Application of longitudinal follow-up data increases power in the identification of genetic loci for type 2 diabetes

Application of longitudinal follow-up data increases power in the identification of genetic loci for type 2 diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Application of longitudinal follow-up data increases power in the identification of genetic loci for type 2 diabetes Seong Beom Cho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8183367/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Genome-wide association studies (GWASs) have identified several genetically susceptible loci associated with type 2 diabetes mellitus (T2DM). However, a large sample size is required to detect such loci, posing challenges for the application of GWASs in translational research. Result Herein, a meta-analysis of repeat GWASs (MERG) was developed to increase the power for susceptible loci discovery. Repeat GWASs refer to GWASs that are performed with follow-up phenotypes of the study population. As the repeat GWAS results have a dependency structure because of overlapping samples between follow-ups, they were integrated into the meta-analysis using an empirical estimation of the structure using a resampling process. The simulation analysis results indicated that the MERG had acceptable type 1 error and statistical power. In the exome data analysis for T2DM, the MERG detected 14 susceptible loci with high reproducibility. Of the 14 significant loci, 12 were identified in previous GWASs. However, conventional GWASs using the same data identified only two significant loci. After clumping, six loci were selected, four of which (rs2206734, rs2233580, rs2237895, and rs2237892) showed reproducibility. Moreover, the mapped genes ( MRGPRX3 and RPL24P7 ) at the remaining two loci (rs12291017 and rs4334660) were associated with T2DM. Conclusion MERG is a powerful method for identifying the genetic loci associated with T2DM in terms of power and reproducibility. This provides additional opportunities to identify novel loci for other traits. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Diabetes Mellitus Type 2 Genome-Wide Association Study Meta-Analysis Longitudinal Studies Cohort Studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Diabetes mellitus is one of the most prevalent diseases associated with various morbidities and mortalities, and the affected patients number continues to increase globally [ 1 ]. Among the subtypes, type 2 diabetes (T2DM) is the most prevalent. Although obesity is the major cause, genetic factors play important roles in the development of T2DM [ 2 ]. Association studies have been conducted to identify genetic loci associated with T2DM. With the advent of high-throughput genotyping technologies, genome-wide association studies (GWASs) have efficiently identified genetic loci of T2DM susceptibility [ 3 ]. However, because most susceptible loci have a mild to moderate effect size, a large sample size of over tens of thousands is required to detect such loci through GWASs [ 4 ]. To circumvent this problem, researchers have been performing meta-analyses that have discovered many susceptible loci for diseases [ 5 ]. Researchers of international collaborations preformed such meta-analyses to increase the power of the GWASs and discovered many susceptible loci that were not found in independent GWASs [ 6 ]. Most GWASs have a case–control design and use a single snapshot of phenotype information. Even if the follow-up phenotype information is available, GWASs can be performed independently with the follow-up phenotypes, and no specific method is available for integrating the results of GWASs with follow-up phenotypes. In the analysis of genetic associations with longitudinal follow-ups, meta-analysis methods can be applied in a straightforward manner. After performing GWASs with phenotype data from each follow-up, all the statistical results of a variant can be applied to a meta-analysis. The hypothesis of this integration was that such a meta-analysis might increase the power of GWASs and make it possible to identify susceptible loci that were not found in the individual GWASs. However, statistical results, including odds ratios (ORs) or P -values, between two distinct follow-ups are likely to be correlated because overlapping samples with identical genotypes are repeatedly used in GWASs at different follow-ups, as is often the case in multitraits GWASs with overlapping samples [ 7 , 8 ]. If the correlation is not controlled, the final statistics of the meta-analysis would have a higher probability of being inflated, which increases the number of false-positive results. Therefore, the dependency structure of the statistical results between follow-up points should be resolved using meta-analysis methods for GWASs with longitudinal follow-ups. For this purpose, a meta-analysis of repeat GWASs (MERG) was developed in this study. Here, repeat GWAS refers to GWASs performed with the phenotype information of each follow-up. To measure dependency, we empirically estimated the dependency structure, which was then integrated into the meta-analysis using the Brown’s method [ 9 ]. In this study, the MERG was applied to identify the susceptible loci for T2DM using cohort datasets with moderate-sized genotype data and multiple follow-up phenotypes. Simulation analyses were implemented to assess the performance of the MERG method, and a proof-of-concept analysis of exome chip data with follow-up information on T2DM status was conducted. Cohort datasets collected from the Korea Epidemiology and Genetics (KoGES) project were constructed to identify environmental and genetic factors associated with non-communicable diseases in the Korean population [ 10 ]. METHODS Overall MERG analysis flow In the repeat GWAS, we performed a GWAS for each follow-up phenotype. Cohort data included follow-up phenotype information containing disease status and related covariates, such as age, body mass index (BMI), and waist circumference (Fig. 1). Although the genotypes of the participants remained unchanged throughout the follow-up period, such covariates varied according to different follow-up time points. Therefore, different GWAS results for the phenotype data from the follow-ups were obtained and Brown method was applied with an empirical estimation of the dependency structure between repeated GWAS results. The Brown method integrates the covariance structure between the P -values of independent studies [9]. The method was developed based on Fisher’s method, which sums the P values of independent studies and applies the test statistics to the chi-squared distribution, as in Eq. (1): (1) where k refers to the number of studies and P i is the P -value of the i -th study. In the repeat GWAS, P -values were obtained from the results of the GWASs performed with the follow-up phenotypes. The Brown method uses the summation of P -values, as in Fisher’s method, but rescales the degrees of freedom using the covariances between the P -values. In Eq. (2), f and c are derived from the expected value and variance of the Ψ . (2) The expectation and variance are estimated with Eq. (3), (3) where those values were estimated with the number of studies and variance of the Ψ that are estimated with the number and covariances between P -values. The c was used to rescale the degrees of freedom in the estimation of P -values for the Brown method. To apply the Brown's method to the repeat GWAS, the covariance matrix for the P -values is necessary, as it captures the dependency structure among them. Since only the vector of P -values from the repeat GWAS is available, an empirical estimation of the covariance matrix was used to approximate this dependency structure. (Fig. 1). First, participants in the cohorts who appeared in all follow-ups were selected. Subsequently, a subset of the participants was sampled, and a repeat GWAS was performed. The number of subsets was calculated by multiplying 0.632 by the number of participants who appeared in all follow-ups. A value of 0.632 was derived from the bootstrap method for the estimation of parameters using multiple sampling. This sampling was conducted by replacing the participants and was repeated 30 times. After this process, we obtained the P -value matrix with a row of 30 P -values from the sampling and a column of different follow-ups. The covariances can be estimated using this matrix. However, the MERG used correlations of the P -values instead of covariances because the powers were greater when using the correlations (See Results section). Analysis of exome chip data from KoGES project The exome array data of participants from the Ansan/Ansung (ASAS) cohort, which is part of the KoGES project, were used for real data analysis [10]. The KoGES project developed three main cohorts: the ASAS, Cardiovascular Disease Association Study (CAVAS), and the Health Examinees (HEXA) cohorts. The Illumina HumanExome BeadChip platform was used to generate the data. The platform contains approximately 250,000 probes for genetic loci in exome regions. These data were originally used to analyze the pleiotropic effects of exome loci on cardiometabolic traits [11]. The entire process of data generation and quality control has been described in the literature. In the present study, we used 50,543 probes that passed the quality control process of previous research, and the missing genotypes were entered using the Beagle software [12]. After obtaining P -values via the MERG, we applied the clump function of the Plink program with default parameters [13]. Type 2 diabetes was defined according to the American Diabetes Association criteria (Supplementary Methods). Repeat GWAS with follow-up information was performed using the plink program, and genomic control (GC)-based P -values and Bonferroni’s correction were used to determine significant results. RESULTS Power and type 1 error analysis with simulation data To assess the performance of the MERG, simulation data were generated as described in the Supplementary Methods section with different combinations of parameters. In the simulation, four parameters, including 1) Minor allele frequency (MAF), 2) number of samples, 3) number of follow-ups, and 4) effect size of the simulated genetic variant, were varied, and the performance was determined according to the different combinations of the parameters. The MAF was set to 0.1, 0.2, and 0.3, and the numbers of follow-ups were two and five. The numbers of baseline samples were set to 4000 and 8000, which were similar to those of the exome chip data of the KoGES cohorts. The