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Correction of phenotype misclassification based on high-discrimination genetic predictive risk models
John P A Ioannidis1, Yi Yu, Johanna M Seddon
1Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA. jioannid@stanford.edu
Epidemiology (Cambridge, Mass.)
|October 2, 2012
Summary
Accurate phenotype classification is crucial for genetic studies. Recoding participants at high risk of disease, even if initially classified as healthy, improves accuracy and aids in discovering new genetic risk factors.
Area of Science:
- Genetics and Epidemiology
- Biostatistics
Background:
- Phenotype misclassification in association studies, particularly genetic risk factor studies, can significantly reduce accuracy.
- Participants may be incorrectly classified as non-diseased due to incomplete diagnostic workup or insufficient follow-up periods.
Purpose of the Study:
- To evaluate the use of validated predictive models to reclassify 'non-diseased' individuals at high risk of developing a disease.
- To determine conditions for maximal net improvement in phenotype classification accuracy through reclassification.
- To assess the impact of reclassification on the power to detect novel genetic risk factors.
Main Methods:
- Simulations were conducted to analyze the effect of reclassification on detecting new risk factors under varying classification accuracy scenarios.
- A validated predictive model for age-related macular degeneration progression (AUC=0.915) was applied to training and validation cohorts.
- Participants initially classified as non-progressors were reclassified as progressors based on model predictions.
Main Results:
- Reclassification yielded maximal net improvement when the model's positive likelihood ratio exceeded the inverse odds of disease in controls.
- In the training cohort (n=2,937), 195-272 non-progressors were reclassified; in the validation cohort (n=1,227), 78-91 were reclassified.
- The framework was successfully applied to a validated model for age-related macular degeneration progression.
Conclusions:
- Correcting phenotype misclassification using highly discriminatory predictive models can enhance the identification of additional genetic and other risk factors.
- This approach is particularly beneficial when validated risk factors with strong discriminating ability are available.
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