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1.  Genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease 
Scott, Robert A. | Freitag, Daniel F. | Li, Li | Chu, Audrey Y. | Surendran, Praveen | Young, Robin | Grarup, Niels | Stancáková, Alena | Chen, Yuning | V.Varga, Tibor | Yaghootkar, Hanieh | Luan, Jian'an | Zhao, Jing Hua | Willems, Sara M. | Wessel, Jennifer | Wang, Shuai | Maruthur, Nisa | Michailidou, Kyriaki | Pirie, Ailith | van der Lee, Sven J. | Gillson, Christopher | Olama, Ali Amin Al | Amouyel, Philippe | Arriola, Larraitz | Arveiler, Dominique | Aviles-Olmos, Iciar | Balkau, Beverley | Barricarte, Aurelio | Barroso, Inês | Garcia, Sara Benlloch | Bis, Joshua C. | Blankenberg, Stefan | Boehnke, Michael | Boeing, Heiner | Boerwinkle, Eric | Borecki, Ingrid B. | Bork-Jensen, Jette | Bowden, Sarah | Caldas, Carlos | Caslake, Muriel | Cupples, L. Adrienne | Cruchaga, Carlos | Czajkowski, Jacek | den Hoed, Marcel | Dunn, Janet A. | Earl, Helena M. | Ehret, Georg B. | Ferrannini, Ele | Ferrieres, Jean | Foltynie, Thomas | Ford, Ian | Forouhi, Nita G. | Gianfagna, Francesco | Gonzalez, Carlos | Grioni, Sara | Hiller, Louise | Jansson, Jan-Håkan | Jørgensen, Marit E. | Jukema, J. Wouter | Kaaks, Rudolf | Kee, Frank | Kerrison, Nicola D. | Key, Timothy J. | Kontto, Jukka | Kote-Jarai, Zsofia | Kraja, Aldi T. | Kuulasmaa, Kari | Kuusisto, Johanna | Linneberg, Allan | Liu, Chunyu | Marenne, Gaëlle | Mohlke, Karen L. | Morris, Andrew P. | Muir, Kenneth | Müller-Nurasyid, Martina | Munroe, Patricia B. | Navarro, Carmen | Nielsen, Sune F. | Nilsson, Peter M. | Nordestgaard, Børge G. | Packard, Chris J. | Palli, Domenico | Panico, Salvatore | Peloso, Gina M. | Perola, Markus | Peters, Annette | Poole, Christopher J. | Quirós, J. Ramón | Rolandsson, Olov | Sacerdote, Carlotta | Salomaa, Veikko | Sánchez, María-José | Sattar, Naveed | Sharp, Stephen J. | Sims, Rebecca | Slimani, Nadia | Smith, Jennifer A. | Thompson, Deborah J. | Trompet, Stella | Tumino, Rosario | van der A, Daphne L. | van der Schouw, Yvonne T. | Virtamo, Jarmo | Walker, Mark | Walter, Klaudia | Abraham, Jean E. | Amundadottir, Laufey T. | Aponte, Jennifer L. | Butterworth, Adam S. | Dupuis, Josée | Easton, Douglas F. | Eeles, Rosalind A. | Erdmann, Jeanette | Franks, Paul W. | Frayling, Timothy M. | Hansen, Torben | Howson, Joanna M. M. | Jørgensen, Torben | Kooner, Jaspal | Laakso, Markku | Langenberg, Claudia | McCarthy, Mark I. | Pankow, James S. | Pedersen, Oluf | Riboli, Elio | Rotter, Jerome I. | Saleheen, Danish | Samani, Nilesh J. | Schunkert, Heribert | Vollenweider, Peter | O'Rahilly, Stephen | Deloukas, Panos | Danesh, John | Goodarzi, Mark O. | Kathiresan, Sekar | Meigs, James B. | Ehm, Margaret G. | Wareham, Nicholas J. | Waterworth, Dawn M.
Science translational medicine  2016;8(341):341ra76.
Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to inform development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in 6 genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing, and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr;rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and lower T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomised controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.
doi:10.1126/scitranslmed.aad3744
PMCID: PMC5219001  PMID: 27252175
2.  Genetic evidence for causal relationships between maternal obesity-related traits and birth weight 
JAMA  2016;315(11):1129-1140.
Structured abstract
Importance
Neonates born to overweight/obese women are larger and at higher risk of birth complications. Many maternal obesity-related traits are observationally associated with birth weight, but the causal nature of these associations is uncertain.
Objective
To test for genetic evidence of causal associations of maternal body mass index (BMI) and related traits with birth weight.
Design, Setting and Participants
We used Mendelian randomization to test whether maternal BMI and obesity-related traits are causally related to offspring birth weight. Mendelian randomization makes use of the fact that genotypes are randomly determined at conception and are thus not confounded by non-genetic factors. Data were analysed on 30,487 women from 18 studies. Participants were of European ancestry from population- or community-based studies located in Europe, North America or Australia and participating in the Early Growth Genetics (EGG) Consortium. Live, term, singleton offspring born between 1929 and 2013 were included. We tested associations between a genetic score of 30 BMI-associated single nucleotide polymorphisms (SNPs) and (i) maternal BMI and (ii) birth weight, to estimate the causal relationship between BMI and birth weight. Analyses were repeated for other obesity-related traits.
Exposures
Genetic scores for BMI, fasting glucose level, type 2 diabetes, systolic blood pressure (SBP), triglyceride level, HDL-cholesterol level, vitamin D status and adiponectin level.
Main Outcome(s) and Measure(s)
Offspring birth weight measured by trained study personnel (n=2 studies), from medical records (n= 10 studies) or from maternal report (n=6 studies).
Results
Among the 30,487 newborns the mean birth weight in the various cohorts ranged from 3325 g to 3679 g. The genetic score for BMI was associated with a 2g (95%CI: 0, 3g) higher offspring birth weight per maternal BMI-raising allele (P=0.008). The maternal genetic scores for fasting glucose and SBP were also associated with birth weight with effect sizes of 8g (95%CI: 6, 10g) per glucose-raising allele (P=7×10−14) and −4g (95%CI: −6, −2g) per SBP-raising allele (P=1×10−5), respectively. A 1 standard deviation (1 SD ≈ 4kg/m2) genetically higher maternal BMI was associated with a 55g (95% CI: 17, 93g) higher birth weight. A 1-SD genetically higher maternal fasting glucose (≈ 0.4mmol/L) or SBP (10mmHg) were associated with a 114g (95%CI: 80, 147g) higher or −208g (95% CI: −394, −21g) lower birth weight, respectively. For BMI and fasting glucose these genetic associations were consistent with the observational associations, but for SBP, the genetic and observational associations were in opposite directions.
Conclusions and Relevance
In this Mendelian randomization study of more than 30,000 women with singleton offspring from 18 studies, genetically elevated maternal BMI and blood glucose levels were potentially causally associated with higher offspring birth weight, whereas genetically elevated maternal systolic blood pressure was shown to be potentially causally related to lower birth weight. If replicated, these findings may have implications for counseling and managing pregnancies to avoid adverse weight-related birth outcomes.
doi:10.1001/jama.2016.1975
PMCID: PMC4811305  PMID: 26978208
3.  Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals 
Human Molecular Genetics  2015;24(12):3582-3594.
Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m2], but their contribution to common obesity (BMI ≥ 30 kg/m2) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06–1.24, P = 6.08 × 10−6) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04–1.10, P = 3.00 × 10−7). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00–0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00–0.03; P = 5.57 × 10−4). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.
doi:10.1093/hmg/ddv097
PMCID: PMC4498155  PMID: 25784503
4.  Genetic studies of body mass index yield new insights for obesity biology 
Locke, Adam E. | Kahali, Bratati | Berndt, Sonja I. | Justice, Anne E. | Pers, Tune H. | Day, Felix R. | Powell, Corey | Vedantam, Sailaja | Buchkovich, Martin L. | Yang, Jian | Croteau-Chonka, Damien C. | Esko, Tonu | Fall, Tove | Ferreira, Teresa | Gustafsson, Stefan | Kutalik, Zoltán | Luan, Jian’an | Mägi, Reedik | Randall, Joshua C. | Winkler, Thomas W. | Wood, Andrew R. | Workalemahu, Tsegaselassie | Faul, Jessica D. | Smith, Jennifer A. | Zhao, Jing Hua | Zhao, Wei | Chen, Jin | Fehrmann, Rudolf | Hedman, Åsa K. | Karjalainen, Juha | Schmidt, Ellen M. | Absher, Devin | Amin, Najaf | Anderson, Denise | Beekman, Marian | Bolton, Jennifer L. | Bragg-Gresham, Jennifer L. | Buyske, Steven | Demirkan, Ayse | Deng, Guohong | Ehret, Georg B. | Feenstra, Bjarke | Feitosa, Mary F. | Fischer, Krista | Goel, Anuj | Gong, Jian | Jackson, Anne U. | Kanoni, Stavroula | Kleber, Marcus E. | Kristiansson, Kati | Lim, Unhee | Lotay, Vaneet | Mangino, Massimo | Leach, Irene Mateo | Medina-Gomez, Carolina | Medland, Sarah E. | Nalls, Michael A. | Palmer, Cameron D. | Pasko, Dorota | Pechlivanis, Sonali | Peters, Marjolein J. | Prokopenko, Inga | Shungin, Dmitry | Stančáková, Alena | Strawbridge, Rona J. | Sung, Yun Ju | Tanaka, Toshiko | Teumer, Alexander | Trompet, Stella | van der Laan, Sander W. | van Setten, Jessica | Van Vliet-Ostaptchouk, Jana V. | Wang, Zhaoming | Yengo, Loïc | Zhang, Weihua | Isaacs, Aaron | Albrecht, Eva | Ärnlöv, Johan | Arscott, Gillian M. | Attwood, Antony P. | Bandinelli, Stefania | Barrett, Amy | Bas, Isabelita N. | Bellis, Claire | Bennett, Amanda J. | Berne, Christian | Blagieva, Roza | Blüher, Matthias | Böhringer, Stefan | Bonnycastle, Lori L. | Böttcher, Yvonne | Boyd, Heather A. | Bruinenberg, Marcel | Caspersen, Ida H. | Chen, Yii-Der Ida | Clarke, Robert | Daw, E. Warwick | de Craen, Anton J. M. | Delgado, Graciela | Dimitriou, Maria | Doney, Alex S. F. | Eklund, Niina | Estrada, Karol | Eury, Elodie | Folkersen, Lasse | Fraser, Ross M. | Garcia, Melissa E. | Geller, Frank | Giedraitis, Vilmantas | Gigante, Bruna | Go, Alan S. | Golay, Alain | Goodall, Alison H. | Gordon, Scott D. | Gorski, Mathias | Grabe, Hans-Jörgen | Grallert, Harald | Grammer, Tanja B. | Gräßler, Jürgen | Grönberg, Henrik | Groves, Christopher J. | Gusto, Gaëlle | Haessler, Jeffrey | Hall, Per | Haller, Toomas | Hallmans, Goran | Hartman, Catharina A. | Hassinen, Maija | Hayward, Caroline | Heard-Costa, Nancy L. | Helmer, Quinta | Hengstenberg, Christian | Holmen, Oddgeir | Hottenga, Jouke-Jan | James, Alan L. | Jeff, Janina M. | Johansson, Åsa | Jolley, Jennifer | Juliusdottir, Thorhildur | Kinnunen, Leena | Koenig, Wolfgang | Koskenvuo, Markku | Kratzer, Wolfgang | Laitinen, Jaana | Lamina, Claudia | Leander, Karin | Lee, Nanette R. | Lichtner, Peter | Lind, Lars | Lindström, Jaana | Lo, Ken Sin | Lobbens, Stéphane | Lorbeer, Roberto | Lu, Yingchang | Mach, François | Magnusson, Patrik K. E. | Mahajan, Anubha | McArdle, Wendy L. | McLachlan, Stela | Menni, Cristina | Merger, Sigrun | Mihailov, Evelin | Milani, Lili | Moayyeri, Alireza | Monda, Keri L. | Morken, Mario A. | Mulas, Antonella | Müller, Gabriele | Müller-Nurasyid, Martina | Musk, Arthur W. | Nagaraja, Ramaiah | Nöthen, Markus M. | Nolte, Ilja M. | Pilz, Stefan | Rayner, Nigel W. | Renstrom, Frida | Rettig, Rainer | Ried, Janina S. | Ripke, Stephan | Robertson, Neil R. | Rose, Lynda M. | Sanna, Serena | Scharnagl, Hubert | Scholtens, Salome | Schumacher, Fredrick R. | Scott, William R. | Seufferlein, Thomas | Shi, Jianxin | Smith, Albert Vernon | Smolonska, Joanna | Stanton, Alice V. | Steinthorsdottir, Valgerdur | Stirrups, Kathleen | Stringham, Heather M. | Sundström, Johan | Swertz, Morris A. | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Tayo, Bamidele O. | Thorand, Barbara | Thorleifsson, Gudmar | Tyrer, Jonathan P. | Uh, Hae-Won | Vandenput, Liesbeth | Verhulst, Frank C. | Vermeulen, Sita H. | Verweij, Niek | Vonk, Judith M. | Waite, Lindsay L. | Warren, Helen R. | Waterworth, Dawn | Weedon, Michael N. | Wilkens, Lynne R. | Willenborg, Christina | Wilsgaard, Tom | Wojczynski, Mary K. | Wong, Andrew | Wright, Alan F. | Zhang, Qunyuan | Brennan, Eoin P. | Choi, Murim | Dastani, Zari | Drong, Alexander W. | Eriksson, Per | Franco-Cereceda, Anders | Gådin, Jesper R. | Gharavi, Ali G. | Goddard, Michael E. | Handsaker, Robert E. | Huang, Jinyan | Karpe, Fredrik | Kathiresan, Sekar | Keildson, Sarah | Kiryluk, Krzysztof | Kubo, Michiaki | Lee, Jong-Young | Liang, Liming | Lifton, Richard P. | Ma, Baoshan | McCarroll, Steven A. | McKnight, Amy J. | Min, Josine L. | Moffatt, Miriam F. | Montgomery, Grant W. | Murabito, Joanne M. | Nicholson, George | Nyholt, Dale R. | Okada, Yukinori | Perry, John R. B. | Dorajoo, Rajkumar | Reinmaa, Eva | Salem, Rany M. | Sandholm, Niina | Scott, Robert A. | Stolk, Lisette | Takahashi, Atsushi | Tanaka, Toshihiro | van ’t Hooft, Ferdinand M. | Vinkhuyzen, Anna A. E. | Westra, Harm-Jan | Zheng, Wei | Zondervan, Krina T. | Heath, Andrew C. | Arveiler, Dominique | Bakker, Stephan J. L. | Beilby, John | Bergman, Richard N. | Blangero, John | Bovet, Pascal | Campbell, Harry | Caulfield, Mark J. | Cesana, Giancarlo | Chakravarti, Aravinda | Chasman, Daniel I. | Chines, Peter S. | Collins, Francis S. | Crawford, Dana C. | Cupples, L. Adrienne | Cusi, Daniele | Danesh, John | de Faire, Ulf | den Ruijter, Hester M. | Dominiczak, Anna F. | Erbel, Raimund | Erdmann, Jeanette | Eriksson, Johan G. | Farrall, Martin | Felix, Stephan B. | Ferrannini, Ele | Ferrières, Jean | Ford, Ian | Forouhi, Nita G. | Forrester, Terrence | Franco, Oscar H. | Gansevoort, Ron T. | Gejman, Pablo V. | Gieger, Christian | Gottesman, Omri | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Alistair S. | Harris, Tamara B. | Hattersley, Andrew T. | Hicks, Andrew A. | Hindorff, Lucia A. | Hingorani, Aroon D. | Hofman, Albert | Homuth, Georg | Hovingh, G. Kees | Humphries, Steve E. | Hunt, Steven C. | Hyppönen, Elina | Illig, Thomas | Jacobs, Kevin B. | Jarvelin, Marjo-Riitta | Jöckel, Karl-Heinz | Johansen, Berit | Jousilahti, Pekka | Jukema, J. Wouter | Jula, Antti M. | Kaprio, Jaakko | Kastelein, John J. P. | Keinanen-Kiukaanniemi, Sirkka M. | Kiemeney, Lambertus A. | Knekt, Paul | Kooner, Jaspal S. | Kooperberg, Charles | Kovacs, Peter | Kraja, Aldi T. | Kumari, Meena | Kuusisto, Johanna | Lakka, Timo A. | Langenberg, Claudia | Marchand, Loic Le | Lehtimäki, Terho | Lyssenko, Valeriya | Männistö, Satu | Marette, André | Matise, Tara C. | McKenzie, Colin A. | McKnight, Barbara | Moll, Frans L. | Morris, Andrew D. | Morris, Andrew P. | Murray, Jeffrey C. | Nelis, Mari | Ohlsson, Claes | Oldehinkel, Albertine J. | Ong, Ken K. | Madden, Pamela A. F. | Pasterkamp, Gerard | Peden, John F. | Peters, Annette | Postma, Dirkje S. | Pramstaller, Peter P. | Price, Jackie F. | Qi, Lu | Raitakari, Olli T. | Rankinen, Tuomo | Rao, D. C. | Rice, Treva K. | Ridker, Paul M. | Rioux, John D. | Ritchie, Marylyn D. | Rudan, Igor | Salomaa, Veikko | Samani, Nilesh J. | Saramies, Jouko | Sarzynski, Mark A. | Schunkert, Heribert | Schwarz, Peter E. H. | Sever, Peter | Shuldiner, Alan R. | Sinisalo, Juha | Stolk, Ronald P. | Strauch, Konstantin | Tönjes, Anke | Trégouët, David-Alexandre | Tremblay, Angelo | Tremoli, Elena | Virtamo, Jarmo | Vohl, Marie-Claude | Völker, Uwe | Waeber, Gérard | Willemsen, Gonneke | Witteman, Jacqueline C. | Zillikens, M. Carola | Adair, Linda S. | Amouyel, Philippe | Asselbergs, Folkert W. | Assimes, Themistocles L. | Bochud, Murielle | Boehm, Bernhard O. | Boerwinkle, Eric | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Cauchi, Stéphane | Chambers, John C. | Chanock, Stephen J. | Cooper, Richard S. | de Bakker, Paul I. W. | Dedoussis, George | Ferrucci, Luigi | Franks, Paul W. | Froguel, Philippe | Groop, Leif C. | Haiman, Christopher A. | Hamsten, Anders | Hui, Jennie | Hunter, David J. | Hveem, Kristian | Kaplan, Robert C. | Kivimaki, Mika | Kuh, Diana | Laakso, Markku | Liu, Yongmei | Martin, Nicholas G. | März, Winfried | Melbye, Mads | Metspalu, Andres | Moebus, Susanne | Munroe, Patricia B. | Njølstad, Inger | Oostra, Ben A. | Palmer, Colin N. A. | Pedersen, Nancy L. | Perola, Markus | Pérusse, Louis | Peters, Ulrike | Power, Chris | Quertermous, Thomas | Rauramaa, Rainer | Rivadeneira, Fernando | Saaristo, Timo E. | Saleheen, Danish | Sattar, Naveed | Schadt, Eric E. | Schlessinger, David | Slagboom, P. Eline | Snieder, Harold | Spector, Tim D. | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | van der Harst, Pim | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Weir, David R. | Wichmann, H-Erich | Wilson, James F. | Zanen, Pieter | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Heid, Iris M. | O’Connell, Jeffrey R. | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Abecasis, Gonçalo R. | Franke, Lude | Frayling, Timothy M. | McCarthy, Mark I. | Visscher, Peter M. | Scherag, André | Willer, Cristen J. | Boehnke, Michael | Mohlke, Karen L. | Lindgren, Cecilia M. | Beckmann, Jacques S. | Barroso, Inês | North, Kari E. | Ingelsson, Erik | Hirschhorn, Joel N. | Loos, Ruth J. F. | Speliotes, Elizabeth K.
Nature  2015;518(7538):197-206.
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10−8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
doi:10.1038/nature14177
PMCID: PMC4382211  PMID: 25673413
5.  A novel common variant in DCST2 is associated with length in early life and height in adulthood 
van der Valk, Ralf J.P. | Kreiner-Møller, Eskil | Kooijman, Marjolein N. | Guxens, Mònica | Stergiakouli, Evangelia | Sääf, Annika | Bradfield, Jonathan P. | Geller, Frank | Hayes, M. Geoffrey | Cousminer, Diana L. | Körner, Antje | Thiering, Elisabeth | Curtin, John A. | Myhre, Ronny | Huikari, Ville | Joro, Raimo | Kerkhof, Marjan | Warrington, Nicole M. | Pitkänen, Niina | Ntalla, Ioanna | Horikoshi, Momoko | Veijola, Riitta | Freathy, Rachel M. | Teo, Yik-Ying | Barton, Sheila J. | Evans, David M. | Kemp, John P. | St Pourcain, Beate | Ring, Susan M. | Davey Smith, George | Bergström, Anna | Kull, Inger | Hakonarson, Hakon | Mentch, Frank D. | Bisgaard, Hans | Chawes, Bo | Stokholm, Jakob | Waage, Johannes | Eriksen, Patrick | Sevelsted, Astrid | Melbye, Mads | van Duijn, Cornelia M. | Medina-Gomez, Carolina | Hofman, Albert | de Jongste, Johan C. | Taal, H. Rob | Uitterlinden, André G. | Armstrong, Loren L. | Eriksson, Johan | Palotie, Aarno | Bustamante, Mariona | Estivill, Xavier | Gonzalez, Juan R. | Llop, Sabrina | Kiess, Wieland | Mahajan, Anubha | Flexeder, Claudia | Tiesler, Carla M.T. | Murray, Clare S. | Simpson, Angela | Magnus, Per | Sengpiel, Verena | Hartikainen, Anna-Liisa | Keinanen-Kiukaanniemi, Sirkka | Lewin, Alexandra | Da Silva Couto Alves, Alexessander | Blakemore, Alexandra I. | Buxton, Jessica L. | Kaakinen, Marika | Rodriguez, Alina | Sebert, Sylvain | Vaarasmaki, Marja | Lakka, Timo | Lindi, Virpi | Gehring, Ulrike | Postma, Dirkje S. | Ang, Wei | Newnham, John P. | Lyytikäinen, Leo-Pekka | Pahkala, Katja | Raitakari, Olli T. | Panoutsopoulou, Kalliope | Zeggini, Eleftheria | Boomsma, Dorret I. | Groen-Blokhuis, Maria | Ilonen, Jorma | Franke, Lude | Hirschhorn, Joel N. | Pers, Tune H. | Liang, Liming | Huang, Jinyan | Hocher, Berthold | Knip, Mikael | Saw, Seang-Mei | Holloway, John W. | Melén, Erik | Grant, Struan F.A. | Feenstra, Bjarke | Lowe, William L. | Widén, Elisabeth | Sergeyev, Elena | Grallert, Harald | Custovic, Adnan | Jacobsson, Bo | Jarvelin, Marjo-Riitta | Atalay, Mustafa | Koppelman, Gerard H. | Pennell, Craig E. | Niinikoski, Harri | Dedoussis, George V. | Mccarthy, Mark I. | Frayling, Timothy M. | Sunyer, Jordi | Timpson, Nicholas J. | Rivadeneira, Fernando | Bønnelykke, Klaus | Jaddoe, Vincent W.V.
