Whitepaper: Validating Orchid’s Hypertension Genetic Risk Score

Introduction

Hypertension is defined by persistently high blood pressure, measured using systolic pressure (during a heartbeat) and diastolic pressure (between heartbeats). It is a major risk factor for cardiovascular disease, with risk increasing continuously as blood pressure rises. Key hypertension risk factors include obesity, physical inactivity, excessive sodium consumption, insufficient dietary potassium intake, and alcohol intake.¹

Hypertension is the single largest risk factor contributing to global all-cause mortality, leading to approximately 9.4 million deaths per year.¹ The CDC estimates that nearly half of U.S. adults have hypertension, with only about a quarter of those affected having it controlled by treatment.² Treatment typically involves a combination of lifestyle modification and blood pressure lowering medication. Common lifestyle interventions include dietary change and increased physical activity.¹

Genetic Risk Score

Hypertension is influenced by both environmental and genetic factors. Rare variants in genes such as PDE3A can cause the disease,¹ but most cases arise from the combined effects of many genetic variants and environmental exposures. Genetic risk scores (GRS), which combine the small effects of many variants into a single score, can estimate genetic risk. Although not diagnostic, a GRS can indicate how likely an individual is to develop the disease.

The hypertension GRS was developed using data from multiple large studies, including the UK Biobank (UKB), FinnGen, the Million Veteran Program (MVP), and All of Us, consisting of individuals of European ancestry.^3–6 For the UKB, a subset of individuals was held out to use in the validation study. Summary statistics were generated for the UKB and All of Us using the Regenie method.⁷ Summary statistics across all studies were then meta-analyzed using METAL.⁸ The final GRS was trained using SBayesRC and includes over 7 million variants.⁹

Risk predictions are adjusted to each individual's ancestry, with predictive power decaying as genetic distance from the predominately European training data increases.¹⁰ Orchid considers a GRS meaningfully predictive if individuals at roughly the 97.7th percentile have an odds ratio (OR) of at least 2. The hypertension GRS meets this criterion for all common ancestry groups.

Evaluation on UK Biobank Data

We evaluated the predictive accuracy of Orchid's hypertension GRS using the UK Biobank (UKB), a research database of roughly 500,000 genotyped individuals from the United Kingdom.³ Because some data from the UKB was used to train the GRS, our validation study included only the set of samples that were held out for testing and not included in the GRS training. We also restricted the analysis to individuals of British ancestry and defined hypertension using the ICD-10 codes I10.x as well as self-reported diagnoses, yielding 35,411 cases and 57,253 controls (38.2% 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 Information.

Table 1 shows the hypertension observed prevalence for individuals in the UKB grouped by GRS percentile range (top 10%, 5%, and 1%), as well as how their risk compares to the baseline risk at the 50th GRS percentile. Those with higher GRS relative to the population baseline also had substantially higher observed prevalence of hypertension, supporting the predictive accuracy of the GRS to identify individuals with elevated risk.

Estimating Lifetime Risk

The average observed prevalence of hypertension in the UKB was 38.2%. This is considerably lower than the lifetime prevalence in the US general population, which has been estimated to be approximately 48.1%.² This is likely due in part to the fact that UKB participants tend to be healthier than the general population, which leads to lower observed disease prevalence.¹¹ Additionally, the observed prevalence in the UKB includes people still living who could develop the disease when they are older, and so does not capture the full lifetime risk of the disease.

Orchid's clinical reports include predicted lifetime disease risk, which we calculate by first estimating how disease risk varies across GRS in the UKB and then rescaling that pattern so the average matches the known lifetime population risk (Figure 2).¹² People at the high end of the GRS distribution are predicted to have an elevated lifetime risk of the disease relative to the population (Table 2).

Conclusion

In this study, we evaluated our hypertension GRS on data from the UKB. We found that it performed well, particularly for identifying individuals with elevated risk of the disease relative to the population. In our embryo and couple reports, we adjust the model to predict lifetime risk, which is generally higher than observed prevalence in the UKB. The hypertension GRS model is available to individuals of all ancestry groups.

Acknowledgments

This research was conducted using the UK Biobank Resource under Application Number 80545.

References

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