Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke

BACKGROUND

Current risk assessment for ischemic stroke (IS) is limited to clinical variables. We hypothesize that polygenic scores (PGS) of IS (PGSIS) and IS-associated diseases such as atrial fibrillation (AF), venous thromboembolism (VTE), coronary artery disease (CAD), hypertension (HTN), and Type 2 diabetes (T2D) may improve the performance of IS risk assessment.

METHODS

Incident IS was followed for 479,476 participants in the UK Biobank who did not have an IS diagnosis prior to the recruitment. Lifestyle variables (obesity, smoking and alcohol) at the time of study recruitment, clinical diagnoses of IS-associated diseases, PGSIS, and five PGSs for IS-associated diseases were tested using the Cox proportional-hazards model. Predictive performance was assessed using the C-statistic and net reclassification index (NRI).

RESULTS

During a median average 12.5-year follow-up, 8374 subjects were diagnosed with IS. Known clinical variables (age, gender, clinical diagnoses of IS-associated diseases, obesity, and smoking) and PGSIS were all independently associated with IS (P < 0.001). In addition, PGSIS and each PGS for IS-associated diseases was also independently associated with IS (P < 0.001). Compared to the clinical model, a joint clinical/PGS model improved the C-statistic for predicting IS from 0.71 to 0.73 (P < 0.001) and significantly reclassified IS risk (NRI = 0.017, P < 0.001), and 6.48% of subjects were upgraded from low to high risk.

CONCLUSIONS

Adding PGSs of IS and IS-associated diseases to known clinical risk factors statistically improved risk assessment for IS, demonstrating the supplementary value of inherited susceptibility measurement . However, its clinical utility is likely limited due to modest improvements in predictive values.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits

The novel metaPGS, integrating multiple fracture-related genetic traits, surpasses traditional polygenic scores in predicting fracture risk. Demonstrating a robust association with incident fractures, this metaPGS offers significant potential for enhancing clinical fracture risk assessment and tailoring prevention strategies.

INTRODUCTION

Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits.

METHODS

We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture.

CONCLUSIONS

The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

Stroke genetics and how it Informs novel drug discovery

INTRODUCTION

Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes.

AREAS COVERED

In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication.

EXPERT OPINION

As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Genetic Determinants of Vascular Dementia

Vascular dementia (VaD) is a prevalent form of cognitive impairment with underlying vascular etiology. In this review, we examine recent genetic advancements in our understanding of VaD, encompassing a range of methodologies including genome-wide association studies, polygenic risk scores, heritability estimates, and family studies for monogenic disorders revealing the complex and heterogeneous nature of the disease. We report well known genetic associations and highlight potential pathways and mechanisms implicated in VaD and its pathological risk factors, including stroke, cerebral small vessel disease, and cerebral amyloid angiopathy. Moreover, we discuss important modifiable risk factors such as hypertension, diabetes, and dyslipidemia, emphasizing the importance of a multifactorial approach in prevention, treatment, and understanding the genetic basis of VaD. Last, we outline several areas of scientific advancements to improve clinical care, highlighting that large-scale collaborative efforts, together with an integromics approach can enhance the robustness of genetic discoveries. Indeed, understanding the genetics of VaD and its pathophysiological risk factors hold the potential to redefine VaD on the basis of molecular mechanisms and to generate novel diagnostic, prognostic, and therapeutic tools.

Joint Exposure to Ambient Air Pollutants, Genetic Risk, and Ischemic Stroke: A Prospective Analysis in UK Biobank

BACKGROUND

Our primary objective was to assess the association between joint exposure to various air pollutants and the risk of ischemic stroke (IS) and the modification of the genetic susceptibility.

METHODS

This observational cohort study included 307 304 British participants from the United Kingdom Biobank, who were stroke-free and possessed comprehensive baseline data on genetics, air pollutant exposure, alcohol consumption, and dietary habits. All participants were initially enrolled between 2006 and 2010 and were followed up until 2022. An air pollution score was calculated to assess joint exposure to 5 ambient air pollutants, namely particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, as well as nitrogen oxide and nitrogen dioxide. To evaluate individual genetic risk, a polygenic risk score for IS was calculated for each participant. We adjusted for demographic, social, economic, and health covariates. Cox regression models were utilized to estimate the associations between air pollution exposure, polygenic risk score, and the incidence of IS.

RESULTS

Over a median follow-up duration of 13.67 years, a total of 2476 initial IS events were detected. The hazard ratios (95% CI) of IS for per 10 µg/m3 increase in particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, nitrogen dioxide, and nitrogen oxide were 1.73 (1.33-2.14), 1.24 (0.88-1.70), 1.13 (0.89-1.33), 1.03 (0.98-1.08), and 1.04 (1.02-1.07), respectively. Furthermore, individuals in the highest quintile of the air pollution score exhibited a 29% to 66% higher risk of IS compared with those in the lowest quintile. Notably, participants with both high polygenic risk score and air pollution score had a 131% (95% CI, 85%-189%) greater risk of IS than participants with low polygenic risk score and air pollution score.

CONCLUSIONS

Our findings suggested that prolonged joint exposure to air pollutants may contribute to an increased risk of IS, particularly among individuals with elevated genetic susceptibility to IS.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction

Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.

Polygenic risk score predicts all-cause death in East Asian patients with prior coronary artery disease

Introduction

Coronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare.

Methods

We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression.

Results and discussion

We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.

Fine Particulate Matter Exposure, Genetic Susceptibility, and the Risk of Incident Stroke: A Prospective Cohort Study

BACKGROUND

Both genetic factors and environmental air pollution contribute to the risk of stroke. However, it is unknown whether the association between air pollution and stroke risk is influenced by the genetic susceptibilities of stroke and its risk factors.

