Abstract
Personalized genomics companies (PG; also called ‘direct-to-consumer genetics’) are businesses marketing genetic testing to consumers over the Internet. While much has been written about these new businesses, little attention has been given to their roles in science communication. This paper provides an analysis of the gene concept presented to customers and the relation between the information given and the science behind PG. Two quite different gene concepts are present in company rhetoric, but only one features in the science. To explain this, we must appreciate the delicate tension between PG, academic science, public expectation, and market forces.
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Notes
It is interesting that PG companies use the number of SNPs included in their tests as marketing tools. Some boast of using 2 million where others used only 1.5. Since only a few thousand of those SNPs confer any information, the gross amount of SNPs tested is little indication of test quality.
Some companies omit (2), requiring you to infer the subjective risk, or omit (3), requiring you to calculate adjusted risk.
There are some interesting and important questions about the degree to which this is accomplished. Many ethnic groups are not represented in the GWAS studies on which PG results are based (see Mountain et al. 2007).
Perhaps the most discussed of these is height, which is estimated to be 80–90 % heritable, but for which SNPs account for only 5 %. The issue of missing heritability is a complex one. The poor risk associations discussed here are but a symptom of this greater problem. On the problem of heritability estimates (see Sesardic 2005). On the problem of missing heritability (see Maher 2008).
To be clear, we cannot access what the PG scientists really think genes are. To access scientists’ inner thoughts isn’t possible. The best we can do is to analyze the material they present to their customers and examine their scientific practices. How they actually conceive of and use the concept is beyond the scope of this sort of investigation. For more on the epistemic and psychological barriers to reconstructing scientific concepts (see Waters 2004).
This likely owes, at least in part, to early linkage disequilibrium (DL) mapping, a technique for mapping polymorphisms, which evolved into contemporary GWAS. Early DL approaches, relying on restriction fragment length polymorphisms and familial transmission data, were predicated on the fact that polymorphisms shared by individuals related ancestrally are often surrounded by shared alleles at nearby loci. The polymorphism is thus treated as a marker for the ‘true’ source of the trait(s) in question. In contemporary studies, however, samples are not restricted to family lines, and thus researches ought not to assume so readily that a polymorphism is indicative of shared alleles. Yet this caveat may have been neglected and surrogate assumption adopted erroneously, in contemporary GWAS (see Kruglyak 2008).
The difficulty in piecing together information about the science will only be compounded by the extremely high reading level required of PG users. Lachance et al. (2010) found that the reading level of PG websites was 15, at least 6 grades above the average reading comprehension of US citizens.
Some recent work has shown just how fruitful (though difficult) it can be to include clinical metrics, like family history, into risk assessments relying on DNA (see Ashley et al. 2010).
This may be set to change, a new company, Personalis, promises to integrate whole-genome scans with clinical risk information (family history, behaviour, etc.). It will be interesting to watch whether, if successful, Personalis prompts other PG companies to follow suit, as I predict.
References
23andMe. (2010a). http://www.23andme.com. Accessed December 9, 2010.
23andMe. (2010b). Keywords for genetics. Retrieved from: https://www.23andme.com/gen101/graphics/genetics/. March 1, 2011.
23andMe. (2011). 23andMe announces additional investment in series C financing [press release]. Retrieved from: https://www.23andme.com/about/press/20110107/.
Ashley, E. A., Butte, A. J., Wheeler, M. T., Chen, R., Klein, T. E., Dewey, F. E., et al. (2010). Clinical assessment incorporating a personal genome. The Lancet, 375(9725), 1525–1535. doi:10.1016/S0140-6736(10)60452-7.
Bates, B. R. (2005). Public culture and public understanding of genetics: A focus group study. Public Understanding of Science, 14(47), 47–65.
Burian, R. M. (2004). Molecular epigenesis, molecular pleiotropy, and molecular gene definitions. History and Philosophy of the Life Sciences, 26, 59–80.
