Gene-gene interaction plays an important role in the etiology of complex diseases,which may exist without a genetic main effect. It would be helpful to develop methods that can detect not only the gene’s main effects but also gene-gene interaction effects regardless of the existence of gene’s main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical andfrequently applied. In this study,wehave developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families.Anew test statistic is proposed for classifying affecteds and unaffecteds in the SVM-based PGMDRapproach to improve performance in detecting gene-gene interactions. The proposed and original approaches have been applied to the simulation study anda realdata examplefor illustrationand comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interaction
