Monthly model for genetic evaluation of laying hens 1. Fixed regression.

1. This paper addresses the possibility of using a monthly model for the genetic evaluation of laying hens, based on the definition of a test day model with fixed regression as used in dairy cattle, in which monthly records were treated as repeated measurements of the same trait. 2. Production records of 6450 hens, daughters of 180 sires and 1335 dams were analysed using an animal model with restricted maximum likelihood (REML). The traits considered were individual monthly egg production and cumulative egg production in 11 months. Four different models were fitted to various combinations of monthly and cumulative records. The covariates were derived from the regression of Ali and Schaeffer (1987). 3. Spearman rank correlations were computed to compare breeding values from different models. Two types of correlations were computed: between individual breeding values and between sire breeding values based on subsets of full-sib records. 4. The results indicated that a monthly model with nested covariates produced higher heritability and permanent environmental variance than the models with non-nested or without covariates. The estimates of heritability obtained from monthly model were lower than the estimates from the cumulative model. The monthly model resulted in higher correlations of sire breeding values between two subsets of full-sib records than those from cumulative models. 5. In conclusion, the monthly model with nested covariates appears to be better than the model with non-nested covariates or without covariate. Although the heritability estimates obtained from the monthly model were lower, the monthly model with nested covariates could be better than the cumulative model for genetic evaluation of laying hens in the 1st cycle of laying period when using either full or part records. The use of information from odd months of production could be of interest for the evaluation of full records.     

Estimation of genetic parameters based on individual and group mean records in laying hens

(1) The study was conducted to estimate the heritability, genetic correlations and breeding values of laying hens based on individual records and group mean records. (2) Records of two pure lines from a commercial breeding programme of White Leghorns from three generations housed in single cages and in group cages were used. A total of 8483 and 8817 individual records of lines A and D, respectively, and a total of 1358 (line A) and 1161 (line D) group mean records were analysed. (3) An animal model using Restricted Maximum Likelihood (REML) was used to estimate variance components of individual records. Group mean records were analysed using the sire model, taking heterogeneity of error variance and correlated residual effects into account. Breeding values of sires were estimated based on the BLUP method using a multivariate sire model. Spearman Rank correlations were used to compare sire breeding values estimated from individual records and from group mean records. The traits studied were monthly egg production, cumulative production and egg weight. (4) Heritability estimates based on individual records were higher than from group mean records. Heritabilities for cumulative production records were higher than for monthly production, based on individual as well as group mean records. The estimates of genetic correlations between monthly egg production and cumulative production were moderate to high. Egg production and egg weight recorded individually were highly genetically correlated with those recorded on group means. Sire breeding values estimated from individual records showed high correlations with those from group mean records. (5) Differences in the ranking of sire breeding values estimated from individual vs group mean records were negligible, indicating that no genotype x environment interaction exists. Selection based on individual performance records of laying hens housed in single cages could give a good response on performance of laying hens housed in group cages. Cumulative egg production over periods 1 to 6 is the best trait for the selection programme.     

Comparison of different models for genetic evaluation of egg weight in Mazandaran fowl

1. The aim of the present study was to compare different models to estimate variance components for egg weight (EW) in laying hens.

2. The data set included 67 542 EW records of 18 245 Mazandaran hens at 24, 28, 30, 32 and 84 weeks of age, during 19 consecutive generations. Variance components were estimated using multi-trait, repeatability, fixed regression and random regression models (MTM, RM, FRM and RRM, respectively) by Average Information-Restricted Maximum Likelihood algorithm (AI-REML). The models were compared based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).

3. The MTM was the best model followed by the Legendre RRMs. A RRM with 2nd degree of fit for fixed regression and 3^rd and 2^nd degrees of fit for random regressions of direct additive genetic and permanent environmental effects, respectively, was the best RRM. The FRM and RM were not proper models to fit the data. However, nesting curves within contemporary groups improved the fit of FRM.

4. Heritability estimates for EW by MTM (0.06–0.41) were close to the estimates obtained by the best RRM (0.09–0.45). In both MTM and RRM, positive genetic correlations were estimated for EW records at different ages, with higher correlations for adjacent records.

5. The results suggest that MTM is the best model for EW data, at least when the records are taken at relatively few age points. Though selection based on EW at higher ages might be more precise, 30 or 32 weeks of age could be considered as the most appropriate time points for selection on EW to maximise genetic improvement per time unit.     

