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1.  Glycemic Exposure and Blood Pressure Influencing Progression and Remission of Diabetic Retinopathy 
Diabetes Care  2013;36(12):3979-3984.
OBJECTIVE
This study sought to investigate the progression and regression of diabetic retinopathy (DR) and the effects of population risk factors on the rates of transition across retinopathy stages.
RESEARCH DESIGN AND METHODS
The study cohort consisted of 44,871 observed DR events between the calendar years 1990 and 2011 for 4,758 diabetic patients who were diagnosed at 35 years of age or older. The first retinal observation was recorded within a year from diagnosis, and the result was recorded as free of retinopathy. A multistate Markov model was applied for analyzing the development of DR and its relation to the patterns of changes in risk factors.
RESULTS
We observed a consistent risk effect of HbA1c on the progression (no retinopathy to mild background DR [BDR] hazard ratio per SD of HbA1c [HR] 1.42 [95% CI 1.32–1.52], mild BDR to observable BDR HR 1.32 [95% CI 1.08–1.60], and observable BDR to severe nonproliferative/proliferative DR HR 2.23 [95% CI 1.16–4.29]). Similarly, systolic blood pressure (SBP) and diastolic blood pressure increased the risk for the transition from the asymptomatic phase to mild BDR (HR 1.20 [95% CI 1.11–1.30]) and the mild BDR to observable BDR (HR 1.87 [95% CI 1.46–2.40]), respectively. Regression from mild BDR to no DR was associated with lower SBP (HR 0.79 [95% CI 0.64–0.97]) and lower HbA1c (HR 0.76 [95% CI 0.64–0.89]).
CONCLUSIONS
Progression and regression of DR were strongly associated with blood pressure and glycemic exposure.
doi:10.2337/dc12-2392
PMCID: PMC3836116  PMID: 24170761
2.  Novel Insights Into the Etiology of Diabetes From Genome-Wide Association Studies 
Diabetes  2009;58(11):2444-2447.
doi:10.2337/db09-1153
PMCID: PMC2768184  PMID: 19875620
3.  Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach 
Diabetes  2013;62(12):4270-4276.
Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39–1.95], P = 8.46 × 10−9) and was moderately heritable (h2 = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34–2.11], P = 6.52 × 10−6) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27–2.75], P = 1 × 10−3). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.
doi:10.2337/db13-0570
PMCID: PMC3837024  PMID: 23884885
4.  Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes 
Yaghootkar, Hanieh | Lamina, Claudia | Scott, Robert A. | Dastani, Zari | Hivert, Marie-France | Warren, Liling L. | Stancáková, Alena | Buxbaum, Sarah G. | Lyytikäinen, Leo-Pekka | Henneman, Peter | Wu, Ying | Cheung, Chloe Y.Y. | Pankow, James S. | Jackson, Anne U. | Gustafsson, Stefan | Zhao, Jing Hua | Ballantyne, Christie M. | Xie, Weijia | Bergman, Richard N. | Boehnke, Michael | el Bouazzaoui, Fatiha | Collins, Francis S. | Dunn, Sandra H. | Dupuis, Josee | Forouhi, Nita G. | Gillson, Christopher | Hattersley, Andrew T. | Hong, Jaeyoung | Kähönen, Mika | Kuusisto, Johanna | Kedenko, Lyudmyla | Kronenberg, Florian | Doria, Alessandro | Assimes, Themistocles L. | Ferrannini, Ele | Hansen, Torben | Hao, Ke | Häring, Hans | Knowles, Joshua W. | Lindgren, Cecilia M. | Nolan, John J. | Paananen, Jussi | Pedersen, Oluf | Quertermous, Thomas | Smith, Ulf | Lehtimäki, Terho | Liu, Ching-Ti | Loos, Ruth J.F. | McCarthy, Mark I. | Morris, Andrew D. | Vasan, Ramachandran S. | Spector, Tim D. | Teslovich, Tanya M. | Tuomilehto, Jaakko | van Dijk, Ko Willems | Viikari, Jorma S. | Zhu, Na | Langenberg, Claudia | Ingelsson, Erik | Semple, Robert K. | Sinaiko, Alan R. | Palmer, Colin N.A. | Walker, Mark | Lam, Karen S.L. | Paulweber, Bernhard | Mohlke, Karen L. | van Duijn, Cornelia | Raitakari, Olli T. | Bidulescu, Aurelian | Wareham, Nick J. | Laakso, Markku | Waterworth, Dawn M. | Lawlor, Debbie A. | Meigs, James B. | Richards, J. Brent | Frayling, Timothy M.
Diabetes  2013;62(10):3589-3598.
Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
doi:10.2337/db13-0128
PMCID: PMC3781444  PMID: 23835345
5.  The CTRB1/2 Locus Affects Diabetes Susceptibility and Treatment via the Incretin Pathway 
Diabetes  2013;62(9):3275-3281.
The incretin hormone glucagon-like peptide 1 (GLP-1) promotes glucose homeostasis and enhances β-cell function. GLP-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors, which inhibit the physiological inactivation of endogenous GLP-1, are used for the treatment of type 2 diabetes. Using the Metabochip, we identified three novel genetic loci with large effects (30–40%) on GLP-1–stimulated insulin secretion during hyperglycemic clamps in nondiabetic Caucasian individuals (TMEM114; CHST3 and CTRB1/2; n = 232; all P ≤ 8.8 × 10−7). rs7202877 near CTRB1/2, a known diabetes risk locus, also associated with an absolute 0.51 ± 0.16% (5.6 ± 1.7 mmol/mol) lower A1C response to DPP-4 inhibitor treatment in G-allele carriers, but there was no effect on GLP-1 RA treatment in type 2 diabetic patients (n = 527). Furthermore, in pancreatic tissue, we show that rs7202877 acts as expression quantitative trait locus for CTRB1 and CTRB2, encoding chymotrypsinogen, and increases fecal chymotrypsin activity in healthy carriers. Chymotrypsin is one of the most abundant digestive enzymes in the gut where it cleaves food proteins into smaller peptide fragments. Our data identify chymotrypsin in the regulation of the incretin pathway, development of diabetes, and response to DPP-4 inhibitor treatment.
