Advertisement

The Ethics of Large-Scale Genomic Research

  • Benjamin E. Berkman
  • Zachary E. Shapiro
  • Lisa Eckstein
  • Elizabeth R. PikeEmail author
Part of the Computational Social Sciences book series (CSS)

Abstract

The potential for big data to advance our understanding of human disease has been particularly heralded in the field of genomics. Recent technological advances have accelerated the massive data generation capabilities of genomic research, which has allowed researchers to undertake larger scale genomic research, with significantly more participants, further spurring the generation of massive amounts of data. The advance of technology has also triggered a significant reduction in cost, allowing large-scale genomic research to be increasingly feasible, even for smaller research sites. The rise of genetic research has triggered the creation of many large-scale genomic repositories (LSGRs) some of which contain the genomic information of millions of research participants. While LSGRs have genuine potential, they also have raised a number of ethical concerns. Most prominently, commentators have raised questions about the privacy implications of LSGRs, given that all genomic data is theoretically re-identifiable. Privacy can be further threatened by the possibility of aggregation of data sets, which can give rise to unexpected, and potentially sensitive, information. Beyond privacy concerns, LSGRs also raise questions about participant autonomy, public trust in research, and justice. In this chapter, we explore these ethical challenges, with the goal of elucidating which ones require closer scrutiny and perhaps policy action. Our analysis suggests that caution is warranted before any major policies are implemented. Much attention has been directed at privacy concerns raised by LSGRs, but perhaps for the wrong reasons, and perhaps at the expense of other relevant concerns. We do not think that there is yet sufficient evidence to motivate enactment of major policy changes in order to safeguard welfare interests, although there might be some stronger reasons to worry about subjects’ non-welfare interests. We also believe that LSGRs raise genuine concerns about autonomy and justice. Big data research, and LSGRs in particular, have the potential to radically advance our understanding of human disease. While these new research resources raise important ethical concerns, any policies implemented concerning LSGRs should be carefully tailored to ensure that research is not unduly burdened.

