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23 results on '"Uh, H.‐W."'

1. Multi-omics integration identifies key upstream regulators of pathomechanisms in hypertrophic cardiomyopathy due to truncating MYBPC3 mutations

Author

Pei, J., Schuldt, M., Nagyova, E., Gu, Z., el Bouhaddani, S., Yiangou, L., Jansen, M., Calis, J. J. A., Dorsch, L. M., Blok, C. Snijders, van den Dungen, N. A. M., Lansu, N., Boukens, B. J., Efimov, I. R., Michels, M., Verhaar, M. C., de Weger, R., Vink, A., van Steenbeek, F. G., Baas, A. F., Davis, R. P., Uh, H. W., Kuster, D. W. D., Cheng, C., Mokry, M., van der Velden, J., Asselbergs, F. W., and Harakalova, M.

Published
2021
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3. Asymptotic normality of kernel type deconvolution estimators

Author

van Es, A. J. and Uh, H. -W.

Subjects
Mathematics - Statistics Theory, 62G05, 62E20
Abstract

We derive asymptotic normality of kernel type deconvolution estimators of the density, the distribution function at a fixed point, and of the probability of an interval. We consider the so called super smooth case where the characteristic function of the known distribution decreases exponentially. It turns out that the limit behavior of the pointwise estimators of the density and distribution function is relatively straightforward while the asymptotics of the estimator of the probability of an interval depends in a complicated way on the sequence of bandwidths., Comment: 26 pages

Published
2001

8. Erratum: Genomewide meta-analysis identifies loci associated with IGF-I and IGEBP-3 levels with impact on age-related traits

Author

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, M-H, Chen, TC, Chen, Y-DI, Chung, J, Miglianico, DGF, 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, JAMJL, Jansson, J-O, Jehmlich, N, Johnson, S, Juul, A, Karlsson, M, Kilpelainen, TO, Kovacs, P, Kraft, P, Li, C, Linneberg, A, Liu, Y, Loos, RJF, Lorentzon, M, Lu, Y, 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, Pierre, SA, Schurmann, C, Seshadri, S, Sjogren, K, Slagboom, PE, Strickler, HD, Stumvoll, M, Suh, Y, Sun, Q, Zhang, C, Svensson, J, Tanaka, T, Tare, A, Tonjes, A, Uh, H-W, Van Duijn, CM, Van Heemst, D, Vandenput, L, Vasan, RS, Volker, U, Willems, SM, Ohlsson, C, Wallaschofski, H, and Kaplan, RC

Subjects
Science & Technology, Geriatrics & Gerontology, Cell Biology, 11 Medical And Health Sciences, 06 Biological Sciences, Life Sciences & Biomedicine, Developmental Biology
Abstract

In the article, ‘Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits’, the published Table 1 was incorrect, due to an error.

Published
2017

9. Human plasma N-glycosylation as analyzed by MALDI-FTICR-MS associates with markers of inflammation and metabolic health

Author

Reiding, KR, Ruhaak, LR, Uh, H-W, El Bouhaddani, S, Van den Akker, EB, Plomp, R, McDonnell, LA, Houwing-Duistermaat, JJ, Slagboom, PE, Beekman, M, and Wuhrer, M

Abstract

Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. While 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 (TPNG), it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large scale studies (n > 200) of the TPNG have been performed with methods of chromatographic and electrophoretic separation, which, while being informative, are limited in resolving the structural complexity of plasma N-glycans. Mass spectrometry (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 TPNGs of 2,144 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 Hex4HexNAc2 to Hex7HexNAc6dHex1Neu5Ac4, 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.

Published
2017

11. Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited

Author

Deelen J., Beekman M., Uh H. -W., Helmer Q., Kuningas M., Christiansen L., Kremer D., van der Breggen R., Suchiman H. E. D., Lakenberg N., van den Akker E. B., Passtoors W. M., Tiemeier H., van Heemst D., de Craen A. J., Rivadeneira F., de Geus E. J., Perola M., van der Ouderaa F. J., Gunn D. A., Boomsma D. I., Uitterlinden A. G., Christensen K., van Duijn C. M., Heijmans B. T., Houwing-Duistermaat J. J., Westendorp R. G. J., Slagboom P. E., Epidemiology, Child and Adolescent Psychiatry / Psychology, Internal Medicine, Biological Psychology, EMGO+ - Mental Health, Deelen J., Beekman M., Uh H.-W., Helmer Q., Kuningas M., Christiansen L., Kremer D., van der Breggen R., Suchiman H.E.D., Lakenberg N., van den Akker E.B., Passtoors W.M., Tiemeier H., van Heemst D., de Craen A.J., Rivadeneira F., de Geus E.J., Perola M., van der Ouderaa F.J., Gunn D.A., Boomsma D.I., Uitterlinden A.G., Christensen K., van Duijn C.M., Heijmans B.T., Houwing-Duistermaat J.J., Westendorp R.G.J., and Slagboom P.E.

Subjects
Adult, Male, Netherlands Twin Register (NTR), Longevity, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Cohort Studies, Apolipoproteins E, Genetic, SDG 3 - Good Health and Well-being, Alzheimer Disease, Humans, Genetic Predisposition to Disease, genetics, Longitudinal Studies, human, Aged, apolipoprotein E, Aged, 80 and over, genome-wide association study, Genome, Human, Forkhead Box Protein O3, aging, Forkhead Transcription Factors, Original Articles, aging apolipoprotein E genetics genome-wide association study human longevity apolipoprotein-e genotype growth-factor-i human longevity leiden longevity familial longevity alzheimers-disease nonagenarian siblings exceptional longevity depressive disorder artery-disease, Middle Aged, humanities, Genetic Loci, Case-Control Studies, Female, Proto-Oncogene Proteins c-akt
Abstract

By studying the loci that contribute to human longevity, we aim to identify mechanisms that contribute to healthy aging. To identify such loci, we performed a genome-wide association study (GWAS) comparing 403 unrelated nonagenarians from long-living families included in the Leiden Longevity Study (LLS) and 1670 younger population controls. The strongest candidate SNPs from this GWAS have been analyzed in a meta-analysis of nonagenarian cases from the Rotterdam Study, Leiden 85-plus study, and Danish 1905 cohort. Only one of the 62 prioritized SNPs from the GWAS analysis (P

