from Innovative Medicines Initiative 2 BigData@Heart, grant no. 116074, during the conduct of the study. Dr Williams has nothing to disclose. Dr Kiliana Suzart-Woischnik has nothing to disclose. Dr Banerjee reports grants from AstraZeneca, outside the submitted work. Professor Asselbergs reports grants from Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No (116074), during the conduct of the study and is supported by UCL Hospitals NIHR Biomedical Research Centre. Professor MJC Eijkemans has nothing to disclose. Professor Gkoutos has nothing to disclose. Professor Kotecha reports grants from the National Institute for Health Research (NIHR CDF-2015-08-074 RATE-AF; NIHR HTA-130280 DaRe2THINK; NIHR EME-132974 D2T-NV), the British Heart Foundation (PG/17/55/33087 and AA/18/2/34218), EU/EFPIA Innovative Medicines Initiative (BigData@Heart 116074), the European Society of Cardiology supported by educational grants from Boehringer Ingelheim/BMS-Pfizer Alliance/Bayer/Daiichi Sankyo/Boston Scientific, the NIHR/University of Oxford Biomedical Research Centre and British Heart Foundation/University of Birmingham Accelerator Award (STEEER-AF NCT04396418), Amomed Pharma and IRCCS San Raffaele/Menarini (Beta-blockers in Heart Failure Collaborative Group NCT0083244); in addition to personal fees from Bayer (Advisory Board), AtriCure (Speaker fees), Protherics Medicines Development (Advisory Board) and Myokardia (Advisory Board)., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
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- 2022
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23. Statistical method for modeling sequencing data from different technologies in longitudinal studies with application to Huntington disease.
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Fuady AM, van Roon-Mom WMC, Kiełbasa SM, Uh HW, and Houwing-Duistermaat JJ
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- Gene Expression Profiling, Humans, Longitudinal Studies, Technology, Huntington Disease genetics
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Advancement of gene expression measurements in longitudinal studies enables the identification of genes associated with disease severity over time. However, problems arise when the technology used to measure gene expression differs between time points. Observed differences between the results obtained at different time points can be caused by technical differences. Modeling the two measurements jointly over time might provide insight into the causes of these different results. Our work is motivated by a study of gene expression data of blood samples from Huntington disease patients, which were obtained using two different sequencing technologies. At time point 1, DeepSAGE technology was used to measure the gene expression, with a subsample also measured using RNA-Seq technology. At time point 2, all samples were measured using RNA-Seq technology. Significant associations between gene expression measured by DeepSAGE and disease severity using data from the first time point could not be replicated by the RNA-Seq data from the second time point. We modeled the relationship between the two sequencing technologies using the data from the overlapping samples. We used linear mixed models with either DeepSAGE or RNA-Seq measurements as the dependent variable and disease severity as the independent variable. In conclusion, (1) for one out of 14 genes, the initial significant result could be replicated with both technologies using data from both time points; (2) statistical efficiency is lost due to disagreement between the two technologies, measurement error when predicting gene expressions, and the need to include additional parameters to account for possible differences., (© 2020 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)
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- 2021
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24. Multi-omics integration identifies key upstream regulators of pathomechanisms in hypertrophic cardiomyopathy due to truncating MYBPC3 mutations.
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Pei J, Schuldt M, Nagyova E, Gu Z, El Bouhaddani S, Yiangou L, Jansen M, Calis JJA, Dorsch LM, Blok CS, van den Dungen NAM, Lansu N, Boukens BJ, Efimov IR, Michels M, Verhaar MC, de Weger R, Vink A, van Steenbeek FG, Baas AF, Davis RP, Uh HW, Kuster DWD, Cheng C, Mokry M, van der Velden J, Asselbergs FW, and Harakalova M
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- Humans, Mutation, Transcriptome, Cardiomyopathy, Hypertrophic genetics, Cardiomyopathy, Hypertrophic physiopathology, Carrier Proteins genetics, DNA Methylation, Gene Expression, Genes, Homeobox, Histones genetics
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Background: Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the cardiac muscle, frequently caused by mutations in MYBPC3. However, little is known about the upstream pathways and key regulators causing the disease. Therefore, we employed a multi-omics approach to study the pathomechanisms underlying HCM comparing patient hearts harboring MYBPC3 mutations to control hearts., Results: Using H3K27ac ChIP-seq and RNA-seq we obtained 9310 differentially acetylated regions and 2033 differentially expressed genes, respectively, between 13 HCM and 10 control hearts. We obtained 441 differentially expressed proteins between 11 HCM and 8 control hearts using proteomics. By integrating multi-omics datasets, we identified a set of DNA regions and genes that differentiate HCM from control hearts and 53 protein-coding genes as the major contributors. This comprehensive analysis consistently points toward altered extracellular matrix formation, muscle contraction, and metabolism. Therefore, we studied enriched transcription factor (TF) binding motifs and identified 9 motif-encoded TFs, including KLF15, ETV4, AR, CLOCK, ETS2, GATA5, MEIS1, RXRA, and ZFX. Selected candidates were examined in stem cell-derived cardiomyocytes with and without mutated MYBPC3. Furthermore, we observed an abundance of acetylation signals and transcripts derived from cardiomyocytes compared to non-myocyte populations., Conclusions: By integrating histone acetylome, transcriptome, and proteome profiles, we identified major effector genes and protein networks that drive the pathological changes in HCM with mutated MYBPC3. Our work identifies 38 highly affected protein-coding genes as potential plasma HCM biomarkers and 9 TFs as potential upstream regulators of these pathomechanisms that may serve as possible therapeutic targets.
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- 2021
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25. Statistical integration of two omics datasets using GO2PLS.
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Gu Z, El Bouhaddani S, Pei J, Houwing-Duistermaat J, and Uh HW
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- Case-Control Studies, Least-Squares Analysis, Computational Biology, Genomics
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Background: Nowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace., Results: The simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease., Conclusions: GO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.
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- 2021
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26. Investigating the impact of Down syndrome on methylation and glycomics with two-stage PO2PLS.
