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1. nature
2. npj genomic medicine
3. perspectives
4. article

Future implications of polygenic risk scores for life insurance underwriting
Download PDF
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* Perspective
* Open access
* Published: 30 March 2024

FUTURE IMPLICATIONS OF POLYGENIC RISK SCORES FOR LIFE INSURANCE UNDERWRITING

* Tatiane Yanes1,
* Jane Tiller ORCID: orcid.org/0000-0003-3906-66322,
* Casey M. Haining3,
* Courtney Wallingford1,
* Margaret Otlowski4,
* Louise Keogh3,
* Aideen McInerney-Leo1 na1 &
* …
* Paul Lacaze ORCID: orcid.org/0000-0002-0902-67982 na1

Show authors

npj Genomic Medicine volume 9, Article number: 25 (2024) Cite this article

* 656 Accesses

* 21 Altmetric

* Metrics details

The use of genetic risk information in life insurance underwriting is a major
ethical, legal, and psychosocial concern1,2,3,4. Genetic discrimination (GD) is
defined as the “differential treatment of asymptomatic individuals or their
relatives on the basis of real or assumed genetic differences or
characteristics”3. In life insurance underwriting, GD stems from the use of
genetic risk information to deny coverage, increase premiums, or place
conditions on products such as disability, death, trauma and income protection
cover5. Reports of insurance discrimination among individuals with rare variants
in monogenic risk genes are well described, including insurance providers
denying coverage or increasing premiums based on positive results, even when
individuals take steps to mitigate risk5,6,7,8,9. However, it is difficult to
quantify the prevalence of GD due to methodological challenges such as ability
to obtain data from insurance industry, thus, most research to date has relied
on self-reported experiences. Nevertheless, fear of GD remains a deterrent to
uptake of genetic testing in clinical and research
settings6,7,8,9,10,11,12,13,14. With the emergence and increased use of new
genetic technologies it is essential that we consider the unique challenges that
may arise regarding GD.

An emerging genomic technology that is likely to present new challenges in GD is
polygenic scores (PGS). PGS provides an estimate of the genetic liability to
health conditions and is typically calculated based on the cumulative impact of
multiple disease-associated common genetic variants or single nucleotide
polymorphisms (SNPs), derived from genome-wide association studies (GWAS)15,16.
Several articles have considered the future clinical implications of
PGS17,18,19,20,21,22, but few have considered insurance implications
specifically23. PGS has the potential to disrupt the insurance industry given
its broad use in risk-stratification for common complex health conditions17.
Furthermore, there is emerging evidence that the risk of insurance
discrimination may negatively impact willingness to undertake PGS testing and
participate in research24. Thus, the increased use of PGS and its possible
impact on life insurance underwriting warrants further consideration. In this
article, we explore the current measures to address GD in insurance underwriting
globally, issues of GD arising from PGS use, and argue that the increased
availability of PGS could shift the way insurers utilize genetic risk
information. As life insurance is the risk-rated product that has been most
frequently evaluated in the context of GD it is the focus of this article.

INTERNATIONAL MEASURES TO ADDRESS GENETIC DISCRIMINATION IN INSURANCE

Box 1 provides a definition of community vs risk-rated insurance, which provides
important context in understanding the impact of genetic information in
insurance underwriting. Internationally, various regulatory measures have been
introduced to address GD in insurance underwriting2,25,26,27,28,29 (see Table 1
for some examples). These measures range from soft forms of regulation such as
industry-led moratoria (e.g., Australia)26 and voluntary agreements between
governments and industry (e.g., UK)25, to relatively more robust regulatory
responses in the form of legislation as found in Canada27. In addition to the
variation in the type of regulation, the scope of the protection each regime
offers varies. For instance, some protections only apply to certain types of
insurance (e.g., the federal US protection extends only to health insurance and
employment, not life insurance)28, and others only apply within prescribed
financial limits (e.g., Australia’s moratorium). Other countries, such as New
Zealand, currently do not have any protections against the use of genetic
information in health or life insurance8. The Australian Government recently
recognized the level of community concern in Australia about genetic
discrimination in life insurance30, and conducted a consultation on options to
address the issue (concluded 31 January 2024)31, which received over 1000
stakeholder submissions to Treasury (Tiller J., Personal Communication Treasury
Department, Feb 06, 2024).

