Ó Springer 2006
Medicine, Health Care and Philosophy (2006) 9:325–337 DOI 10.1007/s11019-006-0001-8
Scientific Contribution Genetic Information in the Age of Genohype Pe´ter Kakuk Department of Behavioural Sciences, Medical and Health Sciences Centre, University of Debrecen, 4032 Nagyerdei krt. 98, P.O. Box 45, Debrecen, Hungary (E-mail:
[email protected])
Abstract. We will analyse the representations and conceptualisation of genetics and genetic information in bioethical discourse. Genetics and genetic information is widely believed to be revolutionizing medicine and is sometimes misconceived as having a high predictive value compared to traditional diagnostics. We will attempt to present the inherent limitations of genetic information within its health care context. We will also argue against the exceptional treatment of genetic information that seems to govern bioethical reflection and regulatory approaches. And finally, we will make the claim that geneticization should be seen as a more serious threat to our privacy, autonomy and freedom, than genetic discrimination. Key words: genetic information, genetic revolution, predictive value, discrimination, geneticization
The ethical debate on the new genetics appeared around the end of the 1960’s, new regulatory institutions were established in the 1970’s and the basic topics within the bioethical discourse took shape by the end of the 1980’s. The initial period was highly influenced by new techniques in manipulating DNA at the molecular level and by the popular term – as coined by Hotchkiss – genetic engineering (Hotchkiss, 1965). At the outset, the possible general environmental risks that the manipulation of genes posed to life on earth became the focus of attention. The ethical problem of human application entered the main stage much later as ‘...genetic engineering was relieved of the stigma of hazard in the 1980s’. The controversy surrounding the biohazard issue all but faded away’ (Wright, 1994). The ethical and social problems of human applications of genetic technologies came to be widely discussed following another highly influential scientific achievement. The idea of mapping the complete human genome emerged at the end of the 1980s, but, at the beginning, it seemed to be an elusive task requiring generations of scientist to fulfil. However, there happened to be a more rapid development of the world most grandiose and expensive scientific project than had been expected. The Human Genome Project was first announced the completion of a ‘working draft’ of the human genome sequence on 28th June 2000.
The possible applications of the new genetic knowledge for humans were surrounded by great hopes as well as fears (U.S. Congress, 1988). The emerging bioethical problems and questions were formed by ethical issues concerning gene therapy, cloning, reproductive technologies, genetic testing, screening, and genetic counselling (National Reference Center for Bioethics, 2004). The issue of genetic information is prominent within this topical division for two reasons. Firstly, genetic information as an underlying theme is closely related to other bioethical topics in genetics, as far as reproductive decisions, genetic testing and screening, and medical diagnosis and treatments are all somehow subject to the concept of genetic information. Secondly, genetic information, unlike issues of gene therapy and human cloning, has fallen within the scope of practical feasibility of its possibilities since the completed project of mapping, because of the accessibility of the practical applications that are based on our knowledge of genetic information. The Human Genome Project was able to obtain unprecedented financial and institutional support because of the promises it could formulate in relation to health care. ‘[The U.S.] Congress was convinced to fund [The Human Genome Project] on the promise that it would lead to diagnosis and cure of genetic disease’ (Andrews, 1997). Moreover, as an early enthusiastic supporter of the
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project, an editor of Science envisaged: the praiseworthy use of the project is ‘to aid the poor, the infirm, and the underprivileged’ (Koshland, 1989). Although this claim might have been an exaggeration in its own time, few were sceptical regarding the more widely held belief that the new knowledge emerging from the project might have an enormous impact on the way medicine would be practiced in the future, both in terms of genetic diagnostics and therapies. This vision on our medical future rests on the assumption that it will be possible to ‘fix’ disease by fixing genes. Although this sounds highly plausible, and is easily understood by the public and the media we need to express our reservations towards this idea. There are two main reasons for being sceptical about the revolutionary possibilities genetics might create in health care. One is that gene therapy trials were not successful, and were proven to be very problematic in spite of the numerous and well financed efforts that have already been realized (Wilson, 1999). As Nicholson noted: ‘gene therapy has yet to produce any of the revolutionary treatments predicted back in 1990’ (Nicholson, 1999). The other source of our scepticism toward the revolutionary role given to genetics in the context of health care comes from a realistic interpretation of the information contained in the DNA of humans that demonstrates a low predictive value of genetic information. There exists a common belief that genetic information has a high predictive value or that with our growing knowledge about the human genome and its relation to diseases it will at least increase our capability and capacity to predict the onset of different diseases. Bioethical authors either implicitly or explicitly refer to this characterization of genetic information as Goldworth does when saying: ‘As our knowledge of the genetic constitution of human beings expands, testing to determine an individual’s disposition toward a given disease will also increase’ (Goldworth, 1999). This theory gave a strong justification both in viewing genetic information as a powerful new diagnostic tool in the context of health care, and as a tool that has a discriminatory potential in the context of health and life insurance, as well as in the workplace. Moreover, it also justifies making the issue of genetic information a special case and one that needs exceptional treatment in the context of policy. Nevertheless, could we justify the commonly held conception that genetic information has a higher predictive value than other medical diagnostic sources? Is it true that as our knowledge
about our genes increases it will result in an increase in our predictive capability/capacity based on DNA diagnostics?
