Improving the Prediction of Complex Diseases by Testing for Multiple Disease-Susceptibility Genes

Studies have argued that genetic testing will provide limited information for predicting the probability of common diseases, because of the incomplete penetrance of genotypes and the low magnitude of associated risks for the general population. Such studies, however, have usually examined the effect of one gene at time. We argue that disease prediction for common multifactorial diseases is greatly improved by considering multiple predisposing genetic and environmental factors concurrently, provided that the model correctly reflects the underlying disease etiology. We show how likelihood ratios can be used to combine information from several genetic tests to compute the probability of developing a multifactorial disease. To show how concurrent use of multiple genetic tests improves the prediction of a multifactorial disease, we compute likelihood ratios by logistic regression with simulated case-control data for a hypothetical disease influenced by multiple genetic and environmental risk factors. As a practical example, we also apply this approach to venous thrombosis, a multifactorial disease influenced by multiple genetic and nongenetic risk factors. Under reasonable conditions, the concurrent use of multiple genetic tests markedly improves prediction of disease. For example, the concurrent use of a panel of three genetic tests (factor V Leiden, prothrombin variant G20210A, and protein C deficiency) increases the positive predictive value of testing for venous thrombosis at least eightfold. Multiplex genetic testing has the potential to improve the clinical validity of predictive testing for common multifactorial diseases.