Keywords

Medical Waste Genetic Discrimination Psychological Harm Privacy Rule Broad Consent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. AAP. (2006, August 8). Warrior Gene” Blamed for Maori Violence. National Nine News.Google Scholar
  2. Bollier, D., & Firestone, C. M. (2010). The promise and peril of big data. Washington, DC: Aspen Institute.Google Scholar
  3. Broadstock, M., Michie, S., & Marteau, T. (2000). Psychological consequences of predictive genetic testing: A systematic review. European Journal of Human Genetics, 8(10), 731–738.CrossRefGoogle Scholar
  4. Bustamante, C. D., Francisco, M., & Burchard, E. G. (2011). Genomics for the world. Nature, 475(7355), 163–165.CrossRefGoogle Scholar
  5. Chen, D. T., Rosenstein, D. L., Muthappan, P. G., Hilsenbeck, S. G., Miller, F. G., Emanuel, E. J., et al. (2005). Research with stored biological samples: What do research participants want? Archives of Internal Medicine, 165, 652–655.Google Scholar
  6. Citizens’ Council on Health Care. (2009). State by state government newborn blood & baby DNA retention practices. Retrieved at http://www.cchfreedom.org/pdf/50_States-Newborn_
  7. Claes, P., Hill, H., & Shriver, M. D. (2014). Toward DNA-based facial composites: Preliminary results and validation. Forensic Science International: Genetics, 13, 208–216.CrossRefGoogle Scholar
  8. Crampton, P., & Parkin, C. (2007). Warrior genes and risk-taking science. New Zealand Medical Journal, 120, U2439.Google Scholar
  9. Dunn, C. K. (2012). Protecting the silent third party: The need for legislative reform with respect to informed consent and research on human biological materials. Charleston Law Review, 6, 635–684.Google Scholar
  10. Eiseman, E. (2000). Stored tissue samples: An inventory of sources in the United States. In National Bioethics Advisory Commission (NBAC), Research involving human biological materials: Ethical issues and policy guidance. Rockville, Maryland: NBAC.Google Scholar
  11. Freeman, W. M., Romero, F. C., & Kanade, S. (2006). Community consultation to assess and minimize group harms. In E. A. Bankert & R. J. Amdur (Eds.), Institutional review board management and function (2nd ed.). Sunderland, MA: Jones and Bartlett.Google Scholar
  12. Geetter, J. S. (2011). Another man’s treasure: The promise and pitfalls of leveraging existing biomedical assets for future use. Journal of Health and Life Science Law, 4, 1–104.Google Scholar
  13. Genetic Information Non-Discrimination Act Charges. (2014). Retrieved at http://www.eeoc.gov/eeoc/statistics/enforcement/genetic.cfm
  14. Genomic Data Sharing. (2014, August 27). Retrieved at https://gds.nih.gov/
  15. Grady, C., Eckstein, L., Berkman, B. E., Brock, D., Cook-Deegan, R., Fullerton, S. M., et al. (2015). Broad consent for research with biological samples: Workshop conclusions. American Journal of Bioethics, 15(9), 34–42.CrossRefGoogle Scholar
  16. Green, R. C., Roberts, J. S., Cupples, L. A., Relkin, N. R., Whitehouse, P. J., Brown, T., & Farrer, L. A. (2009). Disclosure of APOE genotype for risk of Alzheimer’s disease. New England Journal of Medicine, 361(3), 245–254.CrossRefGoogle Scholar
  17. Gymrek, M., McGuire, A. L., Golan, D., Halperin, E., & Erlich, Y. (2013). Identifying personal genomes by surname inference. Science, 339(6117), 321–324.CrossRefGoogle Scholar
  18. Haga, S. B. (2010). Impact of limited population diversity of genome-wide association studies. Genetics in Medicine, 12(2), 81–84.CrossRefGoogle Scholar
  19. Hartl, D. L., & Clark, A. G. (2007). Principles of population genetics (4th ed.). Sunderland, MA: Sinauer Associates.Google Scholar
  20. Hausman, D. M. (2007). Group risks, risks to groups, and group engagement in genetics research. Kennedy Institute of Ethics Journal, 17, 351–369.CrossRefGoogle Scholar
  21. Hausman, D. (2008). Protecting groups from genetic research. Bioethics, 22(3), 157–165.CrossRefGoogle Scholar
  22. Heshka, J. T., Palleschi, C., Howley, H., Wilson, B., & Wells, P. S. (2008). A systematic review of perceived risks, psychological and behavioral impacts of genetic testing. Genetics in Medicine, 10(1), 19–32.CrossRefGoogle Scholar
  23. Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., & Craig, D. W. (2008). Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genetics, 4(8), e1000167.CrossRefGoogle Scholar
  24. Hudson, K. L., Rothenberg, K. H., Andrews, L. B., Kahn, M. E., & Collins, F. S. (1995). Genetic discrimination and health insurance: An urgent need for reform”. Science, 270(5235), 391–393.CrossRefGoogle Scholar
  25. International Human Genome Sequencing Consortium. (2004). Finishing the euchromatic sequence of the human genome. Nature, 431(7011), 931–945.CrossRefGoogle Scholar
