Bovine TB (BTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, which we have previously shown to be substantially reprogrammed during intracellular infection by M. bovis. In the current study we used differential expression with correlation- and interaction-based network approaches to analyze the macrophage transcriptional response to infection with M. bovis to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. The host gene expression data consisted of bovine RNA-seq data from alveolar macrophages infected with M. bovis at 24 and 48 h post-infection. These RNA-seq data were analyzed using 3 distinct analysis pipelines; novel response pathways and modules were further refined using cross-comparison and integration of the results. First, a differential expression analysis was carried out to determine the most significantly differentially expressed (DE) genes between conditions at each time point. Second, 2 networks were constructed at each time point using gene correlation patterns to determine changes in expression across conditions. Functional sub-modules within each correlation network were selected by statistical criteria for modularity. Third, a gene interaction base network of the mammalian host response to mycobacterial infection was generated using the GeneCards database (www.genecards.com) and InnateDB (www.innatedb.com). Differential gene expression data were superimposed on this base network to extract functional modules of interconnected DE genes. Bovine GWAS data were obtained from a published BTB susceptibility/resistance study. The results from the 3 parallel analyses were integrated with this data to determine which of the 3 approaches identified genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Our results indicate distinct and significant overlap in SNP discovery and demonstrate that network-based integration of relevant transcriptomics data can leverage substantial additional information from GWAS data sets.