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Gu Z, El Bouhaddani S, Houwing-Duistermaat J, and Uh HW
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- DNA Methylation, Female, Humans, Polysaccharides, Protein Processing, Post-Translational, Down Syndrome genetics, Glycomics methods
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Down syndrome (DS) is a condition that leads to precocious and accelerated aging in affected subjects. Several alterations in DS cases have been reported at a molecular level, particularly in methylation and glycosylation. Investigating the relation between methylation, glycomics and DS can lead to new insights underlying the atypical aging. We consider a data integration approach, where we investigate how DS affects the parts of glycomics and methylation which are correlated, and which CpG sites and glycans are relevant. Our motivating datasets consist of methylation and glycomics data, measured on 29 DS patients and their unaffected siblings and mothers. The family-based case-control design needs to be taken into account when studying the relationship between methylation, glycomics and DS. We propose a two-stage approach to first integrate methylation and glycomics data, and then link the joint information to Down syndrome. For the data integration step, we consider probabilistic two-way orthogonal partial least squares (PO2PLS). PO2PLS models two omics datasets in terms of low-dimensional joint and omic-specific latent components, and takes into account heterogeneity across the omics data. The relationship between the omics data can be statistically tested. The joint components represent the joint information in methylation and glycomics. In the second stage, we apply a linear mixed model to the relationship between DS and the joint methylation and glycomics components. For the components that are significantly as sociated with DS, we identify the most important CpG sites and glycans. A simulation study is conducted to evaluate the performance of our approach. The results showed that the effects of DS on the omics data can be detected in a large sample size, and the accuracy of the feature selection was high in both small and large sample sizes. Our approach is applied to the DS datasets, a significant effect of DS on the joint components is found. The identified CpG sites and glycans appeared to be related to DS. Our proposed method that jointly analyzes multiple omics data with an outcome variable may provide new insight into the molecular implications of DS at different omics levels., (Copyright: © 2022 by Fabrizio Serra editore, Pisa · Roma.)
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- 2021
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27. Estimation of the effect of surrogate multi-omic biomarkers.
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Fuady AM, El Bouhaddani S, Uh HW, and Houwing-Duistermaat J
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- Biomarkers, Calibration, Female, Humans, Mass Spectrometry methods, Regression Analysis, Polysaccharides
- Abstract
Multiple technologies which measure the same omics data set but are based on different aspects of the molecules exist. In practice, studies use different technologies and have therefore different biomarkers. An example is the glycan age index, which is constructed by three different ultra-performance liquid chromatography (UPLC) IgG glycans, and is a biomarker for biological age. A second technology is liquid chromatography- mass spectrometry (LCMS). To estimate the effect of a biomarker on an outcome variable, two issues need to be addressed. Firstly, a measurement error is needed to map one technology to the other one using a calibration study. Here, we consider two approaches, namely one based on the chemical properties of the two technologies and one based on the estimation of this relationship using O2PLS. Secondly, the use of an approximation of the biomarker in the main study needs to be taken into account by use of a regression calibration method. The performance of the two approaches is studied via simulations. The methods are used to estimate the relationship between glycan age and menopause. We have data from two cohorts, namely Korcula and Vis. In conclusion, (1) both measurement error models give similar results and suggest that there is an association between the glycan age index and the menopause status, (2) the chemical mapping approach outperforms O2PLS in the low measurement error variance, while on the larger measurement error variance, O2PLS works better, (3) statistical efficiency is lost due to increased noise level by adding irrelevant information., (Copyright: © 2022 by Fabrizio Serra editore, Pisa · Roma.)
- Published
- 2021
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28. Prediction of vascular aging based on smartphone acquired PPG signals.
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Dall'Olio L, Curti N, Remondini D, Safi Harb Y, Asselbergs FW, Castellani G, and Uh HW
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- Adolescent, Adult, Aged, Female, Heart Rate, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Young Adult, Aging pathology, Deep Learning, Neural Networks, Computer, Photoplethysmography methods, Smartphone statistics & numerical data, Vascular Diseases diagnosis
- Abstract
Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) - the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking - was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.
- Published
- 2020
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29. Low IgA Associated With Oropharyngeal Microbiota Changes and Lung Disease in Primary Antibody Deficiency.
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Berbers RM, Mohamed Hoesein FAA, Ellerbroek PM, van Montfrans JM, Dalm VASH, van Hagen PM, Paganelli FL, Viveen MC, Rogers MRC, de Jong PA, Uh HW, Willems RJL, and Leavis HL
- Subjects
- Adolescent, Adult, Child, Cross-Sectional Studies, Female, Humans, Immunoglobulin A blood, Male, Young Adult, Immunoglobulin A immunology, Immunologic Deficiency Syndromes complications, Immunologic Deficiency Syndromes immunology, Lung Diseases immunology, Oropharynx microbiology
- Abstract
Common Variable Immunodeficiency (CVID) and X-linked agammaglobulinemia (XLA) are primary antibody deficiencies characterized by hypogammaglobulinemia and recurrent infections, which can lead to structural airway disease (AD) and interstitial lung disease (ILD). We investigated associations between serum IgA, oropharyngeal microbiota composition and severity of lung disease in these patients. In this cross-sectional multicentre study we analyzed oropharyngeal microbiota composition of 86 CVID patients, 12 XLA patients and 49 healthy controls (HC) using next-generation sequencing of the 16S rRNA gene. qPCR was used to estimate bacterial load. IgA was measured in serum. High resolution CT scans were scored for severity of AD and ILD. Oropharyngeal bacterial load was increased in CVID patients with low IgA ( p = 0.013) and XLA ( p = 0.029) compared to HC. IgA status was associated with distinct beta (between-sample) diversity ( p = 0.039), enrichment of ( Allo)prevotella , and more severe radiographic lung disease ( p = 0.003), independently of recent antibiotic use. AD scores were positively associated with Prevotella, Alloprevotella , and Selenomonas , and ILD scores with Streptococcus and negatively with Rothia . In clinically stable patients with CVID and XLA, radiographic lung disease was associated with IgA deficiency and expansion of distinct oropharyngeal bacterial taxa. Our findings highlight IgA as a potential driver of upper respiratory tract microbiota homeostasis., (Copyright © 2020 Berbers, Mohamed Hoesein, Ellerbroek, van Montfrans, Dalm, van Hagen, Paganelli, Viveen, Rogers, de Jong, Uh, Willems and Leavis.)
- Published
- 2020
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30. Choosing proper normalization is essential for discovery of sparse glycan biomarkers.
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Uh HW, Klarić L, Ugrina I, Lauc G, Smilde AK, and Houwing-Duistermaat JJ
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- Algorithms, Calibration, Mass Spectrometry, Biomarkers analysis, Glycomics methods
- Abstract
Rapid progress in high-throughput glycomics analysis enables the researchers to conduct large sample studies. Typically, the between-subject differences in total abundance of raw glycomics data are very large, and it is necessary to reduce the differences, making measurements comparable across samples. Essentially there are two ways to approach this issue: row-wise and column-wise normalization. In glycomics, the differences per subject are usually forced to be exactly zero, by scaling each sample having the sum of all glycan intensities equal to 100%. This total area (row-wise) normalization (TA) results in so-called compositional data, rendering many standard multivariate statistical methods inappropriate or inapplicable. Ignoring the compositional nature of the data, moreover, may lead to spurious results. Alternatively, a log-transformation to the raw data can be performed prior to column-wise normalization and implementing standard statistical tools. Until now, there is no clear consensus on the appropriate normalization method applied to glycomics data. Nor is systematic investigation of impact of TA on downstream analysis available to justify the choice of TA. Our motivation lies in efficient variable selection to identify glycan biomarkers with regard to accurate prediction as well as interpretability of the model chosen. Via extensive simulations we investigate how different normalization methods affect the performance of variable selection, and compare their performance. We also address the effect of various types of measurement error in glycans: additive, multiplicative and two-component error. We show that when sample-wise differences are not large row-wise normalization (like TA) can have deleterious effects on variable selection and prediction.