  26. Joly, Y., Feze, I. N., & Simard, J. (2013). Genetic discrimination and life insurance: A systematic review of the evidence. BMC Medicine, 11, 25–40.CrossRefGoogle Scholar
  27. Lane, J., Stodden, V., Bender, S., & Nissenbaum, H. (Eds.). (2014). Privacy, big data, and the public good: Frameworks for engagement. Cambridge: Cambridge University Press.Google Scholar
  28. McKinnon, W., Banks, K. C., Skelly, J., Kohlmann, W., Bennett, R., Shannon, K., & Wood, M. (2009). Survey of unaffected BRCA and mismatch repair (MMR) mutation positive individuals. Familial Cancer, 8(4), 363–369.CrossRefGoogle Scholar
  29. Meiser, B. (2005). Psychological impact of genetic testing for cancer susceptibility: An update of the literature. Psycho-Oncology, 14, 1060–1074.CrossRefGoogle Scholar
  30. National Science Foundation. (2010). Core techniques and technologies for advancing big data science and engineering program solicitation. Retrieved at http://www.nsf.gov/pubs/2012/nsf12499/nsf12499.htm
  31. Olson, J. (2014, January 14). Minnesota must destroy 1 million newborn blood samples. Star Tribune.Google Scholar
  32. Otlowski, M., Taylor, S., & Bombard, Y. (2012). Genetic discrimination: International perspectives. Annual Review of Genomics and Human Genetics, 13, 433–454.CrossRefGoogle Scholar
  33. Peters, S. A., Laham, S. M., Pachter, N., & Winship, I. M. (2014). The future in clinical genetics: Affective forecasting biases in patient and clinician decision making. Clinical Genetics, 85(4), 312–317.CrossRefGoogle Scholar
  34. Pollitz, K., Peshkin, B. N., Bangit, E., & Lucia, K. (2007). Genetic discrimination in health insurance: current legal protections and industry practices. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 44(3), 350–368.Google Scholar
  35. Prince, A. E., & Berkman, B. E. (2012). When does an illness begin: Genetic discrimination and disease manifestation. The Journal of Law, Medicine & Ethics, 40(3), 655–664.CrossRefGoogle Scholar
  36. Roberts, J. S., Christensen, K. D., & Green, R. C. (2011). Using Alzheimer’s disease as a model for genetic risk disclosure: Implications for personal genomics. Clinical Genetics, 80(5), 407–414.CrossRefGoogle Scholar
  37. Roser, M. A. (2009, December 23). State agrees to destroy more than 5 million stored blood samples from newborns. Statesman.Google Scholar
  38. Rothstein, M. A. (2010). Is deidentification sufficient to protect health privacy in research? The American Journal of Bioethics, 10(9), 3–11.CrossRefGoogle Scholar
  39. Rotimi, C. N. (2012). Health disparities in the genomic era: The case for diversifying ethnic representation. Genome Medicine, 4(8), 65–68.CrossRefGoogle Scholar
  40. Schadt, E. E. (2012). The changing privacy landscape in the era of big data. Molecular Systems Biology, 8(1), 612.Google Scholar
  41. Schadt, E. E., Woo, S., & Hao, K. (2012). Bayesian method to predict individual SNP genotypes from gene expression data. Nature Genetics, 44(5), 603–608.CrossRefGoogle Scholar
  42. Scutti, S. (2014, July 24). The government owns your DNA. What are they doing with It? N ewsweek.Google Scholar
  43. Suter, S. M. (2014). Did you give the government your baby’s DNA? Rethinking consent in newborn Screening. Minnesota Journal of Law Science and Technology, 15, 729–790.Google Scholar
  44. Tomlinson, T. (2009). Protection of non-welfare interests in the research uses of archived biological samples. In K. Dierickx & P. Borry (Eds.), New challenges for biobanks: Ethics, law, governance. Intersentia: Antwerp.Google Scholar
  45. Tomlinson, T., De Vries, R., Ryan, K., Kim, H. M., Lehpamer, N., & Kim, S. Y. (2015). Moral concerns and the willingness to donate to a research biobank. Journal of the American Medical Association, 313(4), 417–419.CrossRefGoogle Scholar
  46. Waldo, A. (2010, March 16). The Texas newborn bloodspot saga has reached a sad—and preventable—conclusion. Genomics Law Report.Google Scholar
  47. Wendler, D. (2006). One-time general consent for research on biological samples. British Medical Journal, 332(7540), 544–547.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland (Outside the USA) 2016

Authors and Affiliations

  • Benjamin E. Berkman
    • 1
  • Zachary E. Shapiro
    • 2
  • Lisa Eckstein
    • 3
  • Elizabeth R. Pike
    • 4
    Email author
  1. 1.Department of Bioethics, Clinical Center, and Bioethics CoreNational Human Genome Research Institute, National Institutes of HealthBethesdaUSA
  2. 2.Harvard Law SchoolCambridgeUSA
  3. 3.The Faculty of LawUniversity of TasmaniaTasmaniaAustralia
  4. 4.Presidential Commission for the Study of Bioethical IssuesWashingtonUSA

Personalised recommendations