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2.  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
3.  Intrahepatic Lipid Content and Insulin Resistance Are More Strongly Associated with Impaired NEFA Suppression after Oral Glucose Loading Than with Fasting NEFA Levels in Healthy Older Individuals 
Introduction. The mechanisms underlying the association between insulin resistance and intrahepatic lipid (IHL) accumulation are not completely understood. We sought to determine whether this association was explained by differences in fasting non-esterified fatty acid (NEFA) levels and/or NEFA suppression after oral glucose loading. Materials and Methods. We performed a cross-sectional analysis of 70 healthy participants in the Hertfordshire Physical Activity Trial (39 males, age 71.3 ± 2.4 years) who underwent oral glucose tolerance testing with glucose, insulin, and NEFA levels measured over two hours. IHL was quantified with magnetic resonance spectroscopy. Insulin sensitivity was measured with the oral glucose insulin sensitivity (OGIS) model, the leptin: adiponectin ratio (LAR), and the homeostasis model assessment (HOMA). Results. Measures of insulin sensitivity were not associated with fasting NEFA levels, but OGIS was strongly associated with NEFA suppression at 30 minutes and strongly inversely associated with IHL. Moreover, LAR was strongly inversely associated with NEFA suppression and strongly associated with IHL. This latter association (beta = 1.11 [1.01, 1.21], P = 0.026) was explained by reduced NEFA suppression (P = 0.24 after adjustment). Conclusions. Impaired postprandial NEFA suppression, but not fasting NEFA, contributes to the strong and well-established association between whole body insulin resistance and liver fat accumulation.
doi:10.1155/2013/870487
PMCID: PMC3659510  PMID: 23737780
4.  No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels 
Scott, Robert A. | Chu, Audrey Y. | Grarup, Niels | Manning, Alisa K. | Hivert, Marie-France | Shungin, Dmitry | Tönjes, Anke | Yesupriya, Ajay | Barnes, Daniel | Bouatia-Naji, Nabila | Glazer, Nicole L. | Jackson, Anne U. | Kutalik, Zoltán | Lagou, Vasiliki | Marek, Diana | Rasmussen-Torvik, Laura J. | Stringham, Heather M. | Tanaka, Toshiko | Aadahl, Mette | Arking, Dan E. | Bergmann, Sven | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brunner, Eric | Bumpstead, Suzannah J. | Brage, Soren | Carlson, Olga D. | Chen, Han | Chen, Yii-Der Ida | Chines, Peter S. | Collins, Francis S. | Couper, David J. | Dennison, Elaine M. | Dowling, Nicole F. | Egan, Josephine S. | Ekelund, Ulf | Erdos, Michael R. | Forouhi, Nita G. | Fox, Caroline S. | Goodarzi, Mark O. | Grässler, Jürgen | Gustafsson, Stefan | Hallmans, Göran | Hansen, Torben | Hingorani, Aroon | Holloway, John W. | Hu, Frank B. | Isomaa, Bo | Jameson, Karen A. | Johansson, Ingegerd | Jonsson, Anna | Jørgensen, Torben | Kivimaki, Mika | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Laakso, Markku | Lecoeur, Cécile | Lévy-Marchal, Claire | Li, Guo | Loos, Ruth J.F. | Lyssenko, Valeri | Marmot, Michael | Marques-Vidal, Pedro | Morken, Mario A. | Müller, Gabriele | North, Kari E. | Pankow, James S. | Payne, Felicity | Prokopenko, Inga | Psaty, Bruce M. | Renström, Frida | Rice, Ken | Rotter, Jerome I. | Rybin, Denis | Sandholt, Camilla H. | Sayer, Avan A. | Shrader, Peter | Schwarz, Peter E.H. | Siscovick, David S. | Stančáková, Alena | Stumvoll, Michael | Teslovich, Tanya M. | Waeber, Gérard | Williams, Gordon H. | Witte, Daniel R. | Wood, Andrew R. | Xie, Weijia | Boehnke, Michael | Cooper, Cyrus | Ferrucci, Luigi | Froguel, Philippe | Groop, Leif | Kao, W.H. Linda | Vollenweider, Peter | Walker, Mark | Watanabe, Richard M. | Pedersen, Oluf | Meigs, James B. | Ingelsson, Erik | Barroso, Inês | Florez, Jose C. | Franks, Paul W. | Dupuis, Josée | Wareham, Nicholas J. | Langenberg, Claudia
Diabetes  2012;61(5):1291-1296.
