The Role of Polygenic Risk Score in the General Population: Current Status and Future Prospects

Abstract

Polygenic risk scores (PRSs), constructed from numerous common single nucleotide polymorphisms (SNPs), have emerged as useful tools for predicting future atherosclerotic cardiovascular disease (ASCVD). PRSs have shown independent associations with ASCVD outcomes and are increasingly being considered to enhance risk stratification and guide primary prevention strategies. However, most evidence to date has been derived from populations of European ancestry, and their generalizability to other populations, including East Asians, remains uncertain. This review summarizes the current epidemiological evidence on the association between PRS and ASCVD outcomes, focusing on findings in Japanese cohorts. We discuss the potential of PRS as a clinical decision support tool, its incremental value over traditional risk factors, and its role in the early identification of high-risk individuals. We also highlight the limited number of prospective studies in the Japanese population, where validation and implementation studies are ongoing. Given the growing accessibility of genetic testing and the potential of PRS to complement conventional risk assessments, further large-scale studies are warranted to evaluate its clinical utility across diverse populations. Expanding ancestry-specific biobanks and improving PRS transferability are essential steps toward the equitable implementation of genomic risk prediction in ASCVD prevention.

1.Introduction

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality worldwide ¹⁾. In Japan, ASCVD has consistently ranked among the leading causes of death, placing a growing burden on the healthcare system and underscoring the urgent need for effective prevention strategies ²⁾.

Considerable efforts have been devoted to risk prediction and modification of known risk factors as a primary prevention strategy to reduce the burden of ASCVD ^{3, 4)}. Over the past five decades, numerous well-designed epidemiological studies have identified key ASCVD risk factors, including blood pressure (BP), lipid levels, glucose levels, smoking, alcohol consumption, obesity, physical inactivity, and diet ^5-11). However, it is widely recognized that these factors do not fully account for the risk of ASCVD, as genetic predisposition also plays a substantial role in its development ¹¹⁾.

Incorporating genetic information into risk assessment may allow earlier identification of individuals at high risk for ASCVD and support the development of personalized prevention strategies from a younger age ^{4, 12)}. To date, genetic risk in clinical settings has typically been assessed using family history. Epidemiological studies have shown that having a parent with a history of ASCVD is strongly associated with an elevated risk of ASCVD, independent of conventional risk factors^{13, 14)}. Although family history is often used as a proxy for inherited genetic risk, it may also reflect shared environmental risk factors, including lifestyle habits. Thus, the observed associations could arise from a combination of genetic and non-genetic influences or even from complex interactions between them. Nonetheless, the fact that this association remained after adjusting for lifestyle-related factors suggests that unidentified genetic components may contribute to susceptibility to ASCVD.

Monogenic risk variants, which are typically inherited according to Mendelian patterns, are rare in the general population, and confer a large increase in disease risk¹⁵⁾. For example, variants in the low-density lipoprotein (LDL) receptor gene that cause familial hypercholesterolemia are strongly associated with ASCVD^{16, 17)}. Because of their clinical significance, these monogenic variants are a primary focus in cardiovascular genetics. However, such variants are found in only a small minority of individuals and account for a limited proportion of heritable cardiovascular disease risk. Many patients who develop ASCVD do not carry any known monogenic variants¹⁸⁾. These observations support growing evidence that in addition to rare monogenic variants, common genetic variants—those present in at least 1% of the population—also contribute substantially to susceptibility to ASCVD^{18, 19)}.

The decreasing cost of genetic testing has facilitated large-scale genome-wide association studies (GWAS), many now involving over one million individuals^20-24). These studies have identified numerous single nucleotide variants (SNVs) associated with ASCVD and related traits, including stroke, myocardial infarction, diabetes, BP, and lipid levels^20-25). Although the effect size of each common variant is modest in comparison to monogenic mutations, their cumulative impact on disease risk is substantial. Polygenic risk scores (PRSs), also known as polygenic scores or polygenic indices, aggregate the effects of multiple SNVs identified through GWAS and are typically weighted by their estimated effect sizes. Multiple studies have demonstrated that the PRS of ASCVD is associated with the risk of ASCVD^{4, 19)}. Unlike rare monogenic mutations, which affect only a small fraction of individuals, PRSs can identify much larger segments of the population at comparable or even higher genetic risk. In this context, PRSs have emerged as a promising risk modifier that can capture genetic predispositions beyond a recorded family history. The enthusiasm for their use has grown rapidly; however, this has arguably outpaced the rigorous evaluation needed to define their clinical utility.

Despite the 2021 European Society of Cardiology (ESC) prevention guidelines recommending against the use of PRSs in clinical practice, these scores are already commercially available and adopted by some clinicians and patients²⁶⁾. Their expanding application reflects both interest and ongoing uncertainty regarding when and how PRSs should be used in cardiovascular risk assessment. A 2022 Scientific Statement from the American Heart Association (AHA) also noted that PRSs offer promising clinical value and are already being used in select contexts⁴⁾. Nonetheless, the statement emphasized that further evaluation, standardization, and evidence development are essential before widespread implementation. The increasing availability of genetic data in large population-based cohorts now provides an opportunity to enhance our understanding of the pathogenesis of ASCVD, particularly components that are not fully explained by environmental exposure alone.

With the growing availability of PRSs through both academic research and direct-to-consumer platforms, the integration of genetic information into cardiovascular disease prevention has moved beyond theoretical interest to practical relevance²⁷⁾. Despite increasing global attention, the clinical utility of PRSs remains underexplored in Asian populations, including Japan, where genetic architectures and environmental exposures may differ significantly from those in Western populations^{28, 29)}. As PRS-based risk stratification gains traction, it is essential to assess its potential role within the context of Japan’s unique healthcare landscape and public health priorities. This review aimed to synthesize the current evidence surrounding the use of PRSs for ASCVD prevention among the Japanese population and to evaluate its implications for risk prediction, early intervention, and the development of population-specific preventive strategies. In doing so, we seek to inform future research and support responsible implementation of genetic risk profiling in the Japanese context.

2.What are PRSs?

A PRS is constructed by aggregating genotypes across an individual genome. Each genotype is weighted by its corresponding effect size estimated from a GWAS conducted on independent datasets³⁰⁾. The process typically consists of three main steps. First, researchers obtain a list of SNVs and their effect sizes (e.g., β coefficients, which represent the association per allele with the outcome in linear models, or log-odds/log-hazard rations in logistic or Cox models), which quantify the strength of the association of each SNV with the trait of interest. Publicly available GWAS summary statistics, such as those found in the GWAS catalog (https://www.ebi.ac.uk/gwas/), are the primary sources of these data. Second, linkage disequilibrium (LD) clumping was performed to account for LD, that is, non-random association of alleles at different loci. This reduces redundancy between correlated SNVs and separates independently associated signals, thereby improving the interpretability and performance of the PRS. Third, a PRS is calculated by summing the weighted genotypes across the selected SNVs. Various P-value thresholds are often used, with the optimal threshold determined on an independent validation dataset to maximize prediction accuracy.

In recent years, modern PRSs have evolved to incorporate millions of common genetic variants across the genome, in addition to those that exceed strict significance thresholds. Advanced methods that go beyond traditional strict P-value thresholds and LD clumping have been developed, such as Bayesian approaches (e.g., LDpred³¹⁾, LDpred2 ³²⁾, LDpred-funct³³⁾, PRS-CS³⁴⁾, AnnoPred³⁵⁾, SBayesC³⁶⁾, Bayesian sparse linear mixed models³⁷⁾, PleioPred³⁸⁾, STMGP³⁹⁾), penalized regression methods (e.g., Lassosum⁴⁰⁾), and ensemble or integrative frameworks (e.g., MegaPRS⁴¹⁾). These methods can consider genome-wide variants while controlling noise by considering the LD structure more comprehensively and applying effect size reduction, thereby improving prediction performance.

Currently, large-scale GWAS involving millions of individuals—including those of Japanese and other East Asian ancestries—is underway, gradually capturing a large portion of the heritability that can be explained by common variants, and genome-wide genotyping data can be used to almost completely estimate an individual’s polygenic predisposition²¹⁾. However, since many large-scale GWAS are based on prevalent cases rather than on incident cases, their ability to predict future disease events needs to be examined²⁶⁾. The majority of current GWAS datasets are derived from populations of European descent, and it is well known that PRSs perform optimally in European populations, while their predictive accuracy decreases in non-European populations⁴²⁾. In recent years, GWAS based on multi-ethnic cohorts has led to the development of PRSs that show improved predictive performance not only in European populations but also in diverse non-European populations such as East Asians and Africans^{43, 44)}. These advances demonstrate the potential of more globally applicable risk prediction tools. However, even with such improvements, disparities in predictive accuracy across ancestries remain, highlighting the need to develop PRSs that ensure equitable performance across all ethnic groups and minimize ancestral bias.

Another important consideration is that the choice of the PRS construction method can have a significant impact on individual risk estimates. Various statistical methods, from Bayesian statistical frameworks to penalized regression, can generate different polygenic scores, even when derived from the same GWAS summary statistics⁴⁵⁾. Variation in PRS output across computational methods can lead to inconsistent risk stratification, especially at the individual level. Thus, the reproducibility and interpretability of polygenic risk estimates must be carefully evaluated, especially in clinical contexts and reproductive applications such as embryo selection, where decision-making may depend on these predictions. Efforts to standardize PRS construction and evaluate consistency across methods are essential to ensure the fair and reliable application in any population.

