Effect of parentage misidentification on estimates of genetic parameters for milk yield in the Mediterranean Italian buffalo population.

The objective of this study was to evaluate the effect of parentage misidentification on estimation of genetic parameters for the Italian buffalo population for milk yield from 45,194 lactation records of 23,104 Italian buffalo cows. Animals were grouped into 10 data sets in which sires and dams were DNA identified, or reported from the pedigree, or unknown. A derivative-free restricted maximum likelihood method was used to estimate components of variance with a repeatability model. The model contained age at calving nested within parity and days from calving to conception as linear covariates, herd-year-seasons as fixed effects, and additive genetic, permanent environmental, and temporary environmental effects as random effects. Estimates of heritability (±SE) ranged from 0.00 ± 0.099 (sires and dams as reported in the pedigree) to 0.39 ± 0.094 (sires DNA identified and dams as reported in the pedigree). When identification of sires was as reported in the pedigree, estimates of heritability were close to zero. These small estimates indicate that a large proportion of reported paternity is incorrect. When sires are unknown and dams are DNA identified, the proportion of variance due to sires seems to be captured in the estimate of permanent environmental variance as a fraction of phenotypic variance. Therefore, as heritability decreased, permanent environmental variance increased about the same amount. Data sets with dams identified from pedigree and sires DNA identified showed the largest estimate of heritability (0.39), which was essentially the same as when dams were DNA identified (0.38). This result supports that most dams are correctly reported from the pedigree. Genetic progress should be much greater with bulls DNA identified because of greater heritability, but without artificial insemination and progeny testing, progress would be slow and would depend mostly on selection of sires based on dam estimated breeding values. Implementation of artificial insemination programs and DNA testing to identify sires are the keys for increasing genetic progress in the Italian buffalo population.

Prediction of genetic values for feed intake from individual body weight gain and total feed intake of the pen.

Records of individual feed intake (FI) and BW gain (GN) were obtained from the Germ Plasm Evaluation (GPE) program at US Meat Animal Research Center (USMARC). Animals were randomly assigned to pens. Only pens with 6 to 9 steers (n = 289) were used for this study (data set 1). Variance components and genetic parameters were estimated using data set 1. Estimated genetic values (EGV) for FI were calculated by 5 methods using single and 2-trait analyses: 1) individual FI and individual GN, 2) individual FI alone, 3) 2-trait with individual GN but with FI missing, 4) individual GN and pen total FI, and 5) pen total FI alone. Analyses were repeated but with some of the same records assigned artificially to 36 pens of 5 and 4 paternal half sibs per pen (data sets 2 and 3). Models included year as a fixed factor and birth and weaning weights, age on test, and days fed as covariates. Estimates of heritability were 0.42 +/- 0.16 and 0.34 +/- 0.17 for FI and GN. The estimate of the genetic correlation was 0.57 +/- 0.23. Empirical responses to selection were calculated as the average EGV for the top and bottom 10% based on rank for each method but with EGV from method 1 substituted for the EGV on which ranking was based. With data set 1, rank correlations between EGV from method 1 and EGV from methods 2, 3, 4, and 5 were 0.99, 0.53, 0.32, and 0.15, respectively. Empirical responses relative to method 1 agreed with the rank correlations. Accuracy of EGV for method 4 (0.44) was greater than for method 3 (0.35) and for method 5 (0.29). Accuracies for methods 4 and 5 were greater than indicated by empirical responses and correlations with EGV from method 1. Comparisons of the 5 methods were similar for data sets 2 and 3. With data set 2, rank correlations between EGV from method 1 and EGV from methods 3, 4, and 5 were 0.47, 0.64, and 0.62. Average accuracies of 56, 75, and 75% relative to method 1 (0.67) generally agreed with the empirical responses to selection. As expected, accuracy using pen total FI and GN to obtain EGV for FI was greater than using GN alone. With data set 1, empirical response to selection with method 4 was one-third of that for method 1, although average accuracy was 65% of that for method 1. With assignment of 5 paternal half sibs to artificial pens, using pen total FI and individual GN was about 81% as effective for selection as using individual FI and GN to obtain EGV for FI and was substantially more effective than use of GN alone.

Effect of pen mates on growth, backfat depth, and longissimus muscle area of swine.

