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The impact of personal genomics on risk perceptions and medical decision-making

Nature Biotechnology volume 34, pages 912–918 (2016)Cite this article

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To the Editor:

The question of whether direct-to-consumer (DTC) personal genomic testing (PGT) will lead to effective preventative medicine instead of a “raiding of the medical commons”1 or inappropriate changes to medical treatment has spurred debate about regulations for PGT. But despite scientific progress and a high-profile regulatory engagement, there is a dearth of empirical evidence on how such services might influence participant decision-making. On one hand, better information about genomic disease risks seems likely to empower consumers and add to the physician's toolkit, encouraging rational screening behavior and monitoring of disease symptoms. On the other hand, however, this information might mislead or alarm consumers, resulting in overuse of medical resources.

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Figure 1: Risk perception changes by risk result group.
Figure 2: Risk perception changes by risk result group and medical condition.
Figure 3: Risk perception changes by risk result group and motivation for testing.
Figure 4: Medical utilization according to maximum and median risk perception change.

References

  1. McGuire, A.L. & Burke, W. J. Am. Med. Assoc. 300, 2669–2671 (2008).

    Article  CAS  Google Scholar 

  2. Bloss, C.S., Schork, N.J. & Topol, E.J. N. Engl. J. Med. 364, 524–534 (2011).

    Article  CAS  Google Scholar 

  3. Bloss, C.S., Topol, E.J. & Schork, N.J. Genet. Epidemiol. 36, 66–70 (2012).

    Article  Google Scholar 

  4. Christensen, K.D., Roberts, J.S., Uhlmann, W.R. & Green, R.C. Genet. Med. 13, 409–414 (2011).

    Article  Google Scholar 

  5. Green, R.C. et al. N. Engl. J. Med. 361, 245–254 (2009).

    Article  CAS  Google Scholar 

  6. Kaufman, D.J., Bollinger, J.M., Dvoskin, R.L. & Scott, J.A. J. Genet. Couns. 21, 413–422 (2012).

    Article  Google Scholar 

  7. Ostergren, J.E. et al. Public Health Genomics 18, 216–224 (2015).

    Article  Google Scholar 

  8. Roberts, J.S. & Ostergren, J. Curr. Genet. Med. Rep. 1, 182–200 (2013).

    Article  Google Scholar 

  9. Shiloh, S. et al. Clin. Genet. 87, 117–123 (2015).

    Article  CAS  Google Scholar 

  10. Bansback, N., Sizto, S., Guh, D. & Anis, A.H. Genet. Test. Mol. Biomarkers 16, 1165–1171 (2012).

    Article  Google Scholar 

  11. Oster, E., Shoulson, I. & Ray Dorsey, E. Am. Econ. Rev. 103, 804–830 (2013).

    Article  Google Scholar 

  12. Oster, E., Shoulson, I., Quaid, K. & Ray Dorsey, E. J. Public Econ. 94, 1041–1050 (2010).

    Article  Google Scholar 

  13. Eil, D. & Rao, J.M. Am. Econ. J. Microecon. 3, 114–138 (2011).

    Article  Google Scholar 

  14. Moutsiana, C. et al. Proc. Natl. Acad. Sci. USA 110, 16396–16401 (2013).

    Article  CAS  Google Scholar 

  15. Sharot, T. et al. Proc. Natl. Acad. Sci. USA 109, 17058–17062 (2012).

    Article  CAS  Google Scholar 

  16. Sharot, T., Korn, C.W. & Dolan, R.J. Nat. Neurosci. 14, 1475–1479 (2011).

    Article  CAS  Google Scholar 

  17. Tversky, A. & Kahneman, D. Science 185, 1124–1131 (1974).

    Article  CAS  Google Scholar 

  18. Kuhnen, C.M. J. Finance 70, 2029–2062 (2015).

    Article  Google Scholar 

  19. Ellsberg, D. Q. J. Econ. 75, 643–669 (1961).

    Article  Google Scholar 

  20. Köszegi, B. J. Health Econ. 22, 1073–1084 (2003).

    Article  Google Scholar 

Download references

Acknowledgements

We thank Tali Sharot, Alec Brandon, Jean Tirole, Alexandra Rodman, Daniel Fehder, Abhishek Nagaraj, Rebecca Grunberg, Roberto Fernandez, Sarah Kalia and members of the PGen Study Group (particularly Kurt Christensen, Deanna Alexis Carere, and Joanna L. Mountain). Members of the PGen Study Group are listed in Supplementary Note 2. The PGen Study is supported by the US National Institutes of Health (NIH) National Human Genome Research Institute (R01-HG005092). R.C.G. was additionally supported by the following NIH grants: U01-HG006500, U19-HD077671, U01-HG008685 and U41-HG006834.

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Authors and Affiliations

  1. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Joshua L Krieger & Fiona Murray

  2. Department of Health Behavior & Health Education, University of Michigan School of Public Health, Ann Arbor, Michigan, USA

    J Scott Roberts

  3. Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Partners HealthCare Personalized Medicine, the Broad Institute and Harvard Medical School, Boston, Massachusetts, USA

    Robert C Green

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  1. Joshua L Krieger
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Contributions

J.L.K. formulated the research question, designed and performed the analyses, prepared the figures and wrote the manuscript. F.M. helped with the research design, analyses, and manuscript writing. R.C.G. and J.S.R. designed and implemented the surveys, in their roles as the primary investigators of the Impact of Personal Genomics (PGen) study, and advised on the preparation of the figures and the manuscript. All authors edited and approved the manuscript.

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Correspondence to Joshua L Krieger.

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Competing interests

R.C.G. receives personal compensation for speaking or consulting from AIA, Helix, Illumina, Invitae, Prudential and Roche.

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Editor's note: This article has been peer-reviewed.

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Supplementary Text and Figures

Supplementary Methods, Supplementary Notes 1 and 2, Supplementary Figures 1–5 and Supplementary Tables 1–9 (PDF 587 kb)

Supplementary Data (XLSX 994 kb)

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Krieger, J., Murray, F., Roberts, J. et al. The impact of personal genomics on risk perceptions and medical decision-making. Nat Biotechnol 34, 912–918 (2016). https://doi.org/10.1038/nbt.3661

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