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Nat Genet. Author manuscript; available in PMC Dec 8, 2012.
Published in final edited form as:
Published online Dec 7, 2008. doi:  10.1038/ng.290
PMCID: PMC2682768
NIHMSID: NIHMS89177
EMSID: EMS4369
Variants in MTNR1B influence fasting glucose levels
Inga Prokopenko,1,2,64 Claudia Langenberg,3,64 Jose C Florez,4-6,64 Richa Saxena,4,7,64 Nicole Soranzo,8,9,64 Gudmar Thorleifsson,10 Ruth J F Loos,3 Alisa K Manning,11 Anne U Jackson,12 Yurii Aulchenko,13 Simon C Potter,8 Michael R Erdos,14 Serena Sanna,15 Jouke-Jan Hottenga,16 Eleanor Wheeler,8 Marika Kaakinen,17 Valeriya Lyssenko,18 Wei-Min Chen,19,20 Kourosh Ahmadi,9 Jacques S Beckmann,21,22 Richard N Bergman,23 Murielle Bochud,24 Lori L Bonnycastle,14 Thomas A Buchanan,25 Antonio Cao,15 Alessandra Cervino,9 Lachlan Coin,26 Francis S Collins,14 Laura Crisponi,15 Eco J C de Geus,16 Abbas Dehghan,13 Panos Deloukas,8 Alex S F Doney,27 Paul Elliott,26 Nelson Freimer,28 Vesela Gateva,12 Christian Herder,29 Albert Hofman,13 Thomas E Hughes,30 Sarah Hunt,8 Thomas Illig,31 Michael Inouye,8 Bo Isomaa,32 Toby Johnson,21,24,33 Augustine Kong,10 Maria Krestyaninova,34 Johanna Kuusisto,35 Markku Laakso,35 Noha Lim,36 Ulf Lindblad,37,38 Cecilia M Lindgren,2 Owen T McCann,8 Karen L Mohlke,39 Andrew D Morris,27 Silvia Naitza,15 Marco Orrù,15 Colin N A Palmer,40 Anneli Pouta,41,42 Joshua Randall,2 Wolfgang Rathmann,43 Jouko Saramies,44 Paul Scheet,12 Laura J Scott,12 Angelo Scuteri,45 Stephen Sharp,3 Eric Sijbrands,46 Jan H Smit,47 Kijoung Song,36 Valgerdur Steinthorsdottir,10 Heather M Stringham,12 Tiinamaija Tuomi,48 Jaakko Tuomilehto,49,50 André G Uitterlinden,46 Benjamin F Voight,4,7 Dawn Waterworth,36 H-Erich Wichmann,31,51 Gonneke Willemsen,16 Jacqueline C M Witteman,13 Xin Yuan,36 Jing Hua Zhao,3 Eleftheria Zeggini,2 David Schlessinger,52 Manjinder Sandhu,3,53 Dorret I Boomsma,16 Manuela Uda,15 Tim D Spector,9 Brenda WJH Penninx,53-55 David Altshuler,4-7 Peter Vollenweider,56 Marjo Riitta Jarvelin,17,26,42 Edward Lakatta,52 Gerard Waeber,56 Caroline S Fox,57,58 Leena Peltonen,8,59,60 Leif C Groop,18 Vincent Mooser,36 L Adrienne Cupples,11 Unnur Thorsteinsdottir,10,61 Michael Boehnke,12 Inês Barroso,8 Cornelia Van Duijn,13 Josée Dupuis,11 Richard M Watanabe,23,62 Kari Stefansson,10,61 Mark I McCarthy,1,2 Nicholas J Wareham,3 James B Meigs,5,63 and Gonçalo R Abecasis12
1Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
2Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
3Medical Research Council Epidemiology Unit, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
4Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
5Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
6Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
7Center for Human Genetic Research, Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
8Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
9Twin Research and Genetic Epidemiology Department, King’s College London, St. Thomas’ Hospital Campus, Lambeth Palace Rd, London SE1 7EH, UK
10deCODE genetics, 101 Reykjavík, Iceland
11Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
12Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
13Department of Epidemiology, Erasmus MC Rotterdam, Postbus 2040, 3000 CA Rotterdam, The Netherlands
14Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
15Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
16Department of Biological Psychology, VU University Amsterdam, van der Boechorstraat 1, 1081 BT Amsterdam, The Netherlands
17Institute of Health Sciences and Biocenter Oulu, P.O. Box 5000, 90014 University of Oulu, Finland
18Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, University Hospital Malmo, Malmo, Sweden
19Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
20Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia 22908, USA
21Department of Medical Genetics, University of Lausanne, Lausanne 1005, Switzerland
22Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne 1011, Switzerland
23Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
24University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne 1011, Switzerland
25Department of Medicine, Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
