Likelihood ratio-based integrated personal risk assessment of type 2 diabetes

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1 Endocrine Journal 2014, 61 (10), Original Likelihood ratio-based integrated personal risk assessment of type 2 diabetes Noriko Sato 1), Nay Chi Htun 1), Makoto Daimon 2), 3), Gen Tamiya 2), Takeo Kato 2), 4), Isao Kubota 2), Yoshiyuki Ueno 2), Hidetoshi Yamashita 2), Akira Fukao 2), Takamasa Kayama 2) and Masaaki Muramatsu 1) 1) Department of Epigenetic Epidemiology/ Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo , Japan 2) Global Center of Excellence Program Study Group, Yamagata University School of Medicine, Yamagata , Japan 3) Department of Endocrinology and Metabolism, Hirosaki University Graduate School of Medicine, Hirosaki , Japan 4) Department of Neurology, Hematology, Metabolism, Endocrinology and Diabetology, Yamagata University School of Medicine, Yamagata , Japan Abstract. To facilitate personalized health care for multifactorial diseases, risks of genetic and clinical/environmental factors should be assessed together for each individual in an integrated fashion. This approach is possible with the likelihood ratio (LR)-based risk assessment system, as this system can incorporate manifold tests. We examined the usefulness of this system for assessing type 2 diabetes (T2D). Our system employed 29 genetic susceptibility variants, body mass index (BMI), and hypertension as risk factors whose LRs can be estimated from openly available T2D association data for the Japanese population. The pretest probability was set at a sex- and age-appropriate population average of diabetes prevalence. The classification performance of our LR-based risk assessment was compared to that of a non-invasive screening test for diabetes called TOPICS (with score based on age, sex, family history, smoking, BMI, and hypertension) using receiver operating characteristic analysis with a community cohort (n = 1263). The area under the receiver operating characteristic curve (AUC) for the LR-based assessment and TOPICS was (95% CI ) and ( ), respectively. These AUCs were much higher than that of a genetic risk score constructed using the same genetic susceptibility variants, ( ). The use of ethnically matched LRs is necessary for proper personal risk assessment. In conclusion, although LR-based integrated risk assessment for T2D still requires additional tests that evaluate other factors, such as risks involved in missing heritability, our results indicate the potential usability of LR-based assessment system and stress the importance of stratified epidemiological investigations in personalized medicine. Key words: Type 2 diabetes, Personalized health care, Multifactorial disease, Likelihood ratio, Integrated risk assessment TYPE 2 DIABETES (T2D) is a common disease, and its prevalence is currently increasing in Japan and other countries. T2D is preventable, as long as preemptive care is taken before development of the disease [1-3]. T2D results from environmental and genetic factors and mutual interactions between them. Although the Submitted Jun. 6, 2014; Accepted Jul. 1, 2014 as EJ Released online in J-STAGE as advance publication Jul. 25, 2014 Correspondence to: Noriko Sato, M.D., Ph.D., Department of Epigenetic Epidemiology/ Molecular Epidemiology, Medical Research Institute, Tokyo Medical and Dental University, Kanda-surugadai, Chiyoda-ku, Tokyo , Japan. nsato.epi@mri.tmd.ac.jp List of abbreviations; T2D, type 2 diabetes; LR, likelihood ratio; SNP, single nucleotide polymorphism; AUC, Area Under the Receiver-Operating Characteristic Curve; OR, odds ratio; RAF, risk allele frequency; BMI, body mass index The Japan Endocrine Society additive effect of genetic information on disease susceptibility is significant, its predictive capacity is small [4-7]. Nevertheless, personal genotype information may still contribute to an appropriate assessment of disease susceptibility in an individual when it is combined with other clinical and/or environmental components. A method using parallel coordinates to integrate manifold risk components would be helpful for visualizing personal disease risk [8]. Attempts to implement clinical assessment of risk genotypes have begun in the United States. One case showed that a man was able to cure himself of T2D by changing his lifestyle immediately upon the onset of diabetes, in response to knowing his genotypic risk for T2D [9]. The genotypic risk of T2D was deduced using the VARiants Informing MEDicine (VARIMED)

2 968 Sato et al. system, which calculates likelihood ratios (LRs) for an input genotype, using data on genotypes of diseaseassociated SNPs, to assess post-test disease probabilities [10-13]. To construct a LR-based risk model, each test is basically assumed to be independent of one another. The LR is the ratio of the probability of being positive for a test in a diseased group to the probability of being positive for a test in a non-diseased group. In the case of genotypic tests, LRs are calculated for three genotypes for each SNP. This method is useful for the risk assessment of a polygenic disease, such as T2D, because it takes prior risk probability into account so that multiple tests can be conducted serially, in a specified order. Special focus was placed on the order of the SNP list; disease-associated SNPs were sorted into descending order of confidence level for their ethnically matched, disease association. The result of personal risk assessment was presented as a genomic nomogram, an intelligible visualization device, which is suitable for interpreting of personal genetic risk. Since T2D is a multifactorial disease, we estimated the LRs for both genotypic and clinical tests to assess personal risk in an integrated manner. We surveyed the published literature, and selected studies on T2D association in the Japanese population as derivation cohorts to obtain data resource for the LR estimation. Importantly, the LRs vary among the populations. Significant differences exist in the allele frequencies between Caucasians and East Asians, especially for T2D genetic variants [12, 14], which affects not only the LRs, but also the selection of variants for risk assessment. In our study, the Japanese-specific genotypic LRs were calculated based largely on the latest genome-wide association study (GWAS) [15]. The Japanese-specific LRs for body mass index (BMI) and hypertension (HT) tests were also calculated based on large-scale epidemiological association studies [16-17]. Thus, we constructed an integrated version of a LR-based T2D risk assessment system. Our system was preliminarily evaluated using retrospective data from a local Japanese cohort. First, we focused on LR-based genotypic risk assessment and its classification performance as examined by receiver operating characteristic (ROC) curve analysis. Second, ethnic matching was confirmed to be important in the overall personal genetic risk assessment. Finally, the integrated version of the LR-based personal risk assessment was evaluated in parallel with the non-invasive screening test for diabetes, TOPICS [18]. We found that the classification performance of the LR-based assessment was comparable to that of the conventional screening test. However, we think that the system can be improved and optimized by better estimating LRs. Here, we provide an example in which the modification of LRs for a HT test by considering age improves system performance. For the LR-based system, the evidence for all of the LRs was based on large-scale epidemiological studies. Publically available epidemiological data were used, which sometimes lacked the age- and sex-stratified information. Such data are crucial for enhancing system performance. Although the system needs future evaluation in large-scale cohorts, our preliminary evaluation substantiates how the LR-based integrated risk assessment can be performed. Materials and Methods SNP selection for Japanese T2D We surveyed the published literature from 2000 to August 2013 in PubMed and HuGE Navigator regarding genetic variants associated with T2D. From more than 3,000 papers on a total of 129 T2D-associated SNPs, 279 papers (including one meeting abstract) on the Japanese population were selected and manually curated. Table S1 provides a list of 68 T2D-associated SNPs investigated for the Japanese population with the results of corresponding association studies in the Caucasian population shown side-by-side. From those SNPs, significantly T2D-associated SNPs in the Japanese population were selected. First, SNPs investigated for the Japanese with more than 80% statistical power were selected. Next, SNPs with positive risk effects in the risk alleles (odds ratio [OR] and 95% confidence interval [CI] values >1) were selected. Finally, when multiple SNPs existed in the same gene locus, the representative SNP identified by the largest-scale study was selected. As a result, 29 SNPs were selected as SNPs with a true impact in the Japanese population (Table 1). An SNP with a reported T2D association in the Caucasian population was regarded as a common SNP. Twenty SNPs are commonly associated with T2D in the Caucasian population (Table 1 and Table S1). LR estimation for genotypic tests We mathematically computed the Japanese-specific genetic LRs for the 28 selected autosomal SNPs using the risk allele frequency (RAF) and OR reported in the latest large-scale genome-wide association study

