Prediction of Clinical Mastitis Outcomes Within and Between Environments Using Whole-genome Markers

The objective of this study was to evaluate genome-enabled predictions of daughter yield deviations for clinical mastitis in Norwegian Red cows within and between environments according to mastitis pathogen status. Genome-based predictions of daughter yield deviations for clinical mastitis for 1,126 bulls within and between 5 environments were performed using Bayesian ridge regression. The environments were defined as herd-5-yr classes with the following prevalence of bacteriological milk samples found positive for contagious mastitis pathogens: <50% (L50), ≥ 50% (H50), ≤ 25% (L75), >25% and <75% (M75), and ≥ 75% (H75). In addition, predictions based on all data across environment groups (the full data set, FD) were calculated to provide a benchmark for comparison. Predictive ability was evaluated using a 10-fold cross validation. A bootstrap procedure was used to obtain 95% confidence intervals for the cross-validation distribution of predictive ability for each data set. Predictive ability ranged from 0.04 for L75 to 0.19 for FD. Similar predictions within and between environments showed no evidence of genotype by environment interaction. The 95% confidence interval for all 5 environmental data sets included zero and ranged from 0.02 to 0.35 for FD. The bootstrap distribution showed large variation within each data set and small variation between data sets. Although we found no evidence of genotype by environment interaction, rank correlations of the single nucleotide polymorphism effects between different environments ranged from 0.15 (L75 - H75) to 0.92 (M75 - FD), indicating that single nucleotide polymorphisms may have a differential contribution to predictive ability in environments with distinct pathogen loads.