The objectives of this research included estimating variance components for ketosis susceptibility and identification of genomic regions associated with ketosis in Jersey cattle. Ketosis is one of the most commonly reported metabolic diseases in dairy herds. Genetic analyses of ketosis have been conducted previously, but few focus specifically on Jersey cattle. Voluntary producer-recorded health event data related to ketosis were available from Dairy Records Management Systems (NCSU, Raleigh, NC). Standardization was implemented to account for the various acronyms used by producers to designate an incidence of ketosis. Ketosis events were restricted to the first reported incidence within 60 d of calving in first through fifth parity. After editing, there were a total of 42,233 records from 23,865 cows. A total of 1,750 genotyped animals were used for analyses using 60,671 markers. Given the binary nature of the trait, a threshold animal model was fitted using THRGIBBS1F90 (version 2.110) using only pedigree information and then also incorporating genomic information using a single-step genomic BLUP approach. postGSf90 (version 1.38) was used to calculate SNP effects as well as variance explained by 10-SNP windows. Heritability of ketosis susceptibility was 0.083 (SD = 0.021) and 0.078 (SD = 0.018) in pedigree-based and genomic analyses, respectively. The marker with the largest estimated SNP effect was located on chromosome 10 at 66.3 Mbp. Additional peaks were identified on chromosomes 11, 14, and 23. The region explaining the largest proportion of variance (0.70%) was located on chromosome 6 at 56.1 Mbp. Additional regions explaining large proportions of variance were located on chromosomes 11 (0.51%), 3 (0.45%), and 25 (0.40%). Genes located in these regions were investigated for having a role in ketosis susceptibility. Results indicate that ketosis susceptibility in Jerseys has a significant genetic component, making feasible the selection for animals more resistant to ketosis. Associated genomic regions could be incorporated into genetic evaluations in the future, as well as used to further understand the underlying biology of this disease.