Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Post Genome-Wide Association Studies of Novel Genes Associated with Type 2 Diabetes Show Gene-Gene Interaction and High Predictive Value

  • Stéphane Cauchi,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • David Meyre,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Emmanuelle Durand,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Christine Proença,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Michel Marre,

    Affiliations INSERM U695, Paris, France, René Diderot-Paris 7 University, Paris, France, Department of Endocrinology-Diabetology and Nutrition, Bichat Claude Bernard Hospital, Paris, France

  • Samy Hadjadj,

    Affiliation CHU de Poitiers, Endocrinologie Diabetologie, CIC INSERM 0801, INSERM U927, Université de Poitiers, UFR Médecine Pharmacie, Poitiers, France

  • Hélène Choquet,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Franck De Graeve,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Stefan Gaget,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Frederic Allegaert,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Jérôme Delplanque,

    Affiliation CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France

  • Marshall Alan Permutt,

    Affiliation Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Jon Wasson,

    Affiliation Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Ilana Blech,

    Affiliation Endocrinology and Metabolism Service, Department of Internal Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

  • Guillaume Charpentier,

    Affiliation Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, Corbeil-Essonnes, France

  • Beverley Balkau,

    Affiliations INSERM U780-IFR69, Villejuif, France, University of Paris-Sud, Paris, France

  • Anne-Claire Vergnaud,

    Affiliation UMR U557 INSERM; U1125 INRA; CNAM; PARIS 13-CRNH-IdF, Bobigny, France

  • Sébastien Czernichow,

    Affiliations UMR U557 INSERM; U1125 INRA; CNAM; PARIS 13-CRNH-IdF, Bobigny, France, Hôpital Avicenne, Département de Santé Publique, Assistance Publique-Hôpitaux de Paris (AP-HP), Bobigny, France,

  • Wolfgang Patsch,

    Affiliation Department of Laboratory Medicine, Paracelsus Medical University and Landeskrankenhaus Salzburg, Salzburg, Austria

  • Mohamed Chikri,

    Affiliation Laboratory of Biochemistry, Faculty of Medicine and Pharmacy of Fez, Route Sidi Harazem, Fez, Morocco

  • Benjamin Glaser,

    Affiliation Endocrinology and Metabolism Service, Department of Internal Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel

  • Robert Sladek,

    Affiliations Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Canada, Department of Medicine, Faculty of Medicine, McGill University, Montreal, Canada, Department of Pediatrics, Faculty of Medicine, McGill University, Montreal, Canada

  •  [ ... ],
  • Philippe Froguel

    Affiliations CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France, Genomic Medicine, Hammersmith Hospital, Imperial College London, London, United Kingdom

  • [ view all ]
  • [ view less ]



Recently, several Genome Wide Association (GWA) studies in populations of European descent have identified and validated novel single nucleotide polymorphisms (SNPs), highly associated with type 2 diabetes (T2D). Our aims were to validate these markers in other European and non-European populations, then to assess their combined effect in a large French study comparing T2D and normal glucose tolerant (NGT) individuals.

Methodology/Principal Findings

In the same French population analyzed in our previous GWA study (3,295 T2D and 3,595 NGT), strong associations with T2D were found for CDKAL1 (ORrs7756992 = 1.30[1.19–1.42], P = 2.3×10−9), CDKN2A/2B (ORrs10811661 = 0.74[0.66–0.82], P = 3.5×10−8) and more modestly for IGFBP2 (ORrs1470579 = 1.17[1.07–1.27], P = 0.0003) SNPs. These results were replicated in both Israeli Ashkenazi (577 T2D and 552 NGT) and Austrian (504 T2D and 753 NGT) populations (except for CDKAL1) but not in the Moroccan population (521 T2D and 423 NGT). In the overall group of French subjects (4,232 T2D and 4,595 NGT), IGFBP2 and CXCR4 synergistically interacted with (LOC38776, SLC30A8, HHEX) and (NGN3, CDKN2A/2B), respectively, encoding for proteins presumably regulating pancreatic endocrine cell development and function. The T2D risk increased strongly when risk alleles, including the previously discovered T2D-associated TCF7L2 rs7903146 SNP, were combined (8.68-fold for the 14% of French individuals carrying 18 to 30 risk alleles with an allelic OR of 1.24). With an area under the ROC curve of 0.86, only 15 novel loci were necessary to discriminate French individuals susceptible to develop T2D.


In addition to TCF7L2, SLC30A8 and HHEX, initially identified by the French GWA scan, CDKAL1, IGFBP2 and CDKN2A/2B strongly associate with T2D in French individuals, and mostly in populations of Central European descent but not in Moroccan subjects. Genes expressed in the pancreas interact together and their combined effect dramatically increases the risk for T2D, opening avenues for the development of genetic prediction tests.