3 Integrated personal T2D risk assessment 969 Table 1 LR values calculated for T2D-associated SNPs appropriate for the Japanese population SNP Locus Risk allele Other allele LR_rr LR_Rr LR_RR SNP analyzed (TAKAHATA) SNP analyzed (HapMap3) Caucasian rs KCNQ1 C T rs rs Yes rs near CDKN2A&B T C rs * rs Yes rs TCF7L2 T C rs rs Yes rs IGF2BP2 C A rs * rs Yes rs CDKAL1 C G rs rs Yes rs near DUSP9 (chr X) A G rs NA Yes rs near HHEX C T rs rs Yes rs MAEA C G rs * rs * Yes rs UBE2E2 G A rs * NA No 1 rs SLC30A8 C T rs rs Yes rs C2CD4A C2CD4B A G rs * rs Yes rs AP3S2 C A rs * rs Yes rs HNF1B G A rs * rs Yes rs MIR129 LEP A G rs NA No 2 rs GPSM1 A G rs * NA No 2 rs SLC16A13 G A rs * NA No 2 rs CDC123 CAMK1D A G rs NA No 3,4 rs KCNK16&17 T G rs * NA No 5 rs ANK1 C T rs * rs Yes rs near SPRY2 G A rs * rs * Yes rs GCKR C T rs rs * Yes rs5215 KCNJ11 C T rs5219 * rs5215 Yes rs near GCC1 G A rs NA No 5,6 rs PEPD A G rs * NA No 5,6 rs HMG20A G A rs rs Yes rs ZFAND3 C T rs NA No 5,6 rs near HMGA2 C G rs * rs Yes rs FITM2 HNF4A G T rs * rs * Yes rs GLIS3 A G rs rs Yes The 29 SNPs are sorted in ascending order of p-value. a An SNP with a reported T2D association in the Caucasian population was regarded as a common SNP (Yes). Some SNPs are thought to be unique to East-Asians (No): 1 Yamauchi et al., Nat Genet. 2010, 42(10):864-8, 2 Hara et al., Hum Mol Genet. 2014, 23(1):239-46, 3 Imamura et al., Diabetologia. 2011, 54(12):3071-7, 4 Shu et al., PLoS Genet 2010, 6(9): e , 5 Cho et al., Nat Genet. 2012, 44(1):67 72, 6 Morris et al., Nat Genet. 2012, 44(9): * : proxy SNP (r2 > 0.8). NA; not applicable (They are not common autosomal SNPs) (GWAS) [15] because the genotype frequency data from this latest study were not publically available. Equations for LR computation are shown in Methods S1. We computed LRs in accordance with the best-fitted genetic model. An additive model is known to fit well for most T2D-associated SNPs [19]; however, as references for this study to estimate best-fitted model, we calculated the lambda value when the genotype information was available (Table S2). The lambda value is the ratio of log OR Rr (the odds ratio between heterozygous genotype Rr and reference homozygous genotype rr) and log OR RR (the odds ratio between risk-allele homozygous genotype RR and reference homozygous genotype rr) [20]. The lambda values 0, 0.5, and 1 correspond to the recessive, additive, and dominant genetic models, respectively. In order to test the accuracy of the computed LRs, they were plotted against the conventional (observed) LRs (Fig. S1). In this accuracy test, the LRs were computed for SNPs identified by all of the past confident studies (with MAF > 0.05) that publically provided the genotype frequency data (Table S2). The computed LRs conformed

4 970 Sato et al. to the observed LRs (Pearson s correlation coefficient = 0.994). The Caucasian-specific genetic LRs were computed similarly using the RAF and reported OR (Table S1) assuming the additive genetic model. For an X chromosome SNP (rs ), only one study (sample size = 9275) provided the homozygote genotype frequency data for the Japanese population [21]. Therefore, the values of LR_RR and LR_rr were calculated in a straightforward manner based on this genotype frequency data. A value of 1 was assigned to LR_Rr (Table S2). No information was available to calculate the LRs of this SNP for the Caucasian population. Pretest probability The pretest probability is a sex- and age-appropriate population average for the prevalence of disease [10]. In our study, the pre-test probability values for the Japanese population were calculated as the mean values of prevalence data from the National Nutrition Survey in Japan between 2005 and 2010 ( mhlw.go.jp/bunya/kenkou/kenkou_eiyou_chousa. html). In this survey, diabetes is assessed from either the affirmative answer to the question Have you ever been told by a doctor that you have diabetes? or HbA1c 6.5 % (NGSP). The pretest probabilities are provided in Table S3. LR estimation for the BMI and HT tests In a published report [16], ten BMI levels were established, and their associations with diabetes (the presence or absence of diabetes) were studied: < 15.0, , , , , , , , , and 35.0 kg/ m 2. The presence or absence of diabetes was derived from self-reported data. To estimate gender- and BMIappropriate LR in our study, BMI levels were categorized into four groups: < 20, 20-25, 25-30, 30 kg/m 2. We used the Ohsaki cohort described in [16] as a derivation cohort in which the population s baseline mean age was the closest to that of our test cohort (Takahata cohort, below). From the data of Japan-Ohsaki males (n = 23, 000) and Japan-Ohsaki females (n = 24,710), we calculated the probability of each BMI category in disease and control groups. From these data, LR values were estimated for the BMI test. A previous study with a large sample size (n = 26,448, mean age = 53.8 years) investigated the association between HT and diabetes [17]. HT was clini- cally defined by systolic and diastolic blood pressures 140/90 mmhg, or by the use of medication to treat hypertension. Diabetes was clinically defined by a fasting glucose level 7.0 mmol/l, a non-fasting glucose level 11.1 mmol/l, or by the use of medication to treat diabetes. The control group was defined by a fasting glucose level < 5.6 mmol/l, and a non-fasting glucose level < 7.8 mmol/l. The probability of HT in diabetics and control was and 0.365, respectively. Using these data, we calculated the LRs for the presence or absence of HT. Several lines of evidence suggest that HT often precedes diabetes onset [22-26]. Considering the mean age (61.3 years) of our test population (Takahata cohort, below), we were aware that the LRs calculated by the study with a much younger mean age (53.8 years) may not fit perfectly. Therefore, we also performed the simulation experiment using our test cohort to search optimal LRs for HT and the boundary age for maximum classification performance (see text). Estimated LRs for the BMI and HT tests are shown in Table S3. Post-test probability The final outcome in the LR-based assessment system is the post-test probability, that is, the probability of disease for individuals estimated after multiple tests. Post-test probability is calculated as: post-test probability = post-test odds / (1 + post-test odds) Here, post-test odds is calculated as the product of pretest odds and LRs: post-test odds = pre-test odds n i=1 LRi (n is the number of tests.) Pre-test odds is calculated from pre-test probability: pre-test odds = pre-test probability / (1 pre-test probability) Takahata cohort Takahata is an agricultural and suburban area approximately 300 km north of Tokyo, Japan. Between 2004 and 2006, 3520 residents were enrolled in the Takahata study. Clinical data were obtained at annual physical examinations. Our study was approved by the Ethics Committee of Yamagata University School of Medicine (approval no. 68) and the Institutional Review Board of Medical Research Institute, Tokyo Medical and Dental University (approval no ). Written, informed consent was obtained from all participants. Genomic DNA was extracted from peripheral blood leukocytes

5 Integrated personal T2D risk assessment 971 from each subject. Of these, 1615 subjects (mean age, 61.3 ± 10.2 years; men/women, 724/891; BMI, 23.4 ± 3.14; Hypertension/others, 844/771) were genotyped using Human660W-Quad (Illumina). All the SNPs analyzed in this study had genotyping rates over 99.4%. None of the subjects had a missing genotyping rate greater than 10%. The number of subjects with nonmissing genotypes for all the 29 SNPs was Diabetes was diagnosed at baseline using criteria employed by the World Health Organization (WHO). Out of 1547 subjects, 146 were diagnosed with diabetes. A great majority of Japanese adults with diabetes have T2D [27]. Therefore, the diabetes group can be regarded as consisting mostly of T2D, but the inclusion of other types of diabetes cannot be ruled out. From the remaining subjects, 1117 were selected as the control group using the following inclusion criteria: no diabetes, no family history of diabetes, and non-borderline (HbA1c < 6% (NGSP) and FPG < 110 mg/dl). Overall T2D genetic risk in HapMap3 JPT and CEU populations From 1184 individuals in HapMap release 3, 111 CEU and 84 JPT founders with no missing genotype data for the 19 common autosomal T2D SNPs were selected to represent the distinct ethnic populations. The overall T2D genetic risk for each individual was estimated by multiplying the ethnic-specific LRs of these 19 SNPs. In order to determine the importance of ethnic matching, we also conducted a test experiment to swap the LR set for an unmatched combination. TOPICS A screening score for undiagnosed diabetes was developed by Toranomon Hospital Health Management Center as a self-assessment tool [18]. The TOPICS Diabetes Screening Score includes age, male sex, family history of diabetes, current smoking habit, BMI, and HT. The details were described previously [18]. Genetic risk score 29 A genetic risk score 29 (GRS29) was constructed by summing the number of risk alleles among the 29 selected T2D-associated SNPs. For an X chromosome SNP, men were coded as a homozygote for either the risk or non-risk allele. Statistical analysis and visualizations Statistical analyses were performed using IBM SPSS Statistics 19 (Statistical Package for Social Science for Windows version 19.0), and a R package, ROCR [28]. All plots were created and calculations performed in the R statistical environment (v3.0.1). As a meta-analysis, the statistical powers of the studies and the required sample sizes for the detection of a significant association (a < 0.05) were calculated using QUANTO software (ver ) ( based on the reported OR and the RAF of the same ethnic population (Table S1 and S2). The population risk (prevalence) of diabetes was estimated to be 10%. The additive model and equal numbers of cases and controls for each study were assumed. The r 2 value and RAF (risk allele frequency) in the general Japanese population for each of the SNPs were retrieved from 1000GENOMES:phase_1_JPT and/or HapMap3-JPT data. Results LR calculations for the Japanese population Population-specific genetic associations with T2D are known [14]. Accordingly, special attention should be paid to ethnicity in the personal genetic risk assessment for T2D. Therefore, the selection and ranking of T2D-associated SNPs with a true impact on the Japanese population is the first priority in determining personal genetic risk for a Japanese subject. Initially, 68 T2D-associated SNPs (58 gene loci) that had been investigated in Japanese populations were extracted (Table S1), from which 29 SNPs (29 gene loci) were selected for the T2D-associated SNPs in the Japanese population. Table 1 provides a list of T2D-associated SNPs with their computed LRs. All of the values given for p-value, OR, and RAF are from the latest GWAS [15], which comprised 5976 Japanese patients with T2D and non-diabetic individuals. No other independent large-scale association study has been conducted in Japan. LRs for BMI and HT tests were estimated based on large-scale epidemiological association studies in Japan [16-17] (Table S3). Post-test probability of genotypic tests and their ability to classify disease status Using retrospective cohort data from a local district (Takahata) in Japan, we evaluated our LR-based genetic risk assessment. We calculated post-test probabilities for the 29 SNPs consecutively in ascending order of p-values (Table S4). The area under the ROC

6 972 Sato et al. Classifier AUC Asymptotic 95% Confidence Interval Lower bound Upper bound Pretest probability Post-test probability (common SNPs) Post-test probability (29 SNPs) Fig. 1 Binary classification performance of post-test probability of genotypic tests. Binary classification was performed for diabetics and controls in the Takahata population by examining the post-test probability of genotypic tests. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) are shown. curve (AUC) was (95% CI ) when the pretest probability was used as the initial classifier (Fig. 1, gray curve). A higher AUC (0.686, 95% CI ) was obtained when the post-test probability employing the 29 SNPs was used as the classifier (Table S4; Fig. 1, red curve). The classification performance of the post-test probability with the 20 common SNPs (AUC 0.683, 95% CI ) was similar to that of the post-test probability with 29 SNPs (Fig. 1, green dashed curve). Ethnic matching is important for overall personal genetic risk assessment In order to validate the importance of exact ethnic matching in the LR calculation, a test experiment was performed comparing risk assessment using the same (JPT_LR) and different (CEU_LR) ethnic LR values for 19 common autosomal SNPs. The overall personal T2D genetic risk was estimated by multiplying the 19 LRs. The Takahata population was tested to compare overall personal genetic risks calculated in two ways: one using the Japanese LRs (ethnically matched LRs) and the other using the Caucasian LRs (ethnically unmatched LRs). Each post-test probability was calculated using the same pretest probability value. The classification performance was almost the same for the two resulting post-test probabilities (Fig. 2A). However, when the distribution of overall personal T2D genetic risk was plotted, the difference was clear. The distribution of overall risk estimated with Caucasian LRs (CEU_LR) shifted towards the left, suggesting that the personal risks are underestimated if the Caucasian (ethnically unmatched) LRs are used (Fig. 2B, black dashed curve). In order to confirm the effect of ethnic discordance, HapMap populations were tested. Overall genetic risks were calculated for the HapMap3 JPT and CEU populations using the same method. The overall genetic T2D risks of both the JPT and CEU populations were normally distributed with 0 as the center when the ethnically matched LRs were used. However, the distribution of overall risk for HapMap3 JPT estimated with Caucasian LRs (CEU_LR) shifted towards the left (Fig. 2C, black dashed curve). This result suggests that overall genetic risk may be misjudged if ethnic matching is neglected. The correlation of effect sizes for the common SNPs between the two populations was high [15] (Pearson s correlation coefficient = for 19 SNPs in this study). Thus, the major reason for such a big difference between Japanese and Caucasian could be the differences in RAF [12]. Fig. 2D illustrates differences between HapMap3 JPT and CEU in the distribution of risk allele counts for the 19 analyzed SNPs. Taken together, the results indicate that the use of ethnically matched LR values is required for a proper personal risk assessment. Integrated risk assessment with genetic variants, BMI, and HT The range of tests for personal risk assessment was expanded to take clinical phenotype into consideration. The effects of adding BMI and HT tests to the personal risk assessment prior to the genotype test were examined with the Takahata cohort (Fig. 3A). The AUC for the post-test probability of clinical tests (BMI and HT) was (95% CI ; Fig. 3A). In contrast,

