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Effect of Common Genetic Variants Associated with and Glycemic Traits on α and βcell Function and Insulin Action in Man

Anna Jonsson1,2, Claes Ladenvall1, Tarunveer Singh Ahluwalia1,2, Jasmina Kravic1, Ulrika Krus1, Jalal Taneera1, Bo Isomaa3,4, Tiinamaija Tuomi3,5, Erik Renström1, Leif Groop1,6 and Valeriya Lyssenko1,7 From the 1Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmö, Sweden, Lund University Diabetes Centre, Malmö, Sweden, 2Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical and Sciences, University of Copenhagen, Copenhagen, Denmark, 3Folkhälsan Research Centre, Helsinki, Finland, 4Department of Social Services and Health Care, Jakobstad, Finland, 5Department of Medicine, Helsinki University Central Hospital, Helsinki, Finland, 6Finnish Institute of Molecular Medicine (FIMM), Helsinki University, Helsinki, Finland, 7Steno Diabetes Center A/S, Gentofte, Denmark

Running title: Genetic variants, insulin and glucagon secretion

Word count: Abstract 197, Main text 1,997

Items on display: 4 Figures, 8 Supplementary Tables, 5 Supplementary Figures

Abbreviations

SNP single nucleotide polymorphism, OGTT oral glucose tolerance test, FPG – fasting plasma glucose, ISI insulin sensitivity index, HOMAIR insulin resistance index, CIR corrected insulin response, DI disposition index.

Address for correspondence

Anna Jonsson Novo Nordisk Foundation Centre for Basic Metabolic Research Section of Metabolic Genetics Faculty of Health and Medical and Sciences University of Copenhagen DIKU Building Universitetsparken 1, 1st floor DK2100 Copenhagen Ø Denmark Phone: 46735364541 EMail: [email protected] 1

Diabetes Publish Ahead of Print, published online April 4, 2013 Diabetes Page 2 of 34

Abstract

Although metaanalyses of genomewide association studies have identified more than 60 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes and/or glycemic traits, there is little information whether these variants also affect αcell function. The aim of the present study was to evaluate the effects of glycemiaassociated genetic loci on islet function in vivo and in vitro. We studied 43 SNPs in 4,654 normoglycemic participants from the Finnish populationbased PPPBotnia study. Islet function was assessed, in vivo, by measuring insulin and glucagon concentrations during OGTT, and, in vitro, by measuring glucose stimulated insulin and glucagon secretion from human pancreatic islets. Carriers of risk variants in BCL11A, HHEX, ZBED3, HNF1A, IGF1 and NOTCH2 showed elevated, while those in CRY2, IGF2BP2, TSPAN8 and KCNJ11 decreased fasting and/or 2hr glucagon concentrations in vivo. Variants in BCL11A, TSPAN8, and NOTCH2 affected glucagon secretion both in vivo and in vitro. The MTNR1B variant was a clear outlier in the relationship analysis between insulin secretion and action, as well as between insulin, glucose and glucagon. Many of the genetic variants shown to be associated with type 2 diabetes or glycemic traits also exert pleiotropic in vivo and in vitro effects on islet function.

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In the last few years genome wide association studies have substantially increased the

knowledge of genetic variants predisposing to type 2 diabetes. Although the majority of these

SNPs seem to influence insulin secretion, few if any studies have assessed their simultaneous

effects on β and αcell function in vivo and in vitro. The aim of the present study was to

provide a comprehensive evaluation of the effects of genetic loci associated with type 2

diabetes [1] and/or glucose and insulin levels [2] on islet function, in vivo, in a large well

characterized populationbased study from the western coast of Finland, the PPPBotnia

study, and, in vitro, in human pancreatic islets. Islet function was assessed by measuring

insulin and glucagon concentrations during an oral glucose tolerance test (OGTT).

Research Design and Methods

Study population

The PPPBotnia Study (Prevalence, Prediction and Prevention of diabetes) is a population

based study from the Botnia region of Western Finland. 4,654 nondiabetic subjects (fasting

plasma glucose (FPG) <7.0 mmol/l and 2hr plasma glucose <11.1 mmol/l) were included in

the current study [3] (Supplementary Table 1).

Measurements

Blood samples were drawn at 0, 30 and 120 minutes of the OGTT. Insulin was measured

using a fluoroimmunometric assay (AutoDelfia, PerkinElmer, Waltham, MA, USA), and

serum glucagon using radioimmunoassay (Millipore, St. Charles, MO, USA). Insulin

sensitivity index (ISI) was calculated as 10,000/√(fasting Pglucose×fasting P

insulin×meanOGTTglucose×meanOGTTinsulin) [4]. Insulin secretion was assessed as corrected

insulin response during OGTT (CIR=100×insulin at 30 min/[glucose at 30 min×(glucose 30 3

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min3.89)]) [5] or as disposition index, i.e. insulin secretion adjusted for insulin sensitivity

(DI=CIR×ISI).

Genotyping

Genotyping was performed either by matrixassisted laser desorptionionization timeofflight mass spectrometry on the MassARRAY platform (Sequenom, San Diego, CA, USA) or by an allelic discrimination method with a TaqMan assay on the ABI 7900 platform (Applied

Biosystems, Foster City, CA, USA). We obtained an average genotyping success rate of

98.1%, and the average concordance rate, based on 10,578 (6.5%) duplicate comparisons was

99.9%. HardyWeinberg equilibrium was fulfilled for all studied variants (P>0.05).

Glucosestimulated insulin and glucagon secretion in human pancreatic islets

Glucosestimulated insulin and glucagon secretion were performed in high (16.7 mmol/l) and low (1.0 mmol/l) glucose concentration in the medium as described previously [6] and were available from 56 nondiabetic human pancreatic islet donors. Given the limited number of donors for genetic analyses, the relationships between absolute genotypic means of insulin and glucagon concentrations and the 95% CI were plotted separately for different genotype carriers rather than contrasting mean differences in phenotypes between genotypes.

Statistical Analysis

Variables are presented as mean ± standard deviation (SD), and if not normally distributed as median [interquartile range (IQR)]. Genotypephenotype correlations were studied using linear regression analyses adjusted for age, gender and BMI. Nonnormally distributed variables were logarithmically (natural) transformed for analyses. All statistical analyses were

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performed with IBM SPSS Statistics version 19.0 (IBM, Armonk, NY) and PLINK version

1.07 [7, 8].

Results

Effect of SNPs on metabolic variables

Insulin secretion We observed the strongest effect on reduced insulin secretion for the

MTNR1B rs10830963 (CIR P=4.3×1022; DI P=3.4×1032). Additionally, we confirmed that

genetic variants in CDKAL1 (CIR P=1.6×107; DI P=2.5×1011), HHEX (CIR P=1.4×104; DI

P=0.0021), GIPR (CIR P=3.1×104; DI P=0.0061), GCK (CIR P=0.0010; DI P=9.2×104),

KCNQ1 (CIR P=0.0032; DI P=0.0054), CDKN2A/2B (CIR P=0.016; DI P=0.0072), FADS1

(CIR P=6.6×104), IGF2BP2 (DI P=0.0013), JAZF1 (DI P=0.015), KCNJ11 (CIR P=0.019),

PPARG (DI P=0.023), ZFAND6 (CIR P=0.026), CHCHD2P9 (DI P=0.039), ARAP1 (CIR

P=0.039) and BCL11A (CIR P=0.048) were associated with reduced insulin secretion

(Supplementary Table 2).

Insulin action The variants in MTNR1B (P=6.3×104), CHCHD2P9 (P=0.011), FADS1

(P=0.013), PPARG (P=0.041) and CDKAL1 (P=0.042) were associated with reduced insulin

sensitivity (Supplementary Table 3).

Glucagon secretion To explore whether these genetic variants would also influence αcell

function, we analyzed glucagon concentrations during OGTT. The variants in BCL11A

(P=0.0069), HHEX (P=0.0096), ZBED3 (P=0.033) and HNF1A (P=0.047) were associated

with elevated, while variants in CRY2 (P=0.0021), IGF2BP2 (P=0.0036) and TSPAN8

(P=0.034) were associated with reduced fasting glucagon levels. Furthermore, variants in

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IGF1 (P=0.0048), ZBED3 (P=0.0094) and NOTCH2 (P=0.016) were associated with increased and the variant in KCNJ11 (P=0.027) with decreased postprandial glucagon levels

(Supplementary Table 4).

Effect of SNPs on relationships between metabolic variables

Insulin secretion vs. sensitivity (CIR vs. ISI) We plotted the effects of the tested genetic variants on CIR and ISI (Figure 1). Most SNPs were related to insulin secretion and sensitivity in a linear fashion, with SNPs in the MTNR1B, CDKAL1, GCK, CDKN2A/2B and

PPARG loci in the lower left quadrant, reflecting both impaired insulin secretion and action, with MTNR1B being the most extreme. In contrast variants in GIPR, FADS1, CAMK1D and

TCF7L2 showed relatively high insulin sensitivity despite low insulin secretion. A genotype centric analysis (Supplementary Figure 1) also showed that homozygous carriers of MTNR1B,

CDKAL1 and GCK risk genotypes had CIR values below the values expected from ISI values whereas TT carriers of TCF7L2 showed rather high insulin sensitivity.

