Prediction has long been a feature of medical genetics. For example, healthcare practitioners commonly screen for simple Mendelian traits with high penetrance and well understood presentation like Huntington’s Disease or the BRCA1/2 breast cancer mutations. Polygenic scores (PGS), used to estimate how a multitude of genetic variants relate to phenotype, represent an exponential shift in the complexity and ambiguity of genetics’ predictive potential. Yet biological mechanisms underlying PGS and their connections to phenotypes are ambiguous, as are the populations and problems to which they apply. If traditional genetic tests spread uncertainty onto users, PGS are characterized by uncertainty that originates in scientific research and is amplified through the application of PGS to clinical, commercial, and potentially policy settings. To date, PGS research has framed uncertainty narrowly as a statistical matter. It is unknown how researchers think about and accommodate other dimensions of uncertainty in the field. This paper contends that this conceals a set of interconnected uncertainties affecting the utility of PGS that must be managed in the context of application. Drawing on interviews with experts and content analysis of genomics research, it conceptualizes the field of PGS research as one beset by multidimensional forms of uncertainty: causal, phenotype, population, practical, and ethical. We argue that if the effects of uncertainty are not well understood, deploying PGS as decision-making tools will be less equitable, effective, and publicly legitimate, and may also lead to greater stigmatization and inaccurate perceptions of disease or health.
Authors: Zachary Griffen, Division of Medical Ethics, NYU Grossman School of Medicine; Aaron Panofsky, Institute for Society and Genetics, UCLA
