Cow activity measurements can be used to define new fertility traits for use in genetic evaluation

Cow activity measurements are widely used in herd management tools for estrus detection. The aim of this study was to examine whether a new trait based on activity measures can be useful for genetic evaluation of cow fertility in Norwegian Red dairy cattle. Data from 284 herds with Lely milking robot were collected. Daily measurements of cow activity level were available from Lely activity tags and included data from 13,224 lactations of 8,139 Norwegian Red cows. We analyzed daily activity and included records from 10 to 150 d in milk for cows with at least 50 records. The trait analyzed was interval from calving to first high activity (CFHA). In total 87% of the cows had at least one episode of high activity recorded. The mean (standard deviation) of CFHA was 42 (28) days. A linear animal repeatability model with fixed effects of month-year of calving, age-parity, and herd, and random animal and permanent environment effects was used for estimation of variance components. The trait CFHA showed significant genetic variation with a heritability of 0.05 (0.01). This trait reflects the cow's ability to return to estrus cycle and show estrus after calving, which are important aspects of cow fertility.

Heritability of subclinical endometritis in Norwegian Red cows

Subclinical endometritis (SCE) is highly prevalent in dairy cows, causing negative effects on reproductive outcomes and the producer economy. Genetic selection for animals with better resilience against uterine disease should be prioritized due to both sustainability and animal welfare. Therefore, the aim of the present study was to estimate the heritability of SCE in the Norwegian Red (NR) population. Moreover, future perspectives of the condition as a fertility phenotype for breeding are discussed. A total of 1,642 NR cows were sampled for SCE at the time of artificial insemination, using cytotape. The percentage of polymorphonuclear cells (PMN) in each sample was established by cytology, through the counting of 300 PMN and epithelial cells. The mean percentage of PMN was 5%. Different trait definitions were examined, and SCE was defined as binary traits, based on the following cut-off levels of PMN: Cyto0 = PMN >0, Cyto3 = PMN >3%, Cyto5 = PMN >5%, Cyto10 = PMN >10%, and Cyto20 = PMN >20%. The mean ranged from 0.07 (Cyto20) to 0.59 (Cyto0). We also analyzed PMN as a continuous variable using percent PMN. Information on the animals and herds was obtained from the Norwegian Dairy Herd Recording System. The pedigree of cows with data included a total of 24,066 animals. A linear animal model was used to estimate the heritability. The only trait definition that had an estimated genetic variance larger than the standard error was Cyto5, with an estimated heritability of 0.04. For all other definitions, the genetic variance was not significantly different from zero. A cut-off level of 5% PMN has been established as a general threshold for the definition of SCE in earlier literature. The standard errors of the estimated variance components were relatively large, and results should be interpreted with caution. However, the current study indicates that SCE is heritable at a similar level to that of clinical endometritis and metritis, and has potential as a future fertility phenotype to be used for breeding purposes. A more feasible method to diagnose SCE is needed to establish larger data sets.

Genetic analysis of semen characteristic traits in Norwegian Red bulls at the artificial insemination center

Compared with cow fertility, genetic analyses of bull fertility are limited and based on relatively few animals. The aim of the present study was to estimate genetic parameters for semen characteristics of Norwegian Red bulls at the artificial insemination (AI) center (Geno AI station, Stange, Norway) and to estimate genetic correlations between some of these traits and andrology traits measured at the performance test station. The data from the AI center consisted of records from 137,919 semen collections from 3,145 bulls with information on semen weight, sperm concentration, motility before and after cryopreservation, motility change during cryopreservation, and number of accepted straws made. Data from the performance test station included 12,522 observations from 3,219 bulls on semen volume, concentration, and motility (%) when fresh and after storing for 24 and 48 h. Genetic parameters were estimated using linear animal repeatability models that included fixed effects of year-month of observation, age of bull, interaction between semen collection number, and interval between collections for all traits and type of diluter for postcryopreservation traits. The random effects included test-day, permanent environmental, and additive genetic effects of the bull. Based on records from the AI center, we found that semen weight, sperm concentration, and number of straws were moderately heritable (0.18-0.20), whereas motility had a lower heritability (0.02-0.08). Heritability of motility (%) was higher after cryopreservation than before. Genetic correlations among the semen characteristics ranged from unfavorable (-0.35) to favorable (0.93), with standard errors ranging from 0.02 to 0.22. Among the most precise genetic correlation estimates, number of straws made from a batch correlated favorably with semen weight (0.62 ± 0.06) and sperm concentration (0.44 ± 0.08), whereas sperm concentration was negatively correlated with weight (-0.33 ± 0.09). The genetic correlation between motility (%) before and after cryopreservation was 0.64 ± 0.14, and motility change during cryopreservation had a strong favorable genetic correlation with motility after cryopreservation (-0.93 ± 0.02). The estimated genetic correlation (standard error) between the traits volume, concentration, and motility when fresh measured at the performance test station and their respective corresponding traits at the AI center were 0.83 (0.05), 0.78 (0.09), and 0.49 (0.31). The final product at the AI center (number of accepted straws) correlated genetically favorably with all semen characteristic traits recorded at the performance test station (ranging from 0.51 to 0.67). Our results show that the andrology testing done at the performance test station is a resource to identify the genetically best bulls for AI production.