A practical longitudinal model for evaluating growth in Gelbvieh cattle

Genetic evaluation of growth in Gelbvieh beef cattle was examined by multiple-trait (MTM) and random regression (RRM) analysis. The data set comprised 541,108 animals with 1,120,086 records. Approximately 15% of the animals in the data set had at least one record measured outside of the accepted MTM age ranges for weaning weight (Wwt) and yearling weight (Ywt). Fourteen percent of Wwt records and 19% of Ywt records were measured outside the accepted ranges for MTM analysis, and thus were excluded from MTM evaluations. Two RRM evaluations were performed using cubic Legendre polynomials (RRML) and linear splines (RRMS) with three knots at 1, 205, and 365 d of age. Data Set 1 (d1) utilized all available records, whereas Data Set 2 (d2) included only records measured within MTM ranges (1 d, 160 to 250 d, and 320 to 410 d). The RRML models did not reach convergence until diagonalization was imposed. After diagonalization, it was found that all longitudinal models required fewer iterations to converge than the MTM. Correlations between the MTM, RRML-d2, and RRMS-d2 evaluations were >or=0.99 for all three traits, indicating that these models were equivalent when predicting breeding values from data within the MTM age ranges. Correlations between MTM, RRML-d1, and RRMS-d1 were >0.99 for Bwt and >0.95 for Wwt and Ywt. The lower correlations for Wwt and Ywt indicate that the added information does affect breeding value prediction. The RRM has the capability to incorporate records measured at all ages into genetic evaluations at a computing cost similar to the MTM.     

Estimation of total genetic effects for survival time in crossbred laying hens showing cannibalism,using pedigree or genomic information

Mortality of laying hens due to cannibalism is a major problem in the egg‐laying industry. Survival depends on two genetic effects: the direct genetic effect of the individual itself (DGE) and the indirect genetic effects of its group mates (IGE). For hens housed in sire‐family groups, DGE and IGE cannot be estimated using pedigree information, but the combined effect of DGE and IGE is estimated in the total breeding value (TBV). Genomic information provides information on actual genetic relationships between individuals and might be a tool to improve TBV accuracy. We investigated whether genomic information of the sire increased TBV accuracy compared with pedigree information, and we estimated genetic parameters for survival time. A sire model with pedigree information (BLUP) and a sire model with genomic information (ssGBLUP) were used. We used survival time records of 7290 crossbred offspring with intact beaks from four crosses. Cross‐validation was used to compare the models. Using ssGBLUP did not improve TBV accuracy compared with BLUP which is probably due to the limited number of sires available per cross (~50). Genetic parameter estimates were similar for BLUP and ssGBLUP. For both BLUP and ssGBLUP, total heritable variance (T²), expressed as a proportion of phenotypic variance, ranged from 0.03 ± 0.04 to 0.25 ± 0.09. Further research is needed on breeding value estimation for socially affected traits measured on individuals kept in single‐family groups.     

Longitudinal analysis of clinical mastitis at different stages of lactation in Norwegian Cattle

Genetic variation and covariation of liability to clinical mastitis in the course of first lactation in Norwegian Cattle (NRF) were investigated. The data consisted of 36,178 first-lactation cows with 354,506 clinical mastitis (absence=0 vs. presence=1) monthly records. A longitudinal binary data analysis was carried out using Bayesian threshold models and Markov chain Monte Carlo (MCMC) procedures. Liability was related to stage of lactation using random regression functions: the Ali–Schaeffer function (AS), the Wilmink function (W) and Legendre Polynomials of order 2, 3 or 4 (L2, L3, L4). Models were compared using a pseudo Bayes factor and an analysis of residuals. The MCMC scheme for the AS function did not converge after 20,000 iterations, and was therefore excluded from further analysis. The pseudo Bayes factor strongly favored the L4 model. Most posterior means of the residuals fell in the range from −0.2 to 0 when cows were healthy (a residual is negative when mastitis is absent and positive otherwise). The L4 model tended to have smaller residuals than the other three models when cows had mastitis. The posterior means of the herd variance and of the cow-specific variance were 0.0645 and 0.1084, respectively, for the fourth order Legendre polynomial. Heritability of liability to clinical mastitis was from 7% to 13% before calving, and ranged between 3% and 11% from calving to 260 days after calving. Most genetic correlations of liability to clinical mastitis between different days of first-lactation ranged from 0.4 to 0.7.     

Variance components and breeding values for growth traits from different statistical models.