Human Molecular Genetics  2014;24(4):1155-1168.
Common genetic variants have been identified for adult height, but not much is known about the genetics of skeletal growth in early life. To identify common genetic variants that influence fetal skeletal growth, we meta-analyzed 22 genome-wide association studies (Stage 1; N = 28 459). We identified seven independent top single nucleotide polymorphisms (SNPs) (P < 1 × 10−6) for birth length, of which three were novel and four were in or near loci known to be associated with adult height (LCORL, PTCH1, GPR126 and HMGA2). The three novel SNPs were followed-up in nine replication studies (Stage 2; N = 11 995), with rs905938 in DC-STAMP domain containing 2 (DCST2) genome-wide significantly associated with birth length in a joint analysis (Stages 1 + 2; β = 0.046, SE = 0.008, P = 2.46 × 10−8, explained variance = 0.05%). Rs905938 was also associated with infant length (N = 28 228; P = 5.54 × 10−4) and adult height (N = 127 513; P = 1.45 × 10−5). DCST2 is a DC-STAMP-like protein family member and DC-STAMP is an osteoclast cell-fusion regulator. Polygenic scores based on 180 SNPs previously associated with human adult stature explained 0.13% of variance in birth length. The same SNPs explained 2.95% of the variance of infant length. Of the 180 known adult height loci, 11 were genome-wide significantly associated with infant length (SF3B4, LCORL, SPAG17, C6orf173, PTCH1, GDF5, ZNFX1, HHIP, ACAN, HLA locus and HMGA2). This study highlights that common variation in DCST2 influences variation in early growth and adult height.
doi:10.1093/hmg/ddu510
PMCID: PMC4447786  PMID: 25281659
6.  Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes 
Yaghootkar, Hanieh | Lamina, Claudia | Scott, Robert A. | Dastani, Zari | Hivert, Marie-France | Warren, Liling L. | Stancáková, Alena | Buxbaum, Sarah G. | Lyytikäinen, Leo-Pekka | Henneman, Peter | Wu, Ying | Cheung, Chloe Y.Y. | Pankow, James S. | Jackson, Anne U. | Gustafsson, Stefan | Zhao, Jing Hua | Ballantyne, Christie M. | Xie, Weijia | Bergman, Richard N. | Boehnke, Michael | el Bouazzaoui, Fatiha | Collins, Francis S. | Dunn, Sandra H. | Dupuis, Josee | Forouhi, Nita G. | Gillson, Christopher | Hattersley, Andrew T. | Hong, Jaeyoung | Kähönen, Mika | Kuusisto, Johanna | Kedenko, Lyudmyla | Kronenberg, Florian | Doria, Alessandro | Assimes, Themistocles L. | Ferrannini, Ele | Hansen, Torben | Hao, Ke | Häring, Hans | Knowles, Joshua W. | Lindgren, Cecilia M. | Nolan, John J. | Paananen, Jussi | Pedersen, Oluf | Quertermous, Thomas | Smith, Ulf | Lehtimäki, Terho | Liu, Ching-Ti | Loos, Ruth J.F. | McCarthy, Mark I. | Morris, Andrew D. | Vasan, Ramachandran S. | Spector, Tim D. | Teslovich, Tanya M. | Tuomilehto, Jaakko | van Dijk, Ko Willems | Viikari, Jorma S. | Zhu, Na | Langenberg, Claudia | Ingelsson, Erik | Semple, Robert K. | Sinaiko, Alan R. | Palmer, Colin N.A. | Walker, Mark | Lam, Karen S.L. | Paulweber, Bernhard | Mohlke, Karen L. | van Duijn, Cornelia | Raitakari, Olli T. | Bidulescu, Aurelian | Wareham, Nick J. | Laakso, Markku | Waterworth, Dawn M. | Lawlor, Debbie A. | Meigs, James B. | Richards, J. Brent | Frayling, Timothy M.
Diabetes  2013;62(10):3589-3598.
Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
doi:10.2337/db13-0128
PMCID: PMC3781444  PMID: 23835345
7.  Multiple type 2 diabetes susceptibility genes following genome-wide association scan in UK samples 
Science (New York, N.Y.)  2007;316(5829):1336-1341.
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls, and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insights into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
doi:10.1126/science.1142364
PMCID: PMC3772310  PMID: 17463249
8.  New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism 
Horikoshi, Momoko | Yaghootkar, Hanieh | Mook-Kanamori, Dennis O. | Sovio, Ulla | Taal, H. Rob | Hennig, Branwen J. | Bradfield, Jonathan P. | St. Pourcain, Beate | Evans, David M. | Charoen, Pimphen | Kaakinen, Marika | Cousminer, Diana L. | Lehtimäki, Terho | Kreiner-Møller, Eskil | Warrington, Nicole M. | Bustamante, Mariona | Feenstra, Bjarke | Berry, Diane J. | Thiering, Elisabeth | Pfab, Thiemo | Barton, Sheila J. | Shields, Beverley M. | Kerkhof, Marjan | van Leeuwen, Elisabeth M. | Fulford, Anthony J. | Kutalik, Zoltán | Zhao, Jing Hua | den Hoed, Marcel | Mahajan, Anubha | Lindi, Virpi | Goh, Liang-Kee | Hottenga, Jouke-Jan | Wu, Ying | Raitakari, Olli T. | Harder, Marie N. | Meirhaeghe, Aline | Ntalla, Ioanna | Salem, Rany M. | Jameson, Karen A. | Zhou, Kaixin | Monies, Dorota M. | Lagou, Vasiliki | Kirin, Mirna | Heikkinen, Jani | Adair, Linda S. | Alkuraya, Fowzan S. | Al-Odaib, Ali | Amouyel, Philippe | Andersson, Ehm Astrid | Bennett, Amanda J. | Blakemore, Alexandra I.F. | Buxton, Jessica L. | Dallongeville, Jean | Das, Shikta | de Geus, Eco J. C. | Estivill, Xavier | Flexeder, Claudia | Froguel, Philippe | Geller, Frank | Godfrey, Keith M. | Gottrand, Frédéric | Groves, Christopher J. | Hansen, Torben | Hirschhorn, Joel N. | Hofman, Albert | Hollegaard, Mads V. | Hougaard, David M. | Hyppönen, Elina | Inskip, Hazel M. | Isaacs, Aaron | Jørgensen, Torben | Kanaka-Gantenbein, Christina | Kemp, John P. | Kiess, Wieland | Kilpeläinen, Tuomas O. | Klopp, Norman | Knight, Bridget A. | Kuzawa, Christopher W. | McMahon, George | Newnham, John P. | Niinikoski, Harri | Oostra, Ben A. | Pedersen, Louise | Postma, Dirkje S. | Ring, Susan M. | Rivadeneira, Fernando | Robertson, Neil R. | Sebert, Sylvain | Simell, Olli | Slowinski, Torsten | Tiesler, Carla M.T. | Tönjes, Anke | Vaag, Allan | Viikari, Jorma S. | Vink, Jacqueline M. | Vissing, Nadja Hawwa | Wareham, Nicholas J. | Willemsen, Gonneke | Witte, Daniel R. | Zhang, Haitao | Zhao, Jianhua | Wilson, James F. | Stumvoll, Michael | Prentice, Andrew M. | Meyer, Brian F. | Pearson, Ewan R. | Boreham, Colin A.G. | Cooper, Cyrus | Gillman, Matthew W. | Dedoussis, George V. | Moreno, Luis A | Pedersen, Oluf | Saarinen, Maiju | Mohlke, Karen L. | Boomsma, Dorret I. | Saw, Seang-Mei | Lakka, Timo A. | Körner, Antje | Loos, Ruth J.F. | Ong, Ken K. | Vollenweider, Peter | van Duijn, Cornelia M. | Koppelman, Gerard H. | Hattersley, Andrew T. | Holloway, John W. | Hocher, Berthold | Heinrich, Joachim | Power, Chris | Melbye, Mads | Guxens, Mònica | Pennell, Craig E. | Bønnelykke, Klaus | Bisgaard, Hans | Eriksson, Johan G. | Widén, Elisabeth | Hakonarson, Hakon | Uitterlinden, André G. | Pouta, Anneli | Lawlor, Debbie A. | Smith, George Davey | Frayling, Timothy M. | McCarthy, Mark I. | Grant, Struan F.A. | Jaddoe, Vincent W.V. | Jarvelin, Marjo-Riitta | Timpson, Nicholas J. | Prokopenko, Inga | Freathy, Rachel M.
Nature genetics  2012;45(1):76-82.
Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood1. Previous genome-wide association studies identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes, and a second variant, near CCNL1, with no obvious link to adult traits2. In an expanded genome-wide association meta-analysis and follow-up study (up to 69,308 individuals of European descent from 43 studies), we have now extended the number of genome-wide significant loci to seven, accounting for a similar proportion of variance to maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes; ADRB1 with adult blood pressure; and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.
doi:10.1038/ng.2477
PMCID: PMC3605762  PMID: 23202124
9.  Evaluation of Common Type 2 Diabetes Risk Variants in a South Asian Population of Sri Lankan Descent 
PLoS ONE  2014;9(6):e98608.
Introduction
Most studies seeking common variant associations with type 2 diabetes (T2D) have focused on individuals of European ancestry. These discoveries need to be evaluated in other major ancestral groups, to understand ethnic differences in predisposition, and establish whether these contribute to variation in T2D prevalence and presentation. This study aims to establish whether common variants conferring T2D-risk in Europeans contribute to T2D-susceptibility in the South Asian population of Sri Lanka.
Methodology
Lead single nucleotide polymorphism (SNPs) at 37 T2D-risk loci attaining genome-wide significance in Europeans were genotyped in 878 T2D cases and 1523 normoglycaemic controls from Sri Lanka. Association testing was performed by logistic regression adjusting for age and sex and by the Cochran-Mantel-Haenszel test after stratifying according to self-identified ethnolinguistic subgroup. A weighted genetic risk score was generated to examine the combined effect of these SNPs on T2D-risk in the Sri Lankan population.
Results
Of the 36 SNPs passing quality control, sixteen showed nominal (p<0.05) association in Sri Lankan samples, fifteen of those directionally-consistent with the original signal. Overall, these association findings were robust to analyses that accounted for membership of ethnolinguistic subgroups. Overall, the odds ratios for 31 of the 36 SNPs were directionally-consistent with those observed in Europeans (p = 3.2×10−6). Allelic odds ratios and risk allele frequencies in Sri Lankan subjects were not systematically different to those reported in Europeans. Genetic risk score and risk of T2D were strongly related in Sri Lankans (per allele OR 1.10 [95%CI 1.08–1.13], p = 1.2×10−17).
Conclusion
Our data indicate that most T2D-risk variants identified in Europeans have similar effects in South Asians from Sri Lanka, and that systematic difference in common variant associations are unlikely to explain inter-ethnic differences in prevalence or presentation of T2D.
doi:10.1371/journal.pone.0098608
PMCID: PMC4057178  PMID: 24926958
10.  A Central Role for GRB10 in Regulation of Islet Function in Man 
PLoS Genetics  2014;10(4):e1004235.
