Maternal genome influences associate with up to 40% of spontaneous preterm births (PTB). Multiple genome wide association studies (GWAS) have been completed to identify genetic variants associated with PTB. Disappointingly, no highly significant SNPs have replicated in independent cohorts so far. We developed an approach combining protein-protein interaction (PPI) network data with tissue specific gene expression data to “find” SNPs of modest significance to identify candidate genes of functional importance that would otherwise be overlooked. This approach is based on the assumption that “high-ranking” SNPs falling short of genome wide significance may nevertheless indicate genes that have substantial biological value in understanding PTB. We mapped highly-ranked candidate SNPs from a meta-analysis of PTB-GWAS to coding genes and developed a PPI network enriched with PTB-SNP carrying genes. This network was scored with gene expression data from term and preterm myometrium to identify subnetworks of PTB-SNP associated genes coordinately expressed with labour onset in myometrial tissue. Our analysis consistently identified significant sub-networks associated with the interacting transcription factors MEF2C and TWIST1, genes not previously associated with PTB, both of which regulate processes clearly relevant to birth timing. Other genes in the significant sub-networks were also associated with inflammatory pathways, as well as muscle function and ion channels. Gene expression level dysregulation was confirmed for eight of these networks by qRT-PCR in an independent set of term and pre-term subjects. Our method identifies novel genes dysregulated in PTB and provides a generalized framework to identify GWAS SNPs that would otherwise be overlooked.