Table 1 Overview of genetic discrimination protective regimes for Australia,
Canada, USA, and United Kingdom and potential applicability to PGS
Full size table

BOX 1 IMPORTANT CONTEXT FOR UNDERSTANDING INSURANCE PRODUCTS

Insurance products are either community-rated or risk-rated55.

* Community rated: each consumer is charged the same premiums. In many
countries, health insurance is community rated.

* Risk-Rated: differentiation of premiums based on individual consumer risk
assessment, which can include genetic risk information. Typically applies for
life, disability, long-term care, and travel insurance.

POLYGENIC SCORES (PGS) IN CLINICAL PRACTICE

PGS is best considered as a risk-stratification or screening tool rather than
diagnostic, and it can be used to predict the possibility of health conditions
or behavioral traits. There are various reported uses of PGS that include
informing population screening programs for common complex conditions, such as
cancers, heart disease and diabetes17. Testing for PGS can also be used to
inform treatment and risk management strategies, predict diagnostic outcomes,
and modify risk for monogenic conditions17. Given the broad use of PGS, it is
important to consider the context in which the information is being used. For
example, the predictive ability of the PGS is bounded by the heritability of the
condition of interest, and therefore may be less useful for conditions with low
heritability32. Furthermore, PGS estimates are calculated based on data derived
from GWAS. Currently, >80% of GWAS data has been obtained from European
populations, thereby limiting the predictive performance of PGS to non-European
populations32,33. There is strong evidence for the clinical validity of PGS
(i.e., the test’s ability to accurately and consistently predict outcomes of
interest), while clinical utility is yet to be determined (i.e., the test’s
ability to improve health outcomes)32,34. Nevertheless, consumers are
increasingly accessing PGS testing through direct-to-consumer companies and
third-party providers35,36, clinical research37, and commercial genetic testing
companies17,18.

Implementation of PGS has the capacity to change the way insurers consider and
use genetic information. The life insurance industry is already aware of the
potential impact of PGS in healthcare and has identified PGS implementation as a
possible challenge for the insurance industry38,39. Specifically, industry
commentators have noted the increased use of genetic testing in the population,
and have proposed potential solutions, such as applying a community rating
structure where assessments are pooled to support claims for conditions that
have a high genetic burden, rather than using an individual risk-rated approach
to underwriting39. Additionally, using an aggregate PGS for 27 common conditions
in an elderly population, Linnér et al.23, reported a 2.6-year shorter median
lifespan in the highest decile group and proposed that this data could be used
to improve mortality risk classification in life insurance. However, mortality
estimates are complex and not easily explained by PGS. Early research suggested
PGS have a fairly moderate predictive capacity, and that a substation proportion
of the associated risk is accounted by common mortality risk factors already
measured in middle age40,41.

CONSIDERATIONS OF PGS AND LIFE INSURANCE UNDERWRITING

INCREASED ACCESSIBILITY OF GENETIC RISK ASSESSMENTS

Traditionally, genetic testing has been used to identify the <5% of the general
global population suspected to have a rare monogenic condition42,43. Guidelines
for monogenic testing vary between countries, organizations, and
conditions44,45,46. However, most criteria for publicly funded genetic testing
(or testing through insurance providers) include risk assessments to identify
those most likely to carry pathogenic variants in disease risk genes. Only a
small portion of those at risk for developing a condition are targeted for
genetic testing, limiting the number of individuals whose genetic test results
might then be used in life insurance underwriting. Conversely, PGS have much
broader clinical application (e.g., population screening programs, and augment
monogenic testing17) and can be developed for most health conditions and
heritable traits (such as obesity47). Widespread implementation of PGS will
result in genetic risk assessments accessible to most of the population across
various settings, potentially amplifying GD in insurance underwriting.