Records on final BW (kg), backfat depth (cm), and LM area (cm(2)) of pigs from a University of Nebraska Large White/Landrace composite population were analyzed to estimate the effects of pen mates. Measurements were at approximately 180 d of age for 3,524 pigs in 351 pens (9 to 11 pigs per pen) farrowed from 1999 to 2005. The area of each pen was 8.13 m(2). The full model (M1) included the fixed effects of contemporary group, sex, line, and the covariates of age and inbreeding coefficient, and included random direct genetic, genetic pen-mate, permanent environmental, pen, litter, and residual effects. A derivative-free algorithm was used to obtain REML estimates of variance components for final BW adjusted to 180 d of age with M1 and 7 reduced models, and with 4 reduced models for the carcass traits. For final BW, likelihood ratio tests showed that M1 did not fit the data better than model 2 (permanent environmental effect omitted from M1) or model 3 (pen omitted from M1). Model 2 was not significantly (P > 0.05) better than model 3, which shows that variance attributable to pen effects and permanent environmental effects cannot be separated. Large sampling variances of estimates of the pen component of variance for models with pen-mate effects also indicate an inability to separate pen effects from the effects of pen mates. When pen-mate genetic effects were not in the model, estimates of components of variance and the fit of the data were the same for models 4 (included both permanent environmental and pen effects), 6 (included pen effects), and 7 (included permanent environmental effects), which shows that including both pen and permanent environmental effects was no better than including one or the other. Models 4, 6, and 7 were significantly better than model 8, which did not include pen-mate effects and pen effects, implying that pen effects are important. The estimate of pen variance with model 2 was approximately (number of pen mates - 1) times the estimate of variance of pen-mate permanent environmental effects with model 3. Patterns of estimates of variance components with models 2, 5, 6, and 8 for backfat depth and LM area were similar to those for final BW. Estimates of direct genetic variance and phenotypic variance were similar for all models. Estimates of heritability for direct genetic effects were approximately 0.40 for final BW, 0.45 for backfat depth, and 0.27 for LM area. Estimates of heritability for pen-mate genetic effects were 0.001 for the 3 traits for models including either pen or permanent environmental effects. Under the management conditions for this experiment, the conclusion is that the model for genetic evaluation should include litter effects and either pen effects or pen-mate permanent environmental effects and possibly genetic pen-mate effects, in general agreement with the results of studies of different populations at other locations.

Effects of social interactions on empirical responses to selection for average daily gain of boars.

Effects of social interactions on responses to selection for ADG were examined with records of 9,720 boars from dam lines (1 and 2) and sire lines (3 and 4) provided by Pig Improvement Company. Each line was analyzed separately. Pens contained 15 boars. Average daily gains were measured from about 71 to 161 d of age and BW from 31 to 120 kg. Models included fixed effects of contemporary groups and initial test age as a covariate and random direct genetic (a), social genetic (c), social environmental (ce), and litter (lt) effects. Estimates of direct heritability with model 1 (the full model with a, c, ce, and lt) were 0.21, 0.28, 0.13, and 0.15 for lines 1 to 4. Estimates of heritability of social effects were near zero. Estimates of total heritable variance were 55, 52, 38, and 96% of phenotypic variance for lines 1 through 4. Empirical responses to selection with model 1 were calculated using the parameter estimates from model 1. For response of 1 genetic SD for both components (a and c), the proportions of expected total gain due to social effects (with economic weights of 1 and pen size-1 = 14) were 54, 28, 65, and 65% for the 4 lines. Genetic superiorities of the top 10% of boars were calculated for boars ranked using reduced models, but with EBV calculated using the full model (model 1). Average total breeding values (ETBV = EBV(a)+14EBV(c)) for the top 10% of boars selected with model 1 were 74.08, 94.26, 31.79, and 92.88 g for lines 1 through 4, respectively. For rankings based on model 2 (a, ce, and lt), but EBV calculated with model 1, average total breeding values for the top 10% were 68.15, 94.03, 7.33, and 84.72 g with empirical correlated responses for genetic social effects from selection for direct effects of 0.93, 1.89, -2.19, and 3.52 g for lines 1 to 4.

Estimation of genetic parameters for average daily gain using models with competition effects.

