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.

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.

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.

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.

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.

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.

Heritability estimates of tarsocrural osteochondrosis and palmar/plantar first phalanx osteochondral fragments in Standardbred trotters

REASONS FOR PERFORMING STUDY

The pathogenesis of osteochondrosis (OC) and palmar/plantar first phalanx osteochondral fragments (POFs) is multifactorial, but specific knowledge of heritability is limited.

OBJECTIVES

To improve the precision of heritability estimates and to estimate the genetic correlation between tarsocrural OC and POFs in Standardbred trotters. Further aims were to examine whether the prevalence of OC/POFs was different in the American and French lineages that have contributed to the Norwegian population, and if the prevalence was affected by heterozygosity.

STUDY DESIGN

Retrospective cohort study.

METHODS

Categorical data on tarsocrural OC and POFs from 2 radiographic studies performed in 1989 and 2007/2008 (n = 1217) were analysed with sire threshold models that included 230 sires.

RESULTS

Heritability of OC at the distal intermediate ridge of the tibia and/or the lateral trochlear ridge of the talus was estimated at 0.29 ± 0.15. For OC at the distal intermediate ridge of the tibia only, the estimate was 0.40 ± 0.17. Heritability of POFs in all 4 limbs was estimated at 0.23 ± 0.13; for metatarsophalangeal POFs this was 0.26 ± 0.13 and for medial metatarsophalangeal POFs 0.32 ± 0.14. Estimates of genetic correlation between OC and POFs ranged from 0.68 ± 0.27 to 0.73 ± 0.28 but were not significantly different from a zero-genetic correlation. Effects of lineages or heterozygosity were not observed.

CONCLUSIONS AND POTENTIAL RELEVANCE

This study confirmed a moderate to high heritability of tarsocrural OC and POF, providing further evidence of the heritable nature of these diseases. Examination of specific lesions yielded the highest heritability; therefore, breeding programmes and future genome-analysis studies should focus on predilection sites rather than the entire disease complex.

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.

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.

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.

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.

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.

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.

Genotype by environment interaction for lamb weaning weight in two Norwegian sheep breeds1

Genotype x environment interaction (G x E) effects on live weaning weights of lambs were studied by using the 2 breeds Norwegian White sheep (NWS; heavy, long-tailed) and Spel sheep (Spel; lighter, short-tailed) as genetic groups (G). A total of 37,338 NWS lambs and 30,075 Spel lambs born from 1989 to 1999 on 40 farms that kept both breeds together were included in the analyses. Environment was characterized by farm x year (E). In a mixed linear model framework, significance of the random G x E effect and breed-specific environmental variances were tested by using a log-likelihood approach. Directions and magnitudes of the effect were described through variance component estimates. An across-genotype environmental correlation was also used. There was a significant G x E effect on lamb BW; significant breed differences were found for variance of flock x year effects, indicating different phenotypic plasticities with changing flock x year environments, with the NWS being more sensitive to environmental change. Further, the breed-specific residual variance was greater for NWS, indicating that the effects of environmental variation were larger for the weaning weights of the NWS breed within flock and year. Further, the correlation between flock x year effects for the 2 breeds was significantly different from unity (0.82 +/- 0.02), indicating that the common environment is "perceived" differently in the 2 breeds. The best environment for one breed is not necessarily best for the other breed, and vice versa. Solutions of flock x year effects may be used to describe how environmental characteristics such as climate and topography affect the production of different genotypes, and for clustering of environments, thus facilitating improvement of breeding programs and management schemes for domestic and wild ungulate populations.

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).

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.

Genetic correlations between reproduction and production traits in swine

Genetic correlations between reproduction and production traits were estimated in swine. Reproduction traits investigated were age at first service (AFS), number of live-born piglets in the first litter (NBA1), interval from weaning to first service after first litter (WTS1), number of live-born piglets in the second litter (NBA2), and interval from weaning to first service after the second litter (WTS2). Females generating the data were Norwegian Landrace born in nucleus herds between 1990 and 2000, and the number of records ranged from 13,792 to 56,932. Genetic correlations were estimated among the main production traits in the breeding goal: adjusted age at 100 kg live weight (A100), percentage of lean meat content (LMC), individual feed consumption from 25 to 100 kg (FC), and bacon side quality (BSQ). Average adjusted backfat thickness (BF) was included as a production trait. The A100 and BF traits were recorded on gilts on-farm with 190,454 records, whereas LMC, BSQ, and FC were recorded on-station with the number of records ranging from 12,487 to 12,992. Analyses were carried out with a multivariate animal model using average information restricted maximum likelihood procedures by first running each reproduction trait with A100 and BF, followed by each reproduction trait with LMC, BSQ, and FC. Average heritabilities for reproduction traits were as follows: AFS (0.38), NBA1 (0.11), WTS1 (0.06), NBA2 (0.12), and WTS2 (0.03); and for production traits: A100 (0.30), BF (0.44), FC (0.22), LMC (0.58), and BSQ (0.23). The highest genetic correlation was estimated between A100 and AFS (r(g)= 0.68), also resulting in a positive genetic correlation between FC and AFS. Growth (A100) was negatively (i.e., unfavorably) genetically correlated to NBA1 and NBA2 (r(g) = 0.60 and rg = 0.42 respectively), and so the genetic correlation to FC also became unfavorable (r(g)= 0.23 and r(g) = 0.20). Single-trait selection for enhanced LMC would also affect NBA1 and NBA2 unfavorably (r(g)= -0.12 and r(g)= -0.24). Correlations between BF at 100 kg live weight and reproduction traits were close to zero; however, a low genetic correlation between BF and WTS1 was obtained (r(g)= -0.12), indicating that selection toward reduced BF at 100 kg live weight may have an unfavorable impact on WTS1.

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.

Bivariate Analysis of Number of Services to Conception and Days Open in Norwegian Red Using a Censored Threshold-Linear Model

A bivariate censored threshold-linear model was used to study genetic parameters of number of services to conception (STC) and days open (DO) in first-lactation Norwegian Red (NRF) cows. Records of 1,454,916 NRF cows, with a first insemination from 1980 to 2004, were analyzed. It was assumed that every cow had at least a first insemination. The number of inseminations was recorded until a cow conceived or was culled, whichever occurred first. If a cow was culled before conception, it was considered censored at the number of services until culling was recorded. Twenty-one percent of cows were censored for both STC and DO. Using an univariate probit link function for STC, unobserved liabilities to STC and DO were modeled jointly as a linear function of age at first calving, month-year at first calving, herd-5-yr period, sire of cow and residual effects. Heritability of liability to STC and DO was 4% for each trait. The genetic and residual correlations between STC and DO were 0.77 and 0.68, respectively. There has been little or no genetic change for DO, whereas STC had favorable genetic and phenotypic trends.

Comparison Between Bivariate Models for 56-Day Nonreturn and Interval from Calving to First Insemination in Norwegian Red

A bivariate threshold-linear (TL) and a bivariate linear-linear (LL) model were assessed for the genetic analysis of 56-d nonreturn (NR56) and interval from calving to first insemination (CFI) in first-lactation Norwegian Red (former Norwegian Dairy Cattle) (NRF). Three different datasets were used to infer genetic parameters and to predict transmitting abilities for NRF sires. Mean progeny group sizes were 147.8, 102.7, and 56.5 daughters, and the corresponding number of sires were 746, 743, and 742 in the 3 datasets. Otherwise, the structures of the 3 datasets were similar. When the TL model was used, heritability of liability to NR56 was 2.8% in the 2 larger datasets and 3.8% in the smallest dataset. In the LL model, the heritability of NR56 in the largest dataset and in the 2 smaller datasets was 1.2 and 0.9%, respectively. For CFI, the heritability was similar in TL and LL models, ranging from 2.4 to 2.7%. The small heritability of the 2 reproductive traits implies that most of the variation is environmental and that large progeny groups are required to get accurate sire PTA. The point estimates of the genetic correlation between NR56 and CFI were near zero in both models. The 2 bivariate models were compared in terms of predictive ability using logistic regression and a chi2 statistic based on differences between observed and predicted outcomes for NR56 in a separate dataset. Comparison was also with respect to ranking of sires and correlations between sire posterior means (TL model) and PTA (LL model). We found very small differences in ability to predict NR56 between the 2 bivariate models, regardless of the dataset used. Correlations between sire posterior means (TL) and sire PTA (LL) and rank correlations between sire evaluations were all >0.98 in the 3 datasets. At present, the LL model is preferred for sire evaluations of NR56 and CFI in NRF. This is because the LL model is less computationally demanding and more robust with respect to the structure of the data than TL.