doi:10.2337/db13-0227
PMCID: PMC3749354  PMID: 23674605
6.  Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture 
Berndt, Sonja I. | Gustafsson, Stefan | Mägi, Reedik | Ganna, Andrea | Wheeler, Eleanor | Feitosa, Mary F. | Justice, Anne E. | Monda, Keri L. | Croteau-Chonka, Damien C. | Day, Felix R. | Esko, Tõnu | Fall, Tove | Ferreira, Teresa | Gentilini, Davide | Jackson, Anne U. | Luan, Jian’an | Randall, Joshua C. | Vedantam, Sailaja | Willer, Cristen J. | Winkler, Thomas W. | Wood, Andrew R. | Workalemahu, Tsegaselassie | Hu, Yi-Juan | Lee, Sang Hong | Liang, Liming | Lin, Dan-Yu | Min, Josine L. | Neale, Benjamin M. | Thorleifsson, Gudmar | Yang, Jian | Albrecht, Eva | Amin, Najaf | Bragg-Gresham, Jennifer L. | Cadby, Gemma | den Heijer, Martin | Eklund, Niina | Fischer, Krista | Goel, Anuj | Hottenga, Jouke-Jan | Huffman, Jennifer E. | Jarick, Ivonne | Johansson, Åsa | Johnson, Toby | Kanoni, Stavroula | Kleber, Marcus E. | König, Inke R. | Kristiansson, Kati | Kutalik, Zoltán | Lamina, Claudia | Lecoeur, Cecile | Li, Guo | Mangino, Massimo | McArdle, Wendy L. | Medina-Gomez, Carolina | Müller-Nurasyid, Martina | Ngwa, Julius S. | Nolte, Ilja M. | Paternoster, Lavinia | Pechlivanis, Sonali | Perola, Markus | Peters, Marjolein J. | Preuss, Michael | Rose, Lynda M. | Shi, Jianxin | Shungin, Dmitry | Smith, Albert Vernon | Strawbridge, Rona J. | Surakka, Ida | Teumer, Alexander | Trip, Mieke D. | Tyrer, Jonathan | Van Vliet-Ostaptchouk, Jana V. | Vandenput, Liesbeth | Waite, Lindsay L. | Zhao, Jing Hua | Absher, Devin | Asselbergs, Folkert W. | Atalay, Mustafa | Attwood, Antony P. | Balmforth, Anthony J. | Basart, Hanneke | Beilby, John | Bonnycastle, Lori L. | Brambilla, Paolo | Bruinenberg, Marcel | Campbell, Harry | Chasman, Daniel I. | Chines, Peter S. | Collins, Francis S. | Connell, John M. | Cookson, William | de Faire, Ulf | de Vegt, Femmie | Dei, Mariano | Dimitriou, Maria | Edkins, Sarah | Estrada, Karol | Evans, David M. | Farrall, Martin | Ferrario, Marco M. | Ferrières, Jean | Franke, Lude | Frau, Francesca | Gejman, Pablo V. | Grallert, Harald | Grönberg, Henrik | Gudnason, Vilmundur | Hall, Alistair S. | Hall, Per | Hartikainen, Anna-Liisa | Hayward, Caroline | Heard-Costa, Nancy L. | Heath, Andrew C. | Hebebrand, Johannes | Homuth, Georg | Hu, Frank B. | Hunt, Sarah E. | Hyppönen, Elina | Iribarren, Carlos | Jacobs, Kevin B. | Jansson, John-Olov | Jula, Antti | Kähönen, Mika | Kathiresan, Sekar | Kee, Frank | Khaw, Kay-Tee | Kivimaki, Mika | Koenig, Wolfgang | Kraja, Aldi T. | Kumari, Meena | Kuulasmaa, Kari | Kuusisto, Johanna | Laitinen, Jaana H. | Lakka, Timo A. | Langenberg, Claudia | Launer, Lenore J. | Lind, Lars | Lindström, Jaana | Liu, Jianjun | Liuzzi, Antonio | Lokki, Marja-Liisa | Lorentzon, Mattias | Madden, Pamela A. | Magnusson, Patrik K. | Manunta, Paolo | Marek, Diana | März, Winfried | Mateo Leach, Irene | McKnight, Barbara | Medland, Sarah E. | Mihailov, Evelin | Milani, Lili | Montgomery, Grant W. | Mooser, Vincent | Mühleisen, Thomas W. | Munroe, Patricia B. | Musk, Arthur W. | Narisu, Narisu | Navis, Gerjan | Nicholson, George | Nohr, Ellen A. | Ong, Ken K. | Oostra, Ben A. | Palmer, Colin N.A. | Palotie, Aarno | Peden, John F. | Pedersen, Nancy | Peters, Annette | Polasek, Ozren | Pouta, Anneli | Pramstaller, Peter P. | Prokopenko, Inga | Pütter, Carolin | Radhakrishnan, Aparna | Raitakari, Olli | Rendon, Augusto | Rivadeneira, Fernando | Rudan, Igor | Saaristo, Timo E. | Sambrook, Jennifer G. | Sanders, Alan R. | Sanna, Serena | Saramies, Jouko | Schipf, Sabine | Schreiber, Stefan | Schunkert, Heribert | Shin, So-Youn | Signorini, Stefano | Sinisalo, Juha | Skrobek, Boris | Soranzo, Nicole | Stančáková, Alena | Stark, Klaus | Stephens, Jonathan C. | Stirrups, Kathleen | Stolk, Ronald P. | Stumvoll, Michael | Swift, Amy J. | Theodoraki, Eirini V. | Thorand, Barbara | Tregouet, David-Alexandre | Tremoli, Elena | Van der Klauw, Melanie M. | van Meurs, Joyce B.J. | Vermeulen, Sita H. | Viikari, Jorma | Virtamo, Jarmo | Vitart, Veronique | Waeber, Gérard | Wang, Zhaoming | Widén, Elisabeth | Wild, Sarah H. | Willemsen, Gonneke | Winkelmann, Bernhard R. | Witteman, Jacqueline C.M. | Wolffenbuttel, Bruce H.R. | Wong, Andrew | Wright, Alan F. | Zillikens, M. Carola | Amouyel, Philippe | Boehm, Bernhard O. | Boerwinkle, Eric | Boomsma, Dorret I. | Caulfield, Mark J. | Chanock, Stephen J. | Cupples, L. Adrienne | Cusi, Daniele | Dedoussis, George V. | Erdmann, Jeanette | Eriksson, Johan G. | Franks, Paul W. | Froguel, Philippe | Gieger, Christian | Gyllensten, Ulf | Hamsten, Anders | Harris, Tamara B. | Hengstenberg, Christian | Hicks, Andrew A. | Hingorani, Aroon | Hinney, Anke | Hofman, Albert | Hovingh, Kees G. | Hveem, Kristian | Illig, Thomas | Jarvelin, Marjo-Riitta | Jöckel, Karl-Heinz | Keinanen-Kiukaanniemi, Sirkka M. | Kiemeney, Lambertus A. | Kuh, Diana | Laakso, Markku | Lehtimäki, Terho | Levinson, Douglas F. | Martin, Nicholas G. | Metspalu, Andres | Morris, Andrew D. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Ouwehand, Willem H. | Palmer, Lyle J. | Penninx, Brenda | Power, Chris | Province, Michael A. | Psaty, Bruce M. | Qi, Lu | Rauramaa, Rainer | Ridker, Paul M. | Ripatti, Samuli | Salomaa, Veikko | Samani, Nilesh J. | Snieder, Harold | Sørensen, Thorkild I.A. | Spector, Timothy D. | Stefansson, Kari | Tönjes, Anke | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | van der Harst, Pim | Vollenweider, Peter | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Wichmann, H.-Erich | Wilson, James F. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Frayling, Timothy | Groop, Leif C. | Haritunian, Talin | Heid, Iris M. | Hunter, David | Kaplan, Robert C. | Karpe, Fredrik | Moffatt, Miriam | Mohlke, Karen L. | O’Connell, Jeffrey R. | Pawitan, Yudi | Schadt, Eric E. | Schlessinger, David | Steinthorsdottir, Valgerdur | Strachan, David P. | Thorsteinsdottir, Unnur | van Duijn, Cornelia M. | Visscher, Peter M. | Di Blasio, Anna Maria | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Morris, Andrew P. | Meyre, David | Scherag, André | McCarthy, Mark I. | Speliotes, Elizabeth K. | North, Kari E. | Loos, Ruth J.F. | Ingelsson, Erik
Nature genetics  2013;45(5):501-512.