A PRS represents an individual’s lifelong and immutable susceptibility to disease and functions as an ultimate antecedent marker of risk. However, it cannot determine whether the pathological processes are actively progressing at a given time. Moreover, elevated PRS values often reflect the cumulative effects of multiple overlapping biological pathways that contribute to disease susceptibility, making it difficult to pinpoint the specific mechanisms that drive the onset of disease. This biological complexity has driven the development of pathway-specific PRSs, which aggregate genetic risk within defined biological pathways to enable more mechanistic and targeted risk stratification^{46, 47)}. In this context, integrating polygenic risk information with other layers of biological data (e.g., genomics, proteomics, and metabolomics) through multi-omics approaches, is becoming increasingly important for elucidating the underlying pathophysiological mechanisms of complex diseases. These dynamic data layers capture the real-time biological consequences of gene–environment interactions, offering proximal markers of subclinical disease activity. Such integrative analyses not only have the potential to improve the accuracy of disease risk prediction, but may also uncover novel therapeutic targets and more directly connect genetic risk stratification to actionable, personalized prevention, and treatment strategies.

Importantly, because germline genotypes remain unchanged throughout life, PRSs, once generated, are stable across the lifespan. This enables the calculation of polygenic scores at any point in time, including birth, providing a lifelong index of inherited disease susceptibility with a single measurement²⁶⁾. This temporal stability represents a distinct advantage over conventional biomarkers, which may fluctuate over time due to environmental or physiological factors.

3. Current Evidence in Japan

Most studies evaluating PRSs have been conducted in individuals of European ancestry, primarily in cohorts based in Europe and the United States. These studies have shown that PRSs can modestly improve the prediction of incident ASCVD when added to traditional risk factors⁴⁾. However, evidence supporting the utility of PRSs in Japanese populations remains limited, and its generalizability to non-European populations is yet to be fully established (Table 1).

Table 1.Summary of the association between PRS and atherosclerotic cardiovascular disease in Japanese population

Trait	Authors	Study Cohort	Study Design	Total participants	Outcome	Cases	SNP	PRS construction Method	GWAS	Covariate	Effect size	Prediction ability	Reference no.
Hypertension	Fujii R, al.et	J-MICC Study	Cross-sectional study	11,252 PRS construction: 3,376 Association:7,876	SBP, DBP, Hypertension	1,254	Hypertension: 30,755 SBP: 37,543 DBP: 24,151	C+T approach	UK Biobank Biobank Japan	age, sex, BMI,smoking status, alcohol intake, dietary total energy intake, sedentary time, top five genetic principal components	The difference (95%CI) of SBP and DBP per SD, 2.56(1.83-3.30) mmHg, 2.53(1.80-3.26)mmHg,respectively	-	49
	Takase M, et al.	TMM Comm Cohort Study	Cross- sectional study	7,027 PRS construction: 1,405 Association: 5,622	Hypertension Home hypertension	Hypertension: 2294 Home hypertension: 2,322	SBP:1,786 home SBP: 1,786	C+T approach	Biobank Japan	age, sex, top. Six genetic principal components, measurement seasons (home SBP only)	OR for the highest tertile, 1.69 (1.47-1.96) for hypertension, 1.68 (1.45-1.94) for home hypertension	C statistic, 0.745 (0.732-0.758) for hypertension, 0.746 (0.733-0.758) for home hypertension (age, sex, top six genetic principal components, seasons, obesity, drinking, regular physical activity, sodium-to-potassium ratio) C statistic,0.748 (0.836–0.761) for hypertension, 0.753 (0.740–0.765) for home hypertension (above model + PRS)	50
	Fujii R, et al.	J-MICC Study	Prospective cohort study	9,296	CVD mortality	41	882,808	PRS-CS C+T approach	Biobank Japan	age, sex, BMI, smoking, alcohol. Dietary sodium,. medications for diabetes, medications for dyslipidemia, top 5 genetic principal components	HR per SD increments in PRS SBP-PRS HR:1.67 (1.21-2.32) DBP-PRS HR:1.62 (1.17-2.26)	C statistics: 0.757 for clinical risk factors 0.763 (0.687-0.838) for clinical + SBP-PRS, 0.761 (0.685-0.836) for clinical + DBP-PRS	52
Diabetes	Bui TH, et al.	Bunkyo Health Study	Cross- sectional study	1,610	Diabetes	126	110	Not shown	Biobank Japan	age, BMI, percentage of body fat, subcutaneous fat area, visceral fat area, and physical activity	OR for the highest tertile, 2.53 (1.69-3.80)	-	55
	Inaishi J, et al.	Hisayama Study	Prospective cohort study	1,465	Diabetes	199	84	Not shown	Biobank Japan, DIAGRAM Consortium, AGEN-T2D Consortium, SAT2D Consortium, MAT2D Consortium	age, sex, family history of diabetes, hypertension, serum total cholesterol, serum HDL cholesterol, serum triglycerides, use of lipid-modifying medication, BMI, smoking habits, alcohol intake, and regular exercise	HR for the highest quintile, 2.61 (1.67-4.05)	C statistic, 0.818 (0.791-0.845) (covariate + fasting plasma glucose + 2 hours plasma glucose) C statistic, 0.823 (0.796-0.850) (covariate + fasting plasma glucose + 2 hours plasma glucose + GRS)	56
	Takase M,et al.	TMM Comm Cohort Study	Cross- sectional study	9,681 PRS construction: 1,936 Association: 7,745	Diabetes	467	42,845	C+T approach	Biobank Japan	age, sex, top six genetic principal components	OR for the highest tertile, 3.16 (2.45-4.12)	C statistic, 0.788 (0.769-0.807) (covariate, smoking, drinking, obesity, GGT, physical activity, family history of diabetes) C statistic, 0.802 (0.783-0.821) (above model + PRS)	57
	Takase M, et al.	TMM Comm Cohort Study	Prospective cohort study	11,014	Diabetes	297	66,950	C+T approach	Biobank Japan	age, sex, top six genetic principal components	OR for the highest tertile, 2.31 (1.71-3.15)	C statistic, 0.703 (0.674-0.732) (covariate, smoking, drinking, obesity, GGT, physical activity, family history of diabetes) C statistic, 0.719 (0.692-0.747) (above model + PRS)	58
Hypercholesterolemia	Tanisawa K, et al.	WASEDA’S Heath Study	Cross- sectional study	1,296	Lipids, Diabetes, Hypertension	High TG: 209 High LDL-C: 455 Low HDL- C: 60 Diabetes: 65 Hypertension: 486	LDpred2 TG: 813,267 LDL-C: 813,267 HDL: 813,267 Diabetes: 813,871 Hypertension: 813,267	LDpred2 C+T approach	Biobank Japan	age, sex, BMI, smoking status, alcohol drinking status, menopause status, history of CVD, history of cancer, TG-lowering medication use, cholesterol lowering medication use, antidiabetic medication use, antihypertensive medication use, and the top 10 genetic principal components	OR per SD increment in PRS High TG: 2.13 (1.77-2.56) High LDL-C: 1.74 (1.52 2.56) Low HDL-C: 2.54 (1.86-3.48) Diabetes: 2.42 (1.79-3.28) Hypertension: 1.56 (1.35 1.80)	Proportion of variance Explained by adding PRS High TG: 0.080 High LDL-C: 0.069 Low HDL-C 0.090 Diabetes: 0.077 Hypertension: 0.027	59
Stroke	Hachiya T,et al.	KyusyuU data, JPJM data	case-control, nested-case control study	KyushuU: 2,194 JPJM: 672	Ischemic Stroke	KyushuU: 1,097 JPJM: 336	357,367	polyGRS (Bayesian mixed model)	Biobank Japan, Tohoku Medical Megabank Project, Japan Public Health Center-based prospective study, Japan Multi- Institutional Collaborative	Hypertension, diabetes, hyperlipidemia, atrial fibrillation	OR for top vs bottom quintile: KyushuU 1.75(1.33–2.31), JPJM 1.99 (1.19–3.33)	ΔC-index: 0.700 (95% CI: 0.679–0.722) NRI: 0.151 (95% CI: 0.068–0.235) IDI: 0.004 (95% CI: 0.001–0.006)	66
	Hachiya T,et al.	Hisayama Study	Prospective cohort study	3,038	Ischemic Stroke	91	357,367	GW-PRS (weighted sum using BLUP from Japanese GWAS)	Biobank Japan, Tohoku Medical Megabank Project, Japan Public Health Center-based prospective study, Japan Multi- Institutional Collaborative	age, sex, hypertension, diabetes, cholesterol, BMI, ECFG, smoking, alcohol, exercise	HR for the highest quintile, 2.44 (1.16-5.12)	-	67

Abbreviations: CVD, cardiovascular disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; GGT, gamma-glutamyl transferase; ECG, electrocardiogram; PRS, polygenic risk score; SNP, single nucleotide polymorphism; GWAS, genome-wide association study; GRS, genetic risk score; TG, triglycerides; HDL, high-density lipoprotein, LDL, low-density lipoprotein; T2D, type 2 diabetes; HR, hazard ratio; OR, odds ratio; CI, confidence interval; AUC, area under the curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement; BLUP, best linear unbiased prediction.

