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There is a growing consideration that gene-gene interactions may play important roles in complex diseases etiology. The simultaneous genotyping of hundreds of thousands of single nucleotide polymorphisms (SNP) have made genetic epidemiology studies come into a new phase. Using molecular network to research complex diseases and find related information is a novel and powerful method. Here, we applied GLOSSI (Gene-loci Set Analysis) to test the association of a group of SNPs (loci-set) with complex disease phenotypes. At the same time, all SNPs were used to construct well designed genetic networks by Polymorphism Interaction Analysis (PIA) algorithm and by which we extracted gene-gene sub-networks associated with complex diseases. The results were compared to explore the most significant SNP groups which might contribute to interpret disease etiology. We apply Rheumatoid Arhtritis (RA) datasets provided by the Genetic Analysis Workshop 15 (GAW 15) Problem 2 and positional gene sets for each human chromosome and each cytogenetic band were derived from the publicly accessible Molecular Signature Database (MSigDB). The results reported here that there were no significant SNP loci sets of MSigDB associated with RA but three SNP groups extracted from genetic networks, offering molecular support for the current grouping of the genes. Furthermore, we found that the hub genes of gene-gene sub-networks were also most significant SNPs, such as CD160 (rs744877) and ALS2 (rs970595), were special relevant to RA, as supported by many previous reports. Our study is a new attempt to mine gene groups with genetic associations to complex diseases, which can be applied to large numbers of genetic markers.