Expression analysis of candidate genes for chronic subclinical mastitis in Norwegian Red cattle

Chronic subclinical mastitis (SCM) is characterized by a long-term inflammation in the udder with high somatic cell count (SCC) in milk. Previously, several novel alternative SCM traits for Norwegian Red (NR) cattle have been defined to improve breeding strategies against chronic SCM. Quantitative trait loci and candidate genes affecting chronic SCM in NR have been identified. The aim of this study was to analyze the expression profiles of 14 selected candidate genes (RAD17, ACOT2, ACOT4, FOS, CXCL1, CXCL8, CCNB1, CDK7, TGFB3, SEL1L, STAT4, C6, GLI2, and SLC18A2). Twenty healthy NR cows with official genomic estimated breeding values (GEBV) for lactation average somatic cell scores (LSCS) were selected. Ten cows had high GEBV for LSCS (cows with low probability to have high SCC in milk during lactation) and 10 cows had low GEBV for LSCS (cows with high probability of having high SCC in milk). We isolated RNA from unstimulated peripheral blood mononuclear cells from these. Two out of the 14 analyzed genes showed significantly different results between groups. The group with high GEBV for LSCS displayed significantly higher expression of the CXCL1 gene than the low GEBV group. Grouping by lactation stage revealed significant differential expression of the FOS gene, with higher expression in early lactation (2-3 mo after calving) compared with late lactation (7-8 mo after calving). In addition, flow cytometry was performed on the peripheral blood mononuclear cells samples to analyze if number and type of isolated cells influenced the gene expression in the groups. The results in the current study provide identified genes that can be considered as possible candidate genes for chronic SCM in NR cows.

A genetic study of new udder health indicator traits with data from automatic milking systems

The current study aimed to investigate new udder health traits based on data from automatic milking systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each milking, whereas OCC was measured per milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11-0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.

Genetic correlations between body weight, daily weight gain, and semen characteristic traits in young Norwegian Red bulls

The aim of this study was to estimate genetic parameters for body weight (BW) at 150 d (Bw_150d), and 330 d (Bw_330d) of age and average daily weight gain (Dwg), and to estimate genetic correlations between these traits and semen characteristic traits: volume; concentration (Conc); motility in fresh, 24-h, and 48-h samples (Mot0h, Mot24h, Mot48h); and sperm defects. Data were collected at the performance test station of young Norwegian Red bulls from 2002 to 2012, before selection of bulls for artificial insemination. The weight and growth data consisted of observations for 3,209 bulls, and andrology information was available for up to 2,034 of these bulls. Genetic parameters were estimated using linear animal models. Models for BW and growth traits included the group and year the bull left the station and the pen they occupied during weighing (group-year-pen) and parity of their dam as fixed effects. Models for andrology traits had group-year, age in months (11 to 15), and the interaction between ejaculate number and days since previous collection included as fixed effects. Estimated heritability was 0.14 for Bw_150d, 0.26 for Bw_330d, and 0.34 for Dwg; the estimated genetic correlations among these traits were all favorable. Both BW traits correlated favorably with all the semen characteristic traits (0.20 to 0.76), whereas Dwg was favorably correlated with volume, Mot24h, Mot48h, and sperm defects, and unfavorably correlated with Conc (-0.25) and Mot0h (-0.53). Our results indicate that the genetic correlations between weight and growth traits and semen characteristics depend on the age of the bulls. Although most genetic correlations were favorable, selection for higher daily weight gain between 150 and 330 d might explain the slight negative genetic trends observed for semen characteristics in young Norwegian Red bulls.

Identification of candidate genes affecting chronic subclinical mastitis in Norwegian Red cattle: combining genome-wide association study, topologically associated domains and pathway enrichment analysis

The aim of this study was to identify genes associated with chronic subclinical mastitis (SCM) in Norwegian Red (NR) cattle. Twelve SCM traits defined based on fixed threshold for test-day somatic cell count (SCC) were, together with lactation-average somatic cell score (LSCS) used for association and pathway enrichment analyses. A GWAS was performed on 3795 genotyped NR bulls with 777K SNP data and phenotypic information from 7 300 847 test-day SCC observations from 3 543 764 cows. At 5% chromosome-wide significance level 36 unique SNP were detected to be associated with one or more of the traits. These SNPs were analysed for linked genes using genomic positions of topologically associated domains (TAD). For the SCM traits with SCC >50 000 and >100 000 cells/ml on two test-days in a row and LSCS, the same top significant genes were identified - checkpoint clamp loader component (RAD17) and cyclin B1 (CCNB1). The SCM traits with SCC >250 000, 300 000, 350 000 or 400 000 cells/ml on two test-days in a row and D400 (number of days before the first case with SCC >400 000 cells/ml) displayed similar top significant genes: acyl-CoA thioesterase 2 and 4 (ACOT2; ACOT4). For the traits SCM200_3 (SCC >200 000 cells/ml on three test-days in a row) and SCM150, SCM200 (SCC >150 000; 200 000 cells/ml on two test-days in a row) a group of chemokine (C-X-C motif) ligand genes and the Fos proto-oncogene, AP-1 transcription factor subunit (FOS) gene, were identified. Further functional studies of these identified candidate genes are necessary to clarify their actual role in development of chronic SCM in NR cattle.

