Genetic risk scores in life insurance underwriting

Richard Karlsson Linnér*, Philipp D. Koellinger

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.

Original languageEnglish
Article number102556
Pages (from-to)1-15
Number of pages15
JournalJournal of Health Economics
Volume81
Early online date15 Nov 2021
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information:
The research was approved by the Research Ethics Review Board (RERB) at the School of Business and Economics (SBE) of Vrije Universiteit Amsterdam (20180927.1.pkr730). We gratefully acknowledge Netspar for financially supporting this research project with a topicality grant (BD2018.01) and thank Bas Werker and Anja de Waegenaere for their valuable feedback on the research questions, study design, and draft manuscript. The study was financially supported by an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe). This work was carried out on the Dutch national e-infrastructure (grant 17148) with support of the SURF Cooperative. We are thankful to Casper Burik for constructing the genetic PCs and to Aysu Okbay for a providing a meta-analysis of educational attainment that excluded the Health and Retirement Study (HRS). We thank Jonathan Beauchamp for valuable input on the draft manuscript, and the Editor and anonymous referees for their helpful and constructive feedback.

Funding Information:
HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029). Genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University in Baltimore, Maryland. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington in Seattle, Washington. Genotype data can be accessed via the database of Genotypes and Phenotypes (dbGaP, http://www.ncbi.nlm.nih.gov/gap , accession number phs000428.v2.p2). Researchers who wish to link genetic data with other HRS measures that are not in dbGaP, such as educational attainment, must apply to the HRS for permission. See the HRS website ( http://hrsonline.isr.umich.edu/gwas ) for details.

Funding Information:
The research was approved by the Research Ethics Review Board (RERB) at the School of Business and Economics (SBE) of Vrije Universiteit Amsterdam (20180927.1.pkr730). We gratefully acknowledge Netspar for financially supporting this research project with a topicality grant (BD2018.01) and thank Bas Werker and Anja de Waegenaere for their valuable feedback on the research questions, study design, and draft manuscript. The study was financially supported by an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe). This work was carried out on the Dutch national e-infrastructure (grant 17148) with support of the SURF Cooperative. We are thankful to Casper Burik for constructing the genetic PCs and to Aysu Okbay for a providing a meta-analysis of educational attainment that excluded the Health and Retirement Study (HRS). We thank Jonathan Beauchamp for valuable input on the draft manuscript, and the Editor and anonymous referees for their helpful and constructive feedback. HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029). Genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University in Baltimore, Maryland. Genotyping quality control and final preparation of the data were performed by the Genetics Coordinating Center at the University of Washington in Seattle, Washington. Genotype data can be accessed via the database of Genotypes and Phenotypes (dbGaP, http://www.ncbi.nlm.nih.gov/gap, accession number phs000428.v2.p2). Researchers who wish to link genetic data with other HRS measures that are not in dbGaP, such as educational attainment, must apply to the HRS for permission. See the HRS website (http://hrsonline.isr.umich.edu/gwas) for details.

Publisher Copyright:
© 2021 The Author(s)

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