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Application of a Polygenic Model of Breast and Ovarian Cancer to Critical Illness Insurance

Published online by Cambridge University Press:  10 May 2011

A. S. Macdonald  and
K. R. McIvor
A. S. Macdonald
Affiliation:
Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, U.K., Email: [email protected]
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Abstract

Mutations in the BRCA1 and BRCA2 genes confer very high risk of breast cancer (BC), but only account for about 25% of the observed familial clustering of BC. Antoniou et al. (2002) proposed a model which included the BRCA1 and BRCA2 genes, and a polygenic component which acted multiplicatively on the rate of onset of BC. We use this model to find premium rates for critical illness insurance: (a) given knowledge of an applicant's polygenotype; and (b) given knowledge of a family history of BC or ovarian cancer. We find that the polygenic component causes large variation in premium rates even among non-mutation carriers, therefore affecting the whole population. In some cases the polygenic contribution is protective enough to reduce or remove the additional risk of a BRCA1/2 mutation, leading to cases where it will be advantageous to disclose genetic test results which are adverse in absolute terms. Premiums based on family history are lower than those found in an earlier study which attributed all genetic BC risk to the BRCA1/2 genes.

Keywords

PolygenotypeBreast CancerCritical Illness InsuranceBRCA1BRCA2Ovarian Cancer
Type
Papers
Information
Annals of Actuarial Science , Volume 1 , Issue 2 , September 2006 , pp. 319 - 343
Copyright
Copyright © Institute and Faculty of Actuaries 2006

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References

Antoniou, A.C., Pharoah, P.D.P., McMullan, G., Day, N.E., Ponder, B.J. & Easton, D.F. (2001). Evidence for further breast cancer susceptibility genes in addition to BRCA1 and BRCA2 in a population-based study. Genetic Epidemiology, 21, 1–18.CrossRefGoogle ScholarPubMed
Antoniou, A.C., Pharoah, P.D.P., McMullan, G., Day, N.E., Stratton, M.R., Peto, J., Ponder, B.J. & Easton, D.F. (2002). A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. British Journal of Cancer, 86, 76–83.CrossRefGoogle ScholarPubMed
Antoniou, A.C., Pharoah, P.P.D., Narod, S., Risch, H.A., Eyfjord, J.E., Hopper, J.L., Loman, N., Olsson, H., Johannsson, O., Borg, A., Pasini, B., Radice, P., Manoukian, S., Eccles, D.M., Tang, N., Olah, E., Anton-Culver, H., Warner, E., Lubinski, J., Gronwald, J., Gorski, B., Tulinius, H., Thorlacius, S., Eerola, H., Nevanlinna, H., Syrjäkoski, K., Kallioniemi, O.-P., Thompson, D., Evans, C., Peto, J., Lalloo, F., Evans, D.G. & Easton, D.F. (2003). Average risks of breast and ovarian cancer associated with mutations in BRCA1 or BRCA2 detected in case series unselected for family history: a combined analysis of 22 studies. American Journal of Human Genetics, 72, 1117–1130.Google Scholar
Cannings, C., Thompson, E.A. & Skolnick, M.H. (1978). Probability functions on complex pedigrees. Advances in Applied Probability, 10(1), 26–61.Google Scholar
Easton, D.F. (1999). How many more breast cancer predisposition genes are there? Breast Cancer Research, 1, 14–17.Google Scholar
Easton, D.F. (2005). Finding new breast cancer genes. Presentation at the University of Sheffield.Google Scholar
Ford, D., Easton, D.F., Stratton, M., Narod, S., Goldgar, D., Devilee, P., Bishop, D.T., Weber, B., Lenoir, G., Chang-Claude, J., Sobol, H., Teare, M.D., Struewing, J., Arason, A., Scherneck, S., Peto, J., Rebbeck, T.R., Tonin, P., Neuhausen, S., Barkardottir, R., Eyfjord, J., Lynch, H., Ponder, B.A.J., Gayther, S.A., Birch, J.M., Lindblom, A., Stoppa-Lyonnet, D., Bignon, Y., Borg, A., Hamann, U., Haites, N., Scott, R.J., Maugard, C.M., Vasen, H., Seitz, S., Cannon-Albright, L.A., Schofield, A., Zelada-Hedman, M. and the Breast Cancer Linkage Consortium (1998). Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families. American Journal of Human Genetics, 62, 676–689.Google Scholar
Gui, E.H., Lu, B., Macdonald, A.S., Waters, H.R. & Wekwete, C.T. (2006). The genetics of breast and ovarian cancer III: a new model of family history. Scandinavian Actuarial Journal (to appear).Google Scholar
Gutiérrez, M.C. & Macdonald, A.S. (2003). Adult polycystic kidney disease and critical illness insurance. North American Actuarial Journal, 7(2), 93–115.CrossRefGoogle Scholar
Hoem, J.M. (1988). The versatility of the Markov chain as a tool in the mathematics of life insurance. Transactions of the 23rd International Congress of Actuaries, Helsinki, S, 171–202.Google Scholar
Lange, K. (1997). An approximate model of polygenic inheritance. Genetics, 147, 1423–1430.CrossRefGoogle ScholarPubMed
Lemaire, J., Subramanian, K., Armstrong, K. & Asch, D.A. (2000). Pricing term insurance in the presence of a family history of breast cancer. North American Actuarial Journal, 4, 75–87.CrossRefGoogle Scholar
Macdonald, A.S., Waters, H.R. & Wekwete, C.T. (2003a). The genetics of breast and ovarian cancer I: A model of family history. Scandinavian Actuarial Journal, 1–27.CrossRefGoogle Scholar
Macdonald, A.S., Waters, H.R. & Wekwete, C.T. (2003b). The genetics of breast and ovarian cancer II: A model of critical illness insurance. Scandinavian Actuarial Journal, 28–50.Google Scholar
ONS (1999). Cancer 1971–1997. CD-ROM, Office for National Statistics, London.Google Scholar
Rebbeck, T.R. (1999). Inherited genetic predisposition in breast cancer. A population-based perspective. Cancer, 86, 2493–2501.3.0.CO;2-Z>CrossRefGoogle ScholarPubMed
Struewing, J.P. (2004). Genomic approaches to identifying breast cancer susceptibility factors. Breast Disease, 19, 3–9.Google Scholar
Subramanian, K., Lemaire, J., Hershey, J.C., Pauly, M.V., Armstrong, K. & Asch, D.A. (1999). Estimating adverse selection costs from genetic testing for breast and ovarian cancer: the case of life insurance. The Journal of Risk and Insurance, 66, 531–550.CrossRefGoogle Scholar