%0 Journal Article %J Nat Genet %D 2010 %T Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. %A Speliotes, Elizabeth K %A Willer, Cristen J %A Berndt, Sonja I %A Monda, Keri L %A Thorleifsson, Gudmar %A Jackson, Anne U %A Lango Allen, Hana %A Lindgren, Cecilia M %A Luan, Jian'an %A Mägi, Reedik %A Randall, Joshua C %A Vedantam, Sailaja %A Winkler, Thomas W %A Qi, Lu %A Workalemahu, Tsegaselassie %A Heid, Iris M %A Steinthorsdottir, Valgerdur %A Stringham, Heather M %A Weedon, Michael N %A Wheeler, Eleanor %A Wood, Andrew R %A Ferreira, Teresa %A Weyant, Robert J %A Segrè, Ayellet V %A Estrada, Karol %A Liang, Liming %A Nemesh, James %A Park, Ju-Hyun %A Gustafsson, Stefan %A Kilpeläinen, Tuomas O %A Yang, Jian %A Bouatia-Naji, Nabila %A Esko, Tõnu %A Feitosa, Mary F %A Kutalik, Zoltán %A Mangino, Massimo %A Raychaudhuri, Soumya %A Scherag, Andre %A Smith, Albert Vernon %A Welch, Ryan %A Zhao, Jing Hua %A Aben, Katja K %A Absher, Devin M %A Amin, Najaf %A Dixon, Anna L %A Fisher, Eva %A Glazer, Nicole L %A Goddard, Michael E %A Heard-Costa, Nancy L %A Hoesel, Volker %A Hottenga, Jouke-Jan %A Johansson, Asa %A Johnson, Toby %A Ketkar, Shamika %A Lamina, Claudia %A Li, Shengxu %A Moffatt, Miriam F %A Myers, Richard H %A Narisu, Narisu %A Perry, John R B %A Peters, Marjolein J %A Preuss, Michael %A Ripatti, Samuli %A Rivadeneira, Fernando %A Sandholt, Camilla %A Scott, Laura J %A Timpson, Nicholas J %A Tyrer, Jonathan P %A van Wingerden, Sophie %A Watanabe, Richard M %A White, Charles C %A Wiklund, Fredrik %A Barlassina, Christina %A Chasman, Daniel I %A Cooper, Matthew N %A Jansson, John-Olov %A Lawrence, Robert W %A Pellikka, Niina %A Prokopenko, Inga %A Shi, Jianxin %A Thiering, Elisabeth %A Alavere, Helene %A Alibrandi, Maria T S %A Almgren, Peter %A Arnold, Alice M %A Aspelund, Thor %A Atwood, Larry D %A Balkau, Beverley %A Balmforth, Anthony J %A Bennett, Amanda J %A Ben-Shlomo, Yoav %A Bergman, Richard N %A Bergmann, Sven %A Biebermann, Heike %A Blakemore, Alexandra I F %A Boes, Tanja %A Bonnycastle, Lori L %A Bornstein, Stefan R %A Brown, Morris J %A Buchanan, Thomas A %A Busonero, Fabio %A Campbell, Harry %A Cappuccio, Francesco P %A Cavalcanti-Proença, Christine %A Chen, Yii-Der Ida %A Chen, Chih-Mei %A Chines, Peter S %A Clarke, Robert %A Coin, Lachlan %A Connell, John %A Day, Ian N M %A den Heijer, Martin %A Duan, Jubao %A Ebrahim, Shah %A Elliott, Paul %A Elosua, Roberto %A Eiriksdottir, Gudny %A Erdos, Michael R %A Eriksson, Johan G %A Facheris, Maurizio F %A Felix, Stephan B %A Fischer-Posovszky, Pamela %A Folsom, Aaron R %A Friedrich, Nele %A Freimer, Nelson B %A Fu, Mao %A Gaget, Stefan %A Gejman, Pablo V %A Geus, Eco J C %A Gieger, Christian %A Gjesing, Anette P %A Goel, Anuj %A Goyette, Philippe %A Grallert, Harald %A Grässler, Jürgen %A Greenawalt, Danielle M %A Groves, Christopher J %A Gudnason, Vilmundur %A Guiducci, Candace %A Hartikainen, Anna-Liisa %A Hassanali, Neelam %A Hall, Alistair S %A Havulinna, Aki S %A Hayward, Caroline %A Heath, Andrew C %A Hengstenberg, Christian %A Hicks, Andrew A %A Hinney, Anke %A Hofman, Albert %A Homuth, Georg %A Hui, Jennie %A Igl, Wilmar %A Iribarren, Carlos %A Isomaa, Bo %A Jacobs, Kevin B %A Jarick, Ivonne %A Jewell, Elizabeth %A John, Ulrich %A Jørgensen, Torben %A Jousilahti, Pekka %A Jula, Antti %A Kaakinen, Marika %A Kajantie, Eero %A Kaplan, Lee M %A Kathiresan, Sekar %A Kettunen, Johannes %A Kinnunen, Leena %A Knowles, Joshua W %A Kolcic, Ivana %A König, Inke R %A Koskinen, Seppo %A Kovacs, Peter %A Kuusisto, Johanna %A Kraft, Peter %A Kvaløy, Kirsti %A Laitinen, Jaana %A Lantieri, Olivier %A Lanzani, Chiara %A Launer, Lenore J %A Lecoeur, Cécile %A Lehtimäki, Terho %A Lettre, Guillaume %A Liu, Jianjun %A Lokki, Marja-Liisa %A Lorentzon, Mattias %A Luben, Robert N %A Ludwig, Barbara %A Manunta, Paolo %A Marek, Diana %A Marre, Michel %A Martin, Nicholas G %A McArdle, Wendy L %A McCarthy, Anne %A McKnight, Barbara %A Meitinger, Thomas %A Melander, Olle %A Meyre, David %A Midthjell, Kristian %A Montgomery, Grant W %A Morken, Mario A %A Morris, Andrew P %A Mulic, Rosanda %A Ngwa, Julius S %A Nelis, Mari %A Neville, Matt J %A Nyholt, Dale R %A O'Donnell, Christopher J %A O'Rahilly, Stephen %A Ong, Ken K %A Oostra, Ben %A Paré, Guillaume %A Parker, Alex N %A Perola, Markus %A Pichler, Irene %A Pietiläinen, Kirsi H %A Platou, Carl G P %A Polasek, Ozren %A Pouta, Anneli %A Rafelt, Suzanne %A Raitakari, Olli %A Rayner, Nigel W %A Ridderstråle, Martin %A Rief, Winfried %A Ruokonen, Aimo %A Robertson, Neil R %A Rzehak, Peter %A Salomaa, Veikko %A Sanders, Alan R %A Sandhu, Manjinder S %A Sanna, Serena %A Saramies, Jouko %A Savolainen, Markku J %A Scherag, Susann %A Schipf, Sabine %A Schreiber, Stefan %A Schunkert, Heribert %A Silander, Kaisa %A Sinisalo, Juha %A Siscovick, David S %A Smit, Jan H %A Soranzo, Nicole %A Sovio, Ulla %A Stephens, Jonathan %A Surakka, Ida %A Swift, Amy J %A Tammesoo, Mari-Liis %A Tardif, Jean-Claude %A Teder-Laving, Maris %A Teslovich, Tanya M %A Thompson, John R %A Thomson, Brian %A Tönjes, Anke %A Tuomi, Tiinamaija %A van Meurs, Joyce B J %A van Ommen, Gert-Jan %A Vatin, Vincent %A Viikari, Jorma %A Visvikis-Siest, Sophie %A Vitart, Veronique %A Vogel, Carla I G %A Voight, Benjamin F %A Waite, Lindsay L %A Wallaschofski, Henri %A Walters, G Bragi %A Widen, Elisabeth %A Wiegand, Susanna %A Wild, Sarah H %A Willemsen, Gonneke %A Witte, Daniel R %A Witteman, Jacqueline C %A Xu, Jianfeng %A Zhang, Qunyuan %A Zgaga, Lina %A Ziegler, Andreas %A Zitting, Paavo %A Beilby, John P %A Farooqi, I Sadaf %A Hebebrand, Johannes %A Huikuri, Heikki V %A James, Alan L %A Kähönen, Mika %A Levinson, Douglas F %A Macciardi, Fabio %A Nieminen, Markku S %A Ohlsson, Claes %A Palmer, Lyle J %A Ridker, Paul M %A Stumvoll, Michael %A Beckmann, Jacques S %A Boeing, Heiner %A Boerwinkle, Eric %A Boomsma, Dorret I %A Caulfield, Mark J %A Chanock, Stephen J %A Collins, Francis S %A Cupples, L Adrienne %A Smith, George Davey %A Erdmann, Jeanette %A Froguel, Philippe %A Grönberg, Henrik %A Gyllensten, Ulf %A Hall, Per %A Hansen, Torben %A Harris, Tamara B %A Hattersley, Andrew T %A Hayes, Richard B %A Heinrich, Joachim %A Hu, Frank B %A Hveem, Kristian %A Illig, Thomas %A Jarvelin, Marjo-Riitta %A Kaprio, Jaakko %A Karpe, Fredrik %A Khaw, Kay-Tee %A Kiemeney, Lambertus A %A Krude, Heiko %A Laakso, Markku %A Lawlor, Debbie A %A Metspalu, Andres %A Munroe, Patricia B %A Ouwehand, Willem H %A Pedersen, Oluf %A Penninx, Brenda W %A Peters, Annette %A Pramstaller, Peter P %A Quertermous, Thomas %A Reinehr, Thomas %A Rissanen, Aila %A Rudan, Igor %A Samani, Nilesh J %A Schwarz, Peter E H %A Shuldiner, Alan R %A Spector, Timothy D %A Tuomilehto, Jaakko %A Uda, Manuela %A Uitterlinden, Andre %A Valle, Timo T %A Wabitsch, Martin %A Waeber, Gérard %A Wareham, Nicholas J %A Watkins, Hugh %A Wilson, James F %A Wright, Alan F %A Zillikens, M Carola %A Chatterjee, Nilanjan %A McCarroll, Steven A %A Purcell, Shaun %A Schadt, Eric E %A Visscher, Peter M %A Assimes, Themistocles L %A Borecki, Ingrid B %A Deloukas, Panos %A Fox, Caroline S %A Groop, Leif C %A Haritunians, Talin %A Hunter, David J %A Kaplan, Robert C %A Mohlke, Karen L %A O'Connell, Jeffrey R %A Peltonen, Leena %A Schlessinger, David %A Strachan, David P %A van Duijn, Cornelia M %A Wichmann, H-Erich %A Frayling, Timothy M %A Thorsteinsdottir, Unnur %A Abecasis, Goncalo R %A Barroso, Inês %A Boehnke, Michael %A Stefansson, Kari %A North, Kari E %A McCarthy, Mark I %A Hirschhorn, Joel N %A Ingelsson, Erik %A Loos, Ruth J F %K Body Height %K Body Mass Index %K Body Size %K Body Weight %K Chromosome Mapping %K European Continental Ancestry Group %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Obesity %K Polymorphism, Single Nucleotide %X