26Department of Epidemiology and Public Health, Imperial College of London, Norfolk Place, London W2 1PG, UK
27Diabetes Research Group, Division of Medicine and Therapeutics, Ninewells Hospital and Medical School, Dundee, UK
28Center for Neurobehavioral Genetics, University of California, 695 Charles E. Young Drive South, Los Angeles, California 90095, USA
29Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute at Heinrich-Heine-University, Düsseldorf, Germany
30Diabetes and Metabolism Disease Area, Novartis Institutes for BioMedical Research, 100 Technology Square, Cambridge, MA 02139, USA
31Helmholtz Zentrum Muenchen, National Research Center for Environmental Health, Institute of Epidemiology, Neuherberg, Germany
32Malmska Municipal Health Center and Hospital, Jakobstad, Finland
33Swiss Institute of Bioinformatics, Switzerland
34EMBL-EBI, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
35Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland
36Medical Genetics/Clinical Pharmacology and Discovery Medicine, Glaxo SmithKline, King of Prussia, Pennsylvania 19406, USA
37Skaraborg Institute, Skovde, Sweden
38Department of Clinical Sciences, Community Medicine, Lund University, University Hospital Malmo, Malmo, Sweden
39Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
40Population Pharmacogenetics Group, Biomedical Research Centre, Ninewells Hospital and Medical School, Dundee, UK
41Department of Obstetrics and Gynaecology, Oulu University Hospital, Finland
42Department of Child and Adolescent Health, National Public Health Institute (KTL), Aapistie 1, P.O. Box 310, FIN-90101 Oulu, Finland
43Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute at Heinrich-Heine-University, Düsseldorf, Germany
44Savitaipale Health Center, 54800 Savitaipale, Finland
45Unità Operativa Geriatria, Istituto per la Patologia Endocrina e Metabolica, Rome, Italy
46Department of Internal Medicine, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands
47Department of Psychiatry, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
48Department of Medicine, Helsinki University Hospital, University of Helsinki, Finland
49Diabetes Unit, Department of Health Promotion and Chronic Disease Prevention, National Public Health Institute, Helsinki 00300, Finland
50South Ostrobothnia Central Hospital, Senäjoki 60220, Finland
51Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, Germany
52Gerontology Research Center, National Institute on Aging, Baltimore, Maryland 21224, USA
53Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
54Department of Psychiatry, Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, the Netherlands
55Department of Psychiatry, EMGO Institute, Institute of Neuroscience, VU University Medical Center, A.J. Ernstraat 887, 1081 HL Amsterdam, The Netherlands
56Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne 1011, Switzerland
57Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
58The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
59Institute of Molecular Medicine, Biomedicum, 00290 Helsinki, Finland
60Massachusetts Institute of Technology, The Broad Institute, Cambridge, Massachusetts 02141, USA
61Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland
62Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA
63General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
Correspondence should be addressed to G.R.A. (goncalo/at/umich.edu), J.B.M. (jmeigs/at/partners.org), N.J.W. (nick.wareham/at/mrc-epid.cam.ac.uk) or M.I.M. (mark.mccarthy/at/drl.ox.ac.uk).
64These authors contributed equally to this work.
AUTHOR CONTRIBUTIONS
Project management: DFS: R.N.B., A. Cao, F.S.C., K.L.M., J.T., D.S., M.U., E.L., L.C.G., M. Boehnke, G.R.A.; ENGAGE: P.E., A.H., J.H.S., H.-E.W., G. Willemsen, D.I.B., B.W.J.H.P., M.R.J., L.P., U.T., C.v.D., K. Stefansson, M.I.M.; FHS: J.D., J.B.M.; GEM: T.D.S., I.B., N.J.W.
Study design: DFS: R.S., V.L., R.N.B., T.A.B., A. Cao, F.S.C., K.L.M., L.J.S., J.T., D.S., M.U., E.L., M. Boehnke, R.M.W., G.R.A.; ENGAGE: L.P., A.H., U.T., C.v.D., K. Stefansson, M.I.M.; FHS: J.D., J.C.F., J.B.M.; GEM: C.L., N.S., R.J.F.L., J.S.B., M. Bochud, D.W., M.S., T.D.S., P.V., G. Waeber, V.M., I.B., N.J.W.