Current GD protections tend to apply to use of ‘genetic tests’ (Table 1), which
is broadly defined in the various protective regimes (e.g., tests that examine
chromosomes and DNA). Some commentators have argued that the broadness of this
definition makes it unclear what types of genetic testing (and hence protection)
are captured48. It is possible, in the absence of guidance to the contrary, that
current protections may extend to PGS. However, the current lack of clarity is
undesirable given that PGS has the potential to increase the volume and
diversity of genetic results available to insurers. If no additional consumer
protections are introduced, there is a danger that PGS will amplify the risk and
frequency of GD in life insurance underwriting.

PGS AS A NASCENT RISK PREDICTION TOOL

Despite commercial availability, there are currently no best practice guidelines
for developing and reporting PGS, and evidence for clinical utility is still
emerging15,16,32. Several professional organizations have released position
statements on the use of PGS in clinical practice, which commonly acknowledge
the potential benefits of PGS, while urging for caution given the limited
evidence for its clinical utility49,50,51. Statistical methods for calculating
PGS are constantly being improved and new GWAS data is being generated. The lack
of ancestry diversity in GWAS, resulting in reduced predictive performance of
PGS in non-European populations, is widely recognized as a major limitation of
PGS33. As such, an individual’s PGS today may differ from one calculated in the
future due to changes to the methodology, new GWAS data, and improvements in
ancestry data, which could result in different risk classifications and altered
medical advice for individuals52.

A PGS is a standalone risk factor, which does not typically consider the impact
of rare monogenic variants or clinical and lifestyle risk factors16. To account
for additional risk factors, PGS is being integrated into comprehensive risk
assessment models, such as the CanRisk tool that provides personalized breast
cancer and ovarian risk based on monogenic, polygenic, family history, clinical
and lifestyle factors53,54. Such complex risk prediction tools increase the
likelihood of risk estimates changing over time. Importantly, these tools
reflect the reality that PGS is not diagnostic information. There is a real
concern that insurance providers will seize the opportunity to use PGS alone to
classify a person’s risk and exclude individuals they consider “high risk”,
without considering the remaining dynamic risk factors. Lastly, it is important
to note that no one person will have a low genetic risk for all possible health
conditions and traits, and it is not known how different conditions and traits
would be weighted by life insurance providers.

POTENTIAL FOR MISINTERPRETATION

Given the nascent state of PGS, there is significant potential for
misinterpretation and misuse of the PGS information by life insurance providers
(Box 2). Despite monogenic testing being available for more than 25 years, there
is evidence that insurance providers still misinterpret results and have failed
to consider the impact of risk-reduction strategies in underwriting5,55,56,57.
Compared to monogenic testing, a PGS is substantially more complex, and
interpretation requires comprehension of genetic and epidemiological concepts.
Aspects of PGS that have the potential for misinterpretation include failure to
appreciate the risk assessment nature of PGS, its limitations for non-European
populations, and limited predictive ability across family members (Box 1).
Research has shown that even genetics professionals currently struggle to
interpret and explain PGS given the lack of existing education and clinical
guidelines for this test58,59. As such, it is anticipated that insurance
underwriters would also have difficulties interpreting and using this
information in risk assessment. As all stakeholders are unlikely to understand
the nuances of a PGS, especially in the early days of implementation, careful
consideration needs to be given to how risk information is delivered to mitigate
both the potential for insurance provider misinterpretation and exacerbation of
GD in life insurance20.

BOX 2 POTENTIAL AREAS FOR MISUNDERSTANDING AND MISUSE OF PGS INFORMATION BY
INSURANCE PROVIDERS

* Misunderstanding PGS as a diagnostic tool, rather than risk stratification:
Insurance providers may fail to understand PGS as a screening tool, whereby
the degree in which the PGS predicts disease risk is based on various
factors, such as disease/trait heritability, background population risk, the
statistical methodology used to generate the PGS, and impact of other genetic
and non-genetic risk factors16,17,19. Despite insurance underwriters’
expertise in assessing risk factors and conducting complex risk assessments,
concern remains regarding the scientific and medical complexity of PGS and
the ability of underwriters to interpret PGS without specialist training.