Published
2011

13. Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals

Author

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. Pearson, Pfeiffer Afh., P S Chines, N Vogelzangs, Susan Redline, Alka M. Kanaya, T B Harris, J. V. van Vliet-Ostaptchouk, Ghislain Rocheleau, Rune R. Frants, Olga D. Carlson, James G. Wilson, Melissa Garcia, Ong Rt-H., Mark J. Caulfield, Tanya M. Teslovich, Loo B-M., Beatrice Knight, Andreas Ziegler, Claudia Langenberg, Yoon Shin Cho, Paul M. Ridker, Mark J. Rieder, Praveen Sethupathy, Bert Bravenboer, J. Viikari, Matt Neville, Ioannis M. Stylianou, Andrew Walley, Jarvelin M-R., Jarred B. McAteer, Ronald M. Krauss, Augustine Kong, Oluf Pedersen, Mark J. Daly, Andrew P. Morris, Anna F. Dominiczak, Stéphane Cauchi, Michael Boehnke, Christopher J. O'Donnell, Barbara Thorand, Peter M. Nilsson, Aaron Isaacs, Deborah A. Nickerson, Roza Blagieva, Mary F. Feitosa, Nicholas J. Wareham, Robert Roberts, J S Kooner, K W van Dijk, Tiinamaija Tuomi, Paul Scheet, Lynda M. Rose, Albert V. Smith, Rafn Benediktsson, Chiara Sabatti, Candace Guiducci, Lee M. Kaplan, Aki S. Havulinna, Toby Johnson, Samuli Ripatti, Erik Ingelsson, Mario A. Morken, Carl G. P. Platou, Anke Tönjes, Qi Sun, Narisu Narisu, S J Bumpstead, Jose M. Ordovas, Alan B. Feranil, L Groop, P Chines, Sara M. Willems, Perry Jrb., Matthew A. Allison, Jan Scott, Cécile Lecoeur, Kastelein Jjp., Herman A. Taylor, Anyuan Cao, Christopher J. Groves, Lincoln D. Stein, Laura J. Scott, John Beilby, Kristin G. Ardlie, Christopher S. Franklin, Yoav Ben-Shlomo, B M Shields, N J Timpson, Marco Orrù, Amélie Bonnefond, Kiran Musunuru, Murielle Bochud, Udo Seedorf, Yongmei Liu, Guillaume Lettre, Lee J-Y., Alan R. Shuldiner, Ryan P. Welch, David J. Hunter, John Whitfield, Klaus Strassburger, Khaw K-T., Hartikainen A-L., Gunnar Sigurðsson, Lu Qi, Richard N. Bergman, G M Lathrop, Sigrid W. Fouchier, T van Herpt, David S. Siscovick, Igor Rudan, Richard M. Watanabe, Themistocles L. Assimes, Nicholas G. Martin, Ozren Polasek, Dhiraj Varma, K Kim, Oliver Hofmann, Nicholas D. Hastie, S Bumpstead, Jose C. Florez, Fernando Rivadeneira, Katharine R. Owen, Braxton D. Mitchell, Alisa K. Manning, Abbas Dehghan, Bruce Bartholow Duncan, Cisca Wijmenga, Timo T. Valle, Jaakko Kaprio, Mika Kivimäki, B Shields, Laila Simpson, Tim D. Spector, Paul W. Franks, Guangju Zhai, María Teresa Martínez-Larrad, Janssens Acjw., Kim L. Ward, Inês Barroso, Xiuqing Guo, Rosa Maria Roccasecca, Zari Dastani, Reijo Laaksonen, Wilmar Igl, Vincent Mooser, Niels Grarup, Cornelia Huth, Christian Gieger, Fabio Marroni, Jaakko Tuomilehto, Doney Asf., Andrew C. Edmondson, Christian Fuchsberger, Meena Kumari, David M. Nathan, Reedik Mägi, Solomon K. Musani, U de Faire, Knut Borch-Johnsen, Masahiro Koseki, Giuseppe Paolisso, Norman Klopp, Caroline S. Fox, Nelson B. Freimer, Mika Kähönen, Peter Henneman, Diana Zelenika, K Willems-Vandijk, Steven A. McCarroll, Paul Elliott, Wichmann H-E., J. C. Bis, Nita G. Forouhi, Antti Jula, Witteman Jcm., Fredrik Karpe, Joseph Hung, Antje Fischer-Rosinsky, Eric J. Brunner, Elena Gonzalez, Soumya Raychaudhuri, Jian'an Luan, Josée Dupuis, Joshua C. Randall, Taesung Park, Francis S. Collins, Lori L. Bonnycastle, Andrew A. Hicks, Peter Kovacs, Thomas Illig, Maja Barbalić, David Couper, Jaspal S. Kooner, Damien C. Croteau-Chonka, Gavin Lucas, P J Wagner, Young-Jin Kim, Yurii S. Aulchenko, Aurelian Bidulescu, Ingrid Meulenbelt, Pilar Galan, Iris M. Heid, Michael N. Weedon, Serena Sanna, Sarah H. Wild, Hivert M-F., Patricia B. Munroe, Johan G. Eriksson, Teresa Ferreira, Robert A. Scott, A. Sandbaek, Kenneth Rice, Veronique Vitart, Xin Yuan, Leslie A. Lange, Hilma Holm, Jorge R. Kizer, Timothy M. Frayling, Marika Kaakinen, Liu C-T., Petersen A-K., Peter Schwarz, G B Walters, Palmer Cna., Jean Tichet, Bernhard Paulweber, Ying Wu, Alyson Hall, Christopher T. Johansen, David Masson, Martin Ladouceur, Christie M. Ballantyne, Tai E-S., Robert Luben, Guillaume Charpentier, Angela Döring, Philip J. Barter, Ruth McPherson, Benjamin F. 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. Adair, Yavuz Ariyurek, Talin Haritunians, Toshiko Tanaka, Albert Hofman, MariaGrazia Franzosi, Nicholas L. Smith, Laura Crisponi, Andrew B. Singleton, A Uitterlinden, Bo Isomaa, Y A Kesaniemi, Anne U. Jackson, Christa Meisinger, Holly E. Syddall, Dorret I. Boomsma, Harry Campbell, Gonçalo R. Abecasis, Lyudmyla Kedenko, Christine Cavalcanti-Proença, G Crawford, Scott M. Grundy, Johnson Prv., Nuotio M-L., I Chen, J.H. Smit, Anuj Goel, M Li, David P. Strachan, Kenechi Ejebe, Beverley Balkau, Neelam Hassanali, Kristian Hveem, Pierre Meneton, R. Gwilliam, A J Swift, Caroline Hayward, J. Graessler, Carina Zabena, B. St Pourcain, Michel Marre, Margot Haun, Lyytikäinen L-P., Ben A. Oostra, Stefan Coassin, M. van Hoek, Nigel W. Rayner, John R. Thompson, Kurt Lohman, Ulla Sovio, Unnur Thorsteinsdottir, Naveed Sattar, Lyle J. Palmer, Ulf Gyllensten, A Elliott, Muredach P. Reilly, A Swift, Luigi Ferrucci, Syvänen A-C., Simon C. Potter, T.W. van Haeften, G Wu, Stefan Böhringer, Grant W. Montgomery, Edward G. Lakatta, Serkalem Demissie, Alex S. F. Doney, Najaf Amin, Lenore J. Launer, Hugh Watkins, Johanna Kuusisto, Lars Lind, Stefan R. Bornstein, Laura J. Rasmussen-Torvik, Terho Lehtimäki, Guillaume Paré, Sophie Visvikis-Siest, S C Heath, David Schlessinger, Juha Sinisalo, Kao Whl., Mark E. Cooper, Kati Kristiansson, Thomas W. Winkler, Thomas Sparsø, Laura J. McCulloch, Taina K. Lajunen, Alex N. Parker, Nabila Bouatia-Naji, Markku S. Nieminen, Peter Vollenweider, Wendy L. McArdle, G K Hovingh, Thomas A. Buchanan, Avan Aihie Sayer, M C Zillikens, Jing Hua Zhao, Naomi Hammond, Vilmundur Gudnason, Björn Zethelius, Panos Deloukas, Jacqueline C. M. Witteman, Eric Boerwinkle, Manuel Serrano-Ríos, Anna L. Gloyn, Katherine S. Elliott, A C Fedson, Torben Jørgensen, Nicole Soranzo, Heather M. Stringham, Bruce M. Psaty, A G Uitterlinden, Stavroula Kanoni, Christian Hengstenberg, Yun Li, Olle Melander, Alan R. Tall, Manuela Uda, Magnusson Pke., Christopher W. Kuzawa, V Mooser, R. M. van Dam, Jerome I. Rotter, Greenwood Cmt., Cyrus Cooper, Pau Navarro, Min Jin Go, Nancy L. Pedersen, Serge Hercberg, Bernhard O. Boehm, Eleanor Wheeler, Epidemiology, Medical Microbiology & Infectious Diseases, Clinical Genetics, Dastani, Z, Hivert, Mf, Timpson, N, Perry, Jr, Yuan, X, Scott, Ra, Henneman, P, Heid, Im, Kizer, Jr, Lyytikäinen, Lp, Fuchsberger, C, Tanaka, T, Morris, Ap, Small, K, Isaacs, A, Beekman, M, Coassin, S, Lohman, K, Qi, L, Kanoni, S, Pankow, J, 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, Willems Vandijk, K, Oostra, Ba, Willems, Sm, Lamina, C, Winkler, Tw, Psaty, Bm, Tracy, Rp, Brody, J, Chen, I, Viikari, J, Kähönen, M, Pramstaller, Pp, Evans, Dm, St Pourcain, B, Sattar, N, Wood, Ar, 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, L, Borja, Jb, DIAGRAM+, Consortium, Magic, Consortium, Glgc, Investigator, Muther, Consortium, 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, Pe, Ferrucci, L, Jula, A, Perola, M, Raitakari, O, Florez, Jc, Salomaa, V, Eriksson, Jg, Frayling, Tm, Hicks, Aa, Lehtimäki, T, Smith, Gd, Siscovick, D, Kronenberg, F, van Duijn, C, Loos, Rj, Waterworth, Dm, Meigs, Jb, Dupuis, J, Richards, Jb, Voight, Bf, Scott, Lj, Steinthorsdottir, V, Dina, C, Welch, Rp, Zeggini, E, Huth, C, Aulchenko, Y, Thorleifsson, G, Mcculloch, Lj, Ferreira, T, Grallert, H, Amin, N, Wu, G, Willer, Cj, Raychaudhuri, S, Mccarroll, Sa, Hofmann, Om, Segrè, Av, van Hoek, M, Navarro, P, Ardlie, K, Balkau, B, Benediktsson, R, Bennett, Aj, Blagieva, R, Boerwinkle, E, Bonnycastle, Ll, Boström, Kb, Bravenboer, B, Bumpstead, S, Burtt, Np, Charpentier, G, Chines, P, Cornelis, M, Crawford, G, Doney, A, Elliott, K, Elliott, Al, Erdos, Mr, Fox, C, Franklin, C, Ganser, M, Gieger, C, Grarup, N, Green, T, Griffin, S, Groves, Cj, Guiducci, C, Hadjadj, S, Hassanali, N, Herder, C, Isomaa, B, Jackson, Au, Johnson, Pr, Jørgensen, T, Kao, Wh, Kong, A, Kraft, P, Kuusisto, J, Lauritzen, T, Li, M, Lieverse, A, Lindgren, Cm, Lyssenko, V, Marre, M, Meitinger, T, Midthjell, K, Morken, Ma, Narisu, N, Nilsson, P, Owen, Kr, Payne, F, Petersen, Ak, Platou, C, Proença, C, Prokopenko, I, Rathmann, W, Rayner, Nw, Robertson, Nr, Rocheleau, G, Roden, M, Sampson, Mj, Saxena, R, Shields, Bm, Shrader, P, Sigurdsson, G, Sparsø, T, Strassburger, K, Stringham, Hm, Sun, Q, Swift, Aj, Thorand, B, Tichet, J, Tuomi, T, van Dam, Rm, van Haeften, Tw, van Herpt, T, van Vliet Ostaptchouk, Jv, Walters, Gb, Weedon, Mn, Wijmenga, C, Witteman, J, Bergman, Rn, Cauchi, S, Collins, F, Gloyn, Al, Gyllensten, U, Hansen, T, Hide, Wa, Hitman, Ga, Hofman, A, Hunter, Dj, 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.