Recently, genome-wide association (GWA) studies have revealed new single nucleotide polymorphisms (SNPs) that are strongly associated with type 2 diabetes (T2D) [1][5]. In our French study, we showed that the well known rs7903146 TCF7L2 polymorphism ranked first for its effect on T2D prevalence followed by four new risk loci: SLC30A8, HHEX, LOC387761 and EXT2 [1]. Subsequently, GWA studies in Finnish, English, Icelandic and Danish populations emphasized the role of CDKAL1, CDKN2A/2B and IGFBP2 on T2D and confirmed the effect of TCF7L2, SLC30A8 and HHEX [2][5]. Additional SNPs, located in MMP26, LDLR, KCTD12, CAMTA1, NGN3, CXCR4, LOC646279, were also among the 15 first signals in the joint stage I and fast track stage II analyses of the French study [1], but their current status is uncertain as they did not rank high in the other GWA scans [2][5]. Although the other loci listed above are likely to be true T2D markers, the actual impact of these genetic variants in other European and non-European populations as well as their cumulative effects and potential interactions remain to be determined.

In the present study, we first investigated the association of CDKAL1, CDKN2A/2B and IGFBP2 SNPs with T2D in the French population that we analyzed in our GWA study followed by a fast track replication [3,295 T2D and 3,595 normal glucose tolerant (NGT)] [1]. Then, 22 SNPs in 14 loci previously associated with T2D in GWA studies were selected and analyzed in four additional independent populations of European [French (937 T2D and 1,000 NGT), Austrian (504 T2D and 753 NGT), Israeli Ashkenazi (577 T2D and 552 NGT)] and Moroccan Arabic (521 T2D and 423 NGT) origin. Finally, in the overall group of French individuals (4,232 T2D and 4,595 NGT), we assessed possible gene-gene interactions and determined the cumulative genetic risk of carrying risk alleles on T2D prevalence.


Clinical characteristics of each population are reported in Table S1. Minimum detectable effect size with a statistical power of 80% was calculated for each SNP in all studied samples and linkage disequilibrium was assessed between genetic variants located in the same locus (Table S2).

Association studies in our original set of French individuals

We first studied whether 3 risk loci (7 SNPs) identified in Finnish, English, and Icelandic-Danish populations conferred risk in our original French population (3,295 T2D and 3,595 NGT). Polymorphisms of CDKAL1, CDKN2A/2B and IGFBP2 were all associated with T2D (ORrs7756992 = 1.30 [1.19–1.42], P = 2.3×10−9; ORrs10811661 = 0.74 [0.66–0.82], P = 3.5×10−8; and ORrs1470579 = 1.17 [1.07–1.27], P = 0.0003), respectively) (Table 1). In this population and for each SNP, the best mode of inheritance (Pmax) was found to be multiplicative.

Table 1. Genotypic distribution by number of individuals (%) of the 7 SNPs studied in the first set of French population

Replication studies in 4 additional independent populations

The 22 SNPs located in the 14 loci that we identified in our previous study were analyzed in other independent populations. For each polymorphism, the best fitting genetic model was selected from our previous whole-genome association study in French individuals [1]. Thus, all polymorphisms were analyzed under a multiplicative genetic model except for EXT2 (dominant model) and KCTD12 (recessive model). For CDKAL1, CDKN2A/2B, IGFBP2, HHEX and EXT2, we studied more than one polymorphism because they have all been reported as associated with T2D in previous studies [2][5].

We first analyzed another independent French group of more modest sample size (937 T2D and 1,000 NGT) (Table 2). Polymorphisms of predicted genes (LOC646279, ORrs1256517 = 1.44 [1.03–2.02], P = 0.03; LOC387761, ORrs7480010 = 1.32 [1.01–1.72], P = 0.04), SLC30A8 (ORrs13266634 = 0.76 [0.59–0.97], P = 0.03), MMP26 (ORrs2499953 = 2.45 [1.19–5.04], P = 0.01) and CXCR4 (ORrs932206 = 0.75 [0.57–0.97], P = 0.03) were more prevalent in T2D subjects than in NGT individuals. A trend towards T2D association was detected for one SNP of EXT2 (ORrs3740878 = 0.50 [0.23–1.07], P = 0.07) and HHEX (ORrs1111875 = 0.79 [0.60–1.02], P = 0.07).

Table 2. Genotypic distribution by number of individuals (%) of the 22 SNPs studied in the second set of French population

Then, the same SNPs were analyzed in Austrians (504 T2D and 753 NGT) (Table 3). Associations between SNPs of CDKN2A/2B (ORrs10811661 = 0.67 [0.52–0.87], P = 0.002), IGFBP2 (ORrs4402960 = 1.25 [1.02–1.53], P = 0.03), SLC30A8 (ORrs13266634 = 0.76 [0.61–0.94], P = 0.01) and NGN3 (ORrs7923837 = 0.73 [0.59–0.91], P = 0.005) loci and T2D were identified.