METHODS

This prospective cohort study included 40 827 Chinese adults without stroke history. Satellite-based monthly fine particulate matter (PM2.5) estimation at 1-km resolution was used for exposure assessment. Based on 534 identified genetic variants from genome-wide association studies in East Asians, we constructed 6 polygenic risk scores for stroke and its risk factors, including atrial fibrillation, blood pressure, type 2 diabetes, body mass index, and triglyceride. The Cox proportional hazards model was applied to evaluate the hazard ratios and 95% CIs for the associations of PM2.5 and polygenic risk score with incident stroke and the potential effect modifications.

RESULTS

Over a median follow-up of 12.06 years, 3147 incident stroke cases were documented. Compared with the lowest quartile of PM2.5 exposure, the hazard ratio (95% CI) for stroke in the highest quartile group was 2.72 (2.42-3.06). Among individuals at high genetic risk, the relative risk of stroke was 57% (1.57; 1.40-1.76) higher than those at low genetic risk. Although no statistically significant interaction was found, participants with both the highest PM2.5 and high genetic risk showed the highest risk of stroke, with ≈4× that of the lowest PM2.5 and low genetic risk group (hazard ratio, 3.55 [95% CI, 2.84-4.44]). Similar upward gradients were observed in the risk of stroke when assessing the joint effects of PM2.5 and genetic risks of blood pressure, type 2 diabetes, body mass index, atrial fibrillation, and triglyceride.

CONCLUSIONS

Long-term exposure to PM2.5 was associated with a higher risk of incident stroke across different genetic susceptibilities. Our findings highlighted the great importance of comprehensive assessment of air pollution and genetic risk in the prevention of stroke.

Modifiable vascular risk factors contribute to stroke in 1080 NOTCH3 R544C carriers in Taiwan Biobank

BACKGROUND AND AIM

Previous studies have suggested cardiovascular risk factors increase the risk of not only common sporadic stroke but also of stroke in patients with monogenic stroke disorders including CADASIL. We investigated the effects of the NOTCH3 Arg544Cys (R544C) variant and associated vascular risk factors on stroke in the Taiwanese population.

METHODS

This study was conducted using data from the Taiwan Biobank, consisting of at least 130,000 Han Chinese participants. The genotype was derived from customized genome-wide arrays for 650,000 to 750,000 single-nucleotide polymorphisms (SNPs). Individuals with NOTCH3 R544C were subsequently matched with noncarriers based on the propensity score at a 1:10 ratio by demographic and cardiovascular risk factors. The odds ratio (OR) for stroke or other phenotypes in NOTCH3 R544C carriers and matched noncarriers was then calculated. Univariate and multivariate regression analyses were performed on cardiovascular risk factors in NOTCH3 R544C carriers with and without stroke. The polygenic risk score (PRS) model, adopted from the UK Biobank, was then applied to evaluate the role of NOTCH3 R544C in stroke.

RESULTS

From the 114,282 participants with both genotype and questionnaire results, 1080 (0.95%) harbored the pathogenic NOTCH3 R544C variant. When compared to the matched controls (n = 10,800), the carriers presented with a history of stroke (OR: 2.52, 95% confidence interval (CI) (1.45, 4.37)), dementia (OR: 30.1, 95% CI (3.13, 289.43)), and sibling history of stroke (OR: 2.48, 95% CI (1.85, 3.34)) phenotypes. The risk of stroke increased with every 10-year increase in age (p = 0.006, Cochran-Mantel-Haenszel test). Among NOTCH3 R544C carriers, 16 (1.3%) of the 1080 carriers with a stroke history were older, male, and more likely to have hypertension, diabetes, dyslipidemia, and a family history of stroke. In the stepwise multivariate analysis, hypertension (OR: 11.28, 95% CI (3.54, 43.3)) and diabetes mellitus (OR: 4.10, 95% CI (1.31, 12.4)) were independently associated with stroke. Harboring the NOTCH3 R544C variant in the Taiwan Biobank is comparable with a 6.74 standard deviations increase in individual's polygenic risk score for stroke.

CONCLUSION

While the NOTCH3 R544C variant alone increased the risk of stroke, modifiable vascular risk factors also played a role in the occurrence of stroke in Taiwanese community-dwelling individuals carrying the NOTCH3 variant.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

Genetics in Ischemic Stroke: Current Perspectives and Future Directions

Ischemic stroke is a heterogeneous condition influenced by a combination of genetic and environmental factors. Recent advancements have explored genetics in relation to various aspects of ischemic stroke, including the alteration of individual stroke occurrence risk, modulation of treatment response, and effectiveness of post-stroke functional recovery. This article aims to review the recent findings from genetic studies related to various clinical and molecular aspects of ischemic stroke. The potential clinical applications of these genetic insights in stratifying stroke risk, guiding personalized therapy, and identifying new therapeutic targets are discussed herein.

Genetic and Nongenetic Components of Stroke Family History: A Population Study of Adopted and Nonadopted Individuals

Background Genetic and nongenetic factors account for the association of family history with disease risk. Comparing adopted and nonadopted individuals provides an opportunity to disentangle those factors. Methods and Results We examined associations between family history of stroke and heart disease with incident stroke and myocardial infarction (MI) in 495 640 UK Biobank participants (mean age, 56.5 years; 55% women) stratified by childhood adoption status (5747 adoptees). We estimated hazard ratios (HRs) per affected family member, and for polygenic risk scores in Cox models adjusted for baseline age and sex. A total of 12 518 strokes and 23 923 MIs occurred over a 13-year follow-up. In nonadoptees, family history of stroke and heart disease was associated with increased stroke and MI risk, with the strongest association of family history of stroke for incident stroke (HR, 1.16 [95% CI, 1.12-1.19]) and family history of heart disease for incident MI (HR, 1.48 [95% CI, 1.45-1.50]). In adoptees, family history of stroke associated with incident stroke (HR, 1.41 [95% CI, 1.06-1.86]), but family history of heart disease was not associated with incident MI (P>0.5). Polygenic risk scores showed strong disease-specific associations in both groups. In nonadoptees, the stroke polygenic risk score mediated 6% risk between family history of stroke and incident stroke, and the MI polygenic risk score mediated 13% risk between family history of heart disease and incident MI. Conclusions Family history of stroke and heart disease increases risk for their respective conditions. Family history of stroke contains substantial potentially modifiable nongenetic risk, indicating a need for novel prevention strategies, whereas family history of heart disease represents predominantly genetic risk.

