Analyzing GWAS data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine GWAS results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this paper, we combine an algorithm for weighted-edge module searching and a probabilistic gene interaction network (STRING) in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and highly probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in three test cases.