Gene–lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13–0.31], P = 1.63 × 10−6). All SNPs were associated with 2-h glucose (β = 0.06–0.12 mmol/allele, P ≤ 1.53 × 10−7), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene–lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.
doi:10.2337/db11-0973
PMCID: PMC3331745  PMID: 22415877
5.  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
6.  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
7.  Effect of Five Genetic Variants Associated with Lung Function on the Risk of Chronic Obstructive Lung Disease, and Their Joint Effects on Lung Function 
Rationale: Genomic loci are associated with FEV1 or the ratio of FEV1 to FVC in population samples, but their association with chronic obstructive pulmonary disease (COPD) has not yet been proven, nor have their combined effects on lung function and COPD been studied.
Objectives: To test association with COPD of variants at five loci (TNS1, GSTCD, HTR4, AGER, and THSD4) and to evaluate joint effects on lung function and COPD of these single-nucleotide polymorphisms (SNPs), and variants at the previously reported locus near HHIP.
Methods: By sampling from 12 population-based studies (n = 31,422), we obtained genotype data on 3,284 COPD case subjects and 17,538 control subjects for sentinel SNPs in TNS1, GSTCD, HTR4, AGER, and THSD4. In 24,648 individuals (including 2,890 COPD case subjects and 13,862 control subjects), we additionally obtained genotypes for rs12504628 near HHIP. Each allele associated with lung function decline at these six SNPs contributed to a risk score. We studied the association of the risk score to lung function and COPD.
Measurements and Main Results: Association with COPD was significant for three loci (TNS1, GSTCD, and HTR4) and the previously reported HHIP locus, and suggestive and directionally consistent for AGER and TSHD4. Compared with the baseline group (7 risk alleles), carrying 10–12 risk alleles was associated with a reduction in FEV1 (β = –72.21 ml, P = 3.90 × 10−4) and FEV1/FVC (β = –1.53%, P = 6.35 × 10−6), and with COPD (odds ratio = 1.63, P = 1.46 × 10−5).
Conclusions: Variants in TNS1, GSTCD, and HTR4 are associated with COPD. Our highest risk score category was associated with a 1.6-fold higher COPD risk than the population average score.
doi:10.1164/rccm.201102-0192OC
PMCID: PMC3398416  PMID: 21965014
FEV1; FVC; genome-wide association study; modeling risk
8.  Common genetic determinants of vitamin D insufficiency: a genome-wide association study 
Wang, Thomas J. | Zhang, Feng | Richards, J. Brent | Kestenbaum, Bryan | van Meurs, Joyce B. | Berry, Diane | Kiel, Douglas | Streeten, Elizabeth A. | Ohlsson, Claes | Koller, Daniel L. | Palotie, Leena | Cooper, Jason D. | O'Reilly, Paul F. | Houston, Denise K. | Glazer, Nicole L. | Vandenput, Liesbeth | Peacock, Munro | Shi, Julia | Rivadeneira, Fernando | McCarthy, Mark I. | Anneli, Pouta | de Boer, Ian H. | Mangino, Massimo | Kato, Bernet | Smyth, Deborah J. | Booth, Sarah L. | Jacques, Paul F. | Burke, Greg L. | Goodarzi, Mark | Cheung, Ching-Lung | Wolf, Myles | Rice, Kenneth | Goltzman, David | Hidiroglou, Nick | Ladouceur, Martin | Hui, Siu L. | Wareham, Nicholas J. | Hocking, Lynne J. | Hart, Deborah | Arden, Nigel K. | Cooper, Cyrus | Malik, Suneil | Fraser, William D. | Hartikainen, Anna-Liisa | Zhai, Guangju | Macdonald, Helen | Forouhi, Nita G. | Loos, Ruth J.F. | Reid, David M. | Hakim, Alan | Dennison, Elaine | Liu, Yongmei | Power, Chris | Stevens, Helen E. | Jaana, Laitinen | Vasan, Ramachandran S. | Soranzo, Nicole | Bojunga, Jörg | Psaty, Bruce M. | Lorentzon, Mattias | Foroud, Tatiana | Harris, Tamara B. | Hofman, Albert | Jansson, John-Olov | Cauley, Jane A. | Uitterlinden, Andre G. | Gibson, Quince | Järvelin, Marjo-Riitta | Karasik, David | Siscovick, David S. | Econs, Michael J. | Kritchevsky, Stephen B. | Florez, Jose C. | Todd, John A. | Dupuis, Josee | Hypponen, Elina | Spector, Timothy D.