7 Integrated personal T2D risk assessment 973 Classifier AUC Asymptotic 95% Confidence Interval Lower Bound Upper Bound Post-test probability (ethnically matched LR_JPT) Post-test probability (ethnically unmatched LR_CEU) autosomal SNPs common for JPT& CEU Fig. 2 Ethnic matching is important for overall personal genetic risk assessment (A) Binary classification was performed for diabetics and controls in the Takahata population by examining the post-test probability using the LRs of 19 common autosomal SNPs specific for Caucasians (black and dashed, ethnically unmatched CEU_LR) or Japanese (red, ethnically matched JPT_LR) as the classifiers. (B) Distributions of the logarithmic values of the overall personal genetic risk for T2D in the Takahata population plotted using a kernel density function. Overall T2D genetic risks were assessed using ethnically matched (Japanese, JPT; red) and unmatched (Caucasian, CEU; black and dashed) LRs of 19 common autosomal SNPs. (C) Distributions of the logarithmic values of the overall personal genetic risk for T2D in the HapMap subpopulation specific to either JPT or CEU are similarly plotted. Risks were assessed with ethnically matched (JPT; red) and unmatched (CEU; black and dashed) LRs for the Japanese population, and with ethnically matched (CEU; blue) LRs for the Caucasian population. (D) Density distributions of the number of risk alleles for 19 autosomal SNPs compared between two populations.

8 974 Sato et al. the AUC for the entire integrated test (BMI, HT, and 29 SNPs) was (95% CI ; Fig. 3A, red spot at the right end and 3B, red curve). The classification performance of the integrated LR-based risk assessment was compared to other risk assessment systems. GRS29 is scored by summing the number of risk alleles for the 29 T2D-associated SNPs. TOPICS is scored by summing the points obtained according to the determined state of six parameters: age, sex, BMI, HT, smoking, and family history [18]. The distributions of these scores are shown in Fig. S2. As expected, the classification performance of GRS29 was lowest (AUC = 0.624, 95% CI ; Fig. 3B, magenta curve), but the classification performance of TOPICS was the best (AUC = 0.719, 95% CI ; Fig. 3B, green curve) and that of the LR-based integrated risk assessment (Fig. 3B, red curve) was almost comparable (AUC = 0.707, 95% CI ) to that of TOPICS. Thus, making best use of information on common genetic variants, an integrated LR-based system is capable to attain the higher predictive risk assessment than the conventional genetic risk score (GRS). Age-dependent adjustment of LRs for HT improved the risk assessment Although blood pressure and blood glucose levels should be able to be used to cross-predict each other [22], no increase in the AUC by the addition of HT component was observed in the above result (Fig. 3A). Classification performance could be improved by better estimating LRs. Importantly, HT is present in a high proportion of patients with T2D, and its development can precede and predict the development of dysglycemia [22]. Therefore, we hypothesized that the risk effect of HT may vary depending on age. Specifically, HT could be a major risk factor in middle-aged subjects, whereas its impact would be less in the upper age bracket according to a previous report [29]. The publically available epidemiological data lacked precise age-stratified information on the HT association with T2D. Thus, the simulation test was conducted using the Takahata population. We searched to determine the boundary age above which the effect of HT risk on T2D should be considered small (Fig. S3A, transverse direction). Simultaneously, the LRs for HT were slid to search the optimum, where the AUC reaches the maximum value (Fig. S3A, longitudinal direction). Interestingly, this simulation experi- Classifier AUC Asymptotic 95% Confidence Interval Lower Bound Upper Bound GRS TOPICS LR based risk assessment Fig. 3 The classification performance of LR-based integrated risk assessment Binary classification was performed for diabetics and controls in the Takahata population. The classification performance of LR-based assessment integrating BMI, HT, and 29 SNPs was [95% CI ] (red spot). The AUC (a solid line) and 95% CI (dashed lines) for every classifier is shown. The classification performance was compared among GRS29 (magenta), TOPICS (green), and LR-based integrated risk assessment (red).

9 Integrated personal T2D risk assessment 975 ment indicated that the impact of HT on diabetes susceptibility can be regarded as minor above the age of years. As a result, the optimum conditions were determined to be the boundary age of 60 years and the LRs for HT and non-ht as 1.15 (smaller than the predetermined value 1.61) and 0.85 (larger than the predetermined value 0.65), respectively. When the optimum LR conditions for HT risk evaluation was applied, the classification performance, the AUC, for the integrated LR-based risk assessment increased to (95% CI ; Fig. S3B, blue lines). Personal T2D risk nomogram Finally, we drew personal risk nomograms with the intent to visualize the contribution level per person of each risk component. In Fig. 4, the nomogram resulting from the LR-based risk assessment is shown for three diabetic subjects. For reference, GRS29 and TOPICS scores are also shown. In these three subjects, the posttest probabilities of clinical tests (BMI and HT) ranged from 12.6 to 18.2%. However, the T2D risks presented by the post-test probabilities were pushed towards the right by genetic risk components. Notably, these subjects did not have a family history of diabetes. Thus, the nomogram informs how the manifold components are assembled into personal disease risk. In addition, the LR-based integrated assessment has a potential advantage of detecting a cryptic risk originating from the inherent genotype of the individual, which could be overlooked by the conventional screening test. The nomogram is also shown for three non-diabetic subjects in Fig. S4. Discussion The onset of T2D can be prevented or delayed by maintaining a healthy weight and being physically active [2-3]. Considering the devastating complications in the late phases of T2D, early detection and prevention is crucial. Currently, self-screening tests with non-invasive parameters are primarily recommended to identify people at high-risk of developing the disease [18, 30-31], such as TOPICS, CANRISK ( and the ADA Diabetes Risk test ( are-you-at-risk/diabetes-risk-test/). In these questionnaires, age, sex, BMI, family history, HT, and physical activity are often used as non-invasive parameters. Compared to these conventional, non-invasive risk factors, the genetic polymorphisms that have been identified thus far have not improved disease prediction. Nevertheless, risk stratification using the GRS has proven useful for highlighting people at high risk for T2D, especially when accompanied with diminished β-cell function [32]. Therefore, disease risk should be assessed in an integrated way, covering both genetic and other factors for one person at a time. The widely used GRS calculation is based on the simple counts of risk alleles in which the respective risk loci are mostly matters of indifference. In contrast, LR-based genetic risk assessment originally developed by Butte, et al. is superior because the contribution of each SNP is clarified by employing their LRs [10]. Moreover, LR-based risk assessment accommodates multiple tests serially so that the integrated risk assessment can be performed. The LRs of T2D differ between ethnic populations and the LRs for each ethnic population should be estimated, which is a laborious job. Here, we first calculated the Japanese-specific LRs using publically available epidemiological information and then examined how the LR-based integrated risk assessment works for T2D in the Japanese population. The confidence level of association data is the most important issue when the outcomes of the association studies are utilized as derivation cohorts for the construction of a predictive risk model. Generally, the sample sizes of association studies and their reproducibility are in question. In Japan, only one large-scale epidemiological study (n > 26,000) for each risk factor (genetic variants, BMI, and HT) has been performed [15-17]. No other independent studies have been reported. As for the genetic components, the LRs values were initially calculated in our study by the conventional method using the genotype counting data collected through surveillance of all the published data (from 2000 Jan to 2013 July) (Table S2). Unfortunately, none of these previous values was taken superior to the current ones in regards to the study sample size. SNPs were chosen to confidently assess the T2D risk for the Japanese population (Table 1). A high concordance in the direction and effect sizes of common risk allele was found between the Japanese and Caucasian populations as described in [15]. Thus, the main factor that confers ethnic differences in the LRs of common SNPs turned out to be the difference in RAFs (Fig. 2D). If the Caucasian LRs were used for an assessment of personal genetic risk in the Japanese population, it