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

%B Nat Genet %V 42 %P 937-48 %8 2010 Nov %G eng %N 11 %1 http://www.ncbi.nlm.nih.gov/pubmed/20935630?dopt=Abstract %R 10.1038/ng.686 %0 Journal Article %J Diabetes %D 2011 %T Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. %A Kanoni, Stavroula %A Nettleton, Jennifer A %A Hivert, Marie-France %A Ye, Zheng %A van Rooij, Frank J A %A Shungin, Dmitry %A Sonestedt, Emily %A Ngwa, Julius S %A Wojczynski, Mary K %A Lemaitre, Rozenn N %A Gustafsson, Stefan %A Anderson, Jennifer S %A Tanaka, Toshiko %A Hindy, George %A Saylor, Georgia %A Renstrom, Frida %A Bennett, Amanda J %A van Duijn, Cornelia M %A Florez, Jose C %A Fox, Caroline S %A Hofman, Albert %A Hoogeveen, Ron C %A Houston, Denise K %A Hu, Frank B %A Jacques, Paul F %A Johansson, Ingegerd %A Lind, Lars %A Liu, Yongmei %A McKeown, Nicola %A Ordovas, Jose %A Pankow, James S %A Sijbrands, Eric J G %A Syvänen, Ann-Christine %A Uitterlinden, André G %A Yannakoulia, Mary %A Zillikens, M Carola %A Wareham, Nick J %A Prokopenko, Inga %A Bandinelli, Stefania %A Forouhi, Nita G %A Cupples, L Adrienne %A Loos, Ruth J %A Hallmans, Göran %A Dupuis, Josée %A Langenberg, Claudia %A Ferrucci, Luigi %A Kritchevsky, Stephen B %A McCarthy, Mark I %A Ingelsson, Erik %A Borecki, Ingrid B %A Witteman, Jacqueline C M %A Orho-Melander, Marju %A Siscovick, David S %A Meigs, James B %A Franks, Paul W %A Dedoussis, George V %K Blood Glucose %K Cation Transport Proteins %K Cohort Studies %K Humans %K Polymorphism, Single Nucleotide %K Zinc %K Zinc Transporter 8 %X