Glucose vs. insulin levels The variant in MTNR1B showed both high fasting glucose and insulin levels (Figure 2, Supplementary Table 5 and 6). At the same glucose level,

CHCHD2P9 showed the highest and FADS1 the lowest fasting insulin concentration, while, at the same fasting insulin concentration, FTO showed the lowest and GCK the highest fasting glucose levels. Homozygous carriers of the TCF7L2 risk variant had the lowest, whereas homozygous carriers of HMGA2 showed the highest insulin levels (Supplementary Figure 2).

These differences were partially also seen at 2hr of the OGTT.

Glucose vs. glucagon levels The highest fasting glucose level related to glucagon level was seen for MTNR1B (Figure 3, Supplementary Figure 3), which was also reflected by high fasting and 2hr insulin/glucose ratios (Supplementary Table 7, Supplementary Figures 4 and 6

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5). In line with previous reports, the TCF7L2 variant showed low glucagon levels [9] and

homozygous Tallele carriers showed high fasting and 2hr glucose levels, despite low

insulin/glucagon ratio.

Glucagon vs. insulin levels in human pancreatic islets The risk allele carriers of variants in

CAMK1D, TCF7L2, GIPR, TSPAN8 and KLF14 showed lower, while the risk allele carriers

of CDKAL1, BCL11A, CRY2, FADS1, NOTCH2, HMGA2 and GCK showed higher glucagon

levels than expected from the 95% CI of the relationship between mean values of insulin and

glucagon concentrations measured at normal glucose levels. At high glucose, carriers of the

risk alleles at DGKB, GIPR, JAZF1, CDKN2A/2B, HMGA2 and NOTCH2 displayed high

glucagon levels, while those of TCF7L2, CAMK1D, KLF14, FTO and ZBED3 showed lower

glucagon levels (Figure 4).

Discussion

In the present study, we demonstrate that the effects of some of the genetic variants associated

with type 2 diabetes and/or glycemic traits seem to involve the crosstalk between hormone

secretion from the β and αcells. The novel findings in the present study were that we

observed associations of variants in 10 loci with glucagon secretion, of which 6 loci (HHEX,

IGF2BP2, CDKAL1, FADS1, GCK and CDKN2A/2B) were also associated with reduced

insulin secretion.

Effects of variants on the relationship between CIR and ISI were similar to those observed by

Grarup et al. [10], but we also showed that MTNR1B and CDKAL1 were clear outliers with

strong effects on both impaired insulin secretion and sensitivity. These analyses used beta

values which reflect fractions of the standard deviations. We therefore extended the analyses

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by analyzing the relationship between the absolute values of CIR and ISI in different genotype carriers, which allowed us to obtain a quantitative effect of SNPs. These analyses differ from those presented by Grarup et al. as we now related the effect of the homozygous risk genotypes to the mean values of a reference population instead of expressing them as allelic changes in fraction of standard deviations. It can, of course, be questioned whether homozygosity for risk alleles would best describe their effect on this relationship, but it has the advantage that it allows us to show the absolute effects of the variants.

Importantly, we show that many of the genetic variants seem to affect the relationship between insulin/glucagon secretion and blood glucose levels both at fasting and 2hr during

OGTT. This would imply that in the risk carriers, reduced early insulin secretion in conjunction with impaired suppression of glucagon secretion and/or action would lead to excessive delivery of glucose into the circulation. An interesting finding was that, again,

MTNR1B was different from the rest of the variants and showed elevated glucose despite high insulin/glucagon ratio. There is no obvious explanation to these findings apart from that the ratio between insulin and glucagon may be more important than the individual insulin and glucagon levels for the glucoseraising effect of MTNR1B. Also, MTNR1B is mostly expressed in βcells, while MTNR1A predominating in αcells. Until now, no variant in the

MTNR1A has been associated with type 2 diabetes or elevated glucose levels.

In keeping with a previous study [11], the rs7903146 variant in TCF7L2 was associated with lower glucagon values both in vivo and in vitro. In contrast to some previous studies [12, 13] we did not observe a significant reduction in fasting nor glucosestimulated insulin secretion for this variant in the current study. The PPPstudy is a populationbased study including people aged 1875 years, most of whom had normal glucose tolerance. In the previous studies the effect on insulin secretion was clearly stronger in hyperglycemic individuals or in those 8

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who subsequently developed diabetes. The same was seen in the present study; if we restrict

the analysis to hyperglycemic individuals (FPG >5.5 mmol/l), we see a clear effect of the

TCF7L2 SNP on reduced insulin secretion (N=1,440, beta 0.109 (0.035), P=0.002). This

effect might be too small to fully explain the glucoseincreasing effect of the SNP, nor is it

easy to see an effect of reduced glucagon levels. One possibility is that the TCF7L2 variant

has effects on hepatic glucose production as previously shown [1315] which cannot be

explained by effects of glucagon on the liver.

From the figures relating the mean values of fasting and 2hr insulin/glucagon with fasting and

2hr glucose concentrations (Supplementary Figure 6), it was obvious that homozygous

carriers of HMGA2 differed most from the others. Thus, while carriers of two HMGA2 risk

alleles showed increased insulin secretion and decreased insulin sensitivity, possibly

protecting them from type 2 diabetes, they demonstrated low insulin/ glucagon, which in turn

led to elevated glucose. In vitro, homozygous risk allele carriers showed reduced suppressive

effect of high glucose on glucagon secretion. HMGA2 encodes for a nonhistone with

structural DNAbinding domains and may act as a transcriptional regulating factor. The

current data suggest that it may involve a complex interplay between islet function and insulin

action.

The present study also provides some novel insight for the potential mechanism by which the

BCL11A variant could increase the risk of type 2 diabetes. BCL11A rs243021 was originally

identified as a risk variant for type 2 diabetes in the DIAGRAM+ metaanalysis [1]. The

protein encoded by BCL11A is a zincfinger protein with highest expression in the fetal brain

and the adult central nervous system. While its biological function is not fully elucidated, it

might be involved in the signal transduction from cell to cell or in the neuronal regulation of

hormonal secretion. Association of variants in BCL11A with reduced insulin secretion has 9

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previously been reported [16]. Here we report novel associations of a BCL11A variant with increased glucagon levels in vivo. Additionally, in vitro, homozygous risk allele carriers of the

BCL11A variant had glucagon levels above the expected 95% CI of the relationship between insulin and glucagon secretion in human pancreatic islets. Together, results from the present paper and previous reports suggest that this variant might predispose to increased risk of type

2 diabetes via reduced insulin and increased glucagon secretion.

In conclusion, we here provide a comprehensive description of how common genetic variants associated with type 2 diabetes and/or glycemic traits influence insulin and glucagon secretion in vivo and in vitro in human pancreatic islets. The data clearly emphasize the need to consider effects on αcell function when studying mechanisms by which these variants increase risk of type 2 diabetes.

Acknowledgements

Studies at LUDC, Malmö were supported by grants from the Swedish Research Council, including a Linné grant (No.31475113580) and a Strategic Research Grant EXODIAB (Dnr 20091039), the Heart and Lung Foundation, the Swedish Diabetes Research Society, the European Community's Seventh Framework Programme grant ENGAGE (HealthF42007 201413) and CEED3 (223211), the Diabetes Programme at the Lund University, the Påhlsson Foundation, the Crafoord Foundation, the Knut and Alice Wallenberg Foundation, Novo Nordisk Foundation and the European Foundation for the Study of Diabetes (EFSD). The PPPBotnia study were supported by grants from the Sigrid Juselius Foundation, Folkhälsan Research Foundation, the Finnish Diabetes Research Society, the Signe and Ane Gyllenberg Foundation, Swedish Cultural Foundation in Finland, Ollqvist Foundation, Ministry of Education of Finland, Foundation for Life and Health in Finland, Jakobstad Hospital, Medical Society of Finland, Närpes Research Foundation and the Vasa and Närpes Health centers. Human islets were provided by the Nordic network for clinical islets transplantation by the courtesy of Dr. Olle Korsgren, Uppsala, Sweden. We thank the patients for their participation and the Botnia Study Group for clinically studying the patients. No potential conflicts of interest relevant to this article were reported. Anna Jonsson participated in genotyping and study design, researched and interpreted the data and wrote the manuscript. Claes Ladenvall, Tarunveer Singh Ahluwalia and Jasmina Kravic researched the data. Ulrika Krus, Jalal Taneera and Erik Renström were responsible for

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mRNA analyses and the insulin and glucagon secretion measurements from the human pancreatic islets. Bo Isomaa and Tiinamaija Tuomi collected and phenotyped the PPPBotnia study population, Leif Groop and Valeriya Lyssenko designed and supervised the study, interpreted the data and contributed to discussion. All coauthors reviewed and approved the final version of the manuscript. Prof. Leif Groop is the guarantor of this work and, as such, had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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References