Table 3. Genotypic distribution by number of individuals (%) for the 22 SNPs studied in the Austrian population

In Israeli Ashkenazi subjects (577 T2D and 552 NGT), CDKN2A/2B (ORrs564398 = 1.26 [1.03–1.53], P = 0.02), CDKAL1 (ORrs7754840 = 1.30 [1.08–1.56], P = 0.005; ORrs7756992 = 1.24 [1.03–1.49], P = 0.02), IGFBP2 (ORrs1470579 = 1.34 [1.12–1.61], P = 0.001) and EXT2 (ORrs729287 = 0.43 [0.21–0.88], P = 0.02) were more prevalent in the T2D group than in NGT individuals (Table 4).

Table 4. Genotypic distribution by number of individuals (%) of the 22 SNPs studied in the Israeli Ashkenazi population

Finally, we also investigated a population of Moroccan origin (521 T2D and 423 NGT), in which only trends for association were found for SNPs of CDKAL1 (ORrs7754840 = 1.20 [0.99–1.46], P = 0.06; ORrs10946398 = 1.21 [0.99–1.47], P = 0.06) and LOC646279 (ORrs1256517 = 0.84 [0.69–1.02], P = 0.07) (Table 5).

Table 5. Genotypic distribution by number of individuals (%) of the 22 SNPs studied in the Morrocan population

Gene-gene interactions and cumulative genetic effects in French subjects

For each studied locus, we selected the most T2D-associated SNP, including TCF7L2 rs7903146 (N = 15) in order to assess their cumulative effects on T2D prevalence as well as potential gene-gene interactions (i.e., deviation from a multiplicative model) in the complete set of French individuals (4,232 T2D and 4,595 NGT) [6].

All pairwise combinations (N = 253) were studied for the 15 SNPs (Figure S1). We found interactions of IGFBP2 rs1470579 with LOC387761 rs7480010 (P = 0.04), SLC30A8 rs13266634 (P = 0.007) and HHEX rs7923837 (P = 0.01). Similarly, we detected interactions of IGFBP2 rs4402960 with the same loci (P = 0.02, P = 0.01 and P = 0.008, respectively). Furthermore, interactions of CXCR4 rs932206 with NGN3 rs10823406 (P = 0.02) and CDKN2A/2B rs10811661 (P = 0.02) were also found. No antagonistic effects were detected.

The percentages of individuals with increasing numbers of risk alleles in T2D subjects and NGT individuals are shown in Figure 1. For all SNPs, including those of EXT2 and KCTD12, we considered their multiplicative allelic effects on T2D prevalence. The ORs for T2D in subjects carrying increasing numbers of risk alleles are presented in Figure 2 in comparison to the 7.5% of the study population in the reference group that have 0 to 10 risk alleles. After adjustment for age, body mass index (BMI) and gender, each additional risk allele increased the odds of disease by 1.24 [1.21–1.27]. Individuals with at least 18 risk alleles (6.1% of NGT individuals and 14.5% of T2D subjects) had an OR of 8.68 [6.37–11.83] compared to the reference group.

Figure 1. Percentage distribution of NGT and T2D individuals by number of risk alleles.

Figure 2. Odds ratios for French individuals carrying increasing numbers of risk alleles.

Fifteen T2D genetic variants identified by GWA studies were analyzed in 4,232 T2D and 4,595 NGT French subjects for their cumulative effects on T2D prevalence. Allelic ORs and 95%-CIs were calculated using logistic regression model adjusted for age, gender and BMI. They are presented for each group, defined by their number of T2D risk alleles. Each additional allele increases in average the risk to develop T2D by 24 % (P = 8×10−71).

The predictive power of tests can be evaluated by the area under the ROC curve (AUC) [7]. The AUC is a measure of the discriminatory power of the test. A perfect test would have an AUC of 1; a test with no discriminatory power, an AUC of 0.5. For the 15 polymorphisms, the area under the ROC curve is 0.86 (Figure 3), corresponding to a high predictive power.

Figure 3. ROC for the information provided by the 15 T2D-associated genetic variants.

We used a logistic regression model adjusted for age, gender and BMI including a covariate for the number of risk alleles. The area under the ROC curve was 0.86. Academic point system: 0.90–1 = excellent; 0.80–0.90 = good; 0.70–0.80 = fair; 0.60–0.70 = poor; 0.50–0.60 = fail.


GWA scans in Europeans [1][5] and other populations [8][11] have recently identified and confirmed new genetic variants associated with T2D. However, the clinical interest of these discoveries will depend on their true contribution in other ethnic groups as well as on their predictive value for T2D risk.