OBJECTIVE: Many genetic variants have been associated with glucose homeostasis and type 2 diabetes in genome-wide association studies. Zinc is an essential micronutrient that is important for β-cell function and glucose homeostasis. We tested the hypothesis that zinc intake could influence the glucose-raising effect of specific variants.

RESEARCH DESIGN AND METHODS: We conducted a 14-cohort meta-analysis to assess the interaction of 20 genetic variants known to be related to glycemic traits and zinc metabolism with dietary zinc intake (food sources) and a 5-cohort meta-analysis to assess the interaction with total zinc intake (food sources and supplements) on fasting glucose levels among individuals of European ancestry without diabetes.

RESULTS: We observed a significant association of total zinc intake with lower fasting glucose levels (β-coefficient ± SE per 1 mg/day of zinc intake: -0.0012 ± 0.0003 mmol/L, summary P value = 0.0003), while the association of dietary zinc intake was not significant. We identified a nominally significant interaction between total zinc intake and the SLC30A8 rs11558471 variant on fasting glucose levels (β-coefficient ± SE per A allele for 1 mg/day of greater total zinc intake: -0.0017 ± 0.0006 mmol/L, summary interaction P value = 0.005); this result suggests a stronger inverse association between total zinc intake and fasting glucose in individuals carrying the glucose-raising A allele compared with individuals who do not carry it. None of the other interaction tests were statistically significant.

CONCLUSIONS: Our results suggest that higher total zinc intake may attenuate the glucose-raising effect of the rs11558471 SLC30A8 (zinc transporter) variant. Our findings also support evidence for the association of higher total zinc intake with lower fasting glucose levels.