Components of variance for ADG with models including competition effects were estimated from data provided by the Pig Improvement Company on 11,235 pigs from 4 selected lines of swine. Fifteen pigs with average age of 71 d were randomly assigned to a pen by line and sex and taken off test after approximately 89 d (off-test BW ranged from 61 to 158 kg). Models included fixed effects of line, sex, and contemporary group and initial test age as a covariate, with random direct genetic, competition (genetic and environmental), pen, litter, and residual effects. With the full model, variances attributable to direct, direct-competition, genetic competition, and litter (co)variance components could be partitioned; genetic competition variance was small but statistically significantly different from zero. Variances attributable to environmental competition, pen, and residual effects could not be partitioned, but combinations of these environmental variances were estimable. Variances could be partitioned with either pen effects or environmental competition effects in the model. Environmental competition effects seemed to be the source of variance associated with pens. With pen as a fixed effect and without environmental competition effects in the model, genetic components of variance could not be partitioned, but combinations of genetic (co)variances were estimable. With both pen and environmental competition effects ignored, estimates of direct-competition and genetic competition (co)variance components were greatly inflated. With competition (genetic and environmental) effects ignored, the estimate of pen variance increased by 39%, with little change in estimates of direct genetic or residual variance. When both pen and competition (genetic and environmental) effects were dropped from the model, variance attributable to direct genetic effects was inflated. Estimates of variance attributable to competition effects were small in this study. Including environmental competition effects as permanent environmental effects in the model did not change estimates of genetic (co)variances. We concluded that including either pen effects or environmental competition effects as random effects in the model avoids bias in estimates of genetic variances but that including pen effects is much easier.

A general review of competition genetic effects with an emphasis on swine breeding.

A review of previous studies is presented on estimates of genetic parameters and responses to selection with traditional breeding approaches, on correlations between agonistic behavior and growth performance, and on theoretical frameworks for selection incorporating interactions among individuals and on practical methods for incorporating competition effects in breeding programs.

Computing numerator relationships between any pair of animals.

We describe a simple method to compute the numerator relationship between any or all pairs of animals in the numerator relationship matrix. The method depends on output of the MTDFNRM program from the MTDFREML set of programs. An option of the MTDFNRM program creates a file that includes the inbreeding coefficient for each animal. The method also makes use of how the inbreeding coefficient is traditionally calculated: one-half of the relationship between the animal's parents. To obtain the numerator relationship between any pair of animals, the original pedigree file is augmented with a dummy animal with an identification number (ID) greater than for any animal in the original pedigree file. The ID of the pair of animals for which the relationship is wanted is included as parents. MTDFNRM is then run with the option to create a file of ordered and original IDs for animals and their parents along with the inbreeding coefficients. We then multiply the inbreeding coefficient for a dummy animal by two to obtain the numerator relationship between the two animals designated as parents.

Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model.

Genetic parameters were estimated with restricted maximum likelihood for individual test-day milk, fat, and protein yields and somatic cell scores with a random regression cubic spline model. Test-day records of Holstein cows that calved from 1994 through early 1999 were obtained from Dairy Records Management Systems in Raleigh, North Carolina, for the analysis. Estimates of heritability for individual test-days and estimates of genetic and phenotypic correlations between test-days were obtained from estimates of variances and covariances from the cubic spline analysis. Estimates were calculated of genetic parameters for the averages of the test days within each of the ten 30-day test intervals. The model included herd test-day, age at first calving, and bovine somatropin treatment as fixed factors. Cubic splines were fitted for the overall lactation curve and for random additive genetic and permanent environmental effects, with five predetermined knots or four intervals between days 0, 50, 135, 220, and 305. Estimates of heritability for lactation one ranged from 0.10 to 0.15, 0.06 to 0.10, 0.09 to 0.15, and 0.02 to 0.06 for test-day one to test-day 10 for milk, fat, and protein yields and somatic cell scores, respectively. Estimates of heritability were greater in lactations two and three. Estimates of heritability increased over the course of the lactation. Estimates of genetic and phenotypic correlations were smaller for test-days further apart.

Estimates of genetic parameters for growth traits in Kermani sheep.