Cohen. (2008). Volleyball and the public perception of genetics. http://scienceblogs.com/worldsfair/2008/08/beach_volleyball_and_the_publi.php.
Condit, C. M. (2011). ‘When do people deploy genetic determinism? A review pointing to the need for multi-factorial theories of public utilization of scientific discourses. Sociology Compass, 5(7), 618–635.
Condit, C. M., Gronnvoll, M., Landau, J., Shen, L., Wright, L., & Harris, T. M. (2009). Believing in both genetic determinism and behavioral action: A materialist framework and implications. Public Understanding of Science, 18(6), 730–746.
deCODEme. (2010). http://www.decodeme.com/. Accessed December 9, 2010.
Eriksson, N., Macpherson, J. M., Tung, J. Y., Hon, L. S., Naughton, B., et al. (2010). Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genetics, 6(6), e1000993.
Fogel, T. (2000). The dissolution of protein coding genes in molecular biology’. In P. Beurton, R. Falk, & H. J. Rheinberger (Eds.), The concept of the gene in development and evolution (pp. 3–25). Cambridge, UK: Cambridge University Press.
Fox Keller, E. (2000). The century of the gene. Cambridge, MA: Harvard University Press.
Fox Keller, E. (2010). The mirage of a space between nature and nurture. NC: Duke University Press.
Gibson, G., & Goldstein, D. B. (2007). Human genetics: The hidden text behind genome-wide associations. Current Biology, 17(21), 929–932.
Goldstein, D. B. (2009). Common genetic variation and human traits. The New England Journal of Medicine, 360(17), 1696.
Grey, R. D. (1992). The gene is dead. In P. E. Griffiths (Ed.), Trees of life (pp. 165–210). Dordrecht: Kluwer.
Griffiths, P. E. (2006). The fearless vampire conservator: Philip Kitcher, genetic determinism and the informational gene. In C. Rehmann-Sutter & E. M. Neumann-Held (Eds.), Genes in development: Rethinking the molecular paradigm (pp. 175–198). NC: Duke University Press.
Griffiths, P. E., & Stotz, K. (2007). Gene. In M. Ruse & D. Hull (Eds.), Cambridge companion to philosophy of biology (pp. 85–102). Cambridge: Cambridge University Press.
Hawkes, C. H. (1997). Twin studies in medicine—What do they tell us? Quarterly Journal of Medicine, 90(5), 311–321.
Hindorff, L. A., Praveen, S., Heather, A. J., Erin, M. R., Jayashri, P. M., Francis, S. C., et al. (2009). Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences of the United States, 106(23), 9362–9367.
Hudson, K. (2008). The health benefits of genomics: Out with the old, in with the new. Health Affairs, 27(6), 1612–1615.
Hunter, D. J., & Chanock, S. J. (2010). Genome wide association studies and ‘the art of the soluble’. Journal of the National Cancer Institute, 102(12), 1–2.
Hunter, D. J., Altshuler, D., & Rader, D. J. (2008a). From Darwin’s finches to canaries in the coal mine—Mining the genome for new biology. New England Journal of Medicine, 358(26), 2760–2763.
Hunter, D. J., Khoury, M., & Drazen, J. M. (2008b). Letting the genome out of the bottle—Will we get our wish? The New England Journal of Medicine, 358(2), 105–107.
Ioannidis, J. P. A. (2009). Personalized genetic prediction: Too limited, too expensive, or too soon? Annals of Internal Medicine, 150(2), 139–141.
Kruglyak, L. (2008). The road to genome-wide association studies. Nature Genetics, 9, 314–318.
Kua, E., Reder, M., & Grossel, M. J. (2004). Science in the news: A study of reporting genomics. Public Understanding of Science, 13, 309–322.
Kaufman, D.J., Bollinger J.M., Dvoskin, R. L., & Scott, J. A. (2012). Risky business: Risk perception and the use of medical services among customers of DTC personal genetic testing. Journal of Genetic Counseling, 21(3), 413–422.