Approaches exploiting extremes of the trait distribution may reveal novel loci for common traits, but it is unknown whether such loci are generalizable to the general population. In a genome-wide search for loci associated with upper vs. lower 5th percentiles of body mass index, height and waist-hip ratio, as well as clinical classes of obesity including up to 263,407 European individuals, we identified four new loci (IGFBP4, H6PD, RSRC1, PPP2R2A) influencing height detected in the tails and seven new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3, ZZZ3) for clinical classes of obesity. Further, we show that there is large overlap in terms of genetic structure and distribution of variants between traits based on extremes and the general population and little etiologic heterogeneity between obesity subgroups.
doi:10.1038/ng.2606
PMCID: PMC3973018  PMID: 23563607
7.  Tmem79/Matt is the matted mouse gene and is a predisposing gene for atopic dermatitis in human subjects 
Background
Atopic dermatitis (AD) is a major inflammatory condition of the skin caused by inherited skin barrier deficiency, with mutations in the filaggrin gene predisposing to development of AD. Support for barrier deficiency initiating AD came from flaky tail mice, which have a frameshift mutation in Flg and also carry an unknown gene, matted, causing a matted hair phenotype.
Objective
We sought to identify the matted mutant gene in mice and further define whether mutations in the human gene were associated with AD.
Methods
A mouse genetics approach was used to separate the matted and Flg mutations to produce congenic single-mutant strains for genetic and immunologic analysis. Next-generation sequencing was used to identify the matted gene. Five independently recruited AD case collections were analyzed to define associations between single nucleotide polymorphisms (SNPs) in the human gene and AD.
Results
The matted phenotype in flaky tail mice is due to a mutation in the Tmem79/Matt gene, with no expression of the encoded protein mattrin in the skin of mutant mice. Mattft mice spontaneously have dermatitis and atopy caused by a defective skin barrier, with mutant mice having systemic sensitization after cutaneous challenge with house dust mite allergens. Meta-analysis of 4,245 AD cases and 10,558 population-matched control subjects showed that a missense SNP, rs6694514, in the human MATT gene has a small but significant association with AD.
Conclusion
In mice mutations in Matt cause a defective skin barrier and spontaneous dermatitis and atopy. A common SNP in MATT has an association with AD in human subjects.
doi:10.1016/j.jaci.2013.08.046
PMCID: PMC3834151  PMID: 24084074
Allergy; association; atopic dermatitis; atopy; eczema; filaggrin; flaky tail; Matt; mattrin; mouse; mutation; Tmem79; AD, Atopic dermatitis; DM, Double mutant; FLG, Filaggrin; HDM, House dust mite; hpf, High-power field; MAPEG, Membrane-associated proteins in eicosanoid and glutathione metabolism; OR, Odds ratio; SNP, Single nucleotide polymorphism; TEWL, Transepidermal water loss; WT, Wild-type
8.  Common variants in the HLA-DRB1-HLA-DQA1 Class II region are associated with susceptibility to visceral leishmaniasis 
Nature genetics  2013;45(2):208-213.
To identify susceptibility loci for visceral leishmaniasis we undertook genome-wide association studies in two populations; 989 cases and 1089 controls from India, and 357 cases in 308 Brazilian families (1970 individuals). The HLA-DRB1-HLA-DQA1 locus was the only region to show strong evidence of association in both populations. Replication at this region was undertaken in a second Indian population comprising 941 cases and 990 controls, resulting in Pcombined=2.76×10−17 and OR(95%CI)=1.41(1.30-1.52) across the three cohorts at rs9271858. A conditional analysis provided evidence for multiple associations within the HLA-DRB1-HLA-DQA1 region, and a model in which risk differed between three groups of haplotypes better explained the signal and was significant in the Indian discovery and replication cohorts. In conclusion the HLA-DRB1-HLA-DQA1 HLA class II region contributes to visceral leishmaniasis susceptibility in India and Brazil, suggesting shared genetic risk factors for visceral leishmaniasis that cross the epidemiological divides of geography and parasite species.
doi:10.1038/ng.2518
PMCID: PMC3664012  PMID: 23291585
9.  Genome-wide association study of intraocular pressure identifies the GLCCI1/ICA1 region as a glaucoma susceptibility locus 
Human Molecular Genetics  2013;22(22):4653-4660.
To discover quantitative trait loci for intraocular pressure, a major risk factor for glaucoma and the only modifiable one, we performed a genome-wide association study on a discovery cohort of 2175 individuals from Sydney, Australia. We found a novel association between intraocular pressure and a common variant at 7p21 near to GLCCI1 and ICA1. The findings in this region were confirmed through two UK replication cohorts totalling 4866 individuals (rs59072263, Pcombined = 1.10 × 10−8). A copy of the G allele at this SNP is associated with an increase in mean IOP of 0.45 mmHg (95%CI = 0.30–0.61 mmHg). These results lend support to the implication of vesicle trafficking and glucocorticoid inducibility pathways in the determination of intraocular pressure and in the pathogenesis of primary open-angle glaucoma.
doi:10.1093/hmg/ddt293
PMCID: PMC3904806  PMID: 23836780
10.  Deep Resequencing Unveils Genetic Architecture of ADIPOQ and Identifies a Novel Low-Frequency Variant Strongly Associated With Adiponectin Variation 
Diabetes  2012;61(5):1297-1301.
Increased adiponectin levels have been shown to be associated with a lower risk of type 2 diabetes. To understand the relations between genetic variation at the adiponectin-encoding gene, ADIPOQ, and adiponectin levels, and subsequently its role in disease, we conducted a deep resequencing experiment of ADIPOQ in 14,002 subjects, including 12,514 Europeans, 594 African Americans, and 567 Indian Asians. We identified 296 single nucleotide polymorphisms (SNPs), including 30 amino acid changes, and carried out association analyses in a subset of 3,665 subjects from two independent studies. We confirmed multiple genome-wide association study findings and identified a novel association between a low-frequency SNP (rs17366653) and adiponectin levels (P = 2.2E–17). We show that seven SNPs exert independent effects on adiponectin levels. Together, they explained 6% of adiponectin variation in our samples. We subsequently assessed association between these SNPs and type 2 diabetes in the Genetics of Diabetes Audit and Research in Tayside Scotland (GO-DARTS) study, comprised of 5,145 case and 6,374 control subjects. No evidence of association with type 2 diabetes was found, but we were also unable to exclude the possibility of substantial effects (e.g., odds ratio 95% CI for rs7366653 [0.91–1.58]). Further investigation by large-scale and well-powered Mendelian randomization studies is warranted.
doi:10.2337/db11-0985
PMCID: PMC3331741  PMID: 22403302
11.  Mendelian Randomization Studies Do Not Support a Role for Raised Circulating Triglyceride Levels Influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance 
Diabetes  2011;60(3):1008-1018.