Using the same study design as our French GWA scan (joint stage I and fast track stage II), we confirmed the strong association of CDKAL1, CKN2A/2B and IGFBP2 SNPs with T2D previously found by other GWA scans [2][5]. Intriguingly, these results were not replicated in another independent French population. This may be due to the modest sample size and/or because T2D cases and NGT controls were not specifically selected from families with or without history of diabetes, respectively, and/or because T2D cases and NGT controls were not matched for BMI. Our data show that the level of association of some SNPs with T2D depends on factors other than just ethnicity. Similar discordant results were also found among European cohorts from Finland [2]. However, we replicated our previous results for SLC30A8, MMP26, CXCR4, LOC387761 and LOC646279 SNPs, suggesting that these loci may be truly involved in T2D risk in the French population.

In another mid-sized population from Central Europe (Austria), associations with T2D were found only with CDKN2A/2B, IGFBP2, SLC30A8, and NGN3 SNPs supporting the value of using large homogeneous populations to characterize the T2D genetic architecture in a given population. In Israeli Ashkenazi individuals, in whom one EXT2 SNP was found to be a T2D genetic marker, CDKAL1, CDKN2A/2B and IGFBP2 SNPs were associated with T2D, as in French subjects. Even though the TCF7L2 rs790146 SNP was previously associated with T2D in this Moroccan group [12], no association was detected with any of the newly studied SNPs. While this lack of association may be due to the modest sample size or hidden biases such as population stratification, it may also signify that other SNPs in the same genes or located in different genes could be associated in the North African population.

Among the 14 studied loci, only 3 (CDKN2A/2B, IGFBP2 and SLC30A8) were found to be associated with T2D in at least 3 independent case-control groups (Table 6). Their association was recently confirmed in other subjects of European or Japanese origin, as for CDKAL1 and HHEX [13][17]. For the other loci, further meta-analyses and systematic replications of GWA studies (phase 2) are now necessary to fully evaluate their contribution to T2D risk. In the light of discrepancies between studied populations, GWA scans in non-European ethnic groups may bring additional insight. Further efforts in re-sequencing [18] are also needed to find etiologic variants causing T2D.

Table 6. Summary of replication results in the 5 case-control groups

There is a growing interest in analyzing the combined effect of these novel loci on T2D susceptibility. This is the first time, to our knowledge, that potential synergistic interactions between novel T2D loci have been shown. IGFBP2 SNPs seem to interact with LOC38776, SLC30A8 and HHEX genetic variants. These data are in agreement with a primary beta-cell dysfunction. IGF2BP2 binds to the key growth and insulin signaling molecule insulin-like growth factor 2 (IGFII) and is highly expressed in pancreatic islets [3]. The zinc transporter ZnT8 (SLC30A8 protein) is specifically expressed in pancreatic endocrine cells and may participate in regulating insulin exocytosis [19], [20]. Similarly, the CXCR4 risk variant was found to interact with NGN3 and CDKN2A/2B SNPs. It was reported that CXCR4-positive pancreatic cells express markers of pancreatic endocrine progenitors such as NGN3 [21]. CDKN2A could be a possible biological candidate for T2D [22] as its over-expression in rodents causes a decrease in islet proliferation [23]. The emerging picture of these possible interactions emphasizes the ability of multiple SNPs to potentiate their deleterious effects in both beta-cell development and function.

The evaluation of the T2D risk in individuals carrying increasing numbers of risk variants is critical for a potential clinical use of a genetic test in the general population. It was previously shown that UK individuals with TCF7L2, PPARG and KCNJ11 risk alleles had an OR of 5.71-fold (95% CI, 1.15 to 28.3) compared to those with no risk alleles, for an AUC of 0.58 [6]. It has been suggested that 20–25 risk variants with allele frequencies greater than 0.1 and ORs of 1.5 are required for an AUC of about 0.8 [24]. In our study, with only 15 SNPs, we reached a good discriminating power to identify individuals with high susceptibility for T2D. After adjustments for age, BMI and gender, subjects with at least 18 risk alleles (14.5% of French T2D subjects) had approximately 9-fold higher risk of developing T2D compared to the reference group, for an AUC of 0.86. If confirmed, this increase in T2D risk due to genetic factors is even higher than that due to severe obesity (OR = 7.37) [25], the most established T2D risk factor.

After many years of limited success, the genetic architecture of T2D (common SNPs) is finally being uncovered by GWA studies. Even though the absence or presence of association in a given population is dependent on many factors (e.g., number of individuals, ethnicity, SNP prevalence, BMI, familial history of T2D and others), replication studies certainly help in validating SNPs truly associated with T2D and in excluding false positives.

Our data support the concept that T2D loci may interact together. Consequently, while single polygenic susceptibility variants may be of limited use in disease prediction, the combined information from a number of these variants should allow the identification of groups of subjects at high and low risk of developing a complex disease [26]. Hence, our study opens up the way to new applications in public health, based on early genetic testing for better prevention and care.