Hypertension

A recent large-scale GWAS identified over 2,103 loci associated with BP regulation⁴⁸⁾. The identification of BP-associated variants has enabled the development of a PRS for BP. In Japan, the Japan Multi-institutional Collaborative Cohort (J-MICC) study conducted a cross-sectional analysis and found that participants in the highest PRS quintile had significantly higher systolic and diastolic BP values than those in the middle quintile: 4.55 mmHg (95% confidence interval [CI], 2.26–6.85) and 2.32 mmHg (95% CI, 0.86–3.78), respectively⁴⁹⁾. Similarly, the Tohoku Medical Megabank Community-based Cohort Study (TMM CommCohort Study) demonstrated that the BP PRS was significantly associated with the prevalence of both clinical and home hypertension, independent of established risk factors such as age, sex, body mass index (BMI), sodium-to-potassium ratio, alcohol consumption, and physical activity⁵⁰⁾. The addition of the PRS to conventional risk models modestly improved discrimination ability. For hypertension, the C-statistic increased from 0.743 (95% CI, 0.731–0.756) to 0.748 (95% CI, 0.736–0.761); for home hypertension, from 0.749 (95% CI, 0.736–0.761) to 0.753 (95% CI, 0.740–0.765)⁵⁰⁾.

Although the BP PRS has primarily been studied in relation to hypertension risk, recent studies have extended their application to hard clinical endpoints, including all-cause and cardiovascular mortalities. In a large trans-biobank study involving approximately 676,000 individuals from the BioBank Japan (BBJ), UK Biobank (UKB), and FinnGen, Sakaue et al. demonstrated that the PRS for systolic BP was consistently associated with all-cause mortality across all three cohorts⁵¹⁾. Likewise, the PRS for diastolic BP also showed a significant association with mortality in each biobank, indicating that genetic predisposition to elevated BP contributes to increased mortality risk across diverse populations. Additionally, Fujii et al. reported a significant association between the BP PRS and cardiovascular mortality in a Japanese general population⁵²⁾. Although no statistically significant interaction was observed between the PRS and modifiable lifestyle factors, stratified analyses suggested that the attributable risk of smoking for cardiovascular mortality may be greater in individuals with higher PRSs. These findings imply that a genetic predisposition could potentially modify the absolute benefits of behavioral interventions. This highlights an important gap in our understanding of how genetic risk interacts with behavioral interventions to influence long-term outcomes.

The utility of PRSs in predicting the development of hypertension remains unclear. Prior studies have not accounted for baseline systolic or diastolic BP, limiting their interpretability⁵⁰⁾. To our knowledge, no prospective studies have evaluated the predictive utility of a PRS for hypertension in the Japanese population. Therefore, it remains unclear whether a PRS can predict incident hypertension or improve risk stratification beyond baseline BP measurements. The current analyses yield inconclusive findings, and larger prospective cohort studies are needed to clarify the role of PRSs in this context. The declining cost of genetic testing, coupled with the ability to compute PRSs for multiple diseases from a single biospecimen, has broadened the potential application of PRSs in clinical and public health settings. However, because BP can be measured easily, inexpensively, and non-invasively using standard monitoring devices, the added value and cost-effectiveness of incorporating a PRS into hypertension risk prediction models remain uncertain. Further research is needed to determine whether the PRS provides sufficient incremental predictive utility to justify its implementation in routine practice.

Diabetes

Several monogenic variants associated with diabetes have been identified, including mutations in genes such as Glucokinase, Hepatocyte Nuclear Factor 1-Alpha, and Pancreatic and Duodenal Homeobox1, and those causing mature-onset diabetes in the young⁵³⁾. However, these monogenic forms account for only a small fraction of the overall risk of diabetes. A recent large-scale genome-wide association study involving over 2.5 million individuals identified 1,289 independent association signals across 611 loci⁵⁴⁾. These signals were categorized into eight distinct clusters based on cardiometabolic traits. Cluster-specific polygenic scores were associated with vascular outcomes such as coronary artery disease and diabetic nephropathy, highlighting the key role of obesity-related mechanisms in the development of diabetes complications⁵⁴⁾.

Several studies have investigated the association between PRSs and diabetes in Japan. A cross-sectional analysis from the Bunkyo Health Study constructed a genome risk score (GRS) using 110 diabetes-related SNVs. The GRS was based only on variants that had previously reached genome-wide significance (P＜5×10^－⁸) in a Japanese GWAS and therefore differs from polygenic risk scores (PRSs), which typically include genome-wide variants regardless of significance. Despite this more conservative approach, the GRS was significantly associated with the prevalence of diabetes⁵⁵⁾. The Hisayama Study developed a GRS based on 84 genome-wide significant variants and demonstrated that a higher GRS was associated with an increased risk of developing diabetes, independent of environmental risk factors⁵⁶⁾. In comparison to individuals in the lowest quintile, those in the highest quintile had a HR of 2.85 (95% CI, 1.83–4.44). Although the association was attenuated after adjusting for fasting blood glucose and 2-hour post-load glucose, it remained significant (HR, 2.05; 95% CI, 1.31–3.21). Similar findings were reported in the TMM CommCohort Study^{57, 58)}, which showed that the PRS, lifestyle factors, and family history were independently associated with incident diabetes. Notably, even among participants with an ideal lifestyle and no relevant family history, those with a high genetic risk had an elevated risk of developing diabetes (Odds ratio [OR], 2.76; 95% CI, 1.70–4.63). In contrast, individuals with a family history of diabetes exhibited an increased risk, even when their genetic risk was low and their lifestyle was ideal (OR, 2.97; 95% CI, 1.16–6.70). The predictive performance of diabetes risk models, as measured by the C-statistic (AUROC), improved modestly with the addition of a PRS to models that included lifestyle and family history. The Hisayama Study also reported enhanced discrimination and improved reclassification with the inclusion of a PRS in models incorporating traditional risk factors⁵⁶⁾. However, the overall improvement in prediction was limited. These findings highlight the potential utility of PRSs in stratifying the risk of diabetes; however, further prospective studies are needed to clarify its role in prevention strategies.

Hypercholesterolemia

Dyslipidemia is a major modifiable risk factor for atherosclerotic cardiovascular disease (ASCVD), and the causative role of elevated LDL cholesterol and triglyceride levels has been well established in epidemiological and genetic studies²⁾. Familial hypercholesterolemia (FH) is a monogenic disease characterized by markedly elevated LDL-C levels and is one of the best-known genetic causes of premature ASCVD²⁾. In recent years, common genetic variants associated with lipid traits have been aggregated into PRSs, which have emerged as additional tools to quantify genetic risk beyond single-gene variants²⁰⁾.

The WASEDA’S Health Study demonstrated that PRS constructed from common variants was significantly associated with a higher prevalence of elevated TG and LDL-C, and low HDL-C levels⁵⁹⁾. Importantly, the study found that high cardiorespiratory fitness (CRF), assessed by peak oxygen uptake per kilogram of body weight, attenuated the genetic predisposition to TG elevation (P for interaction = 0.001), but not LDL-C or HDL-C. These findings suggest that high CRF may provide a particular benefit in individuals with a high PRS for triglycerides. Tam et al. developed genome-wide PRSs for various lipid traits using data from Biobank Japan and tested the PRS among individuals of Chinese ancestry⁶⁰⁾. These scores were significantly associated with their respective lipid traits across individuals of different life stages, including children, adolescents, healthy adults, and adult women. Moreover, the PRS predicted the risk of incident dyslipidemia over a 3-year follow-up period. The predictive performance, as measured by the AUROC, modestly improved when PRSs were added to models that included age, sex, BMI, and genetic principal components.

In previous studies, 20% to 30% of patients clinically diagnosed with FH had a high PRS for LDL cholesterol^61-64). Notably, individuals in the top 5th percentile of the LDL-C PRS exhibited comparable LDL-C levels to individuals with monogenic FH variants⁶⁵⁾. A comprehensive assessment of both monogenic and polygenic factors may improve the diagnostic yield beyond that of FH gene panel testing alone. This approach may play an important role in identifying individuals who could benefit from early and intensive lipid-lowering therapy. However, this hypothesis requires confirmation in prospective studies. Future research should explore the utility of lipid and lipoprotein PRSs in guiding therapeutic interventions and clarifying their clinical significance.

Cardiovascular Disease

In Japan, the Iwate Medical Megabank Organization has played a leading role in investigating the genetic and environmental determinants of stroke, and such efforts were initiated relatively early in comparison to other population-based cohorts. Hachiya et al. developed a PRS for ischemic stroke and assessed its ability to improve risk prediction beyond traditional clinical factors⁶⁶⁾. The PRS was significantly associated with an increased risk of ischemic stroke and its subtypes, including large-vessel disease, small-vessel disease, and cardioembolic stroke, across both case-control and nested case-control study designs. When added to a conventional risk model, including hypertension, diabetes mellitus, hyperlipidemia, and atrial fibrillation, the PRS significantly improved risk discrimination, as indicated by a positive net reclassification index. This association was further confirmed in the Hisayama Study, a prospective population-based cohort study in Japan⁶⁷⁾ where the PRS was independently associated with incident ischemic stroke. Participants in the highest PRS quintile had a significantly higher risk of ischemic stroke than those in the lowest quintile (HR, 2.43; 95% CI, 1.15–5.12). However, no statistically significant interactions were observed between the PRS and lifestyle factors, suggesting that effective management of modifiable risk factors is essential, regardless of genetic predisposition.