OBJECTIVE
The causal nature of associations between circulating triglycerides, insulin resistance, and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes and raise normal fasting glucose levels and hepatic insulin resistance.
RESEARCH DESIGN AND METHODS
We tested 10 common genetic variants robustly associated with circulating triglyceride levels against the type 2 diabetes status in 5,637 case and 6,860 control subjects and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8,271 nondiabetic individuals from four studies.
RESULTS
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (SD 0.59 [95% CI 0.52–0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that the carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio [OR] 0.99 [95% CI 0.97–1.01]; P = 0.26). In nondiabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (SD 0.00 per weighted allele [95% CI −0.01 to 0.02]; P = 0.72) or increased fasting glucose levels (0.00 [−0.01 to 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose, or fasting insulin and, for diabetes, showed a trend toward a protective association (OR per 1-SD increase in log10 triglycerides: 0.61 [95% CI 0.45–0.83]; P = 0.002).
CONCLUSIONS
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes or raise fasting glucose or fasting insulin levels in nondiabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.
doi:10.2337/db10-1317
PMCID: PMC3046819  PMID: 21282362
12.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis 
Sawcer, Stephen | Hellenthal, Garrett | Pirinen, Matti | Spencer, Chris C.A. | Patsopoulos, Nikolaos A. | Moutsianas, Loukas | Dilthey, Alexander | Su, Zhan | Freeman, Colin | Hunt, Sarah E. | Edkins, Sarah | Gray, Emma | Booth, David R. | Potter, Simon C. | Goris, An | Band, Gavin | Oturai, Annette Bang | Strange, Amy | Saarela, Janna | Bellenguez, Céline | Fontaine, Bertrand | Gillman, Matthew | Hemmer, Bernhard | Gwilliam, Rhian | Zipp, Frauke | Jayakumar, Alagurevathi | Martin, Roland | Leslie, Stephen | Hawkins, Stanley | Giannoulatou, Eleni | D’alfonso, Sandra | Blackburn, Hannah | Boneschi, Filippo Martinelli | Liddle, Jennifer | Harbo, Hanne F. | Perez, Marc L. | Spurkland, Anne | Waller, Matthew J | Mycko, Marcin P. | Ricketts, Michelle | Comabella, Manuel | Hammond, Naomi | Kockum, Ingrid | McCann, Owen T. | Ban, Maria | Whittaker, Pamela | Kemppinen, Anu | Weston, Paul | Hawkins, Clive | Widaa, Sara | Zajicek, John | Dronov, Serge | Robertson, Neil | Bumpstead, Suzannah J. | Barcellos, Lisa F. | Ravindrarajah, Rathi | Abraham, Roby | Alfredsson, Lars | Ardlie, Kristin | Aubin, Cristin | Baker, Amie | Baker, Katharine | Baranzini, Sergio E. | Bergamaschi, Laura | Bergamaschi, Roberto | Bernstein, Allan | Berthele, Achim | Boggild, Mike | Bradfield, Jonathan P. | Brassat, David | Broadley, Simon A. | Buck, Dorothea | Butzkueven, Helmut | Capra, Ruggero | Carroll, William M. | Cavalla, Paola | Celius, Elisabeth G. | Cepok, Sabine | Chiavacci, Rosetta | Clerget-Darpoux, Françoise | Clysters, Katleen | Comi, Giancarlo | Cossburn, Mark | Cournu-Rebeix, Isabelle | Cox, Mathew B. | Cozen, Wendy | Cree, Bruce A.C. | Cross, Anne H. | Cusi, Daniele | Daly, Mark J. | Davis, Emma | de Bakker, Paul I.W. | Debouverie, Marc | D’hooghe, Marie Beatrice | Dixon, Katherine | Dobosi, Rita | Dubois, Bénédicte | Ellinghaus, David | Elovaara, Irina | Esposito, Federica | Fontenille, Claire | Foote, Simon | Franke, Andre | Galimberti, Daniela | Ghezzi, Angelo | Glessner, Joseph | Gomez, Refujia | Gout, Olivier | Graham, Colin | Grant, Struan F.A. | Guerini, Franca Rosa | Hakonarson, Hakon | Hall, Per | Hamsten, Anders | Hartung, Hans-Peter | Heard, Rob N. | Heath, Simon | Hobart, Jeremy | Hoshi, Muna | Infante-Duarte, Carmen | Ingram, Gillian | Ingram, Wendy | Islam, Talat | Jagodic, Maja | Kabesch, Michael | Kermode, Allan G. | Kilpatrick, Trevor J. | Kim, Cecilia | Klopp, Norman | Koivisto, Keijo | Larsson, Malin | Lathrop, Mark | Lechner-Scott, Jeannette S. | Leone, Maurizio A. | Leppä, Virpi | Liljedahl, Ulrika | Bomfim, Izaura Lima | Lincoln, Robin R. | Link, Jenny | Liu, Jianjun | Lorentzen, Åslaug R. | Lupoli, Sara | Macciardi, Fabio | Mack, Thomas | Marriott, Mark | Martinelli, Vittorio | Mason, Deborah | McCauley, Jacob L. | Mentch, Frank | Mero, Inger-Lise | Mihalova, Tania | Montalban, Xavier | Mottershead, John | Myhr, Kjell-Morten | Naldi, Paola | Ollier, William | Page, Alison | Palotie, Aarno | Pelletier, Jean | Piccio, Laura | Pickersgill, Trevor | Piehl, Fredrik | Pobywajlo, Susan | Quach, Hong L. | Ramsay, Patricia P. | Reunanen, Mauri | Reynolds, Richard | Rioux, John D. | Rodegher, Mariaemma | Roesner, Sabine | Rubio, Justin P. | Rückert, Ina-Maria | Salvetti, Marco | Salvi, Erika | Santaniello, Adam | Schaefer, Catherine A. | Schreiber, Stefan | Schulze, Christian | Scott, Rodney J. | Sellebjerg, Finn | Selmaj, Krzysztof W. | Sexton, David | Shen, Ling | Simms-Acuna, Brigid | Skidmore, Sheila | Sleiman, Patrick M.A. | Smestad, Cathrine | Sørensen, Per Soelberg | Søndergaard, Helle Bach | Stankovich, Jim | Strange, Richard C. | Sulonen, Anna-Maija | Sundqvist, Emilie | Syvänen, Ann-Christine | Taddeo, Francesca | Taylor, Bruce | Blackwell, Jenefer M. | Tienari, Pentti | Bramon, Elvira | Tourbah, Ayman | Brown, Matthew A. | Tronczynska, Ewa | Casas, Juan P. | Tubridy, Niall | Corvin, Aiden | Vickery, Jane | Jankowski, Janusz | Villoslada, Pablo | Markus, Hugh S. | Wang, Kai | Mathew, Christopher G. | Wason, James | Palmer, Colin N.A. | Wichmann, H-Erich | Plomin, Robert | Willoughby, Ernest | Rautanen, Anna | Winkelmann, Juliane | Wittig, Michael | Trembath, Richard C. | Yaouanq, Jacqueline | Viswanathan, Ananth C. | Zhang, Haitao | Wood, Nicholas W. | Zuvich, Rebecca | Deloukas, Panos | Langford, Cordelia | Duncanson, Audrey | Oksenberg, Jorge R. | Pericak-Vance, Margaret A. | Haines, Jonathan L. | Olsson, Tomas | Hillert, Jan | Ivinson, Adrian J. | De Jager, Philip L. | Peltonen, Leena | Stewart, Graeme J. | Hafler, David A. | Hauser, Stephen L. | McVean, Gil | Donnelly, Peter | Compston, Alastair
Nature  2011;476(7359):214-219.
