Human illness due to foodborne consumption of Salmonella continues to occur, despite efforts to regulate and reduce its occurrence in food. Poultry is one food animal that contributes significantly to this problem. Current molecular tools in place are quite effective at identifying and tracing Salmonella through production chains, and companies have developed extensive mitigation strategies to reduce or eliminate problematic clones once they occur. However, this reactive approach often results in human illness burden before mitigation is achieved. Reducing the gap between identifying and mitigating high-risk clones is a pressing need for producers. This project will address that need using high-resolution genomic analyses, combined with rapid phenotypic screens and predictive modeling. First, emergent or problematic Salmonella serovars in poultry will be investigated at the genomic level using phylogenetic and pangenome approaches. To aid in these analyses, a manually curated Salmonella virulence and fitness gene database will be developed and implemented. Second, genomic analyses will guide selection of strains within each serovar for phenotypic analyses for traits associated with adhesion, invasion, acid and disinfectant tolerance, biofilm formation, and competition within the avian gut niche. These data will be used to identify clades of higher risk, and to establish baseline risk across serovars of poultry relevance. Finally, predictive tools will be developed based upon these data to aid in surveillance by providing a risk score based on genomic and/or phenotypic attributes. This work will aid in understanding of how genomic shifts in poultry Salmonella can be used to proactively mitigate human risk.