Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and somatic traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and somatic traits were calculated in European-ancestry (EUR; n = 5691) participants and, when discovery datasets were available, for African-ancestry (AFR; n = 4918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGSMDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGSBMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and somatic traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

Personalization of primary prevention: Exploring the role of coronary artery calcium and polygenic risk score in cardiovascular diseases

Personalized healthcare is becoming increasingly popular given the vast heterogeneity in disease manifestation between individuals. Many commonly encountered diseases within cardiology are multifactorial in nature and disease progression and response is often variable due to environmental and genetic factors influencing disease states. This makes accurate early identification and primary prevention difficult in certain populations, especially young patients with limited Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Newer strategies, such as coronary artery calcium (CAC) scans and polygenic risk scores (PRS), are being implemented to aid in the detection of subclinical disease and heritable risk, respectively. Data surrounding CAC scans have shown promising results in their ability to detect subclinical atherosclerosis and predict the risk of future coronary events, especially at the extremes; however, predictive variability exists among different patient populations, limiting the test's specificity. Furthermore, relying only on CAC scores and ASCVD risk scores may fail to identify a large group of patients needing primary prevention who lack subclinical disease and traditional risk factors, but harbor genetic variabilities strongly associated with certain cardiovascular diseases. PRS can overcome these limitations. These scores can be measured in individuals as early as birth to identify genetic variants placing them at elevated risk for developing cardiovascular disease, irrespective of their current cardiovascular health status. By applying PRS alongside CAC scores, previously overlooked patient populations can be identified and begin primary prevention strategies early to achieve optimal outcomes. In this review, we expand on the current knowledge surrounding CAC scores and PRS and highlight the future possibilities of these technologies for preventive cardiology.

Non-invasive imaging innovation: FFR-CT combined with plaque characterization, safeguarding your cardiac health

Studies have shown that high-risk plaque features (including thin fibrous caps, lipid-rich cores, large plaque volumes, and intraplaque microcalcifications) are closely associated with the occurrence of acute coronary events. CT-derived fractional flow reserve (CT-FFR) is a non-invasive imaging post-processing technique that utilizes artificial intelligence to analyze data obtained from conventional coronary CT angiography (CCTA). FFR-CT technology offers the hemodynamic assessment of coronary lesions, aiding in the prediction of potential cardiovascular risks. This review summarizes the latest research progress on the complex relationship between FFR-CT, plaque characteristics, and hemodynamics, closely linking plaque volume, composition, and distribution with the clinical significance of coronary artery stenosis. It is hoped that these research findings will provide valuable guidance for clinicians, promoting the application of CT in the non-invasive detection of vulnerable plaques, thereby more effectively preventing and managing coronary artery disease. In the future, further optimization of FFR-CT technology and expansion of its clinical application are expected to significantly reduce the incidence and mortality of coronary artery disease, offering new hope for the prevention and treatment of cardiovascular diseases.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank

BACKGROUND

Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke.

METHODS

We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; ncases=51 929; ncontrols=39 980) and subsequent arterial ischemic stroke (AIS; ncases=45 120; ncontrols=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (protein quantitative trait loci) to determine the effect of 1463 plasma protein abundances on subsequent MACE using Mendelian randomization.

RESULTS

Two variants were significantly associated with subsequent cardiovascular events: rs76472767 near gene RNF220 (odds ratio, 0.75 [95% CI, 0.64-0.85]; P=3.69×10-8) with subsequent AIS and rs13294166 near gene LINC01492 (odds ratio, 1.52 [95% CI, 1.37-1.67]; P=3.77×10-8) with subsequent MACE. Using Mendelian randomization, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 ([C-C motif chemokine 27], effect odds ratio, 0.77 [95% CI, 0.66-0.88]; adjusted P=0.05) and TNFRSF14 ([tumor necrosis factor receptor superfamily member 14], effect odds ratio, 1.42 [95% CI, 1.24-1.60]; adjusted P=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation.

CONCLUSIONS

We found evidence that 2 proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMe Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.

A polygenic risk score added to a QRISK®2 cardiovascular disease risk calculator demonstrated robust clinical acceptance and clinical utility in the primary care setting

AIMS

The aim of the study was to assess the real-world feasibility, acceptability, and impact of an integrated risk tool for cardiovascular disease (CVD IRT, combining the standard QRISK®2 risk algorithm with a polygenic risk score), implemented within routine primary practice in the UK National Health Service.

