241 Validating available genomic predictions marketed for commercial crossbred cattle.

Abstract The incorporation of single nucleotide polymorphisms into genetic evaluations began in 2009. Today, with millions of cattle genotyped and a plethora of functioning evaluations for various breeds of cattle, it is a widely accepted technology among breeders. There are currently three practices available for genomic predictions specifically designed for commercial crossbred cows: Igenity Beef, Inherit Select, and the American Simmental Association’s (ASA) Total Herd Enrollment (THE) commercial option. The ASA’s THE program allows commercial producers to participate in a genetic evaluation similar to that of a registered breeder. Submission of pedigree, phenotypes, and DNA samples provide a genomically enhanced expected progeny difference (EPD). All three approaches are marketed for use on crossbred heifers to genetically rank them for economically relevant traits to help producers select replacements with superior genetic merit. While these products have been available for some time, there has not been any recent third-party validation research conducted to investigate their efficacy. Therefore, the objective of this research is to compare the genomic predictions of commercial crossbred dams to the performance of their progeny to determine their ability to predict genetic merit. DNA samples were collected in triplicate on 888 crossbred cows of varying age and breed composition at the University of Missouri research farms. Phenotypic records on progeny born in 2023 and 2024 were also collected including growth and carcass traits. Pearson and Spearman Correlations were performed first to evaluate the relationships between the genetic predictions and phenotypes. Linear mixed models were used to further investigate the relationship between progeny performance and dam genomic prediction while accounting for variation in calf’s sex, calf’s age, dam’s age, and calf’s contemporary group. There was a significant (P < 0.05) and positive correlation between all three genomic predictions and observed traits of birth weight (r= 0.11-0.29) and weaning weight (r= 0.09-0.17). In addition, linear models evaluating the relationship between a dam’s prediction and her calf’s phenotype showed that Inherit and ASA provide predictions that capture the average performance for birth weight and weaning weight (P < 0.01). In conclusion, all three genomic predictions provide an ability to predict the average genetic merit of progeny from commercial females for both birth and weaning weights. However, this also highlights the need to further investigate these relationships for additional traits of economic importance. Validation of these products will determine accuracy and usefulness of the commercial genomic tools as they have the potential to effectively decrease genetic lag between commercial herds and their seedstock providers.