Genetic analysis of semen characteristic traits in young Norwegian Red bulls

The aim of this study was to estimate genetic parameters and genetic trends for male fertility in Norwegian Red bulls. We analyzed data on semen characteristics traits collected at the performance test station of young bulls from 1994 to 2016, in an andrology test used to ensure acceptable semen quality before being selected as an artificial insemination bull. Traits included were volume, concentration, and motility (percentage of moving sperm cells) in fresh samples and after storing for 24 and 48 h, and sperm defects. The data consisted of 14,972 ejaculates from 3,927 young (11-15 mo) Norwegian Red bulls. Genetic parameters were estimated using bivariate linear animal models that included age in months, group-year, and collection-group (main effect of the interaction between ejaculate number and interval between collections) as fixed effects, and test-day and additive genetic and permanent environment effect of the bull as random effects. Considerable genetic coefficients of variation were found for concentration and volume, with lower values for motility. Estimated heritabilities ranged from 0.02 and 0.03 (for sperm defects and motility in fresh samples) to 0.14 (volume and concentration measured on a continuous scale). All estimated genetic correlations were favorable, but the genetic correlations between volume and concentration and volume and sperm defects were not significantly different from zero. The genetic correlations between concentration and motility traits ranged from 0.53 to 0.83, and those between volume and the motility traits were between 0.24 and 0.57. All traits showed a slightly unfavorable genetic trend. Our results indicate that selection of bulls with better sperm quality is possible.

Genetic analyses of novel temperament and milkability traits in Norwegian Red cattle based on data from automatic milking systems

The number of dairy cows milked in automatic milking systems (AMS) is steadily increasing in Norway. Capacity and efficiency of AMS are highly dependent on the individual cow's milking efficiency, such as milking speed and occupation time in the milking robot. Cows meet new challenges in herds utilizing AMS. Consequently, new or revised traits may be needed for genetic evaluation of dairy cattle. The AMS records relevant information on an individual cow basis. The aims of this study were to estimate genetic parameters of new automatically recorded milkability and temperament traits. Data from 77 commercial herds with Norwegian Red dairy cattle were analyzed by mixed linear animal models. The final data set contained 1,012,912 daily records from 4,883 cows in first to ninth lactation. For variance component estimation, univariate and bivariate models were used. Daily records of box time (BT), average flow rate (FR), kilograms of milk per minute of box time (MEF), handling time (HT), log-transformed HT, milking frequency, and milking interval were analyzed with repeatability models. Among these traits, FR, BT, and MEF showed the highest heritabilities of 0.48, 0.27, and 0.22, respectively, whereas heritability of log-transformed HT, HT, milking frequency, and milking interval was low (0.02-0.07). Unsuccessful milkings expressed as rejected milkings, incomplete milkings (IM), milkings with kick-offs (KO), and teat not found also showed low heritabilities (0.002-0.06). Due to low frequency, KO, rejected milkings, IM, and teat not found were also analyzed as proportions per lactation, which resulted in slightly higher heritability estimates. Genetic correlations were favorable and intermediate to strong between BT, HT, MEF, and FR with absolute values above 0.50. Intermediate and favorable correlations were found for IM and KO with BT, HT, MEF, and FR. Cow milkability in AMS can be improved by selection for reduced number of unsuccessful milkings, faster FR, increased MEF, and shorter BT and HT. Our results confirm that automatically recorded data on milkability and temperament can be valuable sources of information for routine genetic evaluations and that milking efficiency in AMS can be genetically improved.

Alternative subclinical mastitis traits for genetic evaluation in dairy cattle

Chronic subclinical mastitis (SCM), characterized by changes in milk composition and high somatic cell count (SCC) in milk for a prolonged period of time, is often caused by a bacterial infection. Different levels of SCC have been suggested and used as threshold to identify subclinical infection. The aim of this study was to examine different definitions of SCM based on test-day SCC and estimate genetic parameters for these traits and their genetic correlation to milk production. Test-day SCC records from 1,209,128 Norwegian Red cows in lactation 1 to 3 were analyzed. Twelve SCM traits were defined as binary with 2 test-day SCC in a row above SCC thresholds from 50,000 to 400,000 cells/mL (SCM50, SCM100, SCM150, SCM200, SCM250, SCM300, SCM350, and SCM400), with 3 test-day SCC in a row above 200,000 and 400,000 cells/mL (SCM200_3 and SCM400_3), and the number of days before the first case with SCM50 (D50) or SCM400 (D400). The heritability and genetic correlations were estimated for SCM traits and the mean lactation-average somatic cell score (LSCS) using linear animal repeatability models. The total mean frequency of SCM ranged from 1.2% to 51.8%, for different trait definitions, high for low SCC threshold (SCM50) and low for the highest SCC threshold (SCM400_3). For the 2 traits based on number of days, the mean values were 104 (D50) and 117 (D400) days. The mean LSCS was 4.4 (equivalent to around 82,000 SCC). Heritabilities for the 12 alternative SCM traits were low and varied from 0.01 (SCM400_3) to 0.1 (SCM100), whereas for LSCS the estimated heritability was 0.3 and standard error varied from 0.001 to 0.003. Genetic correlations among the SCM traits ranged from 0.7 (D50 and SCM400) to 1 (SCM350 and SCM400), whereas between SCM traits and milk production the correlation ranged from 0.07 (LSCS) to 0.3 (D400). The standard error for genetic correlations varied from 0.001 to 0.06. The heritability was low and the genetic correlations were strong among SCM traits. Genetic correlations lower than 1 suggest that the alternative SCM traits are genetically different from LSCS, the trait currently used in genetic evaluations for Norwegian Red. Hence, the alternative traits will add information and improve breeding for better udder health.