effect size of the genetic variant, which is a coefficient of the logistic model used in the simulation, was set to similar effect sizes as the significant variants in previous GWASs. In the GWASs, most of the effect sizes of the genetic variants were lower than 1.5, which is equivalent to 0.4 of a coefficient in the logistic model. Considering this, the effect size of genetics was set to 0.2, 0.3, and 0.4. In the simulation analysis, the MERG showed substantial power for the detection of genetic loci using follow-up phenotype information. Generally, the power was dependent on the MAF, number of samples, effect size of genetic variants, and number of follow-ups. Supplementary Fig. 1 shows the results of the power analysis when the number of samples was set at 4000. As P -value thresholds increased, the performance decreased. However, when the effect size was set to 0.4, all significant genetic variants were perfectly detected regardless of the MAF. When the effect size was low ( β = 0.2), the powers were dependent on the MAF. Although the overall power increased when the number of samples was set to 8000, the results were consistent (Supplementary Fig. 2). In the figure, the powers appear saturated when the effect size is ≥ 0.3, regardless of the MAF. Only when the effect size (beta value) was set to 0.2, the powers showed obvious differences between the MAFs. In both figures, an increase in the number of follow-ups resulted in an upward shift of power at all threshold P -values. Simulation data containing null genetic variants with follow-up phenotype information were generated to estimate type 1 error of the MERG. The results showed complicated patterns of type 1 errors according to changes in the parameters. In general, type 1 errors increased as the sample size increased, regardless of the number of follow-ups and MAFs (Supplementary Fig. 3). As the number of follow-ups increased, type 1 errors tended to increase for all MAFs and sample sizes. While the two parameters showed a consistent trend in type 1 errors, the MAF showed different trends according to the number of samples. When the number of samples was set to 4000, a greater MAF showed elevated type 1 errors. However, when the number of samples was 8000, the results showed a reverse pattern: a lower MAF induced an elevated type 1 error. In any parameter combination, type 1 errors were acceptable with different P -value thresholds (Supplementary Fig. 3). With a P -value threshold of 0.05, type 1 errors of the MERG ranged from 0.05 to 0.09. However, the type 1 error was under 0.05 with all other P -value thresholds. Comparison with other benchmark methods To compare the performance of the MERG, logistic regression and Fisher’s method were applied to the simulated data. When logistic regression (LR) was applied, the most significant P -value of the repeat GWAS for each simulated follow-up dataset was used. The overall tendency of the power analysis was the same as that of the MERG data (Fig. 2 and Supplementary Figs. 4–9). Figure 2 shows results determined using 4000 (Fig. 2 -(a)) and 8000 samples (Fig. 2 -(b)) with an MAF of 0.1 and beta of 0.2. Clearly, the power of the MERG increased as the sample size increased from 4000 to 8000 (Figs. 2 -(a) and (b)). MERG with covariance matrix (MERG.cov), Fisher’s method, and LR showed the same tendency with the change in sample size. The increases in follow-up, MAF, and effect size also resulted in the elevation of powers. When the sample size and MAF were low, Fisher’s method showed the best performance (Fig. 2 -(a) and Supplementary Fig. 4). However, when the sample size increased, all methods showed equivalent power, especially with larger effect sizes (Supplementary Figs. 4–9). Only MERG.cov showed the lowest power for all combinations of parameters. In the benchmark analysis of type 1 errors, all methods showed various type 1 errors with different thresholds (Fig. 3 and Supplementary Fig. 10). The results shown in Fig. 3 were obtained with 4000 samples, different numbers of follow-ups (n = 2 and 5), and different MAFs (0.1, 0.2, and 0.3). In general, as P -value thresholds became more stringent, type 1 errors rapidly decreased. In particular, when the threshold was below 1e-04, type 1 error became almost zero, except for Fisher’s method. Even if Fisher’s method yielded false-positive results with such a stringent threshold, the magnitude of type 1 error was approximately 0.02. However, with less stringent thresholds, type 1 errors showed differences between the methods. In general, MERG.cov showed the lowest type 1 error, and LR had the greatest error. Notably, LR showed a spike in type 1 error when the number of follow-ups and MAFs increased. When comparing the magnitude of the increase in type 1 errors with changes in the number of follow-ups (two to five), these results seemed to be more prominent because the other methods showed relatively less increase in their type 1 errors. The MERG showed acceptable type 1 error with all P -value thresholds. However, when the threshold was set to 0.05, the MERG showed a relatively higher type 1 error (approximately 0.07). The Fisher method generally showed a greater type 1 error than the MERG with different combinations of parameters. Type 1 errors tended to increase with increasing sample size and number of follow-ups for all methods. However, the changes in type 1 errors with an increase in MAFs were not linear in the three methods (MERG.cov, Fisher, and LR), as shown for the MERG method. In all the methods, type 1 error showed the greatest magnitude with an MAF of 0.2 (Fig. 3 ). However, when the number of follow-ups increased, type 1 errors also increased with a greater number of samples (n = 8000, Supplementary Fig. 10). Analysis results of exome chip data MERG analysis with independent cohorts Baseline and follow-up phenotype information are summarized in Supplementary Table 1. For the MERG analysis, filtering and imputation of exome chip data were conducted first. It is possible that genetic loci of lower MAFs are difficult to apply to the MERG because if the participants with low-frequency loci were lost to follow-up, then the genetic loci data might be missing when the GWAS was performed with follow-up phenotype information. Therefore, genetic loci of MAF < 0.05 were removed to rule out possible computation errors that resulted from the missing data. And, genetic loci for which data were missing from more than 10% of the 14205 participants in the exome chip data were also removed. Finally, 31436 genetic loci were selected, and Beagle software was used to perform missing data imputation using default parameters [ 12 ]. As shown in the simulation analysis, it is possible that the MERG returns higher numbers of false-positive results (> 5%) with a conventional P -value threshold of 0.05. Therefore, in Bonferroni’s multiple testing correction, the nominal P -value threshold was set to 0.01, and the adjusted P -value threshold was 3.19×10 –7 (= 0.01/31346). Although ten follow-up data points were available for the ASAS cohort (Supplementary Table 1), baseline and follow-up phenotype information were used to avoid a possible increase in false-positive results, which was revealed in the simulation analysis. Consequently, the MERG identified 14 significant loci, and six genetic loci remained after clumping (Table 1 and Supplementary Table 2). The six loci included well-known single-nucleotide polymorphisms (SNPs) that have been reported to be associated with the development of T2DM. For example, rs2206734 is an SNP in CDKAL1 and is well known for its association with BMI, blood pressure, and glucose homeostasis [ 14 – 16 ]. In a previous report on the current exome data, rs220674 was significant in 14206 participants [ 17 ]. rs8108269 is located between the GIPR and RN7SL836P genes, and previous studies have reported that this locus is susceptible to T2DM [ 6 , 18 , 19 ]. rs2237895 is within KCNQ1, and its association with T2DM has been reported in previous GWASs [ 20 – 23 ]. Table 1 MERG analysis of the Ansan-Ansung cohort with follow-up phenotype information CHR BP SNP Gene FU OR L95 CI U95 CI P -value MERG P -value 6 20694884 rs2206734 CDKAL1 Baseline 1.37 0.95 1.25 3.74×10 –6 1.26×10 –12 1st FU 1.35 1.03 1.47 7.71×10 –4 2nd FU 1.21 0.89 1.22 1.86×10 –2 3rd FU 1.34 0.99 1.23 1.04×10 –7 4th FU 1.29 1.03 1.26 8.85×10 –7 7 127253550 rs2233580 PAX4 Baseline 1.54 1.20 1.57 1.45×10 –4 7.49×10 –10 1st FU 1.64 1.17 1.66 6.13×10 –4 2nd FU 1.23 1.03 1.41 1.59×10 –1 3rd FU 1.42 1.18 1.46 2.71×10 –4 4th FU 1.41 1.13 1.38 1.97×10 –4 11 18158993 rs12291017 MRGPRX3 Baseline 1.68 1.06 1.40 2.78×10 –3 2.85×10 –8 1st FU 1.66 0.96 1.40 2.60×10 –2 2nd FU 2.04 0.95 1.35 2.95×10 –4 3rd FU 1.66 1.09 1.35 3.32×10 –4 4th FU 1.50 1.12 1.39 4.44×10 –3 10 72015573 rs3812694 NPFFR1 Baseline 1.42 1.16 1.74 5.55×10 –4 6.09×10 –8 1st 1.47 1.13 1.91 3.64×10 –3 2nd FU 1.56 1.23 1.97 2.45×10 –4 3rd FU 1.27 1.07 1.50 6.49×10 –3 4th FU 1.22 1.04 1.44 1.77×10 –2 19 46158513 rs8108269 GIPR,RN7SL836P Baseline 0.88 0.77 1.01 7.51×10 –2 1.50×10 –7 1st FU 0.88 0.73 1.05 1.57×10 –1 2nd FU 0.85 0.72 1.00 5.11×10 –2 3rd FU 0.81 0.72 0.90 1.45×10 –4 4th FU 0.80 0.72 0.89 5.04 ×10 –5 11 2857194 rs2237895 KCNQ1 Baseline 1.22 1.20 1.57 6.70×10 –3 2.89×10 –7 1st FU 1.16 1.14 1.61 1.32×10 –1 2nd FU 1.13 1.03 1.42 1.54×10 –1 3rd FU 1.23 1.20 1.49 4.76×10 –4 4th FU 1.25 1.17 1.43 4.98×10 –5 Chr: chromosome, BP: base position, SNP ID: rs number of dbSNP database, FU: follow-up, OR: odds ratio, L95: lower bound of 95% confidence interval, U95: upper bound of 95% confidence interval, CI: confidence interval, MERG: meta-analysis of repeat genome-wide association study. Although the ASAS cohort showed significant results in the MERG analysis, the CAVAS and HEXA cohorts showed no significant results. The CAVAS included baseline and three follow-up phenotype information, while two different follow-up phenotype information, including baseline and one follow-up, were included in the HEXA cohort. These results seem to be due to the relatively small number of samples used in the exome chip data (3122 and 3144, respectively), which was related to the decreased power of the MERG analysis. After obtaining the MERG results, a meta-analysis with Fisher’s P -value summation was performed to augment the power for detecting significant genetic loci. After summation of the three results, 14 SNPs were found to be significant with the same adjusted P -value that was used in the MERG ( P = 3.19×10 –7 ), and there were six significant results after clumping (Fig. 4 and Supplementary Table 3). Of