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1.  Bayesian refinement of association signals for 14 loci in 3 common diseases 
Nature genetics  2012;44(12):1294-1301.
To further investigate susceptibility loci identified by genome-wide association studies, we genotyped 5,500 SNPs across 14 associated regions in 8,000 samples from a control group and 3 diseases: type 2 diabetes (T2D), coronary artery disease (CAD) and Graves’ disease. We defined, using Bayes theorem, credible sets of SNPs that were 95% likely, based on posterior probability, to contain the causal disease-associated SNPs. In 3 of the 14 regions, TCF7L2 (T2D), CTLA4 (Graves’ disease) and CDKN2A-CDKN2B (T2D), much of the posterior probability rested on a single SNP, and, in 4 other regions (CDKN2A-CDKN2B (CAD) and CDKAL1, FTO and HHEX (T2D)), the 95% sets were small, thereby excluding most SNPs as potentially causal. Very few SNPs in our credible sets had annotated functions, illustrating the limitations in understanding the mechanisms underlying susceptibility to common diseases. Our results also show the value of more detailed mapping to target sequences for functional studies.
doi:10.1038/ng.2435
PMCID: PMC3791416  PMID: 23104008
2.  Common variants at 12q15 and 12q24 are associated with infant head circumference 
Taal, H Rob | Pourcain, Beate St | Thiering, Elisabeth | Das, Shikta | Mook-Kanamori, Dennis O | Warrington, Nicole M | Kaakinen, Marika | Kreiner-Møller, Eskil | Bradfield, Jonathan P | Freathy, Rachel M | Geller, Frank | Guxens, Mònica | Cousminer, Diana L | Kerkhof, Marjan | Timpson, Nicholas J | Ikram, M Arfan | Beilin, Lawrence J | Bønnelykke, Klaus | Buxton, Jessica L | Charoen, Pimphen | Chawes, Bo Lund Krogsgaard | Eriksson, Johan | Evans, David M | Hofman, Albert | Kemp, John P | Kim, Cecilia E | Klopp, Norman | Lahti, Jari | Lye, Stephen J | McMahon, George | Mentch, Frank D | Müller, Martina | O’Reilly, Paul F | Prokopenko, Inga | Rivadeneira, Fernando | Steegers, Eric A P | Sunyer, Jordi | Tiesler, Carla | Yaghootkar, Hanieh | Breteler, Monique M B | Debette, Stephanie | Fornage, Myriam | Gudnason, Vilmundur | Launer, Lenore J | van der Lugt, Aad | Mosley, Thomas H | Seshadri, Sudha | Smith, Albert V | Vernooij, Meike W | Blakemore, Alexandra IF | Chiavacci, Rosetta M | Feenstra, Bjarke | Fernandez-Benet, Julio | Grant, Struan F A | Hartikainen, Anna-Liisa | van der Heijden, Albert J | Iñiguez, Carmen | Lathrop, Mark | McArdle, Wendy L | Mølgaard, Anne | Newnham, John P | Palmer, Lyle J | Palotie, Aarno | Pouta, Annneli | Ring, Susan M | Sovio, Ulla | Standl, Marie | Uitterlinden, Andre G | Wichmann, H-Erich | Vissing, Nadja Hawwa | DeCarli, Charles | van Duijn, Cornelia M | McCarthy, Mark I | Koppelman, Gerard H. | Estivill, Xavier | Hattersley, Andrew T | Melbye, Mads | Bisgaard, Hans | Pennell, Craig E | Widen, Elisabeth | Hakonarson, Hakon | Smith, George Davey | Heinrich, Joachim | Jarvelin, Marjo-Riitta | Jaddoe, Vincent W V
Nature genetics  2012;44(5):532-538.
To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association (GWA) studies (N=10,768 from European ancestry enrolled in pregnancy/birth cohorts) and followed up three lead signals in six replication studies (combined N=19,089). Rs7980687 on chromosome 12q24 (P=8.1×10−9), and rs1042725 on chromosome 12q15 (P=2.8×10−10) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height1, their effects on infant head circumference were largely independent of height (P=3.8×10−7 for rs7980687, P=1.3×10−7 for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P=3.9×10−6). SNPs correlated to the 17q21 signal show genome-wide association with adult intra cranial volume2, Parkinson’s disease and other neurodegenerative diseases3-5, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.
doi:10.1038/ng.2238
PMCID: PMC3773913  PMID: 22504419
3.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
4.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
5.  Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes 
Zeggini, Eleftheria | Scott, Laura J. | Saxena, Richa | Voight, Benjamin F. | Marchini, Jonathan L | Hu, Tainle | de Bakker, Paul IW | Abecasis, Gonçalo R | Almgren, Peter | Andersen, Gitte | Ardlie, Kristin | Boström, Kristina Bengtsson | Bergman, Richard N | Bonnycastle, Lori L | Borch-Johnsen, Knut | Burtt, Noël P | Chen, Hong | Chines, Peter S | Daly, Mark J | Deodhar, Parimal | Ding, Charles | Doney, Alex S F | Duren, William L | Elliott, Katherine S | Erdos, Michael R | Frayling, Timothy M | Freathy, Rachel M | Gianniny, Lauren | Grallert, Harald | Grarup, Niels | Groves, Christopher J | Guiducci, Candace | Hansen, Torben | Herder, Christian | Hitman, Graham A | Hughes, Thomas E | Isomaa, Bo | Jackson, Anne U | Jørgensen, Torben | Kong, Augustine | Kubalanza, Kari | Kuruvilla, Finny G | Kuusisto, Johanna | Langenberg, Claudia | Lango, Hana | Lauritzen, Torsten | Li, Yun | Lindgren, Cecilia M | Lyssenko, Valeriya | Marvelle, Amanda F | Meisinger, Christa | Midthjell, Kristian | Mohlke, Karen L | Morken, Mario A | Morris, Andrew D | Narisu, Narisu | Nilsson, Peter | Owen, Katharine R | Palmer, Colin NA | Payne, Felicity | Perry, John RB | Pettersen, Elin | Platou, Carl | Prokopenko, Inga | Qi, Lu | Qin, Li | Rayner, Nigel W | Rees, Matthew | Roix, Jeffrey J | Sandbæk, Anelli | Shields, Beverley | Sjögren, Marketa | Steinthorsdottir, Valgerdur | Stringham, Heather M | Swift, Amy J | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Timpson, Nicholas J | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Walker, Mark | Watanabe, Richard M | Weedon, Michael N | Willer, Cristen J | Illig, Thomas | Hveem, Kristian | Hu, Frank B | Laakso, Markku | Stefansson, Kari | Pedersen, Oluf | Wareham, Nicholas J | Barroso, Inês | Hattersley, Andrew T | Collins, Francis S | Groop, Leif | McCarthy, Mark I | Boehnke, Michael | Altshuler, David
Nature genetics  2008;40(5):638-645.
