A simulation study for multifactorial genetic disorders to quantify the impact of polygenic risk scores on critical illness insurance

Abstract

With advances in genetic research, the understanding of the genetic structure of disease and the ability to predict disease risk have been enhanced. Polygenic risk scores (PRS) have been developed to assess a person’s risk of developing any heritable disease. PRS has two primary utilities that make it particularly relevant for insurers: the ability to identify high-risk groups when using PRS independently or in combination with standard risk factors; and the ability to inform early interventions that may alter future morbidity and mortality. Using heart disease as a case study, a simulation-based model is designed that introduces polygenic risk scoring into the actuarial analysis framework and then quantifies the adverse selection due to information asymmetry introduced by PRS. Individual and parental disease liability as well as PRS were simulated under a liability threshold model. A series of validations were conducted to confirm the utility of our simulated data sets. We explored three scenarios describing how insurance applicants use their PRS results to guide their insurance purchasing decisions and calculated the increased premiums that insurers would need to change to counteract this. The accuracy of PRS has the most significant impact on premiums and the proportion of individuals who know their PRS also has a substantial impact.

How group structure impacts the numbers at risk for coronary artery disease: polygenic risk scores and non-genetic risk factors in the UK Biobank cohort

“…is the cumulative distribution function of the normal distribution (So et al 2011). The total liability L is assumed to be split into two components, the measurable genetic component and the combination of environmental and unknown risk factors, while PRS can be used to represent the measurable genetic component (Zhao et al 2023). If we assume that the variance explained by PRS is V, then the LTM suggests that…”

“…Comparing the model with only PCE to the model with PCE and PRS, when using a risk threshold of 7.5%, the latter improved net reclassification 4.4% for cases and -0.4% for controls. Incorporating family history and PRS can improve the accuracy of predicting CAD risk in both real-world and simulation study settings (e.g., Hujoel et al (2022) Zhao et al (2023)).…”

Genetic testing and actuarial science

“…Then, the small size of each individual effect, plus the central limit theorem, results in lognormally-distributed overall hazard rates of mortality. 5 Although these are largely mathematical artifacts, the range of the combined risk is usually comparable with other medical risks familiar to underwriters (Macdonald & McIvor, 2009;Adams et al, 2015;Maxwell et al, 2021;Zhao et al, 2023). Therefore, predictive genetics, single-gene disorders apart, may be finding its place in "normal" medicine, and there is no reason to suppose that insurers will be unable to cope with it (Joly et al, 2013;Golinghorst et al, 2022).…”