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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., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to estimate genetic parameters for body weight (BW) at 150 d (Bw&#95;150d), and 330 d (Bw&#95;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&#95;150d, 0.26 for Bw&#95;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., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

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 Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

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., (The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

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., (© 2012 Blackwell Verlag GmbH.)

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).

A Bayesian procedure for analyzing longitudinal binary responses using a periodic cosine function was developed. It was assumed that, after adjustment for "seasonal" effects, the oscillation of the underlying latent variables for longitudinal binary responses was a stationary series. Based on this assumption, a single dimension sinusoidal analysis of longitudinal binary responses using the Gibbs sampling and Metropolis algorithms was implemented in a study of clinical mastitis records of Norwegian Red cows taken over five lactations.

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.

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.

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.

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.

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.

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.

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.

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.

Clinical mastitis records for 36,178 first-lactation daughters of 245 Norwegian Cattle (NRF) sires were analyzed with a Bayesian longitudinal threshold model. For each cow, the period going from 30 d before calving to 300 d after calving was divided into 11 intervals of 30 d length each. Absence or presence of clinical mastitis within each interval was scored as "0" or "1", respectively. A Bayesian threshold model consisting of a set of explanatory variables plus Legendre polynomials on time of order four was used to describe the trajectory of liability to clinical mastitis. Heritability ranged between 0.07 and 0.13 before calving, from 0.04 to 0.15 during the first 270 d after calving, and increased sharply thereafter, as a consequence of the form of the polynomial. Genetic correlations between adjacent days were close to 1, and decreased when days were further apart. Most genetic correlations were moderate to high. A measure of probability of future daughters contracting clinical mastitis during lactation was computed for each sire. A typical curve had a peak near calving followed by a decrease thereafter. The best sires had a low peak around calving and a low expected probability of mastitis among daughters throughout lactation. Expected fraction of days without mastitis was derived from the probability curves and used for ranking of sires. Rank correlations with genetic evaluations of sires obtained from cross-sectional models were high. However, sire selection was affected markedly, especially at high selection intensity. An advantage of the longitudinal model for clinical mastitis is its ability to take multiple treatments and time aspects into account.

Records taken on 13,070 first-lactation daughters of 250 Norwegian Cattle sires were used to examine associations between susceptibility to clinical mastitis and to culling. Clinical mastitis was defined as a binary trait, whereas culling was treated as either binary (culled or not culled) or continuous (length of opportunity period) for two sampling periods (120 or 300 d of lactation). Two Bayesian models were employed; 1) a bivariate threshold model with both mastitis and culling as binary traits, and 2) a bivariate model with mastitis as a threshold binary variable and time to culling as Gaussian. The heritability of liability to clinical mastitis was not affected by either the length of sampling period (120 vs. 300 d) or by whether culling (binary) or length of opportunity period was the second trait in the bivariate analysis. The posterior mean (standard deviation) of heritability of liability to clinical mastitis was 0.06 to 0.07 (0.02) in all analyses. The heritability estimate of length of opportunity period was less than 0.001. Culling (threshold trait) in first lactation had a low heritability, but a high genetic correlation with clinical mastitis. The posterior means (standard deviation) for heritability of liability to culling were 0.01 (0.006) for 120 d and 0.02 (0.009) for 300 d, and the posterior means (standard deviation) of the genetic correlation between liability to clinical mastitis and to culling were 0.48 (0.24) and 0.53 (0.21) for 120 and 300 d, respectively.

An autosomal genome scan for quantitative trait loci (QTL) affecting milk production traits was carried out on the Norwegian Dairy Cattle population. Six half-sibling families with a total of 285 sons organized according to a granddaughter design were analyzed by a multiple marker regression method. Suggestive QTL for one or several of the five milk traits (milk yield, protein percentage, protein yield, fat percentage and fat yield) were detected on chromosomes 3, 5, 6, 11, 13, 18 and 20. Among these results, the findings on chromosomes 3, 6, and 20 are highly supported by literature. The most convincing result was found close to marker FBN9 on chromosome 6, where a QTL was detected with alleles that cause a marked reduction in both protein and fat percentages and an increase in milk yield. The results for fat and protein percentage were highly significant even after accounting for multiple testing across the genome. Using bootstrapping, a 95% confidence interval for the position of the QTL for the percentage traits on chromosome 6 was estimated to 16 cM.

First-lactation records of Norwegian Cattle were used to infer heritability of liability to clinical mastitis with a threshold sire model. Mastitis was defined as a binary response (presence or absence) in a defined period of first lactation (opportunity period). Length of opportunity period (from 30 d before calving up to 120 or 300 d of lactation) had less effect on heritability estimates than data sampling methods (include or exclude records of cows culled before the end of the opportunity period) whereas sire ranking was more affected by the former. Including all cows, whether culled before the end of the opportunity period or not, gave a sharper and more symmetric posterior distribution of heritability of liability to clinical mastitis. When we analyzed data for all cows, model specification had a small effect on heritability estimates, while sire ranking was affected markedly. Posterior means of heritability range from 0.058 to 0.074. A model regressing on the length of the opportunity period for culled cows without mastitis, was shown favorable for the two opportunity periods using Bayes factors and the deviance information criterion for model comparison. This model, in which liability of mastitis depends on time to culling, may allow utilizing information from all first lactations in genetic evaluation, irrespectively of duration and culling outcome.

