Climate sensitivity of milk production traits and milk fatty acids in genotyped Holstein dairy cows

The aim of this study was the evaluation of climate sensitivity via genomic reaction norm models [i.e., to infer cow milk production and milk fatty acid (FA) responses on temperature-humidity index (THI) alterations]. Test-day milk traits were recorded between 2010 and 2016 from 5,257 first-lactation genotyped Holstein dairy cows. The cows were kept in 16 large-scale cooperator herds, being daughters of 344 genotyped sires. The longitudinal data consisted of 47,789 test-day records for the production traits milk yield (MY), fat yield (FY), and protein yield (PY), and of 20,742 test-day records for 6 FA including C16:0, C18:0, saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). After quality control of the genotypic data, 41,057 SNP markers remained for genomic analyses. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI. Genomic reaction norm models were applied to estimate genetic parameters in a single-step approach for production traits and FA in dependency of THI at different lactation stages, and to evaluate the model stability. In a first evaluation strategy (New_sire), all phenotypic records from daughters of genotyped sires born after 2010 were masked, to mimic a validation population. In the second strategy (New_env), only daughter records of the new sires recorded in the most extreme THI classes were masked, aiming at predicting sire genomic estimated breeding values (GEBV) under heat stress conditions. Model stability was the correlation between GEBV of the new sires in the reduced data set with respective GEBV estimated from all phenotypic data. Among all test-day production traits, PY responded as the most sensitive to heat stress. As observed for the remaining production traits, genetic variances were quite stable across THI, but genetic correlations between PY from temperate climates with PY from extreme THI classes dropped to 0.68. Genetic variances in dependency of THI were very similar for C16:0 and SFA, indicating marginal climatic sensitivity. In the early lactation stage, genetic variances for C18:0, MUFA, PUFA, and UFA were significantly larger in the extreme THI classes compared with the estimates under thermoneutral conditions. For C18:0 and MUFA, PUFA, and UFA in the middle THI classes, genetic correlations in same traits from the early and the later lactation stages were lower than 0.50, indicating strong days in milk influence. Interestingly, within lactation stages, genetic correlations for C18:0 and UFA recorded at low and high THI were quite large, indicating similar genetic mechanisms under stress conditions. The model stability was improved when applying the New_env instead of New_sire strategy, especially for FA in the first stage of lactation. Results indicate moderately accurate genomic predictions for milk traits in extreme THI classes when considering phenotypic data from a broad range of remaining THI. Phenotypically, thermal stress conditions contributed to an increase of UFA, suggesting value as a heat stress biomarker. Furthermore, the quite large genetic variances for UFA at high THI suggest the consideration of UFA in selection strategies for improved heat stress resistance.     

Random regression test-day model for clinical mastitis: Genetic parameters,model comparison,and correlations with indicator traits

The objective was to study genetic (co)variance components for binary clinical mastitis (CM), test-day protein yield, and udder health indicator traits [test-day somatic cell score (SCS) and type traits of the udder composite] in the course of lactation with random regression models (RRM). The study used a data set from selected 15 large-scale contract herds including 26,651 Holstein cows. Test-day production and CM data were recorded from 2007 to 2012 and comprised parities 1 to 3. A longitudinal CM data structure was generated by assigning CM records to adjacent official test dates. Bivariate threshold-linear RRM were applied to estimate genetic (co)variance components between longitudinal binary CM (0 = healthy; 1 = diseased) and longitudinal Gaussian distributed protein yield and SCS test-day data. Heritabilities for liability to CM (heritability ~0.15 from 0 to 305 d after calving) were slightly higher than for SCS for corresponding days in milk (DIM) in the course of lactation. Daily genetic correlations between CM and SCS were moderate to high (genetic correlation ~0.70), but substantially decreased at the very end of lactation. Genetic correlations between CM at different test days were close to 1 for adjacent test days, but were close to zero for test days far apart. Daily genetic correlations between CM and protein yield were low to moderate. For identical DIM (e.g., DIM 20, 160, and 300), genetic correlations were −0.03, 0.11, and 0.18, respectively, and disproved pronounced genetic antagonisms between udder health and productivity. Correlations between estimated breeding values (EBV) for CM from the RRM and official EBV for linear type traits of the udder composite, including EBV from 74 influential sires (sires with >60 daughters), were −0.31 for front teat placement, −0.01 for rear teat placement, −0.31 for fore udder attachment, −0.32 for udder depth, and −0.08 for teat length. Estimated breeding values for CM from the RRM were compared with EBV from a multiple-trait model and with EBV from a repeatability model. For test days covering an identical time span and on a lactation level, correlations between EBV from RRM, multiple-trait model, and repeatability model were close to 1. Most relevant results suggest the routine application of threshold RRM to binary CM to (1) allow selection of genetically superior sires for distinct stages of lactation and (2) achieve higher selection response in CM compared with selection strategies based on indicator type traits or based on the indicator-trait SCS.     

Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits

Keeping dairy cows in grassland systems relies on detailed analyses of genetic resistance against endoparasite infections, including between- and within-breed genetic evaluations. The objectives of this study were (1) to compare different Black and White dairy cattle selection lines for endoparasite infections and (2) the estimation of genetic (co)variance components for endoparasite and test-day milk production traits within the Black and White cattle population. A total of 2,006 fecal samples were taken during 2 farm visits in summer and autumn 2015 from 1,166 cows kept in 17 small- and medium-scale organic and conventional German grassland farms. Fecal egg counts were determined for gastrointestinal nematodes (FEC-GIN) and flukes (FEC-FLU), and fecal larvae counts for the bovine lungworm Dictyocaulus viviparus (FLC-DV). The lowest values for gastrointestinal nematode infections were identified for genetic lines adopted to pasture-based production systems, especially selection lines from New Zealand. Heritabilities were low for FEC-GIN (0.05–0.06 ± 0.04) and FLC-DV (0.05 ± 0.04), but moderate for FEC-FLU (0.33 ± 0.06). Almost identical heritabilities were estimated for different endoparasite trait transformations (log-transformation, square root). The genetic correlation between FEC-GIN and FLC-DV was 1.00 ± 0.60, slightly negative between FEC-GIN and FEC-FLU (?0.10 ± 0.27), and close to zero between FLC-DV and FEC-FLU (0.03 ± 0.30). Random regression test-day models on a continuous time scale [days in milk (DIM)] were applied to estimate genetic relationships between endoparasite and longitudinal test-day production traits. Genetic correlations were negative between FEC-GIN and milk yield (MY) until DIM 85, and between FEC-FLU and MY until DIM 215. Genetic correlations between FLC-DV and MY were negative throughout lactation, indicating improved disease resistance for high-productivity cows. Genetic relationships between FEC-GIN and FEC-FLU with milk protein content were negative for all DIM. Apart from the very early and very late lactation stage, genetic correlations between FEC-GIN and milk fat content were negative, whereas they were positive for FEC-FLU. Genetic correlations between FEC-GIN and somatic cell score were positive, indicating similar genetic mechanisms for susceptibility to udder and endoparasite infections. The moderate heritabilities for FEC-FLU suggest inclusion of FEC-FLU into overall organic dairy cattle breeding goals to achieve long-term selection response for disease resistance.     

Joint international evaluation of Milking Shorthorn dairy cattle for production traits

Pedigree information and test-day records for the first 3 parities of Milking Shorthorn dairy cattle from 5 countries were analyzed. After editing, the data included 1,018,528 test-day records from 68,653 cows. A multiple-lactation random regression test-day model with Legendre polynomials of order 4 and a Bayesian method were used to estimate variance components for both single and multiple-countries. Fixed effects included herd-test-day class and regressions on DIM within age at calving-parity-season of calving. Random effects included animal genetic, permanent environmental, and residual effects. Average daily heritabilities from single country analyses ranged from 0.33 to 0.47 for milk yield and from 0.37 to 0.45 for protein yield across lactations and countries. Common sires (66) and their daughters were identified for creating a connected data set for simultaneous (co)variance component estimation of milk yield across all 5 countries. Between-country genetic correlations were low, with values from 0.08 to 0.46 and standard deviations from 0.08 to 0.12. Estimated breeding values for milk were generated for each animal using the same test-day animal model. Correlations among country estimated breeding values were higher than genetic correlations. Top 100 bull lists were generated on the scale of each country, and genetic progress was assessed. Future evaluation with increased genetic ties among countries may facilitate international comparison of Milking Shorthorns.     