Birth weight (BW), weaning weight (WW), 6-month weight (W6), 9-month weight (W9) and yearling weight (YW) of Kermani lambs were used to estimate genetic parameters. The data were collected from Shahrbabak Sheep Breeding Research Station in Iran during the period of 1993-1998. The fixed effects in the model were lambing year, sex, type of birth and age of dam. Number of days between birth date and the date of obtaining measurement of each record was used as a covariate. Estimates of (co)variance components and genetic parameters were obtained by restricted maximum likelihood, using single and two-trait animal models. Based on the most appropriate fitted model, direct and maternal heritabilities of BW, WW, W6, W9 and YW were estimated to be 0.10 +/- 0.06 and 0.27 +/- 0.04, 0.22 +/- 0.09 and 0.19 +/- 0.05, 0.09 +/- 0.06 and 0.25 +/- 0.04, 0.13 +/- 0.08 and 0.18 +/- 0.05, and 0.14 +/- 0.08 and 0.14 +/- 0.06 respectively. Direct and maternal genetic correlations between the lamb weights varied between 0.66 and 0.99, and 0.11 and 0.99. The results showed that the maternal influence on lamb weights decreased with age at measurement. Ignoring maternal effects in the model caused overestimation of direct heritability. Maternal effects are significant sources of variation for growth traits and ignoring maternal effects in the model would cause inaccurate genetic evaluation of lambs.

Technical note: Calculation of standard errors of estimates of genetic parameters with the multiple-trait derivative-free restricted maximal likelihood programs.

The multiple-trait derivative-free REML set of programs was written to handle partially missing data for multiple-trait analyses as well as single-trait models. Standard errors of genetic parameters were reported for univariate models and for multiple-trait analyses only when all traits were measured on animals with records. In addition to estimating (co)variance components for multiple-trait models with partially missing data, this paper shows how the multiple-trait derivative-free REML set of programs can also estimate SE by augmenting the data file when not all animals have all traits measured. Although the standard practice has been to eliminate records with partially missing data, that practice uses only a subset of the available data. In some situations, the elimination of partial records can result in elimination of all the records, such as one trait measured in one environment and a second trait measured in a different environment. An alternative approach requiring minor modifications of the original data and model was developed that provides estimates of the SE using an augmented data set that gives the same residual log likelihood as the original data for multiple-trait analyses when not all traits are measured. Because the same residual vector is used for the original data and the augmented data, the resulting REML estimators along with their sampling properties are identical for the original and augmented data, so that SE for estimates of genetic parameters can be calculated.

Bovine respiratory disease in feedlot cattle: phenotypic, environmental, and genetic correlations with growth, carcass, and longissimus muscle palatability traits.

Bovine respiratory disease (BRD) is the most costly feedlot disease in the United States. Selection for disease resistance is one of several possible interventions to prevent or reduce the economic loss associated with animal disease and to improve animal welfare. Undesirable genetic relationships, however, may exist between production and disease resistance traits. The objectives of this study were to estimate the phenotypic, environmental, and genetic correlations of BRD with growth, carcass, and LM palatability traits. Health records on 18,112 feedlot cattle over a 15-yr period and slaughter data on 1,627 steers over a 4-yr period were analyzed with bivariate animal models. Traits included ADG, adjusted carcass fat thickness at the 12th rib, marbling score, LM area, weight of retail cuts, weight of fat trim, bone weight, Warner-Bratzler shear force, tenderness score, and juiciness score. The estimated heritability of BRD incidence was 0.08 +/- 0.01. Phenotypic, environmental, and genetic correlations of the observed traits with BRD ranged from -0.35 to 0.40, -0.36 to 0.55, and -0.42 to 0.20, respectively. Most correlations were low or negligible. The percentage of carcass bone had moderate genetic, phenotypic, and environmental correlations with BRD (-0.42, -0.35, and -0.36, respectively). Hot carcass weight and weight of retail cuts had moderate, undesirable phenotypic correlations with BRD (0.37 and 0.40, respectively). Correlations of BRD with LM palatability and ADG were not detected. Low or near zero estimates of genetic correlations infer that selection to reduce BRD in feedlot cattle would have negligible correlated responses on growth, carcass, and meat palatability traits or that selection for those traits will have little effect on BRD susceptibility or resistance.