In the GIGASTROKE consortium, a large-scale meta-analysis involving over 2.5 million individuals, 61 novel stroke-associated loci were identified⁶⁸⁾. This study also developed a composite PRS integrating genetic contributions not only from stroke-related loci but also from traits such as BP, BMI, and atrial fibrillation. These associations were consistently observed in both East Asian and European populations. Building on this work, Fujii et al. recently constructed a multi-trait PRS that combines genetic risk scores for systolic BP, BMI, triglycerides, LDL cholesterol, estimated glomerular filtration rate, and HbA1c. This multi-trait PRS was associated with CVD mortality independently of conventional risk factors⁶⁹⁾. In comparison to individuals in the bottom 90%, those in the top 10% of the multi-trait PRS distribution had a 32% higher risk of all-cause mortality and a 163% higher risk of CVD-related mortality. These findings suggest that a comprehensive evaluation of genetic risk for multiple risk factors may enable more precise stratification of ASCVD risk. In the future, it will be necessary to develop frameworks that utilize such trait PRSs to identify high-risk individuals and to implement appropriate primary prevention strategies at an early stage. Moreover, prospective interventional studies are needed to evaluate the effectiveness of preventive interventions in high-risk populations.

4.Practical Challenge for the Implementation of PRSs in Japan

The ultimate goals of implementing PRSs in clinical and public health settings are to prevent disease and identify at-risk individuals early in life. However, several important limitations must be addressed before PRSs can be widely adopted in clinical or public health practice.

According to a scientific statement from the AHA⁴⁾, the following three broad criteria should be considered before the implementation of PRSs: 1) efficacy, 2) harm, and 3) logistics.

The AHA suggested that the clinical efficacy of a PRS is likely appropriate when either of the following is achieved: 1) the integration of a PRS into clinical risk tools substantially improves their accuracy, and 2) PRS risk tools can identify participants at risk to a degree that is at least equivalent to that of individuals with monogenic risk variants⁴⁾.

First, regarding efficacy, the addition of PRSs to established clinical risk models has been shown to improve predictive accuracy for complex diseases such as coronary heart disease, diabetes, and atrial fibrillation, primarily in European populations⁷⁰⁾. However, evidence in Asian populations remains limited, and further studies are needed to validate the clinical utility of PRSs in these groups. In addition, it remains unclear whether early life screening using PRSs alone can effectively identify individuals with a lifetime risk comparable to that of monogenic variant carriers and guide the timely initiation of preventive interventions. Currently, there is no established prevention framework for the early identification of individuals at high polygenic risk. Integrating multi-omics data, such as metabolomics, proteomics, and epigenomics, may help to elucidate the early biological changes associated with the onset of disease in these individuals and uncover actionable targets for intervention. Such insights could ultimately pave the way for precision prevention strategies based on both genetic and molecular profiles.

Similarly, for preventive intervention trials, targeting high-risk individuals enables the design of studies with greater statistical power, using smaller cohorts and shorter follow-up periods. By enriching the study population with individuals with a higher baseline risk, the ability to detect the effects of preventive treatments, such as pharmacological therapies or lifestyle interventions, is substantially enhanced. Therefore, early risk stratification using PRSs is expected to play a pivotal role in streamlining observational and interventional research efforts, even in the absence of fully established preventive measures. However, it remains uncertain whether knowledge of an individual’s genetic influence on PRSs can lead to preventive actions. For example, the GeneRISK study reported that 42.6% of individuals with a high polygenic and clinical risk of ASCVD undertook at least one health-promoting behavior, including visiting a doctor or losing weight, and these behavioral changes were independently associated with both the PRS and clinical risk levels⁷¹⁾. In contrast, the MI-GENES randomized trial showed that disclosure of a CHD genetic risk score led to improved LDL-C levels and higher statin use but did not result in measurable changes in diet or physical activity⁷²⁾. Other trials, such as the P5 Study and GC/LC Study, highlighted modest motivational effects, but no consistent impact on actual health behavior or program attendance^{73, 74)}. Further research is needed to determine whether risk prediction improves and whether disclosing a PRS improves health-related behaviors. It is also crucial to recognize that a high polygenic risk score does not necessarily correspond to a single pathogenic mechanism.

Second, although concerns regarding racial disparities in healthcare have been central to discussions on PRS implementation in Western countries, different challenges are anticipated in Japan. In Japan, where the population is more genetically homogeneous than Western multi-ancestry populations, the major concerns may be related to disparities in socioeconomic status, geographic location, and health literacy rather than ancestry. Access to PRS testing may be limited to those with a higher socioeconomic status or those living in urban areas with specialized medical facilities, potentially leading to increased health disparities. Furthermore, subtle but significant genetic heterogeneity exists within Japan, which may affect the accuracy and transferability of PRSs^75-77). These considerations highlight the need for careful validation of PRSs across diverse regional populations within Japan, and for the development of equitable frameworks to ensure fair access and appropriate interpretation.

Third, several logistical and educational challenges need to be addressed. Currently, the genomic data required for PRS calculation are primarily generated through research initiatives or direct-to-consumer testing services and are largely external to the clinical care setting²⁶⁾. The lack of systematic integration of genomic data into electronic health records limits the ability to leverage PRSs in routine CVD assessments. Moreover, germline genotypes remain unchanged throughout life, and the interpretation of genetic variants continues to evolve with scientific advances and clinical insights, requiring information technology systems that can flexibly update algorithms and revise decision-support tools accordingly. Limited genetic literacy among clinicians and patients further complicates the appropriate understanding and use of PRSs, posing a risk of misinterpretation and inappropriate risk communication²⁶⁾. In this context, the development of personalized risk assessment tools that integrate PRSs with conventional risk factors in a user-friendly and comprehensible manner is essential to support both clinical decision-making and patient understanding. Healthcare providers and genetic counselors will require targeted education to facilitate an accurate explanation of the PRS results, their limitations, and appropriate clinical applications. Furthermore, clear guidelines are required regarding how PRS results should be communicated, including whether risks are best presented as continuous scores, stratified risk categories (e.g., low, intermediate, high), or both, and how they should be combined with traditional risk factors for treatment recommendations^{26, 71)}. Additionally, a shortage of clinical geneticists and genetic counselors is another barrier to providing comprehensive genetic risk counseling at the scale, as is the lack of a regulatory and ethical framework for clinical use, including standards for informed consent, data management, and actionable thresholds. The implementation of PRS-guided care would require substantial investments in genomic infrastructure, health information systems, and provider education, raising concerns about disparities between well-resourced and underresourced institutions. To maximize clinical benefits and minimize unintended disparities, future studies should aim to establish equitable systems for the clinical application of PRSs that are adaptable to the evolving genomic landscape.

Conclusions

PRSs have emerged as promising tools for improving risk stratification for ASCVD by capturing cumulative genetic susceptibility not reflected in traditional clinical factors or family history. Although incorporating PRSs into clinical risk models has led to modest improvements in prediction—particularly among individuals of European ancestry—validation in Asian populations, including Japan, remains limited. Encouragingly, large-scale multi-ethnic initiatives such as the All of Us Research Program^{78, 79)}, Our Future Health⁸⁰⁾, and national efforts like BBJ⁸¹⁾ are beginning to address these critical evidence gaps. To ensure successful clinical implementation, it will be essential to overcome practical challenges, including education, data infrastructure, and ethical governance. Ensuring equitable access to PRS-based care is also vital to avoid exacerbating existing health disparities. As genomic testing becomes more accessible, future clinical guidelines should aim to identify populations that are most likely to benefit from PRS-informed decision-making, thereby advancing the broader goal of precision prevention in ASCVD.

Acknowledgements

This work was supported by grants from the Tohoku Medical Megabank Project of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT); the Japan Agency for Medical Research and Development (AMED; JP22tm0124005); and Grants-in-Aid for Scientific Research (KAKENHI #24K02699 to A.H., KAKENHI #24K23673 to M.T., and KAKENHI #25K20591 to M. T.).

The authors are grateful to Nakaya Naoki, Mana Kogure, Rieko Hatanaka, Kumi Nakaya, Ippei Chiba, Sayuri Tokioka, Akira Narita, Taku Obara, Mami Ishikuro, Hisashi Ohseto, Ippei Takahashi, Tomoko Kobayashi, Eiichi N Kodama, Yohei Hamanaka, Masatsugu Orui, Soichi Ogishima, Satoshi Nagaie, Nobuo Fuse, Junichi Sugawara, Shinichi Kuriyama, Biobank Japan Project; Koichi Matsuda, Yoko Izumi, Kengo Kinoshita, Gen Tamiya, Masayuki Yamamoto and the members of the Tohoku Medical Megabank Organization, including the Genome Medical Research Coordinators, and the office and administrative personnel for their assistance. A complete list of members is available at https://www.megabank.tohoku.ac.jp/english/a240901/. The authors also thank the Japan Atherosclerosis Society, the Asian-Pacific Society of Atherosclerosis and Vascular Diseases, and the Editorial Board of the Journal of Atherosclerosis and Thrombosis for providing the opportunity to write this article.

Competing Interests

There are no competing interests.