Lancet  2010;376(9736):180-188.
Background
Vitamin D is crucial for maintaining musculoskeletal health. Recently, vitamin D insufficiency has been linked to a number of extraskeletal disorders, including diabetes, cancer, and cardiovascular disease. Determinants of circulating 25-hydroxyvitamin D (25-OH D) include sun exposure and dietary intake, but its high heritability suggests that genetic determinants may also play a role.
Methods
We performed a genome-wide association study of 25-OH D among ∼30,000 individuals of European descent from 15 cohorts. Five cohorts were designated as discovery cohorts (n=16,125), five as in silico replication cohorts (n=9,366), and five as de novo replication cohorts (n=8,378). Association results were combined using z-score-weighted meta-analysis. Vitamin D insufficiency was defined as 25-OH D <75 nmol/L or <50 nmol/L.
Findings
Variants at three loci reached genome-wide significance in the discovery cohorts, and were confirmed in the replication cohorts: 4p12 (overall P=1.9 × 10-109 for rs2282679, in GC); 11q12 (P=2.1 × 10-27 for rs12785878, near DHCR7); 11p15 (P=3.3 × 10-20 for rs10741657, near CYP2R1). Variants at an additional locus (20q13, CYP24A1) were genome-wide significant in the pooled sample (P=6.0 × 10-10 for rs6013897). A genotype score was constructed using the three confirmed variants. Those in the top quartile of genotype scores had 2- to 2.5-fold elevated odds of vitamin D insufficiency (P≤1 × 10-26).
Interpretation
Variants near genes involved in cholesterol synthesis (DHCR7), hydroxylation (CYP2R1, CYP24A1), and vitamin D transport (GC) influence vitamin D status. Genetic variation at these loci identifies individuals of European descent who have substantially elevated risk of vitamin D insufficiency.
doi:10.1016/S0140-6736(10)60588-0
PMCID: PMC3086761  PMID: 20541252
9.  Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans 
Diabetes  2010;59(5):1266-1275.
OBJECTIVE
Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action.
RESEARCH DESIGN AND METHODS
We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084).
RESULTS
The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 × 10−71). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction.
CONCLUSIONS
Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.
doi:10.2337/db09-1568
PMCID: PMC2857908  PMID: 20185807
10.  Evaluating the Role of LPIN1 Variation in Insulin Resistance, Body Weight, and Human Lipodystrophy in U.K. Populations 
Diabetes  2008;57(9):2527-2533.
OBJECTIVE— Loss of lipin 1 activity causes lipodystrophy and insulin resistance in the fld mouse, and LPIN1 expression and common genetic variation were recently suggested to influence adiposity and insulin sensitivity in humans. We aimed to conduct a comprehensive association study to clarify the influence of common LPIN1 variation on adiposity and insulin sensitivity in U.K. populations and to examine the role of LPIN1 mutations in insulin resistance syndromes.
RESEARCH DESIGN AND METHOD— Twenty-two single nucleotide polymorphisms tagging common LPIN1 variation were genotyped in Medical Research Council (MRC) Ely (n = 1,709) and Hertfordshire (n = 2,901) population-based cohorts. LPIN1 exons, exon/intron boundaries, and 3′ untranslated region were sequenced in 158 patients with idiopathic severe insulin resistance (including 23 lipodystrophic patients) and 48 control subjects.
