Adaptive robust genetic association tests using case-parents triad families

Using the conditional likelihood, a model-specific score test can be derived under a given genetic model to test genetic association for case-parents triad family data. When the underlying genetic model is correctly specified, the score test is most powerful. However, it can lose substantial power when the model is misspecified. Several robust tests have been proposed to deal with the problem, such as the maximum test statistic, the maximin efficiency robust test, and the constrained likelihood ratio test. These tests have been shown to be robust against model misspecification compared with those model-specific score tests, but they are either time-consuming in computation or not sufficiently high in power robustness under some situations. In this study, a data-driven procedure is proposed to construct two adaptive robust genetic association tests WMERT and WMAX . The WMERT is simple in calculation and has fairly high power robustness. The empirical power of WMAX is quite stable and close to those of the model-specific score tests. The two proposed tests should be beneficial to practical genetic association studies. A real dataset consisting of neural tube defect triad families is used for illustration of the methods. R-scripts are also provided for numerical calculation of the proposed methods in practical studies.