Our Stage 1 genotype scan was performed using Illumina Human1 Beadarrays, which have a

Transcription

1 Supplementary Note Analysis of Stage 1 GWAS and design of the Stage 2 iselect array Our Stage 1 genotype scan was performed using Illumina Human1 Beadarrays, which have a gene-centric design, and Illumina HumanHap300 Beadarrays, which were designed using haplotype tagging. Following quality control assessment (described fully in ref. 1), we successfully obtained genome-wide SNP profiles for a sample population of 1,376 French cases and controls (Supplementary Table 1). Since the two genotyping platforms used different design strategies, they were analyzed separately using the Eigenstrat program 2. The p-value threshold used to pass SNPs from Stage 1 to Stage 2, p < 0.05, was selected to optimize study power using the CaTS software 3 for a two-stage study model with a joint analysis (Supplementary Table 2). For the initial Stage 2 validation set, we selected 15,036 SNPs from the HumanHap300 array and 5,080 from the Human1 array (totalling 19,521 unique SNPs), representing 5% of the SNPs tested on each platform. The gene-centric structure of the Human1 array resulted in many assays that were highly correlated: the number of SNPs from this array was reduced by a tagging approach, keeping SNPs at r 2 < 0.7 with other SNPs selected from either assay platform. The resulting iselect chip contained 16,405 SNPs, of which 13,767 SNPs originated from the analysis of the HumanHap300 chip, 2,001 SNPs from the Human1 chip, and 637 SNPs from the X- chromosome, which was analyzed separately (Table 1). Genotyping and quality control of Stage 2 We successfully genotyped 5,378 French samples using the iselect chip, with a call rate exceeding 95% for 16,360 SNPs (Table 1). Following assessment of the sample quality, we 1

2 discarded genotypes for 27 samples that had call rates below 95% and 64 samples that showed a mismatch between reported and estimated gender (Supplementary Table 3). In addition, 70 pairs of samples were found with first-degree relationships, and from each pair the individual with the lowest call rate was discarded. The resulting set of samples was analyzed for intercontinental stratification using STRUCTURE 4 with a panel of 321 SNPs selected for F st > 0.2 in the Perlegen dataset 5. In total, 296 samples showed a coefficient of ancestry < 90% compared to the HapMap CEU population, and were excluded from further analysis (Supplementary Table 3). The majority of these were also outliers in a plot of the samples projected onto the first two principal components (Supplementary Fig. 1). Following quality control, 4,977 samples remained for the Stage 2 association analysis. Eigenstrat analysis of Stage 2 data A total of 16,273 SNPs with a MAF > 0.01 in cases and controls and p(hwe) > in controls (Supplementary Table 4) were tested for association with T2D in these 4,977 samples using Eigenstrat, in order to correct for further stratification bias. Like other PCA-based methods, Eigenstrat analysis is affected by the correlation structure between SNPs as strongly as between samples. To minimize this effect in our analysis, we have estimated the principal components using a set of 13,566 SNPs without highly correlated SNPs (r 2 < 0.8), as well as MAF > 0.05 and p(hwe) > for controls (Supplementary Fig. 2). In Eigenstrat, the association of the full set of SNP markers was then corrected for the top 10 principal components. 2

3 Investigating the deviation between observed and expected p-values for Stage 2 We found a deviation between observed association p-values and p-values expected under the null hypothesis. This variance inflation was more pronounced in the focused second stage than for the first stage (Supplementary Fig. 3a), an increase that has been observed in previous multiassociation studies 6,7. While this deviation mimics the effects of population stratification, it could result from several other factors intrinsic to the multi-stage design. To distinguish between these sources, we first addressed the possibility of technical artefacts. The low number of SNPs in Hardy-Weinberg disequilibrium (0.3%) suggests that the chip design and genotyping assays worked without technical difficulties (Supplementary Table 4). Our analysis reduced the confounding effects caused by admixture by excluding samples with non-caucasian ancestry (detected by STRUCTURE), and by applying Eigenstrat for the association analysis. A PCA plot for the cleaned study cohort samples projected onto a plane spanned by the two strongest principal components shows a dense central cluster, indicating the absence of any remaining strong sample-specific bias. To investigate whether the remaining shift in the distribution of p- values could be explained by the presence of known risk loci, we removed 42 SNPs from known risk loci detected in the detected in this cohort (TCF7L2, HHEX, SLC30A8, CDKAL1, WFS1) and further 22 SNPs located in other confirmed GWAS detected loci (IGF2BP2, CDKN2A/2B, FTO, KCNJ11, PPARG, JAZF1, THADA). SNPs were removed if they were located in the LD block of the reported association with 100 kb padding. The marked change in the qq-plots that results from removal of these SNPs (Supplementary Fig. 3b) indicates that true associations rather than technical artifacts or admixture is the cause of the initially observed deviation. Indeed, we expect effects from true associations to have a stronger impact in a Stage 2 compared 3