References

• 1)  Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation, 2024; 149: e347-e913
• 2)  Okamura T, Tsukamoto K, Arai H, Fujioka Y, Ishigaki Y, Koba S, Ohmura H, Shoji T, Yokote K, Yoshida H, Yoshida M, Deguchi J, Dobashi K, Fujiyoshi A, Hamaguchi H, Hara M, Harada-Shiba M, Hirata T, Iida M, Ikeda Y, Ishibashi S, Kanda H, Kihara S, Kitagawa K, Kodama S, Koseki M, Maezawa Y, Masuda D, Miida T, Miyamoto Y, Nishimura R, Node K, Noguchi M, Ohishi M, Saito I, Sawada S, Sone H, Takemoto M, Wakatsuki A, Yanai H. Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2022. J Atheroscler Thromb, 2024; 31: 641-853
• 3)  Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, Michos ED, Miedema MD, Muñoz D, Smith SC Jr, Virani SS, Williams KA Sr, Yeboah J, Ziaeian B. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation, 2019; 140: e596-e646. doi: 10.1161/CIR.0000000000000678. Epub 2019 Mar 17. Erratum in: Circulation, 2019; 140: e649-e650. doi: 10.1161/CIR.0000000000000725. Erratum in: Circulation, 2020; 141: e60. doi: 10.1161/CIR.0000000000000755. Erratum in: Circulation, 2020 ; 141: e774
• 4)  O’Sullivan JW, Raghavan S, Marquez-Luna C, Luzum JA, Damrauer SM, Ashley EA, O’Donnell CJ, Willer CJ, Natarajan P; American Heart Association Council on Genomic and Precision Medicine; Council on Clinical Cardiology; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Lifestyle and Cardiometabolic Health; and Council on Peripheral Vascular Disease. Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation, 2022; 146: e93-e118
• 5)  Stampfer MJ, Hu FB, Manson JE, Rimm EB, Willett WC. Primary prevention of coronary heart disease in women through diet and lifestyle. N Engl J Med, 2000; 343: 16-22
• 6)  Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L; INTERHEART Study Investigators. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet, 2004; 364: 937-952
• 7)  Hozawa A, Folsom AR, Sharrett AR, Chambless LE. Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects--Atherosclerosis Risk in Communities Study. Arch Intern Med, 2007; 167: 573-579
• 8)  Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet, 2014; 383: 999-1008
• 9)  Kronmal RA, McClelland RL, Detrano R, Shea S, Lima JA, Cushman M, Bild DE, Burke GL. Risk factors for the progression of coronary artery calcification in asymptomatic subjects: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation, 2007; 115: 2722-2730
• 10)  Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, Brauer M, Kutty VR, Gupta R, Wielgosz A, AlHabib KF, Dans A, Lopez-Jaramillo P, Avezum A, Lanas F, Oguz A, Kruger IM, Diaz R, Yusoff K, Mony P, Chifamba J, Yeates K, Kelishadi R, Yusufali A, Khatib R, Rahman O, Zatonska K, Iqbal R, Wei L, Bo H, Rosengren A, Kaur M, Mohan V, Lear SA, Teo KK, Leong D, O’Donnell M, McKee M, Dagenais G. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet, 2020; 395: 795-808
• 11)  Global Cardiovascular Risk Consortium; Magnussen C, Ojeda FM, Leong DP, Alegre-Diaz J, Amouyel P, Aviles-Santa L, De Bacquer D, Ballantyne CM, Bernabé-Ortiz A, Bobak M, Brenner H, Carrillo-Larco RM, de Lemos J, Dobson A, Dörr M, Donfrancesco C, Drygas W, Dullaart RP, Engström G, Ferrario MM, Ferrières J, de Gaetano G, Goldbourt U, Gonzalez C, Grassi G, Hodge AM, Hveem K, Iacoviello L, Ikram MK, Irazola V, Jobe M, Jousilahti P, Kaleebu P, Kavousi M, Kee F, Khalili D, Koenig W, Kontsevaya A, Kuulasmaa K, Lackner KJ, Leistner DM, Lind L, Linneberg A, Lorenz T, Lyngbakken MN, Malekzadeh R, Malyutina S, Mathiesen EB, Melander O, Metspalu A, Miranda JJ, Moitry M, Mugisha J, Nalini M, Nambi V, Ninomiya T, Oppermann K, d’Orsi E, Pająk A, Palmieri L, Panagiotakos D, Perianayagam A, Peters A, Poustchi H, Prentice AM, Prescott E, Risérus U, Salomaa V, Sans S, Sakata S, Schöttker B, Schutte AE, Sepanlou SG, Sharma SK, Shaw JE, Simons LA, Söderberg S, Tamosiunas A, Thorand B, Tunstall-Pedoe H, Twerenbold R, Vanuzzo D, Veronesi G, Waibel J, Wannamethee SG, Watanabe M, Wild PS, Yao Y, Zeng Y, Ziegler A, Blankenberg S. Global Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality. N Engl J Med, 2023; 389: 1273-1285
• 12)  Ashley EA, Hershberger RE, Caleshu C, Ellinor PT, Garcia JG, Herrington DM, Ho CY, Johnson JA, Kittner SJ, Macrae CA, Mudd-Martin G, Rader DJ, Roden DM, Scholes D, Sellke FW, Towbin JA, Van Eyk J, Worrall BB; American Heart Association Advocacy Coordinating Committee. Genetics and cardiovascular disease: a policy statement from the American Heart Association. Circulation, 2012; 126: 142-157
• 13)  Lloyd-Jones DM, Nam BH, D’Agostino RB, Sr., Levy D, Murabito JM, Wang TJ, Wilson PW, O’Donnell CJ. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA, 2004; 291: 2204-2211
• 14)  Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O’Leary D, Carr JJ, Goff DC, Greenland P, Herrington DM. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA, 2012; 308: 788795
• 15)  Kathiresan S, Srivastava D. Genetics of human cardiovascular disease. Cell, 2012; 148: 1242-1257
• 16)  Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, Wiklund O, Hegele RA, Raal FJ, Defesche JC, Wiegman A, Santos RD, Watts GF, Parhofer KG, Hovingh GK, Kovanen PT, Boileau C, Averna M, Borén J, Bruckert E, Catapano AL, Kuivenhoven JA, Pajukanta P, Ray K, Stalenhoef AF, Stroes E, Taskinen MR, Tybjærg-Hansen A; European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J, 2013; 34: 3478-3490a
• 17)  Khera AV, Won HH, Peloso GM, Lawson KS, Bartz TM, Deng X, van Leeuwen EM, Natarajan P, Emdin CA, Bick AG, Morrison AC, Brody JA, Gupta N, Nomura A, Kessler T, Duga S, Bis JC, van Duijn CM, Cupples LA, Psaty B, Rader DJ, Danesh J, Schunkert H, McPherson R, Farrall M, Watkins H, Lander E, Wilson JG, Correa A, Boerwinkle E, Merlini PA, Ardissino D, Saleheen D, Gabriel S, Kathiresan S. Diagnostic Yield and Clinical Utility of Sequencing Familial Hypercholesterolemia Genes in Patients With Severe Hypercholesterolemia. J Am Coll Cardiol, 2016; 67: 2578-2589
• 18)  Fahed AC, Wang M, Homburger JR, Patel AP, Bick AG, Neben CL, Lai C, Brockman D, Philippakis A, Ellinor PT, Cassa CA, Lebo M, Ng K, Lander ES, Zhou AY, Kathiresan S, Khera AV. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat Commun, 2020; 11: 3635
• 19)  Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet, 2018; 50: 1219-1224
• 20)  Graham SE, Clarke SL, Wu KH, Kanoni S, Zajac GJM, et al. The power of genetic diversity in genome-wide association studies of lipids. Nature, 2021; 600: 675-679. doi: 10.1038/s41586-021-04064-3. Epub 2021 Dec 9. Erratum in: Nature, 202; 618: E19-E20
• 21)  Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, et al. A saturated map of common genetic variants associated with human height. Nature, 2022; 610: 704-712