Lachance, C. R., Erby, L. A. H., Ford, B. M., Allen, V. C., & Kaphingst, K. A. (2010). Informational content, literacy demands, and usability of websites offering health-related genetic tests directly to consumers. Genetics Medicine, 12(5), 304–312.
Lakhman, K. (2010). First Walgreens, now house calls: The increasingly bizarre predicament of DTC genetic testing. The Sample. Retrieved from: http://www.genomeweb.com/blog/first-walgreens-now-house-calls-increasingly-bizarre-predicament-dtc-genetic-tes.
Leighton, J. W., Valverde, K., Bernhardt, B. A. (2012). The general public’s understanding and perception of direct-to-consumer genetic test results. Public Health Genomics, 15, 11–21.
Maher, B. (2008). The case of the missing heritability. Nature, 456, 18–21. doi:10.1038/456018a.
Maston, G. A., Evans, S. K., & Green, M. R. (2006). Transcriptional regulatory elements in the human genome. Annual Review of Genomics and Human Genetics, 7, 29–59.
McGuire, A. L., & Burke, W. (2008). An unwelcome side effect of direct-to-consumer personal genome testing: Raiding the medical commons. The Journal of the American Medical Association, 300(22), 2669–2671.
McGuire, A. L., Diaz, C. M., Wang, T., & Hilsenbeck, S. G. (2009). Social networkers attitudes’ toward direct-to-consumer personal genome testing. American Journal of Bioethics, 9(6–7), 3–10.
Mountain, J., Hsu, A., Macpherson, M., & Naughton, B. (2007). White paper 23-02: Estimating genotype-specific incidence in the context of ethnic variation [white paper]. Retrieved from: https://23andme.https.internapcdn.net/res/6295/pdf/23-02_Incidence_And_Ethnicity.pdf.
Navigenics. (2010a). http://www.navigenics.com/.
Navigenics. (2010b). An open letter to the surgeon general [press release]. Retrieved from: http://www.navigenics.com/visitor/about_us/press/releases/surgeon_general_112310/.
Navigenics. (2010c). Navigenics raises $18 million in series C financing [press release]. Retrieved from: http://www.navigenics.com/visitor/about_us/press/releases/financing_020310/.
Neumann-Held, E. (1999). The gene is dead—Long live the gene! Conceptualizing genes the constructionist way. In P. Koslowski (Ed.), Sociobiology and bio-economics: The theory of evolution in biological and economic theory (pp. 105–137). NY: Springer.
O’Neill, S. C., McBride, C. M., Alford, S. H., & Kaphingst, K. A. (2010). Preferences for genetic and behavioral health information: The impact of risk factors and disease attributions. Annals of Behavioral Medicine, 40(2), 127–137.
Patel, C. J., Bhattacharya, J., & Butte, A. J. (2010). An environment-wide association study (EWAS) on type 2 diabetes mellitus’. PLoS One, 5(5), e10746. doi:10.1371/journal.pone.0010746.
Sesardic, N. (2005). Making sense of heritability. Cambridge: Cambridge University Press.
Turkheimer, E. (2012). Genome wide association studies of behavior are social science. In K. S. Plaisance & T. A. C. Reydon (Eds.), Philosophy of behavioral biology (pp. 43–64). NY: Springer.
Waters, K. (1994). Genes made molecular. Philosophy of Science, 61, 163–185.
Waters, K. (2004). What concept analysis in philosophy of science should be (and why competing philosophical analyses cannot be tested by polling scientists)’. History and Philosophy of the Life Sciences, 26(1), 29–58.
Acknowledgments
I would like to thank Stefan Linquist, T. Ryan Gregory, Greg Radick, and audiences in Windsor, Guelph, Toronto, and Exeter for feedback on this paper. Part of this research was supported by the Social Sciences and Humanities Research Council of Canada.
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Bartol, J. Re-examining the Gene in Personalized Genomics. Sci & Educ 22, 2529–2546 (2013). https://doi.org/10.1007/s11191-012-9484-2
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DOI: https://doi.org/10.1007/s11191-012-9484-2