1. Voight, B.F., et al., Twelve type 2 diabetes susceptibility loci identified through large- scale association analysis. Nat Genet, 2010. 42(7): p. 57989. 2. Dupuis, J., et al., New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet, 2010. 42(2): p. 10516. 3. Isomaa, B., et al., A family history of diabetes is associated with reduced physical fitness in the Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia study. Diabetologia, 2010. 53(8): p. 170913. 4. Matsuda, M. and R.A. DeFronzo, Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care, 1999. 22(9): p. 146270. 5. Hanson, R.L., et al., Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies. Am J Epidemiol, 2000. 151(2): p. 1908. 6. Jonsson, A., et al., A variant in the KCNQ1 gene predicts future type 2 diabetes and mediates impaired insulin secretion. Diabetes, 2009. 58(10): p. 240913. 7. Whole genome association analysis toolset. [cited 2010; Available from: http://pngu.mgh.harvard.edu/~purcell/plink/. 8. Purcell, S., et al., PLINK: a tool set for whole-genome association and population- based linkage analyses. Am J Hum Genet, 2007. 81(3): p. 55975. 9. Alibegovic, A.C., et al., The T-allele of TCF7L2 rs7903146 associates with a reduced compensation of insulin secretion for insulin resistance induced by 9 days of bed rest. Diabetes, 2010. 59(4): p. 83643. 10. Grarup, N., T. Sparso, and T. Hansen, Physiologic characterization of type 2 diabetes- related loci. Curr Diab Rep, 2010. 10(6): p. 48597. 11. Pilgaard, K., et al., The T allele of rs7903146 TCF7L2 is associated with impaired insulinotropic action of incretin hormones, reduced 24 h profiles of plasma insulin and glucagon, and increased hepatic glucose production in young healthy men. Diabetologia, 2009. 52(7): p. 1298307. 12. Lyssenko, V., et al., Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med, 2008. 359(21): p. 222032. 13. Lyssenko, V., et al., Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest, 2007. 117(8): p. 215563. 14. Boj, S.F., et al., Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell, 2012. 151(7): p. 1595607. 15. Norton, L., et al., Chromatin occupancy of factor 7-like 2 (TCF7L2) and its role in hepatic glucose metabolism. Diabetologia, 2011. 54(12): p. 313242. 16. SimonisBik, A.M., et al., Gene variants in the novel type 2 diabetes loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B affect different aspects of pancreatic beta-cell function. Diabetes, 2010. 59(1): p. 293301.

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0.05 BCL11A NOTCH2 HNF1A DUSP9(m) VPS13C ADAMTS9 FTO KCNQ1* HMGA2 KLF14 IGF1 PROX1 0.00 TSPAN8 GLIS3 ZBED3SLC2A2 THADA CAMK1D CHCHD2P9 PRC1 ADCY5 DUSP9(f) MADD TP53INP1 PPARG WFS1 TCF7L2 SLC30A8 CRY2 ADRA2A JAZF1 DGKB IGF2BP2 ARAP1 KCNJ11 ZFAND6 -0.05 KCNQ1† CDKN2A/2B FADS1 HHEX CIR GIPR GCK CDKAL1 -0.10

-0.15 MTNR1B

-0.06 -0.04 -0.02 0.00 0.02 0.04 ISI

Figure 1. Graphic representation of effects of genetic variants on relationship between insulin secretion and insulin sensitivity in nondiabetic participants in the PPPBotnia study.

Effect sizes are betas from linear regression adjusted for age, gender and BMI. Whereas most genetic variants either increased insulin secretion or insulin sensitivity, those in MTNR1B, CDKAL1 and GCK were located to the lower left quadrant, reflecting both impaired insulin secretion and insulin sensitivity, with MTNR1B being the most extreme. In contrast, GIPR and FADS1 showed relatively high insulin sensitivity despite low insulin secretion. KCNQ1† represents rs2237895, while KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05.

A B 0.15 0.15 ADCY5 MTNR1B

GCK

0.10 CDKAL1 0.10 GIPR CDKN2A/2B CHCHD2P9 MTNR1B VPS13C KCNQ1† FTO ZBED3 MADD TP53INP1 GCK 0.05 KCNQ1* KLF14 TCF7L2 TSPAN8 HMGA2 MADD SLC2A2 JAZF1 0.05 CDKAL1 IGF2BP2 THADA CAMK1D SLC30A8 CRY2 CDKN2A/2B PPARG PPARG ADRA2A HNF1A DGKB CRY2 CHCHD2P9 KCNQ1† KCNJ11 DGKB JAZF1 IGF2BP2 2hrglucose 0.00 TCF7L2 HNF1A DUSP9(f) BCL11A FADS1 GLIS3 HMGA2 ARAP1 ADAMTS9 Fastingglucose KCNQ1* WFS1 TP53INP1 PROX1 IGF1 BCL11A HHEX HHEX ZFAND6 KCNJ11 ARAP1 NOTCH2 IGF1 ZBED3 TSPAN8 SLC30A8 VPS13C DUSP9(f) SLC2A2 ZFAND6 WFS1 0.00 ADRA2A THADA DUSP9(m) GLIS3 CAMK1D ADAMTS9 DUSP9(m) PRC1 NOTCH2 -0.05 PRC1 ADCY5 FADS1 GIPR KLF14 PROX1 FTO

-0.05 -0.10 -0.04 -0.02 0.00 0.02 0.04 0.06 -0.05 0.00 0.05 0.10 Fasting insulin 2hr insulin

Figure 2. Graphic representation of effects of genetic variants on relationship between glucose and insulin levels during OGTT in nondiabetic participants of the PPPBotnia study.

Effect sizes are betas from linear regression adjusted for age, gender and BMI. KCNQ1† represents rs2237895, while KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05. Panel A shows the relationship between fasting insulin and fasting glucose. MTNR1B showed the highest effect on fasting glucose despite elevated insulin levels. CHCHD2P9 showed increased, while FADS1 showed decreased fasting insulin. Panel B shows the relationship between 2hr insulin and 2hr glucose. GCK, CDKAL1, ZBED3 and MTNR1B all showed high postprandial glucose levels despite elevated insulin levels.

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A B 0.15 0.15 ADCY5 MTNR1B

GCK

0.10 CDKN2A/2B 0.10 CDKAL1 CHCHD2P9 GIPR MTNR1B VPS13C KCNQ1† ZBED3 FTO TP53INP1 0.05 MADD KCNQ1* GCK JAZF1 TCF7L2 KLF14 MADD TSPAN8 THADA CAMK1D 0.05 SLC2A2 IGF2BP2 HMGA2 CRY2 SLC30A8 CHCHD2P9 CDKN2A/2B CDKAL1 DGKB ADRA2A PPARG HNF1A PPARG CRY2 FADS1 JAZF1 KCNQ1† KCNJ11 DUSP9(f) DGKB

HNF1A 2hrglucose 0.00 HMGA2 GLIS3 ADAMTS9 BCL11A IGF1 TP53INP1 HHEX ARAP1 Fastingglucose IGF2BP2 KCNQ1* TCF7L2 ARAP1 TSPAN8 WFS1 PROX1 ZFAND6 BCL11A HHEX NOTCH2 SLC30A8 ADRA2A IGF1 ZBED3 WFS1 SLC2A2 KCNJ11 DUSP9(m) 0.00 DUSP9(f) VPS13C GLIS3 CAMK1D PRC1 ADCY5 ZFAND6 THADA PRC1 ADAMTS9 DUSP9(m) NOTCH2 -0.05 FADS1 GIPR PROX1 KLF14 FTO

-0.05 -0.10 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 -0.04 -0.02 0.00 0.02 0.04 Fasting glucagon 2hr glucagon

Figure 3. Graphic representation of effects of genetic variants on relationship between glucose and glucagon levels during OGTT in nondiabetic participants of the PPPBotnia study.

Effect sizes are betas from linear regression adjusted for age, gender and BMI. KCNQ1† represents rs2237895, while KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05. Panel A shows the relationship between fasting glucagon and fasting glucose. The highest fasting glucose level related to glucagon level was seen for MTNR1B. IGF2BP2 and CRY2 showed reduced glucagon secretion, while BCL11A and HHEX were associated with increased fasting glucagon. Panel B shows the relationship between 2hr glucagon and 2hr glucose. ZBED3, IGF1 and NOTCH2 showed elevated, while KCNJ11 showed decreased postprandial glucagon secretion.

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A Homozygous non-risk Heterozygous Homozygous risk 25 25 25 JAZF1 HHEX HNF1A ARAP1 MADD CDKN2A/2B VPS13C GIPR FTO 20 20 CAMK1D 20 MTNR1B GCK TP53INP1 PROX1 MADD CDKAL1 HMGA2 DGKB CDKN2A/2B CHCHD2P9 WFS1 NOTCH2 JAZF1 FADS1 TP53INP1 BCL11A FTO 15 HHEX 15 15 THADA CRY2 ADRA2A

10 10 10

SLC2A2 Glucagon(pg/islet/h) Glucagon(pg/islet/h) Glucagon(pg/islet/h) NOTCH2 IGF2BP2 SLC30A8 CRY2 KLF14 TSPAN8

5 ZFAND6 5 5 GIPR TCF7L2 THADA CDKN2A/2B HMGA2 CAMK1D PPARG

0 0 0 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 Insulin (ng/islet/h) Insulin (ng/islet/h) Insulin (ng/islet/h) B Homozygous non-risk Heterozygous Homozygous risk 25 25 25

ARAP1 HNF1A MADD JAZF1 HHEX

TP53INP1 FTO CAMK1D CDKN2A/2B DGKB GIPR PROX1 20 20 GIPR 20 MADD CHCHD2P9 JAZF1 CDKN2A/2B KCNQ1* NOTCH2 THADA HMGA2 MTNR1B GCK DGKB GIPR HHEX 15 15 15 CHCHD2P9 KCNJ11 TP53INP1 DGKB TP53INP1

WFS1 PROX1 BCL11A 10 10 10 IGF2BP2 KCNQ1† ARAP1 KCNJ11 MTNR1B ADRA2A Glucagon(pg/islet/h) Glucagon(pg/islet/h) ADAMTS9 Glucagon(pg/islet/h) PPARG GCK ZBED3 ADRA2A HMGA2 5 5 5 KLF14 SLC2A2 NOTCH2 TCF7L2 CAMK1D CDKN2A/2B PPARG FTO THADA

0 0 0 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 Insulin (ng/islet/h) Insulin (ng/islet/h) Insulin (ng/islet/h)

Figure 4. Relationship between glucagon and insulin secretion in human pancreatic islets at low and high glucose level.

