Despite an increase in severity and prevalence of coral diseases, our knowledge of their pathology, etiology and epizootiology is still limited. One emerging disease in particular, Scleractinian Coral Tissue Loss Disease (SCTLD), affects over 20 species of reef-building coral and some of the most susceptible species have been reduced to less than 3% of their initial population densities in some locations. Understanding how SCTLD manifests at the molecular level and how different species are able to respond is necessary to mitigate further spread and mortality. Five Caribbean coral species, Orbicella annularis, Colpophyllia natans, Porites astreoides, Pseudodiploria strigosa and Montastraea cavernosa , were exposed to SCTLD and a spectrum of disease severity as measured by lesion growth rate was defined. Post-exposure, transcriptomes were sequenced to identify the gene expression patterns and biological processes that distinguish SCTLD-susceptible species from SCTLD-resistant species. Weighted Gene Correlation Network Analysis was used to find modules of highly correlated genes in each species to identify candidate biomarkers for disease severity. In addition, the presence of significant functional categories within each module was tested using Rank-based Gene Ontology Analysis. This bioinformatic approach allows us to link distinct gene expression patterns to varying degrees of disease susceptibility, a crucial first step to enhance our understanding of this emerging disease.