Invited review: Opportunities for genetic improvement of metabolic diseases

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Foot and leg conformation traits have a small effect on genomic predictions of claw disorders in Norwegian Red cows

The aim of this study was to evaluate whether the predictive correlation of genomic breeding values (GEBV) for claw disorders increased by including genetically correlated traits as additional information in the analyses. Predictive correlations of GEBV for claw disorders were calculated based on claw disorders only and by analyzing claw disorders together with genetically correlated foot and leg conformation traits. The claw disorders analyzed were corkscrew claw (CSC); infectious claw disorder, including dermatitis, heel horn erosion, and interdigital phlegmon; and laminitis-related claw disorder, including sole ulcer, white line disorder, and hemorrhage of sole and white line. The foot and leg conformation traits included were hoof quality, foot angle, rear leg rear view new, and rear leg rear view old. The data consisted of 183,728 daughters with claw health records and 421,319 daughters with foot and leg conformation scores. A 25K/54K single nucleotide polymorphism (SNP) data set containing 48,249 SNP was available for the analyses. The number of genotyped sires with daughter information in the analyses was 1,093 including claw disorders and 3,111 including claw disorders and foot and leg conformation traits. Predictive correlations of GEBV for CSC, infectious claw disorder, and laminitis-related claw disorder were calculated from a 10-fold cross-validation and from an additional validation set including the youngest sires. Only sires having daughters with claw health records were in the validation sets, thus increasing the reference population when adding foot and leg conformation traits. The results showed marginal improvement in the predictive correlation of GEBV for CSC when including hoof quality and foot angle, both in 10-fold cross-validation (from 0.35 to 0.37) and in the validation including the youngest sires (from 0.38 to 0.49). For infectious claw disorder and laminitis-related claw disorder, including foot and leg conformation traits had no effect on the predictive correlation of GEBV. Claw disorders are novel traits with a limited amount of historical data and, therefore, a small reference population. Increasing the reference population by including sires with daughter information on foot and leg conformation traits had small effect on the predictive correlation of GEBV. However, the small increase in predictive correlation of GEBV for CSC shows a possible gain when including moderate to high genetically correlated traits.

Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits

For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.

Effect of dams' parity and age on daughters' milk yield in Norwegian Red cows

The effect of age and parity of dams on their daughters' milk yield is not well known. Lactation data from 276,000 cows were extracted from the Norwegian Dairy Herd Recording System and analyzed using a linear animal model to estimate effects of parity and age within parity of dam. The 305-d milk yield of daughters decreased as parity of dam increased. Daughters of first-parity dams produced 149 kg more milk than did daughters of seventh-parity dams. We also observed an effect of age of dam within parity on 305-d milk yield of daughters in first lactation. Dams that were young at first calving gave birth to daughters with a higher milk yield compared with older dams within the same parity. The effect of age within parity of dam was highest for second-parity dams. Extensive use of heifers would have a systematic effect, and age and parity of dam should be included in the model when planning a future strategy.

Genomic predictions based on a joint reference population for the Nordic Red cattle breeds

The main aim of this study was to compare accuracies of imputation and genomic predictions based on single and joint reference populations for Norwegian Red (NRF) and a composite breed (DFS) consisting of Danish Red, Finnish Ayrshire, and Swedish Red. The single nucleotide polymorphism (SNP) data for NRF consisted of 2 data sets: one including 25,000 markers (NRF25K) and the other including 50,000 markers (NRF50K). The NRF25K data set had 2,572 bulls, and the NRF50K data set had 1,128 bulls. Four hundred forty-two bulls were genotyped in both data sets (double-genotyped bulls). The DFS data set (DSF50K) included 50,000 markers of 13,472 individuals, of which around 4,700 were progeny-tested bulls. The NRF25K data set was imputed to 50,000 density using the software Beagle. The average error rate for the imputation of NRF25K decreased slightly from 0.023 to 0.021, and the correlation between observed and imputed genotypes changed from 0.935 to 0.936 when comparing the NRF50K reference and the NRF50K-DFS50K joint reference imputations. A genomic BLUP (GBLUP) model and a Bayesian 4-component mixture model were used to predict genomic breeding values for the NRF and DFS bulls based on the single and joint NRF and DFS reference populations. In the multiple population predictions, accuracies of genomic breeding values increased for the 3 production traits (milk, fat, and protein yields) for both NRF and DFS. Accuracies increased by 6 and 1.3 percentage points, on average, for the NRF and DFS bulls, respectively, using the GBLUP model, and by 9.3 and 1.3 percentage points, on average, using the Bayesian 4-component mixture model. However, accuracies for health or reproduction traits did not increase from the multiple population predictions. Among the 3 DFS populations, Swedish Red gained most in accuracies from the multiple population predictions, presumably because Swedish Red has a closer genetic relationship with NRF than Danish Red and Finnish Ayrshire. The Bayesian 4-component mixture model performed better than the GBLUP model for most production traits for both NRF and DFS, whereas no advantage was found for health or reproduction traits. In general, combining NRF and DFS reference populations was useful in genomic predictions for both the NRF and DFS bulls.