Panel A shows secretion at low glucose (1 mmol/l). The risk allele carriers of variants in CAMK1D, TCF7L2, GIPR, TSPAN8 and KLF14 showed lower glucagon secretion, while the risk allele carriers of CDKAL1, BCL11A, CRY2, FADS1, NOTCH2, HMGA2 and GCK showed glucagon levels above the expected 95% CI of the relationship between insulin and glucagon secretion. Panel B shows secretion at high glucose (16.7 mmol/l). The risk allele carriers of DGKB, GIPR, JAZF1, CDKN2A/2B, HMGA2 and NOTCH2 remained high glucagon secretion, while those of FTO, CAMK1D, TCF7L2, KLF14 and ZBED3 had low glucagon. 15

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72x62mm (600 x 600 DPI)

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1593x701mm (96 x 96 DPI)

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1593x701mm (96 x 96 DPI)

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1599x913mm (96 x 96 DPI)

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Supplementary Tables and Figures

Tables

Supplementary Table 1. Characteristics of the PPP-Botnia study participants. Supplementary Table 2. Effect of genetic variants on insulin secretion in non-diabetic participants of the PPP-Botnia study. Supplementary Table 3. Effect of genetic variants on BMI and insulin sensitivity in non-diabetic participants of the PPP-Botnia study. Supplementary Table 4. Effect of genetic variants on glucagon levels during OGTT in non-diabetic participants of the PPP-Botnia study. Supplementary Table 5. Effect of genetic variants on glucose levels during OGTT in non-diabetic participants of the PPP-Botnia study. Supplementary Table 6. Effect of genetic variants on insulin levels during OGTT in non-diabetic participants of the PPP-Botnia study. Supplementary Table 7. Effect of genetic variants on insulin to glucagon ratios during OGTT in non- diabetic participants of the PPP-Botnia study. Supplementary Table 8. Overview of investigated genetic variants.

Figures

Supplementary Figure 1. Relationship between insulin secretion (CIR) and insulin sensitivity (ISI). Supplementary Figure 2. Relationship between glucose and insulin levels during OGTT in 4,654 non- diabetic participants of the PPP-Botnia study. Supplementary Figure 3. Relationship between glucose and glucagon levels during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study. Supplementary Figure 4. Graphic representation of effects of genetic variants on relationship between glucose and insulin to glucagon ratios during OGTT in non-diabetic participants of the PPP-Botnia study. Supplementary Figure 5. Relationship between glucose levels and insulin to glucagon ratios during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study. Page 21 of 34 Diabetes

Supplementary Table 1. Characteristics of the PPP- Botnia study participants. Number (m/f) 4,654 (2,159/2,495) Age (years) 47.9 ± 15.2 BMI (kg/m2) 26.2 ± 4.3 Insulin sensitivity index 147 [112] Corrected insulin response 150 [154] Disposition index 21,450 [23,450] Data are mean ± SD or median [IQR]. Diabetes Page 22 of 34

Supplementary Table 2. Effect of genetic variants on insulin secretion in non-diabetic participants of the PPP-Botnia study. CIR DI Gene SNP Beta SE P-value Beta SE P-value ADAMTS9 rs4607103 0.010 0.020 0.61 -0.004 0.021 0.86 ADCY5 rs11708067 -0.020 0.022 0.36 -0.013 0.023 0.57 ADRA2A rs10885122 -0.037 0.025 0.14 -0.016 0.026 0.55 ARAP1 rs1552224 -0.042 0.020 0.039 -0.035 0.021 0.10 BCL11A rs243021 0.033 0.016 0.048 0.020 0.017 0.24 CAMK1D rs12779790 -0.010 0.020 0.61 0.017 0.021 0.42 CDKAL1 rs7754840 -0.094 0.018 1.6×10-7 -0.126 0.019 2.5×10-11 CDKN2A/2B rs10811661 -0.056 0.023 0.016 -0.066 0.024 0.0072 CHCHD2P9 rs13292136 -0.010 0.024 0.68 -0.051 0.025 0.039 CRY2 rs11605924 -0.031 0.017 0.061 -0.025 0.017 0.14 DGKB rs2191349 -0.030 0.016 0.064 -0.032 0.017 0.056 DUSP9 (female) rs5945326 -0.013 0.026 0.62 -0.007 0.028 0.80 DUSP9 (male) rs5945326 0.030 0.026 0.12 0.043 0.020 0.026 FADS1 rs174550 -0.055 0.016 6.6×10-4 -0.030 0.017 0.078 FTO rs9939609 0.012 0.017 0.47 0.010 0.018 0.58 GCK rs4607517 -0.082 0.025 0.0010 -0.087 0.026 9.2×10-4 GIPR rs10423928 -0.066 0.018 3.1×10-4 -0.052 0.019 0.0061 GLIS3 rs7034200 0.001 0.016 0.94 -0.005 0.017 0.77 HHEX rs1111875 -0.061 0.016 1.4×10-4 -0.052 0.017 0.0021 HMGA2 rs1531343 -0.002 0.033 0.96 -0.033 0.035 0.34 HNF1A rs7957197 0.022 0.019 0.23 0.019 0.019 0.33 IGF1 rs35767 0.004 0.021 0.85 0.001 0.021 0.96 IGF2BP2 rs4402960 -0.033 0.018 0.062 -0.060 0.019 0.0013 JAZF1 rs864745 -0.028 0.016 0.084 -0.041 0.017 0.015 KCNJ11 rs5219 -0.038 0.016 0.019 -0.030 0.017 0.079 KCNQ1 rs2237895 -0.049 0.017 0.0032 -0.048 0.017 0.0054 KCNQ1 rs231362 0.008 0.016 0.63 -0.008 0.017 0.62 KLF14 rs972283 0.000 0.017 0.98 -0.008 0.017 0.64 MADD rs7944584 -0.019 0.021 0.36 -0.030 0.022 0.17 MTNR1B rs10830963 -0.168 0.017 4.3×10-22 -0.215 0.018 3.4×10-32 NOTCH2 rs10923931 0.029 0.024 0.23 0.023 0.025 0.36 PPARG rs1801282 -0.025 0.023 0.27 -0.055 0.024 0.023 PRC1 rs8042680 -0.011 0.018 0.54 -0.014 0.018 0.44 PROX1 rs340874 -0.003 0.016 0.85 0.002 0.017 0.93 SLC2A2 rs11920090 0.000 0.024 0.995 -0.012 0.025 0.64 SLC30A8 rs13266634 -0.031 0.016 0.059 -0.021 0.017 0.22 TCF7L2 rs7903146 -0.030 0.021 0.16 -0.013 0.022 0.57 THADA rs7578597 -0.009 0.034 0.80 -0.001 0.036 0.97 TP53INP1 rs896854 -0.028 0.016 0.079 -0.023 0.017 0.18 TSPAN8 rs7961581 -0.002 0.019 0.93 -0.007 0.020 0.74 VPS13C rs17271305 0.012 0.017 0.48 -0.003 0.017 0.85 WFS1 rs10010131 -0.020 0.016 0.21 -0.018 0.017 0.28 ZBED3 rs4457053 -0.002 0.019 0.93 -0.016 0.020 0.43 ZFAND6 rs11634397 -0.039 0.017 0.026 -0.018 0.018 0.33 Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI. Page 23 of 34 Diabetes

