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.

The UK Biobank is a large cohort study that recruited over 500,000 British participants aged 40-69 in 2006-2010 at 22 assessment centres from across the UK. Self-reported health outcomes and hospital admission data are two types of records that include participants' disease status. Coronary artery disease (CAD) is the most common cause of death in the UK Biobank cohort. After distinguishing between prevalence and incidence CAD events for all UK Biobank participants, we identified geographical variations in age-standardised rates of CAD between assessment centres. Significant distributional differences were found between the pooled cohort equation scores of UK Biobank participants from England and Scotland using the Mann-Whitney test. Polygenic risk scores of UK Biobank participants from England and Scotland and from different assessment centres differed significantly using permutation tests. Our aim was to discriminate between assessment centres with different disease rates by collecting data on disease-related risk factors. However, relying solely on individual-level predictions and averaging them to obtain group-level predictions proved ineffective, particularly due to the presence of correlated covariates resulting from participation bias. By using the Mundlak model, which estimates a random effects regression by including the group means of the independent variables in the model, we effectively addressed these issues. In addition, we designed a simulation experiment to demonstrate the functionality of the Mundlak model. Our findings have applications in public health funding and strategy, as our approach can be used to predict case rates in the future, as both population structure and lifestyle changes are uncertain.