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Multifactorial disorders and polygenic risk scores: predicting common diseases and the possibility of adverse selection in life and protection insurance

Published online by Cambridge University Press:  14 August 2020

Jessye M. Maxwell ,
Richard A. Russell ,
Hei Man Wu ,
Natasha Sharapova ,
Peter Banthorpe ,
Paul F O’Reilly  and
Cathryn M Lewis
Jessye M. Maxwell
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
Richard A. Russell
Affiliation:
Global Research and Data Analytics, RGA Reinsurance Company, London, UK
Hei Man Wu
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, USA
Natasha Sharapova
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
Peter Banthorpe
Affiliation:
Global Research and Data Analytics, RGA Reinsurance Company, London, UK
Paul F O’Reilly
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York, NY, USA
Cathryn M Lewis*
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK Department of Medical and Molecular Genetics, King’s College London, London, UK
*
*Corresponding author. E-mail: cathryn.lewis@kcl.ac.uk
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Abstract

During the past decade, genetics research has allowed scientists and clinicians to explore the human genome in detail and reveal many thousands of common genetic variants associated with disease. Genetic risk scores, known as polygenic risk scores (PRSs), aggregate risk information from the most important genetic variants into a single score that describes an individual’s genetic predisposition to a given disease. This article reviews recent developments in the predictive utility of PRSs in relation to a person’s susceptibility to breast cancer and coronary artery disease. Prognostic models for these disorders are built using data from the UK Biobank, controlling for typical clinical and underwriting risk factors. Furthermore, we explore the possibility of adverse selection where genetic information about multifactorial disorders is available for insurance purchasers but not for underwriters. We demonstrate that prediction of multifactorial diseases, using PRSs, provides population risk information additional to that captured by normal underwriting risk factors. This research using the UK Biobank is in the public interest as it contributes to our understanding of predicting risk of disease in the population. Further research is imperative to understand how PRSs could cause adverse selection if consumers use this information to alter their insurance purchasing behaviour.

Keywords

Adverse selectionGeneticsPolygenic riskMultifactorial disorderLife insuranceCritical illness insurance
Type
Original Research Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 3 , November 2021 , pp. 488 - 503
Copyright
© Institute and Faculty of Actuaries 2020

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Footnotes

†

The online version of this article has been updated since original publication. A notice detailing the changes has also been published

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