Multiple sclerosis (OMIM 126200) is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability.1 Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals;2,3 and systematic attempts to identify linkage in multiplex families have confirmed that variation within the Major Histocompatibility Complex (MHC) exerts the greatest individual effect on risk.4 Modestly powered Genome-Wide Association Studies (GWAS)5-10 have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects play a key role in disease susceptibility.11 Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the Class I region. Immunologically relevant genes are significantly over-represented amongst those mapping close to the identified loci and particularly implicate T helper cell differentiation in the pathogenesis of multiple sclerosis.
doi:10.1038/nature10251
PMCID: PMC3182531  PMID: 21833088
multiple sclerosis; GWAS; genetics
13.  Combined Effect of Inflammatory Gene Polymorphisms and the Risk of Ischemic Stroke in a Prospective Cohort of Subjects With Type 2 Diabetes: A Go-DARTS Study 
Diabetes  2010;59(11):2945-2948.
OBJECTIVE
We have previously observed that genetic profiles determined by the combination of five functionally significant single nucleotide polymorphisms (SNPs) (rs1800795, rs5498, rs5361, rs1024611, and rs679620) of genes encoding prototypical inflammatory molecules are associated with history of ischemic stroke. Here we tested the ability of this multigenic model to predict stroke risk in a large population-based prospective cohort of subjects with type 2 diabetes.
RESEARCH DESIGN AND METHODS
This study was conducted using a prospective cohort of individuals with type 2 diabetes participating in the Go-DARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) study, which includes genetic and clinical information of patients with diabetes within the Tayside region of Scotland, U.K. The above-mentioned inflammatory SNPs were investigated in 2,182 Go-DARTS participants. We created an inflammatory risk score (IRS), ranging from 0 to 5, according to the number of “at-risk” genotypes concomitantly carried by a given individual. The primary outcome was the occurrence of fatal or nonfatal stroke of any kind. Mean follow-up time was 6.2 ± 1.1 years.
RESULTS
The incidence of stroke increased according to the IRS. The IRS was significantly and independently associated with increased stroke risk after adjustment for other conventional risk factors (hazard ratio 1.34 [95% CI 1.1–1.7]; P = 0.009). The highest hazard ratio for stroke was found in subjects concomitantly carrying >3 proinflammatory variations and in subjects without previous cardiovascular diseases.
CONCLUSIONS
This large prospective cohort study provides evidence that SNPs of genes encoding prototypical inflammatory molecules may be used to create multigenic models that predict stroke risk in subjects with type 2 diabetes.
doi:10.2337/db09-1690
PMCID: PMC2963555  PMID: 20622166
14.  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
15.  Common Variation in the FTO Gene Alters Diabetes-Related Metabolic Traits to the Extent Expected Given Its Effect on BMI 
Diabetes  2008;57(5):1419-1426.
OBJECTIVE
Common variation in the FTO gene is associated with BMI and type 2 diabetes. Increased BMI is associated with diabetes risk factors, including raised insulin, glucose, and triglycerides. We aimed to test whether FTO genotype is associated with variation in these metabolic traits.
RESEARCH DESIGN AND METHODS
We tested the association between FTO genotype and 10 metabolic traits using data from 17,037 white European individuals. We compared the observed effect of FTO genotype on each trait to that expected given the FTO-BMI and BMI-trait associations.
RESULTS
Each copy of the FTO rs9939609 A allele was associated with higher fasting insulin (0.039 SD [95% CI 0.013–0.064]; P = 0.003), glucose (0.024 [0.001– 0.048]; P = 0.044), and triglycerides (0.028 [0.003– 0.052]; P = 0.025) and lower HDL cholesterol (0.032 [0.008 – 0.057]; P = 0.009). There was no evidence of these associations when adjusting for BMI. Associations with fasting alanine aminotransferase, γ-glutamyl-transferase, LDL cholesterol, A1C, and systolic and diastolic blood pressure were in the expected direction but did not reach P < 0.05. For all metabolic traits, effect sizes were consistent with those expected for the per allele change in BMI. FTO genotype was associated with a higher odds of metabolic syndrome (odds ratio 1.17 [95% CI 1.10 –1.25]; P = 3 × 10−6).
CONCLUSIONS
FTO genotype is associated with metabolic traits to an extent entirely consistent with its effect on BMI. Sample sizes of >12,000 individuals were needed to detect associations at P < 0.05. Our findings highlight the importance of using appropriately powered studies to assess the effects of a known diabetes or obesity variant on secondary traits correlated with these conditions.
doi:10.2337/db07-1466
PMCID: PMC3073395  PMID: 18346983
16.  Loss-of-function variants in the filaggrin gene are a significant risk factor for peanut allergy 
Background
IgE-mediated peanut allergy is a complex trait with strong heritability, but its genetic basis is currently unknown. Loss-of-function mutations within the filaggrin gene are associated with atopic dermatitis and other atopic diseases; therefore, filaggrin is a candidate gene in the etiology of peanut allergy.
Objective
To investigate the association between filaggrin loss-of-function mutations and peanut allergy.
Methods
Case-control study of 71 English, Dutch, and Irish oral food challenge–positive patients with peanut allergy and 1000 non peanut-sensitized English population controls. Replication was tested in 390 white Canadian patients with peanut allergy (defined by food challenge, or clinical history and skin prick test wheal to peanut ≥8 mm and/or peanut-specific IgE ≥15 kUL−1) and 891 white Canadian population controls. The most prevalent filaggrin loss-of-function mutations were assayed in each population: R501X and 2282del4 in the Europeans, and R501X, 2282del4, R2447X, and S3247X in the Canadians. The Fisher exact test and logistic regression were used to test for association; covariate analysis controlled for coexistent atopic dermatitis.