10 976 Sato et al. Fig. 4 Personal risk nomogram of T2D Personal risk nomograms with other risk scores (GRS29 and TOPICS) for three diabetic subjects are shown. The abscissa denotes the T2D risk estimated by every post-test probability, displaying the axis of the logarithmic scale. Multiple tests to estimate manifold risks are listed vertically. The pretest probability for each subject was determined by the sex- and ageappropriate population average of diabetes prevalence. Clinical information on BMI and blood pressure was integrated for risk assessment. T2D-associated SNPs are shown in ascending order of significance. Personal clinical phenotypes and genotypes are shown in the second column. Personal LR values are shown in the third column. The post-test probability is plotted with a box in the fourth column. The sizes of the boxes are scaled in proportion to the logarithmic value of the sample size used for the LR estimation. As for SNPs, the level of significance is color-coded in boxes: darker blue represents the SNPs with a more significant association with T2D. Visualization of the priority order of SNPs enables the subject to set the threshold of SNPs to be included for his/her risk assessment when it is necessary. The final post-test probability ranged from %, which is much higher than the average probability ( %). Three subjects shown here did not have a family history of diabetes even though they had strong genetic risks.

11 Integrated personal T2D risk assessment 977 led to underestimating risk scores, although the classification performance was not affected (Fig. 2A-C). The classification performance of the post-test probability employing either genetic risk only or combinations of multiple risks were evaluated using retrospective data from a local cohort (Figs. 1 and 3). This study reproduced the result reported by other researchers [4, 5], which indicates that genetic information is not sufficiently robust for T2D prediction, even if the latest information is used. Therefore, personal T2D risk assessment should not be done using only genetic information. Although genetic components occupy a large proportion of the T2D trait variance explained in the population, the heritability explained by GWAS variants is small. The rest of the genetic component is the missing heritability portion. Although gene-environment interactions partly explain the missing heritability, we currently do not have sufficient information. Thus, personal risk assessment can be assessed using known environmental and genetic factors, as the large-scale epidemiological association studies have accumulated valuable data on these factors. Although the potential for integrating clinical and genetic information in disease risk assessment has been proposed conceptually [8], this study is the first to substantiate such ideas in a LR-based risk assessment system. The performance of the LR-based integrated risk assessment resulted was modest and not yet sufficient for T2D prediction. To address the issues whether the poor performance of the LR-based assessment compared to TOPICS is due to the fact that family history and smoking are not taken into account, we have added the additional ROC analysis with the dataset of diabetes and non-diabetes. As shown in Table S5, the AUC for LR-based assessment and TOPICS was (95% CI ) and (95% CI ), respectively. This result indicates that the performance of TOPICS is better when analyzed with the dataset of diabetes and control. We think that this is because the control group excluded the subjects with family history of diabetes. We are aware that family history and smoking factors would be effective for T2D risk assessment. Due to the lack of the relevant derivation cohorts in the published literature, we could not straightforwardly calculate the LRs for those two factors. However, we elaborated to estimate LRs with the data provided by the TOPICS scoring paper of hospital-based study [18]. As for the TOPICS derivation cohort (n = 33,335), the odds ratios and the numbers of subjects in total, in case and in risk positive were given. We assembled quadratic equations using these data for smoking and family history and calculated back the data components of the contingency tables, which are requisite for LR calculation (Table S6). Then two alternative risk models in a LR-based risk assessment system were constructed. The first model employed sex, age, BMI, HT, smoking and family history, which are the components used in TOPICS. The second model employed sex, age, BMI, HT, smoking and SNP data. There is no well-established method to integrate family history and SNP-based assessments together for predicting disease risk, probably because family history and SNPs (genotypic variants) may not be independent risk factors. Accordingly, we used family history and SNP data alternatively when constructing the risk models. As shown in Table S5, the AUC for LR-based assessment using sex, age, BMI, HT, smoking and family history was (95% CI ), while that for sex, age, BMI, HT, smoking and SNP data was (95% CI ). It shows that the use of family history and SNP data gave mostly equivalent performance. Furthermore, the results indicated that the incorporation of smoking did not largely change the performance. Taken together, personal risk assessment could reasonably be conducted in a LR-based system by bringing as much T2D risk information together as possible. For the moment, the classical LR-based system will ignore gene-gene or gene-environment interactions, since each test is supposed to be independent. However, by stratifying LR estimation, the LR-based system could possibly deal with some simple interactions between two risk factors. In this paper, the idea of LR stratification has been newly proposed (see below). If additional tests that could possibly evaluate other environmental factors and/or risks involved in the missing heritability are to be incorporated, the performance could be improved. The LR-based integrated risk assessment system could be one of the feasible forms that can incorporate future risk components. One potential limitation for the accuracy of the system was the slight difference in the criteria for case and control among the derivation studies used for LR calculation. LRs are the key elements for constructing the risk model in this system. Importantly, different LRs can be set for stratified subjects. The stratified LR estimation is convenient because some risk factors have different impacts on distinct population subtypes. For the

