Genetic parameters for clinical mastitis in the first three lactations of Dutch Holstein cattle

The first breeding value for udder health of a bull is based on the performance of his daughters in their first lactation. However, clinical mastitis (CM) is not a problem in first lactation only. Therefore, the objective of this study was to estimate genetic parameters for CM and somatic cell count (SCC) for the first three lactations of Dutch Holstein cattle. Data from 250 Dutch herds recording CM were used to quantify the genetic variation of CM in parity 1, 2, and 3, respectively. The dataset contained 35,379 lactations from 21,064 animals of different parities. Test-day SCC was available from all lactations. Somatic cell counts were log-transformed to somatic cell scores (SCS) and averaged over test-day records between 5 and 335, 5 and 150, and 151 and 335 days in milk. Variance components for CM and SCS were estimated using a sire-maternal grandsire model. The heritability for CM was approximately 3% in all parities. Genetic correlations between CM in consecutive lactations were high (0.9), but somewhat lower between parity 1 and 3 (0.6). All genetic correlations between CM and SCS were positive, implying that genetic selection on lower SCC will reduce CM-incidence. Estimated genetic correlations were stronger for SCS in the first half of lactation than in the second half of lactation. Selection indices showed that most progress could be achieved when treating CM in parity 1, 2, and 3 as different traits and by including SCS between 5 and 150 days in the udder health index.     

Genetic analysis of fat‐to‐protein ratio,milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model

We estimated the genetic parameters of fat‐to‐protein ratio (FPR) and the genetic correlations between FPR and milk yield or somatic cell score in the first three lactations in dairy cows. Data included 3 079 517 test‐day records of 201 138 Holstein cows in Japan from 2006 to 2011. Genetic parameters were estimated with a multiple‐trait random regression model in which the records within and between parities were treated as separate traits. The phenotypic values of FPR increased soon after parturition and peaked at 10 to 20 days in milk, then decreased slowly in mid‐ and late lactation. Heritability estimates for FPR yielded moderate values. Genetic correlations of FPR among parities were low in early lactation. Genetic correlations between FPR and milk yield were positive and low in early lactation, but only in the first lactation. Genetic correlations between FPR and somatic cell score were positive in early lactation and decreased to become negative in mid‐ to late lactation. By using these results for genetic evaluation it should be possible to improve energy balance in dairy cows.     

Estimation of genetic parameters for somatic cell count in the first three lactations using random regression

Genetic parameters were estimated for log_e somatic cell count (LSCC) for the first three lactations of 31 236 Holstein/Friesian cows with 308 534, 236 277 and 206 729 test day yields in parities 1, 2 and 3, respectively. An animal random regression model was employed in the analyses using Gibbs sampling with each parity regarded as different traits. Linear and quadratic functions were fitted for the animal and permanent environmental effects respectively, using orthogonal polynomials. Daily heritabilities increased with days in milk (DIM) and averaged about 0.07 in all three parities. This increase in heritabilities with DIM was due to an increase in genetic variance and decreases in both permanent and residual environmental variances with DIM. Environmental effects have a large influence on LSCC in early lactation in all three parities. Within lactation, genetic correlations were highest between adjacent DIM but decreased as DIM got further apart. However, this decrease was slowest in parity one and greatest in parity three. The lowest correlation within lactation was 0.10 between DIM 7 and 305 in parity 3. Across lactations, genetic correlations were highest between parities 2 and 3, intermediate between 1 and 3, and lowest between 1 and 2. The genetic correlations computed for completed lactations were 0.69, 0.79 and 0.98 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Corresponding phenotypic correlations between parities were 0.38, 0.31 and 0.52, respectively. A test day model, accounting for these variations in heritabilities and genetic correlations, should result in a more accurate evaluation.     