4 to Stage 1, since the fraction of truly associated SNPs is expected to be higher than in Stage 1 due to the focused SNP selection between the two stages. Stage 3 design, genotyping and analysis. 62 SNPs were associated with p < 7 x 10-4 in the Stage 2 Eigenstrat analysis, falling below an inflexion point on a qq-plot of the results. 28 of these SNPs were genotyped using a Sequenom iplex assay in 7,698 Danish cases and controls. To estimate association strength, a single logistic regression model was used, combining the genotypes from 1,376 samples in Stage 1 (from the Hap300 array), 4,977 samples from Stage 2 and the Danish samples tested with the Sequenom assay. For each SNP, the following model was fitted: log( p 1 p) = α + β + β I + β I + ε 1x st 2 st 2 dan dan where I st 2 1 = 0 if sample genotyped in Stage 2 otherwise I dan 1 = 0 if sample genotyped in Danish cohort otherwise Indicator variables are included in the model to account for possible heterogeneity in effect sizes between the cohorts. The model assumes a common genetic effect within each stage/cohort (e.g. in the additive or dominant model, odds ratio of exp(β 1 ) for heterozygote as compared to homozygote with no at-risk allele) but allows for different risks between stages/cohort (e.g., in 4

5 the additive or dominant model, odds ratio of exp(β st2 ) for heterozygote in Stage 2 as compared to heterozygote in Stage 1). Three genetic models were tested: x = (0,1,2) for additive; (0,1,1) for dominant; (0,0,1) for recessive. To test association between a SNP and the disease (H0: β 1 = 0 vs. H1: β 1 0), we used Plink and computed the p-value of the Wald test for each model. Analysis of SNPs in TCF7L2 was complicated by the design of the custom chip. We included the top 5% of SNPs in Stage 1 in the custom array design, but several of these SNPs resulted in probes that failed Illumina s QC. For example, the strongest hit in TCF7L2 in Stage 1 (rs ) was included in the custom array design but did not result in a functioning assay when the array was manufactured. We included rs in the Stage 2 panel and have provided data for this SNP from the first and third stage of the study in Table 2. Since the Stage 2 SNP in strongest LD with rs (rs , r2 = 0.72, D = 0.91, p = 1.3 x in Stage 2) is not a good proxy, we have not included Stage 2 data for this locus in the Table 2. Quantitative trait analysis. In the Inter99 study, blood samples were drawn from participants after a 12-h overnight fast. Plasma glucose was analyzed by the glucose oxidase method (Granutest; Merck, Darmstadt, Germany). Serum insulin [excluding des-(31,32) split products and intact proinsulin] was measured using the AutoDELFIA insulin kit (Perkin-Elmer, Wallac, Turku, Finland). OGTTs were performed in all participants with measurements of plasma glucose and serum insulin at fasting and at 30 and 120 min. In the DESIR population, venous blood samples were collected in the morning after subjects had fasted for 12 h. Fasting plasma glucose was assayed by the glucose oxidase method applied to fluoro-oxalated plasma using a Technicon RA-1000 automatic 5