%B Diabetes %V 60 %P 2407-16 %8 2011 Sep %G eng %N 9 %1 http://www.ncbi.nlm.nih.gov/pubmed/21810599?dopt=Abstract %R 10.2337/db11-0176 %0 Journal Article %J Nat Genet %D 2013 %T Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. %A Berndt, Sonja I %A Gustafsson, Stefan %A Mägi, Reedik %A Ganna, Andrea %A Wheeler, Eleanor %A Feitosa, Mary F %A Justice, Anne E %A Monda, Keri L %A Croteau-Chonka, Damien C %A Day, Felix R %A Esko, Tõnu %A Fall, Tove %A Ferreira, Teresa %A Gentilini, Davide %A Jackson, Anne U %A Luan, Jian'an %A Randall, Joshua C %A Vedantam, Sailaja %A Willer, Cristen J %A Winkler, Thomas W %A Wood, Andrew R %A Workalemahu, Tsegaselassie %A Hu, Yi-Juan %A Lee, Sang Hong %A Liang, Liming %A Lin, Dan-Yu %A Min, Josine L %A Neale, Benjamin M %A Thorleifsson, Gudmar %A Yang, Jian %A Albrecht, Eva %A Amin, Najaf %A Bragg-Gresham, Jennifer L %A Cadby, Gemma %A den Heijer, Martin %A Eklund, Niina %A Fischer, Krista %A Goel, Anuj %A Hottenga, Jouke-Jan %A Huffman, Jennifer E %A Jarick, Ivonne %A Johansson, Asa %A Johnson, Toby %A Kanoni, Stavroula %A Kleber, Marcus E %A König, Inke R %A Kristiansson, Kati %A Kutalik, Zoltán %A Lamina, Claudia %A Lecoeur, Cécile %A Li, Guo %A Mangino, Massimo %A McArdle, Wendy L %A Medina-Gómez, Carolina %A Müller-Nurasyid, Martina %A Ngwa, Julius S %A Nolte, Ilja M %A Paternoster, Lavinia %A Pechlivanis, Sonali %A Perola, Markus %A Peters, Marjolein J %A Preuss, Michael %A Rose, Lynda M %A Shi, Jianxin %A Shungin, Dmitry %A Smith, Albert Vernon %A Strawbridge, Rona J %A Surakka, Ida %A Teumer, Alexander %A Trip, Mieke D %A Tyrer, Jonathan %A van Vliet-Ostaptchouk, Jana V %A Vandenput, Liesbeth %A Waite, Lindsay L %A Zhao, Jing Hua %A Absher, Devin %A Asselbergs, Folkert W %A Atalay, Mustafa %A Attwood, Antony P %A Balmforth, Anthony J %A Basart, Hanneke %A Beilby, John %A Bonnycastle, Lori L %A Brambilla, Paolo %A Bruinenberg, Marcel %A Campbell, Harry %A Chasman, Daniel I %A Chines, Peter S %A Collins, Francis S %A Connell, John M %A Cookson, William O %A de Faire, Ulf %A de Vegt, Femmie %A Dei, Mariano %A Dimitriou, Maria %A Edkins, Sarah %A Estrada, Karol %A Evans, David M %A Farrall, Martin %A Ferrario, Marco M %A Ferrieres, Jean %A Franke, Lude %A Frau, Francesca %A Gejman, Pablo V %A Grallert, Harald %A Grönberg, Henrik %A Gudnason, Vilmundur %A Hall, Alistair S %A Hall, Per %A Hartikainen, Anna-Liisa %A Hayward, Caroline %A Heard-Costa, Nancy L %A Heath, Andrew C %A Hebebrand, Johannes %A Homuth, Georg %A Hu, Frank B %A Hunt, Sarah E %A Hyppönen, Elina %A Iribarren, Carlos %A Jacobs, Kevin B %A Jansson, John-Olov %A Jula, Antti %A Kähönen, Mika %A Kathiresan, Sekar %A Kee, Frank %A Khaw, Kay-Tee %A Kivimaki, Mika %A Koenig, Wolfgang %A Kraja, Aldi T %A Kumari, Meena %A Kuulasmaa, Kari %A Kuusisto, Johanna %A Laitinen, Jaana H %A Lakka, Timo A %A Langenberg, Claudia %A Launer, Lenore J %A Lind, Lars %A Lindström, Jaana %A Liu, Jianjun %A Liuzzi, Antonio %A Lokki, Marja-Liisa %A Lorentzon, Mattias %A Madden, Pamela A %A Magnusson, Patrik K %A Manunta, Paolo %A Marek, Diana %A März, Winfried %A Mateo Leach, Irene %A McKnight, Barbara %A Medland, Sarah E %A Mihailov, Evelin %A Milani, Lili %A Montgomery, Grant W %A Mooser, Vincent %A Mühleisen, Thomas W %A Munroe, Patricia B %A Musk, Arthur W %A Narisu, Narisu %A Navis, Gerjan %A Nicholson, George %A Nohr, Ellen A %A Ong, Ken K %A Oostra, Ben A %A Palmer, Colin N A %A Palotie, Aarno %A Peden, John F %A Pedersen, Nancy %A Peters, Annette %A Polasek, Ozren %A Pouta, Anneli %A Pramstaller, Peter P %A Prokopenko, Inga %A Pütter, Carolin %A Radhakrishnan, Aparna %A Raitakari, Olli %A Rendon, Augusto %A Rivadeneira, Fernando %A Rudan, Igor %A Saaristo, Timo E %A Sambrook, Jennifer G %A Sanders, Alan R %A Sanna, Serena %A Saramies, Jouko %A Schipf, Sabine %A Schreiber, Stefan %A Schunkert, Heribert %A Shin, So-Youn %A Signorini, Stefano %A Sinisalo, Juha %A Skrobek, Boris %A Soranzo, Nicole %A Stančáková, Alena %A Stark, Klaus %A Stephens, Jonathan C %A Stirrups, Kathleen %A Stolk, Ronald P %A Stumvoll, Michael %A Swift, Amy J %A Theodoraki, Eirini V %A Thorand, Barbara %A Trégouët, David-Alexandre %A Tremoli, Elena %A van der Klauw, Melanie M %A van Meurs, Joyce B J %A Vermeulen, Sita H %A Viikari, Jorma %A Virtamo, Jarmo %A Vitart, Veronique %A Waeber, Gérard %A Wang, Zhaoming %A Widen, Elisabeth %A Wild, Sarah H %A Willemsen, Gonneke %A Winkelmann, Bernhard R %A Witteman, Jacqueline C M %A Wolffenbuttel, Bruce H R %A Wong, Andrew %A Wright, Alan F %A Zillikens, M Carola %A Amouyel, Philippe %A Boehm, Bernhard O %A Boerwinkle, Eric %A Boomsma, Dorret I %A Caulfield, Mark J %A Chanock, Stephen J %A Cupples, L Adrienne %A Cusi, Daniele %A Dedoussis, George V %A Erdmann, Jeanette %A Eriksson, Johan G %A Franks, Paul W %A Froguel, Philippe %A Gieger, Christian %A Gyllensten, Ulf %A Hamsten, Anders %A Harris, Tamara B %A Hengstenberg, Christian %A Hicks, Andrew A %A Hingorani, Aroon %A Hinney, Anke %A Hofman, Albert %A Hovingh, Kees G %A Hveem, Kristian %A Illig, Thomas %A Jarvelin, Marjo-Riitta %A Jöckel, Karl-Heinz %A Keinanen-Kiukaanniemi, Sirkka M %A Kiemeney, Lambertus A %A Kuh, Diana %A Laakso, Markku %A Lehtimäki, Terho %A Levinson, Douglas F %A Martin, Nicholas G %A Metspalu, Andres %A Morris, Andrew D %A Nieminen, Markku S %A Njølstad, Inger %A Ohlsson, Claes %A Oldehinkel, Albertine J %A Ouwehand, Willem H %A Palmer, Lyle J %A Penninx, Brenda %A Power, Chris %A Province, Michael A %A Psaty, Bruce M %A Qi, Lu %A Rauramaa, Rainer %A Ridker, Paul M %A Ripatti, Samuli %A Salomaa, Veikko %A Samani, Nilesh J %A Snieder, Harold %A Sørensen, Thorkild I A %A Spector, Timothy D %A Stefansson, Kari %A Tönjes, Anke %A Tuomilehto, Jaakko %A Uitterlinden, André G %A Uusitupa, Matti %A van der Harst, Pim %A Vollenweider, Peter %A Wallaschofski, Henri %A Wareham, Nicholas J %A Watkins, Hugh %A Wichmann, H-Erich %A Wilson, James F %A Abecasis, Goncalo R %A Assimes, Themistocles L %A Barroso, Inês %A Boehnke, Michael %A Borecki, Ingrid B %A Deloukas, Panos %A Fox, Caroline S %A Frayling, Timothy %A Groop, Leif C %A Haritunian, Talin %A Heid, Iris M %A Hunter, David %A Kaplan, Robert C %A Karpe, Fredrik %A Moffatt, Miriam F %A Mohlke, Karen L %A O'Connell, Jeffrey R %A Pawitan, Yudi %A Schadt, Eric E %A Schlessinger, David %A Steinthorsdottir, Valgerdur %A Strachan, David P %A Thorsteinsdottir, Unnur %A van Duijn, Cornelia M %A Visscher, Peter M %A Di Blasio, Anna Maria %A Hirschhorn, Joel N %A Lindgren, Cecilia M %A Morris, Andrew P %A Meyre, David %A Scherag, Andre %A McCarthy, Mark I %A Speliotes, Elizabeth K %A North, Kari E %A Loos, Ruth J F %A Ingelsson, Erik %K Anthropometry %K Body Height %K Body Mass Index %K Case-Control Studies %K European Continental Ancestry Group %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Genotype %K Humans %K Meta-Analysis as Topic %K Obesity %K Phenotype %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %K Waist-Hip Ratio %X