Estimates of genetic parameters resulting from various analytical models for birth weight (BWT, n = 4,155), 205-d weight (WWT, n = 3,884), and 365-d weight (YWT, n = 3,476) were compared. Data consisted of records for Line 1 Hereford cattle selected for postweaning growth from 1934 to 1989 at ARS-USDA, Miles City, MT. Twelve models were compared. Model 1 included fixed effects of year, sex, age of dam; covariates for birth day and inbreeding coefficients of animal and of dam; and random animal genetic and residual effects. Model 2 was the same as Model 1 but ignored inbreeding coefficients. Model 3 was the same as Model 1 and included random maternal genetic effects with covariance between direct and maternal genetic effects, and maternal permanent environmental effects. Model 4 was the same as Model 3 but ignored inbreeding. Model 5 was the same as Model 1 but with a random sire effect instead of animal genetic effect. Model 6 was the same as Model 5 but ignored inbreeding. Model 7 was a sire model that considered relationships among males. Model 8 was a sire model, assuming sires to be unrelated, but with dam effects as uncorrelated random effects to account for maternal effects. Model 9 was a sire and dam model but with relationships to account for direct and maternal genetic effects; dams also were included as uncorrelated random effects to account for maternal permanent environmental effects. Model 10 was a sire model with maternal grandsire and dam effects all as uncorrelated random effects. Model 11 was a sire and maternal grandsire model, with dams as uncorrelated random effects but with sires and maternal grandsires assumed to be related using male relationships. Model 12 was the same as Model 11 but with all pedigree relationships from the full animal model for sires and maternal grandsires. Rankings on predictions of breeding values were the same regardless of whether inbreeding coefficients for animal and dam were included in the models. Heritability estimates were similar regardless of whether inbreeding effects were in the model. Models 3 and 9 best fit the data for estimation of variances and covariances for direct, maternal genetic, and permanent environmental effects. Other models resulted in changes in ranking for predicted breeding values and for estimates of direct and maternal heritability. Heritability estimates of direct effects were smallest with sire and sire-maternal grandsire models.     

Investigating maternal effects on production traits in Duroc pigs using animal and sire models

Variance components for production traits were estimated using different models to evaluate maternal effects. Data analysed were records from the South African pig performance testing scheme on 22 224 pigs from 18 herds, tested between 1990 and 2008. The traits analysed were backfat thickness (BFAT), test period weight gain (TPG), lifetime weight gain (LTG), test period feed conversion ratio (FCR) and age at slaughter (AGES). Data analyses were performed by REML procedures in ASREML, where random effects were successively fitted into animal and sire models to produce different models. The first animal model had one random effect, the direct genetic effects, while the additional random effects were maternal genetic and maternal permanent environmental effects. In the sire model, the random effects fitted were sire and maternal grand sire effects. The best model considered the covariance between direct and maternal genetic effects or between sire and maternal grand sire effects. Fitting maternal genetic effects into the animal model reduced total additive variance, while the total additive variance increased when maternal grand sire effects were fitted into the sire model. The correlations between direct and maternal genetic effects were all negative, indicating antagonism between these effects, hence the need to consider both effects in selection programmes. Direct genetic correlations were higher than other correlations, except for maternal genetic correlations of FCR with TPG, LTG and AGES. There has been direct genetic improvement and almost constant maternal ability in production traits as shown by trends for estimated (EBVs) and maternal breeding values (MBVs), while phenotypic trends were similar to those for EBVs. These results suggest that maternal genetic effects should be included in selection programmes for these production traits. Therefore, the animal–maternal model may be the most appropriate model to use when estimating genetic parameters for production traits in this population.     

Comparison of models for genetic evaluation of number of inseminations to conception in Danish Holstein cows

Number of inseminations to conception (NINS), an important fertility trait, requires appropriate approaches for genetic evaluation due to its non‐normal distribution and censoring records. In this study, we analyzed NINS in 474 837 Danish Holstein cows at their first lactation by using seven models which deal with the categorical phenotypes and censored records in different manners, further assessed these models with regard to stability, lack of bias and accuracy of prediction. The estimated heritability from four models based on original NINS specified as a linear Gaussian model, categorical threshold model, threshold linear model and survival model were similar (0.031‐0.037). While for the other three models based on the binary response derived from NINS, referred as threshold model (TM), logistic and probit models (LOGM and PROM), the heritability were estimated as 0.027, 0.063 and 0.027, respectively. The model comparison concluded that different models could lead to slightly different sire rankings in terms of breeding values; a more complicated model led to less stability of prediction; the models based on the binary response derived from NINS (TM, LOGM and PROM) had slightly better performances in terms of unbiased and accurate prediction of breeding values.     