Genomic risk scores and oral contraceptive-associated ischemic stroke risk: a call for collaboration

Background

Oral contraceptives (OCs) are generally safe but vascular risk factors increase OC-associated ischemic stroke risk. We performed a case-control study to evaluate whether a genomic risk score for ischemic stroke modifies OC-associated ischemic stroke risk.

Methods

The Genetics of Early-Onset Stroke study includes 332 premenopausal women (136 arterial ischemic stroke cases and 196 controls) with data on estrogen-containing OC use within 30 days before the index event (for cases) or interview (for controls). Using a previously validated genetic risk score (metaGRS) for ischemic stroke based on 19 polygenic risk scores for stroke and stroke-associated risk factors, we stratified our combined case-control sample into tertiles of genomic risk. We evaluated the association between OC use and ischemic stroke within each tertile. We tested if the association between OC use and ischemic stroke depended on the genomic risk of stroke using logistic regression with an OC use × metaGRS interaction term. These analyses were performed with and without adjustment for smoking, hypertension, diabetes, coronary heart disease, and body mass index.

Results

After adjustment for vascular risk factors, the odds ratio of OC use was 3.2 (1.7-6.3) overall and increased from the lower, middle, and upper tertile of genomic risk from 1.6 (0.5-5.4) to 2.5 (0.08-8.2) to 13.7 (3.8-67.3) respectively, and a p-value for interaction of 0.001.

Conclusions

Our results suggest that genomic profile may modify the OC-associated ischemic stroke risk. Larger studies are warranted to determine whether a genomic risk score could be clinically useful in reducing OC-associated ischemic stroke.

Associations of polygenic risk scores with risks of stroke and its subtypes in Chinese

BACKGROUND AND PURPOSE

Previous studies, mostly focusing on the European population, have reported polygenic risk scores (PRSs) might achieve risk stratification of stroke. We aimed to examine the association strengths of PRSs with risks of stroke and its subtypes in the Chinese population.

METHODS

Participants with genome-wide genotypic data in China Kadoorie Biobank were split into a potential training set (n=22 191) and a population-based testing set (n=72 150). Four previously developed PRSs were included, and new PRSs for stroke and its subtypes were developed. The PRSs showing the strongest association with risks of stroke or its subtypes in the training set were further evaluated in the testing set. Cox proportional hazards regression models were used to estimate the association strengths of different PRSs with risks of stroke and its subtypes (ischaemic stroke (IS), intracerebral haemorrhage (ICH) and subarachnoid haemorrhage (SAH)).

RESULTS

In the testing set, during 872 919 person-years of follow-up, 8514 incident stroke events were documented. The PRSs of any stroke (AS) and IS were both positively associated with risks of AS, IS and ICH (p<0.05). The HR for per SD increment (HRSD) of PRSAS was 1.10 (95% CI 1.07 to 1.12), 1.10 (95% CI 1.07 to 1.12) and 1.13 (95% CI 1.07 to 1.20) for AS, IS and ICH, respectively. The corresponding HRSD of PRSIS was 1.08 (95% CI 1.06 to 1.11), 1.08 (95% CI 1.06 to 1.11) and 1.09 (95% CI 1.03 to 1.15). PRSICH was positively associated with the risk of ICH (HRSD=1.07, 95% CI 1.01 to 1.14). PRSSAH was not associated with risks of stroke and its subtypes. The addition of current PRSs offered little to no improvement in stroke risk prediction and risk stratification.

CONCLUSIONS

In this Chinese population, the association strengths of current PRSs with risks of stroke and its subtypes were moderate, suggesting a limited value for improving risk prediction over traditional risk factors in the context of current genome-wide association study under-representing the East Asian population.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection

Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.

Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment

Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. We modeled the implications of initiating guideline-recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false-negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.

The prediction of Alzheimer's disease through multi-trait genetic modeling

To better capture the polygenic architecture of Alzheimer's disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

AI-based multi-PRS models outperform classical single-PRS models

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Genetic and non-genetic components of family history of stroke and heart disease: a population-based study among adopted and non-adopted individuals

Background

It is increasingly clear that genetic and non-genetic factors account for the association of family history with disease risk in offspring. We sought to distinguish the genetic and non-genetic contributions of family history of stroke and heart disease on incident events by examining adopted and non-adopted individuals.

Methods

We examined associations between family history of stroke and heart disease with incident stroke and myocardial infarction (MI) in 495,640 participants of the UK Biobank (mean age 56.5 years, 55% female) stratified by early childhood adoption status into adoptees (n=5,747) and non-adoptees (n=489,893). We estimated hazard ratios (HRs) per affected nuclear family member, and for polygenic risk scores (PRS) for stroke and MI in Cox models adjusted for baseline age and sex.

Results

12,518 strokes and 23,923 MIs occurred over a 13-year follow-up. In non-adoptees, family history of stroke and heart disease were associated with increased stroke and MI risk, with the strongest association of family history of stroke for incident stroke (HR 1.16 [1.12, 1.19]) and family history of heart disease for incident MI (HR 1.48 [1.45, 1.50]). In adoptees, family history of stroke associated with incident stroke (HR 1.41 [1.06, 1.86]), but family history of heart disease did not associate with incident MI (p>0.5). PRS showed strong disease-specific associations in adoptees and non-adoptees. In non-adoptees, the stroke PRS mediated 6% risk between family history of stroke and incident stroke, and the MI PRS mediated 13% risk between family history of heart disease and MI.