Genetic parameters and single versus multi-trait evaluation of udder health traits

Abstract

Genetic parameters were estimated for lactation average somatic cell score (SCS) and clinical mastitis (CM) for the first three lactations of multiparous Finnish Ayrshire cows. A multi-trait linear sire model was used for estimation of covariance components, and the efficiencies of single- versus multi-trait multi-lactation (MT) sire evaluations were compared. Heritability of SCS and CM in the first three lactations ranged from 0.11 to 0.13 and 0.02 to 0.03, respectively. Within lactation, genetic correlations between SCS and CM ranged from 0.68 to 0.72. Within both traits, across-lactation genetic correlations were lowest between 1 and 3, and highest between 2 and 3, with estimates ranging from 0.75 to 0.86 and from 0.81 to 0.98 for CM and SCS, respectively. Residual and phenotypic correlations were low and ranged from 0.09 to 0.13 and from 0.10 to 0.13, respectively. The absolute difference between genetic and residual correlations was from 0.5 to 0.6. Within-lactation genetic correlations between traits that are much less than unity suggest a multi-trait model for genetic evaluation of mastitis resistance. Comparison of model prediction performance between single-trait (ST) and MT models using a data splitting method showed that the MT model was more stable in predicting breeding values in future records of animals. Especially, for young sires and CM, the SD of EBVs from the MT model was 14 to 23% higher than the ST model, indicating more effective use of information in terms of revealing more genetic variation.     

Genetic associations between milk fat‐to‐protein ratio,milk production and fertility in the first two lactations of Thai Holsteins dairy cattle

The aims of this study were to estimate, simultaneously, the genetic parameters of test‐day milk fat‐to‐protein ratio (FPR), test‐day milk yield (MY), and days‐open (DO) in the first two lactations of Thai Holsteins. A total of 76 194 test‐day production records collected from 8874 cows with 8674 DO records between 2001 and 2011 from different lactations were treated as separated traits. The estimates of heritability for test‐day FPR in the first lactation showed an increasing trend, whereas the estimates in the second lactation showed a U‐shape trend. Genetic correlations for FPR‐DO and MY‐DO showed a decreasing trend along days in milk (DIM) in both lactations, whereas genetic correlations for FPR‐MY increased along DIM in the first lactation but decreased in the second lactation. Genetic correlations of FPR between consecutive DIM were moderate to high, which showed the effectiveness of simultaneous analyses. Selection of FPR in the early stage has no adverse effect on MY and DO for the first lactation but has a negative effect on MY and positive effect on DO for the second lactation. This study showed that genetic improvement of the energy balance using FPR, MY and DO with multi‐trait test day model could be applied in a Thailand dairy cattle breeding program.     

Genetic evaluation of Australian dairy cattle for somatic cell scores using multi-trait random regression test-day model

Summary A multi-trait (MT) random regression (RR) test day (TD) model has been developed for genetic evaluation of somatic cell scores for Australian dairy cattle, where first, second and third lactations were considered as three different but correlated traits. The model includes herd-test-day, year-season, age at calving, heterosis and lactation curves modelled with Legendre polynomials as fixed effects, and random genetic and permanent environmental effects modelled with Legendre polynomials. Residual variance varied across the lactation trajectory. The genetic parameters were estimated using asreml . The heritability estimates ranged from 0.05 to 0.16. The genetic correlations between lactations and between test days within lactations were consistent with most of the published results. Preconditioned conjugate gradient algorithm with iteration on data was implemented for solving the system of equations. For reliability approximation, the method of Tier and Meyer was used. The genetic evaluation system was validated with Interbull validation method III by comparing proofs from a complete evaluation with those from an evaluation based on a data set excluding the most recent 4 years. The genetic trend estimate was in the allowed range and correlations between the two sets of proofs were very high. Additionally, the RR model was compared to the previous test day model. The correlations of proofs between both models were high (0.97) for bulls with high reliabilities. The correlations of bulls decreased with increasing incompleteness of daughter performance information. The correlations between the breeding values from two consecutive runs were high ranging from 0.97 to 0.99. The MT RR TD model was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize estimated breeding values for first and later lactations.     

Genetic parameters for mastitis incidence and its indicators based on somatic cell score for Holsteins in Hokkaido,Japan

The objectives of this study were to estimate the heritability of mastitis incidence and genetic correlations between the mastitis and the somatic cell score (SCS) statistics, and to compare the practicability between different models. We used test‐day records with the mastitis incidence and SCS collected from Holstein cows calving from 1988 to 2015 in Hokkaido, Japan. As indicators of mastitis, the average SCS (avSCS), the standard deviation of SCS (sdSCS), and the maximum SCS (maxSCS) were calculated using test‐day records up to the first 305 days in milk within a lactation. We compared a four‐trait repeatability animal model (MTRP) with a four‐trait multiple‐lactation animal model (MTML). The heritability for mastitis was equal to or lower than 0.05 in all the models. Genetic correlations between lactations with MTML within the same trait were positive and close to 1. With MTRP, the estimated genetic correlations of the mastitis incidence with avSCS, sdSCS, and maxSCS were 0.66, 0.79, and 0.82, respectively. A joint evaluation with SCS statistics is expected to give an extra reliability for mastitis because of high and positive genetic correlations among the traits.     

