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ABSTRACT Feeding behaviour traits, such as number, duration or intake per feeder visit, have been associated with feed efficiency in dairy cattle. Those traits, however, do not fully capture cows' feeding patterns throughout the day. The goal of this study was to propose a new phenotype for characterising within‐day feeding patterns and estimate its heritability and genetic correlations with dry matter intake (DMI), secreted milk energy, metabolic body weight and residual feed intake. Feeding patterns were evaluated using 4.8 million bunk visits from 1684 midlactation Holstein cows collected from 2009 to 2023 with an Insentec system. Feed efficiency traits were available from 6099 lactating Holstein cows at six research stations across the United States. Daily bunk visits were ordered, with Time 0 designated as the time of first feed delivery. Intake proportions were calculated by visit for each cow by dividing feed intake per visit by the total intake of the cow for that day. Feeding patterns were characterised by the area under the curve of cumulative feed intake proportions for each cow throughout the day. The feeding pattern phenotype per cow was defined as the average of areas under the curve across days, whereas consistency of feeding pattern was calculated as the natural logarithm of variance of daily area under the curve values. Estimates of heritability and genetic correlations were performed using Bayesian inference with an animal model, considering lactation, days in milk and cohort (trial–treatment) as fixed effects and animal as a random effect. Heritability estimates for average area under the curve and variance of daily area under the curve were 0.35 ± 0.05 and 0.16 ± 0.05, respectively. The genetic correlation between average area under the curve and secreted milk energy was −0.30 ± 0.14. Genetic correlations between average area under the curve and DMI, metabolic body weight and residual feed intake were not statistically significant. Variance of daily area under the curve was genetically correlated with DMI (0.47 ± 0.15), secreted milk energy (0.40 ± 0.17) and metabolic body weight (0.28 ± 0.13). The genetic correlation between variance of daily area under the curve and residual feed intake was not significant. Overall, we provided a reliable method to truly characterise feeding patterns in midlactation dairy cows. Feeding pattern and its consistency were heritable, indicating that a significant proportion of phenotypic variation is explained by additive genetic effects. Genetic correlation estimates indicate that cows with more consistent daily feeding patterns have lower DMI, lower secreted milk energy and lower metabolic body weight. [ABSTRACT FROM AUTHOR]

The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive x additive (AxA) epistasis effects (A + AxA model) to be the best prediction model. Under the A + AxA model, additive heritability was 0.141, and AxA heritability was 0.263 that consisted of 0.260 inter-chromosome AxA heritability and 0.003 intra-chromosome AxA heritability, showing that inter-chromosome AxA effects were responsible for the accuracy increases due to AxA. Under the SNP additivemodel (A-onlymodel), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197--0.333) under A + AxA model and was 0.231 (with range of 0.188--0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + AxA model over the A-only model was 6.49% (with range of 3.02--14.29%). Results in this study showed AxA epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model. [ABSTRACT FROM AUTHOR]

Background: Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Selection for fitness traits is more difficult, since they have low heritabilities and are influenced by various non-genetic factors. The objective of this paper was to investigate the predictive ability of two-stage and single-step genomic selection methods applied to health data collected from on-farm computer systems in the U.S. Methods: Implementation of single-trait and two-trait sire models was investigated using BayesA and single-step methods for mastitis and somatic cell score. Variance components were estimated. The complete dataset was divided into training and validation sets to perform model comparison. Estimated sire breeding values were used to estimate the number of daughters expected to develop mastitis. Predictive ability of each model was assessed by the sum of χ2 values that compared predicted and observed numbers of daughters with mastitis and the proportion of wrong predictions. Results: According to the model applied, estimated heritabilities of liability to mastitis ranged from 0.05 (SD = 0.02) to 0.11 (SD = 0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (SD = 0.01) to 0.18 (SD = 0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (SD = 0.17). The single-step method had the best predictive ability. Conversely, the smallest number of wrong predictions was obtained with the univariate BayesA model. The best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, the latter led to the smallest number of wrong predictions. Conclusions: Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods depends on a multitude of factors. Heritability of traits and reliability of genotyped individuals has a large impact on the performance of genomic evaluation methods. Given the current characteristics of producer-recorded health data, single-step methods have several advantages compared to two-step methods. [ABSTRACT FROM AUTHOR]