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

%B Nat Genet %V 45 %P 501-12 %8 2013 May %G eng %N 5 %1 http://www.ncbi.nlm.nih.gov/pubmed/23563607?dopt=Abstract %R 10.1038/ng.2606 %0 Journal Article %J Am J Clin Nutr %D 2013 %T Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. %A Tanaka, Toshiko %A Ngwa, Julius S %A van Rooij, Frank J A %A Zillikens, M Carola %A Wojczynski, Mary K %A Frazier-Wood, Alexis C %A Houston, Denise K %A Kanoni, Stavroula %A Lemaitre, Rozenn N %A Luan, Jian'an %A Mikkilä, Vera %A Renstrom, Frida %A Sonestedt, Emily %A Zhao, Jing Hua %A Chu, Audrey Y %A Qi, Lu %A Chasman, Daniel I %A de Oliveira Otto, Marcia C %A Dhurandhar, Emily J %A Feitosa, Mary F %A Johansson, Ingegerd %A Khaw, Kay-Tee %A Lohman, Kurt K %A Manichaikul, Ani %A McKeown, Nicola M %A Mozaffarian, Dariush %A Singleton, Andrew %A Stirrups, Kathleen %A Viikari, Jorma %A Ye, Zheng %A Bandinelli, Stefania %A Barroso, Inês %A Deloukas, Panos %A Forouhi, Nita G %A Hofman, Albert %A Liu, Yongmei %A Lyytikäinen, Leo-Pekka %A North, Kari E %A Dimitriou, Maria %A Hallmans, Göran %A Kähönen, Mika %A Langenberg, Claudia %A Ordovas, Jose M %A Uitterlinden, André G %A Hu, Frank B %A Kalafati, Ioanna-Panagiota %A Raitakari, Olli %A Franco, Oscar H %A Johnson, Andrew %A Emilsson, Valur %A Schrack, Jennifer A %A Semba, Richard D %A Siscovick, David S %A Arnett, Donna K %A Borecki, Ingrid B %A Franks, Paul W %A Kritchevsky, Stephen B %A Lehtimäki, Terho %A Loos, Ruth J F %A Orho-Melander, Marju %A Rotter, Jerome I %A Wareham, Nicholas J %A Witteman, Jacqueline C M %A Ferrucci, Luigi %A Dedoussis, George %A Cupples, L Adrienne %A Nettleton, Jennifer A %K Alleles %K Atherosclerosis %K Body Mass Index %K Dietary Carbohydrates %K Dietary Fats %K Dietary Proteins %K Energy Intake %K European Continental Ancestry Group %K Fibroblast Growth Factors %K Follow-Up Studies %K Gene-Environment Interaction %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Genotype %K Humans %K Life Style %K Obesity %K Polymorphism, Single Nucleotide %K Prospective Studies %K Quantitative Trait Loci %K Surveys and Questionnaires %X

BACKGROUND: Macronutrient intake varies substantially between individuals, and there is evidence that this variation is partly accounted for by genetic variants.

OBJECTIVE: The objective of the study was to identify common genetic variants that are associated with macronutrient intake.

DESIGN: We performed 2-stage genome-wide association (GWA) meta-analysis of macronutrient intake in populations of European descent. Macronutrients were assessed by using food-frequency questionnaires and analyzed as percentages of total energy consumption from total fat, protein, and carbohydrate. From the discovery GWA (n = 38,360), 35 independent loci associated with macronutrient intake at P < 5 × 10(-6) were identified and taken forward to replication in 3 additional cohorts (n = 33,533) from the DietGen Consortium. For one locus, fat mass obesity-associated protein (FTO), cohorts with Illumina MetaboChip genotype data (n = 7724) provided additional replication data.

RESULTS: A variant in the chromosome 19 locus (rs838145) was associated with higher carbohydrate (β ± SE: 0.25 ± 0.04%; P = 1.68 × 10(-8)) and lower fat (β ± SE: -0.21 ± 0.04%; P = 1.57 × 10(-9)) consumption. A candidate gene in this region, fibroblast growth factor 21 (FGF21), encodes a fibroblast growth factor involved in glucose and lipid metabolism. The variants in this locus were associated with circulating FGF21 protein concentrations (P < 0.05) but not mRNA concentrations in blood or brain. The body mass index (BMI)-increasing allele of the FTO variant (rs1421085) was associated with higher protein intake (β ± SE: 0.10 ± 0.02%; P = 9.96 × 10(-10)), independent of BMI (after adjustment for BMI, β ± SE: 0.08 ± 0.02%; P = 3.15 × 10(-7)).

CONCLUSION: Our results indicate that variants in genes involved in nutrient metabolism and obesity are associated with macronutrient consumption in humans. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetic and Environmental Determinants of Triglycerides), NCT01331512 (InCHIANTI Study), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

%B Am J Clin Nutr %V 97 %P 1395-402 %8 2013 Jun %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/23636237?dopt=Abstract %R 10.3945/ajcn.112.052183 %0 Journal Article %J J Nutr %D 2013 %T Higher magnesium intake is associated with lower fasting glucose and insulin, with no evidence of interaction with select genetic loci, in a meta-analysis of 15 CHARGE Consortium Studies. %A Hruby, Adela %A Ngwa, Julius S %A Renstrom, Frida %A Wojczynski, Mary K %A Ganna, Andrea %A Hallmans, Göran %A Houston, Denise K %A Jacques, Paul F %A Kanoni, Stavroula %A Lehtimäki, Terho %A Lemaitre, Rozenn N %A Manichaikul, Ani %A North, Kari E %A Ntalla, Ioanna %A Sonestedt, Emily %A Tanaka, Toshiko %A van Rooij, Frank J A %A Bandinelli, Stefania %A Djoussé, Luc %A Grigoriou, Efi %A Johansson, Ingegerd %A Lohman, Kurt K %A Pankow, James S %A Raitakari, Olli T %A Riserus, Ulf %A Yannakoulia, Mary %A Zillikens, M Carola %A Hassanali, Neelam %A Liu, Yongmei %A Mozaffarian, Dariush %A Papoutsakis, Constantina %A Syvänen, Ann-Christine %A Uitterlinden, André G %A Viikari, Jorma %A Groves, Christopher J %A Hofman, Albert %A Lind, Lars %A McCarthy, Mark I %A Mikkilä, Vera %A Mukamal, Kenneth %A Franco, Oscar H %A Borecki, Ingrid B %A Cupples, L Adrienne %A Dedoussis, George V %A Ferrucci, Luigi %A Hu, Frank B %A Ingelsson, Erik %A Kähönen, Mika %A Kao, W H Linda %A Kritchevsky, Stephen B %A Orho-Melander, Marju %A Prokopenko, Inga %A Rotter, Jerome I %A Siscovick, David S %A Witteman, Jacqueline C M %A Franks, Paul W %A Meigs, James B %A McKeown, Nicola M %A Nettleton, Jennifer A %K Blood Glucose %K Female %K Genetic Loci %K Humans %K Insulin %K Magnesium %K Male %K Polymorphism, Single Nucleotide %K Trace Elements %K TRPM Cation Channels %X