Genome-wide association (GWA) studies have identified multiple new genomic loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1-11. Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to discover loci at which common alleles have modest effects, we performed meta-analysis of three T2D GWA scans encompassing 10,128 individuals of European-descent and ~2.2 million SNPs (directly genotyped and imputed). Replication testing was performed in an independent sample with an effective sample size of up to 53,975. At least six new loci with robust evidence for association were detected, including the JAZF1 (p=5.0×10−14), CDC123/CAMK1D (p=1.2×10−10), TSPAN8/LGR5 (p=1.1×10−9), THADA (p=1.1×10−9), ADAMTS9 (p=1.2×10−8), and NOTCH2 (p=4.1×10−8) gene regions. The large number of loci with relatively small effects indicates the value of large discovery and follow-up samples in identifying additional clues about the inherited basis of T2D.
doi:10.1038/ng.120
PMCID: PMC2672416  PMID: 18372903
6.  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, raised 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 type 2 diabetes status in 5637 cases, 6860 controls, and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8271 non-diabetic individuals from four studies.
Results
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (0.59 SD [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 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 non-diabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (0.00 SD per weighted allele [95% CI: −0.01, 0.02]; P = 0.72) or increased fasting glucose levels (0.00 SD per weighted allele [95% CI: −0.01, 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 towards a protective association (OR per 1 SD increase in log10-triglycerides: 0.61 [95% CI: 0.45, 0.83]; P = 0.002).
Conclusion
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes, or raise fasting glucose or fasting insulin levels in non-diabetic 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
7.  Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes 
Nature genetics  2010;43(2):117-120.
Metformin is the most commonly used pharmacological therapy for type 2 diabetes. We carried out a GWA study on glycaemic response to metformin in 1024 Scottish patients with type 2 diabetes. Replication was in two cohorts consisting of 1783 Scottish patients and 1113 patients from the UK Prospective Diabetes Study. In a meta-analysis (n=3920) we observed an association (P=2.9 *10−9) for a SNP rs11212617 at a locus containing the ataxia telangiectasia mutated (ATM) gene with an odds ratio of 1.35 (95% CI 1.22 to 1.49) for treatment success. In a rat hepatoma cell line, inhibition of ATM with KU-55933 attenuated the phosphorylation and activation of AMPK in response to metformin. We conclude that ATM, a gene known to be involved in DNA repair and cell cycle control, plays a role in the effect of metformin upstream of AMPK, and variation in this gene alters glycaemic response to metformin.
doi:10.1038/ng.735
PMCID: PMC3030919  PMID: 21186350
8.  A common variant of HMGA2 is associated with adult and childhood height in the general population 
Nature genetics  2007;39(10):1245-1250.
Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (P = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples. We confirmed the association in 19,064 adults from four further studies (P = 3 × 10−11, overall P = 4 × 10−16, including the genome-wide association data). We also observed the association in children (P = 1 × 10−6, N = 6,827) and a tall/short case-control study (P = 4 × 10−6, N = 3,207). We estimate that rs1042725 explains ~0.3% of population variation in height (~0.4 cm increased adult height per C allele). There are few examples of common genetic variants reproducibly associated with human quantitative traits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.
doi:10.1038/ng2121
PMCID: PMC3086278  PMID: 17767157
9.  Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis 
Voight, Benjamin F | Scott, Laura J | Steinthorsdottir, Valgerdur | Morris, Andrew P | Dina, Christian | Welch, Ryan P | Zeggini, Eleftheria | Huth, Cornelia | Aulchenko, Yurii S | Thorleifsson, Gudmar | McCulloch, Laura J | Ferreira, Teresa | Grallert, Harald | Amin, Najaf | Wu, Guanming | Willer, Cristen J | Raychaudhuri, Soumya | McCarroll, Steve A | Langenberg, Claudia | Hofmann, Oliver M | Dupuis, Josée | Qi, Lu | Segrè, Ayellet V | van Hoek, Mandy | Navarro, Pau | Ardlie, Kristin | Balkau, Beverley | Benediktsson, Rafn | Bennett, Amanda J | Blagieva, Roza | Boerwinkle, Eric | Bonnycastle, Lori L | Boström, Kristina Bengtsson | Bravenboer, Bert | Bumpstead, Suzannah | Burtt, Noisël P | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn | Couper, David J | Crawford, Gabe | Doney, Alex S F | Elliott, Katherine S | Elliott, Amanda L | Erdos, Michael R | Fox, Caroline S | Franklin, Christopher S | Ganser, Martha | Gieger, Christian | Grarup, Niels | Green, Todd | Griffin, Simon | Groves, Christopher J | Guiducci, Candace | Hadjadj, Samy | Hassanali, Neelam | Herder, Christian | Isomaa, Bo | Jackson, Anne U | Johnson, Paul R V | Jørgensen, Torben | Kao, Wen H L | Klopp, Norman | Kong, Augustine | Kraft, Peter | Kuusisto, Johanna | Lauritzen, Torsten | Li, Man | Lieverse, Aloysius | Lindgren, Cecilia M | Lyssenko, Valeriya | Marre, Michel | Meitinger, Thomas | Midthjell, Kristian | Morken, Mario A | Narisu, Narisu | Nilsson, Peter | Owen, Katharine R | Payne, Felicity | Perry, John R B | Petersen, Ann-Kristin | Platou, Carl | Proença, Christine | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, N William | Robertson, Neil R | Rocheleau, Ghislain | Roden, Michael | Sampson, Michael J | Saxena, Richa | Shields, Beverley M | Shrader, Peter | Sigurdsson, Gunnar | Sparsø, Thomas | Strassburger, Klaus | Stringham, Heather M | Sun, Qi | Swift, Amy J | Thorand, Barbara | Tichet, Jean | Tuomi, Tiinamaija | van Dam, Rob M | van Haeften, Timon W | van Herpt, Thijs | van Vliet-Ostaptchouk, Jana V | Walters, G Bragi | Weedon, Michael N | Wijmenga, Cisca | Witteman, Jacqueline | Bergman, Richard N | Cauchi, Stephane | Collins, Francis S | Gloyn, Anna L | Gyllensten, Ulf | Hansen, Torben | Hide, Winston A | Hitman, Graham A | Hofman, Albert | Hunter, David J | Hveem, Kristian | Laakso, Markku | Mohlke, Karen L | Morris, Andrew D | Palmer, Colin N A | Pramstaller, Peter P | Rudan, Igor | Sijbrands, Eric | Stein, Lincoln D | Tuomilehto, Jaakko | Uitterlinden, Andre | Walker, Mark | Wareham, Nicholas J | Watanabe, Richard M | Abecasis, Gonçalo R | Boehm, Bernhard O | Campbell, Harry | Daly, Mark J | Hattersley, Andrew T | Hu, Frank B | Meigs, James B | Pankow, James S | Pedersen, Oluf | Wichmann, H-Erich | Barroso, Inês | Florez, Jose C | Frayling, Timothy M | Groop, Leif | Sladek, Rob | Thorsteinsdottir, Unnur | Wilson, James F | Illig, Thomas | Froguel, Philippe | van Duijn, Cornelia M | Stefansson, Kari | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2010;42(7):579-589.