- Published
- 2020
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31. Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases.
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Klarić L, Tsepilov YA, Stanton CM, Mangino M, Sikka TT, Esko T, Pakhomov E, Salo P, Deelen J, McGurnaghan SJ, Keser T, Vučković F, Ugrina I, Krištić J, Gudelj I, Štambuk J, Plomp R, Pučić-Baković M, Pavić T, Vilaj M, Trbojević-Akmačić I, Drake C, Dobrinić P, Mlinarec J, Jelušić B, Richmond A, Timofeeva M, Grishchenko AK, Dmitrieva J, Bermingham ML, Sharapov SZ, Farrington SM, Theodoratou E, Uh HW, Beekman M, Slagboom EP, Louis E, Georges M, Wuhrer M, Colhoun HM, Dunlop MG, Perola M, Fischer K, Polasek O, Campbell H, Rudan I, Wilson JF, Zoldoš V, Vitart V, Spector T, Aulchenko YS, Lauc G, and Hayward C
- Subjects
- Algorithms, Alleles, Computational Biology methods, Genetic Loci, Genome-Wide Association Study, Glycosylation, Humans, Immunoglobulin G immunology, Linkage Disequilibrium, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Polysaccharides metabolism, Gene Expression Regulation, Immunoglobulin G metabolism, Inflammation genetics, Inflammation metabolism
- Abstract
Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation ( N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)
- Published
- 2020
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32. Roux-Y Gastric Bypass and Sleeve Gastrectomy directly change gut microbiota composition independent of surgery type.
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Paganelli FL, Luyer M, Hazelbag CM, Uh HW, Rogers MRC, Adriaans D, Berbers RM, Hendrickx APA, Viveen MC, Groot JA, Bonten MJM, Fluit AC, Willems RJL, and Leavis HL
- Subjects
- Adult, Bariatric Surgery, Female, Humans, Male, Middle Aged, Obesity microbiology, Weight Loss, Gastrectomy methods, Gastric Bypass methods, Gastrointestinal Microbiome, Obesity diet therapy, Obesity surgery
- Abstract
Bariatric surgery in morbid obesity, either through sleeve gastrectomy (SG) or Roux-Y gastric bypass (RYGB), leads to sustainable weight loss, improvement of metabolic disorders and changes in intestinal microbiota. Yet, the relationship between changes in gut microbiota, weight loss and surgical procedure remains incompletely understood. We determined temporal changes in microbiota composition in 45 obese patients undergoing crash diet followed by SG (n = 22) or RYGB (n = 23). Intestinal microbiota composition was determined before intervention (baseline, S1), 2 weeks after crash diet (S2), and 1 week (S3), 3 months (S4) and 6 months (S5) after surgery. Relative to S1, the microbial diversity index declined at S2 and S3 (p < 0.05), and gradually returned to baseline levels at S5. Rikenellaceae relative abundance increased and Ruminococcaceae and Streptococcaceae abundance decreased at S2 (p < 0.05). At S3, Bifidobacteriaceae abundance decreased, whereas those of Streptococcaceae and Enterobacteriaceae increased (p < 0.05). Increased weight loss between S3-S5 was not associated with major changes in microbiota composition. No significant differences appeared between both surgical procedures. In conclusion, undergoing a crash diet and bariatric surgery were associated with an immediate but temporary decline in microbial diversity, with immediate and permanent changes in microbiota composition, independent of surgery type.
- Published
- 2019
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33. The mixed model for the analysis of a repeated-measurement multivariate count data.
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Martin I, Uh HW, Supali T, Mitreva M, and Houwing-Duistermaat JJ
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- Computer Simulation, Humans, Microbiota, Models, Statistical, Multivariate Analysis, Regression Analysis
- Abstract
Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less interest. Here, we focus on the correlation between time points. In addition, the effects of covariates on the multivariate counts distribution need to be assessed. To fulfill these requirements, a regression model based on the Dirichlet-multinomial distribution for association between covariates and the categorical counts is extended by using random effects to deal with the additional clustering. This model is the Dirichlet-multinomial mixed regression model. Alternatively, a negative binomial regression mixed model can be deployed where the corresponding likelihood is conditioned on the total count. It appears that these two approaches are equivalent when the total count is fixed and independent of the random effects. We consider both subject-specific and categorical-specific random effects. However, the latter has a larger computational burden when the number of categories increases. Our work is motivated by microbiome data sets obtained by sequencing of the amplicon of the bacterial 16S rRNA gene. These data have a compositional structure and are typically overdispersed. The microbiome data set is from an epidemiological study carried out in a helminth-endemic area in Indonesia. The conclusions are as follows: time has no statistically significant effect on microbiome composition, the correlation between subjects is statistically significant, and treatment has a significant effect on the microbiome composition only in infected subjects who remained infected., (© 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)
- Published
- 2019
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34. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.
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Chauhan G, Adams HHH, Satizabal CL, Bis JC, Teumer A, Sargurupremraj M, Hofer E, Trompet S, Hilal S, Smith AV, Jian X, Malik R, Traylor M, Pulit SL, Amouyel P, Mazoyer B, Zhu YC, Kaffashian S, Schilling S, Beecham GW, Montine TJ, Schellenberg GD, Kjartansson O, Guðnason V, Knopman DS, Griswold ME, Windham BG, Gottesman RF, Mosley TH, Schmidt R, Saba Y, Schmidt H, Takeuchi F, Yamaguchi S, Nabika T, Kato N, Rajan KB, Aggarwal NT, De Jager PL, Evans DA, Psaty BM, Rotter JI, Rice K, Lopez OL, Liao J, Chen C, Cheng CY, Wong TY, Ikram MK, van der Lee SJ, Amin N, Chouraki V, DeStefano AL, Aparicio HJ, Romero JR, Maillard P, DeCarli C, Wardlaw JM, Hernández MDCV, Luciano M, Liewald D, Deary IJ, Starr JM, Bastin ME, Muñoz Maniega S, Slagboom PE, Beekman M, Deelen J, Uh HW, Lemmens R, Brodaty H, Wright MJ, Ames D, Boncoraglio GB, Hopewell JC, Beecham AH, Blanton SH, Wright CB, Sacco RL, Wen W, Thalamuthu A, Armstrong NJ, Chong E, Schofield PR, Kwok JB, van der Grond J, Stott DJ, Ford I, Jukema JW, Vernooij MW, Hofman A, Uitterlinden AG, van der Lugt A, Wittfeld K, Grabe HJ, Hosten N, von Sarnowski B, Völker U, Levi C, Jimenez-Conde J, Sharma P, Sudlow CLM, Rosand J, Woo D, Cole JW, Meschia JF, Slowik A, Thijs V, Lindgren A, Melander O, Grewal RP, Rundek T, Rexrode K, Rothwell PM, Arnett DK, Jern C, Johnson JA, Benavente OR, Wasssertheil-Smoller S, Lee JM, Wong Q, Mitchell BD, Rich SS, McArdle PF, Geerlings MI, van der Graaf Y, de Bakker PIW, Asselbergs FW, Srikanth V, Thomson R, McWhirter R, Moran C, Callisaya M, Phan T, Rutten-Jacobs LCA, Bevan S, Tzourio C, Mather KA, Sachdev PS, van Duijn CM, Worrall BB, Dichgans M, Kittner SJ, Markus HS, Ikram MA, Fornage M, Launer LJ, Seshadri S, Longstreth WT Jr, and Debette S
- Abstract
Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts., Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI., Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10
-8 ; and LINC00539/ZDHHC20, p = 5.82 × 10-9 . Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits ( p value for BI, p[BI] = 9.38 × 10-25 ; p[SSBI] = 5.23 × 10-14 for hypertension), smoking ( p[BI] = 4.4 × 10-10 ; p[SSBI] = 1.2 × 10-4 ), diabetes ( p[BI] = 1.7 × 10-8 ; p[SSBI] = 2.8 × 10-3 ), previous cardiovascular disease ( p[BI] = 1.0 × 10-18 ; p[SSBI] = 2.3 × 10-7 ), stroke ( p[BI] = 3.9 × 10-69 ; p[SSBI] = 3.2 × 10-24 ), and MRI-defined white matter hyperintensity burden ( p[BI] = 1.43 × 10-157 ; p[SSBI] = 3.16 × 10-106 ), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI ( p ≤ 0.0022), without indication of directional pleiotropy., Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI., (Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)- Published
- 2019
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35. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders.
