Genetics

The Value of Genetics in Cardiovascular Research

January 28, 2026January 29th, 2026
genetics in cardiovascular research

Key Takeaways:

  • Genetic discovery through genome-wide association studies provides critical insights into cardiovascular disease biology by discovering hundreds of genetic loci that point to novel targetable pathways.
  • Polygenic risk scores identify inherited susceptibility to heart disease earlier than traditional clinical factors, although their current clinical benefit remains modest, and barriers exist to widespread adoption.
  • Genetics-guided drug development can accelerate therapeutic success, such as what was demonstrated with PCSK9. Emerging gene therapy approaches are attempting to directly modify causal genes.

Introduction

The integration of genetics into cardiovascular research has enhanced our understanding of heart disease pathophysiology and opened new avenues for precision medicine. Despite cardiovascular disease remaining the leading global cause of morbidity and mortality1, its heritability has only recently been leveraged effectively for prevention and treatment. The expansion of genome-wide association studies (GWAS), advances in sequencing technologies, and the establishment of large-scale population biobanks have accelerated genetic discovery to uncover variants associated with cardiovascular disease. However, a critical gap persists between genetic discovery and clinical implementation: while we have identified hundreds of genetic variants associated with cardiovascular risk, translating these findings into actionable preventive strategies and targeted therapeutics remains challenging. Key questions regarding how to integrate polygenic risk scores into routine clinical practice, which patient populations benefit most from genetic screening, and how to address disparities in genetic research representation must be resolved to realize the full potential of precision cardiovascular medicine.

Genetic Discoveries Through Genome-Wide Association Studies

GWAS have identified hundreds of loci associated with cardiovascular disease and related traits.2,3 For example, genetic variation at the 9p21.3 locus has emerged as a consistent population-level genetic marker for cardiovascular disease risk.4 GWAS studies have also revealed mechanisms in cardiovascular pathogenesis, including inflammatory signaling5, vascular remodeling6, and cellular senescence pathways7. Currently, GWAS findings primarily inform our biological understanding of disease mechanisms and facilitate drug target discovery rather than routine clinical decision-making.8 While data derived from GWAS show promise for risk stratification, several barriers limit broader clinical application.8 These include questions about clinical utility beyond traditional risk factors, lack of standardized implementation guidelines, challenges in interpreting scores across diverse ancestries, and minimal evidence that genetic risk information alone alters patient behavior for risk factor management.8,9 Several major consortia that continue to contribute to these GWAS developments include:

The CARDIoGRAMplusC4D Consortium is a global initiative that integrates data from large-scale genetic studies to identify loci associated with coronary artery disease and myocardial infarction. By analyzing over a million participants and integrating multi-ancestry data, the CARDIoGRAMplusC4D Consortium recently identified 241 genetic variants linked to coronary artery disease, including 30 new signals.10
HERMES is a global collaboration that uses GWAS to uncover causal pathways in heart failure and validate drug targets for new therapies. A study from HERMES suggests that heart failure might share genetic etiology with coronary artery disease, atrial fibrillation, or reduced left ventricular function.11
The CHARGE Consortium is an international collaboration that conducts GWAS meta-analyses and replication across multiple large cohorts of heart and aging studies. This consortium developed and validated a predictive score for atrial fibrillation.12

Risk Prediction in Cardiovascular Disease

Polygenic risk scores (PRS) in cardiovascular disease prevention combine information from numerous genetic variants to estimate a person’s inherited susceptibility to heart disease.13 Unlike traditional risk assessment methods that rely on clinical factors like cholesterol levels and blood pressure, PRS leverage genetic markers that could enable earlier identification of at-risk individuals.14 While current medical guidelines use established models like pooled cohort equations to determine who should receive preventive treatments15, researchers are investigating whether adding genetic risk scores to these traditional models can better identify high-risk individuals who would benefit from early lifestyle changes or medications. However, studies examining this combination have found that while PRS provide significant improvements in prediction accuracy, the actual clinical benefit is modest and only improves risk classification for a small percentage of people.16

The American Heart Association (AHA) acknowledges that PRS are beginning to enter clinical practice and may be appropriately considered in select cardiovascular scenarios.13 A real-world example of PRS offered to assess atrial fibrillation and coronary artery disease in clinical practice can be found here.17 However, the AHA emphasize that significant challenges remain, including lack of diversity in genetic databases, uncertain cost-effectiveness, unclear clinical utility, and the need for clinician/patient education.8,13

A Demonstration from Discovery to Therapy

PCSK9 is a classic example of the translation from genetic cardiovascular discoveries into therapeutics. Variants of PCSK9 were identified in families with autosomal dominant hypercholesterolemia18 and families with low LDL cholesterol levels and reduced cardiovascular events19. These discoveries prompted therapeutic development, leading to FDA approval of PCSK9 inhibitors, evolocumab20 and alirocumab21, that reduce adverse cardiovascular events in high-risk patients. Additional genetic targets whose therapeutic strategies are being actively investigated in clinical trials include ANGPTL3 (monoclonal antibodies, siRNA inhibitors), ANGPTL4 (antisense oligonucleotides, monoclonal antibodies), APOC3 (antisense oligonucleotides, siRNA inhibitors), and LPA (antisense oligonucleotides, siRNA inhibitors).22