Genetic and Environmental Correlations Among Female Fertility Traits and Milk Production in Different Parities of Swedish Red and White Dairy Cattle

The main objective of this study was to estimate genetic correlations between fertility and production traits in first, second and third lactations as well as between fertility traits measured in the same way at different ages. The fertility traits studied were: number of inseminations per service period, number of treatments for reproductive disturbances, interval between first and last inseminations, interval between calving and first insemination, and interval between calving and last insemination. Early milk production was measured as the average of the energy-corrected milk yield at the second and third monthly testdays in a lactation. The number of records was approximately 450 000, 350 000, 180 000 and 75 000 in the heifer period, first, second, and third lactations, respectively. A linear, trivariate model that included the effects of herd-year, year, month, age and sire of the cow was applied. To reduce the effect of ongoing selection, 305-days kg protein production in first lactation was included as a variate in all of the analyses. Correlations between the herd-year effects indicated that factors of herd-year level conducive to increased production had a tendency to increase the number of inseminations as well as the number of reproductive treatments, although there was an earlier start and termination of the insemination period. Genetic correlations between fertility traits and production were in the range of 0.2-0.4, all of them unfavourable and higher at later parities. The genetic correlations between fertility traits in the heifer period and the same traits in first lactation were 0.7. Genetic correlations between the first and second lactation varied between 0.7 and 0.9, and between the second and third lactation they were all 0.9 or higher. In conclusion, fertility and production traits need to be selected for simultaneously if fertility is going to be maintained along further genetic improvement on production, and such selection should include fertility results from lactating cows.     

Genetic and phenotypic parameters and annual trends for milk production and fertility traits of the Sahiwal cattle in semi arid Kenya

Data comprising 7211 lactation records of 2894 cows were used to estimate genetic and phenotypic parameters for milk production (lactation milk yield, LMY and lactation length, LL) and fertility (calving interval, CI; number of services per conception, NSC and age at first calving, AFC) traits. Genetic, environmental and phenotypic trends were also estimated. Variance components were estimated using univariate, bivariate and trivariate animal models on based restricted maximum likelihood procedures. Univariate models were used for each trait, while bivariate models were used to estimate genetic and phenotypic correlations between milk production and fertility traits and between LMY, LL, CI and NSC within each lactation. Trivariate models were used in the analysis of LMY, LL, CI and NSC in the first three lactations. Heritability estimates from the univariate model were 0.16, 0.07, 0.03, 0.04 and 0.01 for LMY, LL, CI, AFC and NSC, respectively. The heritability estimates from trivariate analysis were higher for milk production traits than those from univariate analyses. Genetic correlations were high and undesirable between milk production and fertility traits, while phenotypic correlations were correspondingly low. Genetic trends were close to zero for all traits, while environmental and phenotypic trends fluctuated over the study period.     

Relationships of first lactation yields with lifetime performance traits in Holstein cows

SUMMARY: Genetic and phenotypic correlations between the first lactation and lifetime yields of milk, fat and protein, herdlife, productive life and number of lactations initiated in the herd were estimated from records of 24,231 progeny of 234 young and 119 proven Holstein sires in 1791 herds using a multivariate REML technique to fit a sire model with relationships among young sires. Proven sires were fitted as fixed effects. Genetic correlations between first lactation and lifetime yields were highest for milk (0.666) followed by fat (0.660) and protein (0.512). Genetic as well as phenotypic correlations of herdlife, productive life and number of lactations were higher with first lactation milk yield than with first lactation fat and protein yields. Direct selection for higher lifetime yields would not be effective because of low heritabilities. However the high, positive genetic correlations of lifetime yields of milk and fat with first lactation yields suggested that first lactation yields might be used for indirect selection for higher lifetime yields. ZUSAMMENFASSUNG: Beziehung zwischen Erstlaktations- und Lebensleistung bei Holstein-Kühen Zwischen Erstlaktations- und Lebensleistung für Milch, Fett, Protein, Verbleibedauer, produktiver Lebensdauer und Zahl von Laktationen in der Herde wurden von 24.291 T?chtern, 234 Jung- und 119 geprüften Holsteintieren in 1.791 Herden genetische Beziehungen gesch?tzt, wobei eine multivariate REML-Technik zur Analyse eines Stiermodells mit Verwandtschaft zwischen jungen Stieren angewendet worden ist. Die geprüften Stiere wurden als fixe Effekte angesehen. Genetische Korrelationen zwischen Erstlaktation und Lebensleistung war am h?chsten für Milch (0,666), gefolgt von Fett (0,660) und Protein (0,512). Genetische und ph?notypische Korrelationen mit Verbleibedauer, produktiver Lebensdauer und Zahl der Laktationen waren ebenfalls für Erstlaktations-milchmenge h?her als bei Fett und Protein. Direkte Selektion auf h?here Lebensleistung würde wegen der niedrigen Heritabilit?t nicht wirksam sein. Allerdings k?nnten die hohen positiven genetischen Korrelationen des Merkmals mit Erstlaktationsleistungen diese als geeignetes indirektes Selektionskriterium für h?here Lebensleistung anzeigen.     

