Logic Regression Analysis for Finding Interaction Effects of Genes Polymorphisms and Other Risk Factors on Low HDL: Tehran Lipid and Glucose Study

Logic regression is a generalized regression method that can identify complex Boolean interactions of binary variables. This method has been successfully used for analyzing single-nucleotide polymorphism data, because in SNP association studies interactions are important. The aim of this study is to investigate the associations between some candidate gene polymorphisms and HDL concentration using Logic Regression. Subjects for this cross sectional study, 436 subjects (172 men and 264 women) aged≥20 with some polymorphisms, were randomly selected from among participants of the Tehran Lipid and Glucose Study (TLGS). Logic regression analysis was used to identify combinations of main genetic effects and interactions associated with HDL. Cross validation and randomization test were done to avoid over fitting of the models. Cross validation test suggested that the Logic model with four Boolean combinations and four predictors was the best logic model, which after fitting, showed that individuals who carry Apoe SNP ε3 or have high TG have an odds ratio of 2.35 (CI 95%:1.3-4.25) for having low HDL compared to other subjects. Also subjects with high TG have odds ratio 2.73 (CI 95%: 1.65,4.53) for having low HDL. Results of this study shows that Logic Regression is a powerful method to determine the interaction effect between high TG and ApoE SNP for having low HDL.