Decomposing type 2 diabetes genetics into population-specific features by 600,000 East-Asians

Decomposing type 2 diabetes genetics into population-specific features by 600,000 East-Asians | 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 Decomposing type 2 diabetes genetics into population-specific features by 600,000 East-Asians Chikashi Terao, Kohei Saito, Hsing-Fang Lu, Tatsuhide Inoue, Takeshi Usui, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6532678/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background : Type 2 diabetes (T2D) is a highly heterogeneous metabolic trait, with a higher prevalence in East Asians. This study aims to elucidate the East Asian-specific genetic architecture underlying T2D. Methods : We conducted the largest single-ancestry GWAS to date (596,778 East Asians) and performed one-to-one comparative analyses at high-resolution and in polygenic levels with a European meta-analysis with comparable effective sample sizes. Findings : The East Asian meta-analysis identified 196 T2D-associated loci, comparable to the 199 loci in the EUR meta-analysis. We found 69 SNPs (p<5x10-8) unique to either population, including six East Asian-specific missense variants with stronger effect sizes. Genetic correlation analysis revealed a strong similarity of polygenic architecture between the populations, yet statistical fine-mapping analyses highlighted distinct variant-gene interactions, particularly in pancreatic cells. We found distinct associations between lipid-related traits and T2D susceptibility—pathway analysis of heterogeneous loci revealed higher enrichment of lipid-related gene pathways in Europeans with a stronger effect size of adipose tissue-related epigenetic markers in Europeans. While genetic predisposition to insulin resistance was associated with increased T2D risk in Europeans, East Asians showed minimal differences between cases and controls. Genetic predisposition to HDL-C and BMI-adjusted waist-hip ratio was significantly associated with T2D risk in Europeans. However, the associations were not observed or much weaker in East Asians. Interpretation : Fine-scale genetic differences between populations, especially in lipid-related traits, underlie T2D susceptibility. Associations between insulin resistance and T2D susceptibility are distinct between East Asians and Europeans, in contrast to insulin secretion. Our findings highlight the importance of expanding single-ancestry genetic studies to gain deeper insights into the biology of complex traits. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes Figures Figure 1 Figure 2 Figure 3 RESEARCH CONTEXT Type 2 diabetes (T2D) affects over 500 million people globally, exhibiting significant heterogeneity in clinical features, prevalence, and underlying pathophysiological mechanisms across populations. East Asians demonstrate a unique T2D phenotype characterized by disease onset at lower body mass indices and notably impaired insulin secretion, contrasting with insulin resistance-driven forms commonly observed in Europeans. Prior genome-wide association studies (GWAS), although extensive, predominantly represented European populations, potentially underestimating or obscuring East Asian-specific genetic signals due to limited representation or multi-ancestry dilution effects. This study addresses the critical gap in understanding population-specific genetic contributions to T2D by conducting the largest single-ancestry GWAS to date, involving nearly 600,000 East Asians. By integrating these findings with similarly scaled European data, researchers elucidate genetic distinctions, particularly focusing on insulin secretion, insulin resistance, and lipid metabolism pathways. Population-specific genetic variants, including rare East Asian missense mutations, highlight differences in disease mechanisms at a granular level. This research underscores the necessity of single-ancestry approaches for accurately identifying and interpreting population-specific genetic architecture, ultimately contributing to more precise risk stratification, tailored therapeutic strategies, and enhanced precision medicine for diverse populations globally. INTRODUCTION Type 2 diabetes (T2D) now affects over 500 million people globally ( 1 ), yet clinical manifestation and prevalence vary considerably across populations ( 2 , 3 ). This heterogeneity arises from complex interactions between genetic predispositions, environmental factors, and key mechanisms such as insulin secretion and insulin resistance ( 4 ). In East Asians, for instance, T2D more commonly occurs at lower body mass indices and is marked by reduced insulin secretory capacity ( 4 ), contributing to the relatively high prevalence of T2D in this population ( 1 ). Despite numerous investigations into these distinct clinical features ( 5 – 8 ), the genetic underpinnings of East Asian–specific T2D remains incompletely understood. Genome-wide association studies (GWAS) have identified numerous loci influencing T2D risk ( 5 – 8 ). Spracklen et al. ( 8 ) conducted a landmark single-ancestry East Asian GWAS in 433,540 individuals, identifying 301 distinct signals—including 61 novel loci—while also noting that many variants overlap with those in Europeans. More recent multi-ancestry GWAS ( 9 , 10 ) have provided a broader view of T2D genetics by incorporating multiple populations, though Europeans often constitute 60–70% of these samples. These large cross-ancestry efforts emphasize shared common variants but may under-detect or dilute population-specific signals—particularly in underrepresented cohorts such as East Asians—and often rely on clustering approaches that may not capture finer-grained genetic heterogeneity in insulin secretion and resistance. A deeper investigation of T2D genetics in larger, single-ancestry East Asian cohorts is, therefore, essential to refine our understanding and advance precision medicine for this population. In this study, we integrated newly available large-scale East Asian GWAS data (163,238 East Asians) with the Spracklen et al. dataset ( 8 ), creating the largest single-ancestry T2D GWAS to date in East Asians. We then performed cross-population comparisons (East Asians vs. Europeans) at single-variant, gene, and polygenic levels to assess how insulin resistance and secretory pathways differ between ancestries. Our results bridge critical gaps in T2D genetics and offer insights into both shared and population-specific mechanisms, paving the way for improved risk assessment and therapeutic strategies tailored to East Asian populations. METHODS East Asian GWAS Meta-Analysis Details, including participant characteristics, study design rationale, type 2 diabetes ascertainment, control selection criteria, genotyping platforms and quality control measures, Hardy–Weinberg equilibrium assessments, genomic reference builds, imputation methods, and analytical procedures are described in the Supplementary Note . In summary, the Japanese cohort comprised 1,488 cases and 9,044 controls, sourced from Shizuoka General Hospital and the National Center for Geriatrics and Gerontology, with an average age of 69.0 ± 13.8 and a proportion of 52.0% females. They were diagnosed with type 2 diabetes by clinical endocrinologists. The Taiwanese cohort from the China Medical University Hospital Precision Medicine Biobank comprised 13,736 cases and 138,858 controls, with an average age of 50.1 ± 17.1 and a proportion of 47.4% females. They were diagnosed with type 2 diabetes based on the International Classification of Diseases, 9th and 10th Revision, Clinical Modification codes. GWAS was conducted using BOLT-LMM v2.4.1 ( 29 ) and SAIGE ( 30 ), for the Japanese and Taiwanese cohorts, respectively. Quality control was performed based on minor allele frequency, call rate, and Hardy-Weinberg equilibrium. We used METAL ( 8 ) to perform a fixed-effects meta‐analysis based on inverse variance‐weighted effect sizes. Although alternative methods (e.g., MR‐MEGA, RE2C, or MANTRA) can more flexibly account for trans‐ethnic heterogeneity, our choice of a fixed‐effects approach reflects the need for direct comparison with previous large‐scale meta‐analyses in T2D. In the EAS-meta, we performed meta-analysis by combining those new Japanese and Taiwanese GWASs with the single-ancestry largest GWAS from East Asians provided by Spracklen’s study ( 8 ) —the EAS-meta. The effect sizes strongly correlated between the Japanese study and the Spracklens (R²=0.86). Additionally, we conducted a multi-ancestry GWAS meta-analysis by combining the EAS-meta with the EUR-meta from the DIAMANTE study, which used the Haplotype Reference Consortium panel ( 5 ). Variants reaching a p-value of less than 5x10 − 8 were considered GWAS significant. LDSC ( 31 ) was utilized to estimate the heritability of the EAS-meta. We adjusted heritability estimates to the liability scale with an assumed disease prevalence of 12% ( 1 ). Primary SNPs, called lead SNPs, were defined within each T2D-associated locus based on the lowest P-values and a minimum separation of 500kb. As for secondary lead SNPs, the COJO (Conditional & Joint) function of the Genome-wide Complex Trait Analysis (GCTA) ( 32 ) was used to discern secondary T2D-associated variants, setting a significance threshold of P < 5x10 − 8 . To evaluate trans‐ethnic heterogeneity in effect sizes, we applied Cochran’s Q‐test (CQ‐test) to the lead SNPs identified in both the EAS‐meta and EUR‐meta datasets. Given that lead SNPs differ across populations, secondary variants were not assessed using CQ‐test. We defined heterogeneity as significant when it met a false discovery rate (FDR) threshold of < 0.05. In instances where we identified strong evidence of population‐specific effects, we highlight these loci in the Results. Genetic correlation analyses The Popcorn tool ( 33 ) was utilized to estimate the genetic correlation between the EAS-meta and the EUR-meta, involving the computation of cross-population scores from a reference panel and heritability and genetic correlation estimation. For an additional genetic correlation analysis, LD pruning was first conducted based on the 1000 genome project dataset to identify independent SNPs using the LD structure unique to each population. This process yielded two distinct sets of SNPs, one from each population. We then stratified shared SNPs between the two populations and calculated the Spearman correlation coefficient of the effect sizes (odds ratios) of these shared SNPs in both results based on the respective LD structures of each population. Stratified LD Score regression comparison analyses We compared results of stratified LD score regression (sLDSC) ( 31 , 34 ) from differenct data sources across EAS-meta and EUR-meta. We used 220 cell-type-specific functional annotations from the Roadmap Epigenomics Project against the LD structure derived from the 1000 Genomes Project Phase 3 data. In addition, we used CHIP-seq data from the CHIP-Atlas database ( https://chip-atlas.org ) to identify transcription factor binding sites in pancreas, including CTCF, FOXA1, FOXA2, GATA4, GATA6, GLIS3, HNF1A, HNF1B, PAX6, PDX1, REST, SIRT1, SOX9, and TCF7L2. Plus, we conducted stratified LDSC analysis using pancreatic single-cell ATAC-seq data ( 17 , 35 ) to specifically probe the genetic architecture of T2D in pancreatic islet cells. Putative and prioritized gene identification To represent the populational difference at finer levels, a comprehensive annotation was performed in our downstream analysis. We devised a scoring algorithm to systematically identify putative genes encapsulating four annotation dimensions: Nearest Gene, VEP, SMR, and the ABC model ( Supplementary Note) . Based on this score, up to five genes was designated as putative genes per lead and secondary SNP, and then a single "prioritized gene" was designated as the one with the highest score and nearest to the SNP. Gene set and pathway enrichment comparison analyses Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) was employed for pathway analysis ( 36 ). Gene sets were created from prioritized genes at significantly heterogeneous lead SNPs in each population. According to their effective size, two gene sets were divided into two sets: prioritized gene set with stronger effect in the EAS-meta, “EAS-strong-group”, and the counterparts, “EUR-strong-group”. This method for selecting one gene per locus ensured comparability and integrity in pathway analysis using FUMA. It allowed us to prevent overrepresentation and highlights