these, 12 SNPs overlapped with the significant SNPs in the MERG analysis of the ASAS cohort. As shown in Fig. 4 , most of the ORs consistently showed the same direction in the results of the association studies using the follow-up phenotypes, although the P -values showed different significances. GWAS with longitudinal follow-ups Using 31346 genetic variants and longitudinal follow-up phenotypes, GWASs were performed using a logistic regression model. The covariates included age, sex, and BMI, as in the MERG analysis. The repeat GWAS of the ASAS cohort had two significant loci, rs2206734 (OR = 1.38, P = 1.221e-07) and rs10440833 (OR = 1.34, P = 1.52e-07); rs2206734 was selected after clumping. No significant differences were found in the GWAS of the other cohorts. When GWAS was performed with the CAVAS and HEXA cohort follow-up phenotype data, no significant results reached the adjusted P -value threshold. Finally, a GWAS was performed with 14205 participants’ baseline phenotype information and genotypes. The results showed that rs2237895 (OR = 1.30, P = 1.45e-08) and rs2233580 (OR = 1.46, P = 6.26e-07) were significant, and were selected after clumping. DISCUSSION In this study, genetic loci associated with susceptibility to T2DM were identified using longitudinal follow-up information. Although the total number of samples was relatively modest compared with that of other GWASs, highly reproducible results were obtained. The MERG, a novel GWAS method that fully utilizes the phenotypic information of follow-up, leverages the power to detect susceptible loci without increasing the sample size. In the power and type 1 error analysis, the MERG method showed substantial performance. Although MERG.cov showed type 1 error, it had insufficient power to detect significant loci. However, the MERG applies a correlation matrix instead of a covariance matrix to determine the dependency structure. This seems to result from the fact that the empirically estimated covariances tend to be biased, which ultimately results in an overestimation of the scale parameter, lowering power, and increasing type 1 error. However, correlations were determined by dividing by the standard deviations, and the scale parameter was reduced. This might lead to leveraging power and slightly elevated type 1 error rates. The performance of the MERG is demonstrated in the simulation study. With various combinations of simulation parameters, the MERG performed better than the logistic regression and Fisher’s P -value summation methods. While Fisher’s method showed greater power, it did not seem to be feasible because type 1 error tended to be higher than that of the MERG. Moreover, when the sample size increased from 4000 to 8000, the power of the MERG became equivalent to that of Fisher’s method. Considering these results, the MERG appears to be appropriate for repeat GWAS. Real data analysis also demonstrated the superior performance of the MERG in the search for genetic susceptibility loci for T2DM. When the MERG was applied to the ASAS cohort data, 14 SNPs were identified as significant without clumping (Supplementary Table 2), and six SNPs were significant when clumping was applied (Table 1 ). Moreover, of the 14 SNPs, 12 (86%) showed significant results in the other studies, which were identified in the GWAS catalog (Supplementary Table 3). This was notable because the overall replication rate in the GWAS was reported to be 40%, even with a relaxed P -value threshold ( P < 10 − 5 ) that was used in the GWAS catalog [ 24 ]. The clustered results of the MERG analysis also showed a greater replication rate. In total, four of six (67%) were previously identified SNPs for their T2DM susceptibility, which was far greater than the average replication rate of the GWAS. These results reveal that the MERG outperformed the LR model used in GWASs. In the clumped results of the MERG analysis of the ASAS cohort, the two loci had no previous association with T2DM. Although these loci were found in the GWAS catalog database, they were not significantly associated with T2DM. However, these may be novel loci associated with T2DM. For example, rs3812694 of NPFFR1 had significant results in the MERG analysis of the ASAS cohort, and the loci were reported as height-related SNPs [ 25 ]. It is well-known that height is inversely correlated with the incidence of T2DM [ 26 , 27 ]. NPFFR1 is a neuropeptide receptor involved in regulating glucose homeostasis. It is a G protein-coupled receptor of NPFF and is associated with nociception, locomotion, and reproduction [ 28 ]. Considering that a mouse model experiment showed that NPFFR2 is involved in the regulation of glucose homeostasis by controlling parasympathetic output, NPFFR1 is likely to be another factor in non-insulin glucose control [ 29 ]. Therefore, it is possible that the SNP is a novel locus for T2DM or has an indirect effect on its development. The rs12291017 of the MRGPRX3 gene was also interesting because it directly interacted with NPFFR1 in the protein-protein interaction network (Supplementary Fig. 9). Although no definite association has been reported for this SNP, it seems worthy of further investigation for its association with T2DM development and glucose control. In a meta-analysis of the MERG results from three different cohort datasets, the same number of SNPs ( n = 14) was found to be significant. Moreover, 12 of the 14 SNPs overlapped with the results of the MERG analysis of the ASAS cohort data. This seemed to result from the relatively smaller sample sizes of the CAVAS and HEXA cohorts. The number of data samples was approximately half the sample size of the ASAS cohort. Therefore, these results did not increase the statistical power of their meta-analysis. Although the number of significant SNPs in the meta-analysis was the same as that in the MERG analysis with the ASAS cohort, the results showed excellent replicability. All 12 SNPs were reported as significant loci in a previous GWAS (Supplementary Table 3). The replication rate was 87%, similar to the results of the MERG analysis with the ASAS cohort data, but far greater than that of a previous report [ 24 ]. Moreover, clumped results also showed a high replication rate (4 of 6, 67%). When compared with the results of the GWAS with baseline total samples ( n = 14205), the number of significant SNPs was higher than that of the baseline GWAS ( n = 2). Consequently, there was clear evidence that the MERG outperformed in the discovery of genetic loci in the association studies with longitudinal follow-up data. One limitation of this study was that relatively rare variants (MAF < 0.05) were not considered. Such variants tend to have missing values during follow-up because only a small number of participants have the risk alleles of the variants, making the estimation of the dependency matrix infeasible. This problem can be bypassed by adopting missing-value estimation methods. In addition, although longitudinal phenotype information augmented the power of association tests, MERG still required almost ten thousands of samples to acquire sufficient power to detect susceptible loci. The sample size is one of the critical factors related to the power of the MERG, as revealed in the simulation analysis; therefore, more replicable and explainable results could be obtained in future research with larger sample sizes of study populations. Conclusion This study revealed that longitudinal phenotypic information increased the power to detect genetic loci for T2DM by applying MERG. These results will help discover new genetic loci for T2DM and other phenotypes. Abbreviations GWAS : genome-wide association study MERG : meta-analysis of repeat GWAS T2DM : type 2 diabetes OR : odds ratio BMI : body mass index KoGES : Korean genetics and epidemiology study ASAS : Ansan-Ansung CAVAS : Cardiovascular disease association study HEXA : Health examinees GC : genomic control MAF : minor allele frequency SNP : single nucleotide polymorphism Declarations Ethics approval and consent to participate This study was approved by institutional review board of Gachon University, Gill Hospital (IRB number: GCIRB2023-203) Consent for publication Not applicable Availability of data and materials The datasets generated and/or analyzed during the current study are available in the CODA repository (https://coda.nih.go.kr/) Competing interests The author declares no competing interests Funding This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT, RS-2022-NR070832). Authors' contributions S.B.C designed the study, developed the method, performed the analysis and wrote the manuscript. Acknowledgements Not applicable References Collaborators, G. B. D. D. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 402 (10397), 203–234 (2023). Bonnefond, A., Florez, J. C., Loos, R. J. F. & Froguel, P. Dissection of type 2 diabetes: a genetic perspective. Lancet Diabetes Endocrinol. 13 (2), 149–164 (2025). Billings, L. K. & Florez, J. C. The genetics of type 2 diabetes: what have we learned from GWAS? Ann. N Y Acad. Sci. 1212 , 59–77 (2010). Visscher, P. M. et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am. J. Hum. Genet. 101 (1), 5–22 (2017). Thompson, J. R., Attia, J. & Minelli, C. The meta-analysis of genome-wide association studies. Brief. Bioinform . 12 (3), 259–269 (2011). Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9 (1), 2941 (2018). Lin, D. Y. & Sullivan, P. F. Meta-analysis of genome-wide association studies with overlapping subjects. Am. J. Hum. Genet. 85 (6), 862–872 (2009). LeBlanc, M. et al. A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genom. 19 (1), 494 (2018). Brown, M. B. 400: A method for combining non-independent, one-sided tests of significance. Biometrics 31 (4), 6 (1975). Kim, Y., Han, B. G. & Ko, G. E. S. Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int. J. Epidemiol. 46 (2), e20 (2017). Kim, Y. K. et al. Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population. Cardiovasc. Diabetol. 15 , 20 (2016). Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81 (5), 1084–1097 (2007). Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81 (3), 559–575 (2007). Okada, Y. et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat. Genet. 44 (3), 302–306 (2012). Palmer, N. D. et al. Genetic Variants Associated With Quantitative Glucose Homeostasis Traits Translate to Type 2 Diabetes in Mexican Americans: The GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium. Diabetes 64 (5), 1853–1866 (2015). Surendran, P. et al. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals. Nat. Genet. 