Bovine respiratory disease (BRD) is the most common disease affecting feedlot cattle and economic losses incurred by affected animals is estimated to be $640 million annually. The objective of the current study was to evaluate single nucleotide polymorphisms (SNP) and their potential associations with bovine respiratory disease susceptibility and carcass traits in feedlot steers. A population of 314 crossbred steers born from 2010 to 2013 and raised at LSU AG Center Central Research Station (CRS) in Baton Rouge, LA and LSU Ag Center Hill Farm Research Station (HFRS) in Homer, Louisiana, were utilized in the current study. Prior to shipping to a commercial feedlot, the measurements of birth weight, weaning weight and hip height were collected from each steer. After weaning, and the completion of a 45-d preconditioning period, steers were shipped to a commercial feedlot. A total of 16 steers over the entire 4-year evaluation period were affected by BRD. A total of 309 steers were harvested at a commercial packing plant and the traits of marbling score, rib eye area, back fat thickness and yield grade were collected. A total of 74 SNP were selected from a previously described BRD QTL region spanning between 40 and 80 MB on BTA 6. This same region has also been identified to have QTL associated with kidney pelvic heart fat percentage. A total of 33 SNP were selected from a previously described BRD QTL region spanning 0–30 MB on BTA 20. This region has been identified to have QTL’s associated with kidney pelvic heart fat percentage, marbling score, intramuscular fat, sheer force and carcass weight. A mixed model design was fit with individual carcass traits, BRD status, and individual SNP genotype as dependent variables and sire breed and year fit as independent variables in the model to identify SNP that were significantly associated with the traits of interest. Although multiple SNP were identified as being significantly associated with carcass traits and BRD susceptibility, these SNP must be validated in larger, and more diverse populations before implementation into selection strategies.