RESULTS— We found no association between LPIN1 single nucleotide polymorphisms and fasting insulin but report a nominal association between rs13412852 and BMI (P = 0.042) in a meta-analysis of 8,504 samples from in-house and publicly available studies. Three rare nonsynonymous variants (A353T, R552K, and G582R) were detected in severely insulin-resistant patients. However, these did not cosegregate with disease in affected families, and Lipin1 protein expression and phosphorylation in patients with variants were indistinguishable from those in control subjects.
CONCLUSIONS— Our data do not support a major effect of common LPIN1 variation on metabolic traits and suggest that mutations in LPIN1 are not a common cause of lipodystrophy in humans. The nominal associations with BMI and other metabolic traits in U.K. cohorts require replication in larger cohorts.
doi:10.2337/db08-0422
PMCID: PMC2518506  PMID: 18591397
11.  Correction: Genome-Wide Association Scan Meta-Analysis Identifies Three Loci Influencing Adiposity and Fat Distribution 
Lindgren, Cecilia M. | Heid, Iris M. | Randall, Joshua C. | Lamina, Claudia | Steinthorsdottir, Valgerdur | Qi, Lu | Speliotes, Elizabeth K. | Thorleifsson, Gudmar | Willer, Cristen J. | Herrera, Blanca M. | Jackson, Anne U. | Lim, Noha | Scheet, Paul | Soranzo, Nicole | Amin, Najaf | Aulchenko, Yurii S. | Chambers, John C. | Drong, Alexander | Luan, Jian'an | Lyon, Helen N. | Rivadeneira, Fernando | Sanna, Serena | Timpson, Nicholas J. | Zillikens, M. Carola | Zhao, Jing Hua | Almgren, Peter | Bandinelli, Stefania | Bennett, Amanda J. | Bergman, Richard N. | Bonnycastle, Lori L. | Bumpstead, Suzannah J. | Chanock, Stephen J. | Cherkas, Lynn | Chines, Peter | Coin, Lachlan | Cooper, Cyrus | Crawford, Gabriel | Doering, Angela | Dominiczak, Anna | Doney, Alex S. F. | Ebrahim, Shah | Elliott, Paul | Erdos, Michael R. | Estrada, Karol | Ferrucci, Luigi | Fischer, Guido | Forouhi, Nita G. | Gieger, Christian | Grallert, Harald | Groves, Christopher J. | Grundy, Scott | Guiducci, Candace | Hadley, David | Hamsten, Anders | Havulinna, Aki S. | Hofman, Albert | Holle, Rolf | Holloway, John W. | Illig, Thomas | Isomaa, Bo | Jacobs, Leonie C. | Jameson, Karen | Jousilahti, Pekka | Karpe, Fredrik | Kuusisto, Johanna | Laitinen, Jaana | Lathrop, G. Mark | Lawlor, Debbie A. | Mangino, Massimo | McArdle, Wendy L. | Meitinger, Thomas | Morken, Mario A. | Morris, Andrew P. | Munroe, Patricia | Narisu, Narisu | Nordström, Anna | Nordström, Peter | Oostra, Ben A. | Palmer, Colin N. A. | Payne, Felicity | Peden, John F. | Prokopenko, Inga | Renström, Frida | Ruokonen, Aimo | Salomaa, Veikko | Sandhu, Manjinder S. | Scott, Laura J. | Scuteri, Angelo | Silander, Kaisa | Song, Kijoung | Yuan, Xin | Stringham, Heather M. | Swift, Amy J. | Tuomi, Tiinamaija | Uda, Manuela | Vollenweider, Peter | Waeber, Gerard | Wallace, Chris | Walters, G. Bragi | Weedon, Michael N. | Witteman, Jacqueline C. M. | Zhang, Cuilin | Zhang, Weihua | Caulfield, Mark J. | Collins, Francis S. | Davey Smith, George | Day, Ian N. M. | Franks, Paul W. | Hattersley, Andrew T. | Hu, Frank B. | Jarvelin, Marjo-Riitta | Kong, Augustine | Kooner, Jaspal S. | Laakso, Markku | Lakatta, Edward | Mooser, Vincent | Morris, Andrew D. | Peltonen, Leena | Samani, Nilesh J. | Spector, Timothy D. | Strachan, David P. | Tanaka, Toshiko | Tuomilehto, Jaakko | Uitterlinden, André G. | van Duijn, Cornelia M. | Wareham, Nicholas J. | Watkins for the PROCARDIS consortia, Hugh | Waterworth, Dawn M. | Boehnke, Michael | Deloukas, Panos | Groop, Leif | Hunter, David J. | Thorsteinsdottir, Unnur | Schlessinger, David | Wichmann, H.-Erich | Frayling, Timothy M. | Abecasis, Gonçalo R. | Hirschhorn, Joel N. | Loos, Ruth J. F. | Stefansson, Kari | Mohlke, Karen L. | Barroso, Inês | McCarthy for the GIANT consortium, Mark I.