Favorable associations between magnesium intake and glycemic traits, such as fasting glucose and insulin, are observed in observational and clinical studies, but whether genetic variation affects these associations is largely unknown. We hypothesized that single nucleotide polymorphisms (SNPs) associated with either glycemic traits or magnesium metabolism affect the association between magnesium intake and fasting glucose and insulin. Fifteen studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided data from up to 52,684 participants of European descent without known diabetes. In fixed-effects meta-analyses, we quantified 1) cross-sectional associations of dietary magnesium intake with fasting glucose (mmol/L) and insulin (ln-pmol/L) and 2) interactions between magnesium intake and SNPs related to fasting glucose (16 SNPs), insulin (2 SNPs), or magnesium (8 SNPs) on fasting glucose and insulin. After adjustment for age, sex, energy intake, BMI, and behavioral risk factors, magnesium (per 50-mg/d increment) was inversely associated with fasting glucose [β = -0.009 mmol/L (95% CI: -0.013, -0.005), P < 0.0001] and insulin [-0.020 ln-pmol/L (95% CI: -0.024, -0.017), P < 0.0001]. No magnesium-related SNP or interaction between any SNP and magnesium reached significance after correction for multiple testing. However, rs2274924 in magnesium transporter-encoding TRPM6 showed a nominal association (uncorrected P = 0.03) with glucose, and rs11558471 in SLC30A8 and rs3740393 near CNNM2 showed a nominal interaction (uncorrected, both P = 0.02) with magnesium on glucose. Consistent with other studies, a higher magnesium intake was associated with lower fasting glucose and insulin. Nominal evidence of TRPM6 influence and magnesium interaction with select loci suggests that further investigation is warranted.

%B J Nutr %V 143 %P 345-53 %8 2013 Mar %G eng %N 3 %1 http://www.ncbi.nlm.nih.gov/pubmed/23343670?dopt=Abstract %R 10.3945/jn.112.172049 %0 Journal Article %J Hum Mol Genet %D 2014 %T FTO genetic variants, dietary intake and body mass index: insights from 177,330 individuals. %A Qi, Qibin %A Kilpeläinen, Tuomas O %A Downer, Mary K %A Tanaka, Toshiko %A Smith, Caren E %A Sluijs, Ivonne %A Sonestedt, Emily %A Chu, Audrey Y %A Renstrom, Frida %A Lin, Xiaochen %A Ängquist, Lars H %A Huang, Jinyan %A Liu, Zhonghua %A Li, Yanping %A Asif Ali, Muhammad %A Xu, Min %A Ahluwalia, Tarunveer Singh %A Boer, Jolanda M A %A Chen, Peng %A Daimon, Makoto %A Eriksson, Johan %A Perola, Markus %A Friedlander, Yechiel %A Gao, Yu-Tang %A Heppe, Denise H M %A Holloway, John W %A Houston, Denise K %A Kanoni, Stavroula %A Kim, Yu-Mi %A Laaksonen, Maarit A %A Jääskeläinen, Tiina %A Lee, Nanette R %A Lehtimäki, Terho %A Lemaitre, Rozenn N %A Lu, Wei %A Luben, Robert N %A Manichaikul, Ani %A Männistö, Satu %A Marques-Vidal, Pedro %A Monda, Keri L %A Ngwa, Julius S %A Perusse, Louis %A van Rooij, Frank J A %A Xiang, Yong-Bing %A Wen, Wanqing %A Wojczynski, Mary K %A Zhu, Jingwen %A Borecki, Ingrid B %A Bouchard, Claude %A Cai, Qiuyin %A Cooper, Cyrus %A Dedoussis, George V %A Deloukas, Panos %A Ferrucci, Luigi %A Forouhi, Nita G %A Hansen, Torben %A Christiansen, Lene %A Hofman, Albert %A Johansson, Ingegerd %A Jørgensen, Torben %A Karasawa, Shigeru %A Khaw, Kay-Tee %A Kim, Mi-Kyung %A Kristiansson, Kati %A Li, Huaixing %A Lin, Xu %A Liu, Yongmei %A Lohman, Kurt K %A Long, Jirong %A Mikkilä, Vera %A Mozaffarian, Dariush %A North, Kari %A Pedersen, Oluf %A Raitakari, Olli %A Rissanen, Harri %A Tuomilehto, Jaakko %A van der Schouw, Yvonne T %A Uitterlinden, André G %A Zillikens, M Carola %A Franco, Oscar H %A Shyong Tai, E %A Ou Shu, Xiao %A Siscovick, David S %A Toft, Ulla %A Verschuren, W M Monique %A Vollenweider, Peter %A Wareham, Nicholas J %A Witteman, Jacqueline C M %A Zheng, Wei %A Ridker, Paul M %A Kang, Jae H %A Liang, Liming %A Jensen, Majken K %A Curhan, Gary C %A Pasquale, Louis R %A Hunter, David J %A Mohlke, Karen L %A Uusitupa, Matti %A Cupples, L Adrienne %A Rankinen, Tuomo %A Orho-Melander, Marju %A Wang, Tao %A Chasman, Daniel I %A Franks, Paul W %A Sørensen, Thorkild I A %A Hu, Frank B %A Loos, Ruth J F %A Nettleton, Jennifer A %A Qi, Lu %K Adult %K African Americans %K Aged %K Alleles %K Asian Continental Ancestry Group %K Body Mass Index %K Dietary Carbohydrates %K Dietary Fats %K Dietary Proteins %K Energy Intake %K European Continental Ancestry Group %K Female %K Gene Frequency %K Humans %K Male %K Middle Aged %K Obesity %K Polymorphism, Single Nucleotide %K Proteins %X