Estimates of correlations among yield traits and somatic cell score with different models to adjust for bovine somatotropin effects on Holstein dairy cows.

Records of Holstein cows from the Dairy Records Processing Center at Raleigh, NC were edited to obtain three data sets: 65,720 first, 50,694 second, and 65,445 later lactations. Correlations among yield traits and somatic cell score were estimated with three different models: 1) bovine somatotropin (bST) administration ignored, 2) bST administration as a fixed effect and 3) administration of bST as part of the contemporary group (herd-year-month-bST). Heritability estimates ranged from 0.13 to 0.17 for milk, 0.12 to 0.20 for fat, 0.14 to 0.16 for protein yields, and 0.08 to 0.09 for somatic cell score. Estimates were less for later than first lactations. Estimates of genetic correlations among yields ranged from 0.35 to 0.85 with no important differences between estimates with the 3 models. Estimates for lactation 2 agreed with estimates for lactation 1. Estimates of genetic correlations for later lactations were generally greater than for lactations 1 and 2 except between milk and protein yields. Estimates of genetic correlations between yields and somatic cell score were mostly negative or small (-0.45 to 0.11). Estimates of environmental correlations among yield traits were similar with all models (0.77 to 0.97). Estimates of environmental correlations between yields and somatic cell score were negative (-0.22 to -0.14). Estimates of phenotypic correlations among yield traits ranged from 0.70 to 0.95. Estimates of phenotypic correlations between yields and somatic cell score were small and negative. For all three data sets and all traits, no important differences in estimates of genetic parameters were found for the two models that adjusted for bST and the model that did not.

Impact of bovine somatotropin on ranking for genetic value of dairy sires for milk yield traits and somatic cell score.

Records of Holstein cows were used to examine how different models account for the effect of bovine somatotropin (bST) treatment on genetic evaluation of dairy sires for yield traits and somatic cell score. Data set 1 included 65,720 first-lactation records. Set 2 included 50,644 second-lactation records. Set 3 included 45,505 records for lactations three, four and five. Estimated breeding values (EBV) of sires were with three different animal models. With Model 1, bST administration was ignored. With Model 2, bST administration was used as a fixed effect. With Model 3, administration of bST was used to define the contemporary group (herd-year-month of calving-bST). Correlations for EBV of 1,366 sires with treated daughters between pairs of the three models were calculated for milk, fat and protein yields and somatic cell score for the three data sets. Correlations for EBV of sires between pairs of models for all traits ranged from 0.971 to 0.999. Fractions of sires with bST-treated progeny selected in common (top 10 to 15%) were 0.94 and usually greater for all pairs of models for all traits and parities. For this study, the method of statistical adjustment for bST treatment resulted in a negligible effect on genetic evaluations of sires when some daughters were treated with bST and suggests that selection of sires to produce the next generation of sires and cows might not be significantly affected by how the effect of bST is modeled for prediction of breeding values for milk, fat and protein yields and somatic cell score.

Across-breed adjustment factors for expected progeny differences for carcass traits.

Adjustment factors to allow comparison of EPD from several breed associations for birth, weaning, and yearling weights have been available for more than 10 yr. This paper describes steps to calculate adjustment factors for EPD for 4 carcass traits: marbling score, fat thickness, ribeye area, and retail product percentage. The required information is the same as for the weight traits: 1) breed of sire solutions based on measurements on progeny at the US Meat Animal Research Center (USMARC) that have sires with breed association EPD, 2) mean EPD of sires weighted by number of progeny at USMARC (USMARC progeny not included in breed association EPD), and 3) mean EPD of nonparents from breed associations (defined as animals born 2 yr prior to calculation of EPD). Records at USM-ARC are adjusted to 100% heterozygosity because the purpose of the adjustment factors is to allow prediction of performance of progeny of sires mated to other breeds of dam. A critical step is to adjust breed of sire solutions, which are based on an earlier sample of sires, to the equivalent of a sample from a more recent nonparent group using the difference between mean EPD from information sources 2) and 3). The difference is multiplied by the coefficient of regression of USMARC progeny on EPD of their sires. With weight traits, these coefficients are not greatly different from unity. With the carcass traits, 2 sets of coefficients can be used depending on whether the EPD are based on carcass or ultrasound measurements. The regression coefficients also reflect differences in conditions for USMARC progeny (all steers) and factors associated with breed association EPD. Only for marbling score and ribeye area were any estimates of the regression coefficients near unity. For other traits, the coefficients ranged from 1.65 to 2.82. The solutions for breed of sire, differences in mean EPD, and regression coefficients are then used to calculate adjustment factors for EPD of 11 breeds including the arbitrary base breed, Angus.