6 analyzer (Bayer, Puteaux, France) or either a Kone Specific or Delta analyzer (Konelab, Evry, France); fasting serum insulin was measured by an enzymoimmunoassay with the IMx system (Abbott, Rungis, France). In the NFBC 1986 population, venous blood samples were drawn after overnight fasting (8.00 a.m. to a.m.) and were analyzed in the Oulu University Hospital laboratory using ongoing internal/external quality control. Fasting plasma glucose was analyzed within 24 hours of sampling by a Cobas Integra 700 automatic analyzer (Roche Diagnostics, Basel, Switzerland) which is based on hexokinase-catalyzed phosphorylation of glucose. Samples for serum insulin were stored at -20º C and analyzed by Phadeseph insulin radioimmunoassay based on a double antibody solid phase technique (Pharmacia Diagnostics, Uppsala, Sweden) within 7 days. Insulin release was assessed by calculating the HOMA-B index, defined as (fasting insulin [mu/l] x 20)/(fasting glucose [pmol/l] - 3.5) (ref. 8). To estimate a surrogate measure of insulin resistance, we used the HOMA-IR, defined as (fasting insulin [mu/l] x fasting glucose [pmol/l])/22.5) (ref. 8). Oral glucose-stimulated insulin release traits were measured for Inter99, but not for DESIR or NFBC In Inter99, we calculated the areas under the curves (AUC) for serum insulin and plasma glucose as well as their ratio. We calculated the corrected insulin response, CIR, calculated as CIR = 100 x serum insulin 30 min /[plasma glucose 30 min x (plasma glucose 30 min )] (ref. 9), and the insulin sensitivity index (ISI), calculated as the reciprocal of homeostasis model assessment of insulin resistance: ISI = 22.5/[plasma glucose 0 min x serum insulin 0 min (pmol/l)] (ref. 8). For CIR and ISI calculations in Inter99, plasma glucose was measured in mmol/l and serum insulin in pmol/l. The conversion factor for serum insulin was 1 U = 6 nmol. (Supplementary Table 8) 6

7 The quantitative effects and ORs for all three study samples were estimated by linear regression and logistic regression models, respectively, adjusted for age, gender and BMI (Supplementary Table 8). Body mass index (BMI) is defined as the individual's body weight (kg) divided by the square of their height (m). In DESIR, HOMA-IR and insulin levels increase slightly, but not significantly over time; however, rs is not significantly associated with the difference in HOMA-IR between 0 years and 9 years in DESIR (additive model, p = 0.19); or with the difference in fasting insulin over the same period (additive model, p = 0.28). The longitudinal aspect of DESIR has been taken into account by using linear mixed models in the analysis approach to study trait data obtained from the 4 times of follow-up. Interaction between rs and IRS1 variant G972R (rs ) Study samples from Inter99, DESIR and NFBC 1986 were genotyped at rs and rs , HOMA-IR was estimated as (fasting insulin [mu/l] x fasting glucose [pmol/l])/22.) (ref. 8) and the following two models were fitted to the data (1 mu/l insulin = 6 pmol/l): y = x2 α + β x + β x + β x + ε and y = 1 1 2x2 α + β x + β + ε where x 1 and x 2 are the genotypes of rs and rs , respectively, coded as (0,1,2). ANOVA was used to compare results from the two models. 7

8 In the Inter99 cohort, an analysis for epistatic effects between G972R and rs shows an interaction for HOMA-IR (p = 0.03, additive model, Supplementary Table 10). However, in the DESIR and NFBC 1986 samples, G972R is not associated with insulin resistance (p = 0.15 and p = 0.61, respectively) and no interaction between G972R and rs was observed for HOMA-IR (p = 0.26 and p = 0.45, respectively, Supplementary Table 10). References 1. Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, (2007). 2. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, (2006). 3. Skol, A.D., Scott, L.J., Abecasis, G.R. & Boehnke, M. Optimal designs for two-stage genome-wide association studies. Genet Epidemiol 31, (2007). 4. Pritchard, J.K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, (2000). 5. Hinds, D.A. et al. Whole-genome patterns of common DNA variation in three human populations. Science 307, (2005). 6. Easton, D.F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, (2007). 7. 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 40, (2008). 8. Matthews, D.R. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, (1985). 9. Sluiter, W.J., Erkelens, D.W., Reitsma, W.D. & Doorenbos, H. Glucose tolerance and insulin release, a mathematical approach I. Assay of the beta-cell response after oral glucose loading. Diabetes 25, (1976). 8

9 Supplementary Figure 1. Identification of samples causing intercontinental stratification in the data set. Most of the 296 samples showing < 90% Caucasian ancestry following analysis with STRUCTURE (red) show deviation from the main cluster (blue) along the first principal component identified by Eigenstrat. 9