PLoS Genetics  2009;5(7):10.1371/annotation/b6e8f9f6-2496-4a40-b0e3-e1d1390c1928.
doi:10.1371/annotation/b6e8f9f6-2496-4a40-b0e3-e1d1390c1928
PMCID: PMC2722420
12.  Genome-Wide Association Scan Meta-Analysis Identifies Three Loci Influencing Adiposity and Fat Distribution 
Lindgren, Cecilia M. | Heid, Iris M. | Randall, Joshua C. | Lamina, Claudia | Steinthorsdottir, Valgerdur | Qi, Lu | Speliotes, Elizabeth K. | Thorleifsson, Gudmar | Willer, Cristen J. | Herrera, Blanca M. | Jackson, Anne U. | Lim, Noha | Scheet, Paul | Soranzo, Nicole | Amin, Najaf | Aulchenko, Yurii S. | Chambers, John C. | Drong, Alexander | Luan, Jian'an | Lyon, Helen N. | Rivadeneira, Fernando | Sanna, Serena | Timpson, Nicholas J. | Zillikens, M. Carola | Zhao, Jing Hua | Almgren, Peter | Bandinelli, Stefania | Bennett, Amanda J. | Bergman, Richard N. | Bonnycastle, Lori L. | Bumpstead, Suzannah J. | Chanock, Stephen J. | Cherkas, Lynn | Chines, Peter | Coin, Lachlan | Cooper, Cyrus | Crawford, Gabriel | Doering, Angela | Dominiczak, Anna | Doney, Alex S. F. | Ebrahim, Shah | Elliott, Paul | Erdos, Michael R. | Estrada, Karol | Ferrucci, Luigi | Fischer, Guido | Forouhi, Nita G. | Gieger, Christian | Grallert, Harald | Groves, Christopher J. | Grundy, Scott | Guiducci, Candace | Hadley, David | Hamsten, Anders | Havulinna, Aki S. | Hofman, Albert | Holle, Rolf | Holloway, John W. | Illig, Thomas | Isomaa, Bo | Jacobs, Leonie C. | Jameson, Karen | Jousilahti, Pekka | Karpe, Fredrik | Kuusisto, Johanna | Laitinen, Jaana | Lathrop, G. Mark | Lawlor, Debbie A. | Mangino, Massimo | McArdle, Wendy L. | Meitinger, Thomas | Morken, Mario A. | Morris, Andrew P. | Munroe, Patricia | Narisu, Narisu | Nordström, Anna | Nordström, Peter | Oostra, Ben A. | Palmer, Colin N. A. | Payne, Felicity | Peden, John F. | Prokopenko, Inga | Renström, Frida | Ruokonen, Aimo | Salomaa, Veikko | Sandhu, Manjinder S. | Scott, Laura J. | Scuteri, Angelo | Silander, Kaisa | Song, Kijoung | Yuan, Xin | Stringham, Heather M. | Swift, Amy J. | Tuomi, Tiinamaija | Uda, Manuela | Vollenweider, Peter | Waeber, Gerard | Wallace, Chris | Walters, G. Bragi | Weedon, Michael N. | Witteman, Jacqueline C. M. | Zhang, Cuilin | Zhang, Weihua | Caulfield, Mark J. | Collins, Francis S. | Davey Smith, George | Day, Ian N. M. | Franks, Paul W. | Hattersley, Andrew T. | Hu, Frank B. | Jarvelin, Marjo-Riitta | Kong, Augustine | Kooner, Jaspal S. | Laakso, Markku | Lakatta, Edward | Mooser, Vincent | Morris, Andrew D. | Peltonen, Leena | Samani, Nilesh J. | Spector, Timothy D. | Strachan, David P. | Tanaka, Toshiko | Tuomilehto, Jaakko | Uitterlinden, André G. | van Duijn, Cornelia M. | Wareham, Nicholas J. | Watkins for the PROCARDIS consortia, Hugh | Waterworth, Dawn M. | Boehnke, Michael | Deloukas, Panos | Groop, Leif | Hunter, David J. | Thorsteinsdottir, Unnur | Schlessinger, David | Wichmann, H.-Erich | Frayling, Timothy M. | Abecasis, Gonçalo R. | Hirschhorn, Joel N. | Loos, Ruth J. F. | Stefansson, Kari | Mohlke, Karen L. | Barroso, Inês | McCarthy for the GIANT consortium, Mark I.