Supplementary Table 3. Effect of genetic variants on BMI and insulin sensitivity in non- diabetic participants of the PPP-Botnia study. Gene SNP BMI ISI Beta SE P-value Beta SE P-value ADAMTS9 rs4607103 -0.239 0.106 0.025 -0.012 0.013 0.36 ADCY5 rs11708067 -0.009 0.119 0.94 0.008 0.015 0.58 ADRA2A rs10885122 0.150 0.133 0.26 0.010 0.017 0.57 ARAP1 rs1552224 -0.195 0.110 0.078 0.002 0.014 0.86 BCL11A rs243021 -0.007 0.088 0.93 -0.011 0.011 0.32 CAMK1D rs12779790 -0.064 0.106 0.55 0.020 0.013 0.13 CDKAL1 rs7754840 0.032 0.098 0.74 -0.025 0.012 0.042 CDKN2A/2B rs10811661 -0.125 0.126 0.32 -0.004 0.016 0.79 CHCHD2P9 rs13292136 0.205 0.126 0.11 -0.041 0.016 0.011 CRY2 rs11605924 0.088 0.088 0.32 0.003 0.011 0.80 DGKB rs2191349 -0.135 0.086 0.12 -0.004 0.011 0.70 DUSP9 (female) rs5945326 0.169 0.150 0.26 0.015 0.017 0.39 DUSP9 (male) rs5945326 -0.057 0.095 0.55 0.009 0.013 0.52 FADS1 rs174550 0.009 0.088 0.92 0.027 0.011 0.013 FTO rs9939609 0.394 0.090 1.3×10-5 0.002 0.011 0.85 GCK rs4607517 -0.033 0.134 0.81 -0.003 0.017 0.84 GIPR rs10423928 -0.222 0.097 0.023 0.023 0.012 0.065 GLIS3 rs7034200 -0.010 0.088 0.91 -0.008 0.011 0.45 HHEX rs1111875 -0.082 0.087 0.34 0.004 0.011 0.69 HMGA2 rs1531343 -0.139 0.175 0.43 -0.027 0.022 0.23 HNF1A rs7957197 0.122 0.100 0.23 -0.007 0.013 0.59 IGF1 rs35767 -0.035 0.110 0.75 -0.002 0.014 0.87 IGF2BP2 rs4402960 -0.118 0.096 0.22 -0.022 0.012 0.066 JAZF1 rs864745 0.096 0.088 0.28 -0.012 0.011 0.26 KCNJ11 rs5219 0.108 0.088 0.22 0.012 0.011 0.30 KCNQ1 rs2237895 -0.016 0.089 0.86 0.003 0.011 0.82 KCNQ1 rs231362 0.017 0.087 0.85 -0.008 0.011 0.47 KLF14 rs972283 0.093 0.089 0.30 -0.009 0.011 0.43 MADD rs7944584 0.285 0.110 0.0097 -0.012 0.014 0.37 MTNR1B rs10830963 0.046 0.094 0.62 -0.040 0.012 6.3×10-4 NOTCH2 rs10923931 -0.234 0.129 0.070 -0.009 0.016 0.57 PPARG rs1801282 -0.125 0.123 0.31 -0.032 0.015 0.041 PRC1 rs8042680 0.138 0.094 0.14 0.001 0.012 0.97 PROX1 rs340874 -0.084 0.088 0.34 0.007 0.011 0.51 SLC2A2 rs11920090 -0.305 0.128 0.017 -0.010 0.016 0.53 SLC30A8 rs13266634 -0.081 0.088 0.36 0.019 0.011 0.091 TCF7L2 rs7903146 -0.031 0.114 0.79 0.014 0.014 0.34 THADA rs7578597 -0.091 0.185 0.62 0.009 0.023 0.69 TP53INP1 rs896854 0.015 0.086 0.86 -0.003 0.011 0.81 TSPAN8 rs7961581 -0.208 0.103 0.043 -0.013 0.013 0.33 VPS13C rs17271305 -0.025 0.089 0.78 -0.014 0.011 0.23 WFS1 rs10010131 -0.007 0.087 0.94 0.006 0.011 0.58 ZBED3 rs4457053 -0.084 0.103 0.42 -0.011 0.013 0.39 ZFAND6 rs11634397 -0.067 0.094 0.47 0.018 0.012 0.12 Beta and SE from linear regression analysis adjusted for age, gender and BMI (for ISI) denotes the effect size by each risk allele (additive model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI (for ISI). Diabetes Page 24 of 34

Supplementary Table 4. Effect of genetic variants on glucagon levels during OGTT in non-diabetic participants of the PPP-Botnia study. Fasting glucagon 2hr glucagon Gene SNP Beta SE P-value Beta SE P- ADAMTS9 rs4607103 -0.001 0.009 0.95 -0.012 0.010 0.20 ADCY5 rs11708067 -0.018 0.010 0.086 -0.017 0.011 0.11 ADRA2A rs10885122 0.001 0.012 0.92 -0.008 0.012 0.50 ARAP1 rs1552224 0.007 0.010 0.49 0.002 0.010 0.83 BCL11A rs243021 0.021 0.008 0.0069 0.011 0.008 0.15 CAMK1D rs12779790 0.014 0.009 0.14 0.003 0.010 0.77 CDKAL1 rs7754840 0.011 0.009 0.22 0.011 0.009 0.23 CDKN2A/2B rs10811661 0.005 0.011 0.67 -0.004 0.012 0.71 CHCHD2P9 rs13292136 0.003 0.011 0.75 -0.021 0.011 0.062 CRY2 rs11605924 -0.024 0.008 0.0021 -0.007 0.008 0.39 DGKB rs2191349 -0.001 0.008 0.90 0.000 0.008 0.98 DUSP9 (female) rs5945326 0.000 0.012 0.995 -0.003 0.013 0.85 DUSP9 (male) rs5945326 0.003 0.009 0.73 0.002 0.009 0.81 FADS1 rs174550 -0.010 0.008 0.18 0.000 0.008 0.99 FTO rs9939609 -0.003 0.008 0.67 -0.003 0.008 0.72 GCK rs4607517 -0.001 0.012 0.93 -0.003 0.012 0.78 GIPR rs10423928 -0.013 0.009 0.14 -0.008 0.009 0.37 GLIS3 rs7034200 0.002 0.008 0.79 0.005 0.008 0.52 HHEX rs1111875 0.020 0.008 0.0096 -0.008 0.008 0.33 HMGA2 rs1531343 -0.012 0.015 0.42 0.007 0.016 0.64 HNF1A rs7957197 0.017 0.009 0.047 0.009 0.009 0.32 IGF1 rs35767 0.004 0.010 0.70 0.028 0.010 0.0048 IGF2BP2 rs4402960 -0.024 0.008 0.0036 -0.011 0.009 0.21 JAZF1 rs864745 -0.005 0.008 0.48 0.006 0.008 0.44 KCNJ11 rs5219 -0.008 0.008 0.30 -0.018 0.008 0.027 KCNQ1 rs2237895 0.002 0.008 0.83 0.005 0.008 0.53 KCNQ1 rs231362 0.002 0.008 0.75 0.010 0.008 0.19 KLF14 rs972283 -0.011 0.008 0.16 -0.012 0.008 0.13 MADD rs7944584 -0.005 0.010 0.62 -0.008 0.010 0.42 MTNR1B rs10830963 -0.004 0.008 0.60 0.001 0.008 0.87 NOTCH2 rs10923931 0.013 0.011 0.26 0.028 0.012 0.016 PPARG rs1801282 0.010 0.011 0.39 -0.001 0.011 0.94 PRC1 rs8042680 -0.001 0.008 0.89 -0.001 0.009 0.88 PROX1 rs340874 0.003 0.008 0.71 -0.002 0.008 0.84 SLC2A2 rs11920090 -0.020 0.012 0.088 -0.015 0.012 0.21 SLC30A8 rs13266634 -0.005 0.008 0.55 0.007 0.008 0.39 TCF7L2 rs7903146 -0.009 0.010 0.39 -0.014 0.010 0.18 THADA rs7578597 0.010 0.016 0.56 0.009 0.017 0.61 TP53INP1 rs896854 0.000 0.008 0.99 -0.011 0.008 0.16 TSPAN8 rs7961581 -0.019 0.009 0.034 -0.012 0.009 0.18 VPS13C rs17271305 0.002 0.008 0.77 -0.008 0.008 0.32 WFS1 rs10010131 -0.003 0.008 0.71 0.005 0.008 0.55 ZBED3 rs4457053 0.019 0.009 0.033 0.024 0.009 0.0094 ZFAND6 rs11634397 -0.008 0.008 0.33 0.002 0.008 0.84 Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI. Page 25 of 34 Diabetes

Supplementary Table 5. Effect of genetic variants on glucose levels during OGTT in non- diabetic participants of the PPP-Botnia study. Gene SNP Fasting glucose 2hr glucose Beta SE P-value Beta SE P-value ADAMTS9 rs4607103 -0.005 0.014 0.73 -0.011 0.038 0.77 ADCY5 rs11708067 -0.010 0.016 0.54 0.139 0.042 9.6×10-4 ADRA2A rs10885122 0.003 0.018 0.89 0.015 0.047 0.75 ARAP1 rs1552224 0.017 0.015 0.24 -0.010 0.039 0.80 BCL11A rs243021 0.011 0.012 0.37 -0.001 0.031 0.97 CAMK1D rs12779790 -0.002 0.014 0.91 0.021 0.038 0.59 CDKAL1 rs7754840 0.045 0.013 4.5×10-4 0.090 0.035 0.0097 CDKN2A/2B rs10811661 0.039 0.017 0.018 0.090 0.045 0.045 CHCHD2P9 rs13292136 0.039 0.017 0.023 0.084 0.045 0.063 CRY2 rs11605924 0.031 0.012 0.0083 0.020 0.032 0.53 DGKB rs2191349 0.034 0.011 0.0033 0.001 0.031 0.98 DUSP9 (female) rs5945326 0.000 0.018 1.00 -0.001 0.048 0.98 DUSP9 (male) rs5945326 -0.005 0.014 0.70 -0.036 0.039 0.36 FADS1 rs174550 0.027 0.012 0.020 -0.051 0.031 0.100 FTO rs9939609 -0.027 0.012 0.025 0.060 0.032 0.064 GCK rs4607517 0.061 0.018 7.1×10-4 0.124 0.048 0.0098 GIPR rs10423928 -0.013 0.013 0.31 0.080 0.035 0.021 GLIS3 rs7034200 0.025 0.012 0.034 -0.038 0.031 0.22 HHEX rs1111875 0.016 0.012 0.16 -0.019 0.031 0.54 HMGA2 rs1531343 0.020 0.023 0.40 0.026 0.063 0.68 HNF1A rs7957197 0.025 0.013 0.060 0.014 0.036 0.69 IGF1 rs35767 0.007 0.015 0.61 -0.015 0.039 0.70 IGF2BP2 rs4402960 0.025 0.013 0.051 0.020 0.034 0.56 JAZF1 rs864745 0.028 0.012 0.015 0.035 0.031 0.27 KCNJ11 rs5219 0.006 0.012 0.61 0.012 0.031 0.71 KCNQ1 rs2237895 0.030 0.012 0.012 0.073 0.032 0.023 KCNQ1 rs231362 0.019 0.012 0.097 0.045 0.031 0.15 KLF14 rs972283 -0.017 0.012 0.15 0.035 0.032 0.26 MADD rs7944584 0.056 0.015 1.4×10-4 0.049 0.039 0.21 MTNR1B rs10830963 0.134 0.012 5.6×10-27 0.071 0.033 0.034 NOTCH2 rs10923931 -0.013 0.017 0.47 -0.019 0.046 0.69 PPARG rs1801282 0.043 0.016 0.0094 0.017 0.044 0.70 PRC1 rs8042680 -0.005 0.013 0.66 -0.046 0.034 0.18 PROX1 rs340874 -0.020 0.012 0.095 -0.015 0.031 0.63 SLC2A2 rs11920090 0.008 0.017 0.64 0.025 0.046 0.59 SLC30A8 rs13266634 0.003 0.012 0.78 0.020 0.032 0.54 TCF7L2 rs7903146 0.021 0.015 0.17 0.034 0.041 0.41 THADA rs7578597 0.047 0.025 0.055 -0.047 0.066 0.48 TP53INP1 rs896854 0.023 0.012 0.049 0.048 0.031 0.12 TSPAN8 rs7961581 0.008 0.014 0.56 0.033 0.037 0.37 VPS13C rs17271305 0.000 0.012 0.997 0.064 0.032 0.043 WFS1 rs10010131 0.019 0.012 0.096 -0.031 0.031 0.32 ZBED3 rs4457053 0.004 0.014 0.76 0.070 0.036 0.056 ZFAND6 rs11634397 0.010 0.012 0.41 -0.041 0.033 0.22 Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI. Diabetes Page 26 of 34