10 Supplementary Figure 2. Influence of SNP selection for Eigenstrat PCA on data structure. The selection of SNPs used for the PCA step of Eigenstrat has a major effect on the data structure in the two main principal components (PC). Four different filters were applied on SNP selection for this step. The two plots show PC1 x PC2 for the full set of Stage 2 samples before removing any individuals because of population stratification. The left plot was done using all SNPs, filtered for call rate > 95%, MAF > 0.05 in cases or controls, p(hwe) > in controls. In the right plot, highly correlated SNPs were removed so that r 2 < 0.8 between any pair of SNPs in the set used for the PCA. Filtering out SNPs in high correlation with other SNPs reduces the trimodal density observed along PC2. The analysis of Stage 2 was done using the principal components estimated using this set of 13,566 SNPs, with the samples in the non-central cloud removed by STRUCTURE before the analysis. 10

11 Supplementary Figure 3. EIGENSTRAT adjusts for population stratification in Stage 1 and Stage 2 of this study. Figure 3a. Negative log 10 of p-values from the additive model is shown, observed against expected, with adjustment for the first ten principal components. Deviation from the null (dashed black) for all SNPs passing MAF > 0.01 in cases or control, phwe > in controls, and call rate > 95% is shown for Stage 1 (black squares unadjusted, green adjusted) and Stage 2 (blue diamonds unadjusted, red adjusted). 11

12 Supplementary Figure 3 (continued). Figure 3b. Stage 2 Eigenstrat association log 10 transformed p-values for an additive model, corrected for the top 10 principal components, show small deviation from the expected under the null hypothesis when SNPs in loci detected in this cohort (red diamonds, TCF7L2, HHEX, SLC30A8, CDKAL1, WFS1) or all confirmed GWAS detected loci (green diamonds, TCF7L2, HHEX, SLC30A8, CDKAL1, WFS1, IGF2BP2, CDKN2A/2B, FTO, KCNJ11, PPARG, JAZF1, THADA) are removed compared to when all SNPs are considered (blue diamonds). SNPs were removed if they were located in the LD block of the reported association and 100 kb padding, resulting in 48 SNPs excluded for this cohort and 70 SNPs excluded for all GWAS loci. 12

13 Supplementary Figure 4. Pairwise linkage disequilibrium diagrams for T2D-associated regions identified in this study. D calculations are based on genotype data from the Stage 1 genome-wide association scan. A corresponding plot for the fourth T2D-associated region, surrounding rs / TCF7l2 has been published previously 1. Supplementary Figure 4a. rs / IRS1. 13

14 Supplementary Figure 4b. rs / CDKAL1. Supplementary Figure 4c. rs / WFS1. 14

15 Supplementary Table 1. Description of DNA sample sets for the case-control and quantitative trait studies. Controls for Stage 1 and 2 were selected from DESIR, and controls for Stage 3 were selected from Inter99, overlapping with samples used for the quantitative trait studies. Stage Glycemic status Number Total French Stage 1 case/control (Human100) Cases 672 Controls 667 1,339 French Stage 1 case/control (Hap300) Cases 679 Controls 697 1,376 French Stage 2 case/control (16,360 SNPs, iselect) Cases 2,245 Controls 2,732 4,977 Danish Stage 3 case/control (28 SNPs, Sequenom) Cases 3,334 Controls 4,364 7,698 COMBINED Case/control Cases 6,258 Controls 7,793 14,051 French DESIR Finnish NFBC 1986 Danish Inter99 TOTAL Case/control and QT Normal 3,351 3,351 Normal 5,183 5,183 Normal 5,824 5,824 17,812 15

16 Supplementary Table 2. Statistical power for one-stage, two-stage joint- and replicationbased designs. In total, 2924 cases and 3429 controls were available for statistical analysis. In Stage 1, 21.8% were genotyped and the remaining samples were allocated to Stage 2. Nominal significance level is set at 5 x 10-8 (significance level of 5% Bonferroni-corrected for 1,000,000 independent tests). Prevalence of T2D in French population is estimated at 7%. GRR = Genotype Relative Risk; MAF = Minor Allele Frequency at T2D-risk SNP; π = proportion of markers for follow-up in Stage 2. Study power (%) One-stage Joint analysis Replication analysis GRR MAF π = 0.01 π = 0.05 π = 0.10 π = 0.01 π = 0.05 π =

17 Supplementary Table 3. Summary of samples excluded from association testing. 457 samples initially genotyped were excluded from analysis based on low call rate, continental stratification as detected by STRUCTURE, sex mismatch between expected and observed genotypes, and one from each pair of related samples (parent-offspring or full siblings). Exclusion criterion Number of samples Call rate < 95% 27 Continental stratification 296 Sex mismatch 64 Related individuals 70 Total