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combinedP < 5 × 10−8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
doi:10.1038/ng.609
PMCID: PMC3080658  PMID: 20581827
10.  Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight 
Freathy, Rachel M | Mook-Kanamori, Dennis O | Sovio, Ulla | Prokopenko, Inga | Timpson, Nicholas J | Berry, Diane J | Warrington, Nicole M | Widen, Elisabeth | Hottenga, Jouke Jan | Kaakinen, Marika | Lange, Leslie A | Bradfield, Jonathan P | Kerkhof, Marjan | Marsh, Julie A | Mägi, Reedik | Chen, Chih-Mei | Lyon, Helen N | Kirin, Mirna | Adair, Linda S | Aulchenko, Yurii S | Bennett, Amanda J | Borja, Judith B | Bouatia-Naji, Nabila | Charoen, Pimphen | Coin, Lachlan J M | Cousminer, Diana L | de Geus, Eco J. C. | Deloukas, Panos | Elliott, Paul | Evans, David M | Froguel, Philippe | Glaser, Beate | Groves, Christopher J | Hartikainen, Anna-Liisa | Hassanali, Neelam | Hirschhorn, Joel N | Hofman, Albert | Holly, Jeff M P | Hyppönen, Elina | Kanoni, Stavroula | Knight, Bridget A | Laitinen, Jaana | Lindgren, Cecilia M | McArdle, Wendy L | O'Reilly, Paul F | Pennell, Craig E | Postma, Dirkje S | Pouta, Anneli | Ramasamy, Adaikalavan | Rayner, Nigel W | Ring, Susan M | Rivadeneira, Fernando | Shields, Beverley M | Strachan, David P | Surakka, Ida | Taanila, Anja | Tiesler, Carla | Uitterlinden, Andre G | van Duijn, Cornelia M | Wijga, Alet H | Willemsen, Gonneke | Zhang, Haitao | Zhao, Jianhua | Wilson, James F | Steegers, Eric A P | Hattersley, Andrew T | Eriksson, Johan G | Peltonen, Leena | Mohlke, Karen L | Grant, Struan F A | Hakonarson, Hakon | Koppelman, Gerard H | Dedoussis, George V | Heinrich, Joachim | Gillman, Matthew W | Palmer, Lyle J | Frayling, Timothy M | Boomsma, Dorret I | Smith, George Davey | Power, Chris | Jaddoe, Vincent W V | Jarvelin, Marjo-Riitta | McCarthy, Mark I
Nature genetics  2010;42(5):430-435.
INTRODUCTORY PARAGRAPH
To identify genetic variants associated with birth weight, we meta-analyzed six genome-wide association (GWA) studies (N=10,623 Europeans from pregnancy/birth cohorts) and followed up two lead signals in thirteen replication studies (N=27,591). Rs900400 near LEKR1 and CCNL1 (P=2×10−35), and rs9883204 in ADCY5 (P=7×10−15) were robustly associated with birth weight. Correlated SNPs in ADCY5 were recently implicated in regulation of glucose levels and type 2 diabetes susceptibility,1 providing evidence that the well described association between lower birth weight and subsequent type 2 diabetes2,3 has a genetic component, distinct from the proposed role of programming by maternal nutrition. Using data from both SNPs, the 9% of Europeans with 4 birth weight-lowering alleles were, on average, 113g (95%CI 89-137g) lighter at birth than the 24% with 0 or 1 allele (Ptrend=7×10−30). The impact on birth weight is similar to that of a mother smoking 4-5 cigarettes per day in the third trimester of pregnancy.4
doi:10.1038/ng.567
PMCID: PMC2862164  PMID: 20372150
11.  Circulating β-carotene levels and Type 2 diabetes: Cause or effect? 
Diabetologia  2009;52(10):2117-2121.
Aims and Hypothesis
Circulating β-carotene levels are inversely associated with type 2 diabetes risk, but the causal direction of this association is not certain. In this study we used a Mendelian Randomization approach to provide evidence for or against the causal role of the anti-oxidant vitamin β-carotene in type 2 diabetes.
Methods
We used a common polymorphism (rs6564851) near the β-carotene 15,15'-Monooxygenase 1 (BCMO1) gene that is strongly associated with circulating β-carotene levels (P = 2×10−24) - each G allele is associated with a 0.27 standard deviation increase in levels. We used data from the InCHIANTI study and the ULSAM study to estimate the association between β-carotene levels and type 2 diabetes. We next used a triangulation approach to estimate the expected effect of rs6564851 on type 2 diabetes risk, and compared this to the observed effect using data from 4549 type 2 diabetes cases and 5579 controls from the DIAGRAM consortium.
Results
A 0.27 standard deviation increase in β-carotene levels is associated with an odds ratio of 0.90 (0.86–0.95) for type 2 diabetes in the InCHIANTI study. This association is similar to that of the ULSAM study, OR (0.90 (0.84–0.97)). In contrast there was no association between rs6564851 and type 2 diabetes (OR 0.98 (0.93–1.04, P = 0.58), and this effect size was smaller than that expected given the known associations between rs6564851 and β-carotene levels and the associations between β-carotene levels and type 2 diabetes.
Conclusion
Our Mendelian Randomization studies are in keeping with randomized controlled trials that suggest β-carotene is not causally protective against type 2 diabetes.
doi:10.1007/s00125-009-1475-8
PMCID: PMC2746424  PMID: 19662379
type 2 diabetes; β-carotene; mendelian randomization
12.  Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge 
Saxena, Richa | Hivert, Marie-France | Langenberg, Claudia | Tanaka, Toshiko | Pankow, James S | Vollenweider, Peter | Lyssenko, Valeriya | Bouatia-Naji, Nabila | Dupuis, Josée | Jackson, Anne U | Kao, W H Linda | Li, Man | Glazer, Nicole L | Manning, Alisa K | Luan, Jian’an | Stringham, Heather M | Prokopenko, Inga | Johnson, Toby | Grarup, Niels | Boesgaard, Trine W | Lecoeur, Cécile | Shrader, Peter | O’Connell, Jeffrey | Ingelsson, Erik | Couper, David J | Rice, Kenneth | Song, Kijoung | Andreasen, Camilla H | Dina, Christian | Köttgen, Anna | Le Bacquer, Olivier | Pattou, François | Taneera, Jalal | Steinthorsdottir, Valgerdur | Rybin, Denis | Ardlie, Kristin | Sampson, Michael | Qi, Lu | van Hoek, Mandy | Weedon, Michael N | Aulchenko, Yurii S | Voight, Benjamin F | Grallert, Harald | Balkau, Beverley | Bergman, Richard N | Bielinski, Suzette J | Bonnefond, Amelie | Bonnycastle, Lori L | Borch-Johnsen, Knut | Böttcher, Yvonne | Brunner, Eric | Buchanan, Thomas A | Bumpstead, Suzannah J | Cavalcanti-Proença, Christine | Charpentier, Guillaume | Chen, Yii-Der Ida | Chines, Peter S | Collins, Francis S | Cornelis, Marilyn | Crawford, Gabriel J | Delplanque, Jerome | Doney, Alex | Egan, Josephine M | Erdos, Michael R | Firmann, Mathieu | Forouhi, Nita G | Fox, Caroline S | Goodarzi, Mark O | Graessler, Jürgen | Hingorani, Aroon | Isomaa, Bo | Jørgensen, Torben | Kivimaki, Mika | Kovacs, Peter | Krohn, Knut | Kumari, Meena | Lauritzen, Torsten | Lévy-Marchal, Claire | Mayor, Vladimir | McAteer, Jarred B | Meyre, David | Mitchell, Braxton D | Mohlke, Karen L | Morken, Mario A | Narisu, Narisu | Palmer, Colin N A | Pakyz, Ruth | Pascoe, Laura | Payne, Felicity | Pearson, Daniel | Rathmann, Wolfgang | Sandbaek, Annelli | Sayer, Avan Aihie | Scott, Laura J | Sharp, Stephen J | Sijbrands, Eric | Singleton, Andrew | Siscovick, David S | Smith, Nicholas L | Sparsø, Thomas | Swift, Amy J | Syddall, Holly | Thorleifsson, Gudmar | Tönjes, Anke | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Valle, Timo T | Waeber, Gérard | Walley, Andrew | Waterworth, Dawn M | Zeggini, Eleftheria | Zhao, Jing Hua | Illig, Thomas | Wichmann, H Erich | Wilson, James F | van Duijn, Cornelia | Hu, Frank B | Morris, Andrew D | Frayling, Timothy M | Hattersley, Andrew T | Thorsteinsdottir, Unnur | Stefansson, Kari | Nilsson, Peter | Syvänen, Ann-Christine | Shuldiner, Alan R | Walker, Mark | Bornstein, Stefan R | Schwarz, Peter | Williams, Gordon H | Nathan, David M | Kuusisto, Johanna | Laakso, Markku | Cooper, Cyrus | Marmot, Michael | Ferrucci, Luigi | Mooser, Vincent | Stumvoll, Michael | Loos, Ruth J F | Altshuler, David | Psaty, Bruce M | Rotter, Jerome I | Boerwinkle, Eric | Hansen, Torben | Pedersen, Oluf | Florez, Jose C | McCarthy, Mark I | Boehnke, Michael | Barroso, Inês | Sladek, Robert | Froguel, Philippe | Meigs, James B | Groop, Leif | Wareham, Nicholas J | Watanabe, Richard M
Nature genetics  2010;42(2):142-148.
Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958–30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, β (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 × 10−15). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 × 10−17; ratio of insulin to glucose area under the curve, P = 1.3 × 10−16) and diminished incretin effect (n = 804; P = 4.3 × 10−4). We also identified variants at ADCY5 (rs2877716, P = 4.2 × 10−16), VPS13C (rs17271305, P = 4.1 × 10−8), GCKR (rs1260326, P = 7.1 × 10−11) and TCF7L2 (rs7903146, P = 4.2 × 10−10) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09–1.15, P = 4.8 × 10−18).
doi:10.1038/ng.521
PMCID: PMC2922003  PMID: 20081857
13.  Linkage disequilibrium mapping of the replicated type 2 diabetes linkage signal on chromosome 1q 
Diabetes  2009;58(7):1704-1709.
Objective
Linkage of the chromosome 1q21-25 region to type 2 diabetes has been demonstrated in multiple ethnic groups. We performed common variant fine-mapping across a 23Mb interval in a multiethnic sample to search for variants responsible for this linkage signal.
Research Design and Methods
In all, 5,290 SNPs were successfully genotyped in 3,179 T2D cases and controls from eight populations with evidence of 1q linkage. Samples were ascertained using strategies designed to enhance power to detect variants causal for 1q-linkage. Following imputation, we estimate ~80% coverage of common variation across the region (r2>0.8, Europeans). Association signals of interest were evaluated through in silico replication and de novo genotyping in approximately 8,500 cases and 12,400 controls.
Results
Association mapping of the 23Mb region identified two strong signals, both restricted to the subset of European-descent samples. The first mapped to the NOS1AP (CAPON) gene region (lead SNP: rs7538490, OR 1.38 (95% CI, 1.21-1.57), p=1.4×10-6 in 999 cases and 1,190 controls): the second within an extensive region of linkage disequilibrium that includes the ASH1L and PKLR genes (lead SNP: rs11264371, OR 1.48 [1.18-1.76], p=1.0×10-5, under a dominant model). However, there was no evidence for association at either signal on replication, and, across all data (>24,000 subjects), no indication that these variants were causally-related to T2D status.
Conclusion
Detailed fine-mapping of the 23Mb region of replicated linkage has failed to identify common variant signals contributing to the observed signal. Future studies should focus on identification of causal alleles of lower frequency and higher penetrance.
doi:10.2337/db09-0081
PMCID: PMC2699860  PMID: 19389826
chromosome 1q; linkage; association
14.  Type 2 Diabetes Risk Alleles are Associated with Reduced Size at Birth 
Diabetes  2009;58(6):1428-1433.
Objective
Low birth weight is associated with an increased risk of type 2 diabetes. The mechanisms underlying this association are unknown and may represent intrauterine programming or two phenotypes of one genotype. The fetal insulin hypothesis proposes that common genetic variants that reduce insulin secretion or action may predispose to type 2 diabetes and also reduce birth weight, since insulin is a key fetal growth factor. We tested whether common genetic variants that predispose to type 2 diabetes also reduce birth weight.
Research design and methods
We genotyped single nucleotide polymorphisms (SNPs) at five recently identified type 2 diabetes loci (CDKAL1, CDKN2A/B, HHEX-IDE, IGF2BP2 and SLC30A8) in 7986 mothers and 19200 offspring from four studies of white Europeans. We tested the association between maternal or fetal genotype at each locus and birth weight of the offspring.
Results
We found that type 2 diabetes risk alleles at the CDKAL1 and HHEX-IDE loci were associated with reduced birth weight when inherited by the fetus: 21g [95%CI:11-31g], P=2×10-5 and 14g [4-23g], P=0.004 lower birth weight per risk allele, respectively. The 4% of offspring carrying four risk alleles at these two loci were 80g [39-120g] lighter at birth than the 8% carrying none (Ptrend =5×10-7). There were no associations between birth weight and fetal genotypes at the three other loci, or maternal genotypes at any locus.
Conclusions
Our results are in keeping with the fetal insulin hypothesis and provide robust evidence that common disease-associated variants can alter size at birth directly through the fetal genotype.