distinct genetic contributions from each region. A comprehensive explanation of FUMA’s functionalities and the detailed methodology of our approach are presented in the Supplementary Note . We defined significance with an adjusted enrichment P < 0.05. Genetic risk score comparison analyses We calculated weighted Genetic Risk Scores (wGRS) for T2D-related traits in two populations: Japanese individuals from Shizuoka General Hospital (EAS; 1,506 T2D cases and 2,638 controls) and unrelated White British individuals from the UK Biobank (EUR; 15,600 T2D cases and 320,000 controls). Details on genotyping quality control and T2D case definitions are provided in the Supplementary Note. We used established GRSs from previous studies to capture various pathophysiological aspects of T2D ( 6 , 37 , 38 ). From Uder et al. ( 38 ), we combined “beta cell” and “proinsulin” GRSs into an “insulin secretion” GRS, and aggregated “Lipodystrophy,” “Liver lipids,” and “Obesity” GRSs into an “insulin resistance” GRS. We additionally incorporated GRSs for insulin sensitivity index (ISI), corrected insulin response (CIR), and insulin secretion rate (ISR) from Prokopenko et al. ( 37 ), and GRSs for lipid traits (HDL-C, LDL, TG, etc.) from Aly et al. ( 6 ) (Supplementary Table 10). For the GRS computation, we utilized dosage values of SNPs, representing the expected number of risk alleles. We then summed up the product of the dosage for each SNP[i] and its corresponding effect size (log odds ratio): wGRS = Σ(dosage for SNP[i] * effect size for SNP[i]). For the calculations, we only used the variants commonly present both in the EAS and EUR genotype datasets. Statistical Analysis for GRS Associations We fitted a multivariable logistic regression model to nine percentile categories (using the 40–60 percentile as the reference) in the EAS dataset to estimate the relative risk (RR) of T2D within each population at each GRS percentile. We adjusted for the first 10 principal components, age, and sex for the EAS dataset, and we adjusted for the first 40 principal components, age, sex, and facility for the EUR dataset. A two-tailed t-test was conducted to determine statistical significance (P < 0.05). Relative Risk Across Populations To compare mean GRS levels between EAS and EUR groups (cases or controls separately), we computed a relative risk (RR) measure defined as exp(µ EAS - µ EUR )), where µ EAS and µ EUR ​ are the mean GRS values in EAS and EUR, respectively. We estimated 95% confidence intervals using a two-sample t-test with unequal variances. This RR evaluates whether one population, on average, carries a higher or lower genetic load compared to the other. RESULTS Construction of the largest East Asian single-ancestry T2D GWAS and comparison with a European dataset Details of the new Japanese and Taiwanese GWAS are presented in the Supplementary Note. Briefly, the genomic control λ for the Taiwanese and Japanese GWAS were 0.993 and 1.034, respectively. These two new GWAS were combined with Spracklen et al.’s East Asian meta-analysis ( 8 ), resulting in an “EAS-meta” that included 92,392 cases and 504,024 controls—the largest single-ancestry T2D GWAS to date in East Asians (Supplementary Fig. 1). Linkage Disequilibrium Score Regression (LDSC) estimated an overall liability scale heritability of 0.340 (SE = 0.017). The genomic control lambda was 1.396, and an LDSC intercept was 0.998 (SE = 0.011), suggesting a strong polygenic architecture of T2D and free from confounding bias in the current results (Supplementary Fig. 1). For comparison, we selected the DIAMANTE European meta-analysis (“EUR-meta”) ( 7 ), which includes 80,514 cases and 853,816 controls. The effective sample sizes—312,317 for the EAS-meta vs. 294,303 for the EUR-meta—were comparable, allowing a fair cross-population examination of T2D-associated loci. Identification of shared and population-specific T2D loci Consistent with the comparable effective sample sizes, the EAS-meta and the EUR-meta had a comparable number of T2D-associated loci: 196 and 199, respectively (Fig. 1 A; Supplementary Table 1; Supplementary Fig. 2) , further providing a solid basis for our multi-ancestry comparisons. GCTA-COJO conditional analysis additionally identified 82 and 60 independent T2D-associated signals in the EAS-meta and the EUR-meta, respectively (Pcojo < 5x10 − 8 ). We identified 33 and 62 significant heterogeneous loci (we primarily evaluated heterogeneity in lead SNPs for fair comparison between the two populations) between the EAS-meta and the EUR-meta, with FDR thresholds of 3.2x10 − 4 and 2.9x10 − 4 , respectively. Of the lead and secondary SNPs, 27 and 42 T2D-associated variants were present only in either EAS-meta or EUR-meta with the evidence of minor allele frequency below 1%—most were < 0.1%—in the counterpart population based on 1000 genomes project phase 3 reference data ( Supplementary Table 2 ). Out of these 27 EAS-specific SNPs, six SNPs were missense variants, including rs2233580 (R192H) and rs3824004 (R192S) in PAX4 , rs3731600 (A122P) in SCTR , rs75536691 (L180S) in GRB14 , and rs147834269 (E737K) and rs144951440 (V412A) in WFS1 . We found the 69 variants, predominantly present in a single population, enriched in the 95 loci with significant heterogeneity ( Supplementary Table 2 ). For example, in the KCNQ1 locus, the stronger effect size in EAS, we observed 4 of these unique SNPs out of 17 secondary SNPs in this locus in EAS, while the EUR-meta had no specific SNPs out of 5 secondary SNPs in this locus. In the TCF7L2 region, the stronger effect size in EUR, the EUR-meta had 6 of these unique SNPs out of 11 secondary SNPs in this region. These findings underscore the importance of exploring variants detected from single-ancestry GWAS and comparing effect sizes between (or among) populations to detect independent signals in the significant loci for elucidating the genetic basis of T2D. Genetic correlation and multi-ancestry meta-analysis While we observed distinct population-specific genetic patterns at a very granular level, a genetic correlation analysis found the two populations’ overall genetic signals of T2D were very similar (p = 1.0). Furthermore, we found strong correlations of beta coefficients in the bins of variants according to p-values (Fig. 1 B). These indicated a considerable overlap in T2D genetic risk between the two populations, even in loci with non-GWAS significant polygenic associations, as far as we focus on common variants shared between the populations. While Spracklen et al ( 8 ) found a strong correlation in per-allele effect size between East Asians and Europeans at a lead SNP level, here we further demonstrated an overlap in loci with non-GWAS significant polygenic associations. Given this overarching genetic similarity across populations, a multi-ancestry meta-analysis in the current study refined shared T2D-associated loci ( Supplementary Note; Supplementary Fig. 3; Supplementary Fig. 3 4 ) and identified an additional 143 T2D-associated loci, all of which were Het p-value > 0.05 except for one; the minimal unadjusted Het p-value was 0.043 ( Supplementary Table 3 ). Furthermore, T2D-associated loci specific to EAS-meta or EUR-meta, including highly heterogeneous loci between populations, became non-significant (Supplementary Table 4). These findings suggest that while multi-ancestry meta-analysis enhances the identification of shared genetic associations, it may also undermine population-specific genetic characteristics by losing or weakening associations dominantly observed in a single population. Genetic signals showing heterogeneity between populations inform the in-depth interpretation of T2D associations. We explored 220-cell-type epigenetic enrichment using stratified LD score regression (sLDSC). The analysis revealed that pancreas-related epigenetic factors showed the highest heritability enrichment in both populations ( Supplementary Fig. 5 ). Building on these findings, we employed ChIP-seq data to examine pancreatic transcription factors ( Methods) . As a result, FOXA1 and FOXA2 showed the most significant heritability enrichment in both populations (Fig. 1 C). HNF1B showed significant heritability enrichment, specifically in the EAS-meta (Nominal P-value = 0.009), while PDX1 showed significant heritability enrichment, specifically in the EUR-meta (Nominal P-value = 0.002). Then, we utilized pancreatic single-cell ATAC-seq data for sLDSC analyses ( Methods ). Our results showed significant enrichment in beta cells, with the highest heritability enrichment observed (Fig. 1 C). Non-beta cells, such as pancreatic alpha, delta, and gamma cells, also showed significant enrichment, and open chromatin regions unique to those non-beta cells showed a suggestive trend toward positive enrichment of T2D heritability ( Supplementary Note; Supplementary Table 5 ). Our comprehensive annotation approach focusing on pancreatic associations signified 616 putative genes in the EAS-meta and 657 in the EUR-meta ( Methods; Supplementary Table 1 ). This approach allowed us to identify finer differences among the two ancestries in variant-gene interactions within T2D-associated pancreatic cell types. One notable finding was the C2CD4A locus, where it was significantly heterogeneous (FDR adjusted p = 0.007) with a stronger effect size in the EAS-meta than in the EUR-meta (Fig. 1 D). At this locus, the lead SNP in the EAS-meta and the EUR-meta are rs8037894 and rs11856307, respectively. The high LD variants (r2 > 0.8) with the rs8037894 showed significant interactions not only in pancreatic beta cells but also in pancreatic alpha cells. In addition, these variants interacted with another gene, C2CD4B , in alpha cells. In contrast, the high LD variants (r2 > 0.8) with rs11856307 significantly interacted only with the C2CD4A only in pancreatic beta cells. Interacting between the variants associated with EAS and multiple genes in pancreatic alpha cells was in line with the stronger association in EAS and suggested the broad roles of these genes in alpha cells in T2D​ ( 11 ). These results suggest the importance of maximizing a single-ancestry GWAS to finely interpret T2D association signals in the context of cell-specific variant-gene interactions. From multiple putative genes at each T2D-associated locus, we selected a single prioritized gene at each T2D-associated locus for the FUMA gene set analysis to ensure a balanced comparison (see Supplementary Note for details on the prioritization strategy and its rationale; the results are shown in Supplementary Table 1). 76 out of the 100 multi-ancestry shared loci showed concordant prioritized genes between the two populations, with 19 (25% of the 76 loci) showing significant heterogeneity of effect sizes. Conversely, 24 loci were discordant, and prioritized genes were different between the two populations, with 11 (approximately 45%) showing significant heterogeneity of effect sizes. This may indicate heterogeneous associations reflecting differences in causal genes of T2D between populations. Notably, the locus where the HNF1B gene was prioritized in both populations showed significant heterogeneity in the two different lead variants in EAS and EUR, each with a strong LD to the other. The stronger association of HNF1B in EAS than in EUR is in line with the sLDSC results with significant heritability enrichment of the HNF1B transcription factor in EAS (Fig. 1 C). Stronger involvement of lipid pathway in Europeans Out of the 33 and 62 significantly heterogeneous loci (at the lead SNP level) in EAS and EUR, respectively, we identified 30 loci with stronger effect sizes in EAS and 56 loci with stronger effect sizes in EUR (Fig. 2 A; Supplementary Table 6 ) without overlap. We generated gene sets from the prioritized genes of each group and compared their pathway enrichment using FUMA ( Methods ). As a result, the EUR-strong heterogeneous loci showed a higher enrichment of lipid-related gene pathways based on GWAS catalog-reported genes (Fig. 2 B; Supplementary Table 7 ), namely, pathways related to HDL-C (adjusted P = 3.2x10 − 11 ) and triglycerides (P = 5.2x10 − 11 ). However, the EAS-strong gene set did not (adjusted P > 0.99, suggesting a lack of significant association in EAS not explained by differences in the number of genes with heterogeneous effect sizes). This finding was supported by the results of sLDSC analysis using tissue-specific epigenetic factors across 220 cell types, which showed a higher coefficient in EUR than EAS for epigenetic factors in adipose