Effects of heat stress on production,somatic cell score and conception rate in Holsteins

We examined the effects of heat stress (HS) on production traits, somatic cell score (SCS) and conception rate at first insemination (CR) in Holsteins in Japan. We used a total of 228 242 records of milk, fat and protein yields, and SCS for the first three lactations, as well as of CR in heifers and in first‐ and second‐lactation cows that had calved for the first time between 2000 and 2012. Records from 47 prefectural weather stations throughout Japan were used to calculate the temperature–humidity index (THI); areas were categorized into three regional groups: no HS (THI < 72), mild HS (72 ≤ THI < 79), and moderate HS (THI ≥ 79). Trait records from the three HS‐region groups were treated as three different traits and trivariate animal models were used. The genetic correlations between milk yields from different HS groups were very high (0.91 to 0.99). Summer calving caused the greatest increase in SCS, and in the first and second lactations this increase became greater as THI increased. In cows, CR was affected by the interaction between HS group and insemination month: with summer and early autumn insemination, there was a reduction in CR, and it was much larger in the mild‐ and moderate‐HS groups than in the no‐HS group.     

Use of a test day model for dairy goat milk yield across lactations in Germany.

Breeding value prediction for dairy goats in Germany is still based on herd mate comparison within breeding society. The objective of this study was to estimate genetic parameters for milk yield based on a test day model. For the analysis 35,308, 30,551 and 23,640 test day records from lactations 1, 2 and 3 from 5079, 4118 and 3132 animals, respectively, were used. The data between 1987 and 2003 were obtained from six German breeding societies. The multiple trait (lactations 1, 2 and 3) repeatability model (RPT) included the fixed effects of breeding society-breed-herd-year, litter size, lambing season, and days in milk of third-order Legendre polynomials nested within herd-year, and the random effects of animal additive and permanent environment. The three-trait random regression model (RR) also included the random regressions based on second-order Legendre polynomials for animal additive and permanent environmental effects. Heritability estimates in RPT were 0.27 +/- 0.02, 0.20 +/- 0.02 and 0.37 +/- 0.02 for the first, second and third lactation, respectively. The genetic correlation between the first and second lactation was 0.69, between the second and third lactation 0.79, and between the first and third lactation 0.45. Heritability estimates from the RR in the first and second lactations decreased from the beginning to the end of the lactation, with average values of 0.28 and 0.27, respectively. Estimates in the third lactation showed a maximum in the middle of lactation, averaging 0.37. Genetic correlations between the first and second lactation averaged 0.64, between the second and third lactation 0.72, and between the first and third lactation 0.46. Despite the small data set and restricted relationship structure the estimates were reasonable with the exception of estimates from the third lactation, which seemed inflated. RR could be used for genetic evaluation of dairy goats in Germany.     

Genetic evaluation of somatic cell score in dairy cattle considering first and later lactations as two different but correlated traits

A test‐day (TD) random regression model (RRM) was described for the genetic evaluation of somatic cell score (SCS) where first and later lactations were considered as two different but correlated traits. A two‐step covariance function procedure was used to estimate variance–covariances and associated genetic parameters. Analysis of estimated breeding values (EBV), ranking of top bulls and cows and some computational aspects were used to compare RRM with TD repeatability model (RPM) and lactation average model (LAM). Residuals were analysed to assess the relative fit of TD models. Comparison between RRM and RPM showed that RRM has lower mean squared error and gave better fit to the data. For young bulls and cows, the standard deviation (SD) of EBVs was highest for RRM and lowest for LAM implying efficient utilization of information on SCS, in terms of revealing more genetic variation. A much lower correlation of EBVs ranging from 0.80 to 0.92 and significant re‐ranking of top bulls and cows were observed between RRM and LAM. The lower across‐lactation correlation between RRM and LAM indicated that LAM is directed to give more weight to first lactation breeding values. The RRM, where SCS in the first and later lactations was considered as two different but correlated traits was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize EBVs for first and later lactations.     

