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-R2 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-R2 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-R2 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-R2 is easy to implement and well suited to evaluate PRS for predicting incident events in cohort studies.
Highlights
With the power of genotyping technologies, genome-wide association studies (GWAS) focusing on complex diseases have identified a large number of genetic variants associated with various traits of interest (Buniello et al, 2019)
Our simulations showed that the value of increases with the value of the strength of the polygenic risk scores (PRS)’ effect
In the range of effects presented in this paper, the maximum value reached by is 0.96 when both α and β take value of 6.21
Summary
With the power of genotyping technologies, genome-wide association studies (GWAS) focusing on complex diseases have identified a large number of genetic variants associated with various traits of interest (e.g., diabetes, cardiovascular diseases, cancer...) (Buniello et al, 2019). These large GWAS have provided various lists of disease-related single nucleotide polymorphisms (SNP) together with their effect size estimates (McCarthy et al, 2008). For each individual, a classical polygenic score consists of a linear combination of the trait-associated alleles carried by the subject and weighted by their effect sizes The list of these risk alleles and their corresponding weights are obtained from published GWAS. In recent years, a burgeoning literature has started to focus on the evaluation of published PRS (for a few: International Multiple Sclerosis Genetics Consortium et al, 2010; Machiela et al, 2011; Khera et al, 2018)
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