Orchid's team of genetic experts has developed a genetic risk score (GRS) for coronary artery disease.
Written by Orchid Team
Orchid has developed advanced genetic risk scores (GRS) for a variety of diseases. Here we present our data on our GRS of coronary artery disease.
Coronary Artery Disease
Coronary artery disease (CAD) is the narrowing or blockage of the coronary arteries, most often caused by plaque buildup (atherosclerosis). Risk factors include smoking, alcohol use, poor diet, inactivity, and hypertension. Symptoms include heart pain, fatigue, and, in severe cases, heart attack.[1]
Genetic Risk Score
CAD is shaped by both environmental and genetic factors. Monogenic testing is not available because no single gene causes the condition. Genetic risk scores (GRS), which combine the small effects of many variants into a single score, are currently the only way to estimate genetic risk. Although not diagnostic, a GRS can indicate how likely an individual is to develop the disease.
Orchid’s CAD GRS was trained following current industry standards.[2][3] The GRS was constructed using the SBayesRC algorithm trained on publicly available FinnGen and Million Veterans Program summary statistics.[4][5] The summary statistics include 152,517 cases and 929,037 controls.[6] The resulting GRS contains over a million variants.
Risk predictions are adjusted to each individual’s ancestry, with predictive power decaying as genetic distance from the predominately European training data increases.[7] Orchid considers a GRS meaningfully predictive if individuals at roughly the 97.7th percentile have an odds ratio (OR) of 2. The CAD GRS meets this criteria for all common ancestry groups.
Clinical Impact and Prevalence
CAD is the leading cause of premature adult death worldwide, accounting for approximately 7.4 million deaths in 2015.[1] A US study estimates a 27% lifetime risk of developing the disease.[8]
Treatment typically involves a combination of lifestyle modification, management of blood pressure and diabetes, and medications to lower LDL cholesterol. Common lifestyle interventions include dietary changes, increased physical activity, and smoking cessation.[1]
Performant Risk Stratification
We evaluated the predictive performance of Orchid’s CAD GRS using the UK Biobank (UKB), a research database of roughly 500,000 genotyped individuals from the United Kingdom. We restricted the analysis to participants of British ancestry and defined CAD using the I25.1 ICD-10 code, yielding 27,972 cases and 380,548 controls (6.8% prevalence). We then grouped individuals by GRS percentile and compared the observed disease prevalence within each group to our model’s predictions (Figure 1). For additional technical details, see the Supplementary Data.
UKB participants tend to be healthier than the general population, which leads to lower observed disease prevalence.[9] Hasbani et al. estimate a 27% lifetime risk of CAD, much higher than the prevalence in the UKB.[8] We adjust our model so that its average predicted risk aligns with this estimate (see Figure 2).[10] People at the tail end of the GRS distribution were at an elevated risk compared to the mean (see Table 3), with adults in the 99th percentile 2.3x more likely to develop CAD than average (62.1% vs 27.0%).
References
1. Libby P, Buring JE, Badimon L, et al. Atherosclerosis. Nat Rev Dis Primers. 2019;5:56. doi:10.1038/s41572-019-0106-z.
2. Moore S, Davidson I, Anomaly J, et al. Development and validation of polygenic scores for within-family prediction of disease risks. medRxiv. 2025. doi:10.1101/2025.08.06.25333145.
3. Cordogan S, Starr DB, Treff NR, et al. Within- and between-family validation of nine polygenic risk scores developed in 1.5 million individuals: implications for IVF, embryo selection, and reduction in lifetime disease risk. medRxiv. 2025. doi:10.1101/2025.10.24.25338613.
4. Zheng, Z., Liu, S., Sidorenko, J. et al. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet 56, 767–777 (2024). https://doi.org/10.1038/s41588-024-01704-y
5. FinnGen. FinnGen+MVP+UKBB Summary Statistics. Available at: https://mvp-ukbb.finngen.fi/about. Accessed 2025-12-05.
6. FinnGen. FinnGen+MVP+UKBB Phenotypes. Available at: https://mvp-ukbb.finngen.fi. Accessed 2025-12-15.
7. Privé, Florian et al. “Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort.” American journal of human genetics vol. 109,1 (2022): 12-23. doi:10.1016/j.ajhg.2021.11.008
8. Hasbani NR, Ligthart S, Brown MR, et al. American Heart Association’s Life’s Simple 7: lifestyle recommendations, polygenic risk, and lifetime risk of coronary heart disease. Circulation. 2022;145(11):808–818. doi:10.1161/CIRCULATIONAHA.121.053730
9. Fry A, Littlejohns TJ, Sudlow C, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186:1026–1034. doi:10.1093/aje/kwx246.
10. Chatterjee N, Shi J, García-Closas M et al. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016;17:392–406. doi:10.1038/nrg.2016.27
Supplementary Figures
Acknowledgements
This research has been conducted using the UK Biobank Resource under Application Number 80545.
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