Subjects
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.

Published
2012

15. Multi bandwidth kernel estimators for nonparametric deconvolution problems: asymptotics and finite sample performance

Author

Van Es, A. J. and Uh, H. W.

Abstract

We consider deconvolution problems where the observations Y are equal in distribution to X+Z with X and Z independent random variables. The distribution of Z is assumed to be known and X has an unknown probability density that we want to estimate. The case where Z has a known Laplace distribution is investigated in detail. We consider an estimator that is the sum of two kernel estimators and investigate the gain to be achieved when we use different bandwidths instead of equal bandwidths. In less detail we review exponential deconvolution and estimation of a linear combination of density derivatives. We derive expansions for the asymptotic mean integrated squared error, asymptotically optimal bandwidths as well as a formula for the ratio of the smallest asymptotic error of the multiple bandwidth and equal bandwidth estimator. The finite sample performance of the multi bandwidth kernel estimators is investigated by computation of the exact mean integrated squared error for several target densities.

Published
2000
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16. Statistical integration of two omics datasets using GO2PLS

Author

Jiayi Pei, Jeanine J. Houwing-Duistermaat, Said el Bouhaddani, Zhujie Gu, Hae-Won Uh, Gu Z., el Bouhaddani S., Pei J., Houwing-Duistermaat J., and Uh H.-W.

Subjects
Computer science, 0206 medical engineering, Feature selection, 02 engineering and technology, lcsh:Computer applications to medicine. Medical informatics, Residual, computer.software_genre, 01 natural sciences, Biochemistry, 010104 statistics & probability, 03 medical and health sciences, Integration of Omics data, Structural Biology, Robustness (computer science), O2PLS, Partial least squares regression, 0101 mathematics, Least-Squares Analysis, lcsh:QH301-705.5, Molecular Biology, 030304 developmental biology, Interpretability, 0303 health sciences, Group structure, Applied Mathematics, Dimensionality reduction, Methodology Article, Computational Biology, Genomics, Covariance, Omics, Linear subspace, Computer Science Applications, lcsh:Biology (General), Case-Control Studies, Dimension reduction, lcsh:R858-859.7, Data mining, computer, 020602 bioinformatics, Subspace topology
Abstract

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.

Published
2021

17. Statistical method for modeling sequencing data from different technologies in longitudinal studies with application to Huntington disease

Author

Willeke M. C. van Roon-Mom, Jeanine J. Houwing-Duistermaat, Hae-Won Uh, Szymon M. Kielbasa, Angga M Fuady, Fuady A.M., van Roon-Mom W.M.C., Kielbasa S.M., Uh H.-W., and Houwing-Duistermaat J.J.

Subjects
Statistics and Probability, Mixed model, Technology, DeepSAGE, Computer science, media_common.quotation_subject, RNA-Seq, 01 natural sciences, Generalized linear mixed model, 010104 statistics & probability, 03 medical and health sciences, Longitudinal and Time‐to‐event Analysis, Statistics, Humans, RNA‐Seq, Longitudinal Studies, 0101 mathematics, Time point, quality control, 030304 developmental biology, media_common, 0303 health sciences, Measure (data warehouse), Variables, Observational error, Gene Expression Profiling, General Medicine, Identification (information), Huntington Disease, Statistics, Probability and Uncertainty, linear mixed model, measurement error, Research Paper
Abstract

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.

Published
2020

18. The mixed model for the analysis of a repeated-measurement multivariate count data

Author

Hae-Won Uh, Ivonne Martin, Taniawati Supali, Jeanine J. Houwing-Duistermaat, Makedonka Mitreva, Martin I., Uh H.-W., Supali T., Mitreva M., and Houwing-Duistermaat J.J.

Subjects
Statistics and Probability, Mixed model, Dirichlet-multinomial, Multivariate statistics, Epidemiology, microbiome, 01 natural sciences, Generalized linear mixed model, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, multivariate, Dirichlet‐multinomial, Overdispersion, Statistics, Humans, Computer Simulation, 030212 general & internal medicine, 0101 mathematics, Categorical variable, Research Articles, Mathematics, Models, Statistical, Microbiota, overdispersion, Regression analysis, Random effects model, conditional model, count, generalized linear mixed model, Multivariate Analysis, Regression Analysis, Research Article, Count data
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.