tissue, a key tissue for lipid deposition (Fig. 2 C). Differential contribution of insulin resistance related genetic factor and HDL levels We compared the distributions of insulin secretion and insulin resistance GRSs between EAS and EUR in both T2D cases and controls (Fig. 3 A; Supplementary Table 8 ). In controls, these GRSs were similar between populations, suggesting comparable baseline genetic predisposition for insulin secretion and resistance. As expected, insulin secretion GRS was significantly higher in T2D cases than in controls in both EAS (RR = 1.26, 95% CI: 1.20–1.33) and EUR (RR = 1.30, 95% CI: 1.28–1.32). Comparing T2D cases between the two populations revealed no significant difference in insulin secretion GRS (RR = 0.95, 95% CI: 0.90-1.00). By contrast, insulin resistance GRS showed marked population-specific effects among T2D cases. In EUR, T2D cases had a higher insulin resistance GRS than controls (RR = 1.14, 95% CI: 1.12–1.16), whereas in EAS, the difference between T2D cases and controls was not significant (RR = 1.01, 95% CI: 0.96–1.07). Moreover, when we compared T2D cases across populations, EAS showed significantly lower insulin resistance GRS than EUR (RR = 0.89, 95% CI: 0.84–0.94). These observations imply that genetically determined insulin resistance contributes more prominently to T2D in Europeans than in East Asians. To avoid possible confounding from the sources of GRS, we conducted the same analyses with different sources of GRS (Methods). As a result, we observed very similar results ( Supplementary Fig. 6 ). In addition, we examined the association between lipid-related wGRS and T2D (Fig. 3 B; Supplementary Table 9). In EUR, the GRS for HDL-C demonstrated a modest but significant trend whereby genetically lower HDL-C was associated with higher T2D risk (bottom decile: OR = 1.1, 95% CI: 1.0-1.2; top decile: OR = 0.9, 95% CI: 0.83–0.95). In EAS, however, the confidence intervals overlapped substantially across the HDL-C GRS deciles (bottom decile: OR = 0.9, 95% CI: 0.7–1.2; top decile: OR = 1.0, 95% CI: 0.8–1.3). GRS for BMI-adjusted waist-hip ratio was strongly associated with T2D in EUR but showed a non-leanear pattern (with wide confidence intervals) in EAS. DISCUSSIONS In this study, we aimed to dissect the genetic basis of T2D in East Asians (EAS) by constructing the largest single-ancestry EAS GWAS meta-analysis to date, involving nearly 600,000 individuals. Our findings underline the importance of population-focused genetic research: although we observed a strong overall genetic correlation of T2D between EAS and Europeans (EUR), we also uncovered important population-specific loci and variant-gene interactions that would likely be masked by multi-ancestry meta-analyses alone. These results illuminate how EAS-specific signals, particularly those related to insulin secretion and lipid metabolism, may contribute to the unique T2D phenotype in this population. We identified six missense variants that were significantly associated with T2D only in EAS. All six have allele frequencies < 1% in EUR, underscoring the contributions of low-frequency variants that are enriched or unique in specific populations. Among these, we newly report rs144951440 (V412A) in WFS1 as a T2D-associated coding variant in East Asians. Previous genetic research from East Asian participants has implicated its relationships with non-syndromic hearing loss ( 12 ) and neonatal diabetes mellitus ( 13 ), highlighting pleiotropic effects in metabolic and non-metabolic traits in East Asians. The variant in GRB14 (rs75536691) is another interesting finding: although reported in a previous single-EAS study ( 8 , 14 ), it was not detected as a lead signal in a large multi-ancestry meta-analysis ( 9 ). These observations collectively reinforce that single-ancestry GWAS, with sufficiently large sample sizes, can capture associations that might be diluted or missed entirely in multi-ancestry datasets where the proportion of EAS participants is relatively small. Fine-mapping analyses and sLDSC analyses further revealed that EAS-strong signals can interact with multiple genes in a manner that is absent or attenuated in EUR. For example, the C2CD4A/C2CD4B locus showed significant heterogeneity, with EAS-specific variants demonstrating broader regulatory signatures in both pancreatic beta and alpha cells. Conversely, the lead variants in the same region in EUR preferentially interacted with C2CD4A in beta cells only. According to recent single-cell ATAC-seq analyses in human pancreatic islets ( 15 ), certain T2D risk–associated variants at this locus physically link to enhancer elements active in alpha cells, thereby specifically modulating C2CD4B expression in alpha cells. In another study, functional ablation experiments in mouse models ( 16 )showed that C2CD4A plays a pivotal role in beta-cell insulin secretion, which aligns with the EUR-associated pattern focusing on beta cells. Taken together, this population-dependent difference in regulatory interactions suggests that EAS variants might confer a more multifaceted impact on both glucagon-secreting alpha cells and insulin-secreting beta cells, potentially reflecting—or even exacerbating—the distinct clinical phenotypes of T2D commonly observed in East Asians, such as lower BMI onset and reduced insulin secretory capacity. Our results with sLDSC also confirmed that pancreatic cell types exhibit the highest heritability enrichment in both EAS and EUR, consistent with T2D’s established biology ( 5 , 14 , 17 – 19 ). Consistent with the previous research from type 1 diabetes participants ( 17 ), our findings from type 2 diabetes indicated potential broader interactions beyond beta cells, including alpha, delta, and gamma cells. Additionally, we saw a stronger heritability enrichment for transcription factor HNF1B in EAS. This finding is consistent with stronger effect sizes at the HNF1B locus in EAS, as also reported previously ( 8 , 9 ). HNF1B is known for its critical role in pancreatic development and insulin secretion ( 4 , 20 – 23 ). Given that EAS populations generally show diminished insulin secretory capacity ( 4 ), it is plausible that HNF1B variants exert a stronger effect on T2D susceptibility in EAS compared to EUR. Collectively, these data underscore how single-ancestry fine-mapping can detect subtle differences in gene-variant and tissue-cell interactions that might otherwise be overshadowed by pooled analyses. One of the most intriguing divergences between EAS and EUR in our study emerges in lipid-related pathways and insulin resistance. Via pathway enrichment analysis of genes in significantly heterogeneous loci, we found that EUR-strong loci were enriched in lipid-related traits, particularly HDL-C and triglycerides, whereas EAS-strong loci showed no such enrichment (adjusted p > 0.99). These findings are consistent with multi-ancestry cluster analyses where strong signals related to lipodystrophy or obesity were dominated by EUR ( 9 , 10 ). At the polygenic level, our GRS analyses of insulin secretion and insulin resistance further suggest that EAS may be less influenced by insulin resistance-related susceptibility compared to EUR. While both EAS and EUR T2D cases showed a higher GRS for insulin secretion defects than their respective controls, insulin resistance GRS was significantly higher in EUR cases than controls but nearly indistinguishable between EAS cases and controls. This lends genetic support to clinical observations that EAS T2D is, on average, more driven by poor insulin secretion and less by classical features of insulin resistance ( 4 ). Together, these results underscore the distinct role of insulin resistance in the pathogenesis of T2D in EAS vs. EUR. Our GRS analysis for HDL-C supports the long-reported association in Europeans, where genetically lower HDL-C is linked to higher T2D risk. In East Asians, however, wide confidence intervals across deciles prevented a statistically significant trend, indicating possible population-specific factors influencing the HDL-C–T2D relationship. Additionally, our pathway analysis of EAS-strong or heterogeneous gene sets points to differences in lipid-related metabolic pathways that align with these observations. This finding suggests that HDL-C functionality, particle composition ( 24 ), or interactions with other metabolic factors might vary by ancestry. To clarify these mechanisms, future single-ancestry studies should apply refined lipid phenotyping—considering, for example, adiponectin (commonly lower in Asians) ( 25 ) and fatty acid-binding protein A ( 26 , 27 )—to detect subtle but important variations. Overall, these results indicate that HDL-C–T2D associations may not be uniform across populations and emphasize the need for diverse genetic and functional research approaches. Shi et al. ( 28 ) showed that many GWAS risk regions deemed “population-specific” actually harbor causal variants shared across ancestries, although differences in linkage disequilibrium or sample size can obscure them in other populations. Reflecting this trend, large multi-ancestry GWAS approaches have become popular for leveraging robust statistical power to identify widely shared T2D genetic loci ( 5 , 9 , 10 ). However, while such meta-analyses are crucial for capturing common signals, certain population-specific variants—particularly in understudied groups—may still be overlooked or diluted. By assembling a large single-ancestry East Asian cohort and directly comparing it with a similarly sized European cohort, we identified EAS-specific missense variants and finer locus-specific associations that would otherwise remain undetected. Our study underscores the importance of recognizing genuinely population-specific variants shaped by distinct demographic histories and environmental factors. Through single-ancestry meta-analyses with comparable effective sample sizes, we demonstrate that pinpointing these EAS-specific loci refines risk prediction models deepens our understanding of T2D pathophysiology and informs population-tailored therapeutic strategies. Ultimately, a balanced approach—continuing large-scale multi-ancestry GWAS to uncover broadly shared variants while simultaneously investing in well-powered single-population investigations—offers the most comprehensive view of complex traits, benefiting both global and population-specific precision medicine efforts. Several limitations warrant mention. First, we focused only on EAS and EUR populations, leaving open the question of whether these findings generalize to other groups with a high T2D burden, such as South Asians. Second, the current study relied on array-based imputation, and rare or ultra-rare variants may still be under-represented; whole-genome sequencing studies in large EAS cohorts are needed for a more comprehensive variant catalog. Third, environmental factors—including diet and physical activity—likely modulate genetic risk, but they were beyond the scope of our current analyses. Fourth, although we inferred distinct roles of insulin resistance and lipid metabolism in EAS, direct assessments of BMI or waist-hip ratio adjustments within the same dataset were hampered by incomplete phenotypic data. Finally, although our study sample was large by historical standards, more expansive EAS datasets could help clarify additional sub-phenotypes, such as early-onset T2D or normal-weight T2D. Our comparison of the largest single-ancestry EAS T2D GWAS to a similarly sized EUR meta-analysis underscores both shared and distinctly population-specific genetic architectures underlying T2D. EAS-specific missense variants, divergent variant-gene interactions in pancreatic cells, and a weaker genetic contribution of insulin resistance collectively help explain why many East Asian individuals develop T2D with relatively low BMIs. On the other hand, lipid pathways and insulin resistance appear to play a comparatively stronger role in EUR. These findings exemplify the indispensable value of large-scale, single-ancestry GWAS for illuminating fine-scale genetic heterogeneity and guiding precision medicine efforts. As GWAS continue to expand in size and diversity, single-ancestry approaches, particularly in underrepresented populations, will remain integral to elucidating the biology of complex diseases like T2D. Ultimately, better characterization of population-specific genetic variation has the potential to improve risk stratification and treatment strategies for T2D worldwide. Future research integrating sophisticated