Supplementary Table 6. Effect of genetic variants on insulin levels during OGTT in non- diabetic participants of the PPP-Botnia study. Gene SNP Fasting insulin 2hr insulin Beta SE P-value Beta SE P-value ADAMTS9 rs4607103 0.011 0.013 0.40 -0.004 0.020 0.83 ADCY5 rs11708067 -0.003 0.015 0.84 -0.010 0.023 0.65 ADRA2A rs10885122 -0.016 0.017 0.36 -0.017 0.026 0.50 ARAP1 rs1552224 0.001 0.014 0.95 -0.013 0.021 0.53 BCL11A rs243021 0.006 0.011 0.58 0.024 0.017 0.16 CAMK1D rs12779790 -0.024 0.013 0.069 -0.015 0.020 0.47 CDKAL1 rs7754840 0.022 0.012 0.075 0.051 0.019 0.0068 CDKN2A/2B rs10811661 0.002 0.015 0.91 0.021 0.024 0.38 CHCHD2P9 rs13292136 0.045 0.016 0.0054 0.047 0.024 0.054 CRY2 rs11605924 -0.006 0.011 0.61 0.005 0.017 0.78 DGKB rs2191349 -0.011 0.011 0.33 0.002 0.016 0.88 DUSP9 (female) rs5945326 -0.005 0.017 0.77 -0.023 0.024 0.34 DUSP9 (male) rs5945326 -0.009 0.013 0.51 -0.014 0.022 0.52 FADS1 rs174550 -0.027 0.011 0.017 -0.041 0.017 0.015 FTO rs9939609 -0.007 0.012 0.52 0.036 0.017 0.037 GCK rs4607517 -0.006 0.017 0.75 0.036 0.026 0.17 GIPR rs10423928 -0.009 0.012 0.48 -0.007 0.019 0.70 GLIS3 rs7034200 -0.005 0.011 0.65 0.008 0.017 0.62 HHEX rs1111875 0.017 0.011 0.12 -0.020 0.017 0.22 HMGA2 rs1531343 0.018 0.023 0.43 0.037 0.034 0.28 HNF1A rs7957197 0.007 0.013 0.58 0.000 0.019 0.98 IGF1 rs35767 -0.011 0.014 0.44 -0.004 0.021 0.87 IGF2BP2 rs4402960 0.015 0.012 0.23 0.025 0.018 0.17 JAZF1 rs864745 0.002 0.011 0.88 0.003 0.017 0.87 KCNJ11 rs5219 -0.008 0.011 0.46 -0.013 0.017 0.45 KCNQ1 rs2237895 -0.004 0.011 0.73 -0.006 0.017 0.73 KCNQ1 rs231362 0.003 0.011 0.78 -0.003 0.017 0.88 KLF14 rs972283 0.006 0.011 0.57 -0.005 0.017 0.77 MADD rs7944584 -0.003 0.014 0.84 -0.008 0.021 0.70 MTNR1B rs10830963 0.019 0.012 0.12 0.066 0.018 2.3×10-4 NOTCH2 rs10923931 0.008 0.016 0.63 -0.003 0.025 0.90 PPARG rs1801282 0.025 0.016 0.11 0.025 0.024 0.29 PRC1 rs8042680 0.006 0.012 0.61 -0.016 0.018 0.39 PROX1 rs340874 -0.002 0.011 0.83 -0.004 0.017 0.81 SLC2A2 rs11920090 0.009 0.016 0.60 -0.012 0.025 0.64 SLC30A8 rs13266634 -0.019 0.011 0.091 0.004 0.017 0.80 TCF7L2 rs7903146 -0.014 0.015 0.36 -0.008 0.022 0.71 THADA rs7578597 -0.005 0.024 0.82 -0.047 0.036 0.18 TP53INP1 rs896854 -0.002 0.011 0.89 0.012 0.017 0.48 TSPAN8 rs7961581 0.019 0.013 0.14 0.009 0.020 0.64 VPS13C rs17271305 0.003 0.011 0.80 0.014 0.017 0.42 WFS1 rs10010131 -0.008 0.011 0.46 -0.020 0.017 0.23 ZBED3 rs4457053 -0.005 0.013 0.70 0.054 0.020 0.0060 ZFAND6 rs11634397 -0.021 0.012 0.077 -0.034 0.018 0.057 Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI. Page 27 of 34 Diabetes

Supplementary Table 7. Effect of genetic variants on insulin to glucagon ratios during OGTT in non-diabetic participants of the PPP-Botnia study. Fasting Insulin/glucagon 2hr Insulin/glucagon Gene SNP Beta SE P-value Beta SE P-value ADAMTS9 rs4607103 0.018 0.018 0.33 0.015 0.027 0.57 ADCY5 rs11708067 0.007 0.020 0.74 0.002 0.030 0.95 ADRA2A rs10885122 -0.018 0.023 0.44 -0.028 0.035 0.43 ARAP1 rs1552224 -0.005 0.019 0.80 -0.022 0.028 0.44 BCL11A rs243021 -0.029 0.015 0.053 0.013 0.023 0.56 CAMK1D rs12779790 -0.034 0.018 0.060 -0.004 0.027 0.88 CDKAL1 rs7754840 0.015 0.017 0.36 0.055 0.025 0.029 CDKN2A/2B rs10811661 0.006 0.021 0.78 0.017 0.033 0.60 CHCHD2P9 rs13292136 0.018 0.022 0.41 0.023 0.033 0.48 CRY2 rs11605924 0.017 0.015 0.26 0.019 0.023 0.41 DGKB rs2191349 -0.002 0.015 0.89 -0.004 0.022 0.84 DUSP9 (female) rs5945326 -0.012 0.024 0.61 0.019 0.033 0.58 DUSP9 (male) rs5945326 -0.015 0.018 0.40 -0.005 0.028 0.85 FADS1 rs174550 -0.013 0.015 0.39 -0.048 0.022 0.032 FTO rs9939609 -0.005 0.015 0.73 0.031 0.023 0.18 GCK rs4607517 -0.002 0.023 0.93 0.053 0.035 0.13 GIPR rs10423928 -0.001 0.017 0.97 -0.006 0.025 0.82 GLIS3 rs7034200 -0.007 0.015 0.65 0.015 0.023 0.51 HHEX rs1111875 0.003 0.015 0.85 0.001 0.022 0.97 HMGA2 rs1531343 0.024 0.029 0.42 0.033 0.044 0.45 HNF1A rs7957197 -0.001 0.017 0.97 -0.023 0.026 0.38 IGF1 rs35767 -0.012 0.019 0.53 -0.026 0.028 0.34 IGF2BP2 rs4402960 0.044 0.016 0.0072 0.024 0.025 0.33 JAZF1 rs864745 0.007 0.015 0.62 0.004 0.023 0.85 KCNJ11 rs5219 0.008 0.015 0.58 0.008 0.022 0.73 KCNQ1 rs2237895 0.005 0.015 0.77 -0.020 0.023 0.39 KCNQ1 rs231362 0.009 0.015 0.55 0.017 0.022 0.44 KLF14 rs972283 0.031 0.015 0.042 0.030 0.023 0.19 MADD rs7944584 0.015 0.019 0.42 0.008 0.028 0.77 MTNR1B rs10830963 0.032 0.016 0.049 0.102 0.024 2.6×10-5 NOTCH2 rs10923931 -0.007 0.022 0.73 -0.027 0.033 0.41 PPARG rs1801282 0.020 0.022 0.36 0.046 0.032 0.15 PRC1 rs8042680 0.006 0.016 0.69 -0.017 0.025 0.49 PROX1 rs340874 -0.001 0.015 0.96 0.009 0.023 0.71 SLC2A2 rs11920090 0.025 0.022 0.26 -0.027 0.034 0.43 SLC30A8 rs13266634 -0.007 0.015 0.63 0.020 0.023 0.37 TCF7L2 rs7903146 -0.005 0.019 0.81 0.012 0.029 0.67 THADA rs7578597 -0.032 0.032 0.32 -0.111 0.048 0.020 TP53INP1 rs896854 0.002 0.015 0.88 0.012 0.023 0.59 TSPAN8 rs7961581 0.043 0.018 0.014 0.030 0.027 0.26 VPS13C rs17271305 -0.004 0.015 0.80 0.008 0.023 0.73 WFS1 rs10010131 -0.017 0.015 0.26 -0.037 0.022 0.10 ZBED3 rs4457053 -0.025 0.017 0.15 0.038 0.026 0.15 ZFAND6 rs11634397 -0.016 0.016 0.33 -0.041 0.024 0.089 Beta and SE from linear regression analysis adjusted for age, gender and BMI denotes the effect size by each risk allele (additive model) on phenotype. Analyses of DUSP9 rs5945326 are stratified by gender and adjusted for age and BMI. Diabetes Page 28 of 34