Conclusions

Family history of stroke and heart disease increase risk for their respective conditions. Family history of stroke contains a substantial proportion of potentially modifiable non-genetic risk, indicating a need for further research to elucidate these elements for novel prevention strategies, whereas family history of heart disease represents predominantly genetic risk.

A Mendelian Randomization Analysis of 55 Genetically Predicted Metabolic Traits with Breast Cancer Survival Outcomes in the Pathways Study

Previous studies suggest associations of metabolic syndromes with breast cancer prognosis, yet the evidence is mixed. In recent years, the maturation of genome-wide association study findings has led to the development of polygenic scores (PGS) for many common traits, making it feasible to use Mendelian randomization to examine associations between metabolic traits and breast cancer outcomes. In the Pathways Study of 3,902 patients and a median follow-up time of 10.5 years, we adapted a Mendelian randomization approach to calculate PGS for 55 metabolic traits and tested their associations with seven survival outcomes. Multivariable Cox proportional hazards models were used to derive HRs and 95% confidence intervals (CI) with adjustment for covariates. The highest tertile (T3) of PGS for cardiovascular disease was associated with shorter overall survival (HR = 1.34, 95% CI = 1.11-1.61) and second primary cancer-free survival (HR = 1.31, 95% CI = 1.12-1.53). PGS for hypertension (T3) was associated with shorter overall survival (HR = 1.20, 95% CI = 1.00-1.43), second primary cancer-free survival (HR = 1.24, 95% CI = 1.06-1.45), invasive disease-free survival (HR = 1.18, 95% CI = 1.01-1.38), and disease-free survival (HR = 1.21, 95% CI = 1.04-1.39). PGS for serum cystatin C levels (T3) was associated with longer disease-free survival (HR = 0.82, 95% CI = 0.71-0.95), breast event-free survival (HR = 0.74, 95% CI = 0.61-0.91), and breast cancer-specific survival (HR = 0.72, 95% CI = 0.54-0.95). The above associations were significant at a nominal P < 0.05 level but not after correcting for multiple testing (Bonferroni P < 0.0009). Our analyses revealed notable associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with breast cancer survival outcomes. These findings implicate metabolic traits in breast cancer prognosis.

Significance

To our knowledge, this is the largest study of PGS for metabolic traits with breast cancer prognosis. The findings revealed significant associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with several breast cancer survival outcomes. These findings implicate an underappreciated role of metabolic traits in breast cancer prognosis that would warrant further exploration.

Polygenic Susceptibility to Hypertension and Blood Pressure Control in Stroke Survivors

BACKGROUND AND OBJECTIVES

Blood pressure (BP) is often not at goal in stroke survivors, leaving individuals vulnerable to additional vascular events. Given that BP is a highly heritable trait, we hypothesize that a higher polygenic susceptibility to hypertension (PSH) leads to worse BP control in stroke survivors.

METHODS

We conducted a study within the UK Biobank evaluating persons of European ancestry who survived an ischemic or hemorrhagic stroke. To model the PSH, we created polygenic risk scores (PRSs) for systolic and diastolic BP using 732 genetic variants. We divided the PRSs into quintiles and used linear/logistic regression to test whether higher PSH led to higher observed BP, uncontrolled BP (systolic BP > 140 mm Hg or diastolic BP > 90 mm Hg), and resistant BP (uncontrolled BP despite being on ≥3 antihypertensive drugs). We conducted an independent replication using data from the Vitamin Intervention for Stroke Prevention (VISP) trial.

RESULTS

We analyzed 5,940 stroke survivors. When comparing stroke survivors with very low vs very high PSH, the mean systolic BP was 137 (SD 18) vs 143 (SD 20, p < 0.001), the mean diastolic BP was 81 (SD 10) vs 84 (SD 11, p < 0.001), the prevalence of uncontrolled BP was 42.8% vs 57.2% (p < 0.001), and the prevalence of resistant hypertension was 3.9% vs 11% (p < 0.001). Results remained significant using multivariable models (p < 0.001) and were replicated in the VISP study (all tests with p < 0.05).

DISCUSSION

A higher PSH is associated with worse BP control in stroke survivors. These findings point to genetic predisposition as an important determinant of poorly controlled BP in this population.

The Future of Ischemic Stroke Diagnosis and a Review of Underrecognized Ischemic Stroke Etiologies

Accurate ischemic stroke etiologic determination and diagnosis form the foundation of excellent cerebrovascular care as from it stems initiation of the appropriate secondary prevention strategy as well as appropriate patient education regarding specific risk factors for that subtype. Recurrent stroke rates are highest among those patients who receive an incorrect initial stroke diagnosis. Patient distrust and patient reported depression are also higher. The cause of the ischemic stroke also informs predicted patient outcomes and the anticipated recovery trajectory. Finally, determining the accurate cause of the ischemic stroke provides the patient the opportunity to enroll in appropriate research studies studying mechanism, or targeting treatment approaches for that particular disease process. Advances in ischemic stroke research, imaging techniques, biomarkers, and the ability to rapidly perform genetic sequencing over the past decade have shown that classifying patients into large etiologic buckets may not always be appropriate and may represent one reason why some patients are labeled as cryptogenic, or for whom an underlying etiology is never found. Aside from the more traditional stroke mechanisms, there is new research emerging regarding clinical findings that are not normative, but the contributions to ischemic stroke are unclear. In this article, we first review the essential steps to accurate ischemic stroke etiologic classification and then transition to a discussion of embolic stroke of undetermined source (ESUS) and other new entities that have been postulated as causal in ischemic stroke (i.e., genetics and subclinical atherosclerosis). We also discuss the limitations that are inherent in the current ischemic stroke diagnostic algorithms and finally review the most recent studies regarding more uncommon diagnoses and the future of stroke diagnostics and classification.

