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.

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.

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.

Precision and additivity of organic matter digestibility obtained via in vitro multi-enzymatic method.

The enzymatic digestibility of organic matter (EDOM) method is an in vitro multi-enzymatic method for estimating the organic matter (OM) digestibility of feeds. The EDOM method previously showed high accuracy with in vivo values for compound feeds. The aim of this study was to evaluate the precision of the EDOM method and determine its additivity, compared with the long-assumed additive property of the chemical components of compound feeds. 149 feed samples, 70 commercial compound feeds and 79 associated ingredients, were analyzed in a laboratory (lab1) for OM digestibility measured by EDOM (OMDEDOM) with 2 repetitions separated in time to estimate repeatability. Of the total samples, 49 compound feeds were further analyzed in a commercial laboratory (lab2) for OMDEDOM to determine reproducibility. The 49 compounds and their 69 associated ingredients were also analyzed by lab2 for dry matter (DM), ash, crude protein (CP), neutral detergent fiber (NDF), and starch. The EDOM method resulted in an intralaboratory correlation of 98.9% and an interlaboratory correlation of 92.6%, with no significant mean bias between the 2 laboratories tested. The formulation of compound feeds, total mixed rations, and mixtures in general assumes that their nutrient content can be calculated by adding together the nutrient supply of individual ingredients. This is of great importance in the feed industry for the creation of compound feeds. Additivity of OMDEDOM for the compound feed samples was evaluated by comparing the sum of the digestible OM (DOMEDOM) of the ingredients (predicted) with DOMEDOM estimated directly in the compound feed (observed). The regression of predicted versus observed showed a coefficient of determination (R2) of 0.93 and root mean square error (RMSE) of 1.07% of total DM, with no linear bias but with a mean bias (0.83% of DM). Additivity of CP, starch, crude fat, and NDF showed an R2 of 0.95, 0.98, 0.95, and 0.93, and RMSE of 1.56, 1.90, 0.39, and 1.46% of DM, respectively, all presenting linear bias. Crude fat also presented mean bias. Although significant, all linear and mean bias for DOMEDOM and chemical components were within the acceptable error limits for declaration of feeds. The results demonstrate the high precision of the EDOM method and its additive property, which is an advantage for the estimation of OM digestibility in compound feeds. Moreover, results of the tests of chemical components confirm their additive property.

Designing a replacement heifer rearing strategy: Effects of growth profile on performance of Norwegian Red heifers and cows.

The primary objective of this study was to design a growth profile from 3 mo through puberty to insemination that allows heifers to enter the milking herd at 22 mo of age without impairing milk production over 3 lactations compared with the current rearing practice leading to an age at first calving of 26 mo. Eighty heifers born into the Norwegian University of Life Sciences herd, 40 each from yr 2010 and 2011, were randomly assigned according to birth order either to a high or low intake energy treatment. Each energy group was further subdivided into 2 protein groups, 1 fed according to requirements and 1 fed 10% excess protein, to ensure that metabolizable protein supply would meet the requirements for rapidly growing bone and muscle of today's genetically improved Norwegian Red heifer. Utilizing growth rate and feed composition the energy and protein supply was regulated with roughage quality in a diet containing 1 kg/d of concentrate of 2 qualities. Average daily gain from 3 mo to confirmed pregnancy ranged from 900 to 1,000 g/d among high-energy animals, with high protein-fed animals growing the fastest. Growth rates for low energy animals were <700 g/d. From confirmed pregnancy to first calving, all animals were fed only grass silage to sustain an average daily gain <500 (high energy) or >600 g/d (low energy), excluding fetal and gravid uterus weight, and they reached a postcalving weight of 530 (high energy) to 570 kg (low energy) with body condition score ranging from 3.42 to 3.93 at calving. We have shown that heifers fed a high-energy treatment with the required amount of protein from 3 mo of age to successful insemination combined with an average daily gain of ∼500 g/d throughout pregnancy will calve at 22 mo without becoming over-conditioned at calving and without impairing performance over 3 lactations. We recommend reducing rearing time by 4 mo, planning for an age at first calving of 22 mo of age. This rearing practice would also improve energy efficiency during the heifer rearing period.

The relationship between Norwegian Red heifer growth and their first-lactation test-day milk yield: A field study.