An autosomal genome scan for quantitative trait loci (QTL) affecting twinning rate was carried out in the Norwegian Cattle population. Suggestive QTL were detected on Chromosomes (Chr) 5, 7, 12, and 23. Among these, the QTL positions on both Chr 5 and Chr 23 are strongly supported by literature in the field. Our results also confirm previous mapping of a QTL for twinning to Chr 7, but definitely suggest a different location of the QTL on this chromosome. The most convincing QTL peak was observed for a region in the middle part of Chr 5 close to the insulin-like growth factor 1 (IGF1) gene. Since IGF1 plays an important role in the regulation of folliculogenesis, a mutation search was performed by sequencing more than 3.5 kb of the gene in actual families. The sequencing revealed three polymorphisms in noncoding regions of the gene that will be important in fine structure mapping and characterization of the QTL.

The overall twinning rate was shown to increase from .6% in the first parity to 4.0% in the sixth parity, and a positive phenotypic trend for twinning rate was observed during the time period considered (1978 to 1995). The distribution of bulls according to the average percentage of multiple births of daughters in first and second parity showed that some bulls had an extremely low twinning frequency and others exceeded the population mean by approximately six times. (Co)variance components were estimated for twinning in first and second parities using a linear sire model. The analysis included either first- and second-crop daughters (1.4 and .9 million records for first- and second-parity cows, respectively) or first-crop daughters only (.6 and .4 million records for first- and second-parity cows, respectively) from 2,043 sires. Heritability estimates were .7 to .8% in the first parity and 2.8% in the second parity. The genetic correlation between twinning in the first and second parities was approximately 1. Part of the phenotypic trend observed can be explained by a genetic trend for twinning rate.

We examined alternative definitions of herd x year effects in models used to estimates genetic trends for clinical mastitis in Norwegian Cattle. The model with random herd x year effects showed significant bias and clearly overestimated genetic trend. To obtain unbiased estimates of genetic trend for clinical mastitis with this data, herd x year should be defined as fixed. Our results emphasized the importance of validating estimates of genetic trend.

Short- and long-term response to marker-assisted selection in two stages was studied using a stochastic simulation of a closed nucleus herd for beef production. First-stage selection was carried out within families based on information at a fully additive quantitative trait locus (QTL). Second-stage selection strategies were based on 1) individual phenotype, 2) individual phenotype precorrected for QTL, 3) a selection index incorporating phenotype and QTL information, 4) a standard animal model BLUP, and 5) a selection index incorporating marker-QTL information and standard animal model BLUP on records precorrected for QTL. All strategies were efficient in moving the favorable allele at the QTL to fixation, but they differed in the time to reach fixation. Mass selection was less efficient in changing allele frequencies than BLUP. Discounted accumulated response, accounting for the time response was realized and inflation rate, was proposed to rank strategies and to elude the conflict between short- and long-term response in marker-assisted selection. Discounted accumulated response at a time horizon of 20 yr for alternative two-stage selection strategies was compared with conventional BLUP carried out in second stage only. Within-family selection increased discounted accumulated response by more than 11% using Strategy 4 and by up to 12% using Strategy 5 at an inflation rate of 2%. The percentage increase in response was less for highly heritable traits and when the proportion of additive variance explained by the QTL was small. Strategy 5 gave larger response with reduced inbreeding. This strategy also resulted in the lowest cost-benefit ratio, requiring less genotyping per unit of response. Cost-benefit ratio for discounted genotyping and for discounted in vitro production of embryos for traits with low heritability was two to four times that for traits with high heritability. The use of first-stage selection slightly increased the level of inbreeding for both mass (Strategy 1) and BLUP selection (Strategies 4 and 5).

The study involved 34 primiparous cows fed ad libitum grass silage and fixed amounts of concentrate per cow and stage of lactation. It revealed that number of days from calving to maximum progesterone concentration in first luteal phase was negatively related to (p < 0.05) energy balance summarized over weeks 3-12 post-partum. One standard deviation improvement of the summarized energy balance relative to the mean reduced the length of the anovulatory period by 12 days. Similarly, an improved energy balance enhanced progesterone secretion during the oestrus cycle and early pregnancy, as measured by 3 variables; 1) maximum progesterone concentration in first luteal phase, 2) cumulative progesterone secretion bounded by the maximum concentrations in first and in third luteal phase and 3) cumulative progesterone secretion in the first month of pregnancy. All results were supported by the estimated regression coefficients of the 4 ovarian activity variables on summarized non-estrified fatty acids and acetoacetate variables.

Abstract: To study the accuracy and precision of intramuscular fat prediction by ultrasound (USIMF) in lean cattle, prediction models were developed based on 325 pure and crossbred beef and dual purpose bulls originating from I) commercial herds (n =180) and II) a performance test station (n =145). The bulls were scanned across the 12th thoracic vertebrae using a Pie 200 SLC scanner. Five images were collected per individual bull for later processing in the lab. After slaughter, a 2.5 cm cross-sectional sample located by the 13th thoracic vertebrae was used for chemical analysis to determine intramuscular fat % (CHIMF; Mean 1.25%, SD 0.6%) at the image site. The data were analysed sequentially involving 1) the Total Dataset, 2) Dataset I and 3) Dataset II. Prediction models were developed separately for each dataset, using stepwise regression procedures. The validation model R 2, RMSE, overall mean bias, SEP, r P and rank correlations for CHIMF and USIMF based on the best prediction model (Dataset I) were 0.48, 0.46%, −0.02%, 0.46%, 0.70 and 0.67, respectively. Further improvement in prediction accuracy can probably be achieved by increasing the size of datasets and the CHIMF variation. The results strongly indicate that ultrasound has potential for IMF prediction in lean cattle, although the value in indirect selection of breeding cattle for meat quality needs to be further evaluated. [Copyright &y& Elsevier]

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 ... [ABSTRACT FROM AUTHOR]