METHODS AND RESULTS

The Healthcare Evaluation of Absolute Risk Testing Study (NCT05294419) evaluated participants undergoing primary care health checks. Both QRISK2 and CVD IRT scores were returned to the healthcare providers (HCPs), who then communicated the results to participants. The primary outcome of the study was feasibility of CVD IRT implementation. Secondary outcomes included changes in CVD risk (QRISK2 vs. CVD IRT) and impact of the CVD IRT on clinical decision-making. A total of 832 eligible participants (median age 55 years, 62% females, 97.5% White ethnicity) were enrolled across 12 UK primary care practices. Cardiovascular disease IRT scores were obtained on 100% of the blood samples. Healthcare providers stated that the CVD IRT could be incorporated into routine primary care in a straightforward manner in 90.7% of reports. Participants stated they were 'likely' or 'very likely' to recommend the use of this test to their family or friends in 86.9% of reports. Participants stated that the test was personally useful (98.8%) and that the results were easy to understand (94.6%). When CVD IRT exceeded QRISK2, HCPs planned changes in management for 108/388 (27.8%) of participants and 47% (62/132) of participants with absolute risk score changes of >2%.

CONCLUSION

Amongst HCPs and participants who agreed to the trial of genetic data for refinement of clinical risk prediction in primary care, we observed that CVD IRT implementation was feasible and well accepted. The CVD IRT results were associated with planned changes in prevention strategies.

Long-Term Body Mass Index Variability and Adverse Cardiovascular Outcomes

Importance

Body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is a commonly used estimate of obesity, which is a complex trait affected by genetic and lifestyle factors. Marked weight gain and loss could be associated with adverse biological processes.

Objective

To evaluate the association between BMI variability and incident cardiovascular disease (CVD) events in 2 distinct cohorts.

Design, Setting, and Participants

This cohort study used data from the Million Veteran Program (MVP) between 2011 and 2018 and participants in the UK Biobank (UKB) enrolled between 2006 and 2010. Participants were followed up for a median of 3.8 (5th-95th percentile, 3.5) years. Participants with baseline CVD or cancer were excluded. Data were analyzed from September 2022 and September 2023.

Exposure

BMI variability was calculated by the retrospective SD and coefficient of variation (CV) using multiple clinical BMI measurements up to the baseline.

Main Outcomes and Measures

The main outcome was incident composite CVD events (incident nonfatal myocardial infarction, acute ischemic stroke, and cardiovascular death), assessed using Cox proportional hazards modeling after adjustment for CVD risk factors, including age, sex, mean BMI, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking status, diabetes status, and statin use. Secondary analysis assessed whether associations were dependent on the polygenic score of BMI.

Results

Among 92 363 US veterans in the MVP cohort (81 675 [88%] male; mean [SD] age, 56.7 [14.1] years), there were 9695 Hispanic participants, 22 488 non-Hispanic Black participants, and 60 180 non-Hispanic White participants. A total of 4811 composite CVD events were observed from 2011 to 2018. The CV of BMI was associated with 16% higher risk for composite CVD across all groups (hazard ratio [HR], 1.16; 95% CI, 1.13-1.19). These associations were unchanged among subgroups and after adjustment for the polygenic score of BMI. The UKB cohort included 65 047 individuals (mean [SD] age, 57.30 (7.77) years; 38 065 [59%] female) and had 6934 composite CVD events. Each 1-SD increase in BMI variability in the UKB cohort was associated with 8% increased risk of cardiovascular death (HR, 1.08; 95% CI, 1.04-1.11).

Conclusions and Relevance

This cohort study found that among US veterans, higher BMI variability was a significant risk marker associated with adverse cardiovascular events independent of mean BMI across major racial and ethnic groups. Results were consistent in the UKB for the cardiovascular death end point. Further studies should investigate the phenotype of high BMI variability.

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.

Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank

Background

Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke.

Methods

We performed genome-wide association studies (GWAS) for subsequent major adverse cardiovascular events (MACE) (Ncases=51,929, Ncntrl=39,980) and subsequent arterial ischemic stroke (AIS) Ncases=45,120, Ncntrl=46,789) after first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (pQTLs) to determine the effect of 1,463 plasma protein abundances on subsequent MACE using Mendelian randomization (MR).

Results

Two variants were significantly associated with subsequent cardiovascular events: rs76472767 (OR=0.75, 95% CI = 0.64-0.85, p= 3.69×10-08) with subsequent AIS and rs13294166 (OR=1.52, 95% CI = 1.37-1.67, p=3.77×10-08) with subsequent MACE. Using MR, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 (effect OR= 0.77, 95% CI = 0.66-0.88, adj. p=0.05), and TNFRSF14 (effect OR=1.42, 95% CI = 1.24-1.60, adj. p=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation.

Conclusions

We found evidence that two proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and medical traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and medical traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and medical traits were calculated in European-ancestry (EUR; n=5,691) participants and, when discovery datasets were available, for African-ancestry (AFR; n=4,918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGS MDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGS BMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and medical traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

Polygenic Risk Score Associates With Atherosclerotic Plaque Characteristics at Autopsy

BACKGROUND

Polygenic risk scores (PRSs) for coronary artery disease (CAD) potentially improve cardiovascular risk prediction. However, their relationship with histopathologic features of CAD has never been examined systematically.

