Polygenic Risk Scoring for Coronary Heart Disease: The First Risk Factor

A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

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.

Clinical applications of polygenic risk score for coronary artery disease through the life course

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, highlighting the limitations of current primary and secondary prevention frameworks. In this review, we detail how the polygenic risk score for CAD can improve our current preventive and treatment frameworks across three clinical applications that span the life course: (i) identification and treatment of people at increased risk early in the life course prior to the onset of clinical risk factors, (ii) improving the precision around risk estimation in middle age, and (ii) guiding treatment decisions and enabling more efficient clinical trials even after the onset of CAD. We end by summarizing the efforts needed as we head towards more widespread use of polygenic risk score for CAD in clinical practice.

Assessment of cardiovascular disease risk: a 2023 update

PURPOSE OF REVIEW

The aim of this study was to highlight the current best practice for atherosclerotic cardiovascular disease (CVD) risk evaluation, including selective use of adjunctive tools for risk stratification [e.g. coronary artery calcium (CAC) scoring] and risk enhancement [e.g. lipoprotein(a) [Lp(a)], polygenic risk scoring (PRS)].

RECENT FINDINGS

New studies have evaluated the efficacy of various risk assessment tools. These studies demonstrate the role of Lp(a) as a risk-enhancing factor ready for more widespread use. CAC is the gold standard method of assessing subclinical atherosclerosis, enabling true risk stratification of patients, and informing net benefit assessment for initiating or titrating lipid-lowering therapy (LLT).

SUMMARY

Lp(a) concentration and CAC scoring, apart from the traditional risk factors, add the most value to the current CVD risk assessment approaches of all available tools, especially in terms of guiding LLT. In addition to new integrative tools such as the MESA CHD Risk Score and Coronary Age calculator, the future of risk assessment may include PRS and more advanced imaging techniques for atherosclerosis burden. Soon, polygenic risk scoring may be used to identify the age at which to begin CAC scoring, with CAC scores guiding preventive strategies.

An In-Depth Prospective Comprehensive View on Myocardial Infarction (MI) in Young Adults

Due to major advancements in myocardial infarction (MI) prevention and effective medical treatment, the death rate and incidence of MI have dropped considerably. We know that their risk factors and prognosis may differ; therefore, increasing primary and secondary prevention activities among young people is crucial. Multiple studies have found that MI is the deadliest form of coronary heart disease (CHD). As a result, we made an effort to illuminate MI in young people in our review of the literature. We found that young people, particularly women, are developing MI. Smoking is a key risk factor that should be targeted in an effort to minimize youth MI rates. It is thus important to create superior methods for measuring risk in young people, which may combine both standard and nonconventional risk factors, such as genetic rate scores and coronary artery calcium testing. Henceforth, addressing modifiable risk factors at a younger age has the greatest impact.

Eradicating Atherosclerosis: Should We Start Statins at Younger Ages and at Lower LDL-Cs

PURPOSE OF REVIEW

Given the increasing burden of cardiovascular disease, we review the literature for earlier initiation of statin therapy at younger ages and lower low-density lipoprotein cholesterol (LDL-C) levels, with the goal of preventing the development of atherosclerosis prior to clinical events.

RECENT FINDINGS

There is a rising prevalence of dyslipidemia among younger adults. Although guidelines offer recommendations for adults over 40, there is little guidance for the management of younger adults with moderately elevated LDL-C levels. Earlier and more aggressive statin use may slow progression, or even halt atherosclerosis, and may likewise be beneficial and cost-effective on a population level. Further research is needed to define the exact age and LDL-C level at which to start statin therapy. Until then, more detailed risk stratification with lab testing and imaging should be used to identify younger adults at the highest risk.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Ten things to know about ten cardiovascular disease risk factors - 2022