Results
Filaggrin loss-of-function mutations showed a strong and significant association with peanut allergy in the food challenge–positive patients (P = 3.0 × 10−6; odds ratio, 5.3; 95% CI, 2.8-10.2), and this association was replicated in the Canadian study (P = 5.4 × 10−5; odds ratio, 1.9; 95% CI, 1.4-2.6). The association of filaggrin mutations with peanut allergy remains significant (P = .0008) after controlling for coexistent atopic dermatitis.
Conclusion
Filaggrin mutations represent a significant risk factor for IgE-mediated peanut allergy, indicating a role for epithelial barrier dysfunction in the pathogenesis of this disease.
doi:10.1016/j.jaci.2011.01.031
PMCID: PMC3081065  PMID: 21377035
Atopic dermatitis; filaggrin; IgE; peanut allergy; risk factor; AD, Atopic dermatitis; ALSPAC, Avon Longitudinal Study of Parents and Children; FLG, Filaggrin; OR, Odds ratio; SPT, Skin prick test; UK, United Kingdom
17.  Dissection of the genetics of Parkinson's disease identifies an additional association 5′ of SNCA and multiple associated haplotypes at 17q21 
Human Molecular Genetics  2010;20(2):345-353.
We performed a genome-wide association study (GWAS) in 1705 Parkinson's disease (PD) UK patients and 5175 UK controls, the largest sample size so far for a PD GWAS. Replication was attempted in an additional cohort of 1039 French PD cases and 1984 controls for the 27 regions showing the strongest evidence of association (P< 10−4). We replicated published associations in the 4q22/SNCA and 17q21/MAPT chromosome regions (P< 10−10) and found evidence for an additional independent association in 4q22/SNCA. A detailed analysis of the haplotype structure at 17q21 showed that there are three separate risk groups within this region. We found weak but consistent evidence of association for common variants located in three previously published associated regions (4p15/BST1, 4p16/GAK and 1q32/PARK16). We found no support for the previously reported SNP association in 12q12/LRRK2. We also found an association of the two SNPs in 4q22/SNCA with the age of onset of the disease.
doi:10.1093/hmg/ddq469
PMCID: PMC3005904  PMID: 21044948
18.  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 23-Mb interval in a multiethnic sample to search for variants responsible for this linkage signal.
RESEARCH DESIGN AND METHODS
In all, 5,290 single nucleotide polymorphisms (SNPs) were successfully genotyped in 3,179 type 2 diabetes case and control subjects from eight populations with evidence of 1q linkage. Samples were ascertained using strategies designed to enhance power to detect variants causal for 1q linkage. After imputation, we estimate ∼80% coverage of common variation across the region (r 2 > 0.8, Europeans). Association signals of interest were evaluated through in silico replication and de novo genotyping in ∼8,500 case subjects and 12,400 control subjects.
RESULTS
Association mapping of the 23-Mb region identified two strong signals, both of which were restricted to the subset of European-descent samples. The first mapped to the NOS1AP (CAPON) gene region (lead SNP: rs7538490, odds ratio 1.38 [95% CI 1.21–1.57], P = 1.4 × 10−6, in 999 case subjects and 1,190 control subjects); the second mapped within an extensive region of linkage disequilibrium that includes the ASH1L and PKLR genes (lead SNP: rs11264371, odds ratio 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), there was no indication that these variants were causally related to type 2 diabetes status.
CONCLUSIONS
Detailed fine-mapping of the 23-Mb 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
19.  Reduced-Function SLC22A1 Polymorphisms Encoding Organic Cation Transporter 1 and Glycemic Response to Metformin: A GoDARTS Study 
Diabetes  2009;58(6):1434-1439.
OBJECTIVE
Metformin is actively transported into the liver by the organic cation transporter (OCT)1 (encoded by SLC22A1). In 12 normoglycemic individuals, reduced-function variants in SLC22A1 were shown to decrease the ability of metformin to reduce glucose excursion in response to oral glucose. We assessed the effect of two common loss-of-function polymorphisms in SLC22A1 on metformin response in a large cohort of patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS
The Diabetes Audit and Research in Tayside Scotland (DARTS) database includes prescribing and biochemistry information and clinical phenotypes of all patients with diabetes within Tayside, Scotland, from 1992 onwards. R61C and 420del variants of SLC22A1 were genotyped in 3,450 patients with type 2 diabetes who were incident users of metformin. We assessed metformin response by modeling the maximum A1C reduction in 18 months after starting metformin and investigated whether a treatment target of A1C <7% was achieved. Sustained metformin effect on A1C between 6 and 42 months was also assessed, as was the time to metformin monotherapy failure. Covariates were SLC22A1 genotype, BMI, average drug dose, adherence, and creatinine clearance.
RESULTS
A total of 1,531 patients were identified with a definable metformin response. R61C and 420del variants did not affect the initial A1C reduction (P = 0.47 and P = 0.92, respectively), the chance of achieving a treatment target (P = 0.83 and P = 0.36), the average A1C on monotherapy up to 42 months (P = 0.44 and P = 0.75), or the hazard of monotherapy failure (P = 0.85 and P = 0.56).
CONCLUSIONS
The SLC22A1 loss-of-function variants, R61C and 420del, do not attenuate the A1C reduction achieved by metformin in patients with type 2 diabetes.
doi:10.2337/db08-0896
PMCID: PMC2682689  PMID: 19336679
20.  Underlying genetic models of inheritance in established type 2 diabetes associations 
American journal of epidemiology  2009;170(5):537-545.
For most associations of common polymorphisms with common diseases, the genetic model of inheritance is unknown. We extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations for type 2 diabetes. For 13 polymorphisms, the data fit very well to an additive model, for 4 polymorphisms the data were consistent with either an additive or dominant model, and for 2 polymorphisms with an additive or recessive model of inheritance for the diabetes risk allele. Results were robust to using different priors and after excluding data where index polymorphisms had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that are very similar to those previously reported based on fixed or random effects models, but uncertainty about several of the effects was substantially larger. We also examined the extent of between-study heterogeneity in the genetic model and found generally small values of the between-study deviation for the genetic model parameter. Heterosis could not be excluded in 4 SNPs. Information on the genetic model of robustly replicated GWA-derived association signals may be useful for predictive modeling, and for designing biological and functional experiments.
doi:10.1093/aje/kwp145
PMCID: PMC2732984  PMID: 19602701
21.  Adiposity-Related Heterogeneity in Patterns of Type 2 Diabetes Susceptibility Observed in Genome-Wide Association Data 
Diabetes  2009;58(2):505-510.
OBJECTIVE—This study examined how differences in the BMI distribution of type 2 diabetic case subjects affected genome-wide patterns of type 2 diabetes association and considered the implications for the etiological heterogeneity of type 2 diabetes.