Genetic Improvement in Mastitis Resistance: Comparison of Selection Criteria from Cross-Sectional and Random Regression Sire Models for Somatic Cell Score

Several selection criteria for reducing incidence of mastitis were developed from a random regression sire model for test-day somatic cell score (SCS). For comparison, sire transmitting abilities were also predicted based on a cross-sectional model for lactation mean SCS. Only first-crop daughters were used in genetic evaluation of SCS, and the different selection criteria were compared based on their correlation with incidence of clinical mastitis in second-crop daughters (measured as mean daughter deviations). Selection criteria were predicted based on both complete and reduced first-crop daughter groups (261 or 65 daughters per sire, respectively). For complete daughter groups, predicted transmitting abilities at around 30 d in milk showed the best predictive ability for incidence of clinical mastitis, closely followed by average predicted transmitting abilities over the entire lactation. Both of these criteria were derived from the random regression model. These selection criteria improved accuracy of selection by approximately 2% relative to a cross-sectional model. However, for reduced daughter groups, the cross-sectional model yielded increased predictive ability compared with the selection criteria based on the random regression model. This result may be explained by the cross-sectional model being more robust, i.e., less sensitive to precision of (co)variance components estimates and effects of data structure.

Genetic Association Between Susceptibility to Clinical Mastitis and Protein Yield in Norwegian Dairy Cattle

The objective of this study was to examine associations between susceptibility to clinical mastitis and protein yield in first-lactation Norwegian Dairy Cattle (NRF) cows. Records from 372,227 first-lactation daughters of 2411 NRF sires were analyzed bivariately, using a threshold-liability model for clinical mastitis and a linear Gaussian model for 305-d protein yield. The mean (SD) of the posterior distribution of heritability was 0.08 (0.004) for susceptibility to clinical mastitis and 0.19 (0.007) for 305-d protein yield. The posterior mean (SD) of the genetic correlation between susceptibility to clinical mastitis and 305-d protein yield was 0.43 (0.03). Posterior means of the correlations between herd-5-yr effects, and between model residuals were 0.19 and -0.008, respectively. Corresponding estimates of genetic, herd-5-yr, and residual correlations from a bivariate linear model analysis were 0.42, 0.18, and -0.008, respectively. An antagonistic genetic relationship between clinical mastitis and protein yield was corroborated.

Genetic Evaluation of Mastitis Resistance Using a First-Passage Time Model for Wiener Processes for Analysis of Time to First Treatment

A Wiener process is a Brownian-motion process initiated in a certain state in a state space, and the first passage time is defined as the time of the process to reach a predefined absorbing state where the process stops. Time from 31 d prepartum to first treatment of clinical mastitis (CM) was modeled as first passage times of such Wiener processes. Two processes were used to allow for several risk factors, and for each process, initiation was at some arbitrary time point, in a certain health state with drift toward or away from absorption (disease). The drift parameter of each process was expressed as linear functions of covariates (year of calving and sire). First passage time was defined as the time from process initiation until the first health status process reached zero (absorption). The model was fitted to records for 36,178 first-lactation daughters of 245 Norwegian cattle sires using a Bayesian approach and Markov chain Monte Carlo methods. Genetic evaluation of sires was carried out by calculating the posterior probability of no CM (the value of the survival function) by d 331, i.e., 300 d after first calving. Alternatively, sire evaluation was based on the integrated area under the survival curve. These measures were highly correlated (0.999), which indicates a small degree of crossings of the sire-dependent survival curves. Hence, sire-specific hazards were close to proportional, resulting in a higher rank-correlation to sire evaluations from a survival model with proportional hazards than to the results from a multivariate threshold model.

Heritabilities, Genetic Correlations, and Genetic Change for Female Fertility and Protein Yield in Norwegian Dairy Cattle

Data from 1,815,581 first insemination records from daughters of 2697 Norwegian Dairy Cattle (NRF) sires were analyzed. A multitrait model was used to estimate genetic parameters and genetic change for 56-d nonreturn rate in virgin heifers (NR56D0), for 56-d nonreturn rate in first lactation cows (NR56D1L), for interval from calving to first insemination (CFI1L), and for protein yield (PY(305)1L). The heritabilities for NR56D0, NR56D1L, CFI1L, and PY(305)1L were 1.08, 0.99, 3.01, and 20.80%, respectively. Genetic correlation between heifer and cow fertility was high and favorable between NR56D0 and NR56D1L (0.54) and moderate and unfavorable between NR56D0 and CFI1L (0.24). The genetic correlations between NR56D1L and CFI1L and between NR56D0 and PY(305)1L were 0.08 and 0.04, respectively. A small, unfavorable genetic correlation between NR56D1L and PY(305)1L (-0.18) was found, while the genetic correlation between PY(305)1L and CFI1L was strongly unfavorable (0.47). Since 1972, NRF sires have been selected for NR56D0 using breeding values estimated from large progeny groups and with considerable weight in the total merit index. A linear regression analysis of sire PTA on year of first insemination of daughters showed an annual genetic change of 0.14% units for NR56D0. Selection was able to stabilize the genetic change of NR56D1L (0.03%/yr) but an undesirable change for CFI1L (0.11 d/yr) was found. The change of sire PTA for PY(305)1L was 0.63 kg/yr.