Genetic analysis of pathogen-specific clinical mastitis in Norwegian Red cows

The objective of this study was to estimate heritabilities and genetic correlations for pathogen-specific clinical mastitis (CM) in Norwegian Red cows. In Norway, breeding values for mastitis are predicted based on records of veterinary treatments of clinical mastitis. Bacteriological milk sample results from the mastitis laboratories have been recorded routinely into the Norwegian Dairy Herd Recording System since 2000, but have so far not been used in genetic analyses. This additional source of data may provide valuable information on pathogen-specific CM. Records from 234,088 first-lactation Norwegian Red cows, daughters of 1,656 sires, were used for genetic analyses of unspecific, Staphylococcus aureus, Streptococcus dysgalactiae, and Escherichia coli CM. The 4 CM traits were defined as binary and scored as 1 if the cow had at least 1 case of the CM in question and 0 otherwise. A Bayesian approach using Gibbs sampling was applied, and a multivariate threshold liability model was used for the analyses. The posterior mean (SD ≤ 0.01) of the heritabilities were 0.06 for liability of unspecific CM, 0.04 for Staph. aureus CM, 0.02 for Strep. dysgalactiae CM, and 0.03 for E. coli CM. The posterior mean (SD) of the genetic correlations were all high, ranging from 0.75 (0.14) to 0.87 (0.07). The highest genetic correlation was found between unspecific CM and Strep. dysgalactiae CM, whereas the lowest was found for E. coli CM and Staph. aureus CM. Genetic correlations lower than 1 indicate that mastitis caused by different pathogens can be considered as partly different traits. In spite of high rank correlations (0.95-0.98), some re-ranking of sires was observed.

Comparison of different models for genetic analysis of clinical mastitis in Austrian Fleckvieh dual-purpose cows

The performance of different models for genetic analyses of clinical mastitis in Austrian Fleckvieh dual-purpose cows was evaluated. The main objective was to compare threshold sire models (probit and logit) with linear sire and linear animal models using REML algorithm. For comparison, data were also analyzed using a Bayesian threshold sire model. The models were evaluated with respect to ranking of sires and their predictive ability in cross-validation. Only minor differences were observed in estimated variance components and heritability from Bayesian and REML probit models. Heritabilities for probit and logit models were 0.06 and 0.08, respectively, whereas heritabilities for linear sire and linear animal models were lower (0.02). Correlations among ranking of sires from threshold and linear sire models were high (>0.99), whereas correlations between any sire model (threshold or linear) and the linear animal model were slightly lower (0.96). The worst sires were ranked very similar across all models, whereas for the best sires some reranking occurred. Further, models were evaluated based on their ability to predict future data, which is one of the main concerns of animal breeders. The predictive ability of each model was determined by using 2 criteria: mean squared error and Pearson correlation between predicted and observed value. Overall, the 5 models did not differ in predictive ability. In contrast to expectations, sire models had the same predictive ability as animal models. Linear models were found to be robust toward departures from normality and performed equally well as threshold models.

Genetic analysis of fertility-related diseases and disorders in Norwegian Red cows

Heritability of and genetic correlations among silent heat (SH), cystic ovaries (CO), metritis (MET), and retained placenta (RP) were inferred. These traits were chosen because they are the 4 most frequent fertility-related diseases and disorders among first-lactation cows in Norway. Records of 503,683 first-lactation daughters of 1,058 Norwegian Red sires with first calving from 2000 through 2006 were analyzed with a 4-variate threshold sire model. Presence or absence of each of the 4 diseases was scored as 1 or 0 based on whether or not the cow had at least 1 veterinary treatment for the disease. The mean frequency was 3.1% for SH, 0.9% for MET, 0.5% for CO, and 1.5% for RP. The model for liability had effects of age at calving and of month-year of calving, herd, sire of the cow, and a residual. Posterior mean (SD) of heritability of liability was 0.06 (0.01) for SH, 0.03 (0.01) for MET, 0.07 (0.01) for CO, and 0.06 (0.01) for RP. The genetic correlation between MET and RP was strong, with posterior mean (SD) 0.64 (0.10). A negative genetic correlation (-0.26) was found between RP and CO. The posterior distributions of the other genetic correlations included zero with high density, and could not be considered different from zero. The frequency of fertility-related diseases and disorders is very low in the Norwegian Red population at present, so there is limited scope for genetic improvement. However, this study indicates that reasonably precise genetic evaluation of sires is feasible for these traits given information from large daughter groups.

Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows

Clinical mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of mastitis, it has rarely been used for studying the genetics of mastitis. Many models have been proposed for genetic analysis of mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for mastitis in dairy cattle. The response variables were clinical mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, ..). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy cows and had a similar performance between them.