Variants in the growth factor receptor-bound protein 10 (GRB10) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes (T2D) if inherited from the father, but inexplicably reduced fasting glucose when inherited from the mother. GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues. GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion, which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion. Together, these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
Author Summary
In this paper, we report the first large genome-wide association study in man for glucose-stimulated insulin secretion (GSIS) indices during an oral glucose tolerance test. We identify seven genetic loci and provide effects on GSIS for all previously reported glycemic traits and obesity genetic loci in a large-scale sample. We observe paradoxical effects of genetic variants in the growth factor receptor-bound protein 10 (GRB10) gene yielding both reduced GSIS and reduced fasting plasma glucose concentrations, specifically showing a parent-of-origin effect of GRB10 on lower fasting plasma glucose and enhanced insulin sensitivity for maternal and elevated glucose and decreased insulin sensitivity for paternal transmissions of the risk allele. We also observe tissue-specific differences in DNA methylation and allelic imbalance in expression of GRB10 in human pancreatic islets. We further disrupt GRB10 by shRNA in human islets, showing reduction of both insulin and glucagon expression and secretion. In conclusion, we provide evidence for complex regulation of GRB10 in human islets. Our data suggest that tissue-specific methylation and imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
doi:10.1371/journal.pgen.1004235
PMCID: PMC3974640  PMID: 24699409
11.  FTO genotype is associated with phenotypic variability of body mass index 
Yang, Jian | Loos, Ruth J. F. | Powell, Joseph E. | Medland, Sarah E. | Speliotes, Elizabeth K. | Chasman, Daniel I. | Rose, Lynda M. | Thorleifsson, Gudmar | Steinthorsdottir, Valgerdur | Mägi, Reedik | Waite, Lindsay | Smith, Albert Vernon | Yerges-Armstrong, Laura M. | Monda, Keri L. | Hadley, David | Mahajan, Anubha | Li, Guo | Kapur, Karen | Vitart, Veronique | Huffman, Jennifer E. | Wang, Sophie R. | Palmer, Cameron | Esko, Tõnu | Fischer, Krista | Zhao, Jing Hua | Demirkan, Ayşe | Isaacs, Aaron | Feitosa, Mary F. | Luan, Jian’an | Heard-Costa, Nancy L. | White, Charles | Jackson, Anne U. | Preuss, Michael | Ziegler, Andreas | Eriksson, Joel | Kutalik, Zoltán | Frau, Francesca | Nolte, Ilja M. | Van Vliet-Ostaptchouk, Jana V. | Hottenga, Jouke-Jan | Jacobs, Kevin B. | Verweij, Niek | Goel, Anuj | Medina-Gomez, Carolina | Estrada, Karol | Bragg-Gresham, Jennifer Lynn | Sanna, Serena | Sidore, Carlo | Tyrer, Jonathan | Teumer, Alexander | Prokopenko, Inga | Mangino, Massimo | Lindgren, Cecilia M. | Assimes, Themistocles L. | Shuldiner, Alan R. | Hui, Jennie | Beilby, John P. | McArdle, Wendy L. | Hall, Per | Haritunians, Talin | Zgaga, Lina | Kolcic, Ivana | Polasek, Ozren | Zemunik, Tatijana | Oostra, Ben A. | Junttila, M. Juhani | Grönberg, Henrik | Schreiber, Stefan | Peters, Annette | Hicks, Andrew A. | Stephens, Jonathan | Foad, Nicola S. | Laitinen, Jaana | Pouta, Anneli | Kaakinen, Marika | Willemsen, Gonneke | Vink, Jacqueline M. | Wild, Sarah H. | Navis, Gerjan | Asselbergs, Folkert W. | Homuth, Georg | John, Ulrich | Iribarren, Carlos | Harris, Tamara | Launer, Lenore | Gudnason, Vilmundur | O’Connell, Jeffrey R. | Boerwinkle, Eric | Cadby, Gemma | Palmer, Lyle J. | James, Alan L. | Musk, Arthur W. | Ingelsson, Erik | Psaty, Bruce M. | Beckmann, Jacques S. | Waeber, Gerard | Vollenweider, Peter | Hayward, Caroline | Wright, Alan F. | Rudan, Igor | Groop, Leif C. | Metspalu, Andres | Khaw, Kay Tee | van Duijn, Cornelia M. | Borecki, Ingrid B. | Province, Michael A. | Wareham, Nicholas J. | Tardif, Jean-Claude | Huikuri, Heikki V. | Cupples, L. Adrienne | Atwood, Larry D. | Fox, Caroline S. | Boehnke, Michael | Collins, Francis S. | Mohlke, Karen L. | Erdmann, Jeanette | Schunkert, Heribert | Hengstenberg, Christian | Stark, Klaus | Lorentzon, Mattias | Ohlsson, Claes | Cusi, Daniele | Staessen, Jan A. | Van der Klauw, Melanie M. | Pramstaller, Peter P. | Kathiresan, Sekar | Jolley, Jennifer D. | Ripatti, Samuli | Jarvelin, Marjo-Riitta | de Geus, Eco J. C. | Boomsma, Dorret I. | Penninx, Brenda | Wilson, James F. | Campbell, Harry | Chanock, Stephen J. | van der Harst, Pim | Hamsten, Anders | Watkins, Hugh | Hofman, Albert | Witteman, Jacqueline C. | Zillikens, M. Carola | Uitterlinden, André G. | Rivadeneira, Fernando | Zillikens, M. Carola | Kiemeney, Lambertus A. | Vermeulen, Sita H. | Abecasis, Goncalo R. | Schlessinger, David | Schipf, Sabine | Stumvoll, Michael | Tönjes, Anke | Spector, Tim D. | North, Kari E. | Lettre, Guillaume | McCarthy, Mark I. | Berndt, Sonja I. | Heath, Andrew C. | Madden, Pamela A. F. | Nyholt, Dale R. | Montgomery, Grant W. | Martin, Nicholas G. | McKnight, Barbara | Strachan, David P. | Hill, William G. | Snieder, Harold | Ridker, Paul M. | Thorsteinsdottir, Unnur | Stefansson, Kari | Frayling, Timothy M. | Hirschhorn, Joel N. | Goddard, Michael E. | Visscher, Peter M.
Nature  2012;490(7419):267-272.
There is evidence across several species for genetic control of phenotypic variation of complex traits1–4, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using 170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype)5–7, is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of 0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI8, possibly mediated by DNA methylation9,10. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.
doi:10.1038/nature11401
PMCID: PMC3564953  PMID: 22982992
12.  Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts 
PLoS Medicine  2013;10(2):e1001383.
A mendelian randomization study based on data from multiple cohorts conducted by Karani Santhanakrishnan Vimaleswaran and colleagues re-examines the causal nature of the relationship between vitamin D levels and obesity.
Background
Obesity is associated with vitamin D deficiency, and both are areas of active public health concern. We explored the causality and direction of the relationship between body mass index (BMI) and 25-hydroxyvitamin D [25(OH)D] using genetic markers as instrumental variables (IVs) in bi-directional Mendelian randomization (MR) analysis.
Methods and Findings
We used information from 21 adult cohorts (up to 42,024 participants) with 12 BMI-related SNPs (combined in an allelic score) to produce an instrument for BMI and four SNPs associated with 25(OH)D (combined in two allelic scores, separately for genes encoding its synthesis or metabolism) as an instrument for vitamin D. Regression estimates for the IVs (allele scores) were generated within-study and pooled by meta-analysis to generate summary effects.
Associations between vitamin D scores and BMI were confirmed in the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 123,864). Each 1 kg/m2 higher BMI was associated with 1.15% lower 25(OH)D (p = 6.52×10−27). The BMI allele score was associated both with BMI (p = 6.30×10−62) and 25(OH)D (−0.06% [95% CI −0.10 to −0.02], p = 0.004) in the cohorts that underwent meta-analysis. The two vitamin D allele scores were strongly associated with 25(OH)D (p≤8.07×10−57 for both scores) but not with BMI (synthesis score, p = 0.88; metabolism score, p = 0.08) in the meta-analysis. A 10% higher genetically instrumented BMI was associated with 4.2% lower 25(OH)D concentrations (IV ratio: −4.2 [95% CI −7.1 to −1.3], p = 0.005). No association was seen for genetically instrumented 25(OH)D with BMI, a finding that was confirmed using data from the GIANT consortium (p≥0.57 for both vitamin D scores).
Conclusions
On the basis of a bi-directional genetic approach that limits confounding, our study suggests that a higher BMI leads to lower 25(OH)D, while any effects of lower 25(OH)D increasing BMI are likely to be small. Population level interventions to reduce BMI are expected to decrease the prevalence of vitamin D deficiency.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the US, for example, a third of the adult population is now obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m2. Although there is a genetic contribution to obesity, people generally become obese by consuming food and drink that contains more energy than they need for their daily activities. Thus, obesity can be prevented by having a healthy diet and exercising regularly. Compared to people with a healthy weight, obese individuals have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. They also have a higher risk of vitamin D deficiency, another increasingly common public health concern. Vitamin D, which is essential for healthy bones as well as other functions, is made in the skin after exposure to sunlight but can also be obtained through the diet and through supplements.
Why Was This Study Done?
Observational studies cannot prove that obesity causes vitamin D deficiency because obese individuals may share other characteristics that reduce their circulating 25-hydroxy vitamin D [25(OH)D] levels (referred to as confounding). Moreover, observational studies cannot indicate whether the larger vitamin D storage capacity of obese individuals (vitamin D is stored in fatty tissues) lowers their 25(OH)D levels or whether 25(OH)D levels influence fat accumulation (reverse causation). If obesity causes vitamin D deficiency, monitoring and treating vitamin D deficiency might alleviate some of the adverse health effects of obesity. Conversely, if low vitamin D levels cause obesity, encouraging people to take vitamin D supplements might help to control the obesity epidemic. Here, the researchers use bi-directional “Mendelian randomization” to examine the direction and causality of the relationship between BMI and 25(OH)D. In Mendelian randomization, causality is inferred from associations between genetic variants that mimic the influence of a modifiable environmental exposure and the outcome of interest. Because gene variants do not change over time and are inherited randomly, they are not prone to confounding and are free from reverse causation. Thus, if a lower vitamin D status leads to obesity, genetic variants associated with lower 25(OH)D concentrations should be associated with higher BMI, and if obesity leads to a lower vitamin D status, then genetic variants associated with higher BMI should be associated with lower 25(OH)D concentrations.
What Did the Researchers Do and Find?
The researchers created a “BMI allele score” based on 12 BMI-related gene variants and two “25(OH)D allele scores,” which are based on gene variants that affect either 25(OH)D synthesis or breakdown. Using information on up to 42,024 participants from 21 studies, the researchers showed that the BMI allele score was associated with both BMI and with 25(OH)D levels among the study participants. Based on this information, they calculated that each 10% increase in BMI will lead to a 4.2% decrease in 25(OH)D concentrations. By contrast, although both 25(OH)D allele scores were strongly associated with 25(OH)D levels, neither score was associated with BMI. This lack of an association between 25(OH)D allele scores and obesity was confirmed using data from more than 100,000 individuals involved in 46 studies that has been collected by the GIANT (Genetic Investigation of Anthropometric Traits) consortium.
What Do These Findings Mean?