12 978 Sato et al. genotypic tests, setting the LRs in the BMI-stratified manner would be beneficial, however, the data required to construct it are not yet available. In contrast, sexstratification is not necessary, because there is no sexual dimorphism in the 29 SNPs selected for T2D association [15]. For the BMI test, the difference in BMI distribution between diabetics and non-diabetics is slightly altered by age and sex according to a previous report [16]. Therefore, we selected the derivation cohort in which the mean age was close to that of the Takahata cohort and the LRs for BMI were calculated separately for males and females. For the HT test, HT can precede and predict the development of dysglycemia in some cases, but in other cases HT and T2D progress simultaneously [22]. Two representative reports presented that HT is a major risk factor for T2D, particularly in people in their 40s [23-24]. Furthermore, age-dichotomized association data [29] supported the hypothesis that impact of HT would be less in the upper age bracket. Thus, the simulation was performed to determine the effects of age-stratified LRs for HT on classification performance (Fig. S3). As a result, stratified LRs for HT slightly improved the performance of the assay. This result should be evaluated by collating the age-stratified large-scale epidemiological data for the association of HT with T2D. As for the pretest probability, the population average data by ten-year age group from the National Nutrition Survey in Japan was used in this study, because of its easy accessibility. If a more stratified data such as five-year or one-year age group prevalence is available, it would increase accuracy and might be worth using it in this system. In principle, a LR-based integrated risk assessment system should be developed that initially employs the large-scale epidemiological data as a training (learning) dataset. Next, it should be evaluated repeatedly in independent cohorts to optimize the parameters and adjust the risk prediction algorithm [8]. Because the sample size of the Takahata cohort is insufficient to perfectly optimize system performance, it should be evaluated in large-scale independent cohorts. The prototype of the genomic risk nomogram has appeared many times in reputable papers [9, 10, 13]. In this study, we provided a modified nomogram that includes simple clinical tests (BMI and HT) to enable an integrated risk assessment for T2D (Figs. 4 and S4). The major advantage of the nomogram would be that the contribution of each risk component is visible. Informing an individual of personal risk in such a way would be helpful for preemptive care. Another attractive advantage of LR-based risk assessment is that it can be easily updated by incorporating a new diseaseassociated SNP. In the nomogram, the evidence level of genotypic tests is indicated by the order of SNPs, as the SNPs are sorted into descending order of statistically confident level. When it is necessary to add a SNP whose evidence level is yet low, it might be performed with the proviso that the result is presented together with the information how much reliable the assessment of the particular SNP is. In conclusion, we constructed an integrated LR-based personal T2D risk assessment system using age, sex, 29 genetic variants, BMI, and HT. All of the LRs for the Japanese population were computed from published genotypic and clinical data in large-scale studies using them as derivation cohorts. In the validation cohort, the classification performance of the risk assessment system was comparable to that of a conventional screening test that scores age, sex, BMI, HT, family history, and smoking status. Because the LR is a versatile tool for integrating manifold risks in an assessment, the system has potential for expansion, which could possibly assess other environmental factors and/or other risk components involved in missing heritability in the future. As the complexity of the system increases, risk stratification and the LR estimates appropriate for the corresponding layers would be crucial. The efficacy of the system would be enhanced by evaluation and optimization in large-scale prospective studies. Acknowledgements The author thanks Ms. Atsuko Hiraishi for useful discussion when writing this manuscript. Disclosure None of the authors have any potential conflicts of interest associated with this research.

13 Integrated personal T2D risk assessment 979 Supplementary Data Table S1 List of T2D-associated SNPs analyzed in the Japanese population No. Position (Build 37) SNP rs # Locus (or adjacent gene) Alleles (forward strand) Risk Other allele allele (R) (r) RAF Reported effect size in Japanese Reported OR[95 % CI] Reported effect size in Europeans P value Power References RAF Reported OR[95 % CI] Power References 1 chr1: rs NOTCH2-ADAM30 T G [ ] 2.4E [1] [ ] [8] 2 chr1: rs PROX1 C T [ ] 3.8E [1] [ ] [9] 3 chr2: rs GCKR C T [ ] 2.10E [1] [ ] [10] 4 chr2: rs THADA T C [ ] 4.28E [1] [ ] [8] 5 chr2: rs BCL11A A G [ ] 4.3E [1] [ ] [11] 6 chr2: rs ITGB6-RBMS1 C T [ ] 8.2E [1] [ ] [12] 7 chr2: rs GRB14 A C [ ] 3.85E [1] [ ] [13] 8 chr2: rs IRS1 C T [ ] 9.42E [1] [ ] [14] 9 chr3: rs PPARG C G [ ] 6.05E [1] [ ] [3] 10 chr3: rs UBE2E2 G A [ ] 3.65E [1] [ ] [2] 11 chr3: rs UBE2E2 T C [ ] 1.83E [2] [ ] [2] 12 chr3: rs UBE2E2 C A [ ] 2.27E [2] [ ] [2] 13 chr3: rs PSMD6 C T [ ] 1.16E [1] [ ] [15] 14 chr3: rs ADAMTS9 C T [ ] 8.1E [1] [ ] [8] 15 chr3: rs ADCY5 A G 0.99 NA NA NA [1] [ ] [9] 16 chr3: rs IGF2BP2 C A [ ] 4.50E [1] [ ] [19] 17 chr3: rs ST6GAL1 C T [ ] 7.00E [1] [ ] [13] 18 chr4: rs MAEA C G [ ] 2.07E [1] [ ]# [15] 19 chr4: rs WFS1 G A [ ] 7.47E [1] [ ] [16] 20 chr5: rs ZBED3 G A [ ] 3.9E [1] [ ] [11] 21 chr6: rs CDKAL1 G A [ ] 1.10E [3] [ ] [3] 22 chr6: rs CDKAL1 C G [ ] 1.7E [1] [ ] [3] 23 chr6: rs CDKAL1 G A [ ] 3.5E [3] [ ] [3] 24 chr6: rs ZFAND3 C T [ ] 2.57E [1] [ ] [15] 25 chr6: rs KCNK16&17 T G [ ] 4.72E [1] [ ] [15] 26 chr7: rs DGKB-TMEM195 T G [ ] 9.7E [1] [ ] [17] 27 chr7: rs JAZF1 T C [ ] 2.E [1] [ ] [8] 28 chr7: rs GCK A G [ ] 3.7E [1] [ ] [9] 29 chr7: rs near_gcc1 G A [ ] 9.61E [1] [ ] [15] 30 chr7: rs MIR129_LEP A G [ ] 4.69E [1] 0.18 NA NA NA 31 chr7: rs KLF14 G A [ ] 9.0E [1] [ ] [11] 32 chr8: rs ANK1 G A [ ] 1.24E [1] [ ] [18] 33 chr8: rs TP53INP1 T C [ ] 1.19E [1] [ ] [11] 34 chr8: rs SLC30A8 C T [ ] 4.59E [1] [ ] [3] 35 chr9: rs GLIS3 A G [ ] 4.49E [1] [ ] [15] 36 chr9: rs PTPRD T C [ ] [1] NA NA NA 37 chr9: rs near_cdkn2a&b A G [ ] 2.80E [3] [ ] [3] 38 chr9: rs near_cdkn2a&b T C [ ] 1.43E [1] [ ] [3] 39 chr9: rs CHCHD2P9-TLE4 C T [ ] 1.7E [1] [ ] [11] 40 chr9: rs GPSM1 A G [ ] 7.12E [1] 0.68 NA NA NA 41 chr10: rs CDC123_CAMK1D A G [ ] 1.35E [1] NA NA NA 42 chr10: rs CDC123_CAMK1D G A [ ] 8.0 E [3] [ ] [8] 43 chr10: rs VPS26A T C [ ] 6.33E [1] [ ] [13] 44 chr10: rs near_hhex C T [ ] 1.76E [1] [ ] [10] 45 chr10: rs TCF7L2 T C [ ] 2.44E [1] [ ] [3] 46 chr10: rs TCF7L2 C G [ ] 4.60E [4] NA NA NA 47 chr11: rs KCNQ1 G A [ ] 2.5E [5] [ ] [11] 48 chr11: rs KCNQ1 G T [ ] 4.60E [6] [ ] [6] 49 chr11: rs KCNQ1 C T [ ] 3.67E [1] [ ] [6] 50 chr11: rs5215 KCNJ11 C T [ ] 3.60E [1] [ ]# [3] 51 chr11: rs ARAP1 A C [ ] 4.22E [1] [ ] [11] 52 chr11: rs MTNR1B G C [ ] 8.23E [1] [ ] [9] 53 chr12: rs near_hmga2 C G [ ] 3.05E [1] [ ] [11] 54 chr12: rs LGR5-TSPAN8 C T [ ] 4.6E [1] [ ] [8] 55 chr13: rs near_spry2 G A [ ] 1.40E [1] [ ] [10] 56 chr15: rs C2CD4A_C2CD4B A G [ ] 1.17E [1] [ ] [2] 57 chr15: rs C2CD4A_C2CD4B C T [ ] 3.0E [26] NA NA NA 58 chr15: rs HMG20A G A [ ] 4.28E [1] [ ] [13] 59 chr15: rs ZFAND6 G A [ ] 6.8E [1] [ ] [11] 60 chr15: rs AP3S2 C A [ ] 2.72E [1] [ ] [13] 61 chr15: rs PRC1 A C NA NA NA [1] [ ] [11] 62 chr17: rs SRR C T [ ] [1] NA NA NA 63 chr17: rs HNF1B G A [ ] 3.89E [1] [ ] [11] 64 chr17: rs SLC16A13 G A [ ] 9.40E [1] 0.02 NA NA NA 65 chr19: rs PEPD A G [ ] 1.51E [1] [ ] [15] 66 chr20: rs FITM2_HNF4A G T [ ] 3.24E [1] [ ] [15] 67 chr20: rs HNF4A A G [ ] 1.39E [7] [ ] [13] 68 chrx: rs near_dusp9 A G [ ] 1.78E [1] [ ] [11] A T2D-SNP list was generated by carefully considering (1) T2D case and control criteria, (2) sample size, (3) genotyping technology, (4) major/ minor/risk alleles with unified strand orientation (all of the alleles were standardized in forward strand orientation), (5) odds ratios and 95% confidence intervals, (6) genetic model and p-value, and (7) genotype counts for both cases and controls. NA: not applicable. : The power to detect the association with a two-sided significance threshold (α) of 0.05 was simply estimated using the reported total sample size, reported OR and RAF (HapMap_JPT or CEU) based on the assumption of additive genetic model and equal-sized cases and controls for a study. In the real studies, the ratios of case to control are frequently higher than one, where the real power is slightly smaller than the values estimated above. Because some of the studies did not provide the real number of case and control, the mode of estimation was standarized as above. : RAF in general population (HapMap_JPT or CEU). # : For MAEA and KCNJ11, the reported effect size in Europeans was indicated by the proxy SNP rs (maea) (r2=0.96; HapMap CEU) and rs5219 (KCNJ11) (r2=1; HapMap CEU), respectively.

