Integrating Whole-Genome Sequencing with Social Determinants Data for Coronary Artery Disease Risk Prediction: Interpretability, Bias, and Real-World Performance, Implementation, and Equity Considerations

Namirimu Sandrah

Department of Pharmacology and Toxicology Kampala International University Uganda

Email: sandrahnamirimu@studwc.kiu.ac.ug

ABSTRACT

Coronary artery disease (CAD) remains the leading cause of morbidity and mortality globally. While traditional risk prediction models rely on clinical, biochemical, and demographic factors, they often omit the contribution of genetic variation and social determinants of health (SDOH). Advances in whole-genome sequencing (WGS) have enabled population-scale assessment of polygenic risk, while SDOH capture environmental and socio-economic influences on disease development. This study integrates WGS-derived polygenic hazard scores with SDOH data to improve CAD risk prediction, assess model interpretability, and evaluate real-world performance across diverse populations. Using data from the UK Biobank and independent cohorts, models combining genomic and social risk factors demonstrated superior predictive performance and improved calibration compared with models using either data type alone. However, disparities in predictive accuracy persist across populations, highlighting challenges in equity and access. Implementation considerations, including infrastructure, governance, patient and clinician engagement, and ethical frameworks, are critical for translating these integrative approaches into clinical practice. Our findings underscore the potential of integrated socio-genomic models to enhance precision medicine while emphasizing the need for careful attention to fairness, transparency, and real-world applicability.

Keywords: Coronary artery disease, Whole-genome sequencing, Polygenic risk score, Social determinants of health, and Equity in precision medicine.

CITE AS: Namirimu Sandrah (2026). Integrating Whole-Genome Sequencing With Social Determinants Data for Coronary Artery Disease Risk Prediction: Interpretability, Bias, and Real-World Performance, Implementation, and Equity Considerations. RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 6(1):86-96. https://doi.org/10.59298/RIJSES/2026/618696