- Author
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Ligthart S, Vaez A, Võsa U, Stathopoulou MG, de Vries PS, Prins BP, Van der Most PJ, Tanaka T, Naderi E, Rose LM, Wu Y, Karlsson R, Barbalic M, Lin H, Pool R, Zhu G, Macé A, Sidore C, Trompet S, Mangino M, Sabater-Lleal M, Kemp JP, Abbasi A, Kacprowski T, Verweij N, Smith AV, Huang T, Marzi C, Feitosa MF, Lohman KK, Kleber ME, Milaneschi Y, Mueller C, Huq M, Vlachopoulou E, Lyytikäinen LP, Oldmeadow C, Deelen J, Perola M, Zhao JH, Feenstra B, Amini M, Lahti J, Schraut KE, Fornage M, Suktitipat B, Chen WM, Li X, Nutile T, Malerba G, Luan J, Bak T, Schork N, Del Greco M F, Thiering E, Mahajan A, Marioni RE, Mihailov E, Eriksson J, Ozel AB, Zhang W, Nethander M, Cheng YC, Aslibekyan S, Ang W, Gandin I, Yengo L, Portas L, Kooperberg C, Hofer E, Rajan KB, Schurmann C, den Hollander W, Ahluwalia TS, Zhao J, Draisma HHM, Ford I, Timpson N, Teumer A, Huang H, Wahl S, Liu Y, Huang J, Uh HW, Geller F, Joshi PK, Yanek LR, Trabetti E, Lehne B, Vozzi D, Verbanck M, Biino G, Saba Y, Meulenbelt I, O'Connell JR, Laakso M, Giulianini F, Magnusson PKE, Ballantyne CM, Hottenga JJ, Montgomery GW, Rivadineira F, Rueedi R, Steri M, Herzig KH, Stott DJ, Menni C, Frånberg M, St Pourcain B, Felix SB, Pers TH, Bakker SJL, Kraft P, Peters A, Vaidya D, Delgado G, Smit JH, Großmann V, Sinisalo J, Seppälä I, Williams SR, Holliday EG, Moed M, Langenberg C, Räikkönen K, Ding J, Campbell H, Sale MM, Chen YI, James AL, Ruggiero D, Soranzo N, Hartman CA, Smith EN, Berenson GS, Fuchsberger C, Hernandez D, Tiesler CMT, Giedraitis V, Liewald D, Fischer K, Mellström D, Larsson A, Wang Y, Scott WR, Lorentzon M, Beilby J, Ryan KA, Pennell CE, Vuckovic D, Balkau B, Concas MP, Schmidt R, Mendes de Leon CF, Bottinger EP, Kloppenburg M, Paternoster L, Boehnke M, Musk AW, Willemsen G, Evans DM, Madden PAF, Kähönen M, Kutalik Z, Zoledziewska M, Karhunen V, Kritchevsky SB, Sattar N, Lachance G, Clarke R, Harris TB, Raitakari OT, Attia JR, van Heemst D, Kajantie E, Sorice R, Gambaro G, Scott RA, Hicks AA, Ferrucci L, Standl M, Lindgren CM, Starr JM, Karlsson M, Lind L, Li JZ, Chambers JC, Mori TA, de Geus EJCN, Heath AC, Martin NG, Auvinen J, Buckley BM, de Craen AJM, Waldenberger M, Strauch K, Meitinger T, Scott RJ, McEvoy M, Beekman M, Bombieri C, Ridker PM, Mohlke KL, Pedersen NL, Morrison AC, Boomsma DI, Whitfield JB, Strachan DP, Hofman A, Vollenweider P, Cucca F, Jarvelin MR, Jukema JW, Spector TD, Hamsten A, Zeller T, Uitterlinden AG, Nauck M, Gudnason V, Qi L, Grallert H, Borecki IB, Rotter JI, März W, Wild PS, Lokki ML, Boyle M, Salomaa V, Melbye M, Eriksson JG, Wilson JF, Penninx BWJH, Becker DM, Worrall BB, Gibson G, Krauss RM, Ciullo M, Zaza G, Wareham NJ, Oldehinkel AJ, Palmer LJ, Murray SS, Pramstaller PP, Bandinelli S, Heinrich J, Ingelsson E, Deary IJ, Mägi R, Vandenput L, van der Harst P, Desch KC, Kooner JS, Ohlsson C, Hayward C, Lehtimäki T, Shuldiner AR, Arnett DK, Beilin LJ, Robino A, Froguel P, Pirastu M, Jess T, Koenig W, Loos RJF, Evans DA, Schmidt H, Smith GD, Slagboom PE, Eiriksdottir G, Morris AP, Psaty BM, Tracy RP, Nolte IM, Boerwinkle E, Visvikis-Siest S, Reiner AP, Gross M, Bis JC, Franke L, Franco OH, Benjamin EJ, Chasman DI, Dupuis J, Snieder H, Dehghan A, and Alizadeh BZ
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, Biomarkers metabolism, Bipolar Disorder genetics, Bipolar Disorder metabolism, Body Mass Index, C-Reactive Protein genetics, Child, Female, Genome-Wide Association Study methods, Humans, Inflammation metabolism, Liver metabolism, Liver pathology, Male, Mendelian Randomization Analysis methods, Middle Aged, Schizophrenia genetics, Schizophrenia metabolism, Young Adult, Genetic Loci genetics, Inflammation genetics, Metabolic Networks and Pathways genetics
- Abstract
C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10
-8 ). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences., (Copyright © 2018 American Society of Human Genetics. All rights reserved.)- Published
- 2018
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36. Integrating omics datasets with the OmicsPLS package.