A Genomic Risk Score Identifies Individuals at High Risk for Intracerebral Hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) has an estimated heritability of 29%. We developed a genomic risk score for ICH and determined its predictive power in comparison to standard clinical risk factors.

METHODS

We combined genome-wide association data from individuals of European ancestry for ICH and related traits in a meta-genomic risk score ([metaGRS]; 2.6 million variants). We tested associations with ICH and its predictive performance in addition to clinical risk factors in a held-out validation dataset (842 cases and 796 controls). We tested associations with risk of incident ICH in the population-based UK Biobank cohort (486 784 individuals, 1526 events, median follow-up 11.3 years).

RESULTS

One SD increment in the metaGRS was significantly associated with 31% higher odds for ICH (95% CI, 1.16-1.48) in age-, sex- and clinical risk factor-adjusted models. The metaGRS identified individuals with almost 5-fold higher odds for ICH in the top score percentile (odds ratio, 4.83 [95% CI, 1.56-21.2]). Predictive models for ICH incorporating the metaGRS in addition to clinical predictors showed superior performance compared to the clinical risk factors alone (c-index, 0.695 versus 0.686). The metaGRS showed similar associations for lobar and nonlobar ICH, independent of the known APOE risk locus for lobar ICH. In the UK Biobank, the metaGRS was associated with higher risk of incident ICH (hazard ratio, 1.15 [95% CI, 1.09-1.21]). The associations were significant within both a relatively high-risk population of antithrombotic medications users, as well as among a relatively low-risk population with a good control of vascular risk factors and no use of anticoagulants.

CONCLUSIONS

We developed and validated a genomic risk score that predicts lifetime risk of ICH beyond established clinical risk factors among individuals of European ancestry. Whether implementation of the score in risk prognostication models for high-risk populations, such as patients under antithrombotic treatment, could improve clinical decision making should be explored in future studies.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Integrating polygenic and clinical risks to improve stroke risk stratification in prospective Chinese cohorts

The utility of the polygenic risk score (PRS) to identify individuals at higher risk of stroke beyond clinical risk remains unclear, and we clarified this using Chinese population-based prospective cohorts. Cox proportional hazards models were used to estimate the 10-year risk, and Fine and Gray's models were used for hazard ratios (HRs), their 95% confidence intervals (CIs), and the lifetime risk according to PRS and clinical risk categories. A total of 41,006 individuals aged 30-75 years with a mean follow-up of 9.0 years were included. Comparing the top versus bottom 5% of the PRS, the HR was 3.01 (95%CI 2.03-4.45) in the total population, and similar findings were observed within clinical risk strata. Marked gradients in the 10-year and lifetime risk across PRS categories were also found within clinical risk categories. Notably, among individuals with intermediate clinical risk, the 10-year risk for those in the top 5% of the PRS (7.3%, 95%CI 7.1%-7.5%) reached the threshold of high clinical risk (⩾7.0%) for initiating preventive treatment, and this effect of the PRS on refining risk stratification was evident for ischemic stroke. Even among those in the top 10% and 20% of the PRS, the 10-year risk would also exceed this level when aged ⩾50 and ⩾60 years, respectively. Overall, the combination of the PRS with the clinical risk score improved the risk stratification within clinical risk strata and distinguished actual high-risk individuals with intermediate clinical risk.

Genetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical Heterogeneity

BACKGROUND

Recently, common genetic risk factors for intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (ASAH) were found to explain a large amount of disease heritability and therefore have potential to be used for genetic risk prediction. We constructed a genetic risk score to (1) predict ASAH incidence and IA presence (combined set of unruptured IA and ASAH) and (2) assess its association with patient characteristics.

METHODS

A genetic risk score incorporating genetic association data for IA and 17 traits related to IA (so-called metaGRS) was created using 1161 IA cases and 407 392 controls from the UK Biobank population study. The metaGRS was validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68 568 controls from the Nordic HUNT population study. Furthermore, we assessed association between the metaGRS and patient characteristics in a cohort of 5560 IA patients.

RESULTS

Per SD increase of metaGRS, the hazard ratio for ASAH incidence was 1.34 (95% CI, 1.20-1.51) and the odds ratio for IA presence 1.09 (95% CI, 1.01-1.18). Upon including the metaGRS on top of clinical risk factors, the concordance index to predict ASAH hazard increased from 0.63 (95% CI, 0.59-0.67) to 0.65 (95% CI, 0.62-0.69), while prediction of IA presence did not improve. The metaGRS was statistically significantly associated with age at ASAH (β=-4.82×10-3 per year [95% CI, -6.49×10-3 to -3.14×10-3]; P=1.82×10-8), and location of IA at the internal carotid artery (odds ratio=0.92 [95% CI, 0.86-0.98]; P=0.0041).

CONCLUSIONS

The metaGRS was predictive of ASAH incidence, although with limited added value over clinical risk factors. The metaGRS was not predictive of IA presence. Therefore, we do not recommend using this metaGRS in daily clinical care. Genetic risk does partly explain the clinical heterogeneity of IA warranting prioritization of clinical heterogeneity in future genetic prediction studies of IA and ASAH.

Genetic Risk Score Improves Risk Stratification for Anticoagulation-Related Intracerebral Hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) is the most devastating adverse outcome for patients on anticoagulants. Clinical risk scores that quantify bleeding risk can guide decision-making in situations when indication or duration for anticoagulation is uncertain. We investigated whether integration of a genetic risk score into an existing risk factor-based CRS could improve risk stratification for anticoagulation-related ICH.