The predictive value of genetic information In the case of so called single gene disorders, (or Mendelian diseases), such as Tay-Sachs, Huntington’s, PKU, Duchene muscular dystrophy, Cystic fibrosis etc..., genetic information has a high predictive value. The identification of mutations in many of these single gene disorders does provide more or less deterministic information, but gene–environment and gene–gene interactions have a significant role as well. Therefore these cases should not be interpreted as clear examples of genetic determinism (Wheatherall et al. 1999; Scriver and Waters, 1999). Nevertheless, in these cases we are right to state that we can predict future events with a more or less reliable probability and also, that these predictions may have health implications for related individuals as well. Are we justified in generalizing and relating these results to other diseases? And are we justified in using this characterization of single gene disorders as a model or paradigm for judging the value of genetic prediction on a more general level? Our answer is no to both questions as these diseases are very rare on a population level. Mendelian diseases are exceptional cases within the family of human diseases and constitute a very small proportion of the disease burden in populations (Baird, 1988). The vast majority of human diseases, the most common diseases in modern societies, are complexly determined. Many types of cancer, cardiovascular disease, and several forms of mental illness result from a complex interaction of genetic and environmental factors. Nevertheless, it has been argued that in the case of complex diseases it is still possible to provide susceptibility testing. With the identification of genes that increase a given individual’s susceptibility to one of these diseases, medicine would do a great job in disease prevention. If you know in advance that you have a higher risk of developing a certain type of cancer, then you could make modifications in your life-style that result in the prevention of the development of disease. On the grounds of this reasoning it has been suggested that population screening programmes should be established for testing everyone for susceptibility genes for common diseases (Collins, 2000). This conception has been criticized with reference to the fact that
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identifying a susceptibility gene in itself is not risk information that could be communicated to a patient, because further and systematic studies are needed to acquire reliable information about a susceptibility gene (Holtzmann, 2001). The usefulness, cost-effectiveness and ethical appropriateness of such a health programme has also been criticized on the grounds that genetic diagnostics has a lower predictive value compared to other medical diagnostic possibilities, and is thus inherently limited in what it can achieve (Baird, 2002). According to Patricia Baird: [...] procuring reliable information on the actual risk is bound to be difficult. In the absence of reliable information on the absolute risk for those with the gene, as well as, on the increased relative risk, susceptibility testing is likely to create unnecessary anxiety among those who have a susceptibility gene but whose risk is nonetheless quite low, and a false sense of security among those who lack a susceptibility gene but whose risk is nonetheless real (Baird, 2002, p. 519)
There is an inherent limit to genetic prediction that can be very elegantly expressed in a quantitative way if we are to deconstruct the mythology surrounding genetic information. Andrew Wilkie presented such an analysis that was mostly based on our knowledge about monozygotic twins (MZT) and genetic diseases (Wilkie, 2001). MZ twins are natural clones and as such, they possess the very same genetic constitution and quite often also share common environment as well as experiences. Despite the identical informational content of their genes and the similarity of their environment, they still differ in significant ways from each other. The quantitative analysis of the similarities and differences of MZTs could provide us with an upper limit to the determinative nature and predictive capability of genetic information. In Wilkie’s definition: The similarity (and correspondingly the difference) between twins for a particular trait can be expressed numerically as the concordance. When one MZ twin has a particular trait, the concordance is the proportion of occasions that the MZ twin partner shares the same trait. The concordances for a wide range of common traits in MZ twins usually fall in the 30–70% range (Wilkie, 2001, p. 621).
From this data Wilkie concludes that: ‘even if geneticists were clever enough to identify all the genetic determinants contributing to a complex
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disease or behaviour, the MZ twin concordance rate would place an upper limit on the average confidence of their prediction’ (Wilkie, 2001). This result should not be surprising in the light of observations that presented other aspects of the distance between a given genotype and its phenotypic expression. In an important study Wulf even questions the concept of ‘monofactorial disease’ or ‘monogenic traits’ taking into consideration the findings on ‘clinical heterogeneity, even in the presence of identical mutations’. (Wulf, 1997). The other quantitative formulation relevant to the problem of genetic prediction in the case of complex diseases is the concept of genotype relative risk (GRR) that expresses the contribution of an allele to a given disease (Risch, 2000). ‘The picture that seems to be emerging is that genetic disease is largely dichotomous: the genetic contributions (GRRs) that individual alleles make to disease tend to be either >50 or