A structural equation model for describing relationships between somatic cell score and milk yield in first-lactation dairy cows

Relationships between production and diseases may involve recursive or simultaneous effects between traits. Four structural equation models (SEqM) for somatic cell score and milk yield, with varying specifications for the effects relating the 2 traits, were compared. Data consisted of repeated records of milk yield and somatic cell score of 33,453 first-lactation daughters of 245 Norwegian Red sires that had their first progeny test in 1991 and 1992. All models included random effects of the sire and of the cow and were fitted using the LISREL software. The Bayesian information criterion clearly favored a model with a recursive effect from somatic cell score on milk yield over the 3 other models fitted (absence of recursive effects; an effect from milk yield on somatic cell score; simultaneity of effects between the 2 traits). This provides evidence that the negative association between milk yield and somatic cell score is more likely due to an effect of infection (measured indirectly by the somatic cell score) on production than to a dilution effect. Estimates indicated that a mastitis event would reduce milk yield in the following 15 d by about 900 g/d. The estimated genetic (co)variances did not change sizably when the specification of recursive or simultaneous effects was varied. However, estimates of the phenotypic covariance were altered when a recursive effect from somatic cell score on milk yield was included in the model.

Short communication: Genetic analysis of respiratory disease in Norwegian Red calves

A genetic analysis of respiratory disease (RD) in Norwegian Red calves was conducted. This is the first genetic study of calf diseases based on data from the Norwegian health recording system, and RD was chosen because it is the most frequent disease in calves. Records on absence or presence of RD before 180 d of age for 250,212 calves (progeny of 728 Norwegian Red artificial insemination sires) were analyzed with a threshold model. A total of 0.7% of the calves had veterinary treatment of RD before 180 d of age. Heritability of RD in the underlying scale was 0.05, with a 95% credibility interval from 0.02 to 0.09. The "worst" sire had a predicted probability of RD that was more than twice as high as that of the "best" one (1.15 vs. 0.44%). Their 95% credibility intervals for predicted probability of RD, however, overlapped. With improved and more extensive recording of calf diseases, the precision in genetic evaluation of sires could increase considerably. The frequency of RD is very low in the Norwegian Red population at present, so there is not much scope for genetic improvement. However, this study indicates that reasonably precise genetic evaluation of sires for resistance to RD could be feasible.

A zero-inflated poisson model for genetic analysis of the number of mastitis cases in Norwegian Red cows

The objective was to extend a zero-inflated Poisson (ZIP) model to account for correlated genetic effects, and to use this model to analyze the number of clinical mastitis cases in Norwegian Red cows. The ZIP model is suitable for analysis of count data containing an excess of zeros relative to what is expected from Poisson sampling. A ZIP model was developed and compared with a corresponding Poisson model. The Poisson parameter followed a hierarchical structure, and a residual term accounting for overdispersion was included. In both models, the Poisson parameter was regressed 1) on the year, month, and age at first calving; 2) on the logarithm of the number of days elapsed from calving to the end of first lactation; and c) on herd and sire effects. Herd and sire effects were assigned normal prior distributions in a Bayesian analysis, corresponding to a random effects treatment in a frequentist analysis. An analysis of residuals favored the Poisson model when there were 2 or more cases of mastitis during first lactation, with very small differences between the ZIP and Poisson models at 0 and 1 cases. However, the residual assessment was not satisfactory for either of the models. The ZIP model, on the other hand, had a better predictive ability than the corresponding Poisson model. Posterior means of the sire, herd, and residual variances in the ZIP model (log scale) were 0.09, 0.37, and 0.36, respectively, highlighting the importance of herds as a source of variation in clinical mastitis. The correlation between sire rankings from the ZIP and Poisson models was 0.98. A weaker correlation would be expected in a population with more severe inflation at zero than the present one. The estimate of the perfect state probability p was 0.32, indicating that 32% of the animals would be in the perfect state, either because they are resistant or because they were not exposed to mastitis.

Study on the association of BoLA-DRB3.2 alleles with clinical mastitis in Norwegian Red cows

Genotyping of bovine leucocyte antigen DRB3.2 (BoLA-DRB3.2) in a total of 523 Norwegian Red (NR) cows from two groups selected for high protein yield and low clinical mastitis, respectively, identified 27 previously reported BoLA-DRB3.2 alleles across the groups. Significant differences in BoLA-DRB3.2 allele frequencies were found between the selection groups. Alleles *13, *18, *22 and *27 had a significantly higher frequency in cows selected for low clinical mastitis, while alleles *3, *9, *11 and *26 had a higher frequency in cows selected for high protein yield. Associations between BoLA-DRB3.2 alleles and clinical mastitis were analysed based on mastitis data from 741,072 first-lactation NR cows, of which 452 were genotyped. Alleles *22 and *26 were found to be associated with increased clinical mastitis, while alleles *7, *11, *18 and *24 had a favourable effect on mastitis resistance. Contradictory results from different studies investigating associations between BoLA-DRB3.2 alleles and mastitis indicate that future studies should focus on associations of mastitis with BoLA haplotypes rather than with single BoLA genes.