METHODS

From 4327 subjects referred to CVPath by the State of Maryland Office Chief Medical Examiner for sudden death between 1994 and 2015, 2455 cases were randomly selected for genotyping. We generated PRS from 291 known CAD risk loci. Detailed histopathologic examination of the coronary arteries was performed in all subjects. The primary study outcome measurements were histopathologic plaque features determining severity of atherosclerosis, including %stenosis, calcification, thin-cap fibroatheromas, and thrombotic CAD.

RESULTS

After exclusion of cases with insufficient DNA sample quality or with missing data, 954 cases (mean age, 48.8±14.7 years; 75.7% men) remained in the final study cohort. Subjects in the highest PRS quintile exhibited more severe atherosclerosis compared with subjects in the lowest quintile, with greater %stenosis (80.3%±27.0% versus 50.4%±38.7%; adjusted P<0.001) and a higher frequency of calcification (69.6% versus 35.8%; adjusted P=0.004) and thin-cap fibroatheroma (26.7% versus 9.5%; adjusted P=0.007). Even after adjustment for traditional CAD risk factors, subjects within the highest PRS quintile had higher odds of severe atherosclerosis (ie, ≥75% stenosis; adjusted odds ratio, 3.77 [95% CI, 2.10-6.78]; P<0.001) and plaque rupture (adjusted odds ratio, 4.05 [95% CI, 2.26-7.24]; P<0.001). Moreover, subjects within the highest quintile had higher odds of CAD-associated cause of death, especially among those aged ≤50 years (adjusted odds ratio, 4.08 [95% CI, 2.01-8.30]; P<0.001). No statistically significant associations were observed with plaque erosion after adjusting for covariates.

CONCLUSIONS

This is the first autopsy study investigating associations between PRS and atherosclerosis severity at the histopathologic level in subjects with sudden death. Our pathological analysis suggests PRS correlates with plaque burden and features of advanced atherosclerosis and may be useful as a method for CAD risk stratification, especially in younger subjects.

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Polygenic Risk Score Associates with Atherosclerotic Plaque Characteristics at Autopsy

Background

Polygenic risk scores (PRS) for coronary artery disease (CAD) potentially improve cardiovascular risk prediction. However, their relationship with histopathologic features of CAD has never been examined systematically.

Methods

From 4,327 subjects referred to CVPath by the State of Maryland Office Chief Medical Examiner (OCME) for sudden death between 1994 and 2015, 2,455 cases were randomly selected for genotyping. We generated PRS from 291 known CAD risk loci. Detailed histopathologic examination of the coronary arteries was performed in all subjects. The primary study outcome measurements were histopathologic plaque features determining severity of atherosclerosis, including %stenosis, calcification, thin-cap fibroatheromas (TCFA), and thrombotic CAD.

Results

After exclusion of cases with insufficient DNA sample quality or with missing data, 954 cases (mean age 48.8±14.7; 75.7% men) remained in the final study cohort. Subjects in the highest PRS quintile exhibited more severe atherosclerosis compared to subjects in the lowest quintile, with greater %stenosis (80.3%±27.0% vs. 50.4%±38.7%; adjusted p<0.001) and a higher frequency of calcification (69.6% vs. 35.8%; adjusted p=0.004) and TCFAs (26.7% vs. 9.5%; adjusted p=0.007). Even after adjustment for traditional CAD risk factors subjects within the highest PRS quintile had higher odds of severe atherosclerosis (i.e., ≥75% stenosis; adjusted OR 3.77; 95%CI 2.10-6.78; p<0.001) and plaque rupture (adjusted OR 4.05; 95%CI 2.26-7.24; p<0.001). Moreover, subjects within the highest quintile had higher odds of CAD-associated cause of death, especially among those aged 50 years and younger (adjusted OR 4.08; 95%CI 2.01-8.30; p<0.001). No associations were observed with plaque erosion.

Conclusions

This is the first autopsy study investigating associations between PRS and atherosclerosis severity at the histopathologic level in subjects with sudden death. Our pathological analysis suggests PRS correlates with plaque burden and features of advanced atherosclerosis and may be useful as a method for CAD risk stratification, especially in younger subjects.

Highlights

In this autopsy study including 954 subjects within the CVPath Sudden Death Registry, high PRS correlated with plaque burden and atherosclerosis severity.The PRS showed differential associations with plaque rupture and plaque erosion, suggesting different etiologies to these two causes of thrombotic CAD.PRS may be useful for risk stratification, particularly in the young. Further examination of individual risk loci and their association with plaque morphology may help understand molecular mechanisms of atherosclerosis, potentially revealing new therapy targets of CAD.

Graphic Abstract

A polygenic risk score, generated from 291 known CAD risk loci, was assessed in 954 subjects within the CVPath Sudden Death Registry. Histopathologic examination of the coronary arteries was performed in all subjects. Subjects in the highest PRS quintile exhibited more severe atherosclerosis as compared to subjects in the lowest quintile, with a greater plaque burden, more calcification, and a higher frequency of plaque rupture.

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.