doi:10.2337/db08-1739
PMCID: PMC2682672  PMID: 19228808
15.  Six new loci associated with body mass index highlight a neuronal influence on body weight regulation 
Willer, Cristen J | Speliotes, Elizabeth K | Loos, Ruth J F | Li, Shengxu | Lindgren, Cecilia M | Heid, Iris M | Berndt, Sonja I | Elliott, Amanda L | Jackson, Anne U | Lamina, Claudia | Lettre, Guillaume | Lim, Noha | Lyon, Helen N | McCarroll, Steven A | Papadakis, Konstantinos | Qi, Lu | Randall, Joshua C | Roccasecca, Rosa Maria | Sanna, Serena | Scheet, Paul | Weedon, Michael N | Wheeler, Eleanor | Zhao, Jing Hua | Jacobs, Leonie C | Prokopenko, Inga | Soranzo, Nicole | Tanaka, Toshiko | Timpson, Nicholas J | Almgren, Peter | Bennett, Amanda | Bergman, Richard N | Bingham, Sheila A | Bonnycastle, Lori L | Brown, Morris | Burtt, Noël P | Chines, Peter | Coin, Lachlan | Collins, Francis S | Connell, John M | Cooper, Cyrus | Smith, George Davey | Dennison, Elaine M | Deodhar, Parimal | Elliott, Paul | Erdos, Michael R | Estrada, Karol | Evans, David M | Gianniny, Lauren | Gieger, Christian | Gillson, Christopher J | Guiducci, Candace | Hackett, Rachel | Hadley, David | Hall, Alistair S | Havulinna, Aki S | Hebebrand, Johannes | Hofman, Albert | Isomaa, Bo | Jacobs, Kevin B | Johnson, Toby | Jousilahti, Pekka | Jovanovic, Zorica | Khaw, Kay-Tee | Kraft, Peter | Kuokkanen, Mikko | Kuusisto, Johanna | Laitinen, Jaana | Lakatta, Edward G | Luan, Jian'an | Luben, Robert N | Mangino, Massimo | McArdle, Wendy L | Meitinger, Thomas | Mulas, Antonella | Munroe, Patricia B | Narisu, Narisu | Ness, Andrew R | Northstone, Kate | O'Rahilly, Stephen | Purmann, Carolin | Rees, Matthew G | Ridderstråle, Martin | Ring, Susan M | Rivadeneira, Fernando | Ruokonen, Aimo | Sandhu, Manjinder S | Saramies, Jouko | Scott, Laura J | Scuteri, Angelo | Silander, Kaisa | Sims, Matthew A | Song, Kijoung | Stephens, Jonathan | Stevens, Suzanne | Stringham, Heather M | Tung, Y C Loraine | Valle, Timo T | Van Duijn, Cornelia M | Vimaleswaran, Karani S | Vollenweider, Peter | Waeber, Gerard | Wallace, Chris | Watanabe, Richard M | Waterworth, Dawn M | Watkins, Nicholas | Witteman, Jacqueline C M | Zeggini, Eleftheria | Zhai, Guangju | Zillikens, M Carola | Altshuler, David | Caulfield, Mark J | Chanock, Stephen J | Farooqi, I Sadaf | Ferrucci, Luigi | Guralnik, Jack M | Hattersley, Andrew T | Hu, Frank B | Jarvelin, Marjo-Riitta | Laakso, Markku | Mooser, Vincent | Ong, Ken K | Ouwehand, Willem H | Salomaa, Veikko | Samani, Nilesh J | Spector, Timothy D | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Uda, Manuela | Uitterlinden, André G | Wareham, Nicholas J | Deloukas, Panagiotis | Frayling, Timothy M | Groop, Leif C | Hayes, Richard B | Hunter, David J | Mohlke, Karen L | Peltonen, Leena | Schlessinger, David | Strachan, David P | Wichmann, H-Erich | McCarthy, Mark I | Boehnke, Michael | Barroso, Inês | Abecasis, Gonçalo R | Hirschhorn, Joel N
Nature genetics  2008;41(1):25-34.
Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 × 10−8): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.
doi:10.1038/ng.287
PMCID: PMC2695662  PMID: 19079261
16.  Genome-wide association analysis identifies 20 loci that influence adult height 
Nature genetics  2008;40(5):575-583.
Adult height is a model polygenic trait, but there has been limited success in identifying the genes underlying its normal variation. To identify genetic variants influencing adult human height, we used genome-wide association data from 13,665 individuals and genotyped 39 variants in an additional 16,482 samples. We identified 20 variants associated with adult height (P < 5 × 10−7, with 10 reaching P < 1 × 10−10). Combined, the 20 SNPs explain ~3% of height variation, with a ~5 cm difference between the 6.2% of people with 17 or fewer ‘tall’ alleles compared to the 5.5% with 27 or more ‘tall’ alleles. The loci we identified implicate genes in Hedgehog signaling (IHH, HHIP, PTCH1), extracellular matrix (EFEMP1, ADAMTSL3, ACAN) and cancer (CDK6, HMGA2, DLEU7) pathways, and provide new insights into human growth and developmental processes. Finally, our results provide insights into the genetic architecture of a classic quantitative trait.
doi:10.1038/ng.121
PMCID: PMC2681221  PMID: 18391952
17.  Common variants in WFS1 confer risk of type 2 diabetes 
Nature genetics  2007;39(8):951-953.
We studied genes involved in pancreatic β cell function and survival, identifying associations between SNPs in WFS1 and diabetes risk in UK populations that we replicated in an Ashkenazi population and in additional UK studies. In a pooled analysis comprising 9,533 cases and 11,389 controls, SNPs in WFS1 were strongly associated with diabetes risk. Rare mutations in WFS1 cause Wolfram syndrome; using a gene-centric approach, we show that variation in WFS1 also predisposes to common type 2 diabetes.
doi:10.1038/ng2067
PMCID: PMC2672152  PMID: 17603484
18.  Common variants near MC4R are associated with fat mass, weight and risk of obesity 
Loos, Ruth J F | Lindgren, Cecilia M | Li, Shengxu | Wheeler, Eleanor | Zhao, Jing Hua | Prokopenko, Inga | Inouye, Michael | Freathy, Rachel M | Attwood, Antony P | Beckmann, Jacques S | Berndt, Sonja I | Bergmann, Sven | Bennett, Amanda J | Bingham, Sheila A | Bochud, Murielle | Brown, Morris | Cauchi, Stéphane | Connell, John M | Cooper, Cyrus | Smith, George Davey | Day, Ian | Dina, Christian | De, Subhajyoti | Dermitzakis, Emmanouil T | Doney, Alex S F | Elliott, Katherine S | Elliott, Paul | Evans, David M | Farooqi, I Sadaf | Froguel, Philippe | Ghori, Jilur | Groves, Christopher J | Gwilliam, Rhian | Hadley, David | Hall, Alistair S | Hattersley, Andrew T | Hebebrand, Johannes | Heid, Iris M | Herrera, Blanca | Hinney, Anke | Hunt, Sarah E | Jarvelin, Marjo-Riitta | Johnson, Toby | Jolley, Jennifer D M | Karpe, Fredrik | Keniry, Andrew | Khaw, Kay-Tee | Luben, Robert N | Mangino, Massimo | Marchini, Jonathan | McArdle, Wendy L | McGinnis, Ralph | Meyre, David | Munroe, Patricia B | Morris, Andrew D | Ness, Andrew R | Neville, Matthew J | Nica, Alexandra C | Ong, Ken K | O'Rahilly, Stephen | Owen, Katharine R | Palmer, Colin N A | Papadakis, Konstantinos | Potter, Simon | Pouta, Anneli | Qi, Lu | Randall, Joshua C | Rayner, Nigel W | Ring, Susan M | Sandhu, Manjinder S | Scherag, André | Sims, Matthew A | Song, Kijoung | Soranzo, Nicole | Speliotes, Elizabeth K | Syddall, Holly E | Teichmann, Sarah A | Timpson, Nicholas J | Tobias, Jonathan H | Uda, Manuela | Vogel, Carla I Ganz | Wallace, Chris | Waterworth, Dawn M | Weedon, Michael N | Willer, Cristen J | Wraight, Vicki L | Yuan, Xin | Zeggini, Eleftheria | Hirschhorn, Joel N | Strachan, David P | Ouwehand, Willem H | Caulfield, Mark J | Samani, Nilesh J | Frayling, Timothy M | Vollenweider, Peter | Waeber, Gerard | Mooser, Vincent | Deloukas, Panos | McCarthy, Mark I | Wareham, Nicholas J | Barroso, Inês | Jacobs, Kevin B | Chanock, Stephen J | Hayes, Richard B | Lamina, Claudia | Gieger, Christian | Illig, Thomas | Meitinger, Thomas | Wichmann, H-Erich | Kraft, Peter | Hankinson, Susan E | Hunter, David J | Hu, Frank B | Lyon, Helen N | Voight, Benjamin F | Ridderstrale, Martin | Groop, Leif | Scheet, Paul | Sanna, Serena | Abecasis, Goncalo R | Albai, Giuseppe | Nagaraja, Ramaiah | Schlessinger, David | Jackson, Anne U | Tuomilehto, Jaakko | Collins, Francis S | Boehnke, Michael | Mohlke, Karen L
Nature genetics  2008;40(6):768-775.
