C  The Journal of Risk and Insurance, 2010, Vol. 77, No. 1, 155-182 DOI: 10.1111/j.1539-6975.2009.01342.x MULTIFACTORIAL GENETIC DISORDERS AND ADVERSE SELECTION:EPIDEMIOLOGY MEETS ECONOMICS Angus Macdonald Pradip Tapadar ABSTRACT The focus of genetics is shifting its contribution to common, complex dis- orders. New genetic risk factors will be discovered, which if undisclosed may allow adverse selection. However, this should happen only if low-risk individuals would reduce their expected utility by insuring at the average price. We explore this boundary, focusing on critical illness insurance and heart attack risk. Adverse selection is, in many cases, impossible. Otherwise, it appears only for lower risk aversion and smaller insured losses, or if the genetic risk is implausibly high. We find no strong evidence that adverse selection from this source is a threat. INTRODUCTION Risk and Insurance The principle behind underwriting is to identify key risk factors that stratify ap- plicants into reasonably homogeneous groups, for each of which the appropriate premium rate can be charged. The risk of death or ill health is affected by, among other things, age, gender, lifestyle, and genotype. However, the use of certain risk factors is sometimes controversial. In particular, this is true of factors over which individuals have no control, such as genotype. As a result, in many countries a ban has been imposed, or moratorium agreed, limiting the use of genetic information. In one country, the United Kingdom, a government-appointed Genetics and Insurance Committee (GAIC) is providing guidance to insurers on the acceptable use of genetic test results. Disorders caused by mutations in single genes, which may be severe and of late onset, but are rare, have been quite extensively studied in the insurance literature (see Macdonald, 2004), for a review. One reason is that the epidemiology of these Angus Macdonald is at the Department of Actuarial Mathematics and Statistics, and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, U.K. Pradip Tapadar is at the School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NF, U.K. The authors can be contacted via e-mail: A.S.Macdonald@ma.hw.ac.uk and P.Tapadar@kent.ac.uk. This work was carried out at the Genetics and Insurance Research Centre at Heriot-Watt University. We would like to thank the sponsors for funding and members of the Steering Committee for helpful comments at various stages. We would also like to thank an anonymous referee for very constructive comments. 155