14 980 Sato et al. Table S2 LR and lambda values calculated for Japanese T2D-associated SNPs based on the reported genotype frequency data No. Position (Build 37) SNP rs# Locus (or adjacent gene) Alleles (forward strand) Risk allele (R) Other allele (r) Number of references with genotype counts in Japanese Number of selected references based on lambda References Lambda value (Log OR_ Rr/Log OR_RR) Sample size of selected references Required sample size for 80% power Attainment level of available sample size * Confidence level LR calculated based on genotype data of selected references Reported sample size and effect size LR_rr LR_Rr LR_RR Sample size RAF LR calculated based on reported OR and RAF [95 OR % CI] LR_rr LR_Rr LR_RR Model chr1: rs PROX1 C T 2 2 [5], [20] sufficient confidence [ ] A chr2: [22], [20], rs GCKR C T sufficient confidence [21] [ ] A chr2: rs IRS1 C T 2 2 [5], [20] insufficient lessconfidence 1.28 [ ] A chr3: rs $ IGF [3], [20], 2BP2 T G sufficient confidence [22], [23] [ ] A chr4: rs MAEA C T 1 1 [18] sufficient confidence [ ] A chr6: rs CDK [3], [22], AL1 C G sufficient confidence [18], [24] [ ] AR chr6: rs CDKAL1 G A 6 5 [3], [20], [22], sufficient confidence [23], [25] [ ] A 8 chr7: rs DGKB- TMEM195 T G 1 1 [7] insufficient lessconfidence [ ] A chr7: rs JAZF1 T C 3 3 [3], [5], [20] sufficient confidence [ ] AR chr8: rs ANK1 G A 1 1 [18] sufficient confidence [ ] AR chr8: rs SLC30A8 C T 5 5 [3], [20], [22], [23], [25] chr9: rs near_ CDKN2A&B T C 6 5 [3], [20], [22], [23], [25] sufficient confidence [ ] A sufficient confidence [ ] A chr10: rs CDC123- CAMK1D A G 2 1 [26] sufficient confidence [ ] AD chr10: rs VPS26A T C 1 1 [7] insufficient lessconfidence [ ] AR chr10: rs near_hhex C T 6 5 [3], [22], sufficient confidence [23],[25],[27] [ ] A chr10: rs TCF7L2 T C 6 4 [3],[4], sufficient confidence [20],[21] [ ] AD chr11: 17 rs KCNQ1 G A 2 1 [5] insufficient lessconfidence [ ] A chr11: rs KCNQ1 G T 1 1 [6] sufficient confidence [ ] A chr11: [3], [4], rs KCNQ1 C T sufficient confidence [20], [28] [ ] A chr11: rs5219 KCNJ11 T C 4 3 [3], [22], [29] sufficient confidence [ ] A chr13: rs near_spry2 G A 2 2 [26], [20] sufficient confidence [ ] A 22 chr15: rs C2CD4A A G 1 1 [20] insufficient lessconfidence 1.13 C2CD4B [ ] AD 23 chr15: rs C2CD4A C2CD4B 1.08 C T 1 1 [26] sufficient confidence R [ ] chr15: rs HMG 1.08 G A 1 1 [7] sufficient confidence A 20A [ ] chr15: rs AP3S2 C A 1 1 [7] insufficient lessconfidence AR [ ] chr16: rs FTO A C 3 3 [3], [25], sufficient confidence D [20] [ ] chr17: rs $ HNF1B T C 2 2 [3], [20] sufficient confidence A [ ] chr19: rs PEPD C T 1 1 [3] sufficient confidence A [ ] chrx: rs near_dusp9 A G 1 1 [7] NA sufficient confidence [ ] NA NA NA NA From the 68 SNPs shown in Table S1, the SNPs were selected, whose genotype counts for the Japanese population were available. The SNPs were sorted by the chromosomal position. To avoid publication bias, we carefully included all of the available genotype counts data regardless of the association test results. Publication bias often arises when studies reporting significant results or large effect sizes are taken into consideration more often than negative studies [20]. Thus, for each study, we calculated lambda, the ratio of log OR Rr to log OR RR. The lambda value for each SNP was assumed to be common across studies if no ethnic diversity existed. When the study has an extremely deviated lambda value (< 0 and > 1), data on its genotype counts were not used for the LR calculation. SNPs analyzed with more than 80% statistical power were regarded as confident SNPs. LRs were either straightforwardly calculated from genotype counts or computed based on reported OR and RAF. NA, not applicable. : This SNP is located on chromosome X and the genotype data of heterozygotes is not available. LR was calculated based on only homozygotes genotypes. : Required sample size to detect significance (a < 0.05) association with 80% power was calculated using the reported OR (assuming that the reported OR in Japanese studies represents a true genetic impact) the and RAF (HapMap_JPT or CEU). Additive genetic model was used. The population risk (prevalence) of diabetes was assumed to be 10%. :If it is available, RAF in control as reported in selected references. If not, RAF in general population (HapMap_JPT) #: Model (A = additive, D = dominant, R = recessive, AR = mean value of additive and recessive model, AD = mean value of additive and dominant model, NA = not applicable) * If the sample size of selected references used for LR calculation (observed LR) is larger than the required sample size for 80% power, the attainment level is regarded as "sufficient". If not, "insufficient". $: Different SNPs from Table S1 were used for IGF2BP2 and HNF1B. IGF2BP2 (rs , r2=0.874) HNF1B (rs , r2=0.832).