These findings suggest that a higher BMI leads to a lower vitamin D status whereas any effects of low vitamin D status on BMI are likely to be small. That is, these findings provide evidence for obesity as a causal factor in the development of vitamin D deficiency but not for vitamin D deficiency as a causal factor in the development of obesity. These findings suggest that population-level interventions to reduce obesity should lead to a reduction in the prevalence of vitamin D deficiency and highlight the importance of monitoring and treating vitamin D deficiency as a means of alleviating the adverse influences of obesity on health.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001383.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish); a data brief provides information about the vitamin D status of the US population
The World Health Organization provides information on obesity (in several languages)
The UK National Health Service Choices website provides detailed information about obesity and a link to a personal story about losing weight; it also provides information about vitamin D
The International Obesity Taskforce provides information about the global obesity epidemic
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
The US Office of Dietary Supplements provides information about vitamin D (in English and Spanish)
MedlinePlus has links to further information about obesity and about vitamin D (in English and Spanish)
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Overview and details of the collaborative large-scale genetic association study (D-CarDia) provide information about vitamin D and the risk of cardiovascular disease, diabetes and related traits
doi:10.1371/journal.pmed.1001383
PMCID: PMC3564800  PMID: 23393431
13.  Adiposity-Related Heterogeneity in Patterns of Type 2 Diabetes Susceptibility Observed in Genome-Wide Association Data 
Diabetes  2009;58(2):505-510.
OBJECTIVE—This study examined how differences in the BMI distribution of type 2 diabetic case subjects affected genome-wide patterns of type 2 diabetes association and considered the implications for the etiological heterogeneity of type 2 diabetes.
RESEARCH DESIGN AND METHODS—We reanalyzed data from the Wellcome Trust Case Control Consortium genome-wide association scan (1,924 case subjects, 2,938 control subjects: 393,453 single-nucleotide polymorphisms [SNPs]) after stratifying case subjects (into “obese” and “nonobese”) according to median BMI (30.2 kg/m2). Replication of signals in which alternative case-ascertainment strategies generated marked effect size heterogeneity in type 2 diabetes association signal was sought in additional samples.
RESULTS—In the “obese-type 2 diabetes” scan, FTO variants had the strongest type 2 diabetes effect (rs8050136: relative risk [RR] 1.49 [95% CI 1.34–1.66], P = 1.3 × 10−13), with only weak evidence for TCF7L2 (rs7901695 RR 1.21 [1.09–1.35], P = 0.001). This situation was reversed in the “nonobese” scan, with FTO association undetectable (RR 1.07 [0.97–1.19], P = 0.19) and TCF7L2 predominant (RR 1.53 [1.37–1.71], P = 1.3 × 10−14). These patterns, confirmed by replication, generated strong combined evidence for between-stratum effect size heterogeneity (FTO: PDIFF = 1.4 × 10−7; TCF7L2: PDIFF = 4.0 × 10−6). Other signals displaying evidence of effect size heterogeneity in the genome-wide analyses (on chromosomes 3, 12, 15, and 18) did not replicate. Analysis of the current list of type 2 diabetes susceptibility variants revealed nominal evidence for effect size heterogeneity for the SLC30A8 locus alone (RRobese 1.08 [1.01–1.15]; RRnonobese 1.18 [1.10–1.27]: PDIFF = 0.04).
CONCLUSIONS—This study demonstrates the impact of differences in case ascertainment on the power to detect and replicate genetic associations in genome-wide association studies. These data reinforce the notion that there is substantial etiological heterogeneity within type 2 diabetes.
doi:10.2337/db08-0906
PMCID: PMC2628627  PMID: 19056611
14.  Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes 
Strawbridge, Rona J. | Dupuis, Josée | Prokopenko, Inga | Barker, Adam | Ahlqvist, Emma | Rybin, Denis | Petrie, John R. | Travers, Mary E. | Bouatia-Naji, Nabila | Dimas, Antigone S. | Nica, Alexandra | Wheeler, Eleanor | Chen, Han | Voight, Benjamin F. | Taneera, Jalal | Kanoni, Stavroula | Peden, John F. | Turrini, Fabiola | Gustafsson, Stefan | Zabena, Carina | Almgren, Peter | Barker, David J.P. | Barnes, Daniel | Dennison, Elaine M. | Eriksson, Johan G. | Eriksson, Per | Eury, Elodie | Folkersen, Lasse | Fox, Caroline S. | Frayling, Timothy M. | Goel, Anuj | Gu, Harvest F. | Horikoshi, Momoko | Isomaa, Bo | Jackson, Anne U. | Jameson, Karen A. | Kajantie, Eero | Kerr-Conte, Julie | Kuulasmaa, Teemu | Kuusisto, Johanna | Loos, Ruth J.F. | Luan, Jian'an | Makrilakis, Konstantinos | Manning, Alisa K. | Martínez-Larrad, María Teresa | Narisu, Narisu | Nastase Mannila, Maria | Öhrvik, John | Osmond, Clive | Pascoe, Laura | Payne, Felicity | Sayer, Avan A. | Sennblad, Bengt | Silveira, Angela | Stančáková, Alena | Stirrups, Kathy | Swift, Amy J. | Syvänen, Ann-Christine | Tuomi, Tiinamaija | van 't Hooft, Ferdinand M. | Walker, Mark | Weedon, Michael N. | Xie, Weijia | Zethelius, Björn | Ongen, Halit | Mälarstig, Anders | Hopewell, Jemma C. | Saleheen, Danish | Chambers, John | Parish, Sarah | Danesh, John | Kooner, Jaspal | Östenson, Claes-Göran | Lind, Lars | Cooper, Cyrus C. | Serrano-Ríos, Manuel | Ferrannini, Ele | Forsen, Tom J. | Clarke, Robert | Franzosi, Maria Grazia | Seedorf, Udo | Watkins, Hugh | Froguel, Philippe | Johnson, Paul | Deloukas, Panos | Collins, Francis S. | Laakso, Markku | Dermitzakis, Emmanouil T. | Boehnke, Michael | McCarthy, Mark I. | Wareham, Nicholas J. | Groop, Leif | Pattou, François | Gloyn, Anna L. | Dedoussis, George V. | Lyssenko, Valeriya | Meigs, James B. | Barroso, Inês | Watanabe, Richard M. | Ingelsson, Erik | Langenberg, Claudia | Hamsten, Anders | Florez, Jose C.
Diabetes  2011;60(10):2624-2634.
OBJECTIVE
Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.
RESEARCH DESIGN AND METHODS
We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.
RESULTS
Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.
CONCLUSIONS
We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.
doi:10.2337/db11-0415
PMCID: PMC3178302  PMID: 21873549
15.  Genome-Wide Association Scan Allowing for Epistasis in Type 2 Diabetes 
Annals of human genetics  2010;75(1):10-19.
Summary
In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10−11). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals (P-value = 5 × 10−4). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings.
doi:10.1111/j.1469-1809.2010.00629.x
PMCID: PMC3430851  PMID: 21133856
Epistasis; simultaneous search; joint effects; genome-wide association
16.  The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits 
PLoS Genetics  2012;8(8):e1002793.
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Author Summary
Recent genetic studies have identified hundreds of regions of the human genome that contribute to risk for type 2 diabetes, coronary artery disease and myocardial infarction, and to related quantitative traits such as body mass index, glucose and insulin levels, blood lipid levels, and blood pressure. These results motivate two central questions: (1) can further genetic investigation identify additional associated regions?; and (2) can more detailed genetic investigation help us identify the causal variants (or variants more strongly correlated with the causal variants) in the regions identified so far? Addressing these questions requires assaying many genetic variants in DNA samples from thousands of individuals, which is expensive and timeconsuming when done a few SNPs at a time. To facilitate these investigations, we designed the “Metabochip,” a custom genotyping array that assays variation in nearly 200,000 sites in the human genome. Here we describe the Metabochip, evaluate its performance in assaying human genetic variation, and describe solutions to methodological challenges commonly encountered in its analysis.
doi:10.1371/journal.pgen.1002793
PMCID: PMC3410907  PMID: 22876189
17.  A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone–Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation 
Coviello, Andrea D. | Haring, Robin | Wellons, Melissa | Vaidya, Dhananjay | Lehtimäki, Terho | Keildson, Sarah | Lunetta, Kathryn L. | He, Chunyan | Fornage, Myriam | Lagou, Vasiliki | Mangino, Massimo | Onland-Moret, N. Charlotte | Chen, Brian | Eriksson, Joel | Garcia, Melissa | Liu, Yong Mei | Koster, Annemarie | Lohman, Kurt | Lyytikäinen, Leo-Pekka | Petersen, Ann-Kristin | Prescott, Jennifer | Stolk, Lisette | Vandenput, Liesbeth | Wood, Andrew R. | Zhuang, Wei Vivian | Ruokonen, Aimo | Hartikainen, Anna-Liisa | Pouta, Anneli | Bandinelli, Stefania | Biffar, Reiner | Brabant, Georg | Cox, David G. | Chen, Yuhui | Cummings, Steven | Ferrucci, Luigi | Gunter, Marc J. | Hankinson, Susan E. | Martikainen, Hannu | Hofman, Albert | Homuth, Georg | Illig, Thomas | Jansson, John-Olov | Johnson, Andrew D. | Karasik, David | Karlsson, Magnus | Kettunen, Johannes | Kiel, Douglas P. | Kraft, Peter | Liu, Jingmin | Ljunggren, Östen | Lorentzon, Mattias | Maggio, Marcello | Markus, Marcello R. P. | Mellström, Dan | Miljkovic, Iva | Mirel, Daniel | Nelson, Sarah | Morin Papunen, Laure | Peeters, Petra H. M. | Prokopenko, Inga | Raffel, Leslie | Reincke, Martin | Reiner, Alex P. | Rexrode, Kathryn | Rivadeneira, Fernando | Schwartz, Stephen M. | Siscovick, David | Soranzo, Nicole | Stöckl, Doris | Tworoger, Shelley | Uitterlinden, André G. | van Gils, Carla H. | Vasan, Ramachandran S. | Wichmann, H.-Erich | Zhai, Guangju | Bhasin, Shalender | Bidlingmaier, Martin | Chanock, Stephen J. | De Vivo, Immaculata | Harris, Tamara B. | Hunter, David J. | Kähönen, Mika | Liu, Simin | Ouyang, Pamela | Spector, Tim D. | van der Schouw, Yvonne T. | Viikari, Jorma | Wallaschofski, Henri | McCarthy, Mark I. | Frayling, Timothy M. | Murray, Anna | Franks, Steve | Järvelin, Marjo-Riitta | de Jong, Frank H. | Raitakari, Olli | Teumer, Alexander | Ohlsson, Claes | Murabito, Joanne M. | Perry, John R. B.
PLoS Genetics  2012;8(7):e1002805.
Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
Author Summary
Sex hormone-binding globulin (SHBG) is the key protein responsible for binding and transporting the sex steroid hormones, testosterone and estradiol, in the circulatory system. SHBG regulates their bioavailability and therefore their effects in the body. SHBG has been linked to chronic diseases including type 2 diabetes and to hormone-sensitive cancers such as breast and prostate cancer. SHBG concentrations are approximately 50% heritable in family studies, suggesting SHBG concentrations are under significant genetic control; yet, little is known about the specific genes that influence SHBG. We conducted a large study of the association of SHBG concentrations with markers in the human genome in ∼22,000 white men and women to determine which loci influence SHBG concentrations. Genes near the identified genomic markers in addition to the SHBG protein coding gene included PRMT6, GCKR, ZBTB10, JMJD1C, SLCO1B1, NR2F2, ZNF652, TDGF3, LHCGR, BAIAP2L1, and UGT2B15. These genes represent a wide range of biologic pathways that may relate to SHBG function and sex steroid hormone biology, including liver function, lipid metabolism, carbohydrate metabolism and type 2 diabetes, and the development and progression of sex steroid hormone-responsive cancers.
doi:10.1371/journal.pgen.1002805
PMCID: PMC3400553  PMID: 22829776
18.  Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases 
Perry, John R. B. | Voight, Benjamin F. | Yengo, Loïc | Amin, Najaf | Dupuis, Josée | Ganser, Martha | Grallert, Harald | Navarro, Pau | Li, Man | Qi, Lu | Steinthorsdottir, Valgerdur | Scott, Robert A. | Almgren, Peter | Arking, Dan E. | Aulchenko, Yurii | Balkau, Beverley | Benediktsson, Rafn | Bergman, Richard N. | Boerwinkle, Eric | Bonnycastle, Lori | Burtt, Noël P. | Campbell, Harry | Charpentier, Guillaume | Collins, Francis S. | Gieger, Christian | Green, Todd | Hadjadj, Samy | Hattersley, Andrew T. | Herder, Christian | Hofman, Albert | Johnson, Andrew D. | Kottgen, Anna | Kraft, Peter | Labrune, Yann | Langenberg, Claudia | Manning, Alisa K. | Mohlke, Karen L. | Morris, Andrew P. | Oostra, Ben | Pankow, James | Petersen, Ann-Kristin | Pramstaller, Peter P. | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, William | Roden, Michael | Rudan, Igor | Rybin, Denis | Scott, Laura J. | Sigurdsson, Gunnar | Sladek, Rob | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vivequin, Sidonie | Weedon, Michael N. | Wright, Alan F. | Hu, Frank B. | Illig, Thomas | Kao, Linda | Meigs, James B. | Wilson, James F. | Stefansson, Kari | van Duijn, Cornelia | Altschuler, David | Morris, Andrew D. | Boehnke, Michael | McCarthy, Mark I. | Froguel, Philippe | Palmer, Colin N. A. | Wareham, Nicholas J. | Groop, Leif | Frayling, Timothy M. | Cauchi, Stéphane
PLoS Genetics  2012;8(5):e1002741.
Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
Author Summary
Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.
doi:10.1371/journal.pgen.1002741
PMCID: PMC3364960  PMID: 22693455
19.  Mendelian Randomization Studies Do Not Support a Role for Raised Circulating Triglyceride Levels Influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance 
Diabetes  2011;60(3):1008-1018.
OBJECTIVE
The causal nature of associations between circulating triglycerides, insulin resistance, and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes and raise normal fasting glucose levels and hepatic insulin resistance.
RESEARCH DESIGN AND METHODS
We tested 10 common genetic variants robustly associated with circulating triglyceride levels against the type 2 diabetes status in 5,637 case and 6,860 control subjects and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8,271 nondiabetic individuals from four studies.
RESULTS
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (SD 0.59 [95% CI 0.52–0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that the carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio [OR] 0.99 [95% CI 0.97–1.01]; P = 0.26). In nondiabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (SD 0.00 per weighted allele [95% CI −0.01 to 0.02]; P = 0.72) or increased fasting glucose levels (0.00 [−0.01 to 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose, or fasting insulin and, for diabetes, showed a trend toward a protective association (OR per 1-SD increase in log10 triglycerides: 0.61 [95% CI 0.45–0.83]; P = 0.002).
CONCLUSIONS
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes or raise fasting glucose or fasting insulin levels in nondiabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.
doi:10.2337/db10-1317
PMCID: PMC3046819  PMID: 21282362
20.  Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution 
Heid, Iris M. | Jackson, Anne U. | Randall, Joshua C. | Winkler, Thomas W. | Qi, Lu | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Zillikens, M. Carola | Speliotes, Elizabeth K. | Mägi, Reedik | Workalemahu, Tsegaselassie | White, Charles C. | Bouatia-Naji, Nabila | Harris, Tamara B. | Berndt, Sonja I. | Ingelsson, Erik | Willer, Cristen J. | Weedon, Michael N. | Luan, Jian'an | Vedantam, Sailaja | Esko, Tõnu | Kilpeläinen, Tuomas O. | Kutalik, Zoltán | Li, Shengxu | Monda, Keri L. | Dixon, Anna L. | Holmes, Christopher C. | Kaplan, Lee M. | Liang, Liming | Min, Josine L. | Moffatt, Miriam F. | Molony, Cliona | Nicholson, George | Schadt, Eric E. | Zondervan, Krina T. | Feitosa, Mary F. | Ferreira, Teresa | Allen, Hana Lango | Weyant, Robert J. | Wheeler, Eleanor | Wood, Andrew R. | Estrada, Karol | Goddard, Michael E. | Lettre, Guillaume | Mangino, Massimo | Nyholt, Dale R. | Purcell, Shaun | Vernon Smith, Albert | Visscher, Peter M. | Yang, Jian | McCaroll, Steven A. | Nemesh, James | Voight, Benjamin F. | Absher, Devin | Amin, Najaf | Aspelund, Thor | Coin, Lachlan | Glazer, Nicole L. | Hayward, Caroline | Heard-Costa, Nancy L. | Hottenga, Jouke-Jan | Johansson, Åsa | Johnson, Toby | Kaakinen, Marika | Kapur, Karen | Ketkar, Shamika | Knowles, Joshua W. | Kraft, Peter | Kraja, Aldi T. | Lamina, Claudia | Leitzmann, Michael F. | McKnight, Barbara | Morris, Andrew P. | Ong, Ken K. | Perry, John R.B. | Peters, Marjolein J. | Polasek, Ozren | Prokopenko, Inga | Rayner, Nigel W. | Ripatti, Samuli | Rivadeneira, Fernando | Robertson, Neil R. | Sanna, Serena | Sovio, Ulla | Surakka, Ida | Teumer, Alexander | van Wingerden, Sophie | Vitart, Veronique | Zhao, Jing Hua | Cavalcanti-Proença, Christine | Chines, Peter S. | Fisher, Eva | Kulzer, Jennifer R. | Lecoeur, Cecile | Narisu, Narisu | Sandholt, Camilla | Scott, Laura J. | Silander, Kaisa | Stark, Klaus | Tammesoo, Mari-Liis | Teslovich, Tanya M. | John Timpson, Nicholas | Watanabe, Richard M. | Welch, Ryan | Chasman, Daniel I. | Cooper, Matthew N. | Jansson, John-Olov | Kettunen, Johannes | Lawrence, Robert W. | Pellikka, Niina | Perola, Markus | Vandenput, Liesbeth | Alavere, Helene | Almgren, Peter | Atwood, Larry D. | Bennett, Amanda J. | Biffar, Reiner | Bonnycastle, Lori L. | Bornstein, Stefan R. | Buchanan, Thomas A. | Campbell, Harry | Day, Ian N.M. | Dei, Mariano | Dörr, Marcus | Elliott, Paul | Erdos, Michael R. | Eriksson, Johan G. | Freimer, Nelson B. | Fu, Mao | Gaget, Stefan | Geus, Eco J.C. | Gjesing, Anette P. | Grallert, Harald | Gräßler, Jürgen | Groves, Christopher J. | Guiducci, Candace | Hartikainen, Anna-Liisa | Hassanali, Neelam | Havulinna, Aki S. | Herzig, Karl-Heinz | Hicks, Andrew A. | Hui, Jennie | Igl, Wilmar | Jousilahti, Pekka | Jula, Antti | Kajantie, Eero | Kinnunen, Leena | Kolcic, Ivana | Koskinen, Seppo | Kovacs, Peter | Kroemer, Heyo K. | Krzelj, Vjekoslav | Kuusisto, Johanna | Kvaloy, Kirsti | Laitinen, Jaana | Lantieri, Olivier | Lathrop, G. Mark | Lokki, Marja-Liisa | Luben, Robert N. | Ludwig, Barbara | McArdle, Wendy L. | McCarthy, Anne | Morken, Mario A. | Nelis, Mari | Neville, Matt J. | Paré, Guillaume | Parker, Alex N. | Peden, John F. | Pichler, Irene | Pietiläinen, Kirsi H. | Platou, Carl G.P. | Pouta, Anneli | Ridderstråle, Martin | Samani, Nilesh J. | Saramies, Jouko | Sinisalo, Juha | Smit, Jan H. | Strawbridge, Rona J. | Stringham, Heather M. | Swift, Amy J. | Teder-Laving, Maris | Thomson, Brian | Usala, Gianluca | van Meurs, Joyce B.J. | van Ommen, Gert-Jan | Vatin, Vincent | Volpato, Claudia B. | Wallaschofski, Henri | Walters, G. Bragi | Widen, Elisabeth | Wild, Sarah H. | Willemsen, Gonneke | Witte, Daniel R. | Zgaga, Lina | Zitting, Paavo | Beilby, John P. | James, Alan L. | Kähönen, Mika | Lehtimäki, Terho | Nieminen, Markku S. | Ohlsson, Claes | Palmer, Lyle J. | Raitakari, Olli | Ridker, Paul M. | Stumvoll, Michael | Tönjes, Anke | Viikari, Jorma | Balkau, Beverley | Ben-Shlomo, Yoav | Bergman, Richard N. | Boeing, Heiner | Smith, George Davey | Ebrahim, Shah | Froguel, Philippe | Hansen, Torben | Hengstenberg, Christian | Hveem, Kristian | Isomaa, Bo | Jørgensen, Torben | Karpe, Fredrik | Khaw, Kay-Tee | Laakso, Markku | Lawlor, Debbie A. | Marre, Michel | Meitinger, Thomas | Metspalu, Andres | Midthjell, Kristian | Pedersen, Oluf | Salomaa, Veikko | Schwarz, Peter E.H. | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Valle, Timo T. | Wareham, Nicholas J. | Arnold, Alice M. | Beckmann, Jacques S. | Bergmann, Sven | Boerwinkle, Eric | Boomsma, Dorret I. | Caulfield, Mark J. | Collins, Francis S. | Eiriksdottir, Gudny | Gudnason, Vilmundur | Gyllensten, Ulf | Hamsten, Anders | Hattersley, Andrew T. | Hofman, Albert | Hu, Frank B. | Illig, Thomas | Iribarren, Carlos | Jarvelin, Marjo-Riitta | Kao, W.H. Linda | Kaprio, Jaakko | Launer, Lenore J. | Munroe, Patricia B. | Oostra, Ben | Penninx, Brenda W. | Pramstaller, Peter P. | Psaty, Bruce M. | Quertermous, Thomas | Rissanen, Aila | Rudan, Igor | Shuldiner, Alan R. | Soranzo, Nicole | Spector, Timothy D. | Syvanen, Ann-Christine | Uda, Manuela | Uitterlinden, André | Völzke, Henry | Vollenweider, Peter | Wilson, James F. | Witteman, Jacqueline C. | Wright, Alan F. | Abecasis, Gonçalo R. | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | Frayling, Timothy M. | Groop, Leif C. | Haritunians, Talin | Hunter, David J. | Kaplan, Robert C. | North, Kari E. | O'Connell, Jeffrey R. | Peltonen, Leena | Schlessinger, David | Strachan, David P. | Hirschhorn, Joel N. | Assimes, Themistocles L. | Wichmann, H.-Erich | Thorsteinsdottir, Unnur | van Duijn, Cornelia M. | Stefansson, Kari | Cupples, L. Adrienne | Loos, Ruth J.F. | Barroso, Inês | McCarthy, Mark I. | Fox, Caroline S. | Mohlke, Karen L. | Lindgren, Cecilia M.
Nature genetics  2010;42(11):949-960.
Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body-mass-index (up to 77,167 participants), following up 16 loci in an additional 29 studies (up to 113,636 subjects). We identified 13 novel loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (P 1.9 × 10−9 to 1.8 × 10−40), and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex-difference 1.9 × 10−3 to 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution, independent of overall adiposity, and reveal powerful gene-by-sex interactions.
doi:10.1038/ng.685
PMCID: PMC3000924  PMID: 20935629
genome-wide association; waist-hip-ratio; body fat distribution; central obesity; meta-analysis; genetics; visceral adipose tissue; metabolism; body composition; Expression Quantitative Trait Loci; sex difference
21.  Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index 
Speliotes, Elizabeth K. | Willer, Cristen J. | Berndt, Sonja I. | Monda, Keri L. | Thorleifsson, Gudmar | Jackson, Anne U. | Allen, Hana Lango | Lindgren, Cecilia M. | Luan, Jian’an | Mägi, Reedik | Randall, Joshua C. | Vedantam, Sailaja | Winkler, Thomas W. | Qi, Lu | Workalemahu, Tsegaselassie | Heid, Iris M. | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Weedon, Michael N. | Wheeler, Eleanor | Wood, Andrew R. | Ferreira, Teresa | Weyant, Robert J. | Segré, Ayellet V. | Estrada, Karol | Liang, Liming | Nemesh, James | Park, Ju-Hyun | Gustafsson, Stefan | Kilpeläinen, Tuomas O. | Yang, Jian | Bouatia-Naji, Nabila | Esko, Tõnu | Feitosa, Mary F. | Kutalik, Zoltán | Mangino, Massimo | Raychaudhuri, Soumya | Scherag, Andre | Smith, Albert Vernon | Welch, Ryan | Zhao, Jing Hua | Aben, Katja K. | Absher, Devin M. | Amin, Najaf | Dixon, Anna L. | Fisher, Eva | Glazer, Nicole L. | Goddard, Michael E. | Heard-Costa, Nancy L. | Hoesel, Volker | Hottenga, Jouke-Jan | Johansson, Åsa | Johnson, Toby | Ketkar, Shamika | Lamina, Claudia | Li, Shengxu | Moffatt, Miriam F. | Myers, Richard H. | Narisu, Narisu | Perry, John R.B. | Peters, Marjolein J. | Preuss, Michael | Ripatti, Samuli | Rivadeneira, Fernando | Sandholt, Camilla | Scott, Laura J. | Timpson, Nicholas J. | Tyrer, Jonathan P. | van Wingerden, Sophie | Watanabe, Richard M. | White, Charles C. | Wiklund, Fredrik | Barlassina, Christina | Chasman, Daniel I. | Cooper, Matthew N. | Jansson, John-Olov | Lawrence, Robert W. | Pellikka, Niina | Prokopenko, Inga | Shi, Jianxin | Thiering, Elisabeth | Alavere, Helene | Alibrandi, Maria T. S. | Almgren, Peter | Arnold, Alice M. | Aspelund, Thor | Atwood, Larry D. | Balkau, Beverley | Balmforth, Anthony J. | Bennett, Amanda J. | Ben-Shlomo, Yoav | Bergman, Richard N. | Bergmann, Sven | Biebermann, Heike | Blakemore, Alexandra I.F. | Boes, Tanja | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brown, Morris J. | Buchanan, Thomas A. | Busonero, Fabio | Campbell, Harry | Cappuccio, Francesco P. | Cavalcanti-Proença, Christine | Chen, Yii-Der Ida | Chen, Chih-Mei | Chines, Peter S. | Clarke, Robert | Coin, Lachlan | Connell, John | Day, Ian N.M. | den Heijer, Martin | Duan, Jubao | Ebrahim, Shah | Elliott, Paul | Elosua, Roberto | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Facheris, Maurizio F. | Felix, Stephan B. | Fischer-Posovszky, Pamela | Folsom, Aaron R. | Friedrich, Nele | Freimer, Nelson B. | Fu, Mao | Gaget, Stefan | Gejman, Pablo V. | Geus, Eco J.C. | Gieger, Christian | Gjesing, Anette P. | Goel, Anuj | Goyette, Philippe | Grallert, Harald | Gräßler, Jürgen | Greenawalt, Danielle M. | Groves, Christopher J. | Gudnason, Vilmundur | Guiducci, Candace | Hartikainen, Anna-Liisa | Hassanali, Neelam | Hall, Alistair S. | Havulinna, Aki S. | Hayward, Caroline | Heath, Andrew C. | Hengstenberg, Christian | Hicks, Andrew A. | Hinney, Anke | Hofman, Albert | Homuth, Georg | Hui, Jennie | Igl, Wilmar | Iribarren, Carlos | Isomaa, Bo | Jacobs, Kevin B. | Jarick, Ivonne | Jewell, Elizabeth | John, Ulrich | Jørgensen, Torben | Jousilahti, Pekka | Jula, Antti | Kaakinen, Marika | Kajantie, Eero | Kaplan, Lee M. | Kathiresan, Sekar | Kettunen, Johannes | Kinnunen, Leena | Knowles, Joshua W. | Kolcic, Ivana | König, Inke R. | Koskinen, Seppo | Kovacs, Peter | Kuusisto, Johanna | Kraft, Peter | Kvaløy, Kirsti | Laitinen, Jaana | Lantieri, Olivier | Lanzani, Chiara | Launer, Lenore J. | Lecoeur, Cecile | Lehtimäki, Terho | Lettre, Guillaume | Liu, Jianjun | Lokki, Marja-Liisa | Lorentzon, Mattias | Luben, Robert N. | Ludwig, Barbara | Manunta, Paolo | Marek, Diana | Marre, Michel | Martin, Nicholas G. | McArdle, Wendy L. | McCarthy, Anne | McKnight, Barbara | Meitinger, Thomas | Melander, Olle | Meyre, David | Midthjell, Kristian | Montgomery, Grant W. | Morken, Mario A. | Morris, Andrew P. | Mulic, Rosanda | Ngwa, Julius S. | Nelis, Mari | Neville, Matt J. | Nyholt, Dale R. | O’Donnell, Christopher J. | O’Rahilly, Stephen | Ong, Ken K. | Oostra, Ben | Paré, Guillaume | Parker, Alex N. | Perola, Markus | Pichler, Irene | Pietiläinen, Kirsi H. | Platou, Carl G.P. | Polasek, Ozren | Pouta, Anneli | Rafelt, Suzanne | Raitakari, Olli | Rayner, Nigel W. | Ridderstråle, Martin | Rief, Winfried | Ruokonen, Aimo | Robertson, Neil R. | Rzehak, Peter | Salomaa, Veikko | Sanders, Alan R. | Sandhu, Manjinder S. | Sanna, Serena | Saramies, Jouko | Savolainen, Markku J. | Scherag, Susann | Schipf, Sabine | Schreiber, Stefan | Schunkert, Heribert | Silander, Kaisa | Sinisalo, Juha | Siscovick, David S. | Smit, Jan H. | Soranzo, Nicole | Sovio, Ulla | Stephens, Jonathan | Surakka, Ida | Swift, Amy J. | Tammesoo, Mari-Liis | Tardif, Jean-Claude | Teder-Laving, Maris | Teslovich, Tanya M. | Thompson, John R. | Thomson, Brian | Tönjes, Anke | Tuomi, Tiinamaija | van Meurs, Joyce B.J. | van Ommen, Gert-Jan | Vatin, Vincent | Viikari, Jorma | Visvikis-Siest, Sophie | Vitart, Veronique | Vogel, Carla I. G. | Voight, Benjamin F. | Waite, Lindsay L. | Wallaschofski, Henri | Walters, G. Bragi | Widen, Elisabeth | Wiegand, Susanna | Wild, Sarah H. | Willemsen, Gonneke | Witte, Daniel R. | Witteman, Jacqueline C. | Xu, Jianfeng | Zhang, Qunyuan | Zgaga, Lina | Ziegler, Andreas | Zitting, Paavo | Beilby, John P. | Farooqi, I. Sadaf | Hebebrand, Johannes | Huikuri, Heikki V. | James, Alan L. | Kähönen, Mika | Levinson, Douglas F. | Macciardi, Fabio | Nieminen, Markku S. | Ohlsson, Claes | Palmer, Lyle J. | Ridker, Paul M. | Stumvoll, Michael | Beckmann, Jacques S. | Boeing, Heiner | Boerwinkle, Eric | Boomsma, Dorret I. | Caulfield, Mark J. | Chanock, Stephen J. | Collins, Francis S. | Cupples, L. Adrienne | Smith, George Davey | Erdmann, Jeanette | Froguel, Philippe | Grönberg, Henrik | Gyllensten, Ulf | Hall, Per | Hansen, Torben | Harris, Tamara B. | Hattersley, Andrew T. | Hayes, Richard B. | Heinrich, Joachim | Hu, Frank B. | Hveem, Kristian | Illig, Thomas | Jarvelin, Marjo-Riitta | Kaprio, Jaakko | Karpe, Fredrik | Khaw, Kay-Tee | Kiemeney, Lambertus A. | Krude, Heiko | Laakso, Markku | Lawlor, Debbie A. | Metspalu, Andres | Munroe, Patricia B. | Ouwehand, Willem H. | Pedersen, Oluf | Penninx, Brenda W. | Peters, Annette | Pramstaller, Peter P. | Quertermous, Thomas | Reinehr, Thomas | Rissanen, Aila | Rudan, Igor | Samani, Nilesh J. | Schwarz, Peter E.H. | Shuldiner, Alan R. | Spector, Timothy D. | Tuomilehto, Jaakko | Uda, Manuela | Uitterlinden, André | Valle, Timo T. | Wabitsch, Martin | Waeber, Gérard | Wareham, Nicholas J. | Watkins, Hugh | Wilson, James F. | Wright, Alan F. | Zillikens, M. Carola | Chatterjee, Nilanjan | McCarroll, Steven A. | Purcell, Shaun | Schadt, Eric E. | Visscher, Peter M. | Assimes, Themistocles L. | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Haritunians, Talin | Hunter, David J. | Kaplan, Robert C. | Mohlke, Karen L. | O’Connell, Jeffrey R. | Peltonen, Leena | Schlessinger, David | Strachan, David P. | van Duijn, Cornelia M. | Wichmann, H.-Erich | Frayling, Timothy M. | Thorsteinsdottir, Unnur | Abecasis, Gonçalo R. | Barroso, Inês | Boehnke, Michael | Stefansson, Kari | North, Kari E. | McCarthy, Mark I. | Hirschhorn, Joel N. | Ingelsson, Erik | Loos, Ruth J.F.
Nature genetics  2010;42(11):937-948.