Published
2019

19. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Author

Symen Ligthart, Ahmad Vaez, Urmo Võsa, Maria G. Stathopoulou, Paul S. de Vries, Bram P. Prins, Peter J. Van der Most, Toshiko Tanaka, Elnaz Naderi, Lynda M. Rose, Ying Wu, Robert Karlsson, Maja Barbalic, Honghuang Lin, René Pool, Gu Zhu, Aurélien Macé, Carlo Sidore, Stella Trompet, Massimo Mangino, Maria Sabater-Lleal, John P. Kemp, Ali Abbasi, Tim Kacprowski, Niek Verweij, Albert V. Smith, Tao Huang, Carola Marzi, Mary F. Feitosa, Kurt K. Lohman, Marcus E. Kleber, Yuri Milaneschi, Christian Mueller, Mahmudul Huq, Efthymia Vlachopoulou, Leo-Pekka Lyytikäinen, Christopher Oldmeadow, Joris Deelen, Markus Perola, Jing Hua Zhao, Bjarke Feenstra, Marzyeh Amini, Jari Lahti, Katharina E. Schraut, Myriam Fornage, Bhoom Suktitipat, Wei-Min Chen, Xiaohui Li, Teresa Nutile, Giovanni Malerba, Jian’an Luan, Tom Bak, Nicholas Schork, Fabiola Del Greco M., Elisabeth Thiering, Anubha Mahajan, Riccardo E. Marioni, Evelin Mihailov, Joel Eriksson, Ayse Bilge Ozel, Weihua Zhang, Maria Nethander, Yu-Ching Cheng, Stella Aslibekyan, Wei Ang, Ilaria Gandin, Loïc Yengo, Laura Portas, Charles Kooperberg, Edith Hofer, Kumar B. Rajan, Claudia Schurmann, Wouter den Hollander, Tarunveer S. Ahluwalia, Jing Zhao, Harmen H.M. Draisma, Ian Ford, Nicholas Timpson, Alexander Teumer, Hongyan Huang, Simone Wahl, YongMei Liu, Jie Huang, Hae-Won Uh, Frank Geller, Peter K. Joshi, Lisa R. Yanek, Elisabetta Trabetti, Benjamin Lehne, Diego Vozzi, Marie Verbanck, Ginevra Biino, Yasaman Saba, Ingrid Meulenbelt, Jeff R. O’Connell, Markku Laakso, Franco Giulianini, Patrik K.E. Magnusson, Christie M. Ballantyne, Jouke Jan Hottenga, Grant W. Montgomery, Fernando Rivadineira, Rico Rueedi, Maristella Steri, Karl-Heinz Herzig, David J. Stott, Cristina Menni, Mattias Frånberg, Beate St. Pourcain, Stephan B. Felix, Tune H. Pers, Stephan J.L. Bakker, Peter Kraft, Annette Peters, Dhananjay Vaidya, Graciela Delgado, Johannes H. Smit, Vera Großmann, Juha Sinisalo, Ilkka Seppälä, Stephen R. Williams, Elizabeth G. Holliday, Matthijs Moed, Claudia Langenberg, Katri Räikkönen, Jingzhong Ding, Harry Campbell, Michele M. Sale, Yii-Der I. Chen, Alan L. James, Daniela Ruggiero, Nicole Soranzo, Catharina A. Hartman, Erin N. Smith, Gerald S. Berenson, Christian Fuchsberger, Dena Hernandez, Carla M.T. Tiesler, Vilmantas Giedraitis, David Liewald, Krista Fischer, Dan Mellström, Anders Larsson, Yunmei Wang, William R. Scott, Matthias Lorentzon, John Beilby, Kathleen A. Ryan, Craig E. Pennell, Dragana Vuckovic, Beverly Balkau, Maria Pina Concas, Reinhold Schmidt, Carlos F. Mendes de Leon, Erwin P. Bottinger, Margreet Kloppenburg, Lavinia Paternoster, Michael Boehnke, A.W. Musk, Gonneke Willemsen, David M. Evans, Pamela A.F. Madden, Mika Kähönen, Zoltán Kutalik, Magdalena Zoledziewska, Ville Karhunen, Stephen B. Kritchevsky, Naveed Sattar, Genevieve Lachance, Robert Clarke, Tamara B. Harris, Olli T. Raitakari, John R. Attia, Diana van Heemst, Eero Kajantie, Rossella Sorice, Giovanni Gambaro, Robert A. Scott, Andrew A. Hicks, Luigi Ferrucci, Marie Standl, Cecilia M. Lindgren, John M. Starr, Magnus Karlsson, Lars Lind, Jun Z. Li, John C. Chambers, Trevor A. Mori, Eco J.C.N. de Geus, Andrew C. Heath, Nicholas G. Martin, Juha Auvinen, Brendan M. Buckley, Anton J.M. de Craen, Melanie Waldenberger, Konstantin Strauch, Thomas Meitinger, Rodney J. Scott, Mark McEvoy, Marian Beekman, Cristina Bombieri, Paul M. Ridker, Karen L. Mohlke, Nancy L. Pedersen, Alanna C. Morrison, Dorret I. Boomsma, John B. Whitfield, David P. Strachan, Albert Hofman, Peter Vollenweider, Francesco Cucca, Marjo-Riitta Jarvelin, J. Wouter Jukema, Tim D. Spector, Anders Hamsten, Tanja Zeller, André G. Uitterlinden, Matthias Nauck, Vilmundur Gudnason, Lu Qi, Harald Grallert, Ingrid B. Borecki, Jerome I. Rotter, Winfried März, Philipp S. Wild, Marja-Liisa Lokki, Michael Boyle, Veikko Salomaa, Mads Melbye, Johan G. Eriksson, James F. Wilson, Brenda W.J.H. Penninx, Diane M. Becker, Bradford B. Worrall, Greg Gibson, Ronald M. Krauss, Marina Ciullo, Gianluigi Zaza, Nicholas J. Wareham, Albertine J. Oldehinkel, Lyle J. Palmer, Sarah S. Murray, Peter P. Pramstaller, Stefania Bandinelli, Joachim Heinrich, Erik Ingelsson, Ian J. Deary, Reedik Mägi, Liesbeth Vandenput, Pim van der Harst, Karl C. Desch, Jaspal S. Kooner, Claes Ohlsson, Caroline Hayward, Terho Lehtimäki, Alan R. Shuldiner, Donna K. Arnett, Lawrence J. Beilin, Antonietta Robino, Philippe Froguel, Mario Pirastu, Tine Jess, Wolfgang Koenig, Ruth J.F. Loos, Denis A. Evans, Helena Schmidt, George Davey Smith, P. Eline Slagboom, Gudny Eiriksdottir, Andrew P. Morris, Bruce M. Psaty, Russell P. Tracy, Ilja M. Nolte, Eric Boerwinkle, Sophie Visvikis-Siest, Alex P. Reiner, Myron Gross, Joshua C. Bis, Lude Franke, Oscar H. Franco, Emelia J. Benjamin, Daniel I. Chasman, Josée Dupuis, Harold Snieder, Abbas Dehghan, Behrooz Z. Alizadeh, H. Marike Boezen, Gerjan Navis, Marianne Rots, Morris Swertz, Bruce H.R. Wolffenbuttel, Cisca Wijmenga, Emelia Benjamin, Tarunveer Singh Ahluwalia, James Meigs, Russell Tracy, Josh Bis, Nathan Pankratz, Alex Rainer, James G. Wilson, Josee Dupuis, Bram Prins, Urmo Vaso, Maria Stathopoulou, Terho Lehtimaki, Yalda Jamshidi, Sophie Siest, Andre G. Uitterlinden, Mohammadreza Abdollahi, Renate Schnabel, Ursula M. Schick, Aldi Kraja, Yi-Hsiang Hsu, Daniel S. Tylee, Alyson Zwicker, Rudolf Uher, George Davey-Smith, Andrew Hicks, Cornelia M. van Duijn, Cavin Ward-Caviness, J. Rotter, Ken Rice, Leslie Lange, Eco de Geus, Kari Matti Makela, David Stacey, Johan Eriksson, Tim M. Frayling, Eline P. Slagboom, Erasmus University Medical Center [Rotterdam] (Erasmus MC), University Medical Center Groningen [Groningen] (UMCG), University of Isfahan, University of Tartu, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), The University of Texas Health Science Center at Houston (UTHealth), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Brigham and Women's Hospital [Boston], University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], University of Split, Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Process & Energy Laboratory, Delft University of Technology (TU Delft), Grand Lyon : communauté urbaine de Lyon, Interuniversity Cardiology Institute