functional genomics tools, detailed metabolic phenotyping, and larger sample sizes will help dissect how environmental and genetic factors jointly contribute to the pathogenesis of T2D in diverse populations. Ethics This study adhered to the Helsinki Declaration's ethical principles. Participants provided written informed consent. Ethical approval was granted by the Shizuoka General Hospital Ethics Committee (SGHIRB 2018064) and the Institutional Review Board of CMUH (approval numbers: CMUH110-REC3-005, CMUH111-REC1-176). The National Center for Geriatrics and Gerontology's Conflict of Interest Committee also reviewed this study (990 − 13). Declarations Author Contributions K.S. conceived and designed the GWAS study, obtained ethical approvals, recruited participants, collected and analyzed DNA data, and drafted the manuscript; H.F.L. conducted analyses of the Taiwanese cohort, wrote sections of the Methods and Results, and assisted in manuscript revision; H.I., K.Ha., K.O., T.I., T.U., T.Sh., T.Sm., and I.T. provided resources, established the Japanese cohort infrastructure, supervised the study, and revised the manuscript; T.O., R.K., and C.K. obtained patient consent, collected DNA samples, and performed clinical diabetes diagnoses; K.Hi. conducted in-depth data analysis; Y.L. provided additional data and oversaw manuscript supervision; M.H. guided manuscript revision; W.L.L., T.Y.L., and F.J.T. provided resources and guidance for the Taiwanese cohort and reviewed the manuscript; C.T. served as the corresponding author, coordinated study design and resources, supervised statistical methods, and led manuscript revisions. All authors contributed to drafting or critically revising the manuscript, approved the final version, and agreed to be accountable for all aspects of the work. Competing interests The authors declare that they have no competing interests. Materials & Correspondence Correspondence and material requests should be addressed to C.T. (Chikashi Terao). Data availability statement The datasets generated and analyzed during the current study are not publicly available due to ethical restrictions but are available from the corresponding author upon reasonable request. The data supporting this study's findings are available from the Shizuoka General Hospital's Clinical Research Support Center Department. Funding : This study was supported by the Medical Research Support Project of the Shizuoka Prefectural Hospital Organization, contract research projects on public health in Shizuoka Prefecture, Japan Agency for Medical Research and Development (AMED) grants 21ek0109555, 21tm0424220, 21ck0106642, 23ek0410114 and 23tm0424225, Japan Society for the Promotion of Science (JSPS) KAKENHI grant JP20H00462, and Takeda Hosho Grants for Research in Medicine. References Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes research and clinical practice. 2022;183:109119. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L. The many faces of diabetes: a disease with increasing heterogeneity. The Lancet. 2014;383(9922):1084-94. Davis T. Ethnic diversity in type 2 diabetes. Diabetic medicine. 2008;25:52-6. Ma RC, Chan JC. 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Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature. 2020;582(7811):240-5. Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024;627(8003):347-57. Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Huerta-Chagoya A, et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nature medicine. 2024;30(4):1065-74. Di Pietro P, Abate AC, Prete V, Damato A, Venturini E, Rusciano MR, et al. C2CD4B Evokes Oxidative Stress and Vascular Dysfunction via a PI3K/Akt/PKCα–Signaling Pathway. Antioxidants. 2024;13(1):101. Choi HJ, Lee JS, Yu S, Cha DH, Gee HY, Choi JY, et al. Whole-exome sequencing identified a missense mutation in WFS1 causing low-frequency hearing loss: a case report. BMC medical genetics. 2017;18:1-5. Teerawattanapong N, Tangjarusritaratorn T, Narkdontri T, Santiprabhob J, Tangjittipokin W. 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Tomura H, Nishigori H, Sho K, Yamagata K, Inoue I, Takeda J. Loss-of-function and dominant-negative mechanisms associated with hepatocyte nuclear factor-1β mutations in familial type 2 diabetes mellitus. Journal of Biological Chemistry. 1999;274(19):12975-8. Wang C, Zhang R, Lu J, Jiang F, Hu C, Zhou J, et al. Phenotypic heterogeneity in Chinese patients with hepatocyte nuclear factor-1β mutations. Diabetes research and clinical practice. 2012;95(1):119-24. Davidson WS, Shah AS. High-Density Lipoprotein Subspecies in Health and Human Disease: Focus on Type 2 Diabetes. Methodist DeBakey cardiovascular journal. 2019;15(1):55-61. Li S, Shin HJ, Ding EL, van Dam RM. Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. Jama. 2009;302(2):179-88. Tso AW, Xu A, Sham PC, Wat NM, Wang Y, Fong CH, et al. Serum adipocyte fatty acid–binding protein as a new biomarker predicting the development of type 2 diabetes: A 10-year prospective study in a Chinese cohort. Diabetes care. 2007;30(10):2667-72. Hsu WC, Okeke E, Cheung S, Keenan H, Tsui T, Cheng K, King GL. A cross-sectional characterization of insulin resistance by phenotype and insulin clamp in East Asian Americans with type 1 and type 2 diabetes. PloS one. 2011;6(12):e28311. Shi H, Burch KS, Johnson R, Freund MK, Kichaev G, Mancuso N, et al. Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data. The American Journal of Human Genetics. 2020;106(6):805-17. Loh P-R, Tucker G, Bulik-Sullivan BK, Vilhjálmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nature genetics. 2015;47(3):284-90. Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nature genetics. 2018;50(9):1335-41. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Consortium SWGotPG, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature genetics. 2015;47(3):291-5. Yang J, Ferreira T, Morris AP, Medland SE, Consortium GIoAT, Replication DG, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature genetics. 2012;44(4):369-75. Brown BC, Ye CJ, Price AL, Zaitlen N. Transethnic genetic-correlation estimates from summary statistics. The American Journal of Human Genetics. 2016;99(1):76-88. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature genetics. 2015;47(11):1228-35. Costanzo MC, von Grotthuss M, Massung J, Jang D, Caulkins L, Koesterer R, et al. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell metabolism. 2023;35(4):695-710. e6. Watanabe K, Umićević Mirkov M, de Leeuw CA, van den Heuvel MP, Posthuma D. Genetic mapping of cell type specificity for complex traits. Nature communications. 2019;10(1):3222. Prokopenko I, Poon W, Maegi R, Prasad B R, Salehi SA, Almgren P, et al. A central role for GRB10 in regulation of islet function in man. PLoS genetics. 2014;10(4):e1004235. Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB, Chiou J, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS medicine. 2018;15(9):e1002654. Additional Declarations There is NO Competing Interest. Supplementary Files SupplNote202503.docx Supplementary Note 3SupplTables202503.xlsx Supplementary Tables Cite Share Download PDF Status: Under Review 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":false,"prefix":"","firstName":"Fuu-Jen","middleName":"","lastName":"Tsai","suffix":""}],"badges":[],"createdAt":"2025-04-26 05:10:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6532678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6532678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82515287,"identity":"5f4365a7-b489-4714-bedf-7ec826c29d8b","added_by":"auto","created_at":"2025-05-12 11:37:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analyses between GWASs in EAS and EUR revealed fine-scale genetic differences in T2D: (A)\u003c/strong\u003e The upper and lower left Manhattan plots highlights T2D-associated loci (p\u0026lt;5x10\u003csup\u003e-8\u003c/sup\u003e; dashed line) either in the EAS-meta (bright red) or the EUR-meta (bright blue colored), respectively. The upper right highlights additionally identified through the multi-ancestry meta-analysis (bright green). P=1.0×10\u003csup\u003e-200\u003c/sup\u003e is the upper limit of the plot showing in these figures.\u003cstrong\u003e \u003c/strong\u003eThe bar graph at the right bottom compares the number of T2D-associated loci shared and unique to each meta-analysis. (B) The plot shows the correlation of T2D genetic risk between East Asian and European populations at various P-value thresholds (\u003cstrong\u003eMethods\u003c/strong\u003e). The circle and triangle markers represent correlation coefficients based on the LD structure of the EAS and EUR population from 1000 genome project data, respectively. Error bars are 95% confidence intervals for each point. \u003cstrong\u003e(C)\u003c/strong\u003e The bar plots are results of stratified LD Score regression based on ATAC-seq data (Left) and CHIP-seq data (Right) (\u003cstrong\u003eMethods\u003c/strong\u003e). The x-axis quantifies the enrichment as -log\u003csub\u003e10\u003c/sub\u003e(FDR). The dashed line marks the threshold of significance (P \u0026lt; 0.05, adjusted). (D)\u003cstrong\u003e \u003c/strong\u003eLocal associations in the \u003cem\u003eCASR-CSTA\u003c/em\u003e region and \u003cem\u003eC2CD4A-C2CD4B \u003c/em\u003eregion\u003cem\u003e \u003c/em\u003eare indicated in each association study. Locus plots are colored by LD (R\u003csup\u003e2\u003c/sup\u003e) with the lead SNPs. The arrows represent significant interaction based on ABC scores (\u0026gt;0.02) between lead SNPs or its tightly linked variants (R2\u0026gt;0.8) and the enhancer peaks generated from single-cell ATAC-seq data for different pancreatic cell types: beta cells with high insulin expression (INS-high), beta cells with low insulin expression (INS-low), alpha cells with high glucagon expression (GCG-high), alpha cells with low glucagon expression (GCG-low), acinar cells, and ductal cells.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6532678/v1/2580e737c11649a1f2bcdae1.jpg"},{"id":82515288,"identity":"42e3c1c3-fae3-4370-88cd-b87c8a04dd76","added_by":"auto","created_at":"2025-05-12 11:37:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation differences in T2D genetics in comparative analysis in heterogeneous effective T2D-associated loci\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(A) Lead variants with heterogeneity are shown in each study. The color-filled plots are significantly heterogeneous lead SNPs, with FDR thresholds of 3.2x10\u003csup\u003e-4\u003c/sup\u003e for the EAS-meta and 2.9x10\u003csup\u003e-4\u003c/sup\u003e for the EUR-meta, respectively. Allelic effect heterogeneity is evaluated using Cochran's Q-test, with significance determined at an FDR\u0026gt;0.05 (Methods). The x-axis and y-axis represent the effect sizes in the EUR and EAS populations, respectively. Error bars represent the standard errors for effect sizes in both populations. The beta axes' upper limit was set at 0.2 to maintain scale uniformity. (B) Pathway analyses results are shown from the two prioritized gene sets, namely, the EAS-stronger gene set (corresponding with pink shade area in the Figure1A) and the EUR-stronger gene set (corresponding with light blue shade area in the Figure1A). The figure includes pathways with top significant associations, capturing the most relevant findings. Full results are provided in Supplementary Table 7. (C) The results of sLDSC using 220 specific annotations based on the Roadmap Project are indicated (Methods). Y axes in the left and right panels indicate the coefficient and coefficients z-scores, respectively. Adipose (pink) and pancreatic (red) cell types are highlighted. \u0026nbsp;Original figures without transparent effect on plots are shown in the Supplementary Not\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6532678/v1/f32e1d2e2827ba18bde51ccd.jpg"},{"id":82515290,"identity":"8d8f3fce-9d3d-40cf-a875-a8d9ed50f5a4","added_by":"auto","created_at":"2025-05-12 11:37:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of GRS distributions highlights population-specific associations in T2D susceptibility. \u003c/strong\u003e(A) The plots show cumulative distribution curves for GRS of insulin secretion and resistance, stratified by case and control status in EAS and EUR populations. The percentages show the cumulative percentage of each population at the point of the mean GRS (log-scaled GRS = 1). (B) The plots display ORs of T2D cases between the indicated percentile bins and the 40-60 percentile (as the reference) based on GRS of insulin secretion, insulin resistance, Waist-Hip Ratio adjusted for BMI (WHRadjBMI), and HDL-C levels. Each point on the graph represents the OR for a given percentile of GRS, with the lines indicating the 95% confidence intervals. In EAS, the insulin resistance GRS shows a mild U-shaped curve when compared to the 40–60% GRS category, but appears nearly flat in the figure because that category serves as the baseline.