• 22)  Mishra A, Malik R, Hachiya T, Jürgenson T, Namba S, Posner DC, Kamanu FK, Koido M, Le Grand Q, Shi M, He Y, Georgakis MK, Caro I, Krebs K, Liaw YC, Vaura FC, Lin K, Winsvold BS, Srinivasasainagendra V, Parodi L, Bae HJ, Chauhan G, Chong MR, Tomppo L, Akinyemi R, Roshchupkin GV, Habib N, Jee YH, Thomassen JQ, Abedi V, Cárcel-Márquez J, Nygaard M, Leonard HL, Yang C, Yonova-Doing E, Knol MJ, Lewis AJ, Judy RL, Ago T, Amouyel P, Armstrong ND, Bakker MK, Bartz TM, Bennett DA, Bis JC, Bordes C, Børte S, Cain A, Ridker PM, Cho K, Chen Z, Cruchaga C, Cole JW, de Jager PL, de Cid R, Endres M, Ferreira LE, Geerlings MI, Gasca NC, Gudnason V, Hata J, He J, Heath AK, Ho YL, Havulinna AS, Hopewell JC, Hyacinth HI, Inouye M, Jacob MA, Jeon CE, Jern C, Kamouchi M, Keene KL, Kitazono T, Kittner SJ, Konuma T, Kumar A, Lacaze P, Launer LJ, Lee KJ, Lepik K, Li J, Li L, Manichaikul A, Markus HS, Marston NA, Meitinger T, Mitchell BD, Montellano FA, Morisaki T, Mosley TH, Nalls MA, Nordestgaard BG, O’Donnell MJ, Okada Y, Onland-Moret NC, Ovbiagele B, Peters A, Psaty BM, Rich SS, Rosand J, Sabatine MS, Sacco RL, Saleheen D, Sandset EC, Salomaa V, Sargurupremraj M, Sasaki M, Satizabal CL, Schmidt CO, Shimizu A, Smith NL, Sloane KL, Sutoh Y, Sun YV, Tanno K, Tiedt S, Tatlisumak T, Torres-Aguila NP, Tiwari HK, Trégouët DA, Trompet S, Tuladhar AM, Tybjærg-Hansen A, van Vugt M, Vibo R, Verma SS, Wiggins KL, Wennberg P, Woo D, Wilson PWF, Xu H, Yang Q, Yoon K; COMPASS Consortium; INVENT Consortium; Dutch Parelsnoer Initiative (PSI) Cerebrovascular Disease Study Group; Estonian Biobank; PRECISE4Q Consortium; FinnGen Consortium; NINDS Stroke Genetics Network (SiGN); MEGASTROKE Consortium; SIREN Consortium; China Kadoorie Biobank Collaborative Group; VA Million Veteran Program; International Stroke Genetics Consortium (ISGC); Biobank Japan; CHARGE Consortium; GIGASTROKE Consortium; Millwood IY, Gieger C, Ninomiya T, Grabe HJ, Jukema JW, Rissanen IL, Strbian D, Kim YJ, Chen PH, Mayerhofer E, Howson JMM, Irvin MR, Adams H, Wassertheil-Smoller S, Christensen K, Ikram MA, Rundek T, Worrall BB, Lathrop GM, Riaz M, Simonsick EM, Kõrv J, França PHC, Zand R, Prasad K, Frikke-Schmidt R, de Leeuw FE, Liman T, Haeusler KG, Ruigrok YM, Heuschmann PU, Longstreth WT, Jung KJ, Bastarache L, Paré G, Damrauer SM, Chasman DI, Rotter JI, Anderson CD, Zwart JA, Niiranen TJ, Fornage M, Liaw YP, Seshadri S, Fernández-Cadenas I, Walters RG, Ruff CT, Owolabi MO, Huffman JE, Milani L, Kamatani Y, Dichgans M, Debette S. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature, 2022; 611: 115-123. doi: 10.1038/s41586-022-05165-3. Epub 2022 Sep 30. Erratum in: Nature, 2022; 612: E7
• 23)  Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature, 2024; 627: 347-357
• 24)  Park S, Kim S, Kim B, Kim DS, Kim J, Ahn Y, Kim H, Song M, Shim I, Jung SH, Cho C, Lim S, Hong S, Jo H, Fahed AC, Natarajan P, Ellinor PT, Torkamani A, Park WY, Yu TY, Myung W, Won HH. Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome. Nat Genet, 2024; 56: 2380-2391
• 25)  Hartiala JA, Han Y, Jia Q, Hilser JR, Huang P, Gukasyan J, Schwartzman WS, Cai Z, Biswas S, Trégouët DA, Smith NL; INVENT Consortium; CHARGE Consortium Hemostasis Working Group; GENIUS-CHD Consortium; Seldin M, Pan C, Mehrabian M, Lusis AJ, Bazeley P, Sun YV, Liu C, Quyyumi AA, Scholz M, Thiery J, Delgado GE, Kleber ME, März W, Howe LJ, Asselbergs FW, van Vugt M, Vlachojannis GJ, Patel RS, Lyytikäinen LP, Kähönen M, Lehtimäki T, Nieminen TVM, Kuukasjärvi P, Laurikka JO, Chang X, Heng CK, Jiang R, Kraus WE, Hauser ER, Ferguson JF, Reilly MP, Ito K, Koyama S, Kamatani Y, Komuro I; Biobank Japan; Stolze LK, Romanoski CE, Khan MD, Turner AW, Miller CL, Aherrahrou R, Civelek M, Ma L, Björkegren JLM, Kumar SR, Tang WHW, Hazen SL, Allayee H. Genome-wide analysis identifies novel susceptibility loci for myocardial infarction. Eur Heart J, 2021; 42: 919-933
• 26)  Heribert Schunkert, Emanuele Di Angelantonio, Michael Inouye, Riyaz S Patel, Samuli Ripatti, Elisabeth Widen, Saskia C Sanderson, Juan Pablo Kaski, John W McEvoy, Panos Vardas, Angela Wood, Victor Aboyans, Vassilios S Vassiliou, Frank L J Visseren, Luis R Lopes, Perry Elliott, Maryam Kavousi. Clinical utility and implementation of polygenic risk scores for predicting cardiovascular disease: A clinical consensus statement of the ESC Council on Cardiovascular Genomics, the ESC Cardiovascular Risk Collaboration, and the European Association of Preventive Cardiology. European Heart Journal, 2025; 46: 1372-1383
• 27)  Hull LE, Aday AW, Bui QM, Luzum JA, Muchira JM, Wand H, Chahal CAA, Chung MK, Kwitek AE, Molossi S, Natarajan P; American Heart Association Data Science and Precision Medicine Committee of the Council on Genomic and Precision Medicine and Council of Clinical Cardiology; Council on Cardiovascular and Stroke Nursing; Council on Lifestyle and Cardiometabolic Health; and Council on Peripheral Vascular Disease. Direct-to-Consumer Genetic Testing for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation, 2025; 151: e905-e917
• 28)  Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019; 51: 584-591. doi: 10.1038/s41588-019-0379-x. Epub 2019. Erratum in: Nat Genet, 2021; 53: 763
• 29)  Tsugane S. Why has Japan become the world’s most long-lived country: insights from a food and nutrition perspective. Eur J Clin Nutr, 2021; 75: 921-928
• 30)  Choi SW, Mak TS, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc, 2020; 15: 2759-2772
• 31)  Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S, Genovese G, Loh PR, Bhatia G, Do R, Hayeck T, Won HH; Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study; Kathiresan S, Pato M, Pato C, Tamimi R, Stahl E, Zaitlen N, Pasaniuc B, Belbin G, Kenny EE, Schierup MH, De Jager P, Patsopoulos NA, McCarroll S, Daly M, Purcell S, Chasman D, Neale B, Goddard M, Visscher PM, Kraft P, Patterson N, Price AL. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am J Hum Genet, 2015; 97: 576-592
• 32)  Privé F, Arbel J, Vilhjálmsson BJ. LDpred2: better, faster, stronger. Bioinformatics, 2021; 36: 5424-5431
• 33)  Márquez-Luna C, Gazal S, Loh PR, Kim SS, Furlotte N, Auton A; 23andMe Research Team; Price AL. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat Commun, 2021; 12: 6052
• 34)  Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun, 2019; 10: 1776
• 35)  Hu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, Xu X, Zhao H. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput Biol, 2017; 13: e1005589
• 36)  Lloyd-Jones LR, Zeng J, Sidorenko J, Yengo L, Moser G, Kemper KE, Wang H, Zheng Z, Magi R, Esko T, Metspalu A, Wray NR, Goddard ME, Yang J, Visscher PM. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat Commun, 2019; 10: 5086
• 37)  Zhou X, Carbonetto P, Stephens M. Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet, 2013; 9: e1003264
• 38)  Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet, 2017 ; 13: e1006836
• 39)  Ueki M, Tamiya G; Alzheimer’s Disease Neuroimaging Initiative. Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection. Genet Epidemiol, 2016; 40: 233-243
• 40)  Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. Polygenic scores via penalized regression on summary statistics. Genet Epidemiol, 2017; 41: 469-480
• 41)  Zhang Q, Privé F, Vilhjálmsson B, Speed D. Improved genetic prediction of complex traits from individual-level data or summary statistics. Nat Commun, 2021; 12: 4192
• 42)  Mars N, Kerminen S, Feng YA, Kanai M, Läll K, Thomas LF, Skogholt AH, Della Briotta Parolo P; Biobank Japan Project; FinnGen; Neale BM, Smoller JW, Gabrielsen ME, Hveem K, Mägi R, Matsuda K, Okada Y, Pirinen M, Palotie A, Ganna A, Martin AR, Ripatti S. Genome-wide risk prediction of common diseases across ancestries in one million people. Cell Genom, 2022; 2: None
• 43)  Patel AP, Wang M, Ruan Y, Koyama S, Clarke SL, Yang X, Tcheandjieu C, Agrawal S, Fahed AC, Ellinor PT; Genes & Health Research Team; the Million Veteran Program; Tsao PS, Sun YV, Cho K, Wilson PWF, Assimes TL, van Heel DA, Butterworth AS, Aragam KG, Natarajan P, Khera AV. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med, 2023; 29: 1793-1803
• 44)  Smith JL, Tcheandjieu C, Dikilitas O, Iyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YI, Sheu WH, Chang KM, Kanoni S, Tsao PS, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. Circ Genom Precis Med, 2024; 17: e004272
• 45)  Namba S, Akiyama M, Hamanoue H, Kato K, Kawashima M, Kushima I, Matsuda K, Nakatochi M, Ogishima S, Sonehara K, Suzuki K, Takata A, Tamiya G, Tanikawa C, Yamamoto K, Yamamoto N; BioBank Japan Project; Ozaki N, Okada Y. Inconsistent embryo selection across polygenic score methods. Nat Hum Behav, 2024; 8: 2264-2267