A comparison of different measures of persistency with special respect to variation of test-day milk yields

Six measures of persistency of milk yield were compared by estimation of heritabilities, genetic correlations and the amount of concentrates required by cows with high and low persistency.Persistency was expressed as ratio between different parts of the lactation (P2:1 and P3:1), as a ratio between maximum test-day milk yield and mean test-day milk yield and as the standard deviation of the test-day milk yields of a lactation. The investigation was based on 39 349 first, 23 910 second and 13 651 third lactation records of Simmental cows from the region of Lower Austria.The heritability estimates ranged from 0.12 to 0.18 for measurements including the first 200 days of lactation and from 0.17 to 0.22 when the whole 305-day lactation was included. The largest values were found for the standard deviation of test-day milk yields (305 days); 0.21, 0.22 and 0.22 for the first, second and third lactation, respectively. Genetic correlations between lactations were high for all measures of persistency, ranging from 0.79 to 0.95. Highly persistent cows required between 69 and 161 kg less concentrate than cows with a poor persistency to produce 5500 kg of milk. The difference was generally larger when the grouping was performed on measures including 305 days of lactation vs. measures including only earlier parts of the lactation; 161 kg was found for P3:1.     

Genetic relationships of alternative somatic cell count traits with milk yield,composition and udder type traits in Italian Jersey cows

The aim of this study was to estimate genetic associations between alternative somatic cell count (SCC) traits and milk yield, composition and udder type traits in Italian Jersey cows. Alternative SCC traits were test‐day (TD) somatic cell score (SCS) averaged over early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), a binary trait indicating absence or presence of at least one TD SCC >400,000 cells/ml in the lactation (Infection) and the ratio of the number of TD SCC >400,000 cells/ml to total number of TD in the lactation (Severity). Heritabilities of SCC traits, including lactation‐mean SCS (SCS_LM), ranged from 0.038 to 0.136. Genetic correlations between SCC traits were moderate to strong, with very few exceptions. Unfavourable genetic associations between milk yield and SCS_SD and Infection indicated that high‐producing cows were more susceptible to variation in SCC than low‐producing animals. Cows with deep udders, loose attachments, weak ligaments and long teats were more susceptible to an increase of SCC in milk. Overall, results suggest that alternative SCC traits can be exploited to improve cow's resistance to mastitis in Italian Jersey breed.     

Genetic relationships among female fertility disorders,female fertility traits and productivity of Holstein dairy cows in the early lactation period

Fertility health disorders from the early lactation period including retained placenta (REPLA), metritis (MET), corpus luteum persistence (CLP), anoestria/acyclia (AOEAC) and ovarial cysts (OC), as well as overall disease categories (disorders during the postpartal period (DPP), ovary infertility (OINF), overall trait definition “fertility disorders” (FD)), were used to estimate genetic (co)variance components with female fertility and test‐day traits. The disease data set comprised 25,142 Holstein cows from parities 1, 2 and 3 resulting in 43,584 lactations. For disease traits, we used the binary trait definition (sick or healthy) and disease count data reflecting the sum of treatments for the same disease within lactation or within lactation periods. Statistical modelling included single and multiple trait repeatability animal models for all trait combinations within a Bayesian framework. Heritabilities for binary disease traits ranged from 0.04 (OC) to 0.10 (REPLA) and were slightly lower for the corresponding sum trait definitions. Correlations between both trait definitions were almost one, for genetic as well as for permanent environmental effects. Moderate to high genetic correlations were found among puerperal disorders DPP, REPLA and MET (0.45–0.98) and among the ovarian disorders OINF, AOEAC, CLP and OC (0.59–0.99). Genetic correlations between puerperal and ovarian disorders were close to zero, apart from the REPLA–OC association (0.55). With regard to fertility disorders and productivity in early lactation, a pronounced genetic antagonistic relationship was only identified between OC and protein yield. Genetic correlations between fertility disorders and test‐day SCS were close to zero. OINF and all diseases contributing to OINF were strongly correlated with the female fertility traits “interval from calving to first service,” “interval from service to pregnancy” and “interval from calving to pregnancy.” The strong correlations imply that fertility disorders could be included in genetic evaluations of economic fertility traits as correlated predictors. Vice versa, a breeding focus on female fertility traits will reduce genetic susceptibility to OC, CLP and AOEAC.     