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 × 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 × 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

%B Hum Mol Genet %V 23 %P 6961-72 %8 2014 Dec 20 %G eng %N 25 %1 http://www.ncbi.nlm.nih.gov/pubmed/25104851?dopt=Abstract %R 10.1093/hmg/ddu411 %0 Journal Article %J Nat Commun %D 2014 %T Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. %A Postmus, Iris %A Trompet, Stella %A Deshmukh, Harshal A %A Barnes, Michael R %A Li, Xiaohui %A Warren, Helen R %A Chasman, Daniel I %A Zhou, Kaixin %A Arsenault, Benoit J %A Donnelly, Louise A %A Wiggins, Kerri L %A Avery, Christy L %A Griffin, Paula %A Feng, QiPing %A Taylor, Kent D %A Li, Guo %A Evans, Daniel S %A Smith, Albert V %A de Keyser, Catherine E %A Johnson, Andrew D %A de Craen, Anton J M %A Stott, David J %A Buckley, Brendan M %A Ford, Ian %A Westendorp, Rudi G J %A Slagboom, P Eline %A Sattar, Naveed %A Munroe, Patricia B %A Sever, Peter %A Poulter, Neil %A Stanton, Alice %A Shields, Denis C %A O'Brien, Eoin %A Shaw-Hawkins, Sue %A Chen, Y-D Ida %A Nickerson, Deborah A %A Smith, Joshua D %A Dubé, Marie Pierre %A Boekholdt, S Matthijs %A Hovingh, G Kees %A Kastelein, John J P %A McKeigue, Paul M %A Betteridge, John %A Neil, Andrew %A Durrington, Paul N %A Doney, Alex %A Carr, Fiona %A Morris, Andrew %A McCarthy, Mark I %A Groop, Leif %A Ahlqvist, Emma %A Bis, Joshua C %A Rice, Kenneth %A Smith, Nicholas L %A Lumley, Thomas %A Whitsel, Eric A %A Stürmer, Til %A Boerwinkle, Eric %A Ngwa, Julius S %A O'Donnell, Christopher J %A Vasan, Ramachandran S %A Wei, Wei-Qi %A Wilke, Russell A %A Liu, Ching-Ti %A Sun, Fangui %A Guo, Xiuqing %A Heckbert, Susan R %A Post, Wendy %A Sotoodehnia, Nona %A Arnold, Alice M %A Stafford, Jeanette M %A Ding, Jingzhong %A Herrington, David M %A Kritchevsky, Stephen B %A Eiriksdottir, Gudny %A Launer, Leonore J %A Harris, Tamara B %A Chu, Audrey Y %A Giulianini, Franco %A MacFadyen, Jean G %A Barratt, Bryan J %A Nyberg, Fredrik %A Stricker, Bruno H %A Uitterlinden, André G %A Hofman, Albert %A Rivadeneira, Fernando %A Emilsson, Valur %A Franco, Oscar H %A Ridker, Paul M %A Gudnason, Vilmundur %A Liu, Yongmei %A Denny, Joshua C %A Ballantyne, Christie M %A Rotter, Jerome I %A Adrienne Cupples, L %A Psaty, Bruce M %A Palmer, Colin N A %A Tardif, Jean-Claude %A Colhoun, Helen M %A Hitman, Graham %A Krauss, Ronald M %A Wouter Jukema, J %A Caulfield, Mark J %K Cholesterol, LDL %K Genome-Wide Association Study %K Humans %K Hydroxymethylglutaryl-CoA Reductase Inhibitors %K Pharmacogenetics %K Polymorphism, Single Nucleotide %X

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

%B Nat Commun %V 5 %P 5068 %8 2014 Oct 28 %G eng %1 http://www.ncbi.nlm.nih.gov/pubmed/25350695?dopt=Abstract %R 10.1038/ncomms6068 %0 Journal Article %J Am J Clin Nutr %D 2015 %T Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians. %A Fretts, Amanda M %A Follis, Jack L %A Nettleton, Jennifer A %A Lemaitre, Rozenn N %A Ngwa, Julius S %A Wojczynski, Mary K %A Kalafati, Ioanna Panagiota %A Varga, Tibor V %A Frazier-Wood, Alexis C %A Houston, Denise K %A Lahti, Jari %A Ericson, Ulrika %A van den Hooven, Edith H %A Mikkilä, Vera %A Kiefte-de Jong, Jessica C %A Mozaffarian, Dariush %A Rice, Kenneth %A Renstrom, Frida %A North, Kari E %A McKeown, Nicola M %A Feitosa, Mary F %A Kanoni, Stavroula %A Smith, Caren E %A Garcia, Melissa E %A Tiainen, Anna-Maija %A Sonestedt, Emily %A Manichaikul, Ani %A van Rooij, Frank J A %A Dimitriou, Maria %A Raitakari, Olli %A Pankow, James S %A Djoussé, Luc %A Province, Michael A %A Hu, Frank B %A Lai, Chao-Qiang %A Keller, Margaux F %A Perälä, Mia-Maria %A Rotter, Jerome I %A Hofman, Albert %A Graff, Misa %A Kähönen, Mika %A Mukamal, Kenneth %A Johansson, Ingegerd %A Ordovas, Jose M %A Liu, Yongmei %A Männistö, Satu %A Uitterlinden, André G %A Deloukas, Panos %A Seppälä, Ilkka %A Psaty, Bruce M %A Cupples, L Adrienne %A Borecki, Ingrid B %A Franks, Paul W %A Arnett, Donna K %A Nalls, Mike A %A Eriksson, Johan G %A Orho-Melander, Marju %A Franco, Oscar H %A Lehtimäki, Terho %A Dedoussis, George V %A Meigs, James B %A Siscovick, David S %K Blood Glucose %K Cohort Studies %K Genetic Association Studies %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Hyperglycemia %K Hyperinsulinism %K Insulin %K Insulin Resistance %K Insulin-Secreting Cells %K Meat %K Meat Products %K Middle Aged %K Polymorphism, Single Nucleotide %K Risk Factors %X

BACKGROUND: Recent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown.

OBJECTIVE: We investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus.

DESIGN: Fourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations.

RESULTS: Processed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance.

CONCLUSION: The association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

%B Am J Clin Nutr %V 102 %P 1266-78 %8 2015 Nov %G eng %N 5 %1 http://www.ncbi.nlm.nih.gov/pubmed/26354543?dopt=Abstract %R 10.3945/ajcn.114.101238 %0 Journal Article %J Hum Mol Genet %D 2015 %T Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry. %A Nettleton, Jennifer A %A Follis, Jack L %A Ngwa, Julius S %A Smith, Caren E %A Ahmad, Shafqat %A Tanaka, Toshiko %A Wojczynski, Mary K %A Voortman, Trudy %A Lemaitre, Rozenn N %A Kristiansson, Kati %A Nuotio, Marja-Liisa %A Houston, Denise K %A Perälä, Mia-Maria %A Qi, Qibin %A Sonestedt, Emily %A Manichaikul, Ani %A Kanoni, Stavroula %A Ganna, Andrea %A Mikkilä, Vera %A North, Kari E %A Siscovick, David S %A Harald, Kennet %A McKeown, Nicola M %A Johansson, Ingegerd %A Rissanen, Harri %A Liu, Yongmei %A Lahti, Jari %A Hu, Frank B %A Bandinelli, Stefania %A Rukh, Gull %A Rich, Stephen %A Booij, Lisanne %A Dmitriou, Maria %A Ax, Erika %A Raitakari, Olli %A Mukamal, Kenneth %A Männistö, Satu %A Hallmans, Göran %A Jula, Antti %A Ericson, Ulrika %A Jacobs, David R %A van Rooij, Frank J A %A Deloukas, Panos %A Sjogren, Per %A Kähönen, Mika %A Djoussé, Luc %A Perola, Markus %A Barroso, Inês %A Hofman, Albert %A Stirrups, Kathleen %A Viikari, Jorma %A Uitterlinden, André G %A Kalafati, Ioanna P %A Franco, Oscar H %A Mozaffarian, Dariush %A Salomaa, Veikko %A Borecki, Ingrid B %A Knekt, Paul %A Kritchevsky, Stephen B %A Eriksson, Johan G %A Dedoussis, George V %A Qi, Lu %A Ferrucci, Luigi %A Orho-Melander, Marju %A Zillikens, M Carola %A Ingelsson, Erik %A Lehtimäki, Terho %A Renstrom, Frida %A Cupples, L Adrienne %A Loos, Ruth J F %A Franks, Paul W %K Adult %K Body Mass Index %K Case-Control Studies %K Diet, Western %K Epistasis, Genetic %K European Continental Ancestry Group %K Female %K Genetic Loci %K Genome-Wide Association Study %K Humans %K Male %K Obesity %K Polymorphism, Single Nucleotide %X

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006-0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.

%B Hum Mol Genet %V 24 %P 4728-38 %8 2015 Aug 15 %G eng %N 16 %1 http://www.ncbi.nlm.nih.gov/pubmed/25994509?dopt=Abstract %R 10.1093/hmg/ddv186 %0 Journal Article %J PLoS One %D 2017 %T Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts. %A Mozaffarian, Dariush %A Dashti, Hassan S %A Wojczynski, Mary K %A Chu, Audrey Y %A Nettleton, Jennifer A %A Männistö, Satu %A Kristiansson, Kati %A Reedik, Mägi %A Lahti, Jari %A Houston, Denise K %A Cornelis, Marilyn C %A van Rooij, Frank J A %A Dimitriou, Maria %A Kanoni, Stavroula %A Mikkilä, Vera %A Steffen, Lyn M %A de Oliveira Otto, Marcia C %A Qi, Lu %A Psaty, Bruce %A Djoussé, Luc %A Rotter, Jerome I %A Harald, Kennet %A Perola, Markus %A Rissanen, Harri %A Jula, Antti %A Krista, Fischer %A Mihailov, Evelin %A Feitosa, Mary F %A Ngwa, Julius S %A Xue, Luting %A Jacques, Paul F %A Perälä, Mia-Maria %A Palotie, Aarno %A Liu, Yongmei %A Nalls, Nike A %A Ferrucci, Luigi %A Hernandez, Dena %A Manichaikul, Ani %A Tsai, Michael Y %A Kiefte-de Jong, Jessica C %A Hofman, Albert %A Uitterlinden, André G %A Rallidis, Loukianos %A Ridker, Paul M %A Rose, Lynda M %A Buring, Julie E %A Lehtimäki, Terho %A Kähönen, Mika %A Viikari, Jorma %A Lemaitre, Rozenn %A Salomaa, Veikko %A Knekt, Paul %A Metspalu, Andres %A Borecki, Ingrid B %A Cupples, L Adrienne %A Eriksson, Johan G %A Kritchevsky, Stephen B %A Bandinelli, Stefania %A Siscovick, David %A Franco, Oscar H %A Deloukas, Panos %A Dedoussis, George %A Chasman, Daniel I %A Raitakari, Olli %A Tanaka, Toshiko %K Adult %K Aged %K Cohort Studies %K Docosahexaenoic Acids %K Eicosapentaenoic Acid %K Europe %K European Continental Ancestry Group %K Female %K Genome-Wide Association Study %K Humans %K Male %K Middle Aged %K Seafood %K United States %X

BACKGROUND: Regular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences.

OBJECTIVE: To identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption.

DESIGN: We conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts.

RESULTS: Heritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (FreqA = 0.015) was associated with 0.029 servings/day (~1 serving/month) lower fish consumption (P = 1.96x10-8). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10-7). Post-hoc calculations demonstrated 95% statistical power to detect a genetic variant associated with effect size of 0.05% for fish and 0.08% for EPA+DHA.

CONCLUSIONS: These novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.

%B PLoS One %V 12 %P e0186456 %8 2017 %G eng %N 12 %R 10.1371/journal.pone.0186456