Frontiers in Operations: Optimal Genetic Testing of Families

Abstract

Problem definition: Through the laws of inheritance, knowing an individual’s genetic status informs disease risk for family members, but current protocols for deciding whom to genetically test only consider one person at a time rather than design an optimal testing plan for the entire family. Methodology/results: We develop a Markov decision process framework for maximizing the net benefits of genetic testing that 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, that is, a plan that dynamically incorporates new test results, revealed sequentially at random, to decide who next to test. In the general case, we show that optimal stopping follows a structure with two-sided thresholds, previously known only for individual testing. Although the optimal testing sequence, in general, is contingent on the family test results, in the special case of sibling-only tests we can identify this sequence a priori. Our numerical case study, which was conducted in a realistic BRCA1/2 testing setting, demonstrates that an optimal policy significantly improves cost-effectiveness over existing policies. Thus, our framework offers a promising and powerful new approach to genetic testing. Managerial implications: In an optimal policy, prioritizing testing family members who might otherwise not have been tested can lead to an overall improvement in familial health value, surpassing even the most cost-effective existing protocols. From a management perspective, healthcare organizations and insurance companies can potentially save costs by implementing this approach for such families. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: D. Adelman is grateful for financial support from Booth School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0057 .