Genome-wide association sampling and genotyping: DFS: M.R.E., L.L.B., A. Cao, L. Crisponi, T.E.H., B.I., U.L., S.N., M.O., A.S., H.M.S., T.T., J.T., M.U., D.A., L.C.G.; ENGAGE: P.E., N.F., A.P., E.S., V.S., A.G.U., J.C.M.W., D.I.B., M.R.J., L.P., U.T., C.v.D., K. Stefansson, M.I.M.; FHS: C.S.F., L.A.C., J.D., J.B.M.; GEM: K.A., A. Cervino, P.D., M.I., O.T.M.
Statistical analysis and informatics: DFS: R.S., A.U.J., S. Sanna, W.-M.C., V.G., P.S., B.F.V., R.M.W.; ENGAGE: I.P., G.T., Y.A., J.-J.H., M. Kaakinen, L. Coin, E.J.C.d.G., A.D., C.H., A.K., M. Krestyaninova, C.M.L., J.R., V.S., E.Z., C.v.D.; FHS: A.K.M., J.D.; GEM: C.L., N.S., S.C.P., E.W., S.H., T.J., N.L., S. Sharp, K. Song, X.Y., J.H.Z.
Replication sampling and genotyping: A.S.F.D., A.H., T.I., M.L., A.D.M., C.N.A.P., W.R., J.S., H.E.W.
MAGIC management committee: I.P., C.L., J.C.F., R.S., N.S., G.T., R.J.F.L., A.U.J., Y.A., E.W., V.L., C.M.L., D.W., D.S., M.S., P.V., G. Waeber., L.C.G., V.M., U.T., M. Boehnke, I.B., J.D., R.M.W., M.I.M., N.J.W., J.B.M., G.R.A.
Writing team: I.P., C.L., J.C.F., R.S., N.S., G.T., M. Boehnke, I.B., C.v.D., J.D., R.M.W., K. Stefansson, M.I.M., N.J.W., J.B.M., G.R.A.
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.
Blood and plasma fasting glucose levels are tightly regulated within a narrow physiologic range by a feedback mechanism that targets a particular fasting glucose set point for each individual1,2. Disruption of normal glucose homeostasis and substantial elevations of fasting glucose are hallmarks of type 2 diabetes (T2D) and typically result from sustained reduction in pancreatic beta-cell function and insulin secretion.
However, even within healthy, nondiabetic populations there is substantial variation in fasting glucose levels. Approximately one-third of this variation is genetic3, but little of this heritability has been explained. There is growing evidence to suggest that common variants contributing to variation in fasting glucose are largely distinct from those associated with major disruptions of beta-cell function that predispose to T2D. Common sequence variants in the GCK (gluco-kinase) promoter4-6, and around genes encoding the islet-specific glucose-6-phosphatase (G6PC2)5,6 and the glucokinase regulatory protein (GCKR)7-9, have each been associated with individual variation in fasting glucose levels, but have, at best, weak effects on T2D risk8,10. Furthermore, although there are now over 15 genetic loci strongly associated with the risk of T2D7,10-14, none shows compelling evidence for association with fasting glucose in the two genome-wide association scans (GWAS) so far reported5,6.
MAGIC (the Meta-Analyses of Glucose and Insulin-related traits Consortium) represents a collaborative effort to combine data from multiple GWAS to identify additional loci that affect glycemic and metabolic traits. Our genetic studies of fasting glucose levels were originally organized as four distinct consortia: (i) European Network for Genetic and Genomic Epidemiology (ENGAGE), combining data from deCODE, Northern Finland Birth Cohort 1966 (NFBC1966), Netherlands Twins Register/Netherlands Study of Depression and Anxiety (NTR/NESDA) and the Rotterdam Study; (ii) Genetics of Energy Metabolism (GEM), a meta-analysis of the Lausanne (CoLaus) and TwinsUK scans; (iii) DFS, involving the Diabetes Genetics Initiative (DGI), Finland-United States Investigation of NIDDM Genetics (FUSION) and SardiNIA scans; and (iv) the Framingham Heart Study (FHS). Details of the ten component studies (n = 1,233-6,479) are provided in Supplementary Table 1 online.
