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Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits

Nature Neuroscience volume 26, pages 932–941 (2023)Cite this article

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Abstract

Genome-wide association studies have led to the identification of robust statistical associations of genetic variants with numerous brain-related traits, including neurological and psychiatric conditions, and psychological and behavioral measures. These results may provide insight into the biology underlying these traits and may facilitate clinically useful predictions. However, these results also carry the risk of harm, including possible negative effects of inaccurate predictions, violations of privacy, stigma and genomic discrimination, raising serious ethical and legal implications. Here, we discuss ethical concerns surrounding the results of genome-wide association studies for individuals, society and researchers. Given the success of genome-wide association studies and the increasing availability of nonclinical genomic prediction technologies, better laws and guidelines are urgently needed to regulate the storage, processing and responsible use of genetic data. Also, researchers should be aware of possible misuse of their results, and we provide guidance to help avoid such negative impacts on individuals and society.

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Fig. 1: Embryo selection for desirable traits may come with some unexpected consequences.
Fig. 2: Genetic exceptionalism and the impossibility to completely anonymize genetic data.

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References

  1. Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Primers. 1, 59 (2021).

    Article  CAS  Google Scholar 

  2. Yengo, L. et al. A saturated map of common genetic variants associated with human height from 5.4 million individuals of diverse ancestries. Nature 610, 704–712 (2022).

  3. Jansen, P. R. et al. Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence. Nat. Commun. 11, 5606 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Choi, S. W., Mak, T. S. H. & O’Reilly, P. F. A guide to performing Polygenic Risk Score analyses. Nat. Protoc. 15, 2759–2772 (2020).

  6. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Landi, I. et al. Prognostic value of polygenic risk scores for adults with psychosis. Nat. Med. 27, 1576–1581 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Adeyemo, A. et al. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat. Med. 27, 1876–1884 (2021). This is a comprehensive summary of the state of polygenic score research, and includes thoughtful discussion on the needs and challenges as PRSs move closer to widespread use in the clinic.

    Article  Google Scholar 

  9. Rossello, X. et al. Risk prediction tools in cardiovascular disease prevention: a report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP). Eur. Heart J. Acute Cardiovasc. Care 9, 522–532 (2020).

    Article  PubMed  Google Scholar 

  10. European Commission. Joint Research Centre. Genome-wide association studies, polygenic scores and social science genetics: overview and policy implications (Publications Office, 2019).

  11. Wray, N. R., Yang, J., Goddard, M. E. & Visscher, P. M. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet. 6, e1000864 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chatterjee, N., Shi, J. & García-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50, 912–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Martin, A. R. et al. Current clinical use of polygenic scores will risk exacerbating health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mostafavi, H. et al. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9, e48376 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Office for Civil Rights: Health Information Privacy (US HHS, accessed 1 April 2023); https://www.hhs.gov/hipaa/index.html

  19. Phillips, A. M. Reading the fine print when buying your genetic self online: direct-to-consumer genetic testing terms and conditions. New Genet. Soc. 36, 273–295 (2017).

    Article  Google Scholar 

  20. Peck, L., Borle, K., Folkersen, L. & Austin, J. Why do people seek out polygenic risk scores for complex disorders, and how do they understand and react to results? Eur. J. Hum. Genet. 30, 81–87 (2022).

    Article  PubMed  Google Scholar 

  21. Shoenbill, K., Fost, N., Tachinardi, U. & Mendonca, E. A. Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations. J. Am. Med. Inform. Assoc. 21, 171–180 (2014).

    Article  PubMed  Google Scholar 

  22. Putt, S. et al. Exploration of experiences with and understanding of polygenic risk scores for bipolar disorder. J. Affect. Disord. 265, 342–350 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Lewis, A. C. F. & Green, R. C. Polygenic risk scores in the clinic: new perspectives needed on familiar ethical issues. Genome Med. 13, 14 (2021). This paper provides a good overview of the different ethical issues related to the use of polygenic risk scores in clinical practice.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Green, R. C., Lautenbach, D. & McGuire, A. L. GINA, genetic discrimination, and genomic medicine. N. Engl. J. Med. 372, 397–399 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Fost, N. Ethical issues in genetics. Pediatr. Clin. North Am. 39, 79–89 (1992).

    Article  CAS  PubMed  Google Scholar 

  26. Joly, Y. et al. The Genetic Discrimination Observatory: confronting novel issues in genetic discrimination. Trends Genet. 37, 951–954 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Tiller, J. et al. Genetic discrimination by Australian insurance companies: a survey of consumer experiences. Eur. J. Hum. Genet. 28, 108–113 (2020).

    Article  PubMed  Google Scholar 

  28. Clayton, E. W., Evans, B. J., Hazel, J. W. & Rothstein, M. A. The law of genetic privacy: applications, implications, and limitations. J. Law Biosci. 6, 1–36 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Phillips, A. et al. Informing relatives of their genetic risk: an examination of the Belgian legal context. Eur. J. Hum. Genet. https://doi.org/10.1038/s41431-021-01016-3 (2022).

