Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?

Abstract

Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed.

Keywords: DNA; autonomy; calibration; discrimination; personal genomics; polygenic; prediction; regulation; risk; transparency.

Figure 1

Risk distributions of four different polygenic algorithms for a hypothetical disease that affects 20% of the population. (a) Uninformative polygenic algorithm that predicts a 20% risk of disease for everyone; (b) Perfect polygenic algorithm that predicts a 100% risk for the 20% of the people who will develop the disease and 0% risk for the other 80%; (c,d) Polygenic algorithms with lower (c) and higher (d) predictive ability.