As a prelude to more extensive data-sharing, the four consortia initially exchanged the identities of between 10 and 20 SNPs prominently associated with fasting glucose in their individual, interim, meta-analyses (n = 6,479-12,389; Supplementary Table 2 online). Comparison of these signals revealed three loci with consistent effects on fasting glucose detected in multiple studies. Two of these represented the previously reported signals in G6PC2 and GCK. In addition, all four groups independently generated evidence for an association between fasting glucose and SNPs around the MTNR1B(melatonin receptor 1B) locus (ENGAGE: rs1387153, P = 2.2 10−17; GEM: rs10830963, P = 7.4 × 10−11; DFS: rs10830963, P = 2.5 × 10−7 FHS: rs11020107, P = 5.8 × 10−4, for the most strongly associated SNP exchanged from each analysis). The association signals at all three loci were confirmed on formal meta-analysis including results from all ten studies, after exclusion of individuals with known diabetes (rs560887 (G6PC2), P = 1.1 × 10−57; rs4607517), (GCK), P = 1.0 × 1.0−25; rs10830963 (MTNR1B), P = P 3.2 × 10−50; Table 1, Fig. 1, Supplementary Fig. 1, Supplementary Table 3 and Supplementary Methods online). Subsequent efforts to harmonize additional aspects of data analysis strategies (including the additional exclusion, where necessary, of individuals with fasting glucose measures ≥7mmol/l) had only a marginal impact on estimates of significance and effect size (Supplementary Table 4 online).
Table 1
Table 1
Association of rs10830963 (MTNR1B) with fasting glucose levels in ten studies within MAGIC and meta-analysis of best SNPs across all ten studies for three loci associated with fasting glucose (MTNR1B, G6PC2 and GCK)
Figure 1
Figure 1
Regional plot of fasting glucose association results for the MTNR1B locus across ten MAGIC GWAS. Meta-analysis −log10 P values are plotted as a function of genomic position (NCBI build 35). The SNP with the strongest signal (rs10830963) is denoted (more ...)
We attempted to refine the location of the MTNR1B association signal by extending the meta-analysis to all SNPs (genotyped and imputed from the HapMap) within the 1-Mb region flanking the gene (n = 35,812; 981 SNPs). In all, 30 genotyped and imputed SNPs showed compelling evidence for association with fasting glucose (P < 10−8). The strongest signal was detected at rs10830963: the minor (G) allele (frequency 0.30 in HapMap CEU15) at this SNP was associated with a per-allele increase of 0.07 (95% CI = 0.06-0.08) evidence for mmol/l in fasting glucose (P = 3.2 × 10−50). Consistent association at rs10830963 was observed in all ten component GWAS, irrespective of whether this SNP was genotyped or imputed, and of the genotyping platform (Table 1 and Supplementary Table 1). Repeat meta-analysis within the region after conditioning on rs10830963 revealed no additional independent signals of association (Supplementary Note online).
The strength of the association between rs10830963 and fasting glucose was unchanged after adjustment for body mass index (Supplementary Table 4). Analyses of fasting insulin levels as well as indices of beta-cell function (HOMA-B) and insulin sensitivity (HOMA-IR) estimated by the homeostasis model assessment16 were possible in ~24,000 participants from the ten studies. These established that the glucose-raising allele at rs10830963 was associated with reduced beta-cell function (P = 1.1 × 10−15), with no appreciable effect on fasting insulin or insulin sensitivity (Supplementary Table 5 and Supplementary Note online).
To determine the impact of variants within MTNR1B on T2D risk, we carried out a large-scale meta-analysis of 13 T2D case-control samples (18,236 T2D cases, 64,453 controls; corresponding to an effective sample size of 21,179 unrelated cases and 21,179 unrelated controls). We combined data from the deCODE13, Rotterdam17, KORA18, FUSION stage 2 (ref. 11) and METSIM10 studies and from several case-control samples from the UK10 with publicly available data from the DIAGRAM consortium (which itself aggregates GWA data from the WTCCC, DGI and FUSION scans)10 (Supplementary Note). We found strong evidence that the minor G allele of rs10830963 was associated with increased risk of T2D (odds ratio = 1.09 (1.05-1.12), P = 3.3 × 10−7; Fig. 2 and Supplementary Table 6 online). The possibility that the fasting glucose association might reflect the inclusion within the cross-sectional study samples of subjects with undiagnosed T2D can be discounted given that exclusion of those with either known diabetes, or a fasting glucose ≥7mmol/l had little impact on the strength of the association signal (Table 1 and Supplementary Table 4). Although the association with T2D does not, despite large-scale replication efforts, reach the 5 × 10−8 threshold consistent with ‘genome-wide significance’15, it seems highly probable, given the strong impact of this variant on beta-cell function (Supplementary Table 5), that this is a genuine effect.