18 Supplementary Table 4. Chromosome distribution of SNPs on the Stage 2 custom genotyping array. 15,652 autosomal SNPs and 621 X chromosome SNPs were successfully assayed (call rate > 95%) and passed MAF > 0.01 in cases or controls, and p(hwe) > in controls. Chromosome SNPs Failed HWE Failed MAF Successful 1 1, , , , , , , , X TOTAL 16, ,273 18

19 Supplementary Table 5. Stage 2 association analysis with Eigenstrat. rs ID Chr. Pos. rs Gene (hit or flanking) FAM110C, LOC MAF (Cases) MAF (Controls) P(HWE) (Controls) OR (Het) OR (Hom) Min P Min P (ES) HWE fail? OR direction different? x x 10-4 NO YES NO rs LOC x x 10-4 NO YES NO rs ZRANB x x x 10-5 YES NO NO rs rs rs rs rs rs rs rs LOC646736, IRS1 LOC646736, IRS1 LOC646736, IRS1 LOC646736, IRS1 LOC646736, IRS1 LOC642692, CENTG2 LOC642692, CENTG2 LOC646736, IRS x x 10-5 NO NO NO x x 10-5 NO NO YES x x 10-5 NO NO YES x x 10-5 NO NO NO x x 10-5 NO NO NO x x 10-5 NO NO YES x x 10-4 NO NO NO x x x 10-6 NO NO YES rs BOC x x 10-4 NO NO YES rs LOC644681, LOC x x 10-4 NO NO YES rs LOC x x 10-4 NO NO YES rs LOC x x 10-4 NO NO NO rs LOC285484, WFS x x 10-7 NO NO YES rs WFS x x 10-6 NO NO NO rs WFS x x 10-6 NO NO NO rs WFS x x 10-6 NO NO NO rs WFS1, PPP2R2C x x 10-6 NO NO NO Test in Stage 3? P-values are the minimum obtained using additive, dominant and recessive models, with and without Eigenstrat correction. 19

20 Supplementary Table 5. Stage 2 association analysis with Eigenstrat (continued). rs ID Chr. Pos. rs Gene (hit or flanking) WFS1, PPP2R2C MAF (Cases) MAF (Controls) P(HWE) (Controls) OR (Het) OR (Hom) Min P Min P (ES) HWE fail? OR direction different? x x 10-4 NO NO NO rs PPP2R2C x x 10-5 NO NO NO rs PPP2R2C x x 10-5 NO NO NO rs PPP2R2C x x 10-6 NO NO NO rs PPP2R2C x x 10-6 NO NO NO rs KCNIP x x 10-5 NO NO YES rs KCNIP x x 10-4 NO YES NO rs KCNIP x x 10-5 NO NO YES rs rs LOC644578, SRD5A2L2 LOC729011, CETN x x 10-4 NO YES NO x x 10-4 NO NO YES rs EFNA x x 10-4 NO NO YES rs LOC x x 10-4 NO NO YES rs CDKAL x x 10-5 NO NO YES rs CDKAL x x 10-5 NO NO NO rs PTPRK x x 10-4 NO NO YES rs rs rs rs QKI, LOC QKI, LOC PBEF1, PIK3CG LOC728795, TRPS x x 10-5 NO NO YES x x 10-4 NO NO YES x x 10-4 NO NO NO x x x 10-4 NO NO YES rs SLC30A x x 10-5 NO NO YES rs LOC646505, SMNP x x 10-5 NO YES NO rs VTI1A x x 10-5 NO NO YES rs TCF7L x x 10-4 NO NO NO rs TCF7L x x 10-9 NO NO NO Test in Stage 3? P-values are the minimum obtained using additive, dominant and recessive models, with and without Eigenstrat correction. 20