To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 × 10−6) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 × 10−15) and 5,988 children aged 7–11 (0.13 Z-score units; P = 1.5 × 10−8). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 × 10−11). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 × 10−4). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.
doi:10.1038/ng.140
PMCID: PMC2669167  PMID: 18454148
19.  Exploring the Developmental Overnutrition Hypothesis Using Parental–Offspring Associations and FTO as an Instrumental Variable 
PLoS Medicine  2008;5(3):e33.
Background
The developmental overnutrition hypothesis suggests that greater maternal obesity during pregnancy results in increased offspring adiposity in later life. If true, this would result in the obesity epidemic progressing across generations irrespective of environmental or genetic changes. It is therefore important to robustly test this hypothesis.
Methods and Findings
We explored this hypothesis by comparing the associations of maternal and paternal pre-pregnancy body mass index (BMI) with offspring dual energy X-ray absorptiometry (DXA)–determined fat mass measured at 9 to 11 y (4,091 parent–offspring trios) and by using maternal FTO genotype, controlling for offspring FTO genotype, as an instrument for maternal adiposity. Both maternal and paternal BMI were positively associated with offspring fat mass, but the maternal association effect size was larger than that in the paternal association in all models: mean difference in offspring sex- and age-standardised fat mass z-score per 1 standard deviation BMI 0.24 (95% confidence interval [CI]: 0.22 to 0.26) for maternal BMI versus 0.13 (95% CI: 0.11, 0.15) for paternal BMI; p-value for difference in effect < 0.001. The stronger maternal association was robust to sensitivity analyses assuming levels of non-paternity up to 20%. When maternal FTO, controlling for offspring FTO, was used as an instrument for the effect of maternal adiposity, the mean difference in offspring fat mass z-score per 1 standard deviation maternal BMI was −0.08 (95% CI: −0.56 to 0.41), with no strong statistical evidence that this differed from the observational ordinary least squares analyses (p = 0.17).
Conclusions
Neither our parental comparisons nor the use of FTO genotype as an instrumental variable, suggest that greater maternal BMI during offspring development has a marked effect on offspring fat mass at age 9–11 y. Developmental overnutrition related to greater maternal BMI is unlikely to have driven the recent obesity epidemic.
Using parental-offspring associations and theFTO gene as an instrumental variable for maternal adiposity, Debbie Lawlor and colleagues found that greater maternal BMI during offspring development does not appear to have a marked effect on offspring fat mass at age 9-11.
Editors' Summary
Background.
Since the 1970s, the proportion of children and adults who are overweight or obese (people who have an unhealthy amount of body fat) has increased sharply in many countries. In the US, 1 in 3 adults is now obese; in the mid-1970s it was only 1 in 7. Similarly, the proportion of overweight children has risen from 1 in 20 to 1 in 5. An adult is considered to be overweight if their body mass index (BMI)—their weight in kilograms divided by their height in meters squared—is between 25 and 30, and obese if it is more than 30. For children, the healthy BMI depends on their age and gender. Compared to people with a healthy weight (a BMI between 18.5 and 25), overweight or obese individuals have an increased lifetime risk of developing diabetes and other adverse health conditions, sometimes becoming ill while they are still young. People become unhealthily fat when they consume food and drink that contains more energy than they need for their daily activities. It should, therefore, be possible to avoid becoming obese by having a healthy diet and exercising regularly.
Why Was This Study Done?
Some researchers think that “developmental overnutrition” may have caused the recent increase in waistline measurements. In other words, if a mother is overweight during pregnancy, high sugar and fat levels in her body might permanently affect her growing baby's appetite control and metabolism, and so her offspring might be at risk of becoming obese in later life. If this hypothesis is true, each generation will tend to be fatter than the previous one and it will be very hard to halt the obesity epidemic simply by encouraging people to eat less and exercise more. In this study, the researchers have used two approaches to test the developmental overnutrition hypothesis. First, they have asked whether offspring fat mass is more strongly related to maternal BMI than to paternal BMI; it should be if the hypothesis is true. Second, they have asked whether a genetic indicator of maternal fatness—the “A” variant of the FTO gene—is related to offspring fat mass. A statistical association between maternal FTO genotype (genetic make-up) and offspring fat mass would support the developmental nutrition hypothesis.
What Did the Researchers Do and Find?
In 1991–1992, the Avon Longitudinal Study of Parents and Children (ALSPAC) enrolled about 14,000 pregnant women and now examines their offspring at regular intervals. The researchers first used statistical methods to look for associations between the self-reported prepregnancy BMI of the parents of about 4,000 children and the children's fat mass at ages 9–11 years measured using a technique called dual energy X-ray absorptiometry. Both maternal and paternal BMI were positively associated with offspring fat mass (that is, fatter parents had fatter children) but the effect of maternal BMI was greater than the effect of paternal BMI. When the researchers examined maternal FTO genotypes and offspring fat mass (after allowing for the offspring's FTO genotype, which would directly affect their fat mass), there was no statistical evidence to suggest that differences in offspring fat mass were related to the maternal FTO genotype.
What Do These Findings Mean?
Although the findings from first approach provide some support for the development overnutrition hypothesis, the effect of maternal BMI on offspring fat mass is too weak to explain the recent obesity epidemic. Developmental overnutrition could, however, be responsible for the much slower increase in obesity that began a century ago. The findings from the second approach provide no support for the developmental overnutrition hypothesis, although these results have wide error margins and need confirming in a larger study. The researchers also note that the effects of developmental overnutrition on offspring fat mass, although weak at age 9–11, might become more important at later ages. Nevertheless, for now, it seems unlikely that developmental overnutrition has been a major driver of the recent obesity epidemic. Interventions that aim to improve people's diet and to increase their physical activity levels could therefore slow or even halt the epidemic.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050033.
See a related PLoS Medicine Perspective article
The MedlinePlus encyclopedia has a page on obesity (in English and Spanish)
The US Centers for Disease Control and Prevention provides information on all aspects of obesity (in English and Spanish)
The UK National Health Service's health Web site (NHS Direct) provides information about obesity
The International Obesity Taskforce provides information about preventing obesity and on childhood obesity
The UK Foods Standards Agency, the United States Department of Agriculture, and Shaping America's Health all provide useful advice about healthy eating for adults and children
The ALSPAC Web site provides information about the Avon Longitudinal Study of Parents and Children and its results so far
doi:10.1371/journal.pmed.0050033
PMCID: PMC2265763  PMID: 18336062
20.  Combining Information from Common Type 2 Diabetes Risk Polymorphisms Improves Disease Prediction 
PLoS Medicine  2006;3(10):e374.
