Webinar: Impacts for Insurers of Polygenic Risk Scores for Genetic Disorders

Event Type
Web Session
Date
Time

Start time: 12.30 pm

End time: 2.00 pm

Venue
Online
Description

A video of this event is now available to view on:

actuview

Jinbo Zhao will present a simulation-based model for multifactorial genetic disorders to quantify the impact of polygenic risk scores (PRS) on life insurance. Heart attacks are used as a case study and individual-level PRS, disease status and family history are simulated employing the liability threshold model and introduced into the simulated individual profiles to form the data source for subsequent analysis. The impact of PRS was measured by calculating the incidence of heart attack for various categories, quantifying the additional information provided by PRS, and comparing the premiums calculated for various PRS risk groups.

This event will be run as a live webinar using Zoom. Click here for information on using Zoom.

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Speakers/Presenters
Jinbo Zhao, Dr Michael Salter Townshend and Dr Adrian O’Hagan
Biographical details

Jinbo Zhao is currently a PhD student in the School of Mathematics and Statistics at University College Dublin and holds an MSc in Actuarial Science from UCD. Her research interests lie in assessing the predictive power of statistical genetic models for individual genetic risk of disease, and in quantifying the potential impact of recent advances in statistical genetics research on life insurance.

Dr Michael Salter-Townshend is an Assistant Professor of Statistics in University College Dublin. His primary research interest is in statistical genetics, with a focus on human genomes. This involves modelling the patterns of shared variation between individuals and can shed light on questions of human demographic history, natural selection, and disease association mapping.

Dr Adrian O’Hagan is an Assistant Professor of Statistics and Actuarial Science and director of the Bachelor of Actuarial and Financial Studies (BAFS) degree at UCD. His research interests lie at the interface of statistics and actuarial science, using techniques in data analytics and machine learning to address emerging research challenges in actuarial science across general, life and health insurance.

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