The American Society for Preventive Cardiology (ASPC) "Ten things to know about ten cardiovascular disease risk factors - 2022" is a summary document regarding cardiovascular disease (CVD) risk factors. This 2022 update provides summary tables of ten things to know about 10 CVD risk factors and builds upon the foundation of prior annual versions of "Ten things to know about ten cardiovascular disease risk factors" published since 2020. This 2022 version provides the perspective of ASPC members and includes updated sentinel references (i.e., applicable guidelines and select reviews) for each CVD risk factor section. The ten CVD risk factors include unhealthful dietary intake, physical inactivity, dyslipidemia, pre-diabetes/diabetes, high blood pressure, obesity, considerations of select populations (older age, race/ethnicity, and sex differences), thrombosis (with smoking as a potential contributor to thrombosis), kidney dysfunction and genetics/familial hypercholesterolemia. Other CVD risk factors may be relevant, beyond the CVD risk factors discussed here. However, it is the intent of the ASPC "Ten things to know about ten cardiovascular disease risk factors - 2022" to provide a tabular overview of things to know about ten of the most common CVD risk factors applicable to preventive cardiology and provide ready access to applicable guidelines and sentinel reviews.

Prevention of atherosclerosis from childhood

Cardiovascular diseases caused by atherosclerosis do not typically manifest before middle age; however, the disease process begins early in life. Preclinical atherosclerosis can be quantified with imaging methods in healthy populations long before clinical manifestations present. Cohort studies have shown that childhood exposure to risk factors, such as dyslipidaemia, elevated blood pressure and tobacco smoking, are associated with adult preclinical atherosclerotic phenotypes. Importantly, these long-term effects are substantially reduced if the individual becomes free from the risk factor by adulthood. As participants in the cohorts continue to age and clinical end points accrue, the strongest evidence linking exposure to risk factors in early life with cardiovascular outcomes has begun to emerge. Although science has deciphered the natural course of atherosclerosis, discovered its causal risk factors and developed effective means to intervene, we are still faced with an ongoing global pandemic of atherosclerotic diseases. In general, atherosclerosis goes undetected for too long, and preventive measures, if initiated at all, are inadequate and/or come too late. In this Review, we give an overview of the available literature suggesting the importance of initiating the prevention of atherosclerosis in early life and provide a summary of the major paediatric programmes for the prevention of atherosclerotic disease. We also highlight the limitations of current knowledge and indicate areas for future research.

Clinical utility of polygenic risk scores for coronary artery disease

Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.

Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome

BACKGROUND

Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS.

METHODS

We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen.

RESULTS

We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P=2.87×10^-8) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction P=1.38×10^-6).

CONCLUSIONS

In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.

Polygenic Risk Scores to Identify CVD Risk and Tailor Therapy: Hope or Hype?

PURPOSE OF REVIEW

The purpose of this review is to understand the conceptual basis and implications of polygenic risk scores (PRS) in assessing risk of future coronary artery disease (CAD).

RECENT FINDINGS

Genetic information from the USA and beyond has been pooled together to create population-based biobanks, composed of millions of genotyped individuals, which have helped further our understanding of the relationship between single nucleotide polymorphisms (SNPs) and CAD. Contemporary PRS composed of millions of SNPs now serve as the gold standard and have been evaluated in several cohort studies to predict risk of CAD and potentially help guide pharmacotherapy. The development of PRS has enhanced our understanding of the relationship between genes and disease, thereby facilitating CAD risk prediction. While certain constraints currently limit their utility in clinical practice, further refinement of this tool will enable clinicians to more fully understand genetic risk and improve preventive care.

How Do We Incorporate Polygenic Risk Scores in Cardiovascular Disease Risk Assessment and Management?

PURPOSE OF REVIEW

The potential of polygenic risk scores (PRS) to improve atherosclerotic cardiovascular disease (ASCVD) risk assessment and management has stoked significant interest in their incorporation into clinical management. The goal of this review is to apprise the readers of the latest developments and evidence of PRS readiness for clinical integration. We also discuss current limitations that must be addressed before PRS can be implemented into routine clinical practice.

RECENT FINDINGS

PRS have been shown to improve risk stratification for ASCVD and to identify patients who may derive increased benefit from primary and secondary prevention. Risk captured by PRS appears largely independent of traditional risk factors and can be ascertained at birth, prior to the development of traditional clinical risk factors. Genetic risk is modifiable through lifestyle modifications and medications. PRS offers a valuable way to improve early identification of actionable CVD risk. However, further work is needed before PRS can be implemented clinically.

