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. 2020 Apr 24;11:408.
doi: 10.3389/fgene.2020.00408. eCollection 2020.

Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort

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Free PMC article

Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort

Julianne Duhazé et al. Front Genet. .
Free PMC article

Abstract

With the increasing use of polygenic risk scores (PRS) there is a need for adapted methods to evaluate the predictivity of these tools. In this work, we propose a new pseudo-R 2 criterion to evaluate PRS predictive accuracy for time-to-event data. This new criterion is related to the score statistic derived under a two-component mixture model. It evaluates the effect of the PRS on both the propensity to experience the event and on the dynamic of the event among the susceptible subjects. Simulation results show that our index has good properties. We compared our index to other implemented pseudo-R 2 for survival data. Along with our index, two other indices have comparable good behavior when the PRS has a non-null propensity effect, and our index is the only one to detect when the PRS has only a dynamic effect. We evaluated the 5-year predictivity of an 18-single-nucleotide-polymorphism PRS for incident breast cancer cases on the CARTaGENE cohort using several pseudo-R 2 indices. We report that our index, which summarizes both a propensity and a dynamic effect, had the highest predictive accuracy. In conclusion, our proposed pseudo-R 2 is easy to implement and well suited to evaluate PRS for predicting incident events in cohort studies.

Keywords: breast cancer; polygenic risk score; pseudo-R2; survival mixture model; survival models.

Figures

Figure 1
Figure 1
Display of the behavior of different pseudo-R2 indices when the effects of the evaluated criterion on the propensity (α) and on the dynamic (β) vary. The graphic shows Δ with 0% (A) and 20% (B) censoring and other indexes with 0% censoring : OXS (C), OF (D), XO (E), N (F), and RMB (G), for 500 subjects and a tail defect of 70%.
Figure 2
Figure 2
Comparison of the evolution of different pseudo-R2 indices according to the effect of the evaluated polygenic risk score on the dynamic (β) when the criterion has no effect on the propensity (α), for 500 subjects, a tail defect of 70% and no censoring.
Figure 3
Figure 3
Comparison of the evolution of different pseudo-R2 indices according to the effect of the evaluated polygenic risk score on the dynamic (β) and the propensity (α), when α = β, for 500 subjects, a tail defect of 70% and no censoring.
Figure 4
Figure 4
Display of the behavior of the pseudo-R2 Δ according to the effect of the evaluated polygenic risk score on the dynamic (β) or the propensity (α), when α or β is null, for 500 subjects, a tail defect of 70% and no censoring.
Figure 5
Figure 5
Distribution of Evans' polygenic risk score in the CARTaGENE cohort (n = 4,554) (A), control women QQ-plot with confidence bands based on an inversion of the Kolmogorov–Smirnov test (B) and incidence of breast cancer in the cohort according to the Evans' score (C).

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