Short Communication: Bivariate Genetic Analysis of Clinical Mastitis and Somatic Cell Count in Norwegian Dairy Cattle

Clinical mastitis (CM) and lactation mean somatic cell score (LSCS) were analyzed with a bivariate linear sire model. Nearly 1.4 million primiparous cows of Norwegian Dairy Cattle from 2043 sires were used. The heritability estimates were 0.03 for CM and 0.11 for LSCS. The estimates of genetic and residual correlations between the 2 traits were 0.53 and 0.10, respectively. It is postulated that the genetic correlation probably is highly population-specific.

Multivariate Threshold Model Analysis of Clinical Mastitis in Multiparous Norwegian Dairy Cattle

A Bayesian multivariate threshold model was fitted to clinical mastitis (CM) records from 372,227 daughters of 2411 Norwegian Dairy Cattle (NRF) sires. All cases of veterinary-treated CM occurring from 30 d before first calving to culling or 300 d after third calving were included. Lactations were divided into 4 intervals: -30 to 0 d, 1 to 30 d, 31 to 120 d, and 121 to 300 d after calving. Within each interval, absence or presence of CM was scored as "0" or "1" based on the CM episodes. A 12-variate (3 lactations x 4 intervals) threshold model was used, assuming that CM was a different trait in each interval. Residuals were assumed correlated within lactation but independent between lactations. The model for liability to CM had interval-specific effects of month-year of calving, age at calving (first lactation), or calving interval (second and third lactations), herd-5-yr-period, sire of the cow, plus a residual. Posterior mean of heritability of liability to CM was 0.09 and 0.05 in the first and last intervals, respectively, and between 0.06 and 0.07 for other intervals. Posterior means of genetic correlations of liability to CM between intervals ranged from 0.24 (between intervals 1 and 12) to 0.73 (between intervals 1 and 2), suggesting interval-specific genetic control of resistance to mastitis. Residual correlations ranged from 0.08 to 0.17 for adjacent intervals, and between -0.01 and 0.03 for nonadjacent intervals. Trends of mean sire posterior means by birth year of daughters were used to assess genetic change. The 12 traits showed similar trends, with little or no genetic change from 1976 to 1986, and genetic improvement in resistance to mastitis thereafter. Annual genetic change was larger for intervals in first lactation when compared with second or third lactation. Within lactation, genetic change was larger for intervals early in lactation, and more so in the first lactation. This reflects that selection against mastitis in NRF has emphasized mainly CM in early first lactation, with favorable correlated selection responses in second and third lactations suggested.

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

Short Communication: Validation of Two Animal Models for Estimation of Genetic Trends for Female Fertility in Norwegian Dairy Cattle

Two animal models were compared with respect to potential bias in genetic trend estimates for female fertility and for their predictive ability. In addition to either a fixed effect for month of first insemination or for month-year of first insemination, the models had fixed effects of age and double insemination and random effects of herd-year and animal. The model with a fixed effect of month of first insemination had a larger positive genetic trend for 56-d nonreturn rate in virgin heifers (0.16% yr), smaller downward bias, and somewhat higher predictive ability. These results demonstrate the importance of verifying models to be used in the calculation of breeding values.

Genetic Analysis of Somatic Cell Score in Norwegian Cattle Using Random Regression Test-Day Models

The dataset used in this analysis contained a total of 341,736 test-day observations of somatic cell scores from 77,110 primiparous daughters of 1965 Norwegian Cattle sires. Initial analyses, using simple random regression models without genetic effects, indicated that use of homogeneous residual variance was appropriate. Further analyses were carried out by use of a repeatability model and 12 random regression sire models. Legendre polynomials of varying order were used to model both permanent environmental and sire effects, as did the Wilmink function, the Lidauer-Mäntysaari function, and the Ali-Schaeffer function. For all these models, heritability estimates were lowest at the beginning (0.05 to 0.07) and higher at the end (0.09 to 0.12) of lactation. Genetic correlations between somatic cell scores early and late in lactation were moderate to high (0.38 to 0.71), whereas genetic correlations for adjacent DIM were near unity. Models were compared based on likelihood ratio tests, Bayesian information criterion, Akaike information criterion, residual variance, and predictive ability. Based on prediction of randomly excluded observations, models with 4 coefficients for permanent environmental effect were preferred over simpler models. More highly parameterized models did not substantially increase predictive ability. Evaluation of the different model selection criteria indicated that a reduced order of fit for sire effects was desireable. Models with zeroth- or first-order of fit for sire effects and higher order of fit for permanent environmental effects probably underestimated sire variance. The chosen model had Legendre polynomials with 3 coefficients for sire, and 4 coefficients for permanent environmental effects. For this model, trajectories of sire variance and heritability were similar assuming either homogeneous or heterogeneous residual variance structure.