Supplementary Table 8. Overview of investigated genetic variants. Gene Chr SNP RA RAF Identified by Reference association to ADAMTS9 3 rs4607103 C 0.77 Type 2 diabetes Zeggini et al. [1] ADCY5 3 rs11708067 A 0.84 Fasting glucose Dupuis et al. [2] ADRA2A 10 rs10885122 G 0.88 Fasting glucose Dupuis et al. [2] ARAP1 11 rs1552224 A 0.79 Type 2 diabetes Voight et al. [3] BCL11A 2 rs243021 A 0.45 Type 2 diabetes Voight et al. [3] CAMK1D 10 rs12779790 G 0.21 Type 2 diabetes Zeggini et al. [1] CDKAL1 6 rs7754840 C 0.31 Type 2 diabetes Saxena et al [4], Zeggini et al. [5], Scott et al. [6] CDKN2A/2B 9 rs10811661 T 0.84 Type 2 diabetes Saxena et al [4], Zeggini et al. [5], Scott et al. [6] CHCHD2P9 9 rs13292136 C 0.86 Type 2 diabetes Voight et al. [3] CRY2 11 rs11605924 A 0.52 Fasting glucose Dupuis et al. [2] DGKB 7 rs2191349 T 0.50 Fasting glucose Dupuis et al. [2] DUSP9 X rs5945326 G 0.25 Type 2 diabetes Voight et al. [3] FADS1 11 rs174550 T 0.59 Fasting glucose Dupuis et al. [2] FTO 16 rs9939609 A 0.38 BMI Frayling et al. [7] GCK 7 rs4607517 A 0.12 Fasting glucose Dupuis et al. [2] GIPR 19 rs10423928 A 0.27 2 hour glucose Saxena et al. [8] GLIS3 9 rs7034200 A 0.50 Fasting glucose Dupuis et al. [2] HHEX 10 rs1111875 G 0.53 Type 2 diabetes Sladek et al. [9] HMGA2 12 rs1531343 C 0.07 Type 2 diabetes Voight et al. [3] HNF1A 12 rs7957197 T 0.26 Type 2 diabetes Voight et al. [3] IGF1 12 rs35767 G 0.79 Fasting insulin Dupuis et al. [2] IGF2BP2 3 rs4402960 A 0.30 Type 2 diabetes Saxena et al [4], Zeggini et al. [5], Scott et al. [6] JAZF1 7 rs864745 A 0.47 Type 2 diabetes Zeggini et al. [1] KCNJ11 11 rs5219 T 0.49 Type 2 diabetes Gloyn et al. [10] KCNQ1 11 rs2237895 C 0.49 Type 2 diabetes Yasuda et al. [11], Unoki et al. [12] KCNQ1 11 rs231362 G 0.49 Type 2 diabetes Voight et al. [3] KLF14 7 rs972283 G 0.51 Type 2 diabetes Voight et al. [3] MADD 11 rs7944584 A 0.81 Fasting glucose Dupuis et al. [2] MTNR1B 11 rs10830963 G 0.30 Fasting glucose Prokopenko et al. [13], Lyssenko et al.[14], Bouatia-Naji et al. [15] NOTCH2 1 rs10923931 T 0.14 Type 2 diabetes Zeggini et al. [1] PPARG 3 rs1801282 C 0.86 Type 2 diabetes Altshuler et al. [16] PRC1 15 rs8042680 A 0.31 Type 2 diabetes Voight et al. [3] PROX1 1 rs340874 C 0.48 Fasting glucose Dupuis et al. [2] SLC2A2 3 rs11920090 T 0.87 Fasting glucose Dupuis et al. [2] SLC30A8 8 rs13266634 C 0.60 Type 2 diabetes Sladek et al. [9] TCF7L2 10 rs7903146 T 0.17 Type 2 diabetes Grant et al. [17] THADA 2 rs7578597 T 0.94 Type 2 diabetes Zeggini et al. [1] TP53INP1 8 rs896854 T 0.48 Type 2 diabetes Voight et al. [3] TSPAN8 12 rs7961581 G 0.23 Type 2 diabetes Zeggini et al. [1] VPS13C 15 rs17271305 G 0.51 2 hour glucose Saxena et al. [8] WFS1 4 rs10010131 G 0.57 Type 2 diabetes Sandhu et al. [18] ZBED3 2 rs4457053 G 0.25 Type 2 diabetes Voight et al. [3] ZFAND6 15 rs11634397 G 0.67 Type 2 diabetes Voight et al. [3] Page 29 of 34 Diabetes

Homozygous non-risk Heterozygous Homozygous risk 300 300 300

280 280 280

260 260 260 ADRA2A ADCY5 240 MTNR1B 240 240 HMGA2 MADD PPARG

220 220 220 DUSP9(f) IGF1 ZBED3 SLC2A2 IGF2BP2 200 CHCHD2P9 200 200 TCF7L2 CIR CIR CIR DUSP9(m) PRC1 DUSP9(m) GIPR ADAMTS9 MTNR1B TSPAN8 180 180 180 CDKAL1

160 160 160 MTNR1B GCK THADA 140 140 140

120 120 120

100 100 100 150 155 160 165 170 175 180 185 190 195 200 150 155 160 165 170 175 180 185 190 195 200 150 155 160 165 170 175 180 185 190 195 200 ISI ISI ISI

Supplementary Figure 1. Relationship between insulin secretion (CIR) and insulin sensitivity (ISI).

Whereas most SNPs showed effects which kept the relationship between ISI and CIR within the confidence interval of 4,654 non-diabetic subjects, there were some obvious outliers. MTNR1B, CDKAL1 and GCK risk genotype carriers had CIR values below the values expected from the ISI values, whereas TT-carriers of TCF7L2 showed rather high insulin sensitivity. N=4,654 non-diabetic participant of the Botnia-PPP study.

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A Homozygous non-risk Heterozygous Homozygous risk 5.5 5.5 5.5

5.4 5.4 5.4 MTNR1B

ADCY5 HMGA2

MTNR1B GCK GCK IGF2BP2 DGKB 5.3 5.3 CDKAL1 5.3 CDKAL1 HNF1A THADA TCF7L2

GCK CHCHD2P9 IGF1 ARAP1 CRY2 5.2 PPARG 5.2 THADA 5.2 GIPR

MTNR1B MADD Fasting glucoseFasting (mmol/l) Fasting glucoseFasting (mmol/l) Fasting glucoseFasting (mmol/l)

5.1 5.1 5.1

5.0 5.0 5.0 33 34 35 36 37 38 39 40 41 42 43 44 45 33 34 35 36 37 38 39 40 41 42 43 44 45 33 34 35 36 37 38 39 40 41 42 43 44 45 Fasting insulin (pmol/l) Fasting insulin (pmol/l) Fasting insulin (pmol/l)

B Homozygous non-risk Heterozygous Homozygous risk 5.6 5.6 5.6

GCK 5.5 5.5 5.5

5.4 5.4 5.4 CDKAL1 IGF2BP2 HNF1A FTO 5.3 5.3 5.3 VPS13C ZBED3 CAMK1D ADAMTS9 WFS1 CDKAL1 KCNJ11 JAZF1 GLIS3 MTNR1B GIPR IGF1 GIPR KLF14 KCNQ1† 5.2 5.2 5.2 PPARG TCF7L2 FADS1

5.1 IGF1 ADCY5 5.1 5.1 2hrglucose (mmol/l) 2hrglucose (mmol/l) 2hrglucose (mmol/l) HMGA2 5.0 5.0 5.0 CDKN2A/2B MADD 4.9 4.9 4.9

4.8 4.8 4.8 150 160 170 180 190 200 210 220 230 150 160 170 180 190 200 210 220 230 150 160 170 180 190 200 210 220 230 2hr Insulin (pmol/l) 2hr Insulin (pmol/l) 2hr Insulin (pmol/l)

Supplementary Figure 2. Relationship between glucose and insulin levels during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.