• 46)  Widén E, Junna N, Ruotsalainen S, Surakka I, Mars N, Ripatti P, Partanen JJ, Aro J, Mustonen P, Tuomi T, Palotie A, Salomaa V, Kaprio J, Partanen J, Hotakainen K, Pöllänen P, Ripatti S. How Communicating Polygenic and Clinical Risk for Atherosclerotic Cardiovascular Disease Impacts Health Behavior: an Observational Follow-up Study. Circ Genom Precis Med, 2022; 15: e003459
• 47)  Choi SW, García-González J, Ruan Y, Wu HM, Porras C, Johnson J; Bipolar Disorder Working group of the Psychiatric Genomics Consortium; Hoggart CJ, O’Reilly PF. PRSet: Pathway-based polygenic risk score analyses and software. PLoS Genet, 2023; 19: e1010624
• 48)  Keaton JM, Kamali Z, Xie T, Vaez A, Williams A, Goleva SB, Ani A, Evangelou E, Hellwege JN, Yengo L, Young WJ, Traylor M, Giri A, Zheng Z, Zeng J, Chasman DI, Morris AP, Caulfield MJ, Hwang SJ, Kooner JS, Conen D, Attia JR, Morrison AC, Loos RJF, Kristiansson K, Schmidt R, Hicks AA, Pramstaller PP, Nelson CP, Samani NJ, Risch L, Gyllensten U, Melander O, Riese H, Wilson JF, Campbell H, Rich SS, Psaty BM, Lu Y, Rotter JI, Guo X, Rice KM, Vollenweider P, Sundström J, Langenberg C, Tobin MD, Giedraitis V, Luan J, Tuomilehto J, Kutalik Z, Ripatti S, Salomaa V, Girotto G, Trompet S, Jukema JW, van der Harst P, Ridker PM, Giulianini F, Vitart V, Goel A, Watkins H, Harris SE, Deary IJ, van der Most PJ, Oldehinkel AJ, Keavney BD, Hayward C, Campbell A, Boehnke M, Scott LJ, Boutin T, Mamasoula C, Järvelin MR, Peters A, Gieger C, Lakatta EG, Cucca F, Hui J, Knekt P, Enroth S, De Borst MH, Polašek O, Concas MP, Catamo E, Cocca M, Li-Gao R, Hofer E, Schmidt H, Spedicati B, Waldenberger M, Strachan DP, Laan M, Teumer A, Dörr M, Gudnason V, Cook JP, Ruggiero D, Kolcic I, Boerwinkle E, Traglia M, Lehtimäki T, Raitakari OT, Johnson AD, Newton-Cheh C, Brown MJ, Dominiczak AF, Sever PJ, Poulter N, Chambers JC, Elosua R, Siscovick D, Esko T, Metspalu A, Strawbridge RJ, Laakso M, Hamsten A, Hottenga JJ, de Geus E, Morris AD, Palmer CNA, Nolte IM, Milaneschi Y, Marten J, Wright A, Zeggini E, Howson JMM, O’Donnell CJ, Spector T, Nalls MA, Simonsick EM, Liu Y, van Duijn CM, Butterworth AS, Danesh JN, Menni C, Wareham NJ, Khaw KT, Sun YV, Wilson PWF, Cho K, Visscher PM, Denny JC; Million Veteran Program; Lifelines Cohort Study; CHARGE consortium; ICBP Consortium; Levy D, Edwards TL, Munroe PB, Snieder H, Warren HR. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits. Nat Genet, 2024; 56: 778-791
• 49)  Fujii R, Hishida A, Nakatochi M, Tsuboi Y, Suzuki K, Kondo T, Ikezaki H, Hara M, Okada R, Tamura T, Shimoshikiryo I, Suzuki S, Koyama T, Kuriki K, Takashima N, Arisawa K, Momozawa Y, Kubo M, Takeuchi K, Wakai K; J-MICC Study Group. Associations of Genome-Wide Polygenic Risk Score and Risk Factors With Hypertension in a Japanese Population. Circ Genom Precis Med, 2022; 15: e003612
• 50)  Takase M, Hirata T, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Narita A, Metoki H, Satoh M, Obara T, Ishikuro M, Ohseto H, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tamiya G, Hozawa A, Yamamoto M; ToMMo investigators. Associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertens Res, 2024; 47: 2064-2074
• 51)  Sakaue S, Kanai M, Karjalainen J, Akiyama M, Kurki M, Matoba N, Takahashi A, Hirata M, Kubo M, Matsuda K, Murakami Y; FinnGen; Daly MJ, Kamatani Y, Okada Y. Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan. Nat Med, 2020; 26: 542-548
• 52)  Fujii R, Hishida A, Nakatochi M, Okumiyama H, Takashima N, Tsuboi Y, Suzuki K, Ikezaki H, Shimanoe C, Kato Y, Tamura T, Ito H, Michihata N, Tanoue S, Suzuki S, Kuriki K, Kadota A, Watanabe T, Momozawa Y, Wakai K, Matsuo K; J-MICC Study Group. Polygenic risk score for blood pressure and lifestyle factors with overall and CVD mortality: a prospective cohort study in a Japanese population. Hypertens Res, 2024; 47: 2284-2294
• 53)  Bonnefond A, Froguel P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab, 2015; 21: 357-368
• 54)  Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature, 2024; 627: 347-357
• 55)  Bui TH, Kaga H, Kakehi S, Someya Y, Tabata H, Yoshizawa Y, Naito H, Tajima T, Ito N, Kadowaki S, Nishida Y, Kawamori R, Watada H, Tamura Y. Factors Associated With Type 2 Diabetes in Older Japanese With Similar Genetic Risk Scores: The Bunkyo Health Study. J Endocr Soc, 2025; 9: bvaf019
• 56)  Inaishi J, Hirakawa Y, Horikoshi M, Akiyama M, Higashioka M, Yoshinari M, Hata J, Mukai N, Kamatani Y, Momozawa Y, Kubo M, Ninomiya T. Association Between Genetic Risk and Development of Type 2 Diabetes in a General Japanese Population: The Hisayama Study. J Clin Endocrinol Metab, 2019; 104: 3213-3222
• 57)  Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, N Kodama E, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tsuji I, Tamiya G, Hozawa A, Yamamoto M. Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study. J Atheroscler Thromb, 2023; 30: 1950-1965
• 58)  Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S; BioBank Japan Project; Matsuda K, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, Yamamoto M; ToMMo investigators. Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population. J Atheroscler Thromb, 2024; 31: 1717-1732
• 59)  Tanisawa K, Tabata H, Nakamura N, Kawakami R, Usui C, Ito T, Kawamura T, Torii S, Ishii K, Muraoka I, Suzuki K, Sakamoto S, Higuchi M, Oka K. Polygenic Risk Score, Cardiorespiratory Fitness, and Cardiometabolic Risk Factors: WASEDA’S Health Study. Med Sci Sports Exerc, 2024; 56: 2026-2038
• 60)  Tam CHT, Lim CKP, Luk AOY, Ng ACW, Lee HM, Jiang G, Lau ESH, Fan B, Wan R, Kong APS, Tam WH, Ozaki R, Chow EYK, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JYY, Tsang MW, Kam G, Lau IT, Li JKY, Yeung VTF, Lau E, Lo S, Fung S, Cheng YL, Chow CC, Hu M, Yu W, Tsui SKW, Huang Y, Lan H, Szeto CC, Tang NLS, Ng MCY, So WY, Tomlinson B, Chan JCN, Ma RCW; Hong Kong Diabetes Register TRS Study Group; Hong Kong Diabetes Biobank Study Group. Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians. Genome Med, 2021; 13: 29
• 61)  Talmud PJ, Shah S, Whittall R, Futema M, Howard P, Cooper JA, Harrison SC, Li K, Drenos F, Karpe F, Neil HA, Descamps OS, Langenberg C, Lench N, Kivimaki M, Whittaker J, Hingorani AD, Kumari M, Humphries SE. Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenic familial hypercholesterolaemia: a case-control study. Lancet, 2013; 381: 1293-1301
• 62)  Futema M, Shah S, Cooper JA, Li K, Whittall RA, Sharifi M, Goldberg O, Drogari E, Mollaki V, Wiegman A, Defesche J, D’Agostino MN, D’Angelo A, Rubba P, Fortunato G, Waluś-Miarka M, Hegele RA, Aderayo Bamimore M, Durst R, Leitersdorf E, Mulder MT, Roeters van Lennep JE, Sijbrands EJ, Whittaker JC, Talmud PJ, Humphries SE. Refinement of variant selection for the LDL cholesterol genetic risk score in the diagnosis of the polygenic form of clinical familial hypercholesterolemia and replication in samples from 6 countries. Clin Chem, 2015; 61: 231-238
• 63)  Khera AV, Won HH, Peloso GM, Lawson KS, Bartz TM, Deng X, van Leeuwen EM, Natarajan P, Emdin CA, Bick AG, Morrison AC, Brody JA, Gupta N, Nomura A, Kessler T, Duga S, Bis JC, van Duijn CM, Cupples LA, Psaty B, Rader DJ, Danesh J, Schunkert H, McPherson R, Farrall M, Watkins H, Lander E, Wilson JG, Correa A, Boerwinkle E, Merlini PA, Ardissino D, Saleheen D, Gabriel S, Kathiresan S. Diagnostic Yield and Clinical Utility of Sequencing Familial Hypercholesterolemia Genes in Patients With Severe Hypercholesterolemia. J Am Coll Cardiol, 2016; 67: 2578-2589