Genetic association of clinical mastitis with test-day somatic cell score and milk yield during first lactation of Finnish Ayrshire cows

In this study the genetic association during lactation of 2 clinical mastitis (CM) traits: CM1 (7 d before to 30 d after calving) and CM2 (31 to 300 d after calving) with test-day somatic cell score (SCS) and milk yield (MY) was assessed using multitrait random regression sire models. The data analyzed were from 27,557 first-lactation Finnish Ayrshire cows. Random regressions on second- and third-order Legendre polynomials were used to model the daily genetic and permanent environmental variances of test-day SCS and MY, respectively, while only the intercept term was fitted for CM. Results showed that genetic correlations between CM and the test-day traits varied during lactation. Genetic correlations between CM1 and CM2 and test-day SCS during lactation varied from 0.41 to 0.77 and from 0.34 to 0.71, respectively. Genetic correlations of test-day MY with CM1 and CM2 ranged from 0.13 to 0.51 and from 0.49 to 0.66, respectively. Correlations between CM1 and SCS were strongest during early lactation, whereas correlations between CM2 and SCS were strongest in late lactation. Genetic correlations lower than unity indicate that CM and SCS measure different aspects of the trait mastitis. Milk yield in early lactation was more strongly correlated with both CM1 and CM2 than milk yield in later lactation. This suggests that selection for higher lactation MY through selection on increased milk yield in early lactation will have a more deleterious effect on genetic resistance to mastitis than selection for higher yield in late lactation. The approach used in this study for the estimation of the genetic associations between test-day and CM traits could be used to combine information from traits with different data structures, such as test-day SCS and CM traits in a multitrait random regression model for the genetic evaluation of udder health.     

Recursive relationships between milk yield and somatic cell score of Canadian Holsteins from finite mixture random regression models

Finite mixture, multiple-trait, random regression animal models with recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test-day were applied to first lactation Canadian Holstein data. All models included fixed herd-test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Causal links between phenotypes for milk yield and SCS were fitted separately for records from healthy cows and cows with a putative, subclinical form of mastitis. Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Bayes factors indicated superiority of the model with recursive link from milk to SCS over the reciprocal recursive model and the standard multiple-trait model. Differences between models measured by other, single-trait model comparison criteria (i.e., weighted mean squared error, squared bias, and correlation between observed and expected data) were negligible. Approximately 20% of test-day records were classified as originating from cows with mastitis in recursive mixture models. The proportion of records from cows infected with mastitis was largest at the beginning of lactation. Recursive mixture models exhibited different distributions of data from healthy and infected cows in different parts of lactation. A negative effect of milk to SCS (up to −0.15 score points for every kilogram of milk for healthy cows from 5 to 45 d in milk) was estimated for both mixture components (healthy and infected) in all stages of lactation for the most plausible model. The magnitude of this effect was stronger for healthy cows than for cows infected with mastitis. Different patterns of genetic and environmental correlations between milk and SCS for healthy and infected records were revealed, due to heterogeneity of structural coefficients between mixture components. Estimated breeding values for SCS from the best fitting model for sires of infected daughters were more related to estimated breeding values for the same trait from the regular multiple-trait model than evaluations for sires of mastitis-free cows.     

Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.     

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.     

Genotype by environment interaction for somatic cell score across bulk milk somatic cell count and days in milk

The objective of this paper was to investigate the importance of a genotype × environment interaction (G × E) for somatic cell score (SCS) across levels of bulk milk somatic cell count (BMSCC), number of days in milk (DIM), and their interaction. Variance components were estimated with a model including random regressions for each sire on herd test-day BMSCC, DIM, and the interaction of BMSCC and DIM. The analyzed data set contained 344,029 test-day records of 24,125 cows, sired by 182 bulls, in 461 herds comprising 13,563 herd test-days. In early lactation, considerable G × E effects were detected for SCS, indicated by 3-fold higher genetic variance for SCS at high BMSCC compared with SCS at low BMSCC, and a genetic correlation of 0.72 between SCS at low and at high BMSCC. Estimated G × E effects were smaller during late lactation. Genetic correlations between SCS at the same level of BMSCC, across DIM, were between 0.43 and 0.89. The lowest genetic correlation between SCS measures on any 2 possible combinations of BMSCC and DIM was 0.42. Correlated responses in SCS across BMSCC and DIM were, on some occasions, less than half the direct response to selection in the response environment. Responses to selection were reasonably high among environments in the second half of the lactation, whereas responses to selection between environments early and late in lactation tended to be low. Selection for reduced SCS yielded the highest direct response early in lactation at high BMSCC.     