Materials and Methods

Study design

We first investigated the association with T2D of CDKAL1, CDKN2A/2B and IGFBP2 SNPs in the same French population analyzed in our previous GWA study (3,295 T2D and 3,595 NGT). These 3 loci were selected for their high degree of association with T2D in GWA studies and their ability to be replicated in different populations of European origin [2][5]. In addition to these 3 loci, the role of 11 additional loci for which we found the 15 highest SNP association signals (except for TCF7L2) in the joint stage I and fast track stage II of the French GWA scan [1] (SLC30A8, HHEX, EXT2, LOC646279, MMP26, KCTD12, LDLR, CAMTA1, LOC38776, NGN3 and CXCR4) was evaluated in 4 additional independent groups (2,539 T2D and 2,728 NGT) of European (French, Austrian and Israeli Ashkenazi) and non-European (Moroccan) origin. Finally, in all French subjects, we assessed the cumulative genetic risk of carrying the studied risk alleles on T2D prevalence and their possible interactions, including the previously discovered T2D-associated TCF7L2 rs7903146 SNP.

Study populations

The main clinical characteristics of each studied population were presented in Table S1. The first French group of T2D subjects and NGT controls was previously described [1].The second set of French samples includes NGT individuals from the “Supplémentation en Vitamines et Minéraux Antioxydants” (SU.VI.MAX) cohort [27] and T2D subjects from the “DIABete de type 2, NEPHROpathie et GENEtique” (DIAB2.NEPHRO.GENE) study [28].

The SU.VI.MAX study was a French population-based prevention trial designed to evaluate the impact of a daily antioxidant supplementation at nutritional doses on the incidence of ischemic heart disease and cancer. All participants were recruited from throughout France between October 1994 and June 1995. [29]. The NGT controls were selected when having no hypoglycemic treatment and a fasting glucose <6.1 mmol/l. At baseline, a 35-mL venous blood sample was obtained from participants who had been fasting for 12 hr at the time of the visit. The samples were collected in vacutainer tubes that do not interfere with the concentration of trace elements (Becton Dickinson). After collection, blood was kept at +4°C in the dark until centrifugation. Centrifugation was realized at standard time, gravity, and temperature. The time elapsing between collection and aliquoting was recorded for all samples; it was less than 1 hr. Aliquots were stored at −20°C in the mobile units and field centers for, at the most, 7 days prior to shipment in dry ice to the reference laboratories and coordinating center.

The DIAB2.NEPHRO.GENE study was a French multi-center case-control study (15 diabetes and 5 nephrology centers from throughout France between 2001 and 2004) designed to assess the genetic determinants of diabetic nephropathy in type 2 diabetes. T2D was diagnosed on clinically determined absence of type 1 or secondary diabetes, in individuals of more than 40 years of age at onset and without insulin treatment within 2 years after disease onset. Each patient record was carefully checked by an adjudication committee to ascertain T2D status and diabetic complications. 21 ml of blood drawn on EDTA were used for DNA extraction using standard procedures (ethanol precipitation) and samples were stored at –80°c until use. The number of recruited people was 11 in December 2001 (first month of the study), 491 subjects in 2002, 514 in 2003, 593 in 2004, 897 in 2005 and 229 thereafter.

The Austrian subjects were of Bavarian and Austrian German descent and came from the greater region of Salzburg, Austria. They were recruited between April 1999 and December 2002. Unrelated patients with type 2 diabetes were recruited from diabetes outpatient clinics of the Landeskliniken Salzburg and the Hospital Hallein (near Salzburg). The diagnosis of T2D was based on use of hypoglycemic agents or plasma glucose values >126 mg/dl (in absence of treatment). The patients were seen repeatedly and managed by the outpatient clinics. Participants in the Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk (SAPHIR) [12], [30][32] who were not using hypoglycemic medications and had fasting plasma glucose levels <110 mg/dL served as NGTcontrols. SAPHIR was a population-based prospective study that investigated the involvement of factors contributing to the control of plasma lipid transport and carbohydrate metabolism in the progression of atherosclerotic vascular disease. Unrelated men and women subjects with an age range between 39 and 67 years who live in the greater Salzburg region and responded to invitations by their family or workplace physician or to announcement in the local press were included in the study. Average rates of recruitment were 10 controls/week and 3 T2D subjects/week. For all samples, whole blood was collected after an overnight fast in tubes containing 1.6 mg/ml EDTA. Plasma was separated <30 min after collection and used immediately for analysis. Aliquots were stored at –70°C. Aliquots of blood collected in EDTA were stored at –70°C. Genomic DNA was extracted from whole blood and stored at –20°C prior to analysis.