Today's Norwegian Red (NR) is markedly different from the one that existed 25 yr ago due to the continuous genetic improvement of economically important traits. Still, current national recommendations on replacement heifer rearing largely are based on results from Danish studies from the late 1980s to the mid 1990s. The objectives of the present study were to gain information on (1) growth and growth profiles of modern NR replacement heifers in commercial dairy herds and (2) how growth during the rearing period affects the heifers' milk yield during their first lactation. To this end, we conducted a field study on 5 high-producing and 5 low-producing commercial dairy farms from each of 3 geographical regions in Norway. On these 30 farms, we combined repeated onsite registrations of growth on all available females from newborn to calving with registrations deriving from the Norwegian Dairy Herd Recording System. Each herd was visited 6 to 8 times over a period of 2 yr. At each visit, heart girth circumference on all available young females was measured. Registrations were made on a total of 3,110 heifers. After imposing restrictions on the data, growth parameters were estimated based on information from 536 animals, whereas 350 of these animals had the required information needed to estimate the relationship between growth and test-day milk yield. Our findings pointed toward an optimal ADG of 830 g/d from 10 to 15 mo of age that would optimize first-lactation yield of heifers in an average Norwegian dairy herd. The optimum will likely increase from selection over time. Utilizing simple proportionality, the ADG between 5 and 10 mo of age ideally should be 879 g/d, taking into account the fact that animal growth rate is higher at low ages and that a high prepubertal growth rate had no negative effect on first-lactation yield. When such a rearing practice is used to meet the requirements of today's genetically improved NR heifer, heifers can both optimize production in their first lactation and enter the milking herd earlier than the current average age of 24.8 mo.

Genetic analyses of herding traits in the Border Collie using sheepdog trial data.

The aim of this study was to evaluate the quality of the data provided from sheepdog trials in Norway, estimate heritabilities, repeatabilities and genetic correlations for the traits included in the trial and make recommendations on how sheepdog trials best can be utilized in the breeding of Border Collies in Norway. The analyses were based on test results from sheepdog trials carried out in Norway from 1993 to 2012. A total of 45 732 records from 3841 Border Collies were available, but after quality assurance only a third was left. The results demonstrated little information in the data. Heritabilities varied between 0.010 and 0.056 with standard errors ranging from 0.010 to 0.023, while repeatabilities ranged from 0.041 to 0.286. There is a need to assure the quality of data to improve the information in the test results. We recommend adding new traits based on the Herding Trait Characterization scheme evaluated in Sweden, and on traits from the predatory motor pattern, regarded as common for all dogs. These new traits may be scored across the elements that make up the current trial system, which should be kept in place to stimulate participation in the genetic evaluation scheme.

Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats.

One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.

How Swedish breeders can substantially increase the genetic gain for the English Setter's hunting traits.

In both Sweden (Swe) and Norway (Nor), the English Setter is used for hunting. Similar field trials are arranged to give breeders information on the dogs' hunting abilities. Our main objective was to study if a joint Swe-Nor genetic evaluation can improve accuracy compared with estimation of breeding values on a national level only. Genetic parameters for six hunting traits were estimated in univariate (within country) and bivariate (across country, of equivalent traits between countries and joint pedigree) analyses utilizing 3620 Swe records from 685 dogs and 94,414 Nor records from 7175 dogs. A mixed linear animal model was used, including fixed effects of sex, type of trial, year, month and interaction between age and class of trial, and random effects of animal, permanent environment, judge and residual. Heritabilities ranged from 0.07 to 0.13 for Swe and from 0.08 to 0.18 for Nor. The accuracies were higher in the bivariate analyses, especially for dogs with Swe trial results with an average increase of 19%. If comparing selection based on a joint genetic evaluation over phenotypic results alone (which is today's method), the potential genetic gain in Swe was almost doubled. Our results suggest that a joint genetic evaluation is especially advantageous for a population with limited information, such as the English Setter population in Swe. However, it should also be beneficial for Norwegian breeders because it makes it easier for them to select Swedish dogs, potentially resulting in faster genetic gain and lowered inbreeding rate.

Optimal contribution selection applied to the Norwegian and the North-Swedish cold-blooded trotter - a feasibility study.