Netherlands, Department of Twin Research and Genetic Epidemiology, King's College London, London, Huazhong University of Science and Technology [Wuhan] (HUST), Division of Statistical Genomics, Washington University School of Medicine, Department of Psychiatry, VU University Medical Center [Amsterdam], Institut fuer Theoretische Physik (Institut fuer Theoretische Physik), Universität Heidelberg [Heidelberg] = Heidelberg University, Molecular Epidemiology, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Mahidol University [Bangkok], Northwest A and F University, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Institute of Genetics and Biophysics, CNR, Naples, Università degli studi di Verona = University of Verona (UNIVR), Department of Molecular Medicine [Scripps Research Institute], The Scripps Research Institute [La Jolla, San Diego], Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Department of Electrical and Computer Engineering [Waterloo] (ECE), University of Waterloo [Waterloo], University of Maryland School of Medicine, University of Maryland System, Institut National de l'Environnement Industriel et des Risques (INERIS), Institute of Pop. Genetics, CNR, Sassari, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, IT University of Copenhagen (ITU), Robertson Centre for Biostatistics, University of Glasgow, Centre for Causal Analyses in Translational Epidemiology, University of Bristol [Bristol]-Medical Research Council, King‘s College London, Jinan University [Guangzhou], Institute of Oceanology [China], School Medicine, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), General Internal Medicine, Johns Hopkins School of Medicine, Johns Hopkins University School of Medicine [Baltimore], Shardna life science Pula Cagliari, Section Molecular Epidemiology, Leiden University Medical Center (LUMC), Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Medstar Research Institute, Department of Cardiology, Ernst-Moritz-Arndt University, Center for Biological Sequence Analysis [Lyngby], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Department of Internal Medicine, University of Groningen and University Medical Center Groningen, Department of Epidemiology, Harvard School of Public Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Metacohorts Consortium, INEOS Technologies (SWITZERLAND), MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, University of Edinburgh, School of Population Health [Crawley, Western Australia], The University of Western Australia (UWA), Institute of Genetics and Biophysics, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), The Scripps Translational Science Institute and Scripps Health, Tulane Center for Cardiovascular Health, Tulane University Health Sciences Center, Centre for Population Health Sciences, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Luleå University of Technology (LUT), Recherche en épidémiologie et biostatistique, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Austrian Institute of Technology [Vienna] (AIT), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Department of Rheumatology and Clinical Epidemiology, Leiden University Medical Center (LUMC), Department of Rheumatology and Clinical Epidemiology [Leiden University Medical Center] (LUMC), Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden-Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Virginia, Tampere University Hospital, Department of Medical Genetics, Université de Lausanne = University of Lausanne (UNIL), Department of Pathological Biochemistry, Royal Infirmary, Oxford University, University of Oxford, University of Newcastle [Callaghan, Australia] (UoN), Department of neurology, Institute of Metabolic Science, MRC, The Wellcome Trust Centre for Human Genetics [Oxford], Uppsala Universitet [Uppsala], QIMR Berghofer Medical Research Institute, Institute of Genetic Epidemiology [Neuherberg, Germany], Institute of Human Genetics, Helmholtz Zentrum München = German Research Center for Environmental Health, Schizophrenia Research Institute [Sydney], Department of Genetics, University of North Carolina System (UNC)-University of North Carolina System (UNC), Vrije Universiteit Brussel (VUB), Population Health Sciences and Education, St George's University of London, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Institute of Health Sciences and Biocenter Oulu, University of Oulu, Medizinische Klinik und Poliklinik, Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Institute of Clinical Chemistry and Laboratory Medicine, Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Departments of Epidemiology and Nutrition, Institute of Epidemiology [Neuherberg] (EPI), Medical University Graz, Transplantation Laboratory [Helsinki], Haartman Institute [Helsinki], Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Department of Chronic Disease Prevention, National Institute for Health and Welfare [Helsinki], Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], CLinical Psychology, Genetics and Pathology, Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Center For Narcolepsy, Stanford University, Centre for Bone and Arthritis Research, University of Gothenburg (GU)-Institute of Medicine, MRC Human Gentics Unit, Inst Genet and Mol Med, Western General Hospital, Edinburgh, University of Maryland School of Medicine [Baltimore, MD, USA], Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Department of Physics [Stockholm], Stockholm University, University of Bristol [Bristol], Universiteit Leiden, Department of Epidemiology, University of Washington, University of Washington [Seattle], Department of Epidemiology [Rotterdam], University of Groningen [Groningen], Dutch Initiative on Crohn and Colitis (ICC), Icelandic Heart Association [Kopavogur, Iceland] (IHA), Department of Physiology and Biophysics [Jackson, MS, USA], University of Southern Mississippi (USM), Human Genetics Branch, National Institutes of Health [Bethesda] (NIH)-National Institute of Mental Health (NIMH), Faculty of Medicine and Life Sciences [Tampere], University of Tampere [Finland], German Center for Cardiovascular Research (DZHK), Berlin Institute of Health (BIH), MRC Centre for Neuropsychiatric Genetics and Genomics, Medical Research Council-Cardiff University, Department