15 Integrated personal T2D risk assessment 981 Table S3 Pretest probability and LR values for BMI and HT tests Pretest probability LRs for BMI test age male female BMI < BMI < BMI < BMI [40-49] male [50-59] female [60-69] [ 70] LRs for HT test HT non HT Table S4 AUC values for consecutive post-test probability classifiers Number of subjects N = 1263 Binary classification DM (146) vs Ctrl * (1117) Classifier AUC Std.error (a) asymptotic sig. (b) Asymptotic 95% Confidence Interval No classifier Lower Bound Upper Bound Pre-test (age, sex) E SNP test ( + KCNQ1_rs ) E SNPs test ( + CDKN2A_rs ) E SNPs test ( + TCF7L2_rs ) E SNPs test ( + IGF2BP2_rs ) E SNPs test ( + CDKAL1_rs ) E SNPs test ( + DUSP9_rs ) E SNPs test ( + HHEX_rs ) E SNPs test ( + MAEA_rs ) E SNPs test ( + UBE2E2_rs ) E SNPs test ( + SLC30A8_rs ) E SNPs test ( + C2CD4A_rs ) E SNPs test ( + AP3S2_rs ) E SNPs test ( + HNF1B_rs ) E SNPs test ( + LEP_rs791595) E SNPs test ( + GPSM1_rs ) E SNPs test ( + SLC16A13_rs312457) E SNPs test ( + CDC123_rs ) E SNPs test ( + KCNK16_rs ) E SNPs test ( + ANK1_rs515071) E SNPs test ( + SPRY2_rs ) E SNPs test ( + GCKR_rs780094) E SNPs test ( + KCNJ11_rs5215) E SNPs test ( + GCC1_rs ) E SNPs test ( + PEPD_rs ) E SNPs test ( + HMG20A_rs ) E SNPs test ( + ZFAND3_rs ) E SNPs test ( + HMGA2_rs ) E SNPs test ( + FITM2_rs ) E SNPs test ( + GLIS3_rs ) E * Inclusion criteria of Ctrl: no diabetes, no family history of diabetes and non-borderline (HbA1c < 6% and FPG < 110mg/dL). a. Under the nonparametric assumption. b. Null hypothesis: true area = 0.5

16 982 Sato et al. Table S5 Integration of family history and smoking into the LR-based assessment system Number of subjects N = 1547 Binary classification DM (146) vs non-dm (1401) Classifier AUC Std.error (a) asymptotic sig. (b) Asymptotic 95% Confidence Interval No classifier Lower Bound Upper Bound TOPICS E Pre-test probability (age, sex) E Post-test probability (BMI + HT + smoking + family history) Post-test probability (BMI + HT + smoking + 29 SNPs test) Post-test probability (BMI + HT + 29 SNPs test) E E E Number of subjects N = 1263 Binary classification DM (146) vs Ctrl*(1117) Classifier AUC Std.error (a) asymptotic sig. (b) Asymptotic 95% Confidence Interval No classifier Lower Bound Upper Bound TOPICS E Pre-test probability (age, sex) E Post-test probability (BMI + HT + smoking + family history) E Post-test probability (BMI + HT + smoking + 29 SNPs test) E Post-test probability (BMI + HT + 29 SNPs test) E *Inclusion criteria of Ctrl: no diabetes, no family history of diabetes and non-borderline (HbA1c < 6% and FPG < 110mg/dL). a. Under the nonparametric assumption b. Null hypothesis: true area = 0.5 Table S6 Estimation of LRs for family history and smoking Given values (from Heianza, et al. JCEM, 2013) Total Number of case 965 Current smoking habit (yes) 8595 Odds ratio (smoking) 1.48 History of parents or siblings having diabetes (yes) 5505 Odds ratio (family history) 2.27 Estimated LRs LR (smoking) 1.32 LR (non-smoking) 0.89 LR (Family history) 1.89 LR (no family history) 0.83 Computed contingency tables Current smoking habit smoking non-smoking Case Control History of parents or siblings having diabetes family history no history Case Control

17 Integrated personal T2D risk assessment 983 References for Tables S1-2 Ref. No. PMID References [1] Hara K, et al. Genome-Wide Association Study Identifies Three Novel Loci for Type 2 Diabetes. Hum Mol Genet Jan 1;23(1): [2] Yamauchi T, et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B. Nat Genet. 2010, 42(10): [3] Takeuchi F, et al. Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes. 2009, 58(7): [4] Miyake K, et al. Association of TCF7L2 polymorphisms with susceptibility to type 2 diabetes in 4,087 Japanese subjects. J Hum Genet. 2008;53(2): [5] Ohshige T, et al. Association of new loci identified in European genome-wide association studies with susceptibility to type 2 diabetes in the Japanese. PLoS One. 2011;6(10):e [6] Yasuda K, et al. 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18 984 Sato et al. Fig. S1 The conventional (observed) and computed LR values for individual genotypes The computed LRs (Table S2) were plotted against the conventional (observed) LRs for all genotypes of T2D SNPs (MAF > 0.05) analyzed with more than 80% statistical power. The computed LRs conformed to the observed LRs (Pearson s correlation coefficient = 0.994). Fig. S2 Distribution of three types of personal risk scores in a community cohort. Distributions of three types of risk score in the Takahata population plotted using a kernel density function: (A) GRS29, (B) TOPICS, and (C) LR-based risk assessment (Post-test probability). Total (black), diabetes (brown), and control (green) populations are shown.

19 Integrated personal T2D risk assessment 985 Fig. S3 Optimization of LR for a HT test. (A) The classification performances of LR-based integrated risk assessment were calculated when two variables, the boundary age and LR for HT, were set to a value in the range (interval =1) and (interval = 0.05), respectively. The prevalence of HT and diabetes was assumed to be 0.5 and 0.1, respectively, based on the National Survey in Japan. On this assumption, the following equation holds: LR non-ht = (9 4 LR HT ) / (LR HT + 4). A pair of values that achieved the maximum AUC, age 60 years and LR 1.15, are indicated by dotted lines and arrows. (B) The AUC (blue solid lines) and 95% CI (blue dashed lines) of the post-test probability estimated using the adjusted LRs for a HT test based on the simulation above are overlaid with the previous result shown in Fig. 3A.