Alternative somatic cell count traits to improve mastitis resistance in Canadian Holsteins

The objective of this study was to investigate whether alternative somatic cell count (SCC) traits are suitable as mastitis indicators in Canadian Holsteins. Mastitis data recorded by producers were available from the national dairy cattle health system in Canada. Mastitis was defined as a binary variable based on whether or not the cow had at least one mastitis case in the period from calving to 305 d after calving. The analyzed alternative SCC traits included mean somatic cell score (SCS) from different time periods, maximum SCS, standard deviation of SCS, excessive test-day SCC, and a peak pattern of test-day records with suspicion of mastitis. Data of 53,626 first-lactation Holstein cows from 1,666 herds across Canada were analyzed using linear animal models. A heritability of 0.02 was obtained for mastitis. For both mean SCS in early and late lactation, a heritability of 0.11 was estimated. Heritabilities of various patterns of SCC ranged from 0.01 to 0.07. Estimated genetic correlations were 0.69 and 0.68 between mastitis and mean SCS in early and late lactation, respectively. Higher genetic correlations were found between mastitis and the different SCC patterns (0.82 to 0.91). Sires with high breeding values for mastitis resistance had consistently higher percentage of healthy daughters than sires with low breeding values for mastitis resistance. Breeding values for mean SCS in early lactation, standard deviation of SCS, and an excessive test-day SCC pattern (at least one SCC test-day above 500,000) were the best predictors of the breeding value for mastitis resistance and explained in total 41% of the variation in relative breeding values for mastitis resistance. The results demonstrated that patterns of SCC provide additional information for genetic evaluations of mastitis resistance that cannot be explained by mean SCS alone.     

Genetic associations between clinical mastitis and somatic cell score in early first-lactation cows

The objectives of this study were to examine genetic associations between clinical mastitis and somatic cell score (SCS) in early first-lactation cows, to estimate genetic correlations between SCS of cows with and without clinical mastitis, and to compare genetic evaluations of sires based on SCS or clinical mastitis. Clinical mastitis records from 15 d before to 30 d after calving and first test-day SCS records (from 6 to 30 d after calving) from 499,878 first-lactation daughters of 2,043 sires were analyzed. Results from a bivariate linear sire model analysis of SCS in cows with and without clinical mastitis suggest that SCS is a heterogeneous trait. Heritability of SCS was 0.03 for mastitic cows and 0.08 for healthy cows, and the genetic correlation between the 2 traits was 0.78. The difference in rank between sire evaluations based on SCS of cows with and without clinical mastitis varied from −994 to 1,125, with mean 0. A bivariate analysis with a threshold-liability model for clinical mastitis and a linear Gaussian model for SCS indicated that heritability of liability to clinical mastitis is at least as large as that of SCS in early lactation. The mean (standard deviation) of the posterior distribution of heritability was 0.085 (0.006) for liability to clinical mastitis and 0.070 (0.003) for SCS. The posterior mean (standard deviation) of the genetic correlation between liability to clinical mastitis and SCS was 0.62 (0.03). A comparison of sire evaluations showed that genetic evaluation based on SCS was not able to identify the best sires for liability to clinical mastitis. The association between sire posterior means for liability to clinical mastitis and sire predicted transmitting ability for SCS was far from perfect.     

Genetic evaluation of dairy cattle with test-day models with autoregressive covariance structures and with a 305-d model