PLoS Genetics  2009;5(6):e1000508.
To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist–hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9×10−11) and MSRA (WC, P = 8.9×10−9). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6×10−8). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.
Author Summary
Here, we describe a meta-analysis of genome-wide association data from 38,580 individuals, followed by large-scale replication (in up to 70,689 individuals) designed to uncover variants influencing anthropometric measures of central obesity and fat distribution, namely waist circumference (WC) and waist–hip ratio (WHR). This work complements parallel efforts that have been successful in defining variants impacting overall adiposity and focuses on the visceral fat accumulation which has particularly strong relationships to metabolic and cardiovascular disease. Our analyses have identified two loci (TFAP2B and MSRA) associated with WC, and a further locus, near LYPLAL1, which shows gender-specific relationships with WHR (all to levels of genome-wide significance). These loci vary in the strength of their associations with overall adiposity, and LYPLAL1 in particular appears to have a specific effect on patterns of fat distribution. All in all, these three loci provide novel insights into human physiology and the development of obesity.
doi:10.1371/journal.pgen.1000508
PMCID: PMC2695778  PMID: 19557161
13.  Evaluating the role of LPIN1 variation on insulin resistance, body weight and human lipodystrophy in UK populations 
Diabetes  2008;57(9):2527-2533.
OBJECTIVE:
Loss of Lpin1 activity causes lipodystrophy and insulin resistance in the fld mouse, and LPIN1 expression and common genetic variation were recently suggested to influence adiposity and insulin sensitivity in humans. We aimed to conduct a comprehensive association study to clarify the influence of LPIN1 common variation on adiposity and insulin sensitivity in UK populations, and to examine the role of LPIN1 mutations in insulin resistance syndromes.
RESEARCH DESIGN AND METHOD:
Twenty-two SNPs tagging LPIN1 common variation were genotyped in MRC Ely (N = 1709) and Hertfordshire (N = 2901) population-based cohorts. LPIN1 exons, exon/intron boundaries and 3′UTR were sequenced in 158 patients with idiopathic severe insulin resistance (including 23 lipodystrophic patients), and 48 controls.
RESULTS:
We found no association between LPIN1 SNPs and fasting insulin, but report a nominal association between rs13412852 and BMI (P = 0.042) in a meta-analysis of 8504 samples from in-house and publicly available studies. Three rare nonsynonymous variants (A353T, R552K and G582R) were detected in severely insulin resistant patients. However, these did not co-segregate with disease in affected families and Lipin1 protein expression and phosphorylation in patients with variants was indistinguishable from controls.
CONCLUSIONS:
Our data do not support a major effect of LPIN1 common variation on metabolic traits and suggest that mutations in LPIN1 are not a common cause of lipodystrophy in humans. The nominal associations with BMI and other metabolic traits in UK cohorts require replication in larger cohorts.
doi:10.2337/db08-0422
PMCID: PMC2518506  PMID: 18591397

Results 1-13 (13)