Genetic and phenotypic parameters for monthly egg production in White Leghorn hens

This article reports genetic and phenotypic parameters of monthly egg production and the influence of Box‐Cox transformation on the parameters from a population of White Leghorns, selected for feed efficiency. A total of 6450 daughters of 180 sires and 1335 dams were analysed by restricted maximum likelihood (REML) using a multivariate animal model. The traits considered were monthly egg productions, cumulative production of the first 5 months (S5), cumulative production of first 10 months (S10), and survivor egg production in the first cycle (S12). Two sets of data were analysed: the original data and with the Box‐Cox method transformed data. The results indicated that there were no great differences in the estimates between untransformed and transformed data. The estimates of heritability for monthly egg production were high for the first period, decreased to reach the lowest during peak production, and increased to the end of lay. The estimates of heritability for cumulative records were generally higher than monthly records. Genetic and phenotypic correlations among monthly egg production totals were generally high for contiguous periods and then decreased as the interval between months increased. The highest genetic correlation between monthly records and S5 was for the second month of production, whereas the correlations between monthly production totals and S10 and S12 reached their peak at the sixth and eighth months of production, respectively.     

Development of genetic evaluation for milk production traits of Holsteins in Japan

The procedure used for the genetic evaluation of dairy cattle in Japan has developed from a lactation sire–MGS model to a multiple‐lactation random regression test‐day animal model. Genetic evaluation of Holstein bulls in Japan began in 1989 with the use of field‐style progeny testing; dairy herd improvement program data from all over Japan were used, along with a sire and maternal grandsire model. In 1993, an animal model was introduced to estimate breeding values for yield and type traits. A random regression test‐day model was first applied in 2010. In the business of breeding dairy cattle, it is very important to users that estimated breeding values are reliable and stable among subsequent routine evaluations. With experience in the genetic evaluation of dairy cattle in Japan, Japanese researchers have found ways to improve the stability of estimated breeding values. These modifications involve changes in data editing, development of evaluation models, changes to the structures of unknown‐parent groups, awareness of the problems of predicting lactation yield from partial test‐day records, and adjustment for heterogeneity within herd variances. Here, I introduce developments in, and our experiences with, the genetic evaluation of yield traits of Holstein cattle in Japan.     

Modelling cumulative egg production in laying hens and parent stocks of broiler chickens using classical growth functions

ABSTRACT

1. The objective of the present study is to introduce fresh insight into modelling of egg production by applying classical growth functions to egg production records reported by Aviagen Management Guide to laying hens and the parent stock of broiler chickens.

2. The functions (monomolecular, logistic, Gompertz, Richards and Morgan) were fitted using nonlinear regression procedures of SAS software, and their performance was assessed using goodness-of-fit statistics (coefficient of determination, residual mean squares, Akaike information criterion and Bayesian information criterion).

3. Overall, except for the logistic and Gompertz, the growth functions evaluated gave an acceptable fit to the cumulative egg production curves, with the Morgan equation ranking first followed by the Richards equation. The Morgan and Richards equations provided satisfactory predictions of weekly egg yield at different egg production stages, from early to late production, whereas the least accurate estimates were obtained with the logistic equation.

4. In conclusion, classical growth functions proved feasible alternatives to fit cumulative egg production curves of laying hens and parent stock of broiler chickens, resulting in suitable statistical performance and accurate estimates of production.     

Predictive ability of alternative models for genetic analysis of clinical mastitis

Mastitis in cows can be defined as a binary trait, reflecting presence or absence of clinical mastitis (CM), or as a count variable, number of mastitis cases (NCM), within a defined time interval. Many different models have been proposed for genetic analyses of mastitis, and the objective of this study was to evaluate the predictive ability and sire predictions of a set of models for genetic evaluation of CM or NCM. Linear- and threshold liability models for CM, and linear, censored ordinal threshold, and zero-inflated Poisson (ZIP) models for NCM were compared in a cross-validation study. To assess the ability of these models to predict future data, records from 620492 first-lactation Norwegian Red cows, which were daughters of 3064 sires, were evaluated in a fourfold cross-validation scheme. The mean squared error of prediction was used for model comparison. All models but ordinal threshold model equally performed when comparing the overall predictive ability. This result was on average, across sick and healthy cows; however, the models behaved differently for each category of animals. For example, healthy cows were predicted better by the threshold and linear models for binary data and ZIP model, whereas for mastitic cows, the ordinal threshold model was by far the best model. Predicted sire effects and rankings of sires were highly correlated across all models. For practical purposes, the linear models are very competitive with the nonlinear models.     