Background
A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed.
Methods and Findings
Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles.
Conclusions
Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases.
Combining information from several known common risk polymorphisms allows the identification of subgroups of the population with markedly differing risks of developing type 2 diabetes.
Editors' Summary
Background.
Diabetes is an important and increasingly common global health problem; the World Health Organization has estimated that about 170 million people currently have diabetes worldwide. One particular form, type 2 diabetes, develops when cells in the body become unable to respond to a hormone called insulin. Insulin is normally released by the pancreas and controls the ability of body cells to take in glucose (sugar). Therefore, when cells become insensitive to insulin as in people with type 2 diabetes, glucose levels in the body are not well controlled and may become dangerously high in the blood. These high levels can have long-term damaging effects on various organs in the body, particularly the eyes, nerves, heart, and kidneys. There are many different factors that affect whether someone is likely to develop type 2 diabetes. These factors can be broadly grouped into two categories: environmental and genetic. Environmental factors such as obesity, a diet high in sugar, and a sedentary lifestyle are all risk factors for developing type 2 diabetes in later life. Genetically, a number of variants in many different genes may affect the risk of developing the disease. Generally, these gene variants are common in human populations but each gene variant only mildly increases the risk that a person possessing it will get type 2 diabetes.
Why Was This Study Done?
The investigators performing this study wanted to understand how different gene variants combine to affect an individual's risk of getting type 2 diabetes. That is, if a person carries many different variants, does their overall risk increase a lot or only a little?
What Did the Researchers Do and Find?
First, the researchers surveyed the published reports to identify those gene variants for which there was strong evidence of an association with type 2 diabetes. They found mutations in three genes that had been shown reproducibly to be associated with type 2 diabetes in different studies: PPARG (whose product is involved in regulation of fat tissue), KCNJ11 (whose product is involved in insulin production), and TCF7L2 (whose product is thought to be involved in controlling sugar levels). Then, they compared two groups of white people in the UK: 2,409 people with type 2 diabetes (“cases”), and 3,668 people from the general population (“controls”). The researchers compared the two groups to see which individuals possessed which gene variants, and did statistical testing to work out to what extent having particular combinations of the gene variants affected an individual's chance of being a “case” versus a “control.” Their results showed that in the groups studied, having an ever-increasing number of gene variants increased the risk of developing diabetes. The risk that someone with none of the gene variants would develop type 2 diabetes was about 2%, while the chance for someone with all gene variants was about10%.
What Do These Findings Mean?
These results show that the risk of developing type 2 diabetes is greater if an individual possesses all of the gene variants that were examined in this study. The analysis also suggests that using information on all three variants, rather than just one, is likely to be more accurate in predicting future risk. How this genetic information should be used alongside other well-known preventative measures such as altered lifestyle requires further study.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030374.
NHS Direct patient information on diabetes
National Diabetes Information Clearinghouse information on type 2 diabetes
World Health Organization Diabetes Programme
Centers for Disease ControlDiabetes Public Health Resource
doi:10.1371/journal.pmed.0030374
PMCID: PMC1584415  PMID: 17020404
21.  The functional "KL-VS" variant of KLOTHO is not associated with type 2 diabetes in 5028 UK Caucasians 
BMC Medical Genetics  2006;7:51.
Background
Klotho has an important role in insulin signalling and the development of ageing-like phenotypes in mice. The common functional "KL-VS" variant in the KLOTHO (KL) gene is associated with longevity in humans but its role in type 2 diabetes is not known. We performed a large case-control and family-based study to test the hypothesis that KL-VS is associated with type 2 diabetes in a UK Caucasian population.
Methods
We genotyped 1793 cases, 1619 controls and 1616 subjects from 509 families for the single nucleotide polymorphism (SNP) F352V (rs9536314) that defines the KL-VS variant. Allele and genotype frequencies were compared between cases and controls. Family-based analysis was used to test for over- or under-transmission of V352 to affected offspring.
Results
Despite good power to detect odds ratios of 1.2, there were no significant associations between alleles or genotypes and type 2 diabetes (V352 allele: odds ratio = 0.96 (0.84–1.09)). Additional analysis of quantitative trait data in 1177 healthy control subjects showed no association of the variant with fasting insulin, glucose, triglycerides, HDL- or LDL-cholesterol (all P > 0.05). However, the HDL-cholesterol levels observed across the genotype groups showed a similar, but non-significant, pattern to previously reported data.
Conclusion
This is the first large-scale study to examine the association between common functional variation in KL and type 2 diabetes risk. We have found no evidence that the functional KL-VS variant is a risk factor for type 2 diabetes in a large UK Caucasian case-control and family-based study.
doi:10.1186/1471-2350-7-51
PMCID: PMC1534014  PMID: 16753056
22.  A Powerful Approach to Sub-Phenotype Analysis in Population-Based Genetic Association Studies 
Genetic Epidemiology  2009;34(4):335-343.
The ultimate goal of genome-wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub-phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub-phenotype analysis is that large disease cohorts are sub-divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub-phenotypes. Simulations demonstrate the power of the multinomial regression-based analysis over existing methods when genetic effects differ between sub-phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome-wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non-obese forms of the disease, and provides evidence of a potential novel association that warrants follow-up in independent replication cohorts.