Genetic Analysis of Calving Difficulty and Stillbirth in Norwegian Red Cows

The objectives of this study were to infer genetic parameters for stillbirth (SB) and calving difficulty (CD) and to evaluate phenotypic and genetic change for these traits in the Norwegian Red breed. Stillbirth is recorded as a binary trait and calving difficulty has 3 categories: 1) easy calving, 2) slight problems, and 3) difficult calving. The overall mean frequency of SB in Norwegian Red was 3% at first calving and 1.5% for second and later calvings; mean frequency of the category "difficult calving" was 2 to 3% for heifers and 1% for cows at second and later calvings. Mean stillbirth rate has remained unchanged from 1978 to 2004. The proportion of the category "difficult calving" has not changed over the years, but the "slight problems" category increased from 4 to 7% for heifers and from 2 to 3% for cows. A total of 528,475 first-calving records were analyzed with a Bayesian bivariate sire-maternal grandsire threshold liability model. Posterior means of direct and maternal heritabilities were 0.13 and 0.09 for CD, and 0.07 and 0.08 for SB, respectively. Strong genetic correlations were found between direct SB and direct CD (0.79), and between maternal SB and maternal CD (0.62), whereas all genetic correlations between direct and maternal effects within or between traits were close to zero. These positive correlations are favorable in the sense that selection for one of the traits would result in a favorable selection response for the second trait. No genetic correlations between direct and maternal effects imply that bulls should be evaluated both as sire of the calf (direct) and sire of the cow (maternal). No genetic change for SB was found, and a slight genetic improvement for CD was detected.

Inferring Relationships Between Somatic Cell Score and Milk Yield Using Simultaneous and Recursive Models

A Bayesian analysis via Markov chain Monte Carlo methods extending the simultaneous and recursive model of Gianola and Sorensen (2004) was proposed to account for possible population heterogeneity. The method was used to infer relationships between milk yield and somatic cell scores of Norwegian Red cows. Data consisted of test-day records of milk yield and somatic cell score of first-lactation cows during the first 120 d of lactation. Results suggested large negative direct effects from somatic cell score to milk yield and small reciprocal effects from milk yield to somatic cell score. The direct effects were larger in the first 60 d of lactation than in the subsequent period. Bayesian model selection strongly favored the simultaneous and recursive models for milk yield and somatic cell score over the corresponding mixed model without considering simultaneity or recursiveness. Estimated effects between milk yield and somatic cell score seemed to be yield-dependent, larger in higher producing cows than in lower producing cows. Heritability estimates from the simultaneous and recursive models were similar to those from the mixed model, but some genetic correlations differed considerably among models.

Selection Responses for Disease Resistance in Two Selection Experiments with Norwegian Red Cows

Genetic trends for clinical mastitis (CM), ketosis (KET), retained placenta (RP), and 305-d protein yield (PY305) were calculated for 2 Norwegian dairy cattle selection experiments. The first experiment, accomplished from 1978 to 1989, included groups selected for high (HMP) and low milk production (LMP). The second experiment started in 1989 and included selection for high protein yield (HPY) and low mastitis frequency (LCM). In both experiments proven sires from the active breeding program of Norwegian Red were used as sires. To take into account that selection of sires was external to the experiment, all available data from the Norwegian Red population, including disease records for 2.7 million first-lactation cows, were analyzed with a multivariate animal model. Estimated breeding values for cows in the experiments were extracted from this analysis to calculate genetic trends in the selection groups. Genetic trends for PY305 were, as expected, positive for the HMP and HPY groups, and negative for LMP and LCM. The HMP group showed increasing genetic trends for all 3 diseases, arguably a correlated response after selection for increased milk production, whereas the LCM group showed decreasing genetic trends for CM, KET, and RP. The genetic trends for KET and RP in the LCM group are most likely correlated responses after selection against CM. After 5 cow-generations the genetic difference between HPY and LCM was 10 percentage units CM, 1.5 percentage units KET, and 0.5 percentage units RP.

Short Communication: Genetic Analysis of Nonreturn Rate and Mastitis in First-Lactation Norwegian Red Cows

Associations between clinical mastitis (CM) and nonreturn rate within 56 d after first insemination (NR56) were examined in Norwegian Red (NRF) cows. Records on absence or presence of CM within each of the intervals, -30 to 30, 31 to 150, and 151 to 300 d after first calving, and records on NR56 for 620,492 first-lactation daughters of 3,064 NRF sires were analyzed with a Bayesian multivariate threshold liability model. Point estimates of genetic correlations between NR56 and the 3 CM traits were between -0.05 and -0.02. Residual correlations were close to zero, and correlations between herd-5-yr effects on NR56 and CM in the 3 lactation intervals ranged from -0.15 to -0.17. It appears that CM and NR56 in first lactation are independent traits.

Genetic Analysis of Number of Mastitis Cases and Number of Services to Conception Using a Censored Threshold Model

The objective of this study was to infer genetic parameters and genetic change for number of clinical mastitis cases (NCM) and number of services to conception (STC) in first-lactation Norwegian Red (NRF) cows. Records on 620,492 daughters of 3,064 NRF sires, with first calving from 1980 through 2004, were analyzed with a bivariate threshold liability model that takes censoring into account. Posterior mean (SD) of heritability of liability was 0.08 (0.004) for NCM and 0.03 (0.002) for STC. The mean (SD) of the posterior distribution of the genetic correlation between the 2 traits was 0.21 (0.04). Posterior means of the correlation between herd-5-yr effects, and between residuals for NCM and STC were 0.17 and 0.05, respectively. To evaluate effects of taking censoring into account, the data were also analyzed with a bivariate ordered threshold model ignoring censoring. The genetic correlation between NCM and STC was lower than in the censored threshold model (0.09 vs. 0.21). Heritability of liability to NCM and STC from this model was also slightly lower, whereas the point estimates of herd-5-yr and residual correlations were 0.15, and -0.01, respectively. These results suggest that genetic (co)variance may be understated in models ignoring censoring. For comparison purposes, the data were analyzed with a bivariate linear sire model and standard REML-BLUP procedures. The correlation (rank correlation) between sire evaluations from the censored threshold model and sire predicted transmitting abilities from the linear model was 0.90 (0.90) for NCM and 0.87 (0.86) for STC. The evolution of average sire posterior means by birth year of daughters was used to assess genetic change, and results indicated genetic reduction (i.e., genetic improvement) of NCM and little or no genetic change for STC in the NRF population.