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6532678/v1/72d7d0e780d7eea5beccc83c.jpg"},{"id":82517211,"identity":"192c6334-600f-4748-97ab-431de86518b4","added_by":"auto","created_at":"2025-05-12 12:01:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1667400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6532678/v1/3c5c2877-13c7-469b-9ba6-a75c8ce1b363.pdf"},{"id":82515298,"identity":"3a3ded1b-2622-404d-870e-405517b605a3","added_by":"auto","created_at":"2025-05-12 11:37:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1362031,"visible":true,"origin":"","legend":"Supplementary Note","description":"","filename":"SupplNote202503.docx","url":"https://assets-eu.researchsquare.com/files/rs-6532678/v1/732bc2c1e30d7dd26b7fad02.docx"},{"id":82516106,"identity":"ba74f60e-070c-4db3-840a-46321e5ce300","added_by":"auto","created_at":"2025-05-12 11:45:58","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":817608,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"3SupplTables202503.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6532678/v1/8fc77af2621166ab8316efba.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Decomposing type 2 diabetes genetics into population-specific features by 600,000 East-Asians","fulltext":[{"header":"RESEARCH CONTEXT","content":"\u003cp\u003eType 2 diabetes (T2D) affects over 500 million people globally, exhibiting significant heterogeneity in clinical features, prevalence, and underlying pathophysiological mechanisms across populations. East Asians demonstrate a unique T2D phenotype characterized by disease onset at lower body mass indices and notably impaired insulin secretion, contrasting with insulin resistance-driven forms commonly observed in Europeans. Prior genome-wide association studies (GWAS), although extensive, predominantly represented European populations, potentially underestimating or obscuring East Asian-specific genetic signals due to limited representation or multi-ancestry dilution effects.\u003c/p\u003e\n\u003cp\u003eThis study addresses the critical gap in understanding population-specific genetic contributions to T2D by conducting the largest single-ancestry GWAS to date, involving nearly 600,000 East Asians. By integrating these findings with similarly scaled European data, researchers elucidate genetic distinctions, particularly focusing on insulin secretion, insulin resistance, and lipid metabolism pathways. Population-specific genetic variants, including rare East Asian missense mutations, highlight differences in disease mechanisms at a granular level. This research underscores the necessity of single-ancestry approaches for accurately identifying and interpreting population-specific genetic architecture, ultimately contributing to more precise risk stratification, tailored therapeutic strategies, and enhanced precision medicine for diverse populations globally.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eType 2 diabetes (T2D) now affects over 500\u0026nbsp;million people globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), yet clinical manifestation and prevalence vary considerably across populations (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This heterogeneity arises from complex interactions between genetic predispositions, environmental factors, and key mechanisms such as insulin secretion and insulin resistance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In East Asians, for instance, T2D more commonly occurs at lower body mass indices and is marked by reduced insulin secretory capacity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), contributing to the relatively high prevalence of T2D in this population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite numerous investigations into these distinct clinical features (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the genetic underpinnings of East Asian\u0026ndash;specific T2D remains incompletely understood.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) have identified numerous loci influencing T2D risk (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Spracklen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) conducted a landmark single-ancestry East Asian GWAS in 433,540 individuals, identifying 301 distinct signals\u0026mdash;including 61 novel loci\u0026mdash;while also noting that many variants overlap with those in Europeans. More recent multi-ancestry GWAS (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) have provided a broader view of T2D genetics by incorporating multiple populations, though Europeans often constitute 60\u0026ndash;70% of these samples. These large cross-ancestry efforts emphasize shared common variants but may under-detect or dilute population-specific signals\u0026mdash;particularly in underrepresented cohorts such as East Asians\u0026mdash;and often rely on clustering approaches that may not capture finer-grained genetic heterogeneity in insulin secretion and resistance.\u003c/p\u003e \u003cp\u003eA deeper investigation of T2D genetics in larger, single-ancestry East Asian cohorts is, therefore, essential to refine our understanding and advance precision medicine for this population. In this study, we integrated newly available large-scale East Asian GWAS data (163,238 East Asians) with the Spracklen et al. dataset (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), creating the largest single-ancestry T2D GWAS to date in East Asians. We then performed cross-population comparisons (East Asians vs. Europeans) at single-variant, gene, and polygenic levels to assess how insulin resistance and secretory pathways differ between ancestries. Our results bridge critical gaps in T2D genetics and offer insights into both shared and population-specific mechanisms, paving the way for improved risk assessment and therapeutic strategies tailored to East Asian populations.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEast Asian GWAS Meta-Analysis\u003c/h2\u003e \u003cp\u003eDetails, including participant characteristics, study design rationale, type 2 diabetes ascertainment, control selection criteria, genotyping platforms and quality control measures, Hardy\u0026ndash;Weinberg equilibrium assessments, genomic reference builds, imputation methods, and analytical procedures are described in the \u003cb\u003eSupplementary Note\u003c/b\u003e. In summary, the Japanese cohort comprised 1,488 cases and 9,044 controls, sourced from Shizuoka General Hospital and the National Center for Geriatrics and Gerontology, with an average age of 69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8 and a proportion of 52.0% females. They were diagnosed with type 2 diabetes by clinical endocrinologists. The Taiwanese cohort from the China Medical University Hospital Precision Medicine Biobank comprised 13,736 cases and 138,858 controls, with an average age of 50.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1 and a proportion of 47.4% females. They were diagnosed with type 2 diabetes based on the International Classification of Diseases, 9th and 10th Revision, Clinical Modification codes. GWAS was conducted using BOLT-LMM v2.4.1 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and SAIGE (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), for the Japanese and Taiwanese cohorts, respectively. Quality control was performed based on minor allele frequency, call rate, and Hardy-Weinberg equilibrium.\u003c/p\u003e \u003cp\u003eWe used METAL (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) to perform a fixed-effects meta‐analysis based on inverse variance‐weighted effect sizes. Although alternative methods (e.g., MR‐MEGA, RE2C, or MANTRA) can more flexibly account for trans‐ethnic heterogeneity, our choice of a fixed‐effects approach reflects the need for direct comparison with previous large‐scale meta‐analyses in T2D. In the EAS-meta, we performed meta-analysis by combining those new Japanese and Taiwanese GWASs with the single-ancestry largest GWAS from East Asians provided by Spracklen\u0026rsquo;s study (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) \u0026mdash;the EAS-meta. The effect sizes strongly correlated between the Japanese study and the Spracklens (R\u0026sup2;=0.86). Additionally, we conducted a multi-ancestry GWAS meta-analysis by combining the EAS-meta with the EUR-meta from the DIAMANTE study, which used the Haplotype Reference Consortium panel (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Variants reaching a p-value of less than 5x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e were considered GWAS significant. LDSC (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) was utilized to estimate the heritability of the EAS-meta. We adjusted heritability estimates to the liability scale with an assumed disease prevalence of 12% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Primary SNPs, called lead SNPs, were defined within each T2D-associated locus based on the lowest P-values and a minimum separation of 500kb. As for secondary lead SNPs, the COJO (Conditional \u0026amp; Joint) function of the Genome-wide Complex Trait Analysis (GCTA) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) was used to discern secondary T2D-associated variants, setting a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e. To evaluate trans‐ethnic heterogeneity in effect sizes, we applied Cochran\u0026rsquo;s Q‐test (CQ‐test) to the lead SNPs identified in both the EAS‐meta and EUR‐meta datasets. Given that lead SNPs differ across populations, secondary variants were not assessed using CQ‐test. We defined heterogeneity as significant when it met a false discovery rate (FDR) threshold of \u0026lt;\u0026thinsp;0.05. In instances where we identified strong evidence of population‐specific effects, we highlight these loci in the Results.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic correlation analyses\u003c/h3\u003e\n\u003cp\u003eThe Popcorn tool (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) was utilized to estimate the genetic correlation between the EAS-meta and the EUR-meta, involving the computation of cross-population scores from a reference panel and heritability and genetic correlation estimation. For an additional genetic correlation analysis, LD pruning was first conducted based on the 1000 genome project dataset to identify independent SNPs using the LD structure unique to each population. This process yielded two distinct sets of SNPs, one from each population. We then stratified shared SNPs between the two populations and calculated the Spearman correlation coefficient of the effect sizes (odds ratios) of these shared SNPs in both results based on the respective LD structures of each population.\u003c/p\u003e\n\u003ch3\u003eStratified LD Score regression comparison analyses\u003c/h3\u003e\n\u003cp\u003eWe compared results of stratified LD score regression (sLDSC) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) from differenct data sources across EAS-meta and EUR-meta. We used 220 cell-type-specific functional annotations from the Roadmap Epigenomics Project against the LD structure derived from the 1000 Genomes Project Phase 3 data. In addition, we used CHIP-seq data from the CHIP-Atlas database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chip-atlas.org\u003c/span\u003e\u003cspan address=\"https://chip-atlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify transcription factor binding sites in pancreas, including CTCF, FOXA1, FOXA2, GATA4, GATA6, GLIS3, HNF1A, HNF1B, PAX6, PDX1, REST, SIRT1, SOX9, and TCF7L2. Plus, we conducted stratified LDSC analysis using pancreatic single-cell ATAC-seq data (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) to specifically probe the genetic architecture of T2D in pancreatic islet cells.