  30. Kraft, S. A., Duenas, D., Wilfond, B. S. & Goddard, K. A. B. The evolving landscape of expanded carrier screening: challenges and opportunities. Genet. Med. 21, 790–797 (2019).

    Article  PubMed  Google Scholar 

  31. Turley, P. et al. Problems with using polygenic scores to select embryos. N. Engl. J. Med. 385, 78–86 (2021). In this paper, the ethical and legal aspect of embryo selection using polygenic risk scores is discussed as well as the gains and the risks on society.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kostick, K., Brannan, C., Pereira, S. & Lázaro‐Muñoz, G. Psychiatric genetics researchers’ views on offering return of results to individual participants. Am. J. Med. Genet. B Neuropsychiatr. Genet. 180, 589–600 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Karavani, E. et al. Screening human embryos for polygenic traits has limited utility. Cell 179, 1424–1435 (2019). In this paper, theory, simulations and real data are used to assess the impact of embryo selection based on polygenic risk scores. It is shown that such predictions have wide confidence intervals and in practice are hardly useful.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ball, P. Polygenic screening of embryos is here, but is it ethical? (The Observer, 2021).

  35. Baruch, S., Kaufman, D. & Hudson, K. L. Genetic testing of embryos: practices and perspectives of US in vitro fertilization clinics. Fertil. Steril. 89, 1053–1058 (2008).

    Article  PubMed  Google Scholar 

  36. Al-Khelaifi, F. et al. Genome-wide association study reveals a novel association between MYBPC3 gene polymorphism, endurance athlete status, aerobic capacity and steroid metabolism. Front. Genet. 11, 595 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Morgan, M. D. et al. Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability. Nat. Commun. 9, 5271 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Dondorp, W. & de Wert, G. Refining the ethics of preimplantation genetic diagnosis: a plea for contextualized proportionality. Bioethics 33, 294–301 (2019).

    Article  PubMed  Google Scholar 

  39. Bayefsky, M. J. Comparative preimplantation genetic diagnosis policy in Europe and the USA and its implications for reproductive tourism. Reprod. Biomed. Soc. Online 3, 41–47 (2016).

    Article  PubMed  Google Scholar 

  40. Corveleyn, A., Morris, M., Zika, E., & Institute for Prospective Technological Studies. Preimplantation genetic diagnosis in Europe (Publications Office, 2007).

  41. Bayefsky, M. Who should regulate preimplantation genetic diagnosis in the United States? AMA J. Ethics 20, 1160–1167 (2018).

    Article  Google Scholar 

  42. Rodriguez, L. L., Brooks, L. D., Greenberg, J. H. & Green, E. D. The complexities of genomic identifiability. Science 339, 275–276 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Homer, N. et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 4, e1000167 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  44. D. Conley. What’s your polygenic score? Scientific American (13 March 2017).

  45. Shabani, M. & Marelli, L. Re‐identifiability of genomic data and the GDPR. EMBO Rep. 20, e48316 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hansson, M. G. et al. The risk of re-identification versus the need to identify individuals in rare disease research. Eur. J. Hum. Genet. 24, 1553–1558 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Beil, A. et al. Disclosure of clinically actionable genetic variants to thoracic aortic dissection biobank participants. BMC Med. Genomics 14, 66 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wolf, S. M. Return of individual research results and incidental findings: facing the challenges of translational science. Annu. Rev. Genomics Hum. Genet. 14, 557–577 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wolf, S. M. The challenge of incidental findings. J. Law. Med. Ethics 36, 216–218 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  50. De Clercq, E., Kaye, J., Wolf, S. M., Koenig, B. A. & Elger, B. S. Returning results in biobank research: global trends and solutions. Genet. Test. Mol. Biomark. 21, 128–131 (2017).

    Article  Google Scholar 

  51. Bredenoord, A. L., Onland-Moret, N. C. & Van Delden, J. J. M. Feedback of individual genetic results to research participants: in favor of a qualified disclosure policy. Hum. Mutat. 32, 861–867 (2011). This article provides a very thoughtful overview of ethical issues concerning whether and when researchers have a moral obligation to return genetic research results to participants. It also includes clear suggestions on a qualified disclosure policy.

    Article  PubMed  Google Scholar 

  52. Jarvik, G. P. et al. Return of genomic results to research participants: the floor, the ceiling, and the choices in between. Am. J. Hum. Genet. 94, 818–826 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dashti, H. S. et al. Genome-wide association study of breakfast skipping links clock regulation with food timing. Am. J. Clin. Nutr. 110, 473–484 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. van de Vegte, Y. J., Said, M. A., Rienstra, M., van der Harst, P. & Verweij, N. Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours. Nat. Commun. 11, 1770 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Coffee and Caffeine Genetics Consortium. et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Mol. Psychiatry 20, 647–656 (2015).