of Social Medicine, School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Department of Medicine [Aurora, CO, USA], University of Colorado [Denver], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Mathematical Institute [Oxford] (MI), Institute of Psychiatry, Psychology & Neuroscience, King's College London, LifeLines Cohort Study, CHARGE Inflammation Working Group, Ligthart, S., Vaez, A., Vosa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., Van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., Wu, Y., Karlsson, R., Barbalic, M., Lin, H., Pool, R., Zhu, G., Mace, A., Sidore, C., Trompet, S., Mangino, M., Sabater-Lleal, M., Kemp, J. P., Abbasi, A., Kacprowski, T., Verweij, N., Smith, A. V., Huang, T., Marzi, C., Feitosa, M. F., Lohman, K. K., Kleber, M. E., Milaneschi, Y., Mueller, C., Huq, M., Vlachopoulou, E., Lyytikainen, L. -P., Oldmeadow, C., Deelen, J., Perola, M., Zhao, J. H., Feenstra, B., Alizadeh, B. Z., Boezen, H. M., Franke, L., van der Harst, P., Navis, G., Rots, M., Snieder, H., Swertz, M., Wolffenbuttel, B. H. R., Wijmenga, C., Amini, M., Benjamin, E., Chasman, D. I., Dehghan, A., Ahluwalia, T. S., Meigs, J., Tracy, R., Bis, J., Eiriksdottir, G., Pankratz, N., Gross, M., Rainer, A., Wilson, J. G., Psaty, B. M., Dupuis, J., Prins, B., Vaso, U., Stathopoulou, M., Lehtimaki, T., Koenig, W., Jamshidi, Y., Siest, S., Uitterlinden, A. G., Abdollahi, M., Schnabel, R., Schick, U. M., Nolte, I. M., Kraja, A., Hsu, Y. -H., Tylee, D. S., Zwicker, A., Uher, R., Davey-Smith, G., Morrison, A. C., Hicks, A., van Duijn, C. M., Ward-Caviness, C., Boerwinkle, E., Rotter, J., Rice, K., Lange, L., de Geus, E., Morris, A. P., Makela, K. M., Stacey, D., Eriksson, J., Frayling, T. M., Slagboom, E. P., Lahti, J., Schraut, K. E., Fornage, M., Suktitipat, B., Chen, W. -M., Li, X., Nutile, T., Malerba, G., Luan, J., Bak, T., Schork, N., Del Greco, M. F., Thiering, E., Mahajan, A., Marioni, R. E., Mihailov, E., Ozel, A. B., Zhang, W., Nethander, M., Cheng, Y. -C., Aslibekyan, S., Ang, W., Gandin, I., Yengo, L., Portas, L., Kooperberg, C., Hofer, E., Rajan, K. B., Schurmann, C., den Hollander, W., Zhao, J., Draisma, H. H. M., Ford, I., Timpson, N., Teumer, A., Huang, H., Wahl, S., Liu, Y., Huang, J., Uh, H. -W., Geller, F., Joshi, P. K., Yanek, L. R., Trabetti, E., Lehne, B., Vozzi, D., Verbanck, M., Biino, G., Saba, Y., Meulenbelt, I., O'Connell, J. R., Laakso, M., Giulianini, F., Magnusson, P. K. E., Ballantyne, C. M., Hottenga, J. J., Montgomery, G. W., Rivadineira, F., Rueedi, R., Steri, M., Herzig, K. -H., Stott, D. J., Menni, C., Franberg, M., S, t. Pourcain B., Felix, S. B., Pers, T. H., Bakker, S. J. L., Kraft, P., Peters, A., Vaidya, D., Delgado, G., Smit, J. H., Grossmann, V., Sinisalo, J., Seppala, I., Williams, S. R., Holliday, E. G., Moed, M., Langenberg, C., Raikkonen, K., Ding, J., Campbell, H., Sale, M. M., Chen, Y. -D. I., James, A. L., Ruggiero, D., Soranzo, N., Hartman, C. A., Smith, E. N., Berenson, G. S., Fuchsberger, C., Hernandez, D., Tiesler, C. M. T., Giedraitis, V., Liewald, D., Fischer, K., Mellstrom, D., Larsson, A., Wang, Y., Scott, W. R., Lorentzon, M., Beilby, J., Ryan, K. A., Pennell, C. E., Vuckovic, D., Balkau, B., Concas, M. P., Schmidt, R., Mendes de Leon, C. F., Bottinger, E. P., Kloppenburg, M., Paternoster, L., Boehnke, M., Musk, A. W., Willemsen, G., Evans, D. M., Madden, P. A. F., Kahonen, M., Kutalik, Z., Zoledziewska, M., Karhunen, V., Kritchevsky, S. B., Sattar, N., Lachance, G., Clarke, R., Harris, T. B., Raitakari, O. T., Attia, J. R., van Heemst, D., Kajantie, E., Sorice, R., Gambaro, G., Scott, R. A., Hicks, A. A., Ferrucci, L., Standl, M., Lindgren, C. M., Starr, J. M., Karlsson, M., Lind, L., Li, J. Z., Chambers, J. C., Mori, T. A., de Geus, E. J. C. N., Heath, A. C., Martin, N. G., Auvinen, J., Buckley, B. M., de Craen, A. J. M., Waldenberger, M., Strauch, K., Meitinger, T., Scott, R. J., Mcevoy, M., Beekman, M., Bombieri, C., Ridker, P. M., Mohlke, K. L., Pedersen, N. L., Boomsma, D. I., Whitfield, J. B., Strachan, D. P., Hofman, A., Vollenweider, P., Cucca, F., Jarvelin, M. -R., Jukema, J. W., Spector, T. D., Hamsten, A., Zeller, T., Nauck, M., Gudnason, V., Qi, L., Grallert, H., Borecki, I. B., Rotter, J. I., Marz, W., Wild, P. S., Lokki, M. -L., Boyle, M., Salomaa, V., Melbye, M., Eriksson, J. G., Wilson, J. F., Penninx, B. W. J. H., Becker, D. M., Worrall, B. B., Gibson, G., Krauss, R. M., Ciullo, M., Zaza, G., Wareham, N. J., Oldehinkel, A. J., Palmer, L. J., Murray, S. S., Pramstaller, P. P., Bandinelli, S., Heinrich, J., Ingelsson, E., Deary, I. J., Magi, R., Vandenput, L., Desch, K. C., Kooner, J. S., Ohlsson, C., Hayward, C., Shuldiner, A. R., Arnett, D. K., Beilin, L. J., Robino, A., Froguel, P., Pirastu, M., Jess, T., Loos, R. J. F., Evans, D. A., Schmidt, H., Slagboom, P. E., Tracy, R. P., Visvikis-Siest, S., Reiner, A. P., Bis, J. C., Franco, O. H., Benjamin, E. J., AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Graduate School, Epidemiology, Internal Medicine, Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), VU University medical center, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, APH - Digital Health, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Universität Heidelberg [Heidelberg], University of Verona (UNIVR), Department of Molecular and Experimental Medicine, The Scripps Research Institute, The Scripps Research Institute, Université Grenoble Alpes - UFR Sciences de l'Homme et de la Société (UGA UFR SHS), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IT University of Copenhagen, Technical University of Denmark [Lyngby] (DTU), Consiglio Nazionale delle Ricerche (CNR), University of Virginia [Charlottesville], Université de Lausanne (UNIL), University of Oxford [Oxford], University of Newcastle [Australia] (UoN), Centre d'économie industrielle i3 (CERNA i3), Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Helmholtz-Zentrum München (HZM), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Universitätsmedizin der Johannes-Gutenberg Universität Mainz, University of Helsinki-University of Helsinki-Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Cardiff University-Medical Research Council, University of California-University of California, and DE CARVALHO, Philippe

