Macdonald AS, Waters HR, Wekwete CT. The genetics of breast and ovarian cancer I: a model of family history. Scand. Actuarial J. 2003; 1: 1-27.We present a Markov model of breast cancer (BC) and ovarian cancer (OC) and estimate its transition intensities, mainly using United Kingdom population data. In the case of BC and OC, we estimate intensities according to BRCA1 and BRCA2 genotype. We use this to estimate the probabilities that an applicant for insurance has a BRCA1 or BRCA2 mutation, given complete or incomplete knowledge of her family history of BC and OC. Life (and other) insurance underwriters typically have incomplete knowledge of family history, for example no information on the number of healthy relatives. We show how these probabilities depend strongly on estimates of the mutation frequencies and penetrances, and conclude that it may not be appropriate to apply risk estimates based on studies of high-risk families to other groups. Key words: Breast cancer, BRCA1 gene, BRCA2 gene, family history, o6arian cancer, underwriting. Macdonald (1997Macdonald ( , 1999) proposed a Markov model for the study of genetics and insurance. The first application to specific genes was by Lemaire et al. (2000) and Subramanian et al. (1999) who studied life insurance underwriting in the presence of genetic tests for mutation in the BRCA1 and BRCA2 genes implicated in breast cancer (BC) and ovarian cancer (OC). The present study considers critical illness (CI) insurance in the presence of the same genetic tests. Life insurance and CI insurance present different problems, partly because of the maturity of the life insurance market compared with the CI market. INTRODUCTIONIn this paper, Part I, we describe the role of the BRCA1 and BRCA2 genes in Section 2. Consideration of the underwriting of a family history of BC or OC leads us to construct a model for the life history of a relative of a woman applying for insurance. We call this the relatives' model. It is described in Section 3, and the estimation of the transition intensities in Section 4.Given the model, we can compute conditional probabilities of the form:

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In Part I we constructed a model for the development of coronary heart disease (CHD) or stroke that either incorporates, or includes pathways through, the major risk factors of interest when underwriting for critical illness (CI) insurance. In Part II we extend this model to include other critical illnesses, for example, cancers and kidney failure, and describe some applications of the model. In particular, we discuss CI premium ratings for applicants with combinations of some or all of high body mass index, smoking, high blood pressure, high cholesterol, and diabetes. We also consider the possible effect on CI premium ratings of genetic conditions that increase the likelihood of high blood pressure, high cholesterol, diabetes, CHD event, or stroke.

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