Relation of milk production loss to milk somatic cell count.

Milk production loss was studied in relation to increased somatic cell count (SCC). Available data were weekly test-day milk yields and SCC (in 1,000 cells/ml), and mastitis incidences. In total, 18,131 records from 274 cows were used. Production loss was determined for test-day kg milk, kg protein, and kg energy-corrected milk. Least-squares analysis of variance was used to estimate the direct effect of Log10(SCC) on production. The recorded measures of production were first corrected for fixed effects, with adjustment factors estimated from a healthy data-set. The average daily milk yield was 19.7 kg/day in first lactation and 22.0 in later lactations. The geometric mean of SCC was 63.1 in first lactation and 107.2 in later lactations. The incidence of clinical mastitis treated by a veterinarian was 19.8% of the lactations-at-risk. Linear relationships were found between the production parameters and Log10(SCC). Quadratic and cubic effects were evaluated, but were found to contribute little to the overall fit of the models. The individual milk yield loss was 1.29 kg/day for each unit increase in Log10(SCC) for cows in first lactation. Milk yield decreased by 2.04 kg/day per unit Log10(SCC) for older cows. Corresponding values for protein yield were 0.042 and 0.067 kg/day for first and later lactations, respectively.     

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.     

Alternative parameterizations of the multiple-trait random regression model for milk yield and somatic cell score via recursive links between phenotypes

Multiple-trait random regression models with recursive phenotypic link from somatic cell score (SCS) to milk yield on the same test day and with different restrictions on co-variances between these traits were fitted to the first-lactation Canadian Holstein data. Bayesian methods with Gibbs sampling were used to derive inferences about parameters for all models. Bayes factor indicated that the recursive model with uncorrelated environmental effects between traits was the most plausible specification in describing the data. Goodness of fit in terms of a within-trait weighted mean square error and correlation between observed and predicted data was the same for all parameterizations. All recursive models estimated similar negative causal effects from SCS to milk yield (up to -0.4 in 46-115 days in milk in lactation). Estimates of heritabilities, genetic and environmental correlations for the first two regression coefficients (overall level of a trait and lactation persistency) within both traits were similar among models. Genetic correlations between milk and SCS were dependent on the restrictions on genetic co-variances for these traits. Recursive model with uncorrelated system genetic effects between milk and SCS gave estimates of genetic correlations of the opposite sign compared with a regular multiple-trait model. Phenotypic recursion between milk and SCS seemed, however, to be the only source of environmental correlations between these two traits. Rankings of sires for total milk yield in lactation, average daily SCS and persistency for both traits were similar among models. Multiple-trait model with recursive links between milk and SCS and uncorrelated random environmental effects could be an attractive alternative for a regular multiple-trait model in terms of model parsimony and accuracy.     

Genetic associations between somatic cell score and pathogen‐specific subclinical mastitis in Norwegian Red cows

The aim of this study was to estimate heritabilities of and genetic correlations between pathogen‐specific subclinical mastitis (SCM) traits and lactation mean somatic cell score (LSCS) in Norwegian Red cattle. Based on data from 130 733 first‐lactation cows four binary pathogen‐specific SCM traits, Staphylococcus aureus, Streptococcus dysgalactiae, Streptococcus uberis and coagulase‐negative staphylococci SCM, were analysed together with unspecific SCM and LSCS using a multivariate sire model with threshold models for binary traits and a linear model for LSCS. Posterior means (SD) of heritabilities were 0.17 (0.01) for LSCS, 0.11 (0.01) for liability to unspecific SCM and ranged from 0.04 (Staph. aureus) to 0.14 (Strep. dysgalactiae) for liability to pathogen‐specific SCM. Genetic correlations were positive and moderate to high, ranging from 0.37 to 0.98. All genetic correlations except the one between LSCS and unspecific SCM were lower than 1, indicating that SCM caused by different pathogens can be considered as partly different traits.