With a large number of quantitative trait loci being identified in genome-wide association studies, researchers have become more interested in detecting interactions among genes or single nucleotide polymorphisms (SNPs). In this research, we carried out a two-stage model selection procedure to detect interacting gene pairs or SNP pairs associated with four important traits of outbred mice, including glucose, high-density lipoprotein cholesterol, diastolic blood pressure and triglyceride. In the first stage, a variance heterogeneity test was used to screen for candidate SNPs. In the second stage, the Lasso method and single pair analysis were used to select two-way interactions. Moreover, the shared Gene Ontology information about the selected interacting gene pairs was considered to study the interactions auxiliarily. Based on this method, we not only replicated the identification of important SNPs associated with each trait of outbred mice, but also found some SNP pairs and gene pairs with significant interaction effects on each trait. Simulation studies were also conducted to evaluate the performance of the two-stage method in different situations.