Panel A shows the fasting values. Homozygous carriers of the risk allele in TCF7L2 had the lowest, whereas homozygous carriers of HMGA2 showed the highest insulin levels. Panel B shows the 2hr values. The GCK risk allele showed the highest postprandial glucose concentrations.

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A Homozygous non-risk Heterozygous Homozygous risk

5.35 5.35 5.35 MTNR1B HMGA2 GCK 5.30 ADCY5 5.30 5.30 IGF2BP2 MTNR1B ADRA2A GCK GLIS3 5.25 5.25 5.25 KCNQ1† CDKAL1 FADS1 HNF1A NOTCH2 KLF14 5.20 5.20 5.20

5.15 5.15 5.15 CDKN2A/2B Fastingglucose (mmol/l) Fastingglucose (mmol/l) Fasting glucoseFasting (mmol/l) 5.10 CHCHD2P9 5.10 5.10 MTNR1B SLC2A2

THADA 5.05 5.05 5.05 MADD

5.00 5.00 5.00 66 68 70 72 74 76 78 80 82 84 66 68 70 72 74 76 78 80 82 84 66 68 70 72 74 76 78 80 82 84 Fasting glucagon (pg/ml) Fasting glucagon (pg/ml) Fasting glucagon (pg/ml)

B Homozygous non-risk Heterozygous Homozygous risk 5.6 5.6 5.6

GCK 5.5 5.5 5.5

5.4 5.4 5.4 IGF2BP2 CDKAL1 HNF1A FTO 5.3 5.3 5.3 ZBED3 ADAMTS9

5.2 5.2 5.2 PPARG KCNJ11

IGF1 5.1 SLC2A2 ADCY5 5.1 5.1 NOTCH2

HMGA2 2hrglucose (mmol/l) 2hrglucose (mmol/l) 2hrglucose (mmol/l) ADCY5 5.0 THADA 5.0 5.0 CDKN2A/2B ADRA2A MADD 4.9 4.9 4.9

4.8 4.8 4.8 60 62 64 66 68 70 72 74 76 78 80 82 60 62 64 66 68 70 72 74 76 78 80 82 60 62 64 66 68 70 72 74 76 78 80 82 2hr glucagon (pg/ml) 2hr glucagon (pg/ml) 2hr glucagon (pg/ml)

Supplementary Figure 3. Relationship between glucose and glucagon levels during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.

Panel A shows the fasting values. HMGA2 and NOTCH2 showed both high glucose and glucagon levels, while MTNR1B, GCK and IGF2BP2 showed high glucose and low glucagon levels. Panel B shows the 2hr values. The HMGA2 variant showed the highest postprandial glucagon level.

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A B 0.15 0.15 ADCY5 MTNR1B GCK

0.10 CDKN2A/2B 0.10 CDKAL1 GIPR CHCHD2P9 KCNQ1† ZBED3 MTNR1B VPS13C FTO MADD TP53INP1 0.05 KCNQ1* GCK JAZF1 KLF14 MADD SLC2A2 TSPAN8 0.05 CAMK1D TCF7L2 HMGA2 THADA CDKAL1 PPARG SLC30A8 CDKN2A/2B CRY2 PPARG ADRA2A IGF2BP2 CHCHD2P9 KCNJ11 DGKB KCNQ1† HNF1A FADS1 GLIS3 CRY2 DUSP9(f) HNF1A JAZF1 2hrglucose 0.00 IGF2BP2 DGKB BCL11A TP53INP1 ARAP1 ADAMTS9 Fastingglucose WFS1 TCF7L2 KCNQ1* HMGA2 IGF1 BCL11A PROX1 ZFAND6 ARAP1 NOTCH2 HHEX SLC2A2 TSPAN8 HHEX IGF1 SLC30A8 KCNJ11 WFS1 0.00 ZBED3 ZFAND6 DUSP9(m) GLIS3 ADRA2A DUSP9(f) VPS13C PRC1 ADAMTS9 THADA PRC1 CAMK1D DUSP9(m) ADCY5 GIPR -0.05 FADS1 NOTCH2 KLF14 PROX1 FTO

-0.05 -0.10 -0.04 -0.02 0.00 0.02 0.04 0.06 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 Fasting insulin/glucagon 2hr insulin/glucagon

Supplementary Figure 4. Graphic representation of effects of genetic variants on relationship between glucose and insulin to glucagon ratios during OGTT in non-diabetic participants of the PPP-Botnia study.

Effect sizes are betas from linear regression adjusted for age, gender and BMI. KCNQ1† represents rs2237895, while KCNQ1* represents rs231362. Black circles P<0.05; White circles P>0.05. Panel A shows the relationship between fasting insulin to glucagon ratio and fasting glucose. MTNR1B was a clear outlier with increased fasting glucose despite high insulin to glucagon ratio. Panel B shows the relationship between 2hr insulin to glucagon ratio and 2hr glucose. Again MTNR1B was a clear outlier with increased 2hr glucose despite the highest insulin to glucagon ratio.

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A Homozygous non-risk Heterozygous Homozygous risk

5.35 5.35 5.35 HMGA2 GCK MTNR1B 5.30 ADCY5 5.30 5.30 IGF2BP2 ADRA2A MTNR1B 5.25 5.25 GCK 5.25 NOTCH2 5.20 PPARG PROX1 5.20 5.20 TCF7L2

5.15 5.15 5.15

5.10 CHCHD2P9 5.10 5.10 MTNR1B SLC2A2

THADA 5.05 MADD 5.05 5.05 Fastingglucose (mmol/l) Fastingglucose (mmol/l) Fastingglucose (mmol/l)

5.00 5.00 5.00

4.95 4.95 4.95

4.90 4.90 4.90 0.080 0.085 0.090 0.095 0.100 0.105 0.080 0.085 0.090 0.095 0.100 0.105 0.080 0.085 0.090 0.095 0.100 0.105 Fasting insulin/glucagon Fasting insulin/glucagon Fasting insulin/glucagon

B Homozygous non-risk Heterozygous Homozygous risk 5.7 5.7 5.7

5.6 5.6 5.6

GCK 5.5 5.5 5.5

5.4 5.4 5.4 CDKAL1 IGF2BP2 HNF1A FTO DUSP9(f) DUSP9(f) ZBED3 DUSP9(f) 5.3 ADAMTS9 5.3 5.3 VPS13C ADCY5 JAZF1 GLIS3 HHEX GIPR PROX1 GIPR KCNJ11 5.2 IGF2BP2 5.2 5.2 KCNQ1† TCF7L2 TP53INP1

NOTCH2 IGF1 5.1 SLC2A2 5.1 5.1 2hrglucose (mmol/l) 2hrglucose (mmol/l) ADCY5 2hrglucose (mmol/l) ADCY5 HMGA2 DUSP9(m) 5.0 THADA 5.0 5.0 CDKN2A/2B MADD ADRA2A 4.9 4.9 4.9

4.8 4.8 4.8 0.40 0.45 0.50 0.55 0.60 0.65 0.40 0.45 0.50 0.55 0.60 0.65 0.40 0.45 0.50 0.55 0.60 0.65 2hr insulin/glucagon 2hr insulin/glucagon 2hr insulin/glucagon

Supplementary Figure 5. Relationship between glucose levels and insulin to glucagon ratios during OGTT in 4,654 non-diabetic participants of the PPP-Botnia study.

Panel A shows the fasting values. Homozygous risk allele carriers of TCF7L2 showed high fasting glucose values despite low insulin to glucagon ratio. Panel B shows the 2hr values. Consistent with the fasting situation, risk allele carriers of TCF7L2 showed high glucose despite high insulin to glucagon ratio.

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References

1. Zeggini, E., et al., Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet, 2008. 40(5): p. 638-45. 2. Dupuis, J., et al., New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet, 2010. 42(2): p. 105-16. 3. Voight, B.F., et al., Twelve type 2 diabetes susceptibility loci identified through large- scale association analysis. Nat Genet, 2010. 42(7): p. 579-89. 4. Saxena, R., et al., Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science, 2007. 316(5829): p. 1331-6. 5. Zeggini, E., et al., Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science, 2007. 316(5829): p. 1336-41. 6. Scott, L.J., et al., A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science, 2007. 316(5829): p. 1341-5. 7. Frayling, T.M., et al., A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science, 2007. 316(5826): p. 889- 94. 8. Saxena, R., et al., Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet, 2010. 42(2): p. 142-8. 9. Sladek, R., et al., A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature, 2007. 445(7130): p. 881-5. 10. Gloyn, A.L., et al., Large-scale association studies of variants in encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes, 2003. 52(2): p. 568-72. 11. Yasuda, K., et al., Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet, 2008. 40(9): p. 1092-7. 12. Unoki, H., et al., SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet, 2008. 40(9): p. 1098-102. 13. Prokopenko, I., et al., Variants in MTNR1B influence fasting glucose levels. Nat Genet, 2009. 41(1): p. 77-81. 14. Lyssenko, V., et al., Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet, 2009. 41(1): p. 82-8. 15. Bouatia-Naji, N., et al., A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet, 2009. 41(1): p. 89-94. 16. Altshuler, D., et al., The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet, 2000. 26(1): p. 76-80. 17. Grant, S.F., et al., Variant of 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet, 2006. 38(3): p. 320-3. 18. Sandhu, M.S., et al., Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet, 2007. 39(8): p. 951-3.

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