• 64)  Wang J, Dron JS, Ban MR, Robinson JF, McIntyre AD, Alazzam M, Zhao PJ, Dilliott AA, Cao H, Huff MW, Rhainds D, Low-Kam C, Dubé MP, Lettre G, Tardif JC, Hegele RA. Polygenic Versus Monogenic Causes of Hypercholesterolemia Ascertained Clinically. Arterioscler Thromb Vasc Biol, 2016; 36: 2439-2445
• 65)  Natarajan P, Peloso GM, Zekavat SM, Montasser M, Ganna A, Chaffin M, Khera AV, Zhou W, Bloom JM, Engreitz JM, Ernst J, O’Connell JR, Ruotsalainen SE, Alver M, Manichaikul A, Johnson WC, Perry JA, Poterba T, Seed C, Surakka IL, Esko T, Ripatti S, Salomaa V, Correa A, Vasan RS, Kellis M, Neale BM, Lander ES, Abecasis G, Mitchell B, Rich SS, Wilson JG, Cupples LA, Rotter JI, Willer CJ, Kathiresan S; NHLBI TOPMed Lipids Working Group. Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nat Commun, 2018; 9: 3391
• 66)  Hachiya T, Kamatani Y, Takahashi A, Hata J, Furukawa R, Shiwa Y, Yamaji T, Hara M, Tanno K, Ohmomo H, Ono K, Takashima N, Matsuda K, Wakai K, Sawada N, Iwasaki M, Yamagishi K, Ago T, Ninomiya T, Fukushima A, Hozawa A, Minegishi N, Satoh M, Endo R, Sasaki M, Sakata K, Kobayashi S, Ogasawara K, Nakamura M, Hitomi J, Kita Y, Tanaka K, Iso H, Kitazono T, Kubo M, Tanaka H, Tsugane S, Kiyohara Y, Yamamoto M, Sobue K, Shimizu A. Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score. Stroke, 2017; 48: 253-258
• 67)  Hachiya T, Hata J, Hirakawa Y, Yoshida D, Furuta Y, Kitazono T, Shimizu A, Ninomiya T. Genome-Wide Polygenic Score and the Risk of Ischemic Stroke in a Prospective Cohort: The Hisayama Study. Stroke, 2020; 51: 759-765
• 68)  Mishra A, Malik R, Hachiya T, Jürgenson T, Namba S, Posner DC, Kamanu FK, Koido M, Le Grand Q, Shi M, He Y, Georgakis MK, Caro I, Krebs K, Liaw YC, Vaura FC, Lin K, Winsvold BS, Srinivasasainagendra V, Parodi L, Bae HJ, Chauhan G, Chong MR, Tomppo L, Akinyemi R, Roshchupkin GV, Habib N, Jee YH, Thomassen JQ, Abedi V, Cárcel-Márquez J, Nygaard M, Leonard HL, Yang C, Yonova-Doing E, Knol MJ, Lewis AJ, Judy RL, Ago T, Amouyel P, Armstrong ND, Bakker MK, Bartz TM, Bennett DA, Bis JC, Bordes C, Børte S, Cain A, Ridker PM, Cho K, Chen Z, Cruchaga C, Cole JW, de Jager PL, de Cid R, Endres M, Ferreira LE, Geerlings MI, Gasca NC, Gudnason V, Hata J, He J, Heath AK, Ho YL, Havulinna AS, Hopewell JC, Hyacinth HI, Inouye M, Jacob MA, Jeon CE, Jern C, Kamouchi M, Keene KL, Kitazono T, Kittner SJ, Konuma T, Kumar A, Lacaze P, Launer LJ, Lee KJ, Lepik K, Li J, Li L, Manichaikul A, Markus HS, Marston NA, Meitinger T, Mitchell BD, Montellano FA, Morisaki T, Mosley TH, Nalls MA, Nordestgaard BG, O’Donnell MJ, Okada Y, Onland-Moret NC, Ovbiagele B, Peters A, Psaty BM, Rich SS, Rosand J, Sabatine MS, Sacco RL, Saleheen D, Sandset EC, Salomaa V, Sargurupremraj M, Sasaki M, Satizabal CL, Schmidt CO, Shimizu A, Smith NL, Sloane KL, Sutoh Y, Sun YV, Tanno K, Tiedt S, Tatlisumak T, Torres-Aguila NP, Tiwari HK, Trégouët DA, Trompet S, Tuladhar AM, Tybjærg-Hansen A, van Vugt M, Vibo R, Verma SS, Wiggins KL, Wennberg P, Woo D, Wilson PWF, Xu H, Yang Q, Yoon K; COMPASS Consortium; INVENT Consortium; Dutch Parelsnoer Initiative (PSI) Cerebrovascular Disease Study Group; Estonian Biobank; PRECISE4Q Consortium; FinnGen Consortium; NINDS Stroke Genetics Network (SiGN); MEGASTROKE Consortium; SIREN Consortium; China Kadoorie Biobank Collaborative Group; VA Million Veteran Program; International Stroke Genetics Consortium (ISGC); Biobank Japan; CHARGE Consortium; GIGASTROKE Consortium; Millwood IY, Gieger C, Ninomiya T, Grabe HJ, Jukema JW, Rissanen IL, Strbian D, Kim YJ, Chen PH, Mayerhofer E, Howson JMM, Irvin MR, Adams H, Wassertheil-Smoller S, Christensen K, Ikram MA, Rundek T, Worrall BB, Lathrop GM, Riaz M, Simonsick EM, Kõrv J, França PHC, Zand R, Prasad K, Frikke-Schmidt R, de Leeuw FE, Liman T, Haeusler KG, Ruigrok YM, Heuschmann PU, Longstreth WT, Jung KJ, Bastarache L, Paré G, Damrauer SM, Chasman DI, Rotter JI, Anderson CD, Zwart JA, Niiranen TJ, Fornage M, Liaw YP, Seshadri S, Fernández-Cadenas I, Walters RG, Ruff CT, Owolabi MO, Huffman JE, Milani L, Kamatani Y, Dichgans M, Debette S. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature, 2022; 611: 115-123. doi: 10.1038/s41586-022-05165-3. Epub 2022. Erratum in: Nature.; 612: E7
• 69)  Fujii R, Nagayoshi M, Nakatochi M, Sato S, Tsuboi Y, Suzuki K, Ikezaki H, Nishida Y, Kubo Y, Tanoue S, Suzuki S, Koyama T, Kuriki K, Takashima N, Katsuura-Kamano S, Momozawa Y, Wakai K, Matsuo K; J‐MICC Study Group. Multi-Trait Polygenic Risk Score, Nongenetic Determinants, and Cardiovascular Disease Death: A Cohort Study of 14 086 Japanese Individuals. J Am Heart Assoc, 2025; 14: e038572
• 70)  Mars N, Koskela JT, Ripatti P, Kiiskinen TTJ, Havulinna AS, Lindbohm JV, Ahola-Olli A, Kurki M, Karjalainen J, Palta P; FinnGen; Neale BM, Daly M, Salomaa V, Palotie A, Widén E, Ripatti S. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med, 2020; 26: 549-557
• 71)  Marjonen H, Marttila M, Paajanen T, Vornanen M, Brunfeldt M, Joensuu A, Halmesvaara O, Aro K, Alanne-Kinnunen M, Jousilahti P, Borodulin K, Koskinen S, Tuomi T, Ilanne-Parikka P, Lindström J, Laine MK, Auro K, Kääriäinen H, Perola M, Kristiansson K. A Web Portal for Communicating Polygenic Risk Score Results for Health Care Use-The P5 Study. Front Genet, 2021; 12: 763159
• 72)  Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN, Haddad RA, Marroush TS, Shameer K, Olson JE, Broeckel U, Green RC, Schaid DJ, Montori VM, Bailey KR. Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial). Circulation, 2016; 133: 1181-1188
• 73)  Liou L, García-González J, Wu HM, Wang Z, Hoggart CJ, Kontorovich AR, Kovacic JC, O’Reilly PF. Clinical and Genomic Prediction of Coronary Artery Disease Subtypes. Arterioscler Thromb Vasc Biol, 2025; 45: 90-103
• 74)  Grant RW, O’Brien KE, Waxler JL, Vassy JL, Delahanty LM, Bissett LG, Green RC, Stember KG, Guiducci C, Park ER, Florez JC, Meigs JB. Personalized genetic risk counseling to motivate diabetes prevention: a randomized trial. Diabetes Care, 2013; 36: 13-19
• 75)  Yamaguchi-Kabata Y, Nakazono K, Takahashi A, Saito S, Hosono N, Kubo M, Nakamura Y, Kamatani N. Japanese population structure, based on SNP genotypes from 7003 individuals compared to other ethnic groups: effects on population-based association studies. Am J Hum Genet, 2008; 83: 445-456
• 76)  Okada Y, Momozawa Y, Sakaue S, Kanai M, Ishigaki K, Akiyama M, Kishikawa T, Arai Y, Sasaki T, Kosaki K, Suematsu M, Matsuda K, Yamamoto K, Kubo M, Hirose N, Kamatani Y. Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese. Nat Commun, 2018; 9: 1631
• 77)  Sakaue S, Hirata J, Kanai M, Suzuki K, Akiyama M, Lai Too C, Arayssi T, Hammoudeh M, Al Emadi S, Masri BK, Halabi H, Badsha H, Uthman IW, Saxena R, Padyukov L, Hirata M, Matsuda K, Murakami Y, Kamatani Y, Okada Y. Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction. Nat Commun, 2020; 11: 1569
• 78)  Khera AV, Kathiresan S. Genetics of coronary artery disease: discovery, biology, and clinical translation. N Engl J Med, 2017; 376: 1391-1399
• 79)  All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature, 2024; 627: 340-346
• 80)  Cook MB, Sanderson SC, Deanfield JE, Reddington F, Roddam A, Hunter DJ, Ali R. Our Future Health: a unique global resource for discovery and translational research. Nat Med, 2025; 31: 728-730
• 81)  Nagai A, Hirata M, Kamatani Y, Muto K, Matsuda K, Kiyohara Y, Ninomiya T, Tamakoshi A, Yamagata Z, Mushiroda T, Murakami Y, Yuji K, Furukawa Y, Zembutsu H, Tanaka T, Ohnishi Y, Nakamura Y; BioBank Japan Cooperative Hospital Group; Kubo M. Overview of the BioBank Japan Project: Study design and profile. J Epidemiol, 2017; 27: S2-S8