METHODS

We constructed 153 genetic risk scores from genome-wide association data of 1545 ICH cases and 1481 controls and validated them in 431 ICH cases and 431 matched controls from the population-based UK Biobank. The score that explained the largest variance in ICH risk was selected and tested for prediction of incident ICH in an independent cohort of 5530 anticoagulant users. A CRS for major anticoagulation-related hemorrhage, based on 8/9 components of the HAS-BLED score, was compared with a combined clinical and genetic risk score incorporating an additional point for high genetic risk for ICH.

RESULTS

Among anticoagulated individuals, 94 ICH occurred over a mean follow-up of 11.9 years. Compared with the lowest genetic risk score tertile, being in the highest tertile was associated with a two-fold increased risk for incident ICH (hazard ratio, 2.08 [95% CI, 1.22-3.56]). Although the CRS predicted incident ICH with a hazard ratio of 1.24 per 1-point increase (95% CI [1.01-1.53]), adding a point for high genetic ICH risk led to a stronger association (hazard ratio of 1.33 per 1-point increase [95% CI, 1.11-1.59]) with improved risk stratification (C index 0.57 versus 0.53) and maintained calibration (integrated calibration index 0.001 for both). The new clinical and genetic risk score showed 19% improvement in high-risk classification among individuals with ICH and a net reclassification improvement of 0.10.

CONCLUSIONS

Among anticoagulant users, a prediction score incorporating genomic information is superior to a clinical risk score alone for ICH risk stratification and could serve in clinical decision-making.

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

Exome-based gene panel analysis in a cohort of acute juvenile ischemic stroke patients:relevance of NOTCH3 and GLA variants

BACKGROUND

Genetic variants are considered to have a crucial impact on the occurrence of ischemic stroke. In clinical routine, the diagnostic value of next-generation sequencing (NGS) in the medical clarification of acute juvenile stroke has not been investigated so far.

MATERIAL AND METHODS

We analyzed an exome-based gene panel of 349 genes in 172 clinically well-characterized patients with magnetic resonance imaging (MRI)-proven, juvenile (age ≤ 55 years), ischemic stroke admitted to a single comprehensive stroke center.

RESULTS

Monogenetic diseases causing ischemic stroke were observed in five patients (2.9%): In three patients with lacunar stroke (1.7%), we identified pathogenic variants in NOTCH3 causing cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Hence, CADASIL was identified at a frequency of 12.5% in the lacunar stroke subgroup. Further, in two male patients (1.2%) suffering from lacunar and cardioembolic stroke, pathogenic variants in GLA causing Fabry's disease were present. Additionally, genetic variants in monogenetic diseases lacking impact on stroke occurrence, variants of unclear significance (VUS) in monogenetic diseases, and (cardiovascular-) risk genes in ischemic stroke were observed in a total of 15 patients (15.7%).

CONCLUSION

Genetic screening for Fabry's disease in cardioembolic and lacunar stroke as well as CADASIL in lacunar stroke might be beneficial in routine medical work-up of acute juvenile ischemic stroke.

Statistical Methods for Disease Risk Prediction with Genotype Data

Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

Association of Vascular Risk Factors and Genetic Factors With Penetrance of Variants Causing Monogenic Stroke

Importance

It is uncertain whether typical variants causing monogenic stroke are associated with cerebrovascular disease in the general population and why the phenotype of these variants varies so widely.

Objective

To determine the frequency of pathogenic variants in the 3 most common monogenic cerebral small vessel diseases (cSVD) and their associations with prevalent and incident stroke and dementia.

Design, Setting, and Participants

This cohort study is a multicenter population-based study of data from UK Biobank participants recruited in 2006 through 2010, with the latest follow-up in September 2021. A total of 9.2 million individuals aged 40 to 69 years who lived in the United Kingdom were invited to join UK Biobank, of whom 5.5% participated in the baseline assessment. Participants eligible for our study (n = 454 756, excluding 48 569 with incomplete data) had whole-exome sequencing and available data pertaining to lacunar stroke-related diseases, namely stroke, dementia, migraine, and epilepsy.

Exposures

NOTCH3, HTRA1, and COL4A1/2 pathogenic variants in monogenic stroke; Framingham cardiovascular risk; and ischemic stroke polygenic risk.

Main Outcomes and Measures

Primary outcomes were prevalent and incident stroke and dementia. Odds ratios (ORs) and hazard ratios (HRs) were adjusted for age, sex, ethnicity, exome sequencing batch, and top 10 genetic principal components.

Results

Of the 454 756 participants (208 027 [45.8%] men; mean [SD] age, 56.5 [8.1] years), 973 participants carried NOTCH3 variants, 546 carried HTRA1 variants, and 336 carried COL4A1/2 variants. Variant carriers were at least 66% more likely to have had stroke. NOTCH3 carriers had increased vascular dementia risk (OR, 5.42; 95% CI, 3.11-8.74), HTRA1 carriers an increased all-cause dementia risk (OR, 2.17; 95% CI, 1.28-3.41), and COL4A1/2 carriers an increased intracerebral hemorrhage risk (OR, 3.56; 95% CI, 1.34-7.53). NOTCH3 variants were associated with incident ischemic stroke and vascular dementia. NOTCH3 and HTRA1 variants were associated with magnetic resonance imaging markers of cSVD. Cardiovascular risk burden was associated with increased stroke risk in NOTCH3 and HTRA1 carriers. Variant location was also associated with risk.

Conclusions and Relevance

In this cohort study, pathogenic variants associated with rare monogenic stroke were more common than expected in the general population and associated with stroke and dementia. Cardiovascular risk burden is associated with the penetrance of such variants. Our results support the hypothesis that cardiovascular risk factor control may improve disease prognosis in individuals with monogenic cSVD variants. This lays the foundation for future studies to evaluate the effect of early identification before symptom onset on mitigating stroke and dementia risk.

Systematic comparison of family history and polygenic risk across 24 common diseases

Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.