Genetic Improvement of Mastitis Resistance: Validation of Somatic Cell Score and Clinical Mastitis as Selection Criteria

Mean daughter deviations for clinical mastitis among second-crop daughters were regressed on predicted transmitting abilities for clinical mastitis and lactation mean somatic cell score in first-crop daughters to validate the predictive ability of these traits as selection criteria for reduced incidence of clinical mastitis. A total of 321 sires had 684,897 second-crop daughters, while predicted transmitting abilities were calculated for 2159 sires, based on 495,681 records of first-crop daughters. Predictive ability, as a measure of efficiency of selection, was 23 to 43% higher for clinical mastitis than for lactation mean somatic cell score. Compared to single-trait selection, predictive ability improved 8 to 13% from utilizing information on both traits. The relative weight that should be assigned to standardized predicted transmitting abilities from univariate genetic analyses were 60 to 67% for clinical mastitis and 33 to 40% for lactation mean somatic cell score. No significant nonlinear genetic relationship between the two traits was found.

Selection responses for clinical mastitis and protein yield in two Norwegian dairy cattle selection experiments

Inferences from two dairy cattle selection experiments, in which sires were selected from external sources, were drawn by using an animal model to analyze data from the entire population. The first selection experiment was carried out in the period from 1978 to 1989 and included groups selected for high milk production (HMP) and low milk production (LMP). Each year, the highest ranking proven sires for milk production, from the most recent group of Norwegian Dairy Cattle (NRF) test bulls, were selected and mated to the cows in the HMP group. A group of sires with low milk production indices from progeny testing in 1978 and 1979 were used as sires in the LMP group during the entire experiment. The second selection experiment, which started in 1989, included one high protein yield (HPY) group and one low clinical mastitis (LCM) group. The highest ranking proven NRF sires for protein yield and mastitis resistance were selected each year from the most recent group of progeny tested bulls and used as sires in the HPY and LCM groups, respectively. Genetic trends for protein yield were positive (as expected) for HMP and HPY cows, and negative for LMP and LCM cows. Estimates of annual genetic trends for clinical mastitis were +0.23, -0.02, +0.04, and -0.91% per year for HMP, LMP, HPY, and LCM cows, respectively. The difference in genetic trend of clinical mastitis between HMP and HPY groups, both selected for increased milk production, reflects the gradual change in the NRF breeding objective towards more weight on health relative to milk over the last 20 yr. After four cow generations, the genetic difference in mastitis between HMP and LMP group cows was 3.1% clinical mastitis, a correlated response to selection for increased milk production. The genetic difference between LCM and HPY cows of 8.6% clinical mastitis after three cow generations is mainly a result of direct selection against clinical mastitis in the LCM group. In the NRF population, an approximately flat genetic trend for clinical mastitis was found for cows born from 1976 to 1990, whereas cows born after 1990 showed a genetic improvement equivalent to a reduction of 0.19% clinical mastitis per year. The results show that it is possible to obtain considerable selection response for clinical mastitis, and that selection for increased milk production will result in an unfavorable correlated increase in mastitis incidence, if mastitis is ignored in the breeding program.

Heifer fertility in Norwegian dairy cattle: variance components and genetic change

Variance components and genetic change were estimated for 56-d nonreturn rate in virgin heifers using first insemination records of a total of 1,632,961 Norwegian Dairy Cattle daughters of 2945 sires. Six univariate mixed linear sire models were compared. The heritabilities varied from 1.2% to 1.4%. All models gave favorable, although different, estimates of genetic change of 56-d nonreturn rate. The method used to validate the genetic trend did not detect bias in any of the models. Goodness-of-fit and predictive ability were used to further validate the models. Three models included service sire, but this effect had little influence on the results. Models treating the herd-year effects as random showed smaller prediction error and higher correlation between observed and predicted values than the models treating herd-year effect as fixed. To avoid confounding of environmental and genetic effects, the model including fixed effect of month-year was preferred over a model with only month of first insemination to estimate the genetic change. Female fertility has been successfully included in the total merit index of Norwegian Dairy Cattle for 20 yr. This has resulted in genetic improvement and the chosen model showed annual genetic change of 0.04% for 56-d nonreturn rate in heifers between 1979 and 2000.