Whitepaper: Validating Orchid’s Atrial Fibrillation Genetic Risk Score

Introduction

Atrial fibrillation is a common cardiac arrhythmia in which the atria beat rapidly and irregularly. It can cause palpitations, dizziness, shortness of breath, and fatigue. Age is the primary risk factor, but hypertension, diabetes, and obesity also increase susceptibility. It can be triggered by binge drinking or elevated stress. Atrial fibrillation can lead to serious complications, including a 5x risk of stroke.^1,2

Atrial fibrillation is the most common form of cardiac arrhythmia and is projected to affect up to 12.1 million Americans by 2030.³ A family history of atrial fibrillation is associated with a 40% increased risk.² It is estimated that 37.1% of individuals over the age of 55 will develop atrial fibrillation during their lifetime.⁴ Treatment is complex and may include medications to slow heart rate, prevent arrhythmia, and prevent stroke complications. Surgical options are rare and include catheter ablation and pacemaker implantation.^2,5

Genetic Risk Score

Atrial fibrillation 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 atrial fibrillation GRS was trained following current industry standards.^6,7 The GRS was constructed using the SBayesRC algorithm trained on publicly available FinnGen and Million Veterans Program summary statistics.^8,9 The summary statistics include 151,410 cases and 778,364 controls.¹⁰ The resulting GRS contains over one 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.¹¹ Orchid considers a GRS meaningfully predictive if individuals at roughly the 97.7th percentile have an odds ratio (OR) of at least 2. The atrial fibrillation GRS meets this criterion for all common ancestry groups.

Evaluation on UK Biobank Data

We evaluated the predictive performance of Orchid's atrial fibrillation GRS using the UK Biobank (UKB), a research database of roughly 500,000 genotyped individuals from the United Kingdom.¹² We restricted the analysis to participants of British ancestry aged 55 or older and defined atrial fibrillation as any diagnoses under ICD-10 codes I48.x, yielding 26,093 cases and 233,099 controls (10.1% 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 observed prevalence of atrial fibrillation 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 atrial fibrillation, supporting the predictive accuracy of the GRS to identify individuals with elevated risk.

Estimating Lifetime Risk

The average observed prevalence of atrial fibrillation in the UKB was 10.1%. This is considerably lower than the lifetime prevalence in the general population, which has been estimated to be approximately 37.1%.⁴ 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.¹³ 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).¹⁴ 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 atrial fibrillation 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 atrial fibrillation 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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