Adapting best linear unbiased prediction (BLUP) for timely genetic evaluation: I. Progeny traits in a single contemporary group for each sex

The first step of a procedure to partially circumvent the voluminous calculations with extremely large matrices for the usual algorithms for a BLUP (best linear unbiased prediction) approach is presented. This procedure, specific for a hierarchical portion of a model relevant to many animal breeding populations, is pertinent especially for polytocous species such as swine and poultry. For these, the occurrence of full-sib families makes the inclusion of dam effects in the model more necessary than in dairy or beef cattle models, where dam effects often are omitted. The formulas are derived for the hierarchical model for sires, dams within sires, individuals within full-sib family, and records within individuals, showing a relatively simple structure for such predictors. These formulas provide the basis for an alternative computing algorithm for obtaining evaluations having the statistical properties of best linear unbiased prediction. Formulas also are developed to approximate the prediction error variances for such models. Following this, the methodology for combining separate BLUP predictors, both error-independent and correlated, is developed.     

Accessing genotype by environment interaction using within- and across-country test-day random regression sire models

First-lactation test-day (TD) milk records of Luxembourg and Tunisian Holsteins were analysed for evidence of genotype by environment interaction (G × E). The joint data included 730 810 TD records of 87 734 cows and 231 common sires. Random regression TD sire models with fourth-order Legendre polynomials were used to estimate genetic parameters via within- and across-country analyses. Daily heritability estimates of milk yield from within-country analysis were between 0.11 and 0.32, and 0.03 and 0.13 in Luxembourg and Tunisia, respectively. Heritability estimates for 305-day milk yield and persistency (defined as the breeding value for milk yield on DIM 280 minus the breeding value on DIM 80) were lower for Tunisian Holsteins compared with the Luxembourg population. Specifically, heritability for 305-day milk yield was 0.16 for within- and 0.11 for across-country analyses for Tunisian Holsteins and 0.38 for within- and 0.40 for across-country analyses for Luxembourg Holsteins. Heritability for apparent persistency was 0.02 for both within- and across-country analyses for Tunisian Holsteins and 0.08 for within- and 0.09 for across-country analyses for Luxembourg Holsteins. Genetic correlations between the two countries were 0.50 for 305-day milk yield and 0.43 for apparent persistency. Moreover, rank correlations between the estimated breeding values of common sires for 305-day milk yield and persistency, estimated separately in each country, were low. Low genetic correlations are evidence for G × E for milk yield production while low rank correlations suggest different rankings of sires in both environments. Results from this study indicate that milk production of daughters of the same sires depends greatly on the production environment and that importing high merit semen for limited input systems might not be an effective strategy to improve milk production.     

Studies on multiple trait and random regression models for genetic evaluation of beef cattle for growth

A simulation study examined issues important for genetic evaluation of growth in beef cattle by random regression models with cubic Legendre polynomials (RRML) and linear splines with three knots (RRMS) compared with multiple-trait models (MTM). Parameters for RRML were obtained by conversion from covariance functions. Parameters for MTM and RRMS were extracted from RRML at 1, 205, and 365 d; parameters for RRMS were the same as MTM for all effects except the permanent environment and the residual. Four data sets were generated assuming RRML included records at 1, 205, and 365 d; at 1, 160 to 250, and 320 to 410 d; at 1, 100, 205, 300, and 365 d; and at 1, 55 to 145, 160 to 250, 275 to 325, and 320 to 410 d. Accuracies were computed as correlations between the true (simulated) and predicted breeding values. With the first data set, excellent agreement in accuracy was obtained for all models. With the second data set, the accuracy of MTM dropped by up to 1.5% compared with the first data set, but accuracy was unchanged for both RRML and RRMS. With the third (fourth) data set, accuracies of RRML were up to 2.4% (2.5%) higher than with the first (second) data set. Small differences in accuracy between RRML and RRMS were found with the third and fourth data sets, which were traced to inflated correlations especially between 1 and 205 d in RRMS; inflation could be decreased by adding one extra knot at 100 d to RRMS. Diagonalization of random coefficients was crucial for RRML but not for RRMS, resulting in approximately six (two) times faster convergence with RRML (RRMS). Reduction of dimensionality in RRML associated with small eigenvalues caused a less accurate evaluation for birth weight. Genetic evaluation of growth by RRM requires careful implementation. The RRMS is simpler to implement than the RRML.     