All Israeli NGT/T2D subjects lived and were ascertained in Israel with the help of 15 major diabetes treatment centers throughout the country. They were of Ashkenazi Jewish origin, defined as having all 4 grandparents born in Northern or Eastern Europe. Subjects with known or suspected Sephardic Jewish or non-Jewish ancestry were excluded. Thus, only Jewish subjects were recruited and Jewish patients from the Mediterranean basin, the Persian Gulf region (Iraq, Iran), Yemen, Ethiopia and other areas that were populated with Jews before the major Roman exile of 70 C.E. were not included. The T2D patients were ascertained between 2002 and 2004 in 15 diabetes clinics throughout Israel by the “Israel Diabetes Research Group” and were defined according to WHO criteria (fasting glucose >140mg/dl on two or more occasions, or random glucose >200 mg/dl on two or more occasions). To avoid late-onset type 1 diabetics, patients who became insulin-dependent within 2 years of diagnosis were excluded. The average age at diagnosis was 47 years old. The NGT controls were defined as Ashkenazi (Northern and Eastern European ancestry) with no history of glucose intolerance or T2D and were purchased from the National Laboratory for the Genetics of Israeli Populations ( They denied ever having been diagnosed with elevated blood glucose level, T2D or glucose intolerance. Whole blood samples were obtained in vacuum tubes containing EDTA. The samples were stored at 4°C and transferred to the Endocrine Laboratory at the Hadassah Hospital, Jerusalem Israel within 72h of collection. DNA was extracted using the Puregene Genomic DNA extraction kit purchased from Gentra Systems, Minneapolis, MN, U.S.A. according to the manufacturer's recommendations. Concentrated DNA was stored at −80°C, diluted stocks were stored at −20°C and working solutions were stored at 4°C.

Moroccan subjects were recruited, between February and July 2006, by the Faculty of Medicine (Fes) within the framework of the Genetic project Diabetes Morocco (GenDiabM: ∼180 subjects by month) and were subjected to a standardized clinical examination at the Hassan II Hospital and in regional health centers. The NGT and T2D subjects were from two Moroccan regions: Fes-Tounate (central-North region) and Rabat-Sale (western region). Patients with T2D were recruited from a registry of associations for T2D and health centers when they had a family history of T2D in first degree relatives. The diagnosis of T2D was made according to the 1997 American Diabetes Association criteria or on being treated with medications for diabetes. The NGT controls were recruited from an unselected population undergoing a routine health check-up at the same health centers. All control individuals ≥40 years of age, not previously diagnosed for T2D, with no history of T2D in first-degree relatives, and with fasting plasma glucose <6.1 mmol/l. Blood and serum samples (9 ml) were collected from all individuals and were immediately stored in frozen conditions (serum at −20°C and blood at −80°C) until use. Genomic DNA was then extracted from whole blood and stored at −20°C.

In all populations, the studied individuals were unrelated to each other (with no first-degree relatives). This genetic study was approved by local Ethical Committees and written informed consent was obtained from all participants.

SNP genotyping

All the polymorphisms were genotyped using an allelic discrimination assay-by-design TaqMan method on ABI 7900 (Applied Biosystems). All genotypic distributions were in Hardy-Weinberg equilibrium. The genotyping success rate was higher than 98% for each SNP. For each SNP, the genotyping error rate was reported in Table S2 and assessed by randomly re-genotyping 384 participants in each population.

Statistical analysis

Odds ratios were assessed by logistic regression models adjusted for age, BMI and gender. In Tables 25 and in Table S2, we tested the effect of the minor allele. In these tables, the risk allele is the minor allele if the OR is >1 and is the major allele if the OR is <1. The replication of an association with T2D was considered positive on the condition that the risk allele was not different from what was found in previous GWA studies. For each population, a simple Bonferroni correction (multiplication by the number of SNPs) was applied to the P values for multiple comparisons. After correction, no association remained significant, except for those found in our original French population. However, in the context of replication, it remains unlikely to detect an effect due to statistical fluctuation only. We explored the effect of multiple SNPs using a logistic regression model including a variable for the number of risk alleles in order to quantify the risk per supplementary allele for all the variants included in the model. OR corresponding to a given number of risk alleles compared to the reference group was also calculated. We evaluated the ability of this model to discriminate between NGT and T2D individuals with a Receiver Operating Characteristic (ROC) curve [33] using a logistic regression model adjusted for age, gender and BMI including a covariate for the number of risk alleles of the 15 studied variants. The Area Under the ROC Curve (AUC) was calculated as a measure of the discriminative power of the test. Gene-gene interactions (i.e., deviation from a multiplicative model) were tested by comparing a logistic regression model including only the main effects to another model including the main effects and an interaction term with a likelihood ratio test. We further explored the way two variants interact with interaction plots. We used Quanto ( for power calculations. Pairwise linkage disequilibrium between genetic markers was assessed using the R “genetics” package (version 1.3.2). All P values are two-sided. SPSS (version 14.0.2) and R statistics (version 2.5.1) software were used for general statistical analysis.

