Optimal Genetic Testing of Families
Through the laws of inheritance, knowing an individual's genetic status informs disease risk for family members, but current protocols for deciding who to genetically test only consider one person at a time rather than design an optimal testing plan for the entire family. We develop a Markov decision process framework for maximizing the net-benefits of genetic testing, which integrates a Bayesian network of genetic statuses, with a functional representation of cost-effectiveness. Our model provides a contingent sequence of family members to test one-at-a-time, i.e., a plan that dynamically incorporates new test results, revealed sequentially at random, to decide who next to test. Using the BRCA1/2 genes as a test case, we show that our policy can simultaneously increase quality-adjusted life years while decreasing testing costs, resulting in substantial benefits to social welfare. Thus, our framework offers a promising and powerful new approach to genetic testing of populations.