This study compared genetic evaluations from 3 test-day (TD) models with different assumptions about the environmental covariance structure for TD records and genetic evaluations from 305-d lactation records for dairy cows. Estimates of genetic values of 12,071 first-lactation Holstein cows were obtained with the 3 TD models using 106,472 TD records. The compound symmetry (CS) model was a simple test-day repeatability animal model with compound symmetry covariance structure for TD environmental effects. The AR_s and AR_e models also used TD records but with a first-order autoregressive covariance structure among short-term environmental effects or residuals, respectively. Estimates of genetic values with the TD models were also compared with those from a model using 305-d lactation records. Animals were genetically evaluated for milk, fat, and protein yields, and somatic cell score (SCS). The largest average estimates of accuracy of predicted breeding values were obtained with the AR_s model and the smallest were with the 305-d model. The 305-d model resulted in smaller estimates of correlations between average predicted breeding values of the parents and lactation records of their daughters for milk and protein yields and SCS than did the CS and AR_e models. Predicted breeding values with the 3 TD models were highly correlated (0.98 to 1.00). Predicted breeding values with 305-d lactation records were moderately correlated with those with TD models (0.71 to 0.87 for sires and 0.80 to 0.87 for cows). More genetic improvement can be achieved by using TD models to select for animals for higher milk, fat, and protein yields, and lower SCS than by using models with 305-d lactation records.     

Genetic correlations between the cumulative pseudo-survival rate,milk yield,and somatic cell score during lactation in Holstein cattle in Japan using a random regression model

Trends in genetic correlations between longevity, milk yield, and somatic cell score (SCS) during lactation in cows are difficult to trace. In this study, changes in the genetic correlations between milk yield, SCS, and cumulative pseudo-survival rate (PSR) during lactation were examined, and the effect of milk yield and SCS information on the reliability of estimated breeding value (EBV) of PSR were determined. Test day milk yield, SCS, and PSR records were obtained for Holstein cows in Japan from 2004 to 2013. A random subset of the data was used for the analysis (825 herds, 205,383 cows). This data set was randomly divided into 5 subsets (162–168 herds, 83,389–95,854 cows), and genetic parameters were estimated in each subset independently. Data were analyzed using multiple-trait random regression animal models including either the residual effect for the whole lactation period (H0), the residual effects for 5 lactation stages (H5), or both of these residual effects (HD). Milk yield heritability increased until 310 to 351 d in milk (DIM) and SCS heritability increased until 330 to 344 DIM. Heritability estimates for PSR increased with DIM from 0.00 to 0.05. The genetic correlation between milk yield and SCS increased negatively to under ?0.60 at 455 DIM. The genetic correlation between milk yield and PSR increased until 342 to 355 DIM (0.53–0.57). The genetic correlation between the SCS and PSR was ?0.82 to ?0.83 at around 180 DIM, and decreased to ?0.65 to ?0.71 at 455 DIM. The reliability of EBV of PSR for sires with 30 or more recorded daughters was 0.17 to 0.45 when the effects of correlated traits were ignored. The maximum reliability of EBV was observed at 257 (H0) or 322 (HD) DIM. When the correlations of PSR with milk yield and SCS were considered, the reliabilities of PSR estimates increased to 0.31–0.76. The genetic parameter estimates of H5 were the same as those for HD. The rank correlation coefficients of the EBV of PSR between H0 and H5 or HD were greater than 0.9. Additionally, the reliabilities of EBV of PSR of H0 were similar to those for H5 and HD. Therefore, the genetic parameter estimates in H0 were not substantially different from those in H5 and HD. When milk yield and SCS, which were genetically correlated with PSR, were used, the reliability of PSR increased. Estimates of the genetic correlations between PSR and milk yield and between PSR and SCS are useful for management and breeding decisions to extend the herd life of cows.     

The effect of intramammary infection with coagulase-negative staphylococci in early lactating heifers on milk yield throughout first lactation revisited