* Failure to appreciate limitations of PGS across diverse populations: The
validity of PGS is inherently dependent on the quality of the GWAS data on
which it was based. Currently, greater than 80% of GWAS data has been
obtained from populations of European ancestry68, resulting in PGS that have
reduced predictive performance in individuals from other ancestries.
Insurance providers may fail to consider the impact of ancestry on PGS, which
in turn may compromise the accuracy of their risk estimates.

* Misinterpretation of risk for family members: Genetic testing has
traditionally been considered within a familial context, with Mendelian
inherence patterns used to estimate risk to relatives. Further, insurance
underwriting models also use genetic information, including a family history
of disease as a predictor of risk for related individuals. However, current
PGS are personalized and cannot be used to estimate risk for close relatives’
results. While there is some association between siblings PGS, this
relationship and predictive capacity becomes weak for parents and 2nd degree
relatives69,70. It is possible insurance companies will interpret familial
risk based on PGS, potentially leading to further insurance discrimination.

ARGUMENTS BY INSURANCE COMPANIES

The insurance industry commonly raises concerns about adverse selection and the
impact of risk prediction on the affordability of insurance60,61. When the
Canadian Genetic Nondiscrimination Act 2017 was being considered, the Canadian
Privacy Commissioner commissioned two statistical experts to conduct modeling to
consider the potential impact of banning the use of genetic test results in life
and health insurance62,63. Both found that the impact of a ban on the insurance
market in the medium term would be negligible. No modeling was conducted at the
time regarding the impact of PGS on insurance affordability, and such studies
would be worthwhile. We anticipate that although PGS would be relevant to the
entire population, the lower predictive value relative to monogenic tests means
that the results are not deterministic, and thus, the impact on the market is
not likely to be substantial17.

Arguments about adverse selection become less significant when considering
population-level risk stratification. Adverse selection refers to the notion
that people at higher risk will take out more expensive policies, therefore
skewing the affordability of insurance for all64. However, if PGS is used as a
population-level risk stratification tool, every person in the population is
likely to have higher PGS for some disease types and lower PGS for others.
Furthermore, ethically, we note that insurance is supposed to be a risk-pooling
exercise, not an exercise in eliminating high-risk individuals from the risk
pool65.

MOVING FORWARD

As PGS is increasingly utilized in research and clinical practice, it is pivotal
that careful consideration is given to the potential insurance implications of
PGS to ensure consumer protection against GD. For the full potential benefits of
PGS to be realized, and its clinical utility determined across various use
cases, individuals will need to be confident that they can participate in
research studies and access clinical genetic testing without fear of insurance
discrimination. Clarification is needed regarding the extent to which existing
protections and legislation relating to monogenic testing may also extend to PGS
test results. Given there is little enforceable protection against GD in life
insurance in various countries (Table 1) further legislative protection should
be introduced, which clearly includes PGS in its protection. Additionally, clear
guidelines, best practice protocols, and training are needed to support accurate
interpretation of genetic risk information among insurance providers and
minimize the risk of misinterpreting results. Finally, further research is
needed to evaluate future issues of GD arising from PGS implementation.

RECOMMENDATIONS

The ethical, legal, and social issues described above highlight a pressing need
for improved consumer protection, and improved implementation research to
support the equitable implementation of PGS into clinical practice. In our view,
the use of PGS as a tool to further deny coverage is both ethically questionable
and actuarially problematic. We recommend that a ban on the use of PGS results
in risk-rated insurance underwriting should be introduced. This recommendation
stands alongside calls to prohibit the use of genetic test results more broadly
in life insurance underwriting, which authors of this paper and others have
made66. Consideration should be given to the most appropriate regulatory tools
to achieve this end in each jurisdiction, given the human genetics field is
rapidly evolving67. At a minimum, it is crucial that:

* any regulation creates enforceable remedies for individuals and is subject to
independent oversight by a body with meaningful sanction powers;

* any regulation has sufficient flexibility to respond adequately to advances
in the field of genetics;

* all current regulations/consumer protections explicitly apply to both
monogenic testing and PGS (or are amended to provide protection where it is
determined that they do not apply); and

* insurers are educated about the limitations of PGS as risk prediction tools.

