Assessing gene-gene interactions (GxG) at the gene level can permit examination of epistasis at biologically functional units with amplified interaction signals from marker-marker pairs. Current gene-based GxG methods tend to be designed for studying interactions among two or a few genes. For complex traits, it is often common to have a list of many candidate genes to explore GxG. In this work, we propose a pathway-guided approach based on penalized regression for detecting interactions among genes. Specifically, we apply the principal component analysis to summarize the multi-SNP genotypes and SNP-SNP interaction between a gene pair, and to identify important main and interaction effects using an L1 penalty, which incorporates adaptive weights based on biological guidance and trait supervision. Our approach aims to combine the advantages of biological guidance and data adaptiveness, and yields credible findings that have both biological and statistical support and may be likely to shed insights in order to formulate biological hypotheses for further cellular and molecular studies. The proposed approach can be used to explore the gene-gene interactions with a list of many candidate genes and is applicable even when sample size is smaller than the number of predictors studied. We evaluate the utility of the pathway-guided penalized GxG regression using simulation and real data analysis. The numerical studies suggest improved performance over methods not utilizing pathway and trait guidance.