Friday, February 15, 2013
Room 313 (Hynes Convention Center)
Genetic data is largely probabilistic and few certainties exist with regard to its interpretation in the medical setting. These inherent uncertainties will continue – and will indeed be amplified - in a genomic era. As whole genomes are increasingly sequenced vast numbers of variants of uncertain significance will be routinely identified in any given individual. Moreover, the genetic influences that impact common diseases are highly probabilistic and not deterministic. Thus interpreting and using genomic data in the setting of common disease presents significant challenges. Finally, estimates of the penetrance of most genetic diseases will likely decline as we ascertain cases from a “genotype first” perspective.
Navigating these complexities and incorporating the probabilistic nature of genetic information into our interpretations will be necessary as we more broadly apply genomic analysis in both the research setting and in the clinic. Espececially in the context of the clinic, we must set defined and rigorous criteria for the interpretation of genomic data so as to avoid the substantial harm that can occur from false positive results. This presentation will explore these challenges and how best to assess and deal with the inherent uncertainties which will inevitably arise as we begin to practice genomic medicine.