    Article  Google Scholar 

  56. Gerard, R. W. The role of pure science. Science 88, 361–368 (1938).

    Article  CAS  PubMed  Google Scholar 

  57. Carlson, J. & Harris, K. Quantifying and contextualizing the impact of bioRxiv preprints through automated social media audience segmentation. PLoS Biol. 18, e3000860 (2020). This paper introduces a method to assess the impact of preprints, which can aid in tracking online (mis)appropriation of GWAS results.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Winkelman, W. D., Missmer, S. A., Myers, D. & Ginsburg, E. S. Public perspectives on the use of preimplantation genetic diagnosis. J. Assist. Reprod. Genet. 32, 665–675 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Xu, M. CCR5-Δ32 biology, gene editing, and warnings for the future of CRISPR–Cas9 as a human and humane gene editing tool. Cell Biosci. 10, 48 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  61. van Beers, B. C. Rewriting the human genome, rewriting human rights law? Human rights, human dignity, and human germline modification in the CRISPR era. J. Law Biosci. 7, lsaa006 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Coller, B. S. Ethics of human genome editing. Annu. Rev. Med. 70, 289–305 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Read the Affordable Care Act, Health Care Law (HealthCare, accessed 1 April 2023); https://www.healthcare.gov/where-can-i-read-the-affordable-care-act/1

  64. EU Law - EUR-Lex (accessed 1 April 2023, EUR-Lex); https://eur-lex.europa.eu/homepage.html?locale=en

  65. Taylor, M. J., Wallace, S. E. & Prictor, M. United Kingdom: transfers of genomic data to third countries. Hum. Genet. 137, 637–645 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wertz, D. C. & Knoppers, B. M. Serious genetic disorders: can or should they be defined? Am. J. Med. Genet. 108, 29–35 (2002).

    Article  PubMed  Google Scholar 

  67. Botkin, J. R. Fetal privacy and confidentiality. Hastings Cent. Rep. 25, 32–39 (1995).

    Article  CAS  PubMed  Google Scholar 

  68. Lázaro-Muñoz, G., Pereira, S., Carmi, S. & Lencz, T. Screening embryos for polygenic conditions and traits: ethical considerations for an emerging technology. Genet. Med. 23, 432–434 (2021). This is a very clear commentary on ethical issues related to polygenic embryonic screening.

    Article  PubMed  Google Scholar 

  69. Ganna, A. et al. Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science 365, eaat7693 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Dawood, K., Bailey, J. M. & Martin, N. G. Genetic and Environmental Influences on Sexual Orientation. in Handbook of Behavior Genetics (ed. Kim, Y. K.) 11 (Springer, 2009).

  71. Check Hayden, E. Ethics: taboo genetics. Nature 502, 26–28 (2013).

    Article  CAS  Google Scholar 

  72. Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49, 1107–1112 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hsu, S. Super-intelligent humans are coming. Nautilus (2 October 2014).

  74. Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    Article  CAS  PubMed  Google Scholar 

  76. Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Brainstorm Consortium. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Article  Google Scholar 

  78. Hill, W. D., Harris, S. E. & Deary, I. J. What genome-wide association studies reveal about the association between intelligence and mental health. Curr. Opin. Psychol. 27, 25–30 (2019).

    Article  PubMed  Google Scholar 

  79. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Lencz, T. et al. Concerns about the use of polygenic embryo screening for psychiatric and cognitive traits. Lancet Psychiatry https://doi.org/10.1016/S2215-0366(22)00157-2 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Wedow, R., Martschenko, D. O. & Trejo, S. Scientists must consider the risk of racist misappropriation of research (Scientific American, 2022).

  82. Molteni, M. Buffalo shooting ignites a debate over the role of genetics researchers in white supremacist ideology. STAT (23 May 2022).

  83. Wills, M. Are clusters races? A discussion of the rhetorical appropriation of Rosenberg et al.’s ‘Genetic structure of human populations’. Philos. Theor. Pract. Biol. 9, 12 (2017).

  84. Lewis, A. C. F. et al. Getting genetic ancestry right for science and society. Science 376, 250–252 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Piffer, D. New genes, same results: group-level genotypic intelligence for 26 and 52 populations. topseudoscience (2 June 2017).

  86. McKinley, J., Traub, A. & Closson, T. Gunman kills 10 at Buffalo supermarket in racist attack (The New York Times, 2022).

  87. Jones, D. What is the ‘great replacement’ and how is it tied to the Buffalo shooting suspect? (NPR, 2022).

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Acknowledgements

This work is supported by the Netherlands Organization for Scientific Research—Gravitation project ‘BRAINSCAPES: a Roadmap from Neurogenetics to Neurobiology’ (024.004.012) and the European Research Council advanced grant ‘From GWAS to Function’ (ERC-2018-ADG 834057). We thank E. Uffelmann and P. Jansen for critical reading and fruitful discussions on earlier versions of this paper, and E. Bunnik for critically reading a pre-final version from a bioethical point of view.

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  1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Matthieu C. de Hemptinne & Danielle Posthuma

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de Hemptinne, M.C., Posthuma, D. Addressing the ethical and societal challenges posed by genome-wide association studies of behavioral and brain-related traits. Nat Neurosci 26, 932–941 (2023). https://doi.org/10.1038/s41593-023-01333-4

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