Association of a Multiancestry Genome-Wide Blood Pressure Polygenic Risk Score With Adverse Cardiovascular Events

BACKGROUND

Traditional cardiovascular risk factors and the underlying genetic risk of elevated blood pressure (BP) determine an individual's composite risk of developing adverse cardiovascular events. We sought to evaluate the relative contributions of the traditional cardiovascular risk factors to the development of adverse cardiovascular events in the context of varying BP genetic risk profiles.

METHODS

Genome-wide polygenic risk score (PRS) was computed using multiancestry genome-wide association estimates among US adults who underwent whole-genome sequencing in the Trans-Omics for Precision program. Individuals were stratified into high, intermediate, and low genetic risk groups (>80th, 20-80th, and <20th centiles of systolic BP [SBP] PRS). Based on the ACC/AHA Pooled Cohort Equations, participants were stratified into low and high (10 year-atherosclerotic cardiovascular disease [CVD] risk: <10% or ≥10%) cardiovascular risk factor profile groups. The primary study outcome was incident cardiovascular event (composite of incident heart failure, incident stroke, and incident coronary heart disease).

RESULTS

Among 21 897 US adults (median age: 56 years; 56.0% women; 35.8% non-White race/ethnicity), 1 SD increase in the SBP PRS, computed using 1.08 million variants, was associated with SBP (β: 4.39 [95% CI, 4.13-4.65]) and hypertension (odds ratio, 1.50 [95% CI, 1.46-1.55]), respectively. This association was robustly seen across racial/ethnic groups. Each SD increase in SBP PRS was associated with a higher risk of the incident CVD (multivariable-adjusted hazards ratio, 1.07 [95% CI, 1.04-1.10]) after controlling for ACC/AHA Pooled Cohort Equations risk scores. Among individuals with a high SBP PRS, low atherosclerotic CVD risk was associated with a 58% lower hazard for incident CVD (multivariable-adjusted hazards ratio, 0.42 [95% CI, 0.36-0.50]) compared to those with high atherosclerotic CVD risk. A similar pattern was noted in intermediate and low genetic risk groups.

CONCLUSIONS

In a multiancestry cohort of >21 000 US adults, genome-wide SBP PRS was associated with BP traits and adverse cardiovascular events. Adequate control of modifiable cardiovascular risk factors may reduce the predisposition to adverse cardiovascular events among those with a high SBP PRS.

Genetic Predisposition, Fruit Intake and Incident Stroke: A Prospective Chinese Cohort Study

The aim of this study was to evaluate the association between fruit intake and stroke risk considering the genetic predisposition. We used data from 34,871 participants from the project of Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR project) from 2007 to 2020. A polygenic risk score comprising 534 genetic variants associated with stroke and its related factors was constructed to categorize individuals into low, intermediate, and high genetic risk groups. The associations of genetic and fruit intake with incident stroke were assessed by the Cox proportional hazard regression. We documented 2586 incident strokes during a median follow-up of 11.2 years. Compared with fruit intake < 200 g/week, similar relative risk reductions in stroke with adherence to fruit intake > 100 g/day across the genetic risk categories were observed (28−32%), but the absolute risk reductions were relatively larger in the highest genetic risk group (p for trend = 0.03). In comparison to those with a fruit intake < 200 g/week, those with a fruit intake >100 g/day in the low, intermediate, and high genetic risk groups had an average of 1.45 (95% CI, 0.61−2.31), 2.12 (1.63−2.59), and 2.19 (1.13−3.22) additional stroke-free years at aged 35, respectively. Our findings suggest that individuals with a high genetic risk could gain more absolute risk reductions and stroke-free years than those with a low genetic risk from increasing fruit intake for the stroke primary prevention.

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine

Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.

Implementation of individualised polygenic risk score analysis: a test case of a family of four

Background

Polygenic risk scores (PRS) have been widely applied in research studies, showing how population groups can be stratified into risk categories for many common conditions. As healthcare systems consider applying PRS to keep their populations healthy, little work has been carried out demonstrating their implementation at an individual level.

Case presentation

We performed a systematic curation of PRS sources from established data repositories, selecting 15 phenotypes, comprising an excess of 37 million SNPs related to cancer, cardiovascular, metabolic and autoimmune diseases. We tested selected phenotypes using whole genome sequencing data for a family of four related individuals. Individual risk scores were given percentile values based upon reference distributions among 1000 Genomes Iberians, Europeans, or all samples. Over 96 billion allele effects were calculated in order to obtain the PRS for each of the individuals analysed here.

Conclusions

Our results highlight the need for further standardisation in the way PRS are developed and shared, the importance of individual risk assessment rather than the assumption of inherited averages, and the challenges currently posed when translating PRS into risk metrics.

Long-Lived Individuals Show a Lower Burden of Variants Predisposing to Age-Related Diseases and a Higher Polygenic Longevity Score

Longevity is a complex phenotype influenced by both environmental and genetic factors. The genetic contribution is estimated at about 25%. Despite extensive research efforts, only a few longevity genes have been validated across populations. Long-lived individuals (LLI) reach extreme ages with a relative low prevalence of chronic disability and major age-related diseases (ARDs). We tested whether the protection from ARDs in LLI can partly be attributed to genetic factors by calculating polygenic risk scores (PRSs) for seven common late-life diseases (Alzheimer's disease (AD), atrial fibrillation (AF), coronary artery disease (CAD), colorectal cancer (CRC), ischemic stroke (ISS), Parkinson's disease (PD) and type 2 diabetes (T2D)). The examined sample comprised 1351 German LLI (≥94 years, including 643 centenarians) and 4680 German younger controls. For all ARD-PRSs tested, the LLI had significantly lower scores than the younger control individuals (areas under the curve (AUCs): ISS = 0.59, p = 2.84 × 10-35; AD = 0.59, p = 3.16 × 10-25; AF = 0.57, p = 1.07 × 10-16; CAD = 0.56, p = 1.88 × 10-12; CRC = 0.52, p = 5.85 × 10-3; PD = 0.52, p = 1.91 × 10-3; T2D = 0.51, p = 2.61 × 10-3). We combined the individual ARD-PRSs into a meta-PRS (AUC = 0.64, p = 6.45 × 10-15). We also generated two genome-wide polygenic scores for longevity, one with and one without the TOMM40/APOE/APOC1 gene region (AUC (incl. TOMM40/APOE/APOC1) = 0.56, p = 1.45 × 10-5, seven variants; AUC (excl. TOMM40/APOE/APOC1) = 0.55, p = 9.85 × 10-3, 10,361 variants). Furthermore, the inclusion of nine markers from the excluded region (not in LD with each other) plus the APOE haplotype into the model raised the AUC from 0.55 to 0.61. Thus, our results highlight the importance of TOMM40/APOE/APOC1 as a longevity hub.