A comparison between multivariate Slash, Student's t and probit threshold models for analysis of clinical mastitis in first lactation cows

Robust threshold models with multivariate Student's t or multivariate Slash link functions were employed to infer genetic parameters of clinical mastitis at different stages of lactation, with each cow defining a cluster of records. The robust fits were compared with that from a multivariate probit model via a pseudo-Bayes factor and an analysis of residuals. Clinical mastitis records on 36 178 first-lactation Norwegian Red cows from 5286 herds, daughters of 245 sires, were analysed. The opportunity for infection interval, going from 30 days pre-calving to 300 days postpartum, was divided into four periods: (i) -30 to 0 days pre-calving; (ii) 1-30 days; (iii) 31-120 days; and (iv) 121-300 days of lactation. Within each period, absence or presence of clinical mastitis was scored as 0 or 1 respectively. Markov chain Monte Carlo methods were used to draw samples from posterior distributions of interest. Pseudo-Bayes factors strongly favoured the multivariate Slash and Student's t models over the probit model. The posterior mean of the degrees of freedom parameter for the Slash model was 2.2, indicating heavy tails of the liability distribution. The posterior mean of the degrees of freedom for the Student's t model was 8.5, also pointing away from a normal liability for clinical mastitis. A residual was the observed phenotype (0 or 1) minus the posterior mean of the probability of mastitis. The Slash and Student's t models tended to have smaller residuals than the probit model in cows that contracted mastitis. Heritability of liability to clinical mastitis was 0.13-0.14 before calving, and ranged from 0.05 to 0.08 after calving in the robust models. Genetic correlations were between 0.50 and 0.73, suggesting that clinical mastitis resistance is not the same trait across periods, corroborating earlier findings with probit models.

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.

A Bayesian threshold-normal mixture model for analysis of a continuous mastitis-related trait

Mastitis is associated with elevated somatic cell count in milk, inducing a positive correlation between milk somatic cell score (SCS) and the absence or presence of the disease. In most countries, selection against mastitis has focused on selecting parents with genetic evaluations that have low SCS. Univariate or multivariate mixed linear models have been used for statistical description of SCS. However, an observation of SCS can be regarded as drawn from a 2- (or more) component mixture defined by the (usually) unknown health status of a cow at the test-day on which SCS is recorded. A hierarchical 2-component mixture model was developed, assuming that the health status affecting the recorded test-day SCS is completely specified by an underlying liability variable. Based on the observed SCS, inferences can be drawn about disease status and parameters of both SCS and liability to mastitis. The prior probability of putative mastitis was allowed to vary between subgroups (e.g., herds, families), by specifying fixed and random effects affecting both SCS and liability. Using simulation, it was found that a Bayesian model fitted to the data yielded parameter estimates close to their true values. The model provides selection criteria that are more appealing than selection for lower SCS. The proposed model can be extended to handle a wide range of problems related to genetic analyses of mixture traits.

Genetic analysis of clinical mastitis, milk fever, ketosis, and retained placenta in three lactations of Norwegian red cows

The objectives were to infer heritability and genetic correlations between clinical mastitis (CM), milk fever (MF), ketosis (KET), and retained placenta (RP) within and between the first 3 lactations and to estimate genetic change over time for these traits. Records of 372,227 daughters of 2411 Norwegian Red (NRF) sires were analyzed with a 12-variate (4 diseases x 3 lactations) threshold model. Within each lactation, absence or presence of each of the 4 diseases was scored based on the cow's health recordings. Each disease was assumed to be a different trait in each of the 3 lactations. The model for liability had trait-specific effects of year-season of calving and age of calving (first lactation) or month-year of calving and calving interval (second and third lactations), herd-5-yr, sire of the cow, and a residual. Posterior means of heritability of liability in first, second, and third lactations were 0.08, 0.07, and 0.07, respectively, for CM; 0.09, 0.11, and 0.13 for MF; 0.14, 0.16, and 0.15 for KET, and 0.08 in all 3 lactations for RP. Posterior means of genetic correlations between liability to CM, MF, KET, and RP, within disease between lactations, ranged from 0.19 to 0.86, and were highest between KET in different lactations. Correlations involving first lactation MF were low and had higher standard deviations. Genetic correlations between diseases were low or moderate (from -0.10 to 0.40), within as well as between lactations; the largest estimates were for MF and KET, and the lowest involved MF or KET and RP. Positive genetic correlations between diseases suggest that some general disease resistance factor with a genetic component exists. Trends of average sire posterior means by birth-year of daughters were used to assess genetic change, and the results indicated genetic improvement of resistance to CM and KET and no genetic change for MF and RP in the NRF population.