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The Neuroethics of BiomarkersWhat the Development of Bioprediction Means for Moral Responsibility, Justice, and the Nature of Mental Disorder$

Matthew L. Baum

Print publication date: 2016

Print ISBN-13: 9780190236267

Published to Oxford Scholarship Online: March 2016

DOI: 10.1093/acprof:oso/9780190236267.001.0001

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(p.169) Appendix I A Brief Note on Genetic versus Non-Genetic Biomarkers

(p.169) Appendix I A Brief Note on Genetic versus Non-Genetic Biomarkers

The Neuroethics of Biomarkers

Matthew L. Baum

Oxford University Press

Many countries extend certain protections to people against the disadvantageous use of predictive genetic information (DNA, RNA, or family history), especially by insurance companies. With the advance of proteomics, lipidomics, medical imaging, and the army of other biomarkers, however, the extent to which it is becoming possible to estimate risk of future illness (the diagnoses themselves serving as surrogate endpoints for other harms) via non-genetic biological means is on the verge of exploding. Because these non-genetic predictive biomarkers, as a class and through multiplexing, show potential for much higher predictive validity than their genetic counterparts, they prompt us to ask the question: Should we extend non-discrimination policies to all forms of bioprediction? And if not, what are the morally relevant differences between predictive genetic and non-genetic biomarkers that justify such different national policies? A rigorous response to these questions is beyond the scope of this appendix. I will, however, raise the following potential differences in predictive power, family privacy, identity, and brute luck, though I think they fall short of justifying such exceptionalism for genetic biomarkers in social policy.

Predictive Power

A first argument one might appeal to is that the predictive value of genetic biomarkers is usually either very low (common variants of small effect) or very high (the dominant mutations in the Huntington’s gene). Because the high predictive value variants are rare but well known, one might worry that there is a greater potential that the common variants will be overweighted in an insurance company’s risk calculus, and that this would be unfair. Because non-genetic biomarkers span the whole intermediate range of predictive power, one might think it less likely that these biomarkers would be over-weighted. I think that this argument falls short for two reasons. The first is that one might (p.170) reasonably imagine that quantitatively oriented insurance companies would be the least likely to overweight, and second, that even if insurance accurately captured the risk information in genetic variants, proponents of policies like GINA would be unlikely to think that using genetic information is acceptable.

Privacy of Family

A second argument one may appeal to is to observe that genetic risk testing in one person is likely to produce information on the risk status of other family members who also may possess the same variants, and this shared information is reason to treat genetic biomarkers differently from non-genetic ones. Non-genetic biomarkers associated with heritable conditions, however, would also provide the same sort of shared risk information.

Identity and Privacy

A third argument might be to posit that genetic information requires special protections because it is more intimately tied to identity; this is the “you are your genes” argument. However, it is unclear as to why genes would be more integral to identity than your proteome, which more faithfully integrates your individual environmental influences, or for that matter, than the size and function of your hippocampus (a region of the brain very important in the consolidation of new memories and often disrupted in illness, from schizophrenia, to depression, to Alzheimer’s disease).

Brute Luck

A fourth, and I think strongest, argument takes a page out of luck egalitarianism. Luck egalitarians argue that we have social obligations to eliminate limitations in opportunity due to factors that are not the result of one’s choices (Arneson 2000); this would include both the effects of the “social lottery” (whether the family you happened to be born into is wealthy or poor, etc.) as well as the “genetic lottery” (what genes you get). While it is clear that differential susceptibilities to disease due purely to genetic variation are not due to choice, the case is less clear with non-genetic biomarkers, as they integrate the effects of environment, which are sometimes a matter of choice, or “option luck.” Luck egalitarians, therefore, may argue that non-genetic biomarkers do not raise the same social obligations as genetic ones. At a bare minimum, however, even luck egalitarians would be committed to minimizing the limitations due to biomarkers in children up until the age at which they can be considered responsible in the way that would disqualify the claim of the biomarker.