RESEARCH DESIGN AND METHODS—We reanalyzed data from the Wellcome Trust Case Control Consortium genome-wide association scan (1,924 case subjects, 2,938 control subjects: 393,453 single-nucleotide polymorphisms [SNPs]) after stratifying case subjects (into “obese” and “nonobese”) according to median BMI (30.2 kg/m2). Replication of signals in which alternative case-ascertainment strategies generated marked effect size heterogeneity in type 2 diabetes association signal was sought in additional samples.
RESULTS—In the “obese-type 2 diabetes” scan, FTO variants had the strongest type 2 diabetes effect (rs8050136: relative risk [RR] 1.49 [95% CI 1.34–1.66], P = 1.3 × 10−13), with only weak evidence for TCF7L2 (rs7901695 RR 1.21 [1.09–1.35], P = 0.001). This situation was reversed in the “nonobese” scan, with FTO association undetectable (RR 1.07 [0.97–1.19], P = 0.19) and TCF7L2 predominant (RR 1.53 [1.37–1.71], P = 1.3 × 10−14). These patterns, confirmed by replication, generated strong combined evidence for between-stratum effect size heterogeneity (FTO: PDIFF = 1.4 × 10−7; TCF7L2: PDIFF = 4.0 × 10−6). Other signals displaying evidence of effect size heterogeneity in the genome-wide analyses (on chromosomes 3, 12, 15, and 18) did not replicate. Analysis of the current list of type 2 diabetes susceptibility variants revealed nominal evidence for effect size heterogeneity for the SLC30A8 locus alone (RRobese 1.08 [1.01–1.15]; RRnonobese 1.18 [1.10–1.27]: PDIFF = 0.04).
CONCLUSIONS—This study demonstrates the impact of differences in case ascertainment on the power to detect and replicate genetic associations in genome-wide association studies. These data reinforce the notion that there is substantial etiological heterogeneity within type 2 diabetes.
doi:10.2337/db08-0906
PMCID: PMC2628627  PMID: 19056611
22.  Genetic evidence that raised sex hormone binding globulin (SHBG) levels reduce the risk of type 2 diabetes 
Human Molecular Genetics  2009;19(3):535-544.
Epidemiological studies consistently show that circulating sex hormone binding globulin (SHBG) levels are lower in type 2 diabetes patients than non-diabetic individuals, but the causal nature of this association is controversial. Genetic studies can help dissect causal directions of epidemiological associations because genotypes are much less likely to be confounded, biased or influenced by disease processes. Using this Mendelian randomization principle, we selected a common single nucleotide polymorphism (SNP) near the SHBG gene, rs1799941, that is strongly associated with SHBG levels. We used data from this SNP, or closely correlated SNPs, in 27 657 type 2 diabetes patients and 58 481 controls from 15 studies. We then used data from additional studies to estimate the difference in SHBG levels between type 2 diabetes patients and controls. The SHBG SNP rs1799941 was associated with type 2 diabetes [odds ratio (OR) 0.94, 95% CI: 0.91, 0.97; P = 2 × 10−5], with the SHBG raising allele associated with reduced risk of type 2 diabetes. This effect was very similar to that expected (OR 0.92, 95% CI: 0.88, 0.96), given the SHBG-SNP versus SHBG levels association (SHBG levels are 0.2 standard deviations higher per copy of the A allele) and the SHBG levels versus type 2 diabetes association (SHBG levels are 0.23 standard deviations lower in type 2 diabetic patients compared to controls). Results were very similar in men and women. There was no evidence that this variant is associated with diabetes-related intermediate traits, including several measures of insulin secretion and resistance. Our results, together with those from another recent genetic study, strengthen evidence that SHBG and sex hormones are involved in the aetiology of type 2 diabetes.
doi:10.1093/hmg/ddp522
PMCID: PMC2798726  PMID: 19933169
23.  Assessing the Combined Impact of 18 Common Genetic Variants of Modest Effect Sizes on Type 2 Diabetes Risk 
Diabetes  2008;57(11):3129-3135.
OBJECTIVES—Genome-wide association studies have dramatically increased the number of common genetic variants that are robustly associated with type 2 diabetes. A possible clinical use of this information is to identify individuals at high risk of developing the disease, so that preventative measures may be more effectively targeted. Here, we assess the ability of 18 confirmed type 2 diabetes variants to differentiate between type 2 diabetic case and control subjects.
RESEARCH DESIGN AND METHODS—We assessed index single nucleotide polymorphisms (SNPs) for the 18 independent loci in 2,598 control subjects and 2,309 case subjects from the Genetics of Diabetes Audit and Research Tayside Study. The discriminatory ability of the combined SNP information was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes and by calculating the area under the receiver-operator characteristic curve (AUC).
RESULTS—Individuals carrying more risk alleles had a higher risk of type 2 diabetes. For example, 1.2% of individuals with >24 risk alleles had an odds ratio of 4.2 (95% CI 2.11–8.56) against the 1.8% with 10–12 risk alleles. The AUC (a measure of discriminative accuracy) for these variants was 0.60. The AUC for age, BMI, and sex was 0.78, and adding the genetic risk variants only marginally increased this to 0.80.
CONCLUSIONS—Currently, common risk variants for type 2 diabetes do not provide strong predictive value at a population level. However, the joint effect of risk variants identified subgroups of the population at substantially different risk of disease. Further studies are needed to assess whether individuals with extreme numbers of risk alleles may benefit from genetic testing.
doi:10.2337/db08-0504
PMCID: PMC2570411  PMID: 18591388
24.  Gene-Environment Interaction in the Onset of Eczema in Infancy: Filaggrin Loss-of-Function Mutations Enhanced by Neonatal Cat Exposure  
PLoS Medicine  2008;5(6):e131.
Background
Loss-of-function variants in the gene encoding filaggrin (FLG) are major determinants of eczema. We hypothesized that weakening of the physical barrier in FLG-deficient individuals may potentiate the effect of environmental exposures. Therefore, we investigated whether there is an interaction between FLG loss-of-function mutations with environmental exposures (pets and dust mites) in relation to the development of eczema.
Methods and Findings
We used data obtained in early life in a high-risk birth cohort in Denmark and replicated the findings in an unselected birth cohort in the United Kingdom. Primary outcome was age of onset of eczema; environmental exposures included pet ownership and mite and pet allergen levels. In Copenhagen (n = 379), FLG mutation increased the risk of eczema during the first year of life (hazard ratio [HR] 2.26, 95% confidence interval [CI] 1.27–4.00, p = 0.005), with a further increase in risk related to cat exposure at birth amongst children with FLG mutation (HR 11.11, 95% CI 3.79–32.60, p < 0.0001); dog exposure was moderately protective (HR 0.49, 95% CI 0.24–1.01, p = 0.05), but not related to FLG genotype. In Manchester (n = 503) an independent and significant association of the development of eczema by age 12 mo with FLG genotype was confirmed (HR 1.95, 95% CI 1.13–3.36, p = 0.02). In addition, the risk increased because of the interaction of cat ownership at birth and FLG genotype (HR 3.82, 95% CI 1.35–10.81, p = 0.01), with no significant effect of the interaction with dog ownership (HR 0.59, 95% CI 0.16–2.20, p = 0.43). Mite-allergen had no effects in either cohort. The observed effects were independent of sensitisation.