Sire x herd interactions for weaning weight in beef cattle.

Weaning weight records of 44,357 Australian Angus calves produced by 1,020 sires in 90 herds were used to evaluate the importance of sire x herd interactions. Models fitted fixed effects of contemporary group (herd-year-date of weighing subclass), sex, calf age, and dam age and random effects of sire or of sire and sire x herd interaction using REML. Effects of standardizing the data, including sire relationships and including dam maternal breeding values (MBV) as a covariate were also investigated. Sire x herd interactions were found (P less than .05) in all cases and, in the most complete model, accounted for 3.3% of phenotypic variance. Across-herd heritabilities ranged from .19 to .28. Differential nonrandom mating among herds seemed to occur in the data. Significant sire x herd effects were observed for dam MBV, and adjustment for dam MBV yielded the smallest estimates of interaction variance and across-herd heritability. If sire x herd interactions were due only to genotype x environment interaction, within-herd heritabilities would range from .33 to .49. These estimates are larger than previously reported estimates. Thus, unreported environmental effects common to progeny of individual sires may also be involved in the observed interaction but could not be disentangled from true genotype x environment interaction effects using these data. Results of these analyses suggest that some accommodation of sire x herd interaction effects on weaning weight may be needed in beef cattle genetic evaluations, but a compelling case for development of herd-specific breeding value prediction cannot be made.     

Evaluation of a contract breeding management program in selected Ohio dairy herds with event-time analysis I. Cox proportional hazards models

An observational study was conducted in order to assess the impact of a contract breeding program on the reproductive performance in a selected group of Ohio dairies using event-time analysis. The contract breeding program was offered by a breeding co-operative and featured tail chalking and daily evaluation of cows for insemination by co-operative technicians. Dairy employees no longer handled estrus detection activities. Between early 2002 and mid-2004, test-day records related to production and reproduction were obtained for 16,453 lactations representing 11,398 cows in a non-random sample of 31 dairies identified as well-managed client herds of the breeding co-operative. Of the 31 herds, 15 were using the contract breeding at the start of the data acquisition period, having started in the previous 2 years. The remaining 16 herds managed their own breeding program and used the co-operative for semen purchase.

Cox proportional hazards modeling techniques were used to estimate the association of the contract breeding, as well as the effect of other significant predictors, with the hazard of pregnancy. Two separate Cox models were developed and compared: one that only considered fixed covariates and a second that included both fixed and time-varying covariates. Estimates of effects were expressed as the hazard ratio (HR) for pregnancy.

Results of the fixed covariates model indicated that, controlling for breed, herd size, use of ovulation synchronization protocols in the herd, whether somatic cell score exceeded 4.5 prior to pregnancy or censoring, parity, calving season, and maximum test-day milk prior to pregnancy or censoring, the contract breeding program was associated with an increased hazard of pregnancy (HR = 1.315; 95% CI 1.261–1.371). The results of the time-varying covariates model, which controlled for breed, herd size, use of ovulation synchronization protocols, somatic cell score above 4.5, parity, calving season, and testing season also found that the program was associated with an increased hazard of pregnancy (HR = 1.387; 95% CI 1.327–1.451).

The fixed and time-varying covariates models both found similar sets of predictors when analyzing the association of the contract breeding program with hazard of pregnancy. Both models identified a 30% or greater increase in hazard of pregnancy associated with use of the contract breeding program, suggesting that herds subscribing to the program achieved pregnancies in a more timely fashion.     

Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses

Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.     

Sire X environment interactions in beef cattle weaning weight field data

Weaning weight field records, supplied by the American Polled Hereford Association, were used to examine sire X environment interactions. Sire X herd/region and sire X contemporary group/herd interactions were evaluated from a data set containing 19,503 records. Sire X region interaction was evaluated from a data set containing 8,659 records. The genetic correlations of sire progeny performance across contemporary groups/herd were .59 and .37 across herds and contemporary groups/region. The average genetic correlation of sire progeny performance across regions was .64. Heritability of weaning weight was .11 across regions, .17 within region and .28 within herd. Mixed-model sire analyses of Polled Hereford weaning weight field records should include sire X herd/region and sire X contemporary group/herd random effects to reduce the sire X environment effects particular to any herd or contemporary group, and to account for the distribution of sire progeny across herds and contemporary groups in the estimation of prediction error variance. It may be necessary to perform separate sire analyses for some regions to evaluate the breeding values of sires in regions where rank changes are likely to occur.