The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation

We present the Polygenic Score (PGS) Catalog (https://www.PGSCatalog.org ), an open resource of published scores (including variants, alleles and weights) and consistently curated metadata required for reproducibility and independent applications. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with a platform for PGS dissemination, research and translation.

Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians

Background

The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear.

Methods

We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients.

Results

Our PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10^− 103 < P < 1.3 × 10^− 75) and 3-year lipid changes (1.4 × 10^− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10^− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10^− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, P_trend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10^− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association.

Conclusions

The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00831-z.

Ten things to know about ten cardiovascular disease risk factors

Given rapid advancements in medical science, it is often challenging for the busy clinician to remain up-to-date on the fundamental and multifaceted aspects of preventive cardiology and maintain awareness of the latest guidelines applicable to cardiovascular disease (CVD) risk factors. The “American Society for Preventive Cardiology (ASPC) Top Ten CVD Risk Factors 2021 Update” is a summary document (updated yearly) regarding CVD risk factors. This “ASPC Top Ten CVD Risk Factors 2021 Update” summary document reflects the perspective of the section authors regarding ten things to know about ten sentinel CVD risk factors. It also includes quick access to sentinel references (applicable guidelines and select reviews) for each CVD risk factor section. The ten CVD risk factors include unhealthful nutrition, physical inactivity, dyslipidemia, hyperglycemia, high blood pressure, obesity, considerations of select populations (older age, race/ethnicity, and sex differences), thrombosis/smoking, kidney dysfunction and genetics/familial hypercholesterolemia. For the individual patient, other CVD risk factors may be relevant, beyond the CVD risk factors discussed here. However, it is the intent of the “ASPC Top Ten CVD Risk Factors 2021 Update” to provide a succinct overview of things to know about ten common CVD risk factors applicable to preventive cardiology.

Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians

BACKGROUND

Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population.

OBJECTIVES

This analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians.

METHODS

This GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India.

RESULTS

The GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p < 0.001 for each). Next, individuals of the Indian case-control study were projected onto static reference distributions, observing an OR/SD of 1.66 (p < 0.001). Compared with the middle quintile, risk for CAD was most pronounced for those in the top 5% of the GPSCAD distribution-ORs of 4.16, 2.46, and 3.22 in the South Asian UK Biobank, Bangladeshi, and Indian studies, respectively (p < 0.05 for each).

CONCLUSIONS

The new GPSCAD has been developed and tested using 3 distinct South Asian studies, and provides a generalizable framework for ancestry-specific GPS assessment.

Recent trends in acute myocardial infarction among the young

PURPOSE OF REVIEW

In recent decades, the incidence of myocardial infarction (MI) has declined among the general population. However, MI rates in the young have not decreased as much as has been observed among older individuals. This review will focus on recent trends of MI in young patients, factors that may account for these trends, and implications for future prevention.

RECENT FINDINGS

MI rates in young patients, particularly in women, have not decreased in the same fashion as they have for their older counterparts, with some studies reporting an increase. The reasons for these findings include underestimation of cardiovascular risk, and accordingly treatment, in the young, as well as an increasing prevalence of risk factors such as obesity and diabetes.

SUMMARY

Better recognition and treatment of cardiovascular risk factors among young adults may improve outcomes. There is a need for improved methods to assess and treat cardiovascular risk in young individuals.

The emerging field of polygenic risk scores and perspective for use in clinical care

Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.

Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications

An individual's susceptibility to atherosclerotic cardiovascular disease is influenced by numerous clinical and lifestyle factors, motivating the multifaceted approaches currently endorsed for primary and secondary cardiovascular disease prevention. With growing knowledge of the genetic basis of atherosclerotic cardiovascular disease-in particular, coronary artery disease-and its contribution to disease pathogenesis, there is increased interest in understanding the potential clinical utility of a genetic predictor that might further refine the assessment and management of atherosclerotic cardiovascular disease risk. Rapid scientific and technological advances have enabled widespread genotyping efforts and dynamic research in the field of coronary artery disease genetic risk prediction. In this review, we describe how genomic analyses of coronary artery disease have been leveraged to create polygenic risk scores. We then discuss evaluations of the clinical utility of these scores, pertinent mechanistic insights gleaned, and practical considerations relevant to the implementation of polygenic risk scores in the health care setting.

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.