The aim of this study was to examine how to apply optimal contribution selection (OCS) in the Norwegian and the North-Swedish cold-blooded trotter and give practical recommendations for the future. OCS was implemented using the software Gencont with overlapping generations and selected a few, but young sires, as these turn over the generations faster and thus is less related to the mare candidates. In addition, a number of Swedish sires were selected as they were less related to the selection candidates. We concluded that implementing OCS is feasible to select sires (there is no selection on mares), and we recommend the number of available sire candidates to be continuously updated because of amongst others deaths and geldings. In addition, only considering sire candidates with phenotype above average within a year class would allow selection candidates from many year classes to be included and circumvent current limitation on number of selection candidates in Gencont (approx. 3000). The results showed that mare candidates can well be those being mated the previous year. OCS will, dynamically, recruit young stallions and manage the culling or renewal of annual breeding permits for stallions that had been previously approved. For the annual mating proportion per sire, a constraint in accordance with the maximum that a sire can mate naturally is recommended.

Estimates of autozygosity derived from runs of homozygosity: empirical evidence from selected cattle populations.

Using genome-wide SNP data, we calculated genomic inbreeding coefficients (FROH  > 1  Mb , FROH  > 2 Mb , FROH  > 8 Mb and FROH  > 16 Mb ) derived from runs of homozygosity (ROH) of different lengths (>1, >2, >8 and > 16 Mb) as well as from levels of homozygosity (FHOM ). We compared these values of inbreeding coefficients with those calculated from pedigrees (FPED ) of 1422 bulls comprising Brown Swiss (304), Fleckvieh (502), Norwegian Red (499) and Tyrol Grey (117) cattle breeds. For all four breeds, population inbreeding levels estimated by the genomic inbreeding coefficients FROH  > 8 Mb and FROH  > 16 Mb were similar to the levels estimated from pedigrees. The lowest values were obtained for Fleckvieh (FPED  = 0.014, FROH  > 8 Mb  = 0.019 and FROH  > 16 Mb  = 0.008); the highest, for Brown Swiss (FPED  = 0.048, FROH  > 8 Mb  = 0.074 and FROH  > 16 Mb  = 0.037). In contrast, inbreeding estimates based on the genomic coefficients FROH  > 1 Mb and FROH  > 2 Mb were considerably higher than pedigree-derived estimates. Standard deviations of genomic inbreeding coefficients were, on average, 1.3-1.7-fold higher than those obtained from pedigrees. Pearson correlations between genomic and pedigree inbreeding coefficients ranged from 0.50 to 0.62 in Norwegian Red (lowest correlations) and from 0.64 to 0.72 in Tyrol Grey (highest correlations). We conclude that the proportion of the genome present in ROH provides a good indication of inbreeding levels and that analysis based on ROH length can indicate the relative amounts of autozygosity due to recent and remote ancestors.

Validation of alternative models in genetic evaluation of racing performance in North Swedish and Norwegian cold-blooded trotters.

There have been several approaches to the estimation of breeding values of performance in trotters, and the objective of this study was to validate different alternatives for genetic evaluation of racing performance in the North Swedish and Norwegian cold-blooded trotters. The current bivariate approach with the traits racing status (RACE) and earnings (EARN) was compared with a threshold-linear animal model and the univariate alternative with the performance trait only. The models were compared based on cross-validation of standardized earnings, using mean-squared errors of prediction (MSEP) and the correlation between the phenotype (Y) and the estimated breeding value (EBV). Despite possible effects of selection, a rather high estimate of heritability of EARN was found in our univariate analysis. The genetic trend estimate for EARN was clearly higher in the bivariate specification than in the univariate model, as a consequence of the considerable size of estimated heritability of RACE and its high correlation with EARN (approximately 0.8). RACE is highly influenced by ancestry rather than the on-farm performance of the horse itself. Consequently, the use of RACE in the genetic analysis may inflate the genetic trend of EARN because of a double counting of pedigree information. Although, because of the higher predictive ability of the bivariate specification, the improved ranking of animals within a year-class and the inability to discriminate between models for genetic trend, we propose to base prediction of breeding values on the current bivariate model.

A method for the prediction of multitrait breeding values for use in stochastic simulation to compare progeny-testing schemes, with large progeny groups for proven sires.

A method of approximating estimated breeding values (EBV) from a multivariate distribution of true breeding values (TBV) and EBV is proposed for use in large-scale stochastic simulation of alternative breeding schemes with a complex breeding goal. The covariance matrix of the multivariate distributions includes the additive genetic (co)variances and approximated prediction error (co)variances at different selection stages in the life of the animal. The prediction error (co)variance matrix is set up for one animal at a time, utilizing information on the selection candidate and its offspring, the parents, as well as paternal and maternal half- sibs. The EBV are a regression on TBV taking individual uncertainty into account, but with additional 'free' variation drawn at random. With the current information included in the calculation of the prediction error variance of a selection candidate, it is concluded that the method can be used to optimize progeny-testing schemes, where the progeny-tested sires are utilized with large progeny groups, e.g. through artificial insemination.