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Bouhaddani SE, Uh HW, Jongbloed G, Hayward C, Klarić L, Kiełbasa SM, and Houwing-Duistermaat J
- Subjects
- Humans, Least-Squares Analysis, Software, Genomics methods, Metabolomics methods
- Abstract
Background: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS., Results: We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data., Conclusions: We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages("OmicsPLS").
- Published
- 2018
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37. Facial Wrinkles in Europeans: A Genome-Wide Association Study.
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Hamer MA, Pardo LM, Jacobs LC, Deelen J, Uitterlinden AG, Slagboom E, van Heemst D, Uh HW, Beekman M, Kayser M, Liu F, Gunn DA, and Nijsten T
- Subjects
- Cohort Studies, Face, Gene Frequency genetics, Genome-Wide Association Study, Humans, Linkage Disequilibrium genetics, Polymorphism, Single Nucleotide genetics, Chromosomes, Human, Pair 5 genetics, Skin Aging genetics, White People genetics
- Published
- 2018
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38. Subclass-specific IgG glycosylation is associated with markers of inflammation and metabolic health.
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Plomp R, Ruhaak LR, Uh HW, Reiding KR, Selman M, Houwing-Duistermaat JJ, Slagboom PE, Beekman M, and Wuhrer M
- Subjects
- Adult, Aged, Biomarkers analysis, Biomarkers metabolism, Cohort Studies, Female, Glycosylation, Humans, Immunoglobulin Fc Fragments metabolism, Immunoglobulin G metabolism, Inflammation metabolism, Male, Metabolomics methods, Middle Aged, Health Status, Immunoglobulin Fc Fragments blood, Immunoglobulin G blood, Inflammation blood
- Abstract
This study indicates that glycosylation of immunoglobulin G, the most abundant antibody in human blood, may convey useful information with regard to inflammation and metabolic health. IgG occurs in the form of different subclasses, of which the effector functions show significant variation. Our method provides subclass-specific IgG glycosylation profiling, while previous large-scale studies neglected to measure IgG2-specific glycosylation. We analysed the plasma Fc glycosylation profiles of IgG1, IgG2 and IgG4 in a cohort of 1826 individuals by liquid chromatography-mass spectrometry. For all subclasses, a low level of galactosylation and sialylation and a high degree of core fucosylation associated with poor metabolic health, i.e. increased inflammation as assessed by C-reactive protein, low serum high-density lipoprotein cholesterol and high triglycerides, which are all known to indicate increased risk of cardiovascular disease. IgG2 consistently showed weaker associations of its galactosylation and sialylation with the metabolic markers, compared to IgG1 and IgG4, while the direction of the associations were overall similar for the different IgG subclasses. These findings demonstrate the potential of IgG glycosylation as a biomarker for inflammation and metabolic health, and further research is required to determine the additive value of IgG glycosylation on top of biomarkers which are currently used.
- Published
- 2017
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39. Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health.
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Reiding KR, Ruhaak LR, Uh HW, El Bouhaddani S, van den Akker EB, Plomp R, McDonnell LA, Houwing-Duistermaat JJ, Slagboom PE, Beekman M, and Wuhrer M
- Subjects
- Aged, Body Mass Index, C-Reactive Protein metabolism, Cyclotrons, Fourier Analysis, Glycosylation, Humans, Male, Middle Aged, Biomarkers blood, Inflammation metabolism, Proteomics instrumentation, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization instrumentation
- Abstract
Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species.Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from Hex
4 HexNAc2 to Hex7 HexNAc6 dHex1 Neu5Ac4 , as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health., (© 2017 by The American Society for Biochemistry and Molecular Biology, Inc.)- Published
- 2017
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40. Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data.
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Bartzis G, Deelen J, Maia J, Ligterink W, Hilhorst HWM, Houwing-Duistermaat JJ, van Eeuwijk F, and Uh HW
- Abstract
Introduction: In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is inferring biochemical interactions from different molecular entities such as metabolites. In this area, the metabolome possesses a unique place for reflecting "true exposure" by being sensitive to variation coming from genetics, time, and environmental stimuli. While influenced by many different reactions, often the research interest needs to be focused on variation coming from a certain source, i.e. a certain covariable [Formula: see text]., Objective: Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to [Formula: see text]. Metabolite values are based on information coming from individuals' [Formula: see text] status which might interact with other covariables., Methods: Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to [Formula: see text] is further used. For two datasets, two different network estimation methods are considered. The first is weighted gene co-expression network analysis based on correlation coefficients. The second method is graphical LASSO based on partial correlations., Results: We observed that when using the parts related to the specific covariable of interest, resulting estimated networks display higher interconnectedness. Additionally, several groups of biologically associated metabolites (very large density lipoproteins, lipoproteins, etc.) were identified in the human data example., Conclusions: This work demonstrates how information on the study design can be incorporated to estimate metabolite networks. As a result, sets of interconnected metabolites can be clustered together with respect to their relation to a covariable of interest.
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- 2017
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41. Classification for Longevity Potential: The Use of Novel Biomarkers.
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Beekman M, Uh HW, van Heemst D, Wuhrer M, Ruhaak LR, Gonzalez-Covarrubias V, Hankemeier T, Houwing-Duistermaat JJ, and Slagboom PE
- Abstract
Background: In older people, chronological age may not be the best predictor of residual lifespan and mortality, because with age the heterogeneity in health is increasing. Biomarkers for biological age and residual lifespan are being developed to predict disease and mortality better at an individual level than chronological age. In the current paper, we aim to classify a group of older people into those with longevity potential or controls., Methods: In the Leiden Longevity Study participated 1671 offspring of nonagenarian siblings, as the group with longevity potential, and 744 similarly aged controls. Using known risk factors for cardiovascular disease, previously reported markers for human longevity and other physiological measures as predictors, classification models for longevity potential were constructed with multiple logistic regression of the offspring-control status., Results: The Framingham Risk Score (FRS) is predictive for longevity potential [area under the receiver operating characteristic curve (AUC) = 64.7]. Physiological parameters involved in immune responses and glucose, lipid and energy metabolism further improve the prediction performance for longevity potential (AUCmale = 71.4, AUCfemale = 68.7)., Conclusion: Using the FRS, the classification of older people in groups with longevity potential and controls is moderate, but can be improved to a reasonably good classification in combination with markers of immune response, glucose, lipid, and energy metabolism. We show that individual classification of older people for longevity potential may be feasible using biomarkers from a wide variety of different biological processes.
- Published
- 2016
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42. Gene coexpression network analysis for family studies based on a meta-analytic approach.
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Tissier R, Uh HW, van den Akker E, Balliu B, Tsonaka S, and Houwing-Duistermaat J
- Abstract
For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches-ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model-and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis.