Subjects
0301 basic medicine, Male, Netherlands Twin Register (NTR), Bipolar Disorder, LD SCORE REGRESSION, [SDV]Life Sciences [q-bio], Genome-wide association study, [SDV.GEN] Life Sciences [q-bio]/Genetics, Body Mass Index, inflammatory disorder, 80 and over, WIDE ASSOCIATION, EPIDEMIOLOGY, ta318, International HapMap Project, Child, Genetics (clinical), 2. Zero hunger, Genetics, Genetics & Heredity, Aged, 80 and over, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, C-reactive proteingenome-wide association studyinflammationMendelian randomizationinflammatory disordersDEPICTcoronary artery diseaseschizophreniasystem biology, system biology, DEPICT, Mendelian Randomization Analysis, 11 Medical And Health Sciences, Middle Aged, C-reactive protein, coronary artery disease, genome-wide association study, inflammation, inflammatory disorders, Mendelian randomization, schizophrenia, Adolescent, Adult, Aged, Biomarkers, C-Reactive Protein, Female, Genetic Loci, Genome-Wide Association Study, Humans, Inflammation, Liver, Metabolic Networks and Pathways, Schizophrenia, Young Adult, 3. Good health, [SDV] Life Sciences [q-bio], Medical genetics, Biomarker (medicine), Life Sciences & Biomedicine, Human, medicine.medical_specialty, CHARGE Inflammation Working Group, Biology, IMMUNITY, ta3111, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, CORONARY-HEART-DISEASE, Mendelian Randomization Analysi, 1000 Genomes Project, METAANALYSIS, Genetic association, [SDV.GEN]Life Sciences [q-bio]/Genetics, Science & Technology, ta1184, Metabolic Networks and Pathway, Biomarker, INSTRUMENTS, 06 Biological Sciences, 030104 developmental biology, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, LifeLines Cohort Study
Abstract

International audience; 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

20. Integrating omics datasets with the OmicsPLS package

Author

Jeanine J. Houwing-Duistermaat, Szymon M. Kielbasa, Lucija Klaric, Said el Bouhaddani, Caroline Hayward, Geurt Jongbloed, Hae-Won Uh, el Bouhaddani S., Uh H.-W., Jongbloed G., Hayward C., Klaric L., Kielbasa S.M., and Houwing-Duistermaat J.

