The Human Genetics Commission intends to study the use of family history in underwriting, regarding it as genetic information in a broad sense. We survey the developments in genetics and insurance that led up to this decision. We propose a model for studying the possible costs of adverse selection in the mortgage-related life insurance market (which is important in the United Kingdom) with and without moratoria on the use of family history. We consider both level and decreasing term assurances, finding that the cost of adverse selection is slightly higher for the latter. Several assumptions about mutation penetrance, incidence of additional mortality, prevalence of genetic testing and insurance-buying behaviour are considered. Overall we conclude that: (a) a moratorium on genetic test results alone will lead to negligible adverse selection costs; while (b) a moratorium extended to family history will lead to premium increases just by redefining underwriting classes, with adverse selection being possible as well, but only in extreme circumstances would all premium increases be likely to exceed about 10%. However, we give examples in a model of a smaller life insurance market that show that our benign conclusions might not apply more generally.

Hereditary nonpolyposis colorectal cancer (HNPCC) is characterised by the familial aggregation of cancer of the colon and rectum (CRC). It may be caused by any of five mutations in DNA mismatch repair (MMR) genes or by non-genetic factors, such as life style. However, it accounts for only about 2% of CRC, which is a very common cancer. Previous actuarial models, of diseases with only genetic causes, assumed that a family history of the disease shows mutations to be present, but this is not true of HNPCC. This is a significant limitation, since the best information available to an underwriter (especially if the use of genetic test results is banned) is likely to be knowledge of a family history of CRC. We present a Markov model of CRC and HNPCC, which includes the presence of a family history of CRC as a state, and estimate its intensities allowing for MMR genotype. Using this we find the MMR mutation probabilities for an insurance applicant with a family history of CRC. Our model greatly simplifies the intensive computational burden of finding such probabilities by integrating over complex models of hidden family structure. We estimate the costs of critical illness insurance given the applicant's genotype or the presence of a family history. We then consider what the cost of adverse selection might be, if insurers are unable to use genetic tests or family history information. We also consider the effect of using alternative definitions of a family history in underwriting.