Model selection is an important topic in data analysis. If the model is selected appropriately, we can use it to predict well. We introduced three tools for model selection in this paper. These three tools are QIC (Quasi-likelihood under the Independence model Criterion), L1-regularization path algorithm for generalized linear model, and L2-penalized logistic regression with a stepwise variable selection. The method QIC can be used for the correlated data such as family data. L1-regularization path algorithm and L2-penalized algorithm can be used for high-dimensional data such as microarray data. If we focus on gene interactions, the method L2-penalized algorithm may be useful. Our data from the SAPPHIRe (Stanford Asian Pacific Program for Hypertension and Insulin Resistance) project is from family data hence correlated. We use these three methods for the data set and compare the models selected by different methods and evaluate the performance of the prediction.

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