21 Supplementary Table 5. Stage 2 association analysis with Eigenstrat (continued). rs ID Chr. Pos. Gene (hit or flanking) MAF (Cases) MAF (Controls) P(HWE) (Controls) OR (Het) OR (Hom) Min P Min P (ES) HWE fail? OR direction different? rs TCF7L x x NO NO NO rs TCF7L x x NO NO NO rs TCF7L x x 10-5 NO NO NO rs rs rs OR52B6, TRIM6 NAALADL1, CDCA5 TPCN2, MYEOV x x 10-4 NO NO YES x x 10-4 NO NO YES x x 10-4 NO NO YES rs CCND x x 10-4 NO NO YES rs SPIC, MYBPC x x 10-4 NO YES NO rs SLC7A x x 10-4 NO YES NO rs rs rs FLJ38723, MGC15885 LOC729922, LOC GGT6, LOC x x 10-4 NO YES NO x x 10-5 NO YES NO x x 10-4 NO NO YES rs ACCN x x 10-5 NO NO YES rs ACCN x x 10-4 NO NO YES rs GRN x x 10-4 NO YES NO rs CLTC x x x 10-4 YES YES NO rs KIAA x x 10-5 NO YES NO rs APOBEC3F x x 10-4 NO YES NO P-values are the minimum obtained using additive, dominant and recessive models, with and without Eigenstrat correction. Test in Stage 3? 21

22 Supplementary Table 6. Meta-analysis of the top SNPs across Stage1, Stage2 and the Danish replication study (Stage 3). rs_id Nearest Gene(s) CHR POS Meta analysis Stage 1 Stage 2 Stage 3 OR OR OR Minor Major MAF MAF Risk OR Inheritance p-value all. all. cases controls all. [95% CI] model rs LOC64673, IRS T C C 1.19 [ ] 9.28 x ADD rs LOC64673, IRS C T C 1.12 [ ] 9.13 x 10-6 ADD rs LOC64269, CENTG T C C 1.07 [ ] 2.73 x 10-4 DOM rs LOC64468, LOC G T G 1.01 [ ] 6.61 x 10-4 REC rs BOC T C T 1.09 [ ] 6.48 x 10-4 ADD rs LOC730168, TBL1XR T C C 1.01 [ ] 5.96 x 10-4 REC rs LOC G A A 1.11 [ ] 1.35 x 10-5 DOM rs WFS1, PPP2R2C C T T 1.16 [ ] 1.11 x 10-8 ADD rs KCNIP A G G 1.02 [ ] DOM rs KCNIP C T T 1.04 [ ] DOM rs LOC72901, CETN C T C 1.16 [ ] 6.89 x 10-7 DOM rs EFNA A C C 1.07 [ ] DOM rs LOC T C T 1.03 [ ] DOM rs CDKAL G A G 1.20 [ ] 2.20 x ADD rs PTPRK C A A 1.01 [ ] 9.39 x 10-3 REC rs QKI, LOC A G G 1.10 [ ] 5.00 x 10-5 DOM rs QKI, LOC C T T 1.10 [ ] 3.40 x 10-5 ADD rs SLC30A C T T 1.01 [ ] REC rs SLC30A T C C 1.16 [ ] 8.13 x 10-8 ADD rs VTI1A T C C 1.11 [ ] 1.61 x 10-3 ADD rs TCF7L NA 1.36 T C T 1.48 [ ] 1.21 x ADD rs OR52B6, TRIM T C T 1.02 [ ] REC rs NAALADL, CDCA G A G 1.07 [ ] 5.25 x 10-4 DOM rs TPCN2, MYEOV G A G 1.10 [ ] 2.47 x 10-5 DOM rs CCND T C C 1.09 [ ] 4.42 x 10-4 ADD rs GGT6, LOC G A G 1.04 [ ] REC rs ACCN A G G 1.07 [ ] ADD rs ACCN A G G 1.02 [ ] REC P-values are the minimum obtained using additive, dominant and recessive models. Alleles are mapped to forward strand. 22

23 Supplementary Table 7. Quantitative trait analysis for T2D-associated SNPs in the Inter99 cohort. SNP HOMA-IR HOMA-B Effect size P (Model) Effect size P (Model) rs (IRS1) ( ) ( ) ( ) (A) (D) 0.59 (R) ( ) ( ) ( ) (A) (D) 0.32 (R) rs (WFS1) ( ) ( ) ( ) (A) (D) (R) ( ) ( ) ( ) 0.02 (A) (D) (R) rs (CDKAL1) ( ) ( ) ( ) 0.78 (A) 0.67 (D) 0.93 (R) ( ) ( ) ( ) 0.72 (A) 0.91 (D) 0.56 (R) Effect sizes are presented as means ± SD. P values are adjusted for age, sex and BMI for each genetic model (Additive (A), Dominant (D) and Recessive (R)). 23