Polygenic Risk, Midlife Life's Simple 7, and Lifetime Risk of Stroke

Background

Recent genetic discoveries in stroke have unleashed the potential of using genetic information for risk prediction and health interventions aimed at disease prevention. We sought to estimate the lifetime risk of stroke (LTRS) by levels of genetic risk and to investigate whether optimal cardiovascular health can offset the negative impact of high genetic risk on lifetime risk of stroke.

Methods and Results

Study participants were 11 568 middle‐aged adults (56% women, 23% Black adults), who were free of stroke at baseline and were followed up for a median of 28 years. The remaining LTRS was estimated according to levels of genetic risk based on a validated stroke polygenic risk score, and to levels of cardiovascular health based on the American Heart Association Life's Simple 7 recommendations. At age 45, individuals with high, intermediate, and low polygenic risk score had a remaining LTRS of 23.2% (95% CI, 20.8%–25.5%), 13.8% (95% CI, 11.7%–15.8%), and 9.6% (95% CI, 7.3%–11.8%), respectively. Those with both a high genetic risk and an inadequate Life's Simple 7 experienced the highest LTRS: 24.8% (95% CI, 22.0%–27.6%). Across all polygenic risk score categories, those with an optimal Life's Simple 7 had a ≈30% to 43% lower LTRS than those with an inadequate Life's Simple 7. This corresponded to almost 6 additional years lived free of stroke.

Conclusions

The LTRS varies by levels of polygenic risk and cardiovascular health. Maintaining an optimal cardiovascular health can partially offset a high genetic risk, emphasizing the importance of modifiable risk factors and illustrating the potential of personalizing genetic risk information to motivate lifestyle changes for stroke prevention.

Predicting mortality among ischemic stroke patients using pathways-derived polygenic risk scores

We aim to determine whether ischemic stroke(IS)-related PRSs are also associated with and further predict 3-year all-cause mortality. 1756 IS patients with European ancestry were randomly split into training (n = 1226) and testing (n = 530) groups with 3-year post-event observations. Univariate Cox proportional hazards regression model (CoxPH) was used for primary screening of individual prognostic PRSs. Only the significantly associated PRSs and clinical risk factors with the same direction for a causal relationship with IS were used to construct a multivariate CoxPH. Feature selection was conducted by the LASSO method. After feature selection, a prediction model with 11 disease-associated pathway-specific PRSs outperformed the base model, as demonstrated by a higher concordance index (0.751, 95%CI [0.693–0.809] versus 0.729, 95%CI [0.676–0.782]) in the testing sample. A PRS derived from endothelial cell apoptosis showed independent predictability in the multivariate CoxPH (Hazard Ratio = 1.193 [1.027–1.385], p = 0.021). These PRSs fine-tuned the model by better stratifying high, intermediate, and low-risk groups. Several pathway-specific PRSs were associated with clinical risk factors in an age-dependent manner and further confirmed some known etiologies of IS and all-cause mortality. In conclusion, Pathway-specific PRSs for IS are associated with all-cause mortality, and the integrated multivariate risk model provides prognostic value in this context.

Genomic risk score for advanced osteoarthritis in older adults

Objective

Prevention of osteoarthritis (OA) remains important, as there are no disease‐modifying treatments. A personalized approach has the potential to better target prevention strategies. In the present study, we used recently identified genetic risk variants from genome‐wide association analysis for advanced OA to calculate polygenic risk scores (PRS) for knee and hip OA and assessed PRS performance in an independent population of older community‐dwelling adults.

Methods

PRS were calculated in 12,093 individuals of European genetic descent ages ≥70 years who were enrolled in the Aspirin in Reducing Events in the Elderly trial. The outcome measure was knee and hip replacement (hospitalizations during the trial and self‐reported joint replacements before enrollment). PRS were considered as continuous (per SD) and categorical (low risk [0–20%], medium risk [21–80%], high risk [81–100%]) variables. Logistic regression was used to examine associations between PRS and risk of joint replacement, adjusted for age, sex, body mass index, and socioeconomic status.

Results

Among the participants, 1,422 (11.8%) had knee replacements and 1,297 (10.7%) had hip replacements. PRS (per SD) were associated with a risk of knee replacement (odds ratio [OR] 1.13 [95% confidence interval (95% CI) 1.07–1.20]) and hip replacement (OR 1.23 [95% CI 1.16–1.30]). Participants with high PRS had an increased risk of knee replacement (OR 1.44 [95% CI 1.20–1.73]) and hip replacement (OR 1.88 [95% CI 1.56–2.26]), compared to those with low PRS. Associations were stronger for PRS and hip replacement risk in women than in men. Associations were similar in sensitivity analyses that examined joint replacements before and during the trial separately.

Conclusion

PRS have the potential to improve prevention of severe knee and hip OA by providing a personalized approach and identifying individuals who may benefit from early intervention.