Obesity is globally prevalent and highly heritable, but the underlying genetic factors remain largely elusive. To identify genetic loci for obesity-susceptibility, we examined associations between body mass index (BMI) 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 BMI (P<5×10−8), one of which includes a copy number variant near GPRC5B. Some loci (MC4R, POMC, SH2B1, BDNF) map near key hypothalamic regulators of energy balance, and one is near GIPR, an incretin receptor. Furthermore, genes in other newly-associated loci may provide novel insights into human body weight regulation.
doi:10.1038/ng.686
PMCID: PMC3014648  PMID: 20935630
22.  Hundreds of variants clustered in genomic loci and biological pathways affect human height 
Lango Allen, Hana | Estrada, Karol | Lettre, Guillaume | Berndt, Sonja I. | Weedon, Michael N. | Rivadeneira, Fernando | Willer, Cristen J. | Jackson, Anne U. | Vedantam, Sailaja | Raychaudhuri, Soumya | Ferreira, Teresa | Wood, Andrew R. | Weyant, Robert J. | Segrè, Ayellet V. | Speliotes, Elizabeth K. | Wheeler, Eleanor | Soranzo, Nicole | Park, Ju-Hyun | Yang, Jian | Gudbjartsson, Daniel | Heard-Costa, Nancy L. | Randall, Joshua C. | Qi, Lu | Smith, Albert Vernon | Mägi, Reedik | Pastinen, Tomi | Liang, Liming | Heid, Iris M. | Luan, Jian'an | Thorleifsson, Gudmar | Winkler, Thomas W. | Goddard, Michael E. | Lo, Ken Sin | Palmer, Cameron | Workalemahu, Tsegaselassie | Aulchenko, Yurii S. | Johansson, Åsa | Zillikens, M.Carola | Feitosa, Mary F. | Esko, Tõnu | Johnson, Toby | Ketkar, Shamika | Kraft, Peter | Mangino, Massimo | Prokopenko, Inga | Absher, Devin | Albrecht, Eva | Ernst, Florian | Glazer, Nicole L. | Hayward, Caroline | Hottenga, Jouke-Jan | Jacobs, Kevin B. | Knowles, Joshua W. | Kutalik, Zoltán | Monda, Keri L. | Polasek, Ozren | Preuss, Michael | Rayner, Nigel W. | Robertson, Neil R. | Steinthorsdottir, Valgerdur | Tyrer, Jonathan P. | Voight, Benjamin F. | Wiklund, Fredrik | Xu, Jianfeng | Zhao, Jing Hua | Nyholt, Dale R. | Pellikka, Niina | Perola, Markus | Perry, John R.B. | Surakka, Ida | Tammesoo, Mari-Liis | Altmaier, Elizabeth L. | Amin, Najaf | Aspelund, Thor | Bhangale, Tushar | Boucher, Gabrielle | Chasman, Daniel I. | Chen, Constance | Coin, Lachlan | Cooper, Matthew N. | Dixon, Anna L. | Gibson, Quince | Grundberg, Elin | Hao, Ke | Junttila, M. Juhani | Kaplan, Lee M. | Kettunen, Johannes | König, Inke R. | Kwan, Tony | Lawrence, Robert W. | Levinson, Douglas F. | Lorentzon, Mattias | McKnight, Barbara | Morris, Andrew P. | Müller, Martina | Ngwa, Julius Suh | Purcell, Shaun | Rafelt, Suzanne | Salem, Rany M. | Salvi, Erika | Sanna, Serena | Shi, Jianxin | Sovio, Ulla | Thompson, John R. | Turchin, Michael C. | Vandenput, Liesbeth | Verlaan, Dominique J. | Vitart, Veronique | White, Charles C. | Ziegler, Andreas | Almgren, Peter | Balmforth, Anthony J. | Campbell, Harry | Citterio, Lorena | De Grandi, Alessandro | Dominiczak, Anna | Duan, Jubao | Elliott, Paul | Elosua, Roberto | Eriksson, Johan G. | Freimer, Nelson B. | Geus, Eco J.C. | Glorioso, Nicola | Haiqing, Shen | Hartikainen, Anna-Liisa | Havulinna, Aki S. | Hicks, Andrew A. | Hui, Jennie | Igl, Wilmar | Illig, Thomas | Jula, Antti | Kajantie, Eero | Kilpeläinen, Tuomas O. | Koiranen, Markku | Kolcic, Ivana | Koskinen, Seppo | Kovacs, Peter | Laitinen, Jaana | Liu, Jianjun | Lokki, Marja-Liisa | Marusic, Ana | Maschio, Andrea | Meitinger, Thomas | Mulas, Antonella | Paré, Guillaume | Parker, Alex N. | Peden, John F. | Petersmann, Astrid | Pichler, Irene | Pietiläinen, Kirsi H. | Pouta, Anneli | Ridderstråle, Martin | Rotter, Jerome I. | Sambrook, Jennifer G. | Sanders, Alan R. | Schmidt, Carsten Oliver | Sinisalo, Juha | Smit, Jan H. | Stringham, Heather M. | Walters, G.Bragi | Widen, Elisabeth | Wild, Sarah H. | Willemsen, Gonneke | Zagato, Laura | Zgaga, Lina | Zitting, Paavo | Alavere, Helene | Farrall, Martin | McArdle, Wendy L. | Nelis, Mari | Peters, Marjolein J. | Ripatti, Samuli | van Meurs, Joyce B.J. | Aben, Katja K. | Ardlie, Kristin G | Beckmann, Jacques S. | Beilby, John P. | Bergman, Richard N. | Bergmann, Sven | Collins, Francis S. | Cusi, Daniele | den Heijer, Martin | Eiriksdottir, Gudny | Gejman, Pablo V. | Hall, Alistair S. | Hamsten, Anders | Huikuri, Heikki V. | Iribarren, Carlos | Kähönen, Mika | Kaprio, Jaakko | Kathiresan, Sekar | Kiemeney, Lambertus | Kocher, Thomas | Launer, Lenore J. | Lehtimäki, Terho | Melander, Olle | Mosley, Tom H. | Musk, Arthur W. | Nieminen, Markku S. | O'Donnell, Christopher J. | Ohlsson, Claes | Oostra, Ben | Palmer, Lyle J. | Raitakari, Olli | Ridker, Paul M. | Rioux, John D. | Rissanen, Aila | Rivolta, Carlo | Schunkert, Heribert | Shuldiner, Alan R. | Siscovick, David S. | Stumvoll, Michael | Tönjes, Anke | Tuomilehto, Jaakko | van Ommen, Gert-Jan | Viikari, Jorma | Heath, Andrew C. | Martin, Nicholas G. | Montgomery, Grant W. | Province, Michael A. | Kayser, Manfred | Arnold, Alice M. | Atwood, Larry D. | Boerwinkle, Eric | Chanock, Stephen J. | Deloukas, Panos | Gieger, Christian | Grönberg, Henrik | Hall, Per | Hattersley, Andrew T. | Hengstenberg, Christian | Hoffman, Wolfgang | Lathrop, G.Mark | Salomaa, Veikko | Schreiber, Stefan | Uda, Manuela | Waterworth, Dawn | Wright, Alan F. | Assimes, Themistocles L. | Barroso, Inês | Hofman, Albert | Mohlke, Karen L. | Boomsma, Dorret I. | Caulfield, Mark J. | Cupples, L.Adrienne | Erdmann, Jeanette | Fox, Caroline S. | Gudnason, Vilmundur | Gyllensten, Ulf | Harris, Tamara B. | Hayes, Richard B. | Jarvelin, Marjo-Riitta | Mooser, Vincent | Munroe, Patricia B. | Ouwehand, Willem H. | Penninx, Brenda W. | Pramstaller, Peter P. | Quertermous, Thomas | Rudan, Igor | Samani, Nilesh J. | Spector, Timothy D. | Völzke, Henry | Watkins, Hugh | Wilson, James F. | Groop, Leif C. | Haritunians, Talin | Hu, Frank B. | Kaplan, Robert C. | Metspalu, Andres | North, Kari E. | Schlessinger, David | Wareham, Nicholas J. | Hunter, David J. | O'Connell, Jeffrey R. | Strachan, David P. | Wichmann, H.-Erich | Borecki, Ingrid B. | van Duijn, Cornelia M. | Schadt, Eric E. | Thorsteinsdottir, Unnur | Peltonen, Leena | Uitterlinden, André | Visscher, Peter M. | Chatterjee, Nilanjan | Loos, Ruth J.F. | Boehnke, Michael | McCarthy, Mark I. | Ingelsson, Erik | Lindgren, Cecilia M. | Abecasis, Gonçalo R. | Stefansson, Kari | Frayling, Timothy M. | Hirschhorn, Joel N
Nature  2010;467(7317):832-838.
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ∼10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ∼16% of phenotypic variation (∼20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
doi:10.1038/nature09410
PMCID: PMC2955183  PMID: 20881960
23.  Common Variation in the FTO Gene Alters Diabetes-Related Metabolic Traits to the Extent Expected Given Its Effect on BMI 
Diabetes  2008;57(5):1419-1426.
OBJECTIVE
Common variation in the FTO gene is associated with BMI and type 2 diabetes. Increased BMI is associated with diabetes risk factors, including raised insulin, glucose, and triglycerides. We aimed to test whether FTO genotype is associated with variation in these metabolic traits.
RESEARCH DESIGN AND METHODS
We tested the association between FTO genotype and 10 metabolic traits using data from 17,037 white European individuals. We compared the observed effect of FTO genotype on each trait to that expected given the FTO-BMI and BMI-trait associations.
RESULTS
Each copy of the FTO rs9939609 A allele was associated with higher fasting insulin (0.039 SD [95% CI 0.013–0.064]; P = 0.003), glucose (0.024 [0.001– 0.048]; P = 0.044), and triglycerides (0.028 [0.003– 0.052]; P = 0.025) and lower HDL cholesterol (0.032 [0.008 – 0.057]; P = 0.009). There was no evidence of these associations when adjusting for BMI. Associations with fasting alanine aminotransferase, γ-glutamyl-transferase, LDL cholesterol, A1C, and systolic and diastolic blood pressure were in the expected direction but did not reach P < 0.05. For all metabolic traits, effect sizes were consistent with those expected for the per allele change in BMI. FTO genotype was associated with a higher odds of metabolic syndrome (odds ratio 1.17 [95% CI 1.10 –1.25]; P = 3 × 10−6).
CONCLUSIONS
FTO genotype is associated with metabolic traits to an extent entirely consistent with its effect on BMI. Sample sizes of >12,000 individuals were needed to detect associations at P < 0.05. Our findings highlight the importance of using appropriately powered studies to assess the effects of a known diabetes or obesity variant on secondary traits correlated with these conditions.
doi:10.2337/db07-1466
PMCID: PMC3073395  PMID: 18346983
24.  Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus 
PLoS ONE  2010;5(11):e14040.
Recent multi-dimensional approaches to the study of complex disease have revealed powerful insights into how genetic and epigenetic factors may underlie their aetiopathogenesis. We examined genotype-epigenotype interactions in the context of Type 2 Diabetes (T2D), focussing on known regions of genomic susceptibility. We assayed DNA methylation in 60 females, stratified according to disease susceptibility haplotype using previously identified association loci. CpG methylation was assessed using methylated DNA immunoprecipitation on a targeted array (MeDIP-chip) and absolute methylation values were estimated using a Bayesian algorithm (BATMAN). Absolute methylation levels were quantified across LD blocks, and we identified increased DNA methylation on the FTO obesity susceptibility haplotype, tagged by the rs8050136 risk allele A (p = 9.40×10−4, permutation p = 1.0×10−3). Further analysis across the 46 kb LD block using sliding windows localised the most significant difference to be within a 7.7 kb region (p = 1.13×10−7). Sequence level analysis, followed by pyrosequencing validation, revealed that the methylation difference was driven by the co-ordinated phase of CpG-creating SNPs across the risk haplotype. This 7.7 kb region of haplotype-specific methylation (HSM), encapsulates a Highly Conserved Non-Coding Element (HCNE) that has previously been validated as a long-range enhancer, supported by the histone H3K4me1 enhancer signature. This study demonstrates that integration of Genome-Wide Association (GWA) SNP and epigenomic DNA methylation data can identify potential novel genotype-epigenotype interactions within disease-associated loci, thus providing a novel route to aid unravelling common complex diseases.
doi:10.1371/journal.pone.0014040
PMCID: PMC2987816  PMID: 21124985
25.  Underlying Genetic Models of Inheritance in Established Type 2 Diabetes Associations 
American Journal of Epidemiology  2009;170(5):537-545.
For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.
doi:10.1093/aje/kwp145
PMCID: PMC2732984  PMID: 19602701
Bayes theorem; diabetes mellitus, type 2; meta-analysis; models, genetic; polymorphism, genetic; population characteristics

Results 1-25 (38)