Whitepaper: Validating Orchid’s Inflammatory Bowel Disease Genetic Risk Score

Introduction

Inflammatory bowel disease (IBD) is a term for two conditions, Crohn’s disease and ulcerative colitis, that are characterized by chronic inflammation of the gastrointestinal tract. It can cause chronic abdominal pain, diarrhea, ulcers, fatigue, weight loss, and anemia. Crohn’s disease can also cause perianal lesions. Surprisingly, smoking is associated with an increased risk of Crohn’s disease but a reduced risk of ulcerative colitis. Diet may also play a role in the development and management of both diseases.¹ IBD affects approximately 1.3% of US adults per a 2015 survey.² Current treatments emphasize mucosal healing through the reduction of gut inflammation, with biologics associated with increased remission and mucosal healing rates in moderate to severe cases.¹

Genetic Risk Score

IBD 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 IBD GRS was trained following current industry standards.^3,4 The GRS was constructed using the SBayesRC algorithm trained on publicly available FinnGen and Million Veterans Program summary statistics.^5,6 The summary statistics include 20,764 cases and 1,104,431 controls.⁷ 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.⁸ Orchid considers a GRS meaningfully predictive if individuals at roughly the 97.7th percentile have an odds ratio (OR) of at least 2. The IBD GRS meets this criterion for all common ancestry groups.

Evaluation on UK Biobank Data

We evaluated the predictive accuracy of Orchid’s IBD GRS using the UK Biobank (UKB), a research database of roughly 500,000 genotyped individuals from the United Kingdom.⁹ We restricted the analysis to participants of British ancestry and defined IBD using the K50.x (Crohn’s disease) and K51.x (ulcerative colitis) ICD-10 codes, yielding 5,987 cases and 402,533 controls (1.5% 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 IBD 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 IBD, supporting the predictive accuracy of the GRS to identify individuals with elevated risk.

Estimating Lifetime Risk

Xu et al. estimate a 1.3% prevalence of IBD,² similar to the computed 1.5% prevalence in the UKB. We adjust our model so that its average prevalence aligns with the Xu et al. estimate (Figure 2).¹⁰ 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 IBD 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 risk consistent with the estimated prevalence in the US general population. The IBD 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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