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Download references

ACKNOWLEDGEMENTS

The project is supported by a grant from the Australian Government’s Medical
Research Future Fund (MRFF), ref 76721. T.Y. is funded by a National Health and
Medical Research Council (NHMRC) EL1 Grant (APP2009136). C.W. is supported by an
Australian Government Research Training Program Scholarship. During this
project, A.M.L. held an NHMRC Early Career Fellowship (APP1158111) and is
currently supported by a University of Queensland Faculty of Medicine
Fellowship. P.L. is supported by a National Heart Foundation Future Leader
Fellowship (ID 102604).

AUTHOR INFORMATION

Author notes

1. These authors jointly supervised this work: Aideen McInerney-Leo, Paul
Lacaze.

AUTHORS AND AFFILIATIONS

1. Frazer Institute, The University of Queensland, Dermatology Research Centre,
Brisbane, QLD, Australia

Tatiane Yanes, Courtney Wallingford & Aideen McInerney-Leo

2. Department of Epidemiology and Preventive Medicine, School of Public Health
and Preventive Medicine, Monash University, Melbourne, Australia

Jane Tiller & Paul Lacaze

3. Centre for Health Equity, Melbourne School of Population and Global Health,
University of Melbourne, Victoria, Australia

Casey M. Haining & Louise Keogh

4. Centre for Law and Genetics, Faculty of Law, University of Tasmania,
Churchill Avenue, Hobart, Tasmania, Australia

Margaret Otlowski

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All authors conceptualized this paper. T.Y. wrote the original draft and authors
J.T. and C.H. generated Table 1. All authors reviewed and approved the final
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Yanes, T., Tiller, J., Haining, C.M. et al. Future implications of polygenic
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https://doi.org/10.1038/s41525-024-00407-x

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SUBJECTS

* Genomics
* Health policy

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* Sections
* References

* International measures to address genetic discrimination in insurance
* Polygenic scores (PGS) in clinical practice
* Considerations of PGS and life insurance underwriting
* References
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Article CAS PubMed PubMed Central Google Scholar

58. Smit, A. K. et al. Knowledge, views and expectations for cancer polygenic
risk testing in clinical practice: a cross-sectional survey of health
professionals. Clin. Genet. 100, 430–439 (2021).

Article CAS PubMed Google Scholar

60. Armstrong, K. et al. Life insurance and breast cancer risk assessment:
adverse selection, genetic testing decisions, and discrimination. Am. J.
Med. Genet. A 120, 359–364 (2003).

Article Google Scholar

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Article Google Scholar

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and Health Insurance Context. In: Canada OotPCo, editor. (2011).

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genetic information for life and health insurance. In: Canada OotPCo,
editor. (2012).

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markets. J. Risk Insurance 77, 39–84 (2010).

Article Google Scholar

65. Baumann, J. & Loi, M. Fairness and risk: an ethical argument for a group
fairness definition insurers can use. Philos. Technol. 36, 45 (2023).

Article PubMed PubMed Central Google Scholar

66. Tiller, J. et al. Final Stakeholder Report of the Australian Genetics and
Life Insurance Moratorium: Monitoring the Effectiveness and Response
(A-GLIMMER) Project; 2023.

67. Golru, S. Regulating the Use of Genetic Information in the Life Insurance
Industry. UNSW Law Journal. Forum 7, 1–18 (2020).

Google Scholar

68. Martin, A. R. et al. Clinical use of current polygenic risk scores may
exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

Article CAS PubMed PubMed Central Google Scholar

Article PubMed PubMed Central Google Scholar

70. Cox, D. G., Heudel, P. E., Henry, J. & Pivot, X. Transmission of breast
cancer polygenic risk based on single nucleotide polymorphisms. Breast 41,
14–18 (2018).

Article PubMed Google Scholar

npj Genomic Medicine (npj Genom. Med.) ISSN 2056-7944 (online)

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