Conclusions
We have demonstrated a significant interaction between FLG loss-of-function main mutations (501x and 2282del4) and cat ownership at birth on the development of early-life eczema in two independent birth cohorts. Our data suggest that cat but not dog ownership substantially increases the risk of eczema within the first year of life in children with FLG loss-of-function variants, but not amongst those without. FLG-deficient individuals may need to avoid cats but not dogs in early life.
In two independent cohorts of children, Hans Bisgaard and colleagues show an association between mutations in the filaggrin gene (FLG) and ownership of cats, but not dogs, with development of eczema.
Editors' Summary
Background.
Eczema is a skin condition characterized by dry, red, and itchy patches on the skin. Eczema is associated with asthma and allergy, though allergy rarely plays a role in development or severity of eczema. Eczema usually begins during infancy, typically on the face, scalp, neck, extensor sides of the forearms, and legs. Up to one in five infants develops eczema, but in more than half of them, the condition improves or disappears completely before they are 15 years old. If eczema persists into adulthood, it usually affects the face and the skin inside the knees and elbows. There is no cure for eczema but it can be controlled by avoiding anything that makes its symptoms worse. These triggers include irritants such as wool, strong soaps, perfumes, and dry environments. A good skin-care routine and frequent moisturizing can also help to keep eczema under control, but in many cases, corticosteroid creams and ointments may be necessary to reduce inflammation.
Why Was This Study Done?
Eczema tends to run in families. This suggests that eczema is caused by genetic factors as well as by environmental factors. Recently, researchers discovered that two common “loss-of-function” variants in the gene encoding filaggrin (FLG) predispose people to eczema. People who inherit one or two defective genes make no filaggrin, a protein that normally forms a physical barrier in the skin that protects the body from potentially harmful substances in the environment. Might the weakening of this barrier in filaggrin-deficient individuals affect their responses to environmental substances to which the skin is exposed? In this study, the researchers test this potential explanation for how genetic and environmental factors (in particular, exposure to pets) might interact to determine an individual's chances of developing eczema.
What Did the Researchers Do and Find?
To test their hypothesis, the researchers studied two independent groups of infants during their first year of life—a high-risk group consisting of infants born in Copenhagen, Denmark to mothers with asthma and a group of infants born to women from the general population in Manchester, United Kingdom. The researchers determined which FLG variants each child had inherited and classified those with either one or two defective copies of FLG as having an FLG mutation. They determined pet exposure in early life by asking whether a dog or a cat was living in the parental home when the child was born (“pet ownership”) and then analyzed how these genetic and environmental factors affected the age of onset of eczema. In both groups, children with FLG mutations were twice as likely to develop eczema during the first year of life as children without FLG mutations. For children without FLG mutations, cat ownership at birth had no effect on eczema risk but for children with FLG mutations, cat ownership at birth (but not dog ownership) further increased the risk of developing eczema.
What Do These Findings Mean?
These findings show that FLG mutations and cat ownership at birth interact to determine the chances of a child developing eczema during the first year of life. They provide support, therefore, for the researchers' suggestion that the weakening of the skin's protective barrier that is caused by filaggrin deficiency increases the child's susceptibility to factors associated with cat exposure. Only a small number of children in this study carried FLG mutations and were exposed to cats from birth, so these findings need confirming in independent studies. In addition, it is still not clear how exposure to cats drives the development of eczema. Allergy was not the mechanism as the FLG-deficient children exposed to cat and who developed eczema did not develop cat-specific immunoglobin E antibodies. Nevertheless, these findings suggest that, to reduce their risk of developing eczema, filaggrin-deficient individuals should avoid cats (but not dogs) during the first few months of life.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050131.
The MedlinePlus Encyclopedia has a page on eczema (in English and Spanish); links to further information are provided by MedlinePlus
EczemaNet is a comprehensive online information resource about eczema provided by the American Academy of Dermatologists
The US National Institute of Arthritis and Musculoskeletal and Skin Diseases provides information on eczema
The UK National Health Service Direct health encyclopedia provides information for patients on eczema (in several languages)
The Copenhagen Studies on Asthma in Childhood (COPSAC) and Manchester Asthma and Allergy Study (MAAS) Web sites provide more information about the children involved in this research
doi:10.1371/journal.pmed.0050131
PMCID: PMC2504043  PMID: 18578563
25.  Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus 
Mahajan, Anubha | Sim, Xueling | Ng, Hui Jin | Manning, Alisa | Rivas, Manuel A. | Highland, Heather M. | Locke, Adam E. | Grarup, Niels | Im, Hae Kyung | Cingolani, Pablo | Flannick, Jason | Fontanillas, Pierre | Fuchsberger, Christian | Gaulton, Kyle J. | Teslovich, Tanya M. | Rayner, N. William | Robertson, Neil R. | Beer, Nicola L. | Rundle, Jana K. | Bork-Jensen, Jette | Ladenvall, Claes | Blancher, Christine | Buck, David | Buck, Gemma | Burtt, Noël P. | Gabriel, Stacey | Gjesing, Anette P. | Groves, Christopher J. | Hollensted, Mette | Huyghe, Jeroen R. | Jackson, Anne U. | Jun, Goo | Justesen, Johanne Marie | Mangino, Massimo | Murphy, Jacquelyn | Neville, Matt | Onofrio, Robert | Small, Kerrin S. | Stringham, Heather M. | Syvänen, Ann-Christine | Trakalo, Joseph | Abecasis, Goncalo | Bell, Graeme I. | Blangero, John | Cox, Nancy J. | Duggirala, Ravindranath | Hanis, Craig L. | Seielstad, Mark | Wilson, James G. | Christensen, Cramer | Brandslund, Ivan | Rauramaa, Rainer | Surdulescu, Gabriela L. | Doney, Alex S. F. | Lannfelt, Lars | Linneberg, Allan | Isomaa, Bo | Tuomi, Tiinamaija | Jørgensen, Marit E. | Jørgensen, Torben | Kuusisto, Johanna | Uusitupa, Matti | Salomaa, Veikko | Spector, Timothy D. | Morris, Andrew D. | Palmer, Colin N. A. | Collins, Francis S. | Mohlke, Karen L. | Bergman, Richard N. | Ingelsson, Erik | Lind, Lars | Tuomilehto, Jaakko | Hansen, Torben | Watanabe, Richard M. | Prokopenko, Inga | Dupuis, Josee | Karpe, Fredrik | Groop, Leif | Laakso, Markku | Pedersen, Oluf | Florez, Jose C. | Morris, Andrew P. | Altshuler, David | Meigs, James B. | Boehnke, Michael | McCarthy, Mark I. | Lindgren, Cecilia M. | Gloyn, Anna L.
PLoS Genetics  2015;11(1):e1004876.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Author Summary
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
doi:10.1371/journal.pgen.1004876
PMCID: PMC4307976  PMID: 25625282

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