\u003c/p\u003e\n\u003ch3\u003ePutative and prioritized gene identification\u003c/h3\u003e\n\u003cp\u003eTo represent the populational difference at finer levels, a comprehensive annotation was performed in our downstream analysis. We devised a scoring algorithm to systematically identify putative genes encapsulating four annotation dimensions: Nearest Gene, VEP, SMR, and the ABC model (\u003cb\u003eSupplementary Note)\u003c/b\u003e. Based on this score, up to five genes was designated as putative genes per lead and secondary SNP, and then a single \"prioritized gene\" was designated as the one with the highest score and nearest to the SNP.\u003c/p\u003e\n\u003ch3\u003eGene set and pathway enrichment comparison analyses\u003c/h3\u003e\n\u003cp\u003eFunctional Mapping and Annotation of Genome-Wide Association Studies (FUMA) was employed for pathway analysis (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Gene sets were created from prioritized genes at significantly heterogeneous lead SNPs in each population. According to their effective size, two gene sets were divided into two sets: prioritized gene set with stronger effect in the EAS-meta, \u0026ldquo;EAS-strong-group\u0026rdquo;, and the counterparts, \u0026ldquo;EUR-strong-group\u0026rdquo;. This method for selecting one gene per locus ensured comparability and integrity in pathway analysis using FUMA. It allowed us to prevent overrepresentation and highlights distinct genetic contributions from each region. A comprehensive explanation of FUMA\u0026rsquo;s functionalities and the detailed methodology of our approach are presented in the \u003cb\u003eSupplementary Note\u003c/b\u003e. We defined significance with an adjusted enrichment P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic risk score comparison analyses\u003c/h2\u003e \u003cp\u003eWe calculated weighted Genetic Risk Scores (wGRS) for T2D-related traits in two populations: Japanese individuals from Shizuoka General Hospital (EAS; 1,506 T2D cases and 2,638 controls) and unrelated White British individuals from the UK Biobank (EUR; 15,600 T2D cases and 320,000 controls). Details on genotyping quality control and T2D case definitions are provided in the Supplementary Note. We used established GRSs from previous studies to capture various pathophysiological aspects of T2D (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). From Uder et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), we combined \u0026ldquo;beta cell\u0026rdquo; and \u0026ldquo;proinsulin\u0026rdquo; GRSs into an \u0026ldquo;insulin secretion\u0026rdquo; GRS, and aggregated \u0026ldquo;Lipodystrophy,\u0026rdquo; \u0026ldquo;Liver lipids,\u0026rdquo; and \u0026ldquo;Obesity\u0026rdquo; GRSs into an \u0026ldquo;insulin resistance\u0026rdquo; GRS. We additionally incorporated GRSs for insulin sensitivity index (ISI), corrected insulin response (CIR), and insulin secretion rate (ISR) from Prokopenko et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), and GRSs for lipid traits (HDL-C, LDL, TG, etc.) from Aly et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) (Supplementary Table\u0026nbsp;10). For the GRS computation, we utilized dosage values of SNPs, representing the expected number of risk alleles. We then summed up the product of the dosage for each SNP[i] and its corresponding effect size (log odds ratio): wGRS\u0026thinsp;=\u0026thinsp;Σ(dosage for SNP[i] * effect size for SNP[i]). For the calculations, we only used the variants commonly present both in the EAS and EUR genotype datasets.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analysis for GRS Associations\u003c/h3\u003e\n\u003cp\u003e We fitted a multivariable logistic regression model to nine percentile categories (using the 40\u0026ndash;60 percentile as the reference) in the EAS dataset to estimate the relative risk (RR) of T2D within each population at each GRS percentile. We adjusted for the first 10 principal components, age, and sex for the EAS dataset, and we adjusted for the first 40 principal components, age, sex, and facility for the EUR dataset. A two-tailed t-test was conducted to determine statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eRelative Risk Across Populations\u003c/h3\u003e\n\u003cp\u003eTo compare mean GRS levels between EAS and EUR groups (cases or controls separately), we computed a relative risk (RR) measure defined as exp(\u0026micro;\u003csub\u003eEAS\u003c/sub\u003e - \u0026micro;\u003csub\u003eEUR\u003c/sub\u003e)), where \u0026micro;\u003csub\u003eEAS\u003c/sub\u003e and \u0026micro;\u003csub\u003eEUR\u003c/sub\u003e ​ are the mean GRS values in EAS and EUR, respectively. We estimated 95% confidence intervals using a two-sample t-test with unequal variances. This RR evaluates whether one population, on average, carries a higher or lower genetic load compared to the other.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the largest East Asian single-ancestry T2D GWAS and comparison with a European dataset\u003c/h2\u003e \u003cp\u003eDetails of the new Japanese and Taiwanese GWAS are presented in the Supplementary Note. Briefly, the genomic control λ for the Taiwanese and Japanese GWAS were 0.993 and 1.034, respectively. These two new GWAS were combined with Spracklen et al.\u0026rsquo;s East Asian meta-analysis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), resulting in an \u0026ldquo;EAS-meta\u0026rdquo; that included 92,392 cases and 504,024 controls\u0026mdash;the largest single-ancestry T2D GWAS to date in East Asians (Supplementary Fig.\u0026nbsp;1). Linkage Disequilibrium Score Regression (LDSC) estimated an overall liability scale heritability of 0.340 (SE\u0026thinsp;=\u0026thinsp;0.017). The genomic control lambda was 1.396, and an LDSC intercept was 0.998 (SE\u0026thinsp;=\u0026thinsp;0.011), suggesting a strong polygenic architecture of T2D and free from confounding bias in the current results (Supplementary Fig.\u0026nbsp;1). For comparison, we selected the DIAMANTE European meta-analysis (\u0026ldquo;EUR-meta\u0026rdquo;) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), which includes 80,514 cases and 853,816 controls. The effective sample sizes\u0026mdash;312,317 for the EAS-meta vs. 294,303 for the EUR-meta\u0026mdash;were comparable, allowing a fair cross-population examination of T2D-associated loci.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of shared and population-specific T2D loci\u003c/h2\u003e \u003cp\u003eConsistent with the comparable effective sample sizes, the EAS-meta and the EUR-meta had a comparable number of T2D-associated loci: 196 and 199, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; \u003cb\u003eSupplementary Table\u0026nbsp;1; Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e, further providing a solid basis for our multi-ancestry comparisons. GCTA-COJO conditional analysis additionally identified 82 and 60 independent T2D-associated signals in the EAS-meta and the EUR-meta, respectively (Pcojo\u0026thinsp;\u0026lt;\u0026thinsp;5x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). We identified 33 and 62 significant heterogeneous loci (we primarily evaluated heterogeneity in lead SNPs for fair comparison between the two populations) between the EAS-meta and the EUR-meta, with FDR thresholds of 3.2x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and 2.9x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOf the lead and secondary SNPs, 27 and 42 T2D-associated variants were present only in either EAS-meta or EUR-meta with the evidence of minor allele frequency below 1%\u0026mdash;most were \u0026lt;\u0026thinsp;0.1%\u0026mdash;in the counterpart population based on 1000 genomes project phase 3 reference data (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Out of these 27 EAS-specific SNPs, six SNPs were missense variants, including rs2233580 (R192H) and rs3824004 (R192S) in \u003cem\u003ePAX4\u003c/em\u003e, rs3731600 (A122P) in \u003cem\u003eSCTR\u003c/em\u003e, rs75536691 (L180S) in \u003cem\u003eGRB14\u003c/em\u003e, and rs147834269 (E737K) and rs144951440 (V412A) in \u003cem\u003eWFS1\u003c/em\u003e. We found the 69 variants, predominantly present in a single population, enriched in the 95 loci with significant heterogeneity (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). For example, in the \u003cem\u003eKCNQ1\u003c/em\u003e locus, the stronger effect size in EAS, we observed 4 of these unique SNPs out of 17 secondary SNPs in this locus in EAS, while the EUR-meta had no specific SNPs out of 5 secondary SNPs in this locus. In the \u003cem\u003eTCF7L2\u003c/em\u003e region, the stronger effect size in EUR, the EUR-meta had 6 of these unique SNPs out of 11 secondary SNPs in this region. These findings underscore the importance of exploring variants detected from single-ancestry GWAS and comparing effect sizes between (or among) populations to detect independent signals in the significant loci for elucidating the genetic basis of T2D.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlation and multi-ancestry meta-analysis\u003c/h2\u003e \u003cp\u003eWhile we observed distinct population-specific genetic patterns at a very granular level, a genetic correlation analysis found the two populations\u0026rsquo; overall genetic signals of T2D were very similar (p\u0026thinsp;=\u0026thinsp;1.0). Furthermore, we found strong correlations of beta coefficients in the bins of variants according to p-values (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These indicated a considerable overlap in T2D genetic risk between the two populations, even in loci with non-GWAS significant polygenic associations, as far as we focus on common variants shared between the populations. While Spracklen et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) found a strong correlation in per-allele effect size between East Asians and Europeans at a lead SNP level, here we further demonstrated an overlap in loci with non-GWAS significant polygenic associations.\u003c/p\u003e \u003cp\u003eGiven this overarching genetic similarity across populations, a multi-ancestry meta-analysis in the current study refined shared T2D-associated loci (\u003cb\u003eSupplementary Note; Supplementary Fig.\u0026nbsp;3; Supplementary Fig.\u0026nbsp;3 4\u003c/b\u003e) and identified an additional 143 T2D-associated loci, all of which were Het p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 except for one; the minimal unadjusted Het p-value was 0.043 (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Furthermore, T2D-associated loci specific to EAS-meta or EUR-meta, including highly heterogeneous loci between populations, became non-significant \u003cb\u003e(Supplementary Table\u0026nbsp;4).\u003c/b\u003e These findings suggest that while multi-ancestry meta-analysis enhances the identification of shared genetic associations, it may also undermine population-specific genetic characteristics by losing or weakening associations dominantly observed in a single population.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenetic signals showing heterogeneity between populations inform the in-depth interpretation of T2D associations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe explored 220-cell-type epigenetic enrichment using stratified LD score regression (sLDSC). The analysis revealed that pancreas-related epigenetic factors showed the highest heritability enrichment in both populations (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e). Building on these findings, we employed ChIP-seq data to examine pancreatic transcription factors (\u003cb\u003eMethods)\u003c/b\u003e. As a result, FOXA1 and FOXA2 showed the most significant heritability enrichment in both populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). HNF1B showed significant heritability enrichment, specifically in the EAS-meta (Nominal P-value\u0026thinsp;=\u0026thinsp;0.009), while PDX1 showed significant heritability enrichment, specifically in the EUR-meta (Nominal P-value\u0026thinsp;=\u0026thinsp;0.002). Then, we utilized pancreatic single-cell ATAC-seq data for sLDSC analyses (\u003cb\u003eMethods\u003c/b\u003e). Our results showed significant enrichment in beta cells, with the highest heritability enrichment observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Non-beta cells, such as pancreatic alpha, delta, and gamma cells, also showed significant enrichment, and open chromatin regions unique to those non-beta cells showed a suggestive trend toward positive enrichment of T2D heritability (\u003cb\u003eSupplementary Note; Supplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eOur comprehensive annotation approach focusing on pancreatic associations signified 616 putative genes in the EAS-meta and 657 in the EUR-meta (\u003cb\u003eMethods; Supplementary Table\u0026nbsp;1\u003c/b\u003e). This approach allowed us to identify finer differences among the two ancestries in variant-gene interactions within T2D-associated pancreatic cell types. One notable finding was the \u003cem\u003eC2CD4A\u003c/em\u003e locus, where it was significantly heterogeneous (FDR adjusted p\u0026thinsp;=\u0026thinsp;0.007) with a stronger effect size in the EAS-meta than in the EUR-meta (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). At this locus, the lead SNP in the EAS-meta and the EUR-meta are rs8037894 and rs11856307, respectively. The high LD variants (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.8) with the rs8037894 showed significant interactions not only in pancreatic beta cells but also in pancreatic alpha cells. In addition, these variants interacted with another gene, \u003cem\u003eC2CD4B\u003c/em\u003e, in alpha cells. In contrast, the high LD variants (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.8) with rs11856307 significantly interacted only with the \u003cem\u003eC2CD4A\u003c/em\u003e only in pancreatic beta cells. Interacting between the variants associated with EAS and multiple genes in pancreatic alpha cells was in line with the stronger association in EAS and suggested the broad roles of these genes in alpha cells in T2D​ (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These results suggest the importance of maximizing a single-ancestry GWAS to finely interpret T2D association signals in the context of cell-specific variant-gene interactions.