Genomic Prediction for Abortion in Lactating Holstein Dairy Cows

Simple Summary Pregnancy losses in dairy cattle result in impaired animal health and welfare, as well as economic losses due to increased culling, reduced milk production, calf losses, and increased reproductive costs, among others. Advances in the last few decades have allowed the dairy industry to make significant progress in reproductive efficiency, but pregnancy losses continue to be an unresolved problem. The origin of abortions can be infectious disease, metabolic disorders, heat stress, and a genetic predisposition among many others. We have developed genomic predictions (Z_Abort) to identify Holstein dairy cows with a greater risk of abortion. This allows dairy producers and veterinarians to select more productive and profitable cows. The objectives of the study were to (1) describe the development of the genomic predictions for cow abortions in lactating Holstein dairy cattle based on producer-recorded data and ssGBLUP methodology and (2) evaluate the efficacy of genomic predictions for cow abortions in commercial herds of US Holstein cows using data from herds that do not contribute phenotypic information to the evaluation. The results of the present study show that the genomic predictions for cow abortion trait (Z_Abort) can effectively predict the risk of abortion of lactating Holstein dairy cows and, hence, allow genetic selection towards healthier and more profitable cows. Abstract Abortion in dairy cattle causes great economic losses due to reduced animal health, increase in culling rates, reduction in calf production, and milk yield, among others. Although the etiology of abortions can be of various origins, previous research has shown a genetic component. The objectives of this study were to (1) describe the development of the genomic prediction for cow abortions in lactating Holstein dairy cattle based on producer-recorded data and ssGBLUP methodology and (2) evaluate the efficacy of genomic predictions for cow abortions in commercial herds of US Holstein cows using data from herds that do not contribute phenotypic information to the evaluation. We hypothesized that cows with greater genomic predictions for cow abortions (Z_Abort STA) would have a reduced incidence of abortion. Phenotypic data on abortions, pedigree, and genotypes were collected directly from commercial dairy producers upon obtaining their permission. Abortion was defined as the loss of a confirmed pregnancy after 42 and prior to 260 days of gestation, treated as a binary outcome (0, 1), and analyzed using a threshold model. Data from a different subset of animals were used to test the efficacy of the prediction. The additive genetic variance for the cow abortion trait (Z_Abort) was 0.1235 and heritability was 0.0773. For all animals with genotypes (n = 1,662,251), mean reliability was 42%, and genomic predicted transmitting abilities (gPTAs) ranged from −8.8 to 12.4. Z_Abort had a positive correlation with cow and calf health traits and reproductive traits, and a negative correlation with production traits. Z_Abort effectively identified cows with a greater or lesser risk of abortion (16.6% vs. 11.0% for the worst and best genomics groups, respectively; p < 0.0001). The inclusion of cow abortion genomic predictions in a multi-trait selection index would allow dairy producers and consultants to reduce the incidence of abortion and to select high-producing, healthier, and more profitable cows.