52 (12), 1314–1332 (2020). Cho, S. B., Jang, J. H., Chung, M. G. & Kim, S. C. Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus. Diabetes Metab. J. 45 (2), 231–240 (2021). Replication, D. I. G. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46(3):234–244. (2014). Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44 (9), 981–990 (2012). Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50 (11), 1505–1513 (2018). Tsai, F. J. et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 6 (2), e1000847 (2010). Carey, C. E. et al. Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation. Nat. Hum. Behav. 8 (8), 1599–1615 (2024). Huang, J. et al. Genomics and phenomics of body mass index reveals a complex disease network. Nat. Commun. 13 (1), 7973 (2022). Marigorta, U. M., Rodriguez, J. A., Gibson, G. & Navarro, A. Replicability and Prediction: Lessons and Challenges from GWAS. Trends Genet. 34 (7), 504–517 (2018). Yengo, L. et al. A saturated map of common genetic variants associated with human height. Nature 610 (7933), 704–712 (2022). Rhee, E. J. et al. Relation between Baseline Height and New Diabetes Development: A Nationwide Population-Based Study. Diabetes Metab. J. 43 (6), 794–803 (2019). Wittenbecher, C., Kuxhaus, O., Boeing, H., Stefan, N. & Schulze, M. B. Associations of short stature and components of height with incidence of type 2 diabetes: mediating effects of cardiometabolic risk factors. Diabetologia 62 (12), 2211–2221 (2019). Koller, J., Herzog, H. & Zhang, L. The distribution of Neuropeptide FF and Neuropeptide VF in central and peripheral tissues and their role in energy homeostasis control. Neuropeptides 90 , 102198 (2021). Zhang, L., Koller, J., Gopalasingam, G., Qi, Y. & Herzog, H. Central NPFF signalling is critical in the regulation of glucose homeostasis. Mol. Metab. 62 , 101525 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMethodandResults.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8183367","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":561328414,"identity":"1fd2f28c-07f4-4849-b53e-811132c8a477","order_by":0,"name":"Seong Beom 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13:24:51","extension":"html","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116951,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/33e53f24853faa25ea0ec619.html"},{"id":98520362,"identity":"764a3200-8a18-41e8-8a08-cbc8fbcf57e6","added_by":"auto","created_at":"2025-12-18 13:24:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall process of meta-analysis of repeat GWAS (MERG) analysis. \u003c/strong\u003eIn the MERG, a repeat genome-wide association study (GWAS) is performed based on genotype and phenotype data from follow-ups. Here, a repeat GWAS is performed on multiple GWASs with each of the phenotype information from follow-ups. Therefore, for each variant, \u003cem\u003eP\u003c/em\u003e-values of follow-up points are obtained. In the MERG, the \u003cem\u003eP\u003c/em\u003e-values are summarized with a dependency matrix, which is adopted from Brown’s method (See Methods section). For estimation of the matrix, repeated subsampling of participants and determination of \u003cem\u003eP\u003c/em\u003e-values from association studies are performed first. This procedure is repeated with each set of follow-up data. After obtaining the \u003cem\u003eP\u003c/em\u003e-value vectors from each follow-up, dependencies between follow-ups are determined. For each variant, these steps are applied, and statistical significance is determined with test statistics and multiple testing correction. The determination of the test statistics is dependent on Brown’s method.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/be9ac3223b0ff0120550de68.png"},{"id":98520391,"identity":"bf035342-6977-4900-8833-43479e14c3f6","added_by":"auto","created_at":"2025-12-18 13:24:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":524537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of results from power analysis of MERG, Fisher’s method, and logistic regression. \u003c/strong\u003e\u0026nbsp;Simulation data were generated with the minimal parameters (number of follow-ups = 2, minor allele frequency = 0.1, β = 0.2), and the number of samples was set to (a) 4,000 and (b) 8,000. Note that Fisher’s method shows the greatest power, especially with more stringent P-value thresholds. On the other hand, MERG.cov has the weakest performance, and this is apparent as the P-value thresholds decrease. The LR shows comparable power under less stringent P-value thresholds. However, the difference in power compared to Fisher’s method and MERG becomes greater with stringent P-value thresholds. Although the difference in performance between MERG and Fisher’s method increases as the P-value thresholds decrease, the magnitudes are less than those of LR and MERG.cov. MERG, meta-analysis of repeat genome-wide association; LR, logistic regression, MERG.cov, MERG with covariance matrix.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/70e5f82ea18eddc28cc4d920.png"},{"id":98520364,"identity":"242e5c67-0faa-451a-a104-e89aa4c1c98f","added_by":"auto","created_at":"2025-12-18 13:24:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":364100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eType 1 errors of MERG, Fisher’s method, and logistic regression with simulation data. \u003c/strong\u003eThe results are determined with the number of samples set to 4000. Note that an increase in type 1 error in logistic regression (LR) occurs when the number of follow-ups becomes 5. In all comparisons, the order of type 1 errors is that of LR, Fisher’s method, MERG, and MERG.cov in a descending direction, although there are ties between the methods. The MERG shows acceptable type 1 error (\u0026lt; 0.05) with all \u003cem\u003eP\u003c/em\u003e-value thresholds except that of 0.05. On the other hand, the type 1 error of LR becomes up to 0.2 when the number of follow-ups increases to 5 and MAF is 0.2. MERG: meta-analysis of repeat genome-wide association study; MERG.cov, MERG with covariance matrix; LR, logistic regression.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/0505b879df20fd7ac58187ca.png"},{"id":98520386,"identity":"53f61222-c1c7-4422-b9eb-ea546d905452","added_by":"auto","created_at":"2025-12-18 13:24:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":603718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta-analysis results of MERG analyses with ASAS, CAVAS, and HEXA cohorts. \u003c/strong\u003eThe forest plot shows odds ratios (ORs) and their confidence intervals (CIs) from the results of the association analysis using phenotype information of each follow-up. In the plot, blue squares indicate ORs, and their sizes are proportional to the magnitude of the ORs. Horizontal lines crossing the squares represent Cis, and the ends of the lines are located at the lower and upper 95% confidence limits. In the plot of 6-(e), upper CI limits are represented with arrows, which means that the upper limits are beyond the range of the x-axis in the plots. MERG, meta-analysis of repeat genome-wide association study; ASAS, Ansan/Ansung; CAVAS, Cardiovascular Disease Association Study; HEXA, Heath Examinees.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/9895b0a34e6b228d40ee2cea.png"},{"id":108182634,"identity":"3e223ca2-c5bb-49bb-896b-691ecc2c4cef","added_by":"auto","created_at":"2026-04-30 08:59:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2311573,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/513e2868-fc07-45fa-a405-2e4191dee465.pdf"},{"id":98625208,"identity":"4923acb7-4229-47d3-9e0d-265f235fb6a4","added_by":"auto","created_at":"2025-12-19 17:08:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2620393,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethodandResults.docx","url":"https://assets-eu.researchsquare.com/files/rs-8183367/v1/12dc7b2b475c1a1390bc8d56.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of longitudinal follow-up data increases power in the identification of genetic loci for type 2 diabetes","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes mellitus is one of the most prevalent diseases associated with various morbidities and mortalities, and the affected patients number continues to increase globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the subtypes, type 2 diabetes (T2DM) is the most prevalent. Although obesity is the major cause, genetic factors play important roles in the development of T2DM [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAssociation studies have been conducted to identify genetic loci associated with T2DM. With the advent of high-throughput genotyping technologies, genome-wide association studies (GWASs) have efficiently identified genetic loci of T2DM susceptibility [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, because most susceptible loci have a mild to moderate effect size, a large sample size of over tens of thousands is required to detect such loci through GWASs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To circumvent this problem, researchers have been performing meta-analyses that have discovered many susceptible loci for diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Researchers of international collaborations preformed such meta-analyses to increase the power of the GWASs and discovered many susceptible loci that were not found in independent GWASs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost GWASs have a case\u0026ndash;control design and use a single snapshot of phenotype information. Even if the follow-up phenotype information is available, GWASs can be performed independently with the follow-up phenotypes, and no specific method is available for integrating the results of GWASs with follow-up phenotypes. In the analysis of genetic associations with longitudinal follow-ups, meta-analysis methods can be applied in a straightforward manner. After performing GWASs with phenotype data from each follow-up, all the statistical results of a variant can be applied to a meta-analysis. The hypothesis of this integration was that such a meta-analysis might increase the power of GWASs and make it possible to identify susceptible loci that were not found in the individual GWASs. However, statistical results, including odds ratios (ORs) or \u003cem\u003eP\u003c/em\u003e-values, between two distinct follow-ups are likely to be correlated because overlapping samples with identical genotypes are repeatedly used in GWASs at different follow-ups, as is often the case in multitraits GWASs with overlapping samples [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. If