Research
Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability
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* Corresponding author: A CJW Janssens a.janssens@erasmusmc.nl
1 Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands
2 Office of Minority Health and Health Disparities, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30341, USA
3 Office of Public Health Genomics, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30341, USA
Genome Medicine 2011, 3:51 doi:10.1186/gm267
Published: 28 July 2011Abstract
Background
Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.
Methods
We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.
Results
We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.
Conclusions
The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC.