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How accurate can genetic predictions be?

Jonathan M Dreyfuss12*, Daniel Levner34, James E Galagan56, George M Church34 and Marco F Ramoni178

  • * Corresponding author: Jonathan M Dreyfuss

  • † Equal contributors

Author Affiliations

1 Partners Center for Personalized Genetic Medicine, Boston, MA, 02115, USA

2 Graduate Program in Bioinformatics, Boston University, Boston, MA, 02215, USA

3 Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA

4 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA

5 Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA

6 Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA

7 Harvard-MIT Division of Health Sciences and Technology, Boston, MA, 02139, USA

8 Children’s Hospital Informatics Program, Boston, MA, 02115, USA

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BMC Genomics 2012, 13:340  doi:10.1186/1471-2164-13-340

Published: 24 July 2012



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.


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.


Knowledge of such limits is valuable in understanding the implications of genetic testing even before additional associations are identified.