Supporting Information

Figure S1.

Significant gene-gene interactions between genetic variants and T2D. 1: Homozygous for the protective allele; 2: Heterozygous; 3: Homozygous for the risk allele. The minimum number of samples by genotype intersection and by status is presented thereafter. A: 48, B: 27, C: 43, D: 37, E: 41, F: 31, G: 42, H: 16.

(0.24 MB TIF)

Table S1.

Clinical characteristics of the studied populations.

(0.04 MB DOC)

Table S2.

Linkage disequilibrium and minimum detectable effect size with a statistical power of 80% for the 22 SNPs in the 5 case-control groups.

(0.12 MB DOC)


We are indebted to all subjects who participated to this study. We thank Marianne Deweirder for the DNA bank management. The DIAB2.NEPHRO.GENE study participating centers are acknowledged here: Poitiers-Diabetology clinic (coordinating center) : F Duengler, R Maréchaud, D Gendre, F Torremocha, L Labbe; Poitiers-Nephrology clinic: G Touchard, F Bridoux, A Thierry, R Abou-Ayache, S Belmouaz; Poitiers-Cardiovascular disease prevention center : D Herpin, Ph Sosner, Angers-Diabetology clinic : G Guilloteau, B, Bouhanick; PH Ducluzeau, P. Ritz; Angoulême-Diabetology clinic : A-M Chameau-Lagarde, M. Bonnefoy; Corbeil-Essonnes-Diabetology clinic : JP Riveline, D Dardari, C. Petit ; Limoges-Diabetology clinic : F. Archambeaux-Mouveroux, S. Gallina; Nancy-Diabetology clinic : Ph Bohme, B Guerci, I. Glatz, H. Hallage; Narbonne -Diabetology clinic : N. Vigier, JP Courrèges; Niort-Nephrology clinic: A Séchet, M Baron ; Papeete-Diabetology clinic : F. Rachedi ; Paris Bichat-Diabetology clinic : A Ankotche, R Roussel; Rennes-Diabetology clinic : C Faure-Gérard, JY Poirier, D Maugendre; St Mandé-HIA Begin-Diabetology clinic : B. Bauduceau, O. Dupuy, H. Mayaudon, J. Margery, R. Poyet; Toulouse-Diabetology clinic: P Gourdy, M Matta, H Hanaire; Tours-CIC, Diabetology clinic and Nephrology clinic : JM Halimi, P Lecomte, M. François; Valenciennes-Diabetology clinic : O. Verier-Mine; Valenciennes-Nephrology clinic : P Vanhile. S Ragot & J Guilhot (Clinical Research Center- Poitiers), C Merceron, E Rogeon, F Abriat & the staff of Diabetology Department- Poitiers are acknowledged for their help in data collection and monitoring. S Leroux & G Mauco (both INSERM ERM 324 and Biochemistry-Poitiers) are thanked for biological determinations.

Author Contributions

Conceived and designed the experiments: PF DM MP BB IB BG RS SCzernichow SH GC SCauchi WP MC MM JW AV. Performed the experiments: ED HC JD. Analyzed the data: SCauchi CP. Contributed reagents/materials/analysis tools: FD SG FA. Wrote the paper: SCauchi .


  1. 1. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445: 881–885.
  2. 2. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, et al. (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316: 1341–1345.
  3. 3. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, et al. (2007) Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316: 1336–1341.
  4. 4. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, et al. (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316: 1331–1336.
  5. 5. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, et al. (2007) A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39: 770–775.
  6. 6. Weedon MN, McCarthy MI, Hitman G, Walker M, Groves CJ, et al. (2006) Combining Information from Common Type 2 Diabetes Risk Polymorphisms Improves Disease Prediction. PLoS Med 3:
  7. 7. Janssens AC, Pardo MC, Steyerberg EW, van Duijn CM (2004) Revisiting the clinical validity of multiplex genetic testing in complex diseases. Am J Hum Genet 74: 585–588; author reply 588–589.
  8. 8. Hayes MG, Pluzhnikov A, Miyake K, Sun Y, Ng MC, et al. (2007) Identification of Type 2 Diabetes Genes in Mexican Americans Through Genome-wide Association Studies. Diabetes.
  9. 9. Hanson RL, Bogardus C, Duggan D, Kobes S, Knowlton M, et al. (2007) A Search for Variants Associated with Young-Onset Type 2 Diabetes in American Indians in a 100k Genotyping Array. Diabetes.
  10. 10. Rampersaud E, Damcott CM, Fu M, Shen H, McArdle P, et al. (2007) Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: Evidence for replication from diabetes-related quantitative traits and from independent populations. Diabetes.
  11. 11. Florez JC, Manning AK, Dupuis J, McAteer J, Irenze K, et al. (2007) A 100k Genome-Wide Association Scan for Diabetes and Related Traits in the Framingham Heart Study: Replication and Integration with Other Genome-Wide Datasets. Diabetes.
  12. 12. Cauchi S, El Achhab Y, Choquet H, Dina C, Krempler F, et al. (2007) TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups: a global meta-analysis. J Mol Med 85: 777–782.
  13. 13. Schulze MB, Al-Hasani H, Boeing H, Fisher E, Doring F, et al. (2007) Variation in the HHEX-IDE gene region predisposes to type 2 diabetes in the prospective, population-based EPIC-Potsdam cohort. Diabetologia 50: 2405–2407.
  14. 14. Grarup N, Rose CS, Andersson EA, Andersen G, Nielsen AL, et al. (2007) Studies of association of variants near the HHEX, CDKN2A/B and IGF2BP2 genes with type 2 diabetes and impaired insulin release in 10,705 Danish subjects validation and extension of genome-wide association studies. Diabetes.
  15. 15. Horikoshi M, Hara K, Ito C, Shojima N, Nagai R, et al. (2007) Variations in the HHEX gene are associated with increased risk of type 2 diabetes in the Japanese population. Diabetologia.
  16. 16. Furukawa Y, Shimada T, Furuta H, Matsuno S, Kusuyama A, et al. (2008) Polymorphisms in the IDE-KIF11-HHEX Gene Locus Are Reproducibly Associated with Type 2 Diabetes in a Japanese Population. J Clin Endocrinol Metab 93: 310–314.
  17. 17. Omori S, Tanaka Y, Takahashi A, Hirose H, Kashiwagi A, et al. (2007) Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8 and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes.
  18. 18. Helgason A, Palsson S, Thorleifsson G, Grant SF, Emilsson V, et al. (2007) Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive evolution. Nat Genet.
  19. 19. Chimienti F, Favier A, Seve M (2005) ZnT-8, a pancreatic beta-cell-specific zinc transporter. Biometals 18: 313–317.
  20. 20. Chimienti F, Devergnas S, Pattou F, Schuit F, Garcia-Cuenca R, et al. (2006) In vivo expression and functional characterization of the zinc transporter ZnT8 in glucose-induced insulin secretion. J Cell Sci 119: 4199–4206.
  21. 21. Koblas T, Zacharovova K, Berkova Z, Mindlova M, Girman P, et al. (2007) Isolation and characterization of human CXCR4-positive pancreatic cells. Folia Biol (Praha) 53: 13–22.
  22. 22. Frayling TM (2007) Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet 8: 657–662.
  23. 23. Krishnamurthy J, Ramsey MR, Ligon KL, Torrice C, Koh A, et al. (2006) p16INK4a induces an age-dependent decline in islet regenerative potential. Nature 443: 453–457.
  24. 24. Yang Q, Khoury MJ, Friedman J, Little J, Flanders WD (2005) How many genes underlie the occurrence of common complex diseases in the population? Int J Epidemiol 34: 1129–1137.
  25. 25. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, et al. (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. Jama 289: 76–79.
  26. 26. Yang Q, Khoury MJ, Botto L, Friedman JM, Flanders WD (2003) Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes. Am J Hum Genet 72: 636–649.
  27. 27. Hercberg S, Galan P, Preziosi P, Bertrais S, Mennen L, et al. (2004) The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals. Arch Intern Med 164: 2335–2342.
  28. 28. Hadjadj S, Fumeron F, Roussel R, Saulnier PJ, Gallois Y, et al. (Submitted) Prognostic Value of the Insertion/ Deletion polymorphism of Angiotensin I Converting Enzyme gene for people with type 2 diabetes and raised excretion of urinary albumin : results from the DIABHYCAR study.
  29. 29. Hercberg S, Preziosi P, Briancon S, Galan P, Triol I, et al. (1998) A primary prevention trial using nutritional doses of antioxidant vitamins and minerals in cardiovascular diseases and cancers in a general population: the SU.VI.MAX study–design, methods, and participant characteristics. SUpplementation en VItamines et Mineraux AntioXydants. Control Clin Trials 19: 336–351.
  30. 30. Esterbauer H, Schneitler C, Oberkofler H, Ebenbichler C, Paulweber B, et al. (2001) A common polymorphism in the promoter of UCP2 is associated with decreased risk of obesity in middle-aged humans. Nat Genet 28: 178–183.
  31. 31. Oberkofler H, Linnemayr V, Weitgasser R, Klein K, Xie M, et al. (2004) Complex haplotypes of the PGC-1alpha gene are associated with carbohydrate metabolism and type 2 diabetes. Diabetes 53: 1385–1393.
  32. 32. Meyre D, Bouatia-Naji N, Tounian A, Samson C, Lecoeur C, et al. (2005) Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes. Nat Genet 37: 863–867.
  33. 33. Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21: 3940–3941.