Subjects
0301 basic medicine, Computer science, Data-specific variation, Metabolomic, Variation (game tree), lcsh:Computer applications to medicine. Medical informatics, computer.software_genre, Biochemistry, 03 medical and health sciences, Search engine, Software, Structural Biology, O2PLS, Humans, Metabolomics, Least-Squares Analysis, lcsh:QH301-705.5, Molecular Biology, Least-Squares Analysi, Thesaurus (information retrieval), business.industry, Joint principal components, Applied Mathematics, R package, Joint principal component, Genomics, Omics data integration, Omics, Computer Science Applications, Data set, Task (computing), 030104 developmental biology, lcsh:Biology (General), Genomic, lcsh:R858-859.7, Data mining, business, computer, Data integration, Human
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”). Electronic supplementary material The online version of this article (10.1186/s12859-018-2371-3) contains supplementary material, which is available to authorized users.

Published
2018

21. Probabilistic partial least squares model: Identifiability, estimation and application

Author

Caroline Hayward, Hae-Won Uh, Jeanine J. Houwing-Duistermaat, Geurt Jongbloed, Said el Bouhaddani, el Bouhaddani S., Uh H.-W., Hayward C., Jongbloed G., and Houwing-Duistermaat J.

Subjects
0301 basic medicine, Statistics and Probability, FOS: Computer and information sciences, INFORMATION, Probabilistic partial least squares, Generalized least squares, 01 natural sciences, Methodology (stat.ME), 010104 statistics & probability, 03 medical and health sciences, REGRESSION-MODELS, Inference, Statistics, Partial least squares regression, Expectation–maximization algorithm, TOOL, Identifiability, 0101 mathematics, Total least squares, MAXIMUM-LIKELIHOOD, EM algorithm, METAANALYSIS, Statistics - Methodology, Mathematics, Numerical Analysis, Probabilistic logic, Statistical model, 030104 developmental biology, Non-linear least squares, Dimension reduction, Probability and Uncertainty, Statistics, Probability and Uncertainty, Algorithm
Abstract

With a rapid increase in volume and complexity of data sets, there is a need for methods that can extract useful information, for example the relationship between two data sets measured for the same persons. The Partial Least Squares (PLS) method can be used for this dimension reduction task. Within life sciences, results across studies are compared and combined. Therefore, parameters need to be identifiable, which is not the case for PLS. In addition, PLS is an algorithm, while epidemiological study designs are often outcome-dependent and methods to analyze such data require a probabilistic formulation. Moreover, a probabilistic model provides a statistical framework for inference. To address these issues, we develop Probabilistic PLS (PPLS). We derive maximum likelihood estimators that satisfy the identifiability conditions by using an EM algorithm with a constrained optimization in the M step. We show that the PPLS parameters are identifiable up to sign. A simulation study is conducted to study the performance of PPLS compared to existing methods. The PPLS estimates performed well in various scenarios, even in high dimensions. Most notably, the estimates seem to be robust against departures from normality. To illustrate our method, we applied it to IgG glycan data from two cohorts. Our PPLS model provided insight as well as interpretable results across the two cohorts., Comment: Accepted in Journal of Multivariate Analysis

Published
2018

22. Evaluation of O2PLS in Omics data integration

Author

Perttu Salo, Said el Bouhaddani, Hae-Won Uh, Geurt Jongbloed, Markus Perola, Jeanine J Houwing-Duistermaat, Bouhaddani S., Houwing-Duistermaat J., Salo P., Perola M., Jongbloed G., and Uh H.-W.

Subjects
Adult, Male, 0301 basic medicine, Latent variable regression, Relation (database), Computer science, Systems biology, Statistics as Topic, computer.software_genre, Biochemistry, Set (abstract data type), 03 medical and health sciences, Metabolomics, Integration of Omics data, O2PLS, Structural Biology, Partial least squares regression, Statistics, Metabolome, Humans, Obesity, Least-Squares Analysis, Molecular Biology, Aged, Systems Biology, Applied Mathematics, Dimensionality reduction, Genomics, Middle Aged, Diet, Computer Science Applications, Proceedings, 030104 developmental biology, Cohort, Dimension reduction, Data analysis, Female, Data mining, Transcriptome, computer
Abstract

BackgroundRapid 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.ResultsA 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.ConclusionsSimulations 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

23. Prediction of vascular aging based on smartphone acquired PPG signals

Author

Yosef Safi Harb, Hae-Won Uh, Nico Curti, Gastone Castellani, Folkert W. Asselbergs, Lorenzo Dall'Olio, Daniel Remondini, Dall'Olio L., Curti N., Remondini D., Safi Harb Y., Asselbergs F.W., Castellani G., Uh H.-W., and Cardiology

Subjects
Male, Aging, Computer science, lcsh:Medicine, 030204 cardiovascular system & hematology, Convolutional neural network, 0302 clinical medicine, Aging/pathology, Computer-Assisted, Heart Rate, Vascular Diseases/diagnosis, lcsh:Science, Signal processing, Multidisciplinary, Artificial neural network, Signal Processing, Computer-Assisted, Aerificial Intelligence, Statistical learning, pulse oximetry, Middle Aged, Photoplethysmography/methods, Female, Smartphone, Smartphone/statistics & numerical data, Adult, Adolescent, Neural Networks, Noise reduction, Cardiology, Article, 03 medical and health sciences, Computer, Young Adult, Medical research, Deep Learning, Photoplethysmogram, medicine, Humans, Vascular Diseases, Photoplethysmography, Second derivative, Aged, business.industry, Deep learning, lcsh:R, Pattern recognition, medicine.disease, Computational biology and bioinformatics, Vascular ageing, Signal Processing, Arterial stiffness, lcsh:Q, Neural Networks, Computer, Artificial intelligence, Data pre-processing, business, Biomarkers, 030217 neurology & neurosurgery
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.

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