Figure 2
Figure 2
Association of rs10830963 with type 2 diabetes (T2D) in 13 case-control studies.
The analyses we performed interrogate only a fraction of common sequence variants in a given region—it is likely that the causal variant for this locus is yet to be identified. The SNP with the strongest statistical evidence so far, rs10830963, maps within the single 11.5-kb intron of MTNR1B but does not seem to disrupt consensus transcription factor binding or cryptic alternative splice sites. The association signal is bounded by recombination hot spots defining a ~60-kb interval within which all our strongly associated SNPs lie and the causal variant is likely to reside. This interval contains the entire coding region of MTNR1B. The only other nearby genes (the coding regions of which lie well outside this 60-kb region) are SLC36A4 and FAT3, neither of which are compelling candidates. SLC36A4 encodes a proton/amino acid transmembrane transporter moderately similar to Rattus norvegicus lysosomal amino acid transporter 1, and FAT3 encodes a cadherin family member which is the human homolog of the Drosophila melanogaster FAT tumor suppressor gene. Ultimately, detailed fine mapping and functional analyses will be required to define the causal allele(s) and to confirm that this effect is mediated through altered function or expression of MTNR1B.
The size of the MAGIC dataset also allowed us to examine the G6PC2 and GCK regions in greater detail than had previously been possible. In the G6PC2 region, rs560887, within intron 3 of the gene, remained the strongest signal whether or not imputed data were included (P = 1.1 × 10−57 across all ten studies; Supplementary Fig. 1 online). This is the same SNP reported in one recent paper5 and is in substantial linkage disequilibrium (LD; r2 = 0.72 in HapMap CEU) with the lead SNP (rs563694) identified in another6. In the GCK region, rs4607517, which lies 6.6-kb upstream of the gene, was the most strongly associated SNP (P = 1.0 × 10−25; Supplementary Fig. 1 and Table 1). This SNP is also in strong LD (r2 1 in HapMap CEU) with the GCK promoter SNP (rs1799884) that was featured in previous reports4. Repeat meta-analysis after conditioning on the respective lead SNPs revealed no additional independent association signals at either locus (Supplementary Note).
As with the variant in MTNR1B, the magnitude of the fasting glucose associations for both these signals was unchanged after adjustment for BMI (Supplementary Table 4). Glucose-raising alleles at GCK and G6PC2 were associated with reduced beta-cell function (rs4607517[A], P = 9.8 × 10−6; rs560887[C], P = 1.2 × 10−26; Supplementary Table 5 and Supplementary Note). However, in line with previous reports4,9, neither signal was strongly associated with T2D in the large-scale meta-analysis: in fact, the glucose-raising allele at G6PC2 was weakly associated with reduced T2D risk (rs4607517[A], per-allele OR = 1.05 (1.00-1.10), P = 0.031; rs560887[C], 0.93 (0.89-0.97), P = 0.0017; Supplementary Table 6).
We found no influence of the noncoding lead SNPs rs10830963, rs560887 or rs4607517 on gene expression of MTNR1B, SLC36A4, FAT3, G6PC2 or GCK in genome-wide expression QTL datasets from lymphocyte-derived cell lines19,20, cerebral cortex21 or liver22, and no evidence for epistatic effects among the three lead SNPs was observed (P for two-way interactions >0.19 in each of the seven studies including only unrelated individuals; interactions were not examined in the other three studies).
MTNR1B encodes one of two known human melatonin receptors23. Although this is the first study to implicate genetic variation in MTNR1B in the regulation of fasting glucose levels and predisposition to T2D, this relationship is biologically credible. As well as being highly expressed in the brain, retina and elsewhere24​, MTNR1B is transcribed in human islets and rodent insulinoma cell lines25, and the translated receptor is thought to mediate the inhibitory effect of melatonin on insulin secretion26. Melatonin release is characterized by marked circadian variability and these inhibitory effects on insulin secretion may contribute to the entrainment of circadian patterns of insulin release27. There is substantial evidence in human and rodent studies linking disturbances of circadian rhythmicity to metabolic conditions including diabetes28,29, and overexpression of melatonin receptors has been observed in islets from individuals with T2D as compared to nondiabetic controls30. Taken together, these findings suggest that the association with raised fasting glucose and T2D may be driven by variants that augment expression and/or activity of islet melatonin receptors.
