The paper discusses the possibility that the benefits of pharmacogenomics will not be distributed equally and will create orphan populations. I argue that since these inequalities are not substantially different from those produced by ‘traditional’ drugs and are not generated with the intention to discriminate, their production needs not be unethical. Still, the final result is going against deep-seated moral feelings and intuitions, as well as broadly accepted principles of just distribution of health outcomes and healthcare. I thus propose (...) two provisos that would prevent the most offensive outcomes and moderate the scope of the produced inequalities. The first proviso rejects pharmacogenomics innovations that worsen existing group inequalities and aggravate the disadvantage of communities with a history of discrimination. The second proviso requires that there is a strategy in place to even out as much as possible the distribution of benefits in the future and that a system of compensations is in place for pharmacogenomic orphans. Given that only one moral problem generated by pharmacogenomics has been tackled, the list of provisos might be expanded when other issues are considered. (shrink)

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological (...) and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics. (shrink)

How do we find what is clinically significant in the swarms of data being generated by today’s diagnostic technologies? As electronic records become ever more prevalent – and digital imaging and genomic, proteomic, salivaomics, metabalomics, pharmacogenomics, phenomics and transcriptomics techniques become commonplace – fdifferent clinical and biological disciplines are facing up to the need to put their data houses in order to avoid the consequences of an uncontrolled explosion of different ways of describing information. We describe a new strategy (...) to advance the consistency of data in the dental research community. The strategy is based on the idea that existing systems for data collection in dental research will continue to be used, but proposes a methodology in which past, present and future data will be described using a consensus-based controlled structured vocabulary called the Ontology for Dental Research (ODR). (shrink)