Conclusion

The convergence of genetics and cardiovascular research represents a period in which molecular-level discoveries are redefining approaches to disease prevention, diagnosis, and therapeutic intervention. Large-scale genomic investigations have elucidated essential pathogenic pathways underlying cardiovascular disease. Realizing the full potential of genetics in cardiovascular disease requires addressing several key priorities, including validating and implementing risk scores into clinical practice, expanding genetic research to underrepresented populations, and developing infrastructure for gene-based therapeutic platforms. The continued growth of genomic repositories and advancements in data analytics will accelerate the adoption of precision cardiology to contribute to improved patient outcomes.

References

  1. World Health Organization 2025 data.who.int, United States of America [Country overview]. Accessed December 10, 2025. https://data.who.int/countries/840
  2. Nikpay M, Goel A, Won HH, et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015;47(10):1121-1130. doi:10.1038/ng.3396
  3. Tcheandjieu C, Zhu X, Hilliard AT, et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat Med. 2022;28(8):1679-1692. doi:10.1038/s41591-022-01891-3
  4. Ruotsalainen AK, Kettunen S, Suoranta T, Kaikkonen MU, Ylä-Herttuala S, Aherrahrou R. The mechanisms of Chr.9p21.3 risk locus in coronary artery disease: where are we today? Am J Physiol Heart Circ Physiol. 2025;328(2):H196-H208. doi:10.1152/ajpheart.00580.2024
  5. Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7(1):131. doi:10.1038/s41392-022-00955-7
  6. Quaye LNK, Dalzell CE, Deloukas P, Smith AJP. The Genetics of Coronary Artery Disease: A Vascular Perspective. Cells. 2023;12(18). doi:10.3390/cells12182232
  7. Xu C, Qiu Z, Guo Q, Huang Y, Zhao Y, Zhao R. The role of cellular senescence in cardiovascular disease. Cell Death Discov. 2025;11(1):431. doi:10.1038/s41420-025-02720-5
  8. Landstrom AP, Ferguson JF, James CA, et al. Genetic and Genomic Testing in Cardiovascular Disease: A Policy Statement From the American Heart Association. Circulation. 2025;152(24):e474-e489. doi:10.1161/CIR.0000000000001385
  9. Hollands GJ, French DP, Griffin SJ, et al. The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. BMJ. 2016;352:i1102. doi:10.1136/bmj.i1102
  10. Aragam KG, Jiang T, Goel A, et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet. 2022;54(12):1803-1815. doi:10.1038/s41588-022-01233-6
  11. Shah S, Henry A, Roselli C, et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat Commun. 2020;11(1):163. doi:10.1038/s41467-019-13690-5
  12. Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013;2(2):e000102. doi:10.1161/JAHA.112.000102
  13. O’Sullivan JW, Raghavan S, Marquez-Luna C, et al. Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2022;146(8):e93-e118. doi:10.1161/CIR.0000000000001077
  14. Inouye M, Abraham G, Nelson CP, et al. Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention. J Am Coll Cardiol. 2018;72(16):1883-1893. doi:10.1016/j.jacc.2018.07.079
  15. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596-e646. doi:10.1161/CIR.0000000000000678
  16. Elliott J, Bodinier B, Bond TA, et al. Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease. JAMA. 2020;323(7):636-645. doi:10.1001/jama.2019.22241
  17. Laboratory for Molecular Medicine: Polygenic Risk Testing. Mass General Brigham. Accessed January 23, 2026. https://www.massgeneralbrigham.org/en/research-and-innovation/centers-and-programs/personalized-medicine/molecular-medicine/tests/polygenic-risk
  18. Abifadel M, Varret M, Rabès JP, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. 2003;34(2):154-156. doi:10.1038/ng1161
  19. Cohen JC, Boerwinkle E, Mosley THJ, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med. 2006;354(12):1264-1272. doi:10.1056/NEJMoa054013
  20. Sabatine MS, Giugliano RP, Keech AC, et al. Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease. N Engl J Med. 2017;376(18):1713-1722. doi:10.1056/NEJMoa1615664
  21. Schwartz GG, Steg PG, Szarek M, et al. Alirocumab and Cardiovascular Outcomes after Acute Coronary Syndrome. N Engl J Med. 2018;379(22):2097-2107. doi:10.1056/NEJMoa1801174
  22. Gurevitz C, Bajaj A, Khera AV, et al. Gene therapy and genome editing for lipoprotein disorders. Eur Heart J. 2025;46(35):3420-3433. doi:10.1093/eurheartj/ehaf411
Share