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- 2016
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43. Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits.
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Teumer A, Qi Q, Nethander M, Aschard H, Bandinelli S, Beekman M, Berndt SI, Bidlingmaier M, Broer L, Cappola A, Ceda GP, Chanock S, Chen MH, Chen TC, Chen YD, Chung J, Del Greco Miglianico F, Eriksson J, Ferrucci L, Friedrich N, Gnewuch C, Goodarzi MO, Grarup N, Guo T, Hammer E, Hayes RB, Hicks AA, Hofman A, Houwing-Duistermaat JJ, Hu F, Hunter DJ, Husemoen LL, Isaacs A, Jacobs KB, Janssen JA, Jansson JO, Jehmlich N, Johnson S, Juul A, Karlsson M, Kilpelainen TO, Kovacs P, Kraft P, Li C, Linneberg A, Liu Y, Loos RJ, Lorentzon M, Lu Y, Maggio M, Magi R, Meigs J, Mellström D, Nauck M, Newman AB, Pollak MN, Pramstaller PP, Prokopenko I, Psaty BM, Reincke M, Rimm EB, Rotter JI, Saint Pierre A, Schurmann C, Seshadri S, Sjögren K, Slagboom PE, Strickler HD, Stumvoll M, Suh Y, Sun Q, Zhang C, Svensson J, Tanaka T, Tare A, Tönjes A, Uh HW, van Duijn CM, van Heemst D, Vandenput L, Vasan RS, Völker U, Willems SM, Ohlsson C, Wallaschofski H, and Kaplan RC
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- Adult, Aging blood, Female, Gene Expression Regulation, Humans, Insulin-Like Growth Factor Binding Protein 3 blood, Male, Metabolome genetics, Quantitative Trait Loci genetics, Regulatory Sequences, Nucleic Acid genetics, Aging genetics, Genome-Wide Association Study, Insulin-Like Growth Factor Binding Protein 3 genetics, Insulin-Like Growth Factor I genetics, Quantitative Trait, Heritable
- Abstract
The growth hormone/insulin-like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF-related proteins including IGF-I and IGF-binding protein-3 (IGFBP-3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Through genomewide association study of up to 30 884 adults of European ancestry from 21 studies, we confirmed and extended the list of previously identified loci associated with circulating IGF-I and IGFBP-3 concentrations (IGF1, IGFBP3, GCKR, TNS3, GHSR, FOXO3, ASXL2, NUBP2/IGFALS, SORCS2, and CELSR2). Significant sex interactions, which were characterized by different genotype-phenotype associations between men and women, were found only for associations of IGFBP-3 concentrations with SNPs at the loci IGFBP3 and SORCS2. Analyses of SNPs, gene expression, and protein levels suggested that interplay between IGFBP3 and genes within the NUBP2 locus (IGFALS and HAGH) may affect circulating IGF-I and IGFBP-3 concentrations. The IGF-I-decreasing allele of SNP rs934073, which is an eQTL of ASXL2, was associated with lower adiposity and higher likelihood of survival beyond 90 years. The known longevity-associated variant rs2153960 (FOXO3) was observed to be a genomewide significant SNP for IGF-I concentrations. Bioinformatics analysis suggested enrichment of putative regulatory elements among these IGF-I- and IGFBP-3-associated loci, particularly of rs646776 at CELSR2. In conclusion, this study identified several loci associated with circulating IGF-I and IGFBP-3 concentrations and provides clues to the potential role of the IGF axis in mediating effects of known (FOXO3) and novel (ASXL2) longevity-associated loci., (© 2016 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.)
- Published
- 2016
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44. The MC1R Gene and Youthful Looks.
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Liu F, Hamer MA, Deelen J, Lall JS, Jacobs L, van Heemst D, Murray PG, Wollstein A, de Craen AJ, Uh HW, Zeng C, Hofman A, Uitterlinden AG, Houwing-Duistermaat JJ, Pardo LM, Beekman M, Slagboom PE, Nijsten T, Kayser M, and Gunn DA
- Subjects
- Aged, Aging genetics, Cohort Studies, Female, Genome-Wide Association Study, Haplotypes, Humans, Middle Aged, Polymorphism, Single Nucleotide, Aging physiology, Receptor, Melanocortin, Type 1 genetics, Skin Aging genetics, Skin Aging physiology
- Abstract
Looking young for one's age has been a desire since time immemorial. This desire is attributable to the belief that appearance reflects health and fecundity. Indeed, perceived age predicts survival [1] and associates with molecular markers of aging such as telomere length [2]. Understanding the underlying molecular biology of perceived age is vital for identifying new aging therapies among other purposes, but studies are lacking thus far. As a first attempt, we performed genome-wide association studies (GWASs) of perceived facial age and wrinkling estimated from digital facial images by analyzing over eight million SNPs in 2,693 elderly Dutch Europeans from the Rotterdam Study. The strongest genetic associations with perceived facial age were found for multiple SNPs in the MC1R gene (p < 1 × 10(-7)). This effect was enhanced for a compound heterozygosity marker constructed from four pre-selected functional MC1R SNPs (p = 2.69 × 10(-12)), which was replicated in 599 Dutch Europeans from the Leiden Longevity Study (p = 0.042) and in 1,173 Europeans of the TwinsUK Study (p = 3 × 10(-3)). Individuals carrying the homozygote MC1R risk haplotype looked on average up to 2 years older than non-carriers. This association was independent of age, sex, skin color, and sun damage (wrinkling, pigmented spots) and persisted through different sun-exposure levels. Hence, a role for MC1R in youthful looks independent of its known melanin synthesis function is suggested. Our study uncovers the first genetic evidence explaining why some people look older for their age and provides new leads for further investigating the biological basis of how old or young people look., (Copyright © 2016 Elsevier Ltd. All rights reserved.)
- Published
- 2016
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45. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels.