the correlation is not controlled, the final statistics of the meta-analysis would have a higher probability of being inflated, which increases the number of false-positive results. Therefore, the dependency structure of the statistical results between follow-up points should be resolved using meta-analysis methods for GWASs with longitudinal follow-ups. For this purpose, a meta-analysis of repeat GWASs (MERG) was developed in this study. Here, repeat GWAS refers to GWASs performed with the phenotype information of each follow-up. To measure dependency, we empirically estimated the dependency structure, which was then integrated into the meta-analysis using the Brown\u0026rsquo;s method [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the MERG was applied to identify the susceptible loci for T2DM using cohort datasets with moderate-sized genotype data and multiple follow-up phenotypes. Simulation analyses were implemented to assess the performance of the MERG method, and a proof-of-concept analysis of exome chip data with follow-up information on T2DM status was conducted. Cohort datasets collected from the Korea Epidemiology and Genetics (KoGES) project were constructed to identify environmental and genetic factors associated with non-communicable diseases in the Korean population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eOverall MERG analysis flow\u003c/h2\u003e\n \u003cp\u003eIn the repeat GWAS, we performed a GWAS for each follow-up phenotype. Cohort data included follow-up phenotype information containing disease status and related covariates, such as age, body mass index (BMI), and waist circumference (Fig. 1). Although the genotypes of the participants remained unchanged throughout the follow-up period, such covariates varied according to different follow-up time points. Therefore, different GWAS results for the phenotype data from the follow-ups were obtained and Brown method was applied with an empirical estimation of the dependency structure between repeated GWAS results.\u003c/p\u003e\n \u003cp\u003eThe Brown method integrates the covariance structure between the \u003cem\u003eP\u003c/em\u003e-values of independent studies [9]. The method was developed based on Fisher’s method, which sums the \u003cem\u003eP\u003c/em\u003e values of independent studies and applies the test statistics to the chi-squared distribution, as in Eq. (1):\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/h3\u003e\n\u003cdiv\u003e(1)\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e refers to the number of studies and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the \u003cem\u003eP\u003c/em\u003e-value of the \u003cem\u003ei\u003c/em\u003e-th study. In the repeat GWAS, \u003cem\u003eP\u003c/em\u003e-values were obtained from the results of the GWASs performed with the follow-up phenotypes.\u003c/p\u003e\n\u003cp\u003eThe Brown method uses the summation of \u003cem\u003eP\u003c/em\u003e-values, as in Fisher’s method, but rescales the degrees of freedom using the covariances between the \u003cem\u003eP\u003c/em\u003e-values. In Eq. (2), \u003cem\u003ef\u003c/em\u003e and \u003cem\u003ec\u003c/em\u003e are derived from the expected value and variance of the \u003cem\u003eΨ\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/h3\u003e\n\u003cdiv\u003e(2)\u003c/div\u003e\n\u003cp\u003eThe expectation and variance are estimated with Eq.\u0026nbsp;(3),\u003c/p\u003e\n\u003ch3\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/h3\u003e\n\u003cdiv\u003e(3)\u003c/div\u003e\n\u003cp\u003ewhere those values were estimated with the number of studies and variance of the \u003cem\u003eΨ\u003c/em\u003e that are estimated with the number and covariances between \u003cem\u003eP\u003c/em\u003e-values. The \u003cem\u003ec\u003c/em\u003e was used to rescale the degrees of freedom in the estimation of \u003cem\u003eP\u003c/em\u003e-values for the Brown method.\u003c/p\u003e\n\u003cp\u003eTo apply the Brown's method to the repeat GWAS, the covariance matrix for the \u003cem\u003eP\u003c/em\u003e-values is necessary, as it captures the dependency structure among them. Since only the vector of \u003cem\u003eP\u003c/em\u003e-values from the repeat GWAS is available, an empirical estimation of the covariance matrix was used to approximate this dependency structure. (Fig. 1). First, participants in the cohorts who appeared in all follow-ups were selected. Subsequently, a subset of the participants was sampled, and a repeat GWAS was performed. The number of subsets was calculated by multiplying 0.632 by the number of participants who appeared in all follow-ups. A value of 0.632 was derived from the bootstrap method for the estimation of parameters using multiple sampling. This sampling was conducted by replacing the participants and was repeated 30 times. After this process, we obtained the \u003cem\u003eP\u003c/em\u003e-value matrix with a row of 30 \u003cem\u003eP\u003c/em\u003e-values from the sampling and a column of different follow-ups. The covariances can be estimated using this matrix. However, the MERG used correlations of the \u003cem\u003eP\u003c/em\u003e-values instead of covariances because the powers were greater when using the correlations (See Results section).\u003c/p\u003e\n\u003ch3\u003eAnalysis of exome chip data from KoGES project\u003c/h3\u003e\n\u003cp\u003eThe exome array data of participants from the Ansan/Ansung (ASAS) cohort, which is part of the KoGES project, were used for real data analysis [10]. The KoGES project developed three main cohorts: the ASAS, Cardiovascular Disease Association Study (CAVAS), and the Health Examinees (HEXA) cohorts. The Illumina HumanExome BeadChip platform was used to generate the data. The platform contains approximately 250,000 probes for genetic loci in exome regions. These data were originally used to analyze the pleiotropic effects of exome loci on cardiometabolic traits [11]. The entire process of data generation and quality control has been described in the literature. In the present study, we used 50,543 probes that passed the quality control process of previous research, and the missing genotypes were entered using the Beagle software [12]. After obtaining \u003cem\u003eP\u003c/em\u003e-values via the MERG, we applied the clump function of the Plink program with default parameters [13]. Type 2 diabetes was defined according to the American Diabetes Association criteria (Supplementary Methods).\u003c/p\u003e\n\u003cp\u003eRepeat GWAS with follow-up information was performed using the plink program, and genomic control (GC)-based \u003cem\u003eP\u003c/em\u003e-values and Bonferroni’s correction were used to determine significant results.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePower and type 1 error analysis with simulation data\u003c/h2\u003e \u003cp\u003eTo assess the performance of the MERG, simulation data were generated as described in the Supplementary Methods section with different combinations of parameters. In the simulation, four parameters, including 1) Minor allele frequency (MAF), 2) number of samples, 3) number of follow-ups, and 4) effect size of the simulated genetic variant, were varied, and the performance was determined according to the different combinations of the parameters. The MAF was set to 0.1, 0.2, and 0.3, and the numbers of follow-ups were two and five. The numbers of baseline samples were set to 4000 and 8000, which were similar to those of the exome chip data of the KoGES cohorts. The effect size of the genetic variant, which is a coefficient of the logistic model used in the simulation, was set to similar effect sizes as the significant variants in previous GWASs. In the GWASs, most of the effect sizes of the genetic variants were lower than 1.5, which is equivalent to 0.4 of a coefficient in the logistic model. Considering this, the effect size of genetics was set to 0.2, 0.3, and 0.4.\u003c/p\u003e \u003cp\u003eIn the simulation analysis, the MERG showed substantial power for the detection of genetic loci using follow-up phenotype information. Generally, the power was dependent on the MAF, number of samples, effect size of genetic variants, and number of follow-ups. Supplementary Fig.\u0026nbsp;1 shows the results of the power analysis when the number of samples was set at 4000. As \u003cem\u003eP\u003c/em\u003e-value thresholds increased, the performance decreased. However, when the effect size was set to 0.4, all significant genetic variants were perfectly detected regardless of the MAF. When the effect size was low (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2), the powers were dependent on the MAF. Although the overall power increased when the number of samples was set to 8000, the results were consistent (Supplementary Fig.\u0026nbsp;2). In the figure, the powers appear saturated when the effect size is \u0026ge;\u0026thinsp;0.3, regardless of the MAF. Only when the effect size (beta value) was set to 0.2, the powers showed obvious differences between the MAFs. In both figures, an increase in the number of follow-ups resulted in an upward shift of power at all threshold \u003cem\u003eP\u003c/em\u003e-values.\u003c/p\u003e \u003cp\u003eSimulation data containing null genetic variants with follow-up phenotype information were generated to estimate type 1 error of the MERG. The results showed complicated patterns of type 1 errors according to changes in the parameters. In general, type 1 errors increased as the sample size increased, regardless of the number of follow-ups and MAFs (Supplementary Fig.\u0026nbsp;3). As the number of follow-ups increased, type 1 errors tended to increase for all MAFs and sample sizes. While the two parameters showed a consistent trend in type 1 errors, the MAF showed different trends according to the number of samples. When the number of samples was set to 4000, a greater MAF showed elevated type 1 errors. However, when the number of samples was 8000, the results showed a reverse pattern: a lower MAF induced an elevated type 1 error. In any parameter combination, type 1 errors were acceptable with different \u003cem\u003eP\u003c/em\u003e-value thresholds (Supplementary Fig.\u0026nbsp;3). With a \u003cem\u003eP\u003c/em\u003e-value threshold of 0.05, type 1 errors of the MERG ranged from 0.05 to 0.09. However, the type 1 error was under 0.05 with all other \u003cem\u003eP\u003c/em\u003e-value thresholds.