\u003c/p\u003e \u003cp\u003eFrom multiple putative genes at each T2D-associated locus, we selected a single prioritized gene at each T2D-associated locus for the FUMA gene set analysis to ensure a balanced comparison (see Supplementary Note for details on the prioritization strategy and its rationale; the results are shown in Supplementary Table\u0026nbsp;1). 76 out of the 100 multi-ancestry shared loci showed concordant prioritized genes between the two populations, with 19 (25% of the 76 loci) showing significant heterogeneity of effect sizes. Conversely, 24 loci were discordant, and prioritized genes were different between the two populations, with 11 (approximately 45%) showing significant heterogeneity of effect sizes. This may indicate heterogeneous associations reflecting differences in causal genes of T2D between populations. Notably, the locus where the \u003cem\u003eHNF1B\u003c/em\u003e gene was prioritized in both populations showed significant heterogeneity in the two different lead variants in EAS and EUR, each with a strong LD to the other. The stronger association of HNF1B in EAS than in EUR is in line with the sLDSC results with significant heritability enrichment of the HNF1B transcription factor in EAS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStronger involvement of lipid pathway in Europeans\u003c/h2\u003e \u003cp\u003eOut of the 33 and 62 significantly heterogeneous loci (at the lead SNP level) in EAS and EUR, respectively, we identified 30 loci with stronger effect sizes in EAS and 56 loci with stronger effect sizes in EUR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e) without overlap. We generated gene sets from the prioritized genes of each group and compared their pathway enrichment using FUMA (\u003cb\u003eMethods\u003c/b\u003e). As a result, the EUR-strong heterogeneous loci showed a higher enrichment of lipid-related gene pathways based on GWAS catalog-reported genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e), namely, pathways related to HDL-C (adjusted P\u0026thinsp;=\u0026thinsp;3.2x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) and triglycerides (P\u0026thinsp;=\u0026thinsp;5.2x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e). However, the EAS-strong gene set did not (adjusted P\u0026thinsp;\u0026gt;\u0026thinsp;0.99, suggesting a lack of significant association in EAS not explained by differences in the number of genes with heterogeneous effect sizes). This finding was supported by the results of sLDSC analysis using tissue-specific epigenetic factors across 220 cell types, which showed a higher coefficient in EUR than EAS for epigenetic factors in adipose tissue, a key tissue for lipid deposition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferential contribution of insulin resistance related genetic factor and HDL levels\u003c/h2\u003e \u003cp\u003eWe compared the distributions of insulin secretion and insulin resistance GRSs between EAS and EUR in both T2D cases and controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). In controls, these GRSs were similar between populations, suggesting comparable baseline genetic predisposition for insulin secretion and resistance. As expected, insulin secretion GRS was significantly higher in T2D cases than in controls in both EAS (RR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.20\u0026ndash;1.33) and EUR (RR\u0026thinsp;=\u0026thinsp;1.30, 95% CI: 1.28\u0026ndash;1.32). Comparing T2D cases between the two populations revealed no significant difference in insulin secretion GRS (RR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.90-1.00). By contrast, insulin resistance GRS showed marked population-specific effects among T2D cases. In EUR, T2D cases had a higher insulin resistance GRS than controls (RR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.12\u0026ndash;1.16), whereas in EAS, the difference between T2D cases and controls was not significant (RR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 0.96\u0026ndash;1.07). Moreover, when we compared T2D cases across populations, EAS showed significantly lower insulin resistance GRS than EUR (RR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.84\u0026ndash;0.94). These observations imply that genetically determined insulin resistance contributes more prominently to T2D in Europeans than in East Asians. To avoid possible confounding from the sources of GRS, we conducted the same analyses with different sources of GRS (Methods). As a result, we observed very similar results (\u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we examined the association between lipid-related wGRS and T2D (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; \u003cb\u003eSupplementary Table\u0026nbsp;9).\u003c/b\u003e In EUR, the GRS for HDL-C demonstrated a modest but significant trend whereby genetically lower HDL-C was associated with higher T2D risk (bottom decile: OR\u0026thinsp;=\u0026thinsp;1.1, 95% CI: 1.0-1.2; top decile: OR\u0026thinsp;=\u0026thinsp;0.9, 95% CI: 0.83\u0026ndash;0.95). In EAS, however, the confidence intervals overlapped substantially across the HDL-C GRS deciles (bottom decile: OR\u0026thinsp;=\u0026thinsp;0.9, 95% CI: 0.7\u0026ndash;1.2; top decile: OR\u0026thinsp;=\u0026thinsp;1.0, 95% CI: 0.8\u0026ndash;1.3). GRS for BMI-adjusted waist-hip ratio was strongly associated with T2D in EUR but showed a non-leanear pattern (with wide confidence intervals) in EAS.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSIONS","content":"\u003cp\u003eIn this study, we aimed to dissect the genetic basis of T2D in East Asians (EAS) by constructing the largest single-ancestry EAS GWAS meta-analysis to date, involving nearly 600,000 individuals. Our findings underline the importance of population-focused genetic research: although we observed a strong overall genetic correlation of T2D between EAS and Europeans (EUR), we also uncovered important population-specific loci and variant-gene interactions that would likely be masked by multi-ancestry meta-analyses alone. These results illuminate how EAS-specific signals, particularly those related to insulin secretion and lipid metabolism, may contribute to the unique T2D phenotype in this population.\u003c/p\u003e \u003cp\u003eWe identified six missense variants that were significantly associated with T2D only in EAS. All six have allele frequencies\u0026thinsp;\u0026lt;\u0026thinsp;1% in EUR, underscoring the contributions of low-frequency variants that are enriched or unique in specific populations. Among these, we newly report rs144951440 (V412A) in \u003cem\u003eWFS1\u003c/em\u003e as a T2D-associated coding variant in East Asians. Previous genetic research from East Asian participants has implicated its relationships with non-syndromic hearing loss (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and neonatal diabetes mellitus (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), highlighting pleiotropic effects in metabolic and non-metabolic traits in East Asians. The variant in \u003cem\u003eGRB14\u003c/em\u003e (rs75536691) is another interesting finding: although reported in a previous single-EAS study (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), it was not detected as a lead signal in a large multi-ancestry meta-analysis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These observations collectively reinforce that single-ancestry GWAS, with sufficiently large sample sizes, can capture associations that might be diluted or missed entirely in multi-ancestry datasets where the proportion of EAS participants is relatively small.\u003c/p\u003e \u003cp\u003eFine-mapping analyses and sLDSC analyses further revealed that EAS-strong signals can interact with multiple genes in a manner that is absent or attenuated in EUR. For example, the \u003cem\u003eC2CD4A/C2CD4B\u003c/em\u003e locus showed significant heterogeneity, with EAS-specific variants demonstrating broader regulatory signatures in both pancreatic beta and alpha cells. Conversely, the lead variants in the same region in EUR preferentially interacted with \u003cem\u003eC2CD4A\u003c/em\u003e in beta cells only. According to recent single-cell ATAC-seq analyses in human pancreatic islets (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), certain T2D risk\u0026ndash;associated variants at this locus physically link to enhancer elements active in alpha cells, thereby specifically modulating C2CD4B expression in alpha cells. In another study, functional ablation experiments in mouse models (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)showed that C2CD4A plays a pivotal role in beta-cell insulin secretion, which aligns with the EUR-associated pattern focusing on beta cells. Taken together, this population-dependent difference in regulatory interactions suggests that EAS variants might confer a more multifaceted impact on both glucagon-secreting alpha cells and insulin-secreting beta cells, potentially reflecting\u0026mdash;or even exacerbating\u0026mdash;the distinct clinical phenotypes of T2D commonly observed in East Asians, such as lower BMI onset and reduced insulin secretory capacity.\u003c/p\u003e \u003cp\u003eOur results with sLDSC also confirmed that pancreatic cell types exhibit the highest heritability enrichment in both EAS and EUR, consistent with T2D\u0026rsquo;s established biology (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Consistent with the previous research from type 1 diabetes participants (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), our findings from type 2 diabetes indicated potential broader interactions beyond beta cells, including alpha, delta, and gamma cells. Additionally, we saw a stronger heritability enrichment for transcription factor HNF1B in EAS. This finding is consistent with stronger effect sizes at the \u003cem\u003eHNF1B\u003c/em\u003e locus in EAS, as also reported previously (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). \u003cem\u003eHNF1B\u003c/em\u003e is known for its critical role in pancreatic development and insulin secretion (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Given that EAS populations generally show diminished insulin secretory capacity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), it is plausible that \u003cem\u003eHNF1B\u003c/em\u003e variants exert a stronger effect on T2D susceptibility in EAS compared to EUR. Collectively, these data underscore how single-ancestry fine-mapping can detect subtle differences in gene-variant and tissue-cell interactions that might otherwise be overshadowed by pooled analyses.