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Kilpeläinen TO, Carli JF, Skowronski AA, Sun Q, Kriebel J, Feitosa MF, Hedman ÅK, Drong AW, Hayes JE, Zhao J, Pers TH, Schick U, Grarup N, Kutalik Z, Trompet S, Mangino M, Kristiansson K, Beekman M, Lyytikäinen LP, Eriksson J, Henneman P, Lahti J, Tanaka T, Luan J, Del Greco M F, Pasko D, Renström F, Willems SM, Mahajan A, Rose LM, Guo X, Liu Y, Kleber ME, Pérusse L, Gaunt T, Ahluwalia TS, Ju Sung Y, Ramos YF, Amin N, Amuzu A, Barroso I, Bellis C, Blangero J, Buckley BM, Böhringer S, I Chen YD, de Craen AJ, Crosslin DR, Dale CE, Dastani Z, Day FR, Deelen J, Delgado GE, Demirkan A, Finucane FM, Ford I, Garcia ME, Gieger C, Gustafsson S, Hallmans G, Hankinson SE, Havulinna AS, Herder C, Hernandez D, Hicks AA, Hunter DJ, Illig T, Ingelsson E, Ioan-Facsinay A, Jansson JO, Jenny NS, Jørgensen ME, Jørgensen T, Karlsson M, Koenig W, Kraft P, Kwekkeboom J, Laatikainen T, Ladwig KH, LeDuc CA, Lowe G, Lu Y, Marques-Vidal P, Meisinger C, Menni C, Morris AP, Myers RH, Männistö S, Nalls MA, Paternoster L, Peters A, Pradhan AD, Rankinen T, Rasmussen-Torvik LJ, Rathmann W, Rice TK, Brent Richards J, Ridker PM, Sattar N, Savage DB, Söderberg S, Timpson NJ, Vandenput L, van Heemst D, Uh HW, Vohl MC, Walker M, Wichmann HE, Widén E, Wood AR, Yao J, Zeller T, Zhang Y, Meulenbelt I, Kloppenburg M, Astrup A, Sørensen TI, Sarzynski MA, Rao DC, Jousilahti P, Vartiainen E, Hofman A, Rivadeneira F, Uitterlinden AG, Kajantie E, Osmond C, Palotie A, Eriksson JG, Heliövaara M, Knekt PB, Koskinen S, Jula A, Perola M, Huupponen RK, Viikari JS, Kähönen M, Lehtimäki T, Raitakari OT, Mellström D, Lorentzon M, Casas JP, Bandinelli S, März W, Isaacs A, van Dijk KW, van Duijn CM, Harris TB, Bouchard C, Allison MA, Chasman DI, Ohlsson C, Lind L, Scott RA, Langenberg C, Wareham NJ, Ferrucci L, Frayling TM, Pramstaller PP, Borecki IB, Waterworth DM, Bergmann S, Waeber G, Vollenweider P, Vestergaard H, Hansen T, Pedersen O, Hu FB, Eline Slagboom P, Grallert H, Spector TD, Jukema JW, Klein RJ, Schadt EE, Franks PW, Lindgren CM, Leibel RL, and Loos RJ
- Subjects
- Adipose Tissue metabolism, Animals, Gene Knockdown Techniques, Leptin genetics, Male, Mice, RNA, Messenger genetics, RNA, Messenger metabolism, Tissue Culture Techniques, Gene Expression Regulation physiology, Genome-Wide Association Study, Leptin blood, Leptin metabolism
- Abstract
Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P<10(-6) in 19,979 additional individuals. We identify five loci robustly associated (P<5 × 10(-8)) with leptin levels in/near LEP, SLC32A1, GCKR, CCNL1 and FTO. Although the association of the FTO obesity locus with leptin levels is abolished by adjustment for BMI, associations of the four other loci are independent of adiposity. The GCKR locus was found associated with multiple metabolic traits in previous GWAS and the CCNL1 locus with birth weight. Knockdown experiments in mouse adipose tissue explants show convincing evidence for adipogenin, a regulator of adipocyte differentiation, as the novel causal gene in the SLC32A1 locus influencing leptin levels. Our findings provide novel insights into the regulation of leptin production by adipose tissue and open new avenues for examining the influence of variation in leptin levels on adiposity and metabolic health.
- Published
- 2016
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46. Evaluation of O2PLS in Omics data integration.
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Bouhaddani SE, Houwing-Duistermaat J, Salo P, Perola M, Jongbloed G, and Uh HW
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- Adult, Aged, Diet, Female, Humans, Least-Squares Analysis, Male, Middle Aged, Obesity genetics, Obesity metabolism, Systems Biology methods, Genomics methods, Metabolomics methods, Statistics as Topic methods, Transcriptome
- Abstract
Background: Rapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation., Results: A simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret., Conclusions: Simulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.
- Published
- 2016
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47. A Genome-Wide Association Study Identifies the Skin Color Genes IRF4, MC1R, ASIP, and BNC2 Influencing Facial Pigmented Spots.
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Jacobs LC, Hamer MA, Gunn DA, Deelen J, Lall JS, van Heemst D, Uh HW, Hofman A, Uitterlinden AG, Griffiths CEM, Beekman M, Slagboom PE, Kayser M, Liu F, and Nijsten T
- Subjects
- Cohort Studies, Facial Dermatoses genetics, Female, Genome-Wide Association Study, Humans, Male, Melanosis genetics, Middle Aged, Netherlands, Nevus, Pigmented genetics, Phenotype, Photography, Prospective Studies, Sensitivity and Specificity, Skin Neoplasms genetics, Agouti Signaling Protein genetics, DNA-Binding Proteins genetics, Genetic Predisposition to Disease epidemiology, Interferon Regulatory Factors genetics, Receptor, Melanocortin, Type 1 genetics, Skin Pigmentation genetics
- Abstract
Facial pigmented spots are a common skin aging feature, but genetic predisposition has yet to be thoroughly investigated. We conducted a genome-wide association study for pigmented spots in 2,844 Dutch Europeans from the Rotterdam Study (mean age: 66.9±8.0 years; 47% male). Using semi-automated image analysis of high-resolution digital facial photographs, facial pigmented spots were quantified as the percentage of affected skin area (mean women: 2.0% ±0.9, men: 0.9% ±0.6). We identified genome-wide significant association with pigmented spots at three genetic loci: IRF4 (rs12203592, P=1.8 × 10(-27)), MC1R (compound heterozygosity score, P=2.3 × 10(-24)), and RALY/ASIP (rs6059655, P=1.9 × 10(-9)). In addition, after adjustment for the other three top-associated loci the BNC2 locus demonstrated significant association (rs62543565, P=2.3 × 10(-8)). The association signals observed at all four loci were successfully replicated (P<0.05) in an independent Dutch cohort (Leiden Longevity Study n=599). Although the four genes have previously been associated with skin color variation and skin cancer risk, all association signals remained highly significant (P<2 × 10(-8)) when conditioning the association analyses on skin color. We conclude that genetic variations in IRF4, MC1R, RALY/ASIP, and BNC2 contribute to the acquired amount of facial pigmented spots during aging, through pathways independent of the basal melanin production.
- Published
- 2015
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48. Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI.