The objective of this study was to further scrutinize the previously found positive association between intramammary infection (IMI) caused by coagulase-negative staphylococci (CNS) in early lactating heifers and test-day daily milk yield (MY) throughout first lactation, with a specific focus on the effect of the heifers’ genetically determined milk production levels and the incidence of clinical mastitis. Two precise longitudinal data sets were analyzed using a series of statistical models including potential confounding and intermediate variables. The final database included the IMI status at calving, composite milk somatic cell count (SCC) and MY records at test day up to 285 d in milk (DIM), farmer-recorded clinical mastitis (CM) cases between 14 and 285 DIM, estimated new IMI incidence based on a SCC threshold of 100,000 cells/mL between 14 and 285 DIM, DIM, average 305-d MY at the herd level, and the heifers’ genetic merit for MY from 240 dairy heifers from 29 dairy herds. Seventy-one (29.6%) early lactating heifers were noninfected, 108 heifers (45.0%) were CNS infected, and 61 heifers (25.4%) were infected with any major pathogen. The positive effect of CNS IMI in early lactation on test-day MY was estimated at 1.32 kg/d using a first basic mixed regression model. Correcting for the confounder genetic merit for MY reduced this effect to 1.17 kg. Interestingly, taking into account the confounding effect of herd resulted in an increase of the estimate from 1.32 to 2.2 kg/d. The positive effect of CNS IMI in early lactation on MY after correcting the model for both confounders was estimated at 2.05 kg/d. Heifers infected with CNS in the first DIM tended to have fewer CM cases throughout lactation compared with the noninfected herd mates. Including the intermediate variable CM in the model explained 0.16 kg/d of the corrected effect of 2.05 kg/d. Inclusion of test-day SCC, another intermediate variable, however, increased the estimate by 0.11 kg/d. With an appropriate correction for several confounders and biologically understood intermediate variables such as CM, test-day SCC, and new IMI based on SCC threshold of 100,000 cells/mL, an unexplained test-day MY difference between CNS-infected and noninfected heifers of 2.0 kg/d remained.     

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from −0.48 to −0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (−0.58 ± 0.02) and MFAIL and FRM (−0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.     

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024–0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from −0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from −0.42 to −0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from −0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.     

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows’ comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates.     

Genotype by heat stress interactions for production and functional traits in dairy cows from an across-generation perspective

The aim of this study was to analyze time-lagged heat stress (HS) effects during late gestation on genetic co(variance) components in dairy cattle across generations for production, female fertility, and health traits. The data set for production and female fertility traits considered 162,492 Holstein Friesian cows from calving years 2003 to 2012, kept in medium-sized family farms. The health data set included 69,986 cows from calving years 2008 to 2016, kept in participating large-scale co-operator herds. Production traits were milk yield (MKG), fat percentage (fat%), and somatic cell score (SCS) from the first official test-day in first lactation. Female fertility traits were the nonreturn rate after 56 d (NRR56) in heifers and the interval from calving to first insemination (ICFI) in first-parity cows. Health traits included clinical mastitis (MAST), digital dermatitis (DD), and endometritis (EM) in the early lactation period in first-parity cows. Meteorological data included temperature and humidity from public weather stations in closest herd distance. The HS indicator was the temperature-humidity index (THI) during dams' late gestation, also defined as in utero HS. For the genetic analyses of production, female fertility, and health traits in the offspring generation, a sire-maternal grandsire random regression model with Legendre polynomials of order 3 for the production and of order 2 for the fertility and health traits on prenatal THI, was applied. All statistical models additionally considered a random maternal effect. THI from late gestation (i.e., prenatal climate conditions), influenced genetic parameter estimates in the offspring generation. For MKG, heritabilities and additive genetic variances decreased in a wave-like pattern with increasing THI. Especially for THI >58, the decrease was very obvious with a minimal heritability of 0.08. For fat% and SCS, heritabilities increased slightly subjected to prenatal HS conditions at THI >67. The ICFI heritabilities differed marginally across THI [heritability (h²) = 0.02–0.04]. For NRR56, MAST, and DD, curves for heritabilities and genetic variances were U-shaped, with largest estimates at the extreme ends of the THI scale. For EM, heritability increased from THI 25 (h² = 0.13) to THI 71 (h² = 0.39). The trait-specific alterations of genetic parameters along the THI gradient indicate pronounced genetic differentiation due to intrauterine HS for NRR56, MAST, DD, and EM, but decreasing genetic variation for MKG and ICFI. Genetic correlations smaller than 0.80 for NRR56, MAST, DD, and EM between THI 65 with corresponding traits at remaining THI indicated genotype by environment interactions. The lowest genetic correlations were identified when considering the most distant THI. For MKG, fat%, SCS, and ICFI, genetic correlations throughout were larger than 0.80, disproving concerns for any genotype by environment interactions. Variations in genetic (co)variance components across prenatal THI may be due to epigenetic modifications in the offspring genome, triggered by in utero HS. Epigenetic modifications have a persistent effect on phenotypic responses, even for traits recorded late in life. However, it is imperative to infer the underlying epigenetic mechanisms in ongoing molecular experiments.