doi:10.1002/gepi.20486
PMCID: PMC2964510  PMID: 20039379
multinomial regression; sub-phenotype analysis; genome-wide association study; type 2 diabetes; obesity
23.  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 | Smith, Albert Vernon | Visscher, Peter M | Yang, Jian | McCarroll, Steven A | Nemesh, James | Voight, Benjamin 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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, 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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 (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new 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 P = 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 P = 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
doi:10.1038/ng.685
PMCID: PMC3000924  PMID: 20935629
24.  New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk 
Dupuis, Josée | Langenberg, Claudia | Prokopenko, Inga | Saxena, Richa | Soranzo, Nicole | Jackson, Anne U | Wheeler, Eleanor | Glazer, Nicole L | Bouatia-Naji, Nabila | Gloyn, Anna L | Lindgren, Cecilia M | Mägi, Reedik | Morris, Andrew P | Randall, Joshua | Johnson, Toby | Elliott, Paul | Rybin, Denis | Thorleifsson, Gudmar | Steinthorsdottir, Valgerdur | Henneman, Peter | Grallert, Harald | Dehghan, Abbas | Hottenga, Jouke Jan | Franklin, Christopher S | Navarro, Pau | Song, Kijoung | Goel, Anuj | Perry, John R B | Egan, Josephine M | Lajunen, Taina | Grarup, Niels | Sparsø, Thomas | Doney, Alex | Voight, Benjamin F | Stringham, Heather M | Li, Man | Kanoni, Stavroula | Shrader, Peter | Cavalcanti-Proença, Christine | Kumari, Meena | Qi, Lu | Timpson, Nicholas J | Gieger, Christian | Zabena, Carina | Rocheleau, Ghislain | Ingelsson, Erik | An, Ping | O’Connell, Jeffrey | Luan, Jian'an | Elliott, Amanda | McCarroll, Steven A | Payne, Felicity | Roccasecca, Rosa Maria | Pattou, François | Sethupathy, Praveen | Ardlie, Kristin | Ariyurek, Yavuz | Balkau, Beverley | Barter, Philip | Beilby, John P | Ben-Shlomo, Yoav | Benediktsson, Rafn | Bennett, Amanda J | Bergmann, Sven | Bochud, Murielle | Boerwinkle, Eric | Bonnefond, Amélie | Bonnycastle, Lori L | Borch-Johnsen, Knut | Böttcher, Yvonne | Brunner, Eric | Bumpstead, Suzannah J | Charpentier, Guillaume | Chen, Yii-Der Ida | Chines, Peter | Clarke, Robert | Coin, Lachlan J M | Cooper, Matthew N | Cornelis, Marilyn | Crawford, Gabe | Crisponi, Laura | Day, Ian N M | de Geus, Eco | Delplanque, Jerome | Dina, Christian | Erdos, Michael R | Fedson, Annette C | Fischer-Rosinsky, Antje | Forouhi, Nita G | Fox, Caroline S | Frants, Rune | Franzosi, Maria Grazia | Galan, Pilar | Goodarzi, Mark O | Graessler, Jürgen | Groves, Christopher J | Grundy, Scott | Gwilliam, Rhian | Gyllensten, Ulf | Hadjadj, Samy | Hallmans, Göran | Hammond, Naomi | Han, Xijing | Hartikainen, Anna-Liisa | Hassanali, Neelam | Hayward, Caroline | Heath, Simon C | Hercberg, Serge | Herder, Christian | Hicks, Andrew A | Hillman, David R | Hingorani, Aroon D | Hofman, Albert | Hui, Jennie | Hung, Joe | Isomaa, Bo | Johnson, Paul R V | Jørgensen, Torben | Jula, Antti | Kaakinen, Marika | Kaprio, Jaakko | Kesaniemi, Y Antero | Kivimaki, Mika | Knight, Beatrice | Koskinen, Seppo | Kovacs, Peter | Kyvik, Kirsten Ohm | Lathrop, G Mark | Lawlor, Debbie A | Le Bacquer, Olivier | Lecoeur, Cécile | Li, Yun | Lyssenko, Valeriya | Mahley, Robert | Mangino, Massimo | Manning, Alisa K | Martínez-Larrad, María Teresa | McAteer, Jarred B | McCulloch, Laura J | McPherson, Ruth | Meisinger, Christa | Melzer, David | Meyre, David | Mitchell, Braxton D | Morken, Mario A | Mukherjee, Sutapa | Naitza, Silvia | Narisu, Narisu | Neville, Matthew J | Oostra, Ben A | Orrù, Marco | Pakyz, Ruth | Palmer, Colin N A | Paolisso, Giuseppe | Pattaro, Cristian | Pearson, Daniel | Peden, John F | Pedersen, Nancy L. | Perola, Markus | Pfeiffer, Andreas F H | Pichler, Irene | Polasek, Ozren | Posthuma, Danielle | Potter, Simon C | Pouta, Anneli | Province, Michael A | Psaty, Bruce M | Rathmann, Wolfgang | Rayner, Nigel W | Rice, Kenneth | Ripatti, Samuli | Rivadeneira, Fernando | Roden, Michael | Rolandsson, Olov | Sandbaek, Annelli | Sandhu, Manjinder | Sanna, Serena | Sayer, Avan Aihie | Scheet, Paul | Scott, Laura J | Seedorf, Udo | Sharp, Stephen J | Shields, Beverley | Sigurðsson, Gunnar | Sijbrands, Erik J G | Silveira, Angela | Simpson, Laila | Singleton, Andrew | Smith, Nicholas L | Sovio, Ulla | Swift, Amy | Syddall, Holly | Syvänen, Ann-Christine | Tanaka, Toshiko | Thorand, Barbara | Tichet, Jean | Tönjes, Anke | Tuomi, Tiinamaija | Uitterlinden, André G | van Dijk, Ko Willems | van Hoek, Mandy | Varma, Dhiraj | Visvikis-Siest, Sophie | Vitart, Veronique | Vogelzangs, Nicole | Waeber, Gérard | Wagner, Peter J | Walley, Andrew | Walters, G Bragi | Ward, Kim L | Watkins, Hugh | Weedon, Michael N | Wild, Sarah H | Willemsen, Gonneke | Witteman, Jaqueline C M | Yarnell, John W G | Zeggini, Eleftheria | Zelenika, Diana | Zethelius, Björn | Zhai, Guangju | Zhao, Jing Hua | Zillikens, M Carola | Borecki, Ingrid B | Loos, Ruth J F | Meneton, Pierre | Magnusson, Patrik K E | Nathan, David M | Williams, Gordon H | Hattersley, Andrew T | Silander, Kaisa | Salomaa, Veikko | Smith, George Davey | Bornstein, Stefan R | Schwarz, Peter | Spranger, Joachim | Karpe, Fredrik | Shuldiner, Alan R | Cooper, Cyrus | Dedoussis, George V | Serrano-Ríos, Manuel | Morris, Andrew D | Lind, Lars | Palmer, Lyle J | Hu, Frank B. | Franks, Paul W | Ebrahim, Shah | Marmot, Michael | Kao, W H Linda | Pankow, James S | Sampson, Michael J | Kuusisto, Johanna | Laakso, Markku | Hansen, Torben | Pedersen, Oluf | Pramstaller, Peter Paul | Wichmann, H Erich | Illig, Thomas | Rudan, Igor | Wright, Alan F | Stumvoll, Michael | Campbell, Harry | Wilson, James F | Hamsten, Anders | Bergman, Richard N | Buchanan, Thomas A | Collins, Francis S | Mohlke, Karen L | Tuomilehto, Jaakko | Valle, Timo T | Altshuler, David | Rotter, Jerome I | Siscovick, David S | Penninx, Brenda W J H | Boomsma, Dorret | Deloukas, Panos | Spector, Timothy D | Frayling, Timothy M | Ferrucci, Luigi | Kong, Augustine | Thorsteinsdottir, Unnur | Stefansson, Kari | van Duijn, Cornelia M | Aulchenko, Yurii S | Cao, Antonio | Scuteri, Angelo | Schlessinger, David | Uda, Manuela | Ruokonen, Aimo | Jarvelin, Marjo-Riitta | Waterworth, Dawn M | Vollenweider, Peter | Peltonen, Leena | Mooser, Vincent | Abecasis, Goncalo R | Wareham, Nicholas J | Sladek, Robert | Froguel, Philippe | Watanabe, Richard M | Meigs, James B | Groop, Leif | Boehnke, Michael | McCarthy, Mark I | Florez, Jose C | Barroso, Inês
Nature genetics  2010;42(2):105-116.
Circulating glucose levels are tightly regulated. To identify novel glycemic loci, we performed meta-analyses of 21 genome-wide associations studies informative for fasting glucose (FG), fasting insulin (FI) and indices of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 non-diabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with FG/HOMA-B and two associated with FI/HOMA-IR. These include nine new FG loci (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and FAM148B) and one influencing FI/HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB/TMEM195 with type 2 diabetes (T2D). Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify T2D risk loci, as well as loci that elevate FG modestly, but do not cause overt diabetes.
doi:10.1038/ng.520
PMCID: PMC3018764  PMID: 20081858

Results 1-24 (24)