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Verhaaren BF, Debette S, Bis JC, Smith JA, Ikram MK, Adams HH, Beecham AH, Rajan KB, Lopez LM, Barral S, van Buchem MA, van der Grond J, Smith AV, Hegenscheid K, Aggarwal NT, de Andrade M, Atkinson EJ, Beekman M, Beiser AS, Blanton SH, Boerwinkle E, Brickman AM, Bryan RN, Chauhan G, Chen CP, Chouraki V, de Craen AJ, Crivello F, Deary IJ, Deelen J, De Jager PL, Dufouil C, Elkind MS, Evans DA, Freudenberger P, Gottesman RF, Guðnason V, Habes M, Heckbert SR, Heiss G, Hilal S, Hofer E, Hofman A, Ibrahim-Verbaas CA, Knopman DS, Lewis CE, Liao J, Liewald DC, Luciano M, van der Lugt A, Martinez OO, Mayeux R, Mazoyer B, Nalls M, Nauck M, Niessen WJ, Oostra BA, Psaty BM, Rice KM, Rotter JI, von Sarnowski B, Schmidt H, Schreiner PJ, Schuur M, Sidney SS, Sigurdsson S, Slagboom PE, Stott DJ, van Swieten JC, Teumer A, Töglhofer AM, Traylor M, Trompet S, Turner ST, Tzourio C, Uh HW, Uitterlinden AG, Vernooij MW, Wang JJ, Wong TY, Wardlaw JM, Windham BG, Wittfeld K, Wolf C, Wright CB, Yang Q, Zhao W, Zijdenbos A, Jukema JW, Sacco RL, Kardia SL, Amouyel P, Mosley TH, Longstreth WT Jr, DeCarli CC, van Duijn CM, Schmidt R, Launer LJ, Grabe HJ, Seshadri SS, Ikram MA, and Fornage M
- Subjects
- Aged, Aged, 80 and over, Chromosomes, Human genetics, Female, Humans, Male, Meta-Analysis as Topic, Middle Aged, Genetic Loci, Genome-Wide Association Study, Models, Genetic, Racial Groups, Stroke ethnology, Stroke genetics, Stroke pathology, White Matter
- Abstract
Background: The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies., Methods and Results: We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10(-19)) and identified novel loci on chr10q24 (P=1.6×10(-9)) and chr2p21 (P=4.4×10(-8)). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10(-8)) and chr2p16 (P=1.5×10(-8)). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16)., Conclusions: We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms., (© 2015 American Heart Association, Inc.)
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- 2015
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49. Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.
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van Leeuwen EM, Karssen LC, Deelen J, Isaacs A, Medina-Gomez C, Mbarek H, Kanterakis A, Trompet S, Postmus I, Verweij N, van Enckevort DJ, Huffman JE, White CC, Feitosa MF, Bartz TM, Manichaikul A, Joshi PK, Peloso GM, Deelen P, van Dijk F, Willemsen G, de Geus EJ, Milaneschi Y, Penninx BW, Francioli LC, Menelaou A, Pulit SL, Rivadeneira F, Hofman A, Oostra BA, Franco OH, Mateo Leach I, Beekman M, de Craen AJ, Uh HW, Trochet H, Hocking LJ, Porteous DJ, Sattar N, Packard CJ, Buckley BM, Brody JA, Bis JC, Rotter JI, Mychaleckyj JC, Campbell H, Duan Q, Lange LA, Wilson JF, Hayward C, Polasek O, Vitart V, Rudan I, Wright AF, Rich SS, Psaty BM, Borecki IB, Kearney PM, Stott DJ, Adrienne Cupples L, Jukema JW, van der Harst P, Sijbrands EJ, Hottenga JJ, Uitterlinden AG, Swertz MA, van Ommen GJ, de Bakker PI, Eline Slagboom P, Boomsma DI, Wijmenga C, and van Duijn CM
- Subjects
- Gene Frequency, Genetic Association Studies, Humans, Netherlands, ATP-Binding Cassette Transporters genetics, Cholesterol blood, Mutation, Missense genetics
- Abstract
Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (~35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value <6.61 × 10(-4)), including a rare missense variant in ABCA6 (rs77542162, p.Cys1359Arg, frequency 0.034), which is predicted to be deleterious. The frequency of this ABCA6 variant is 3.65-fold increased in the Dutch and its effect (βLDL-C=0.135, βTC=0.140) is estimated to be very similar to those observed for single variants in well-known lipid genes, such as LDLR.
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- 2015
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50. Genetic studies of body mass index yield new insights for obesity biology.
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Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T, Fall T, Ferreira T, Gustafsson S, Kutalik Z, Luan J, Mägi R, Randall JC, Winkler TW, Wood AR, Workalemahu T, Faul JD, Smith JA, Zhao JH, Zhao W, Chen J, Fehrmann R, Hedman ÅK, Karjalainen J, Schmidt EM, Absher D, Amin N, Anderson D, Beekman M, Bolton JL, Bragg-Gresham JL, Buyske S, Demirkan A, Deng G, Ehret GB, Feenstra B, Feitosa MF, Fischer K, Goel A, Gong J, Jackson AU, Kanoni S, Kleber ME, Kristiansson K, Lim U, Lotay V, Mangino M, Leach IM, Medina-Gomez C, Medland SE, Nalls MA, Palmer CD, Pasko D, Pechlivanis S, Peters MJ, Prokopenko I, Shungin D, Stančáková A, Strawbridge RJ, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van Setten J, Van Vliet-Ostaptchouk JV, Wang Z, Yengo L, Zhang W, Isaacs A, Albrecht E, Ärnlöv J, Arscott GM, Attwood AP, Bandinelli S, Barrett A, Bas IN, Bellis C, Bennett AJ, Berne C, Blagieva R, Blüher M, Böhringer S, Bonnycastle LL, Böttcher Y, Boyd HA, Bruinenberg M, Caspersen IH, Chen YI, Clarke R, Daw EW, de Craen AJM, Delgado G, Dimitriou M, Doney ASF, Eklund N, Estrada K, Eury E, Folkersen L, Fraser RM, Garcia ME, Geller F, Giedraitis V, Gigante B, Go AS, Golay A, Goodall AH, Gordon SD, Gorski M, Grabe HJ, Grallert H, Grammer TB, Gräßler J, Grönberg H, Groves CJ, Gusto G, Haessler J, Hall P, Haller T, Hallmans G, Hartman CA, Hassinen M, Hayward C, Heard-Costa NL, Helmer Q, Hengstenberg C, Holmen O, Hottenga JJ, James AL, Jeff JM, Johansson Å, Jolley J, Juliusdottir T, Kinnunen L, Koenig W, Koskenvuo M, Kratzer W, Laitinen J, Lamina C, Leander K, Lee NR, Lichtner P, Lind L, Lindström J, Lo KS, Lobbens S, Lorbeer R, Lu Y, Mach F, Magnusson PKE, Mahajan A, McArdle WL, McLachlan S, Menni C, Merger S, Mihailov E, Milani L, Moayyeri A, Monda KL, Morken MA, Mulas A, Müller G, Müller-Nurasyid M, Musk AW, Nagaraja R, Nöthen MM, Nolte IM, Pilz S, Rayner NW, Renstrom F, Rettig R, Ried JS, Ripke S, 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- Subjects
- Adipogenesis genetics, Adiposity genetics, Age Factors, Energy Metabolism genetics, Europe ethnology, Female, Genetic Predisposition to Disease genetics, Glutamic Acid metabolism, Humans, Insulin metabolism, Insulin Secretion, Male, Polymorphism, Single Nucleotide genetics, Quantitative Trait Loci genetics, Racial Groups genetics, Synapses metabolism, Body Mass Index, Genome-Wide Association Study, Obesity genetics, Obesity metabolism
- Abstract
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 × 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 ∼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
- Full Text
- View/download PDF
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