\u003c/p\u003e \u003cp\u003eOne of the most intriguing divergences between EAS and EUR in our study emerges in lipid-related pathways and insulin resistance. Via pathway enrichment analysis of genes in significantly heterogeneous loci, we found that EUR-strong loci were enriched in lipid-related traits, particularly HDL-C and triglycerides, whereas EAS-strong loci showed no such enrichment (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.99). These findings are consistent with multi-ancestry cluster analyses where strong signals related to lipodystrophy or obesity were dominated by EUR (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). At the polygenic level, our GRS analyses of insulin secretion and insulin resistance further suggest that EAS may be less influenced by insulin resistance-related susceptibility compared to EUR. While both EAS and EUR T2D cases showed a higher GRS for insulin secretion defects than their respective controls, insulin resistance GRS was significantly higher in EUR cases than controls but nearly indistinguishable between EAS cases and controls. This lends genetic support to clinical observations that EAS T2D is, on average, more driven by poor insulin secretion and less by classical features of insulin resistance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Together, these results underscore the distinct role of insulin resistance in the pathogenesis of T2D in EAS vs. EUR.\u003c/p\u003e \u003cp\u003eOur GRS analysis for HDL-C supports the long-reported association in Europeans, where genetically lower HDL-C is linked to higher T2D risk. In East Asians, however, wide confidence intervals across deciles prevented a statistically significant trend, indicating possible population-specific factors influencing the HDL-C\u0026ndash;T2D relationship. Additionally, our pathway analysis of EAS-strong or heterogeneous gene sets points to differences in lipid-related metabolic pathways that align with these observations. This finding suggests that HDL-C functionality, particle composition (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), or interactions with other metabolic factors might vary by ancestry. To clarify these mechanisms, future single-ancestry studies should apply refined lipid phenotyping\u0026mdash;considering, for example, adiponectin (commonly lower in Asians) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and fatty acid-binding protein A (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u0026mdash;to detect subtle but important variations. Overall, these results indicate that HDL-C\u0026ndash;T2D associations may not be uniform across populations and emphasize the need for diverse genetic and functional research approaches.\u003c/p\u003e \u003cp\u003eShi et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) showed that many GWAS risk regions deemed \u0026ldquo;population-specific\u0026rdquo; actually harbor causal variants shared across ancestries, although differences in linkage disequilibrium or sample size can obscure them in other populations. Reflecting this trend, large multi-ancestry GWAS approaches have become popular for leveraging robust statistical power to identify widely shared T2D genetic loci (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, while such meta-analyses are crucial for capturing common signals, certain population-specific variants\u0026mdash;particularly in understudied groups\u0026mdash;may still be overlooked or diluted. By assembling a large single-ancestry East Asian cohort and directly comparing it with a similarly sized European cohort, we identified EAS-specific missense variants and finer locus-specific associations that would otherwise remain undetected. Our study underscores the importance of recognizing genuinely population-specific variants shaped by distinct demographic histories and environmental factors. Through single-ancestry meta-analyses with comparable effective sample sizes, we demonstrate that pinpointing these EAS-specific loci refines risk prediction models deepens our understanding of T2D pathophysiology and informs population-tailored therapeutic strategies. Ultimately, a balanced approach\u0026mdash;continuing large-scale multi-ancestry GWAS to uncover broadly shared variants while simultaneously investing in well-powered single-population investigations\u0026mdash;offers the most comprehensive view of complex traits, benefiting both global and population-specific precision medicine efforts.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant mention. First, we focused only on EAS and EUR populations, leaving open the question of whether these findings generalize to other groups with a high T2D burden, such as South Asians. Second, the current study relied on array-based imputation, and rare or ultra-rare variants may still be under-represented; whole-genome sequencing studies in large EAS cohorts are needed for a more comprehensive variant catalog. Third, environmental factors\u0026mdash;including diet and physical activity\u0026mdash;likely modulate genetic risk, but they were beyond the scope of our current analyses. Fourth, although we inferred distinct roles of insulin resistance and lipid metabolism in EAS, direct assessments of BMI or waist-hip ratio adjustments within the same dataset were hampered by incomplete phenotypic data. Finally, although our study sample was large by historical standards, more expansive EAS datasets could help clarify additional sub-phenotypes, such as early-onset T2D or normal-weight T2D.\u003c/p\u003e \u003cp\u003eOur comparison of the largest single-ancestry EAS T2D GWAS to a similarly sized EUR meta-analysis underscores both shared and distinctly population-specific genetic architectures underlying T2D. EAS-specific missense variants, divergent variant-gene interactions in pancreatic cells, and a weaker genetic contribution of insulin resistance collectively help explain why many East Asian individuals develop T2D with relatively low BMIs. On the other hand, lipid pathways and insulin resistance appear to play a comparatively stronger role in EUR. These findings exemplify the indispensable value of large-scale, single-ancestry GWAS for illuminating fine-scale genetic heterogeneity and guiding precision medicine efforts. As GWAS continue to expand in size and diversity, single-ancestry approaches, particularly in underrepresented populations, will remain integral to elucidating the biology of complex diseases like T2D. Ultimately, better characterization of population-specific genetic variation has the potential to improve risk stratification and treatment strategies for T2D worldwide. Future research integrating sophisticated functional genomics tools, detailed metabolic phenotyping, and larger sample sizes will help dissect how environmental and genetic factors jointly contribute to the pathogenesis of T2D in diverse populations.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003e This study adhered to the Helsinki Declaration's ethical principles. Participants provided written informed consent. Ethical approval was granted by the Shizuoka General Hospital Ethics Committee (SGHIRB 2018064) and the Institutional Review Board of CMUH (approval numbers: CMUH110-REC3-005, CMUH111-REC1-176). The National Center for Geriatrics and Gerontology's Conflict of Interest Committee also reviewed this study (990\u0026thinsp;\u0026minus;\u0026thinsp;13).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.S. conceived and designed the GWAS study, obtained ethical approvals, recruited participants, collected and analyzed DNA data, and drafted the manuscript; H.F.L. conducted analyses of the Taiwanese cohort, wrote sections of the Methods and Results, and assisted in manuscript revision; H.I., K.Ha., K.O., T.I., T.U., T.Sh., T.Sm., and I.T. provided resources, established the Japanese cohort infrastructure, supervised the study, and revised the manuscript; T.O., R.K., and C.K. obtained patient consent, collected DNA samples, and performed clinical diabetes diagnoses; K.Hi. conducted in-depth data analysis; Y.L. provided additional data and oversaw manuscript supervision; M.H. guided manuscript revision; W.L.L., T.Y.L., and F.J.T. provided resources and guidance for the Taiwanese cohort and reviewed the manuscript; C.T. served as the corresponding author, coordinated study design and resources, supervised statistical methods, and led manuscript revisions. All authors contributed to drafting or critically revising the manuscript, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMaterials \u0026amp; Correspondence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and material requests should be addressed to C.T. (Chikashi Terao).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to ethical restrictions but are available from the corresponding author upon reasonable request. The data supporting this study\u0026apos;s findings are available from the Shizuoka General Hospital\u0026apos;s Clinical Research Support Center Department.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was supported by the Medical Research Support Project of the Shizuoka Prefectural Hospital Organization, contract research projects on public health in Shizuoka Prefecture, Japan Agency for Medical Research and Development (AMED) grants 21ek0109555, 21tm0424220, 21ck0106642, 23ek0410114 and 23tm0424225, Japan Society for the Promotion of Science (JSPS) KAKENHI grant JP20H00462, and Takeda Hosho Grants for Research in Medicine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. 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A central role for GRB10 in regulation of islet function in man. PLoS genetics. 2014;10(4):e1004235.\u003c/li\u003e\n\u003cli\u003eUdler MS, Kim J, von Grotthuss M, Bon\u0026agrave;s-Guarch S, Cole JB, Chiou J, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS medicine. 2018;15(9):e1002654.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6532678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6532678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Type 2 diabetes (T2D) is a highly heterogeneous metabolic trait, with a higher prevalence in East Asians. This study aims to elucidate the East Asian-specific genetic architecture underlying T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted the largest single-ancestry GWAS to date (596,778 East Asians) and performed one-to-one comparative analyses at high-resolution and in polygenic levels with a European meta-analysis with comparable effective sample sizes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: The East Asian meta-analysis identified 196 T2D-associated loci, comparable to the 199 loci in the EUR meta-analysis. We found 69 SNPs (p\u0026lt;5x10-8) unique to either population, including six East Asian-specific missense variants with stronger effect sizes. Genetic correlation analysis revealed a strong similarity of polygenic architecture between the populations, yet statistical fine-mapping analyses highlighted distinct variant-gene interactions, particularly in pancreatic cells. We found distinct associations between lipid-related traits and T2D susceptibility—pathway analysis of heterogeneous loci revealed higher enrichment of lipid-related gene pathways in Europeans with a stronger effect size of adipose tissue-related epigenetic markers in Europeans. While genetic predisposition to insulin resistance was associated with increased T2D risk in Europeans, East Asians showed minimal differences between cases and controls. Genetic predisposition to HDL-C and BMI-adjusted waist-hip ratio was significantly associated with T2D risk in Europeans. However, the associations were not observed or much weaker in East Asians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e: Fine-scale genetic differences between populations, especially in lipid-related traits, underlie T2D susceptibility. Associations between insulin resistance and T2D susceptibility are distinct between East Asians and Europeans, in contrast to insulin secretion. Our findings highlight the importance of expanding single-ancestry genetic studies to gain deeper insights into the biology of complex traits.\u003c/p\u003e","manuscriptTitle":"Decomposing type 2 diabetes genetics into population-specific features by 600,000 East-Asians","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 11:37:53","doi":"10.21203/rs.3.rs-6532678/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8bb23f88-0dcc-45d2-8678-9a7f96e5e09d","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48217017,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"},{"id":48217018,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes"}],"tags":[],"updatedAt":"2026-04-04T06:35:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 11:37:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6532678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6532678","identity":"rs-6532678","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}