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparison with other benchmark methods\u003c/h3\u003e\n\u003cp\u003eTo compare the performance of the MERG, logistic regression and Fisher\u0026rsquo;s method were applied to the simulated data. When logistic regression (LR) was applied, the most significant \u003cem\u003eP\u003c/em\u003e-value of the repeat GWAS for each simulated follow-up dataset was used. The overall tendency of the power analysis was the same as that of the MERG data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Figs.\u0026nbsp;4\u0026ndash;9). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows results determined using 4000 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-(a)) and 8000 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-(b)) with an MAF of 0.1 and beta of 0.2. Clearly, the power of the MERG increased as the sample size increased from 4000 to 8000 (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-(a) and (b)). MERG with covariance matrix (MERG.cov), Fisher\u0026rsquo;s method, and LR showed the same tendency with the change in sample size. The increases in follow-up, MAF, and effect size also resulted in the elevation of powers. When the sample size and MAF were low, Fisher\u0026rsquo;s method showed the best performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-(a) and Supplementary Fig.\u0026nbsp;4). However, when the sample size increased, all methods showed equivalent power, especially with larger effect sizes (Supplementary Figs.\u0026nbsp;4\u0026ndash;9). Only MERG.cov showed the lowest power for all combinations of parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the benchmark analysis of type 1 errors, all methods showed various type 1 errors with different thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Fig.\u0026nbsp;10). The results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were obtained with 4000 samples, different numbers of follow-ups (n\u0026thinsp;=\u0026thinsp;2 and 5), and different MAFs (0.1, 0.2, and 0.3). In general, as \u003cem\u003eP\u003c/em\u003e-value thresholds became more stringent, type 1 errors rapidly decreased. In particular, when the threshold was below 1e-04, type 1 error became almost zero, except for Fisher\u0026rsquo;s method. Even if Fisher\u0026rsquo;s method yielded false-positive results with such a stringent threshold, the magnitude of type 1 error was approximately 0.02. However, with less stringent thresholds, type 1 errors showed differences between the methods. In general, MERG.cov showed the lowest type 1 error, and LR had the greatest error. Notably, LR showed a spike in type 1 error when the number of follow-ups and MAFs increased. When comparing the magnitude of the increase in type 1 errors with changes in the number of follow-ups (two to five), these results seemed to be more prominent because the other methods showed relatively less increase in their type 1 errors. The MERG showed acceptable type 1 error with all \u003cem\u003eP\u003c/em\u003e-value thresholds. However, when the threshold was set to 0.05, the MERG showed a relatively higher type 1 error (approximately 0.07). The Fisher method generally showed a greater type 1 error than the MERG with different combinations of parameters. Type 1 errors tended to increase with increasing sample size and number of follow-ups for all methods. However, the changes in type 1 errors with an increase in MAFs were not linear in the three methods (MERG.cov, Fisher, and LR), as shown for the MERG method. In all the methods, type 1 error showed the greatest magnitude with an MAF of 0.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, when the number of follow-ups increased, type 1 errors also increased with a greater number of samples (n\u0026thinsp;=\u0026thinsp;8000, Supplementary Fig.\u0026nbsp;10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis results of exome chip data\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eMERG analysis with independent cohorts\u003c/h2\u003e \u003cp\u003eBaseline and follow-up phenotype information are summarized in Supplementary Table\u0026nbsp;1. For the MERG analysis, filtering and imputation of exome chip data were conducted first. It is possible that genetic loci of lower MAFs are difficult to apply to the MERG because if the participants with low-frequency loci were lost to follow-up, then the genetic loci data might be missing when the GWAS was performed with follow-up phenotype information. Therefore, genetic loci of MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were removed to rule out possible computation errors that resulted from the missing data. And, genetic loci for which data were missing from more than 10% of the 14205 participants in the exome chip data were also removed. Finally, 31436 genetic loci were selected, and Beagle software was used to perform missing data imputation using default parameters [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs shown in the simulation analysis, it is possible that the MERG returns higher numbers of false-positive results (\u0026gt;\u0026thinsp;5%) with a conventional \u003cem\u003eP\u003c/em\u003e-value threshold of 0.05. Therefore, in Bonferroni\u0026rsquo;s multiple testing correction, the nominal \u003cem\u003eP\u003c/em\u003e-value threshold was set to 0.01, and the adjusted \u003cem\u003eP\u003c/em\u003e-value threshold was 3.19\u0026times;10\u003csup\u003e\u0026ndash;7\u003c/sup\u003e (=\u0026thinsp;0.01/31346). Although ten follow-up data points were available for the ASAS cohort (Supplementary Table\u0026nbsp;1), baseline and follow-up phenotype information were used to avoid a possible increase in false-positive results, which was revealed in the simulation analysis. Consequently, the MERG identified 14 significant loci, and six genetic loci remained after clumping (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;2). The six loci included well-known single-nucleotide polymorphisms (SNPs) that have been reported to be associated with the development of T2DM. For example, rs2206734 is an SNP in CDKAL1 and is well known for its association with BMI, blood pressure, and glucose homeostasis [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a previous report on the current exome data, rs220674 was significant in 14206 participants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. rs8108269 is located between the GIPR and RN7SL836P genes, and previous studies have reported that this locus is susceptible to T2DM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. rs2237895 is within KCNQ1, and its association with T2DM has been reported in previous GWASs [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMERG analysis of the Ansan-Ansung cohort with follow-up phenotype information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL95 CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU95 CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMERG \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20694884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ers2206734\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCDKAL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e3.74\u0026times;10\u003csup\u003e\u0026ndash;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e \u003cp\u003e1.26\u0026times;10\u003csup\u003e\u0026ndash;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e7.71\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.86\u0026times;10\u003csup\u003e\u0026ndash;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.04\u0026times;10\u003csup\u003e\u0026ndash;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e8.85\u0026times;10\u003csup\u003e\u0026ndash;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127253550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ers2233580\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePAX4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.45\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e \u003cp\u003e7.49\u0026times;10\u003csup\u003e\u0026ndash;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e6.13\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.59\u0026times;10\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e2.71\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.97\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18158993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ers12291017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMRGPRX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e2.78\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e \u003cp\u003e2.85\u0026times;10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e2.60\u0026times;10\u003csup\u003e\u0026ndash;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e2.95\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e3.32\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e4.44\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72015573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ers3812694\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNPFFR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e5.55\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e \u003cp\u003e6.09\u0026times;10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e3.64\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e2.45\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e6.49\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.77\u0026times;10\u003csup\u003e\u0026ndash;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46158513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ers8108269\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGIPR,RN7SL836P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e7.51\u0026times;10\u003csup\u003e\u0026ndash;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e \u003cp\u003e1.50\u0026times;10\u003csup\u003e\u0026ndash;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.57\u0026times;10\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e5.11\u0026times;10\u003csup\u003e\u0026ndash;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.45\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e5.04 \u0026times;10\u003csup\u003e\u0026ndash;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2857194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ers2237895\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKCNQ1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e6.70\u0026times;10\u003csup\u003e\u0026ndash;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c10\"\u003e \u003cp\u003e2.89\u0026times;10\u003csup\u003e\u0026ndash;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.32\u0026times;10\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e1.54\u0026times;10\u003csup\u003e\u0026ndash;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e4.76\u0026times;10\u003csup\u003e\u0026ndash;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th FU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c9\"\u003e \u003cp\u003e4.98\u0026times;10\u003csup\u003e\u0026ndash;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eChr: chromosome, BP: base position, SNP ID: rs number of dbSNP database, FU: follow-up, OR: odds ratio, L95: lower bound of 95% confidence interval, U95: upper bound of 95% confidence interval, CI: confidence interval, MERG: meta-analysis of repeat genome-wide association study.