Individual genome-wide polygenic risk scores (GPSs) for assessing disease susceptibility have been shown to yield both reliable and clinically meaningful results. However, certain impediments and outdated ways of thinking about health maintenance must be overcome before GPSs are adopted in routine care streams.
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References
Khera, A. V. et al. Nat. Genet. https://doi.org/10.1038/s41588-018-0183-z (2018).
Torkamani, A., Wineinger, N. E. & Topol, E. J. Nat. Rev. Genet. https://doi.org/10.1038/s41576-018-0018-x (2018).
Marigorta, U. M., Rodríguez, J. A., Gibson, G. & Navarro, A. Trends Genet. 34, 504–517 (2018).
Martin, A. R. et al. Am. J. Hum. Genet. 100, 635–649 (2017).
Yang, Q. et al. Am. J. Hum. Genet. 85, 786–800 (2009).
Desikan, R. S. et al. PLoS Med. 14, e1002258 (2017).
Patel, C. J. et al. Genome Med. 5, 58 (2013).
Schork, N. J. Genome Med. 5, 54 (2013).
Bloss, C. S., Schork, N. J. & Topol, E. J. N. Engl. J. Med. 364, 524–534 (2011).
Helgesson, G. J. Law Med. Ethics 42, 28–37 (2014).
Nguyen, T. N. et al. Am. Health Drug Benefits 9, 475–485 (2016).
Brainstorm Consortium et al. Science 360, eaap8757 (2018).
Wang, Y. et al. Mult. Scler. 22, 1783–1793 (2016).
Yokoyama, J. S. et al. JAMA Neurol. 73, 691–697 (2016).
Schork, N. J. & Topol, E. J. J. Biopharm. Stat. 20, 315–333 (2010).
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Schork, A.J., Schork, M.A. & Schork, N.J. Genetic risks and clinical rewards. Nat Genet 50, 1210–1211 (2018). https://doi.org/10.1038/s41588-018-0213-x
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DOI: https://doi.org/10.1038/s41588-018-0213-x
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