24 Supplementary Table 8. Effects of rs on quantitative metabolic traits in European non-diabetic population-based subjects. Metabolic trait Cohort rs C/C C/T T/T P add P dom P rec NFBC Age DESIR 47.4 ± ± ± 10.1 INTER ± ± ± 7.6 NFBC / /1, /348 Sex DESIR 693/ / /224 INTER / / /426 NFBC ± ± ± BMI (kg/m 2 ) DESIR 24.6 ± ± ± INTER ± ± ± Fasting plasma NFBC ± ± ± glucose DESIR 5.31 ± ± ± (mmol/l) INTER ± ± ± NFBC ± ± ± Fasting serum insulin DESIR 53.1 ± ± ± (pmol/l) INTER ± ± ± x x NFBC ± ± ± HOMA-B DESIR ± ± ± INTER ± ± ± x NFBC ± ± ± HOMA-IR DESIR 2.1 ± ± ± INTER ± ± ± x x Serum insulin ± ± ± x Serum insulin ± ± ± x x x 10-4 AUC insulin ± ± ± x x Plasma glucose ± ± ± Plasma glucose ± ± ± AUC glucose INTER ± ± ± AUC insulin/auc 33.0 ± ± ± x x glucose CIR 989 ± ± ± ISI ± ± ± x x Disposition Index (CIR * ISI) 145 ± ± ±

25 Data are presented as mean ± standard deviation. P-values are from linear regression models adjusted for gender, age and BMI using additive, dominant and recessive models. In DESIR, mixed models were used, taking into account the 4 times of follow-up. Serum insulin was measured in pmol/l, plasma glucose in mmol/l. HOMA-B and HOMA-IR were calculated using serum insulin concentration in mu/l, where 1 U = 1 nmol serum insulin. 25

26 Supplementary Table 9. Association of rs near IRS1 with quantitative traits in 190 Danish twins. rs CC rs CT rs TT P Add P Dom P Rec n (male/female) 74 (35/39) 88 (51/37) 28 (10/18) Age (years) 42.5 ± ± ± BMI (kg/m 2 ) 25.0 ± ± ± R d insulin clamp (mg/kg FFM /min) 10.4 ± ± ± D i (x 10-7 ) 1.7 ± ± ± IRS-1 protein basal (AU) ± ± ± IRS-1 protein insulin (AU) ± ± ± IRS-1-associated PI3K activity basal (AU) IRS-1-associated PI3K activity insulin (AU) 25.0 ± ± ± ± ± ± Data are presented as means ± SD. Protein and activity levels are shown in arbitrary units (AU). P values are adjusted for age, sex, BMI, and twin zygosity and pair status, except for the P value for BMI which is adjusted for age, sex, and twin zygosity and pair status. 26

27 Supplementary Table 10. Tests for epistatic interaction between rs and rs (G972R) in the Inter99, DESIR and NFBC 1986 cohorts. An analysis of interaction between rs and rs in Inter99 shows an epistatic effect on HOMA-IR with p = in an additive model compared to main effects only. No epistatic interaction is observed in members of the DESIR or NFBC 1986 cohorts. The tables show HOMA-IR values for each cohort ± one standard deviation, and the number of samples in parentheses for each cohort. Inter99 rs CC rs CT rs TT rs AA ± (1945) ± (2281) ± (707) rs AG ± (235) ± (268) ± (81) rs GG ± (4) ± (8) ± (3) DESIR rs CC rs CT rs TT rs AA 1.92 ± 1.00 (961) 1.83 ± 0.96 (1085) 1.90 ± 0.93 (313) rs AG 1.95 ± 1.04 (167) 1.81 ± 0.96 (194) 1.74 ± 0.84 (63) rs GG 1.79 ± 0.33 (7) 1.29 ± 0.41 (2) 1.30 ± 0.07 (2) NFBC 1986 rs CC rs CT rs TT rs AA 2.52 ± 1.59 (1913) 2.46 ± 1.60 (2055) 2.33 ± 1.13 (571) rs AG 2.52 ± 1.81 (210) 2.57 ± 1.41 (236) 2.20 ± 0.95 (72) rs GG 2.23 ± 0.56 (3) 2.69 ± 1.57 (14) 2.18 ± 0.27 (3) 27