Selection based on progeny testing induces rapid changes in myostatin allele frequencies - a case study in sheep.

In this study we show that selection based on progeny testing is able to induce a rapid change in allele frequency, even when a fairly broad and balanced breeding goal is applied. The myostatin 3'-UTR mutation (c.*1232G>A) previously found to affect muscularity in Texel sheep is also present in the Norwegian White Sheep population. By genotyping the rams used for artificial insemination (born in1977-2006), a rapid increase in the c.*1232G>A allele frequency was observed, from 0.31 in 1990 to 0.82 in 2006. The major increase was observed after BLUP-based breeding values and the EUROP classification system for carcass quality was implemented in 1991 and 1996, respectively. The MSTN frameshift mutation c.960delG, recently identified in this population, did not show a similar increase in allele frequency during the same period, in spite that it has a strong desirable effect on meat and fat traits. The results also illustrate that unwanted side effects can rapidly be introduced into a population using an efficient breeding scheme. A system for monitoring changes in phenotypic traits additional to those under selection is therefore recommended to identify possible side effects at an early stage.

Association of gut microbiota with metabolism in juvenile Atlantic salmon.

The gut microbiome plays a key role in animal health and metabolism through the intricate functional interconnection between the feed, gut microbes, and the host. Unfortunately, in aquaculture, the links between gut microbes and fish genetics and production phenotypes are not well understood.In this study, we investigate the associations between gut microbial communities, fish feed conversion, and fish genetics in the domestic Atlantic salmon. Microbial community composition was determined for 230 juvenile fish from 23 full-sib families and was then regressed on growth, carbon and nitrogen metabolism, and feed efficiency. We only found weak associations between host genetics and microbial composition. However, we did identify significant (p < 0.05) associations between the abundance of three microbial operational taxonomical units (OTUs) and fish metabolism phenotypes. Two OTUs were associated with both carbon metabolism in adipose tissue and feed efficiency, while a third OTU was associated with weight gain.In conclusion, this study demonstrates an intriguing association between host lipid metabolism and the gut microbiota composition in Atlantic salmon. Video Abstract.

A frameshift mutation in the coding region of the myostatin gene (MSTN) affects carcass conformation and fatness in Norwegian White Sheep (Ovis aries).

Mutations in the coding region of the myostatin gene (MSTN) are known to cause an increased muscle mass (IMM) phenotype in several mammals, including mice, dogs, cattle and humans. In sheep, a mutation in the 3'-UTR region introducing a microRNA target site has been reported to cause an IMM-like phenotype because of downregulation of translation. Here we report a novel single base deletion in the coding region of the myostatin gene causing an IMM phenotype in Norwegian White Sheep, characterized by a high carcass conformation class and low fat class (EUROP classification system). The deletion disrupts the reading frame from amino acid (aa) position 320, ending in a premature stop codon in aa position 359. In our material, these MSTN mutations segregated in a pattern showing that they reside in two different haplotypes. The phenotypic effect of the single base deletion is more profound than that of the 3'-UTR mutation.

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.

Environmental sensitivity of milk production in extensive environments: a comparison of Simmental, Brown Swiss, and Tyrol Grey using random regression models.

A total of 25,160 milk test-day records from 2,516 cows in first lactation of 3 dairy cattle breeds [Simmental (n = 1,900), Brown Swiss (n = 444), and Tyrol Grey (n = 172)] in Kosovo were analyzed using nested repeatability and random regression test-day models with varying (co)variance structures. The different models were compared based on likelihood-based criteria. The best model was a second-order random regression model, with heterogeneous cow variance per breed and heterogeneous residual variance per lactation month and breed, which was used for further analysis. The highest milk production was found in Brown Swiss, followed by Simmental and Tyrol Grey. Substantial breed differences were found for the trajectories of cow and residual variances by month of lactation, with the highest variances found for Brown Swiss, followed by Simmental and Tyrol Grey. High cow and residual variances indicated a high degree of environmental sensitivity on the macro- and microenvironmental levels, respectively. Thus, these results indicate increased environmental sensitivity for breeds with higher genetic potential for milk production. These results support the conclusion that dairy cattle production under the current environmental conditions of Kosovo should be based on a breed with moderate production that is robust to the diet offered (e.g., Tyrol Grey).

Validation of test-day models for genetic evaluation of dairy goats in Norway.

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.

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.