Effect of competition on gain in feedlot bulls from Hereford selection lines.

This study examined competition effects on ADG in the feedlot of 1,882 Hereford bulls representing 8 birth years from a selection experiment. Each year, 8 feedlot pens were used to feed bulls in groups, with 2 pens nested within each of the 4 selection lines. Gains were recorded for up to 8 periods of 28 d. Models for analyses included pen effects (fixed or random), fixed effects such as year and line, and random direct genetic, competition genetic (and in some analyses competition environmental), and environmental effects. Each pen mate as a competitor affected the records of all others in the pen. All lines traced to common foundation animals, so the numerator relationships among and within pens were the bases for separating direct and competition genetic effects and pen effects. For this population and pen conditions (average of 30 bulls per pen), the major results were 1) competition genetic effects seemed present for the first 28-d period but not for the following 7 periods; 2) models with pens considered as fixed effects could not separate variances and covariance due to direct and competition genetic effects; 3) models without competition effects had large estimates of the variance component due to pen effects for gain through 8 periods; and 4) models with genetic and environmental competition effects accounted for nearly all of the variance traditionally attributed to pen effects (even though estimates of the competition variance component were small, the estimates of pen variance were near zero).

Technical note: Use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood.

The widespread use of the set of multiple-trait derivative-free REML programs for prediction of breeding values and estimation of variance components has led to significant improvement in traits of economic importance. The initial version of this software package, however, was generally limited to pedigree-based relationships. With continued advances in genomic research and the increased availability of genotyping, relationships based on molecular markers are obtainable and desirable. The addition of a new program to the set of multiple-trait derivative-free REML programs is described that allows users the flexibility to calculate relationships using standard pedigree files or an arbitrary relationship matrix based on genetic marker information. The strategy behind this modification and its design is described. An application is illustrated in a QTL association study for canine hip dysplasia.

Genetic parameters for yield traits of cows treated or not treated with bovine somatotropin.

The objective of this study was to estimate genetic correlations between yield traits of cows treated with bovine somatotropin (bST) and the same yield traits of untreated cows. Lactation records from registered Holstein cows were divided by parity into 3 data sets: 1, 2, and 3 through 5. Approximately 10% of the records in each data set were from cows treated with bST. The numbers of records of treated and untreated cows in the data sets were 4,337 and 48,765; 3,730 and 37,796; and 3,645 and 33,957. Two-trait animal models (records for cows treated or not treated) were used to estimate genetic parameters for milk production traits and somatic cell score (SCS). Estimates of heritability for milk yield for records of treated and untreated cows for the 3 data sets were 0.13, 0.16, and 0.09, and 0.18, 0.18, and 0.14, respectively, with estimates of repeatability of 0.50 and 0.41 for data set 3. Estimates of heritability for fat yield for records of treated and untreated cows were 0.31, 0.16, and 0.12, and 0.27, 0.21, and 0.16. Estimates of repeatability were 0.50 and 0.43 for data set 3. Heritability estimates for protein yield for records of treated and untreated cows were 0.13, 0.17, and 0.12, and 0.20, 0.23, and 0.16, with estimates of repeatability of 0.52 and 0.47. Estimates of heritability for SCS for treated and untreated cows were 0.08, 0.15, and 0.13, and 0.11, 0.13, and 0.13 with repeatability estimates of 0.52 and 0.45. Estimates of genetic correlations between milk yields with and without bST treatment in lactations 1, 2, and 3 to 5 were all 0.99. Estimates of genetic correlations for fat and protein yields were 0.96 for all data sets. Estimates for SCS were 0.99. Estimates of genetic correlations between records of treated and untreated cows were large enough to conclude that records of treated and untreated cows could be considered to be one trait, with treatment as a fixed effect to account for differences in means.