Our findings bring the number of common variant loci influencing fasting glucose levels to four, three of which were detected in the present study. Variants in GCKR have a smaller effect size than the others7,9, and the present study design (based on exchange of a limited number of prominent signals between component groups) was not well-powered to detect these. However, subsequent meta-analysis of GCKR variants across all ten study samples confirms the association with fasting glucose (rs780094, P = 8.5 10−9; Supplementary Table 4). The total variance in fasting glucose now attributable to these four signals is 1.5%, indicating that additional loci remain to be found3. In comparison with GCK and G6PC2, variants in MTNR1B seem to have a more marked effect on risk of T2D, the effect size being comparable in magnitude (OR = 1.09 (1.05-1.12)) to several other T2D-susceptibility genes recently identified in GWAS10. Thus, although the physiological regulation of fasting glucose set point and the pathological decline in beta-cell function that characterizes common forms of T2D generally seem to involve different processes, the MTNR1B finding suggests that this is not always the case. Not only can the study of diabetes-related quantitative traits provide an important path to the identification of additional T2D susceptibility loci, but there may also be opportunities for useful therapeutic overlap.
Supplementary Material
Supplementary Information
ACKNOWLEDGMENTS
The authors would like to thank the many colleagues who contributed to collection and phenotypic characterization of the clinical samples, as well as genotyping and analysis of the GWA data. They would also like to acknowledge those who agreed to participate in these studies. Major funding for the work described in this paper comes from Academy of Finland (124243); the Administration of Lanusei, Ilbono, Arzana and Elini (Sardinia, Italy); American Diabetes Association (1-05-RA-140); the Center for Inherited Disease Research; Clinical Research Institute (HUCH); Diabetes UK; the European Bioinformatics Institute; the European Commission (contracts LSHM-CT-2006-037197, LSHM-CT-2003-503041, QLK6-CT-2002-02629, QLG2-CT-2002-01254, HEALTH-F4-2007-201413, LSHG-CT-2004-512066, QLRT-2001-01254, LSHG-CT-2004-518153); the Faculty of Biology and Medicine of Lausanne; Finnish Diabetes Research Foundation; Folkhalsan Research Foundation; Foundation of the NIH (GAIN initiative); German Federal Ministry of Education and Research; German Federal Ministry of Health and Social Security; German National Genome Research Network; GlaxoSmithKline; GSF-National Research Center for Environment and Health; LMUinnovativ; Ministry of Science and Research of the State North-Rhine Westphalia; Municipality of Rotterdam; US National Institutes of Health (HG-02651, HL-084729, HL-087679, HC-25195, N02-HL-6-4278, DK-078616, DK-080140, DK-065978, RR-163736, MH059160, DK069922, DA-021519, DK-062370, DK-072193, US National Human Genome Research Institute intramural project HG-000024; and the Intramural Program of the National Institute on Aging); the UK National Institute for Health Research (Oxford Biomedical Research Centre and Guys and St. Thomas’ Biomedical Research Centre); the Netherlands Ministry of Education, Culture and Science; the Netherlands Ministry of Health, Welfare and Sports; Novartis; NWO (904-61-090, 904-61-193, 480-04-004, 400-05-717); NWOGenomics; NWOInvestments; Research Institute for Diseases in the Elderly (RIDE); Sigrid Juselius Foundation; Spinozapremie; Swedish Research Council (349-2006-237); UK Medical Research Council (G0500539, G0000649, G016121); UK National Health Services Research and Development; the Wellcome Trust (including intramural support for the Wellcome Trust Sanger Institute, GR069224, Strategic Awards 076113 and 083948, Biomedical Collections Grant GR072960); and ZonMw (10-000-1002).
Footnotes
COMPETING INTERESTS STATEMENT
The authors declare competing financial interests: details accompany the full-text HTML version of the paper at http://www.nature.com/naturegenetics/.
Note: Supplementary information is available on the Nature Genetics website.
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