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough the ASAS cohort showed significant results in the MERG analysis, the CAVAS and HEXA cohorts showed no significant results. The CAVAS included baseline and three follow-up phenotype information, while two different follow-up phenotype information, including baseline and one follow-up, were included in the HEXA cohort. These results seem to be due to the relatively small number of samples used in the exome chip data (3122 and 3144, respectively), which was related to the decreased power of the MERG analysis.\u003c/p\u003e \u003cp\u003eAfter obtaining the MERG results, a meta-analysis with Fisher\u0026rsquo;s \u003cem\u003eP\u003c/em\u003e-value summation was performed to augment the power for detecting significant genetic loci. After summation of the three results, 14 SNPs were found to be significant with the same adjusted \u003cem\u003eP\u003c/em\u003e-value that was used in the MERG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.19\u0026times;10\u003csup\u003e\u0026ndash;7\u003c/sup\u003e), and there were six significant results after clumping (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Table\u0026nbsp;3). Of these, 12 SNPs overlapped with the significant SNPs in the MERG analysis of the ASAS cohort. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, most of the ORs consistently showed the same direction in the results of the association studies using the follow-up phenotypes, although the \u003cem\u003eP\u003c/em\u003e-values showed different significances.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGWAS with longitudinal follow-ups\u003c/h2\u003e \u003cp\u003eUsing 31346 genetic variants and longitudinal follow-up phenotypes, GWASs were performed using a logistic regression model. The covariates included age, sex, and BMI, as in the MERG analysis. The repeat GWAS of the ASAS cohort had two significant loci, rs2206734 (OR\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.221e-07) and rs10440833 (OR\u0026thinsp;=\u0026thinsp;1.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.52e-07); rs2206734 was selected after clumping. No significant differences were found in the GWAS of the other cohorts. When GWAS was performed with the CAVAS and HEXA cohort follow-up phenotype data, no significant results reached the adjusted \u003cem\u003eP\u003c/em\u003e-value threshold. Finally, a GWAS was performed with 14205 participants\u0026rsquo; baseline phenotype information and genotypes. The results showed that rs2237895 (OR\u0026thinsp;=\u0026thinsp;1.30, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.45e-08) and rs2233580 (OR\u0026thinsp;=\u0026thinsp;1.46, P\u0026thinsp;=\u0026thinsp;6.26e-07) were significant, and were selected after clumping.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, genetic loci associated with susceptibility to T2DM were identified using longitudinal follow-up information. Although the total number of samples was relatively modest compared with that of other GWASs, highly reproducible results were obtained. The MERG, a novel GWAS method that fully utilizes the phenotypic information of follow-up, leverages the power to detect susceptible loci without increasing the sample size.\u003c/p\u003e \u003cp\u003eIn the power and type 1 error analysis, the MERG method showed substantial performance. Although MERG.cov showed type 1 error, it had insufficient power to detect significant loci. However, the MERG applies a correlation matrix instead of a covariance matrix to determine the dependency structure. This seems to result from the fact that the empirically estimated covariances tend to be biased, which ultimately results in an overestimation of the scale parameter, lowering power, and increasing type 1 error. However, correlations were determined by dividing by the standard deviations, and the scale parameter was reduced. This might lead to leveraging power and slightly elevated type 1 error rates.\u003c/p\u003e \u003cp\u003eThe performance of the MERG is demonstrated in the simulation study. With various combinations of simulation parameters, the MERG performed better than the logistic regression and Fisher\u0026rsquo;s \u003cem\u003eP\u003c/em\u003e-value summation methods. While Fisher\u0026rsquo;s method showed greater power, it did not seem to be feasible because type 1 error tended to be higher than that of the MERG. Moreover, when the sample size increased from 4000 to 8000, the power of the MERG became equivalent to that of Fisher\u0026rsquo;s method. Considering these results, the MERG appears to be appropriate for repeat GWAS.\u003c/p\u003e \u003cp\u003eReal data analysis also demonstrated the superior performance of the MERG in the search for genetic susceptibility loci for T2DM. When the MERG was applied to the ASAS cohort data, 14 SNPs were identified as significant without clumping (Supplementary Table\u0026nbsp;2), and six SNPs were significant when clumping was applied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, of the 14 SNPs, 12 (86%) showed significant results in the other studies, which were identified in the GWAS catalog (Supplementary Table\u0026nbsp;3). This was notable because the overall replication rate in the GWAS was reported to be 40%, even with a relaxed \u003cem\u003eP\u003c/em\u003e-value threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) that was used in the GWAS catalog [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The clustered results of the MERG analysis also showed a greater replication rate. In total, four of six (67%) were previously identified SNPs for their T2DM susceptibility, which was far greater than the average replication rate of the GWAS. These results reveal that the MERG outperformed the LR model used in GWASs.\u003c/p\u003e \u003cp\u003eIn the clumped results of the MERG analysis of the ASAS cohort, the two loci had no previous association with T2DM. Although these loci were found in the GWAS catalog database, they were not significantly associated with T2DM. However, these may be novel loci associated with T2DM. For example, rs3812694 of \u003cem\u003eNPFFR1\u003c/em\u003e had significant results in the MERG analysis of the ASAS cohort, and the loci were reported as height-related SNPs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is well-known that height is inversely correlated with the incidence of T2DM [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eNPFFR1\u003c/em\u003e is a neuropeptide receptor involved in regulating glucose homeostasis. It is a G protein-coupled receptor of NPFF and is associated with nociception, locomotion, and reproduction [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Considering that a mouse model experiment showed that \u003cem\u003eNPFFR2\u003c/em\u003e is involved in the regulation of glucose homeostasis by controlling parasympathetic output, \u003cem\u003eNPFFR1\u003c/em\u003e is likely to be another factor in non-insulin glucose control [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, it is possible that the SNP is a novel locus for T2DM or has an indirect effect on its development. The rs12291017 of the \u003cem\u003eMRGPRX3\u003c/em\u003e gene was also interesting because it directly interacted with \u003cem\u003eNPFFR1\u003c/em\u003e in the protein-protein interaction network (Supplementary Fig.\u0026nbsp;9). Although no definite association has been reported for this SNP, it seems worthy of further investigation for its association with T2DM development and glucose control.\u003c/p\u003e \u003cp\u003eIn a meta-analysis of the MERG results from three different cohort datasets, the same number of SNPs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14) was found to be significant. Moreover, 12 of the 14 SNPs overlapped with the results of the MERG analysis of the ASAS cohort data. This seemed to result from the relatively smaller sample sizes of the CAVAS and HEXA cohorts. The number of data samples was approximately half the sample size of the ASAS cohort. Therefore, these results did not increase the statistical power of their meta-analysis. Although the number of significant SNPs in the meta-analysis was the same as that in the MERG analysis with the ASAS cohort, the results showed excellent replicability. All 12 SNPs were reported as significant loci in a previous GWAS (Supplementary Table\u0026nbsp;3). The replication rate was 87%, similar to the results of the MERG analysis with the ASAS cohort data, but far greater than that of a previous report [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, clumped results also showed a high replication rate (4 of 6, 67%). When compared with the results of the GWAS with baseline total samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14205), the number of significant SNPs was higher than that of the baseline GWAS (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2). Consequently, there was clear evidence that the MERG outperformed in the discovery of genetic loci in the association studies with longitudinal follow-up data.