Estimates of correlations among yield traits and somatic cell score with different models to adjust for bovine somatotropin effects on Holstein dairy cows

Records of Holstein cows from the Dairy Records Processing Center at Raleigh, NC were edited to obtain three data sets: 65,720 first, 50,694 second, and 65,445 later lactations. Correlations among yield traits and somatic cell score were estimated with three different models: 1) bovine somatotropin (bST) administration ignored, 2) bST administration as a fixed effect and 3) administration of bST as part of the contemporary group (herd-year-month-bST). Heritability estimates ranged from 0.13 to 0.17 for milk, 0.12 to 0.20 for fat, 0.14 to 0.16 for protein yields, and 0.08 to 0.09 for somatic cell score. Estimates were less for later than first lactations. Estimates of genetic correlations among yields ranged from 0.35 to 0.85 with no important differences between estimates with the 3 models. Estimates for lactation 2 agreed with estimates for lactation 1. Estimates of genetic correlations for later lactations were generally greater than for lactations 1 and 2 except between milk and protein yields. Estimates of genetic correlations between yields and somatic cell score were mostly negative or small (-0.45 to 0.11). Estimates of environmental correlations among yield traits were similar with all models (0.77 to 0.97). Estimates of environmental correlations between yields and somatic cell score were negative (-0.22 to -0.14). Estimates of phenotypic correlations among yield traits ranged from 0.70 to 0.95. Estimates of phenotypic correlations between yields and somatic cell score were small and negative. For all three data sets and all traits, no important differences in estimates of genetic parameters were found for the two models that adjusted for bST and the model that did not

Impact of bovine somatotropin on ranking for genetic value of dairy sires for milk yield traits and somatic cell score

Records of Holstein cows were used to examine how different models account for the effect of bovine somatotropin (bST) treatment on genetic evaluation of dairy sires for yield traits and somatic cell score. Data set 1 included 65,720 first-lactation records. Set 2 included 50,644 second-lactation records. Set 3 included 45,505 records for lactations three, four and five. Estimated breeding values (EBV) of sires were with three different animal models. With Model 1, bST administration was ignored. With Model 2, bST administration was used as a fixed effect. With Model 3, administration of bST was used to define the contemporary group (herd-year-month of calving-bST). Correlations for EBV of 1,366 sires with treated daughters between pairs of the three models were calculated for milk, fat and protein yields and somatic cell score for the three data sets. Correlations for EBV of sires between pairs of models for all traits ranged from 0.971 to 0.999. Fractions of sires with bST-treated progeny selected in common (top 10 to 15%) were 0.94 and usually greater for all pairs of models for all traits and parities. For this study, the method of statistical adjustment for bST treatment resulted in a negligible effect on genetic evaluations of sires when some daughters were treated with bST and suggests that selection of sires to produce the next generation of sires and cows might not be significantly affected by how the effect of bST is modeled for prediction of breeding values for milk, fat and protein yields and somatic cell score

Estimates of genetic parameters for Holstein cows for test-day yield traits with a random regression cubic spline model

Genetic parameters were estimated with restricted maximum likelihood for individual test-day milk, fat, and protein yields and somatic cell scores with a random regression cubic spline model. Test-day records of Holstein cows that calved from 1994 through early 1999 were obtained from Dairy Records Management Systems in Raleigh, North Carolina, for the analysis. Estimates of heritability for individual test-days and estimates of genetic and phenotypic correlations between test-days were obtained from estimates of variances and covariances from the cubic spline analysis. Estimates were calculated of genetic parameters for the averages of the test days within each of the ten 30-day test intervals. The model included herd test-day, age at first calving, and bovine somatropin treatment as fixed factors. Cubic splines were fitted for the overall lactation curve and for random additive genetic and permanent environmental effects, with five predetermined knots or four intervals between days 0, 50, 135, 220, and 305. Estimates of heritability for lactation one ranged from 0.10 to 0.15, 0.06 to 0.10, 0.09 to 0.15, and 0.02 to 0.06 for test-day one to test-day 10 for milk, fat, and protein yields and somatic cell scores, respectively. Estimates of heritability were greater in lactations two and three. Estimates of heritability increased over the course of the lactation. Estimates of genetic and phenotypic correlations were smaller for test-days further apart.