title: How accurate can genetic predictions be?
abstract: BACKGROUND
  Pre-symptomatic prediction of disease and drug response based on genetic testing is a critical component of personalized medicine. Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution.
  
  
  RESULTS
  Here, we mathematically derive the absolute limits that these factors impose on test accuracy in the absence of any distributional assumptions on risk. We present these limits in terms of the best-case receiver-operating characteristic (ROC) curve, consisting of the best-case test sensitivities and specificities, and the AUC (area under the curve) measure of accuracy. We apply our method to genetic prediction of type 2 diabetes and breast cancer, and we additionally show the best possible accuracy that can be obtained from integrated predictors, which can incorporate non-genetic features.
  
  
  CONCLUSIONS
  Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.
authors: Partners Center for Personalized Genetic Medicine, Boston, MA 02115, USA. jdreyf@bu.edu
authors: Jonathan M
authors: JM
authors: Dreyfuss
authors: Daniel
authors: D
authors: Levner
authors: James E
authors: JE
authors: Galagan
authors: George M
authors: GM
authors: Church
authors: Marco F
authors: MF
authors: Ramoni
mesh_terms: D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics*; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000235:genetics*; D005260:Female; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D057285:Precision Medicine; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve