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Open AccessCommentary

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

Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA
Genes 2019, 10(6), 448; https://doi.org/10.3390/genes10060448
Received: 22 May 2019 / Revised: 8 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Algorithms for Personal Genomics)
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. View Full-Text
Keywords: personal genomics; DNA; polygenic; risk; regulation; discrimination; calibration; prediction; transparency; autonomy personal genomics; DNA; polygenic; risk; regulation; discrimination; calibration; prediction; transparency; autonomy
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Janssens, A.C.J. Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It? Genes 2019, 10, 448.

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