\u003c/p\u003e \u003cp\u003eOne limitation of this study was that relatively rare variants (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were not considered. Such variants tend to have missing values during follow-up because only a small number of participants have the risk alleles of the variants, making the estimation of the dependency matrix infeasible. This problem can be bypassed by adopting missing-value estimation methods. In addition, although longitudinal phenotype information augmented the power of association tests, MERG still required almost ten thousands of samples to acquire sufficient power to detect susceptible loci. The sample size is one of the critical factors related to the power of the MERG, as revealed in the simulation analysis; therefore, more replicable and explainable results could be obtained in future research with larger sample sizes of study populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed that longitudinal phenotypic information increased the power to detect genetic loci for T2DM by applying MERG. These results will help discover new genetic loci for T2DM and other phenotypes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e: genome-wide association study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMERG\u003c/strong\u003e: meta-analysis of repeat GWAS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT2DM\u003c/strong\u003e: type 2 diabetes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e: odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e: body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKoGES\u003c/strong\u003e: Korean genetics and epidemiology study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eASAS\u003c/strong\u003e: Ansan-Ansung\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAVAS\u003c/strong\u003e: Cardiovascular disease association study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHEXA\u003c/strong\u003e: Health examinees\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGC\u003c/strong\u003e: genomic control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAF\u003c/strong\u003e: minor allele frequency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e: single nucleotide polymorphism\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by institutional review board of Gachon University, Gill Hospital (IRB number: GCIRB2023-203)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the CODA repository (https://coda.nih.go.kr/)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT, RS-2022-NR070832).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.B.C designed the study, developed the method, performed the analysis and wrote the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaborators, G. B. D. D. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e402\u003c/b\u003e (10397), 203\u0026ndash;234 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonnefond, A., Florez, J. C., Loos, R. J. F. \u0026amp; Froguel, P. Dissection of type 2 diabetes: a genetic perspective. \u003cem\u003eLancet Diabetes Endocrinol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (2), 149\u0026ndash;164 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBillings, L. K. \u0026amp; Florez, J. C. The genetics of type 2 diabetes: what have we learned from GWAS? \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1212\u003c/b\u003e, 59\u0026ndash;77 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisscher, P. M. et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e (1), 5\u0026ndash;22 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson, J. R., Attia, J. \u0026amp; Minelli, C. The meta-analysis of genome-wide association studies. \u003cem\u003eBrief. Bioinform\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (3), 259\u0026ndash;269 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (1), 2941 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, D. Y. \u0026amp; Sullivan, P. F. Meta-analysis of genome-wide association studies with overlapping subjects. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e (6), 862\u0026ndash;872 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeBlanc, M. et al. A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. \u003cem\u003eBMC Genom.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 494 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, M. B. 400: A method for combining non-independent, one-sided tests of significance. \u003cem\u003eBiometrics\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (4), 6 (1975).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, Y., Han, B. G. \u0026amp; Ko, G. E. S. Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (2), e20 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, Y. K. et al. Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 20 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrowning, S. R. \u0026amp; Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e (5), 1084\u0026ndash;1097 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e (3), 559\u0026ndash;575 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkada, Y. et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e (3), 302\u0026ndash;306 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer, N. D. et al. Genetic Variants Associated With Quantitative Glucose Homeostasis Traits Translate to Type 2 Diabetes in Mexican Americans: The GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium. \u003cem\u003eDiabetes\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e (5), 1853\u0026ndash;1866 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSurendran, P. et al. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (12), 1314\u0026ndash;1332 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho, S. B., Jang, J. H., Chung, M. G. \u0026amp; Kim, S. C. Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus. \u003cem\u003eDiabetes Metab. J.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (2), 231\u0026ndash;240 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReplication, D. I. G. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. \u003cem\u003eNat Genet\u003c/em\u003e 46(3):234\u0026ndash;244. (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e (9), 981\u0026ndash;990 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (11), 1505\u0026ndash;1513 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai, F. J. et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. \u003cem\u003ePLoS Genet.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (2), e1000847 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarey, C. E. et al. Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation. \u003cem\u003eNat. Hum. Behav.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (8), 1599\u0026ndash;1615 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, J. et al. Genomics and phenomics of body mass index reveals a complex disease network. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 7973 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarigorta, U. M., Rodriguez, J. A., Gibson, G. \u0026amp; Navarro, A. Replicability and Prediction: Lessons and Challenges from GWAS. \u003cem\u003eTrends Genet.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (7), 504\u0026ndash;517 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYengo, L. et al. A saturated map of common genetic variants associated with human height. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e610\u003c/b\u003e (7933), 704\u0026ndash;712 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhee, E. J. et al. Relation between Baseline Height and New Diabetes Development: A Nationwide Population-Based Study. \u003cem\u003eDiabetes Metab. J.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (6), 794\u0026ndash;803 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWittenbecher, C., Kuxhaus, O., Boeing, H., Stefan, N. \u0026amp; Schulze, M. B. Associations of short stature and components of height with incidence of type 2 diabetes: mediating effects of cardiometabolic risk factors. \u003cem\u003eDiabetologia\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e (12), 2211\u0026ndash;2221 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoller, J., Herzog, H. \u0026amp; Zhang, L. The distribution of Neuropeptide FF and Neuropeptide VF in central and peripheral tissues and their role in energy homeostasis control. \u003cem\u003eNeuropeptides\u003c/em\u003e \u003cb\u003e90\u003c/b\u003e, 102198 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, L., Koller, J., Gopalasingam, G., Qi, Y. \u0026amp; Herzog, H. Central NPFF signalling is critical in the regulation of glucose homeostasis. \u003cem\u003eMol. Metab.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 101525 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetes Mellitus, Type 2, Genome-Wide Association Study, Meta-Analysis, Longitudinal Studies, Cohort Studies","lastPublishedDoi":"10.21203/rs.3.rs-8183367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8183367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGenome-wide association studies (GWASs) have identified several genetically susceptible loci associated with type 2 diabetes mellitus (T2DM). However, a large sample size is required to detect such loci, posing challenges for the application of GWASs in translational research.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eHerein, a meta-analysis of repeat GWASs (MERG) was developed to increase the power for susceptible loci discovery. Repeat GWASs refer to GWASs that are performed with follow-up phenotypes of the study population. As the repeat GWAS results have a dependency structure because of overlapping samples between follow-ups, they were integrated into the meta-analysis using an empirical estimation of the structure using a resampling process. The simulation analysis results indicated that the MERG had acceptable type 1 error and statistical power. In the exome data analysis for T2DM, the MERG detected 14 susceptible loci with high reproducibility. Of the 14 significant loci, 12 were identified in previous GWASs. However, conventional GWASs using the same data identified only two significant loci. After clumping, six loci were selected, four of which (rs2206734, rs2233580, rs2237895, and rs2237892) showed reproducibility. Moreover, the mapped genes (\u003cem\u003eMRGPRX3\u003c/em\u003e and \u003cem\u003eRPL24P7\u003c/em\u003e) at the remaining two loci (rs12291017 and rs4334660) were associated with T2DM.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMERG is a powerful method for identifying the genetic loci associated with T2DM in terms of power and reproducibility. This provides additional opportunities to identify novel loci for other traits.\u003c/p\u003e","manuscriptTitle":"Application of longitudinal follow-up data increases power in the identification of genetic loci for type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 13:23:29","doi":"10.21203/rs.3.rs-8183367/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b22970fa-a034-4114-96dd-71aad0ebed9f","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59768909,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59768911,"name":"Health sciences/Diseases"},{"id":59768912,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2026-04-30T02:25:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 13:23:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8183367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8183367","identity":"rs-8183367","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}