Genetic evaluation of calving to first insemination using natural and artificial insemination mating data

Mating and calving records for 51,084 first-parity heifers in Australian Angus herds were used to examine the relationship between probability of calving to first insemination (CFI) in artificial insemination and natural service (NS) mating data. Calving to first insemination was defined as a binary trait for both sources of data. Two Bayesian models were employed: 1) a bivariate threshold model with CFI in AI data regarded as a trait separate from CFI in NS data and 2) a univariate threshold model with CFI regarded as the same trait for both sources of data. Posterior means (SD) of additive variance in the bivariate analysis were similar: 0.049 (0.013) and 0.075 (0.021) for CFI in AI and NS data, respectively, indicating lack of heterogeneity for this parameter. A similar trend was observed for heritability in the bivariate analysis, with posterior means (SD) of 0.025 (0.007) and 0.048 (0.012) for AI and NS data, respectively. The posterior means (SD) of the additive covariance and corresponding genetic correlation between the traits were 0.048 (0.006) and 0.821 (0.138), respectively. Differences were observed between posterior means for herd-year variance: 0.843 vs. 0.280 for AI and NS data, respectively, which may reflect the higher incidence of 100% conception rates within a herd-year class (extreme category problem) in AI data. Parameter estimates under the univariate model were close to the weighted average of the corresponding parameters under the bivariate model. Posterior means (SD) for additive, herd-year, and service sire variance and heritability under the univariate model were 0.063 (0.007), 0.56 (0.029), 0.131 (0.013), and 0.036 (0.007), respectively. These results indicate that, genetically, cows with a higher probability of CFI when mated using AI also have a high probability of CFI when mated via NS. The high correlation between the two traits, along with the lack of heterogeneity for the additive variance, implies that a common additive variance could be used for AI and NS data. A single-trait analysis of CFI with heterogeneous variances for herd-year and service sire could be implemented. The low estimates of heritability indicate that response to selection for probability of calving to first insemination would be expected to be low.     

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.     

Genetic analysis of the rates of conception using a longitudinal threshold model with random regression in dairy crossbreeding within a tropical environment

This study was designed to: (i) estimate genetic parameters and breeding values for conception rates (CR) using the repeatability threshold model (RP‐THM) and random regression threshold models (RR‐THM); and (ii) compare covariance functions for modeling the additive genetic (AG) and permanent environmental (PE) effects in the RR‐THM. The CR was defined as the outcome of an insemination. A data set of 130 592 first‐lactation insemination records of 55 789 Thai dairy cows, calving between 1996 and 2011, was used in the analyses. All models included fixed effects of year × month of insemination, breed × day in milk to insemination class and age at calving. The random effects consisted of herd × year interaction, service sire, PE, AG and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Heritability estimates of CR ranged from 0.032 to 0.067, 0.037 to 0.165 and 0.045 to 0.218 for RR‐THM with the second, third and fourth‐order of Legendre polynomials, respectively. The heritability estimated from RP‐THM was 0.056. Model comparisons based on goodness of fit, predictive abilities, predicted service results of animal, and pattern of genetic parameter estimates, indicated that the model which fit the desired outcome of insemination was the RR‐THM with two regression coefficients.     

Threshold-linear analysis of measures of fertility in artificial insemination data and days to calving in beef cattle

Mating and calving records for 47,533 first-calf heifers in Australian Angus herds were used to examine the relationship between days to calving (DC) and two measures of fertility in AI data: 1) calving to first insemination (CFI) and 2) calving success (CS). Calving to first insemination and calving success were defined as binary traits. A threshold-linear Bayesian model was employed for both analyses: 1) DC and CFI and 2) DC and CS. Posterior means (SD) of additive covariance and corresponding genetic correlation between the DC and CFI were -0.62 d (0.19 d) and -0.66 (0.12), respectively. The corresponding point estimates between the DC and CS were -0.70 d (0.14 d) and -0.73 (0.06), respectively. These genetic correlations indicate a strong, negative relationship between DC and both measures of fertility in AI data. Selecting for animals with shorter DC intervals genetically will lead to correlated increases in both CS and CFI. Posterior means (SD) for additive and residual variance and heritability for DC for the DC-CFI analysis were 23.5 d2 (4.1 d2), 363.2 d2 (4.8 d2), and 0.06 (0.01), respectively. The corresponding parameter estimates for the DC-CS analysis were very similar. Posterior means (SD) for additive, herd-year and service sire variance and heritability for CFI were 0.04 (0.01), 0.06 (0.06), 0.14 (0.16), and 0.03 (0.01), respectively. Posterior means (SD) for additive, herd-year, and service sire variance and heritability for CS were 0.04 (0.01), 0.07 (0.07), 0.14 (0.16), and 0.03 (0.01), respectively. The similarity of the parameter estimates for CFI and CS suggest that either trait could be used as a measure of fertility in AI data. However, the definition of CFI allows the identification of animals that not only record a calving event, but calve to their first insemination, and the value of this trait would be even greater in a more complete dataset than that used in this study. The magnitude of the correlations between DC and CS-CFI suggest that it may be possible to use a multitrait approach in the evaluation of AI and natural service data, and to report one genetic value that could be used for selection purposes.     

Bayesian inference strategies for the prediction of genetic merit using threshold models with an application to calving ease scores in Italian Piemontese cattle

First parity calving difficulty scores from Italian Piemontese cattle were analysed using a threshold mixed effects model. The model included the fixed effects of age of dam and sex of calf and their interaction and the random effects of sire, maternal grandsire, and herd‐year‐season. Covariances between sire and maternal grandsire effects were modelled using a numerator relationship matrix based on male ancestors. Field data consisted of 23 953 records collected between 1989 and 1998 from 4741 herd‐year‐seasons. Variance and covariance components were estimated using two alternative approximate marginal maximum likelihood (MML) methods, one based on expectation‐maximization (EM) and the other based on Laplacian integration. Inferences were compared to those based on three separate runs or sequences of Markov Chain Monte Carlo (MCMC) sampling in order to assess the validity of approximate MML estimates derived from data with similar size and design structure. Point estimates of direct heritability were 0.24, 0.25 and 0.26 for EM, Laplacian and MCMC (posterior mean), respectively, whereas corresponding maternal heritability estimates were 0.10, 0.11 and 0.12, respectively. The covariance between additive direct and maternal effects was found to be not different from zero based on MCMC‐derived confidence sets. The conventional joint modal estimates of sire effects and associated standard errors based on MML estimates of variance and covariance components differed little from the respective posterior means and standard deviations derived from MCMC. Therefore, there may be little need to pursue computation‐intensive MCMC methods for inference on genetic parameters and genetic merits using conventional threshold sire and maternal grandsire models for large datasets on calving ease.     

Dairy sire evaluation fitting some of the herd-year-season effects as random

Three models of sire evaluation using different environmental groupings were compared. Effects fitted were herd, period (either 6 or 12 months) within herd, season (either 1 or 2 or 4 months) within period within herd, sire and linear and quadratic regressions on age at calving. Models differed in fitting (1) the effect of herd-period-season fixed, or (2) herd-period fixed and herd-period-season random, or (3) herd fixed, herd-period and herd-period-season random. The overall effects of period and season of calving were regarded as fixed, and were removed by precorrection. Records of first lactation fat yield on 49 242 progeny of 69 widely used proven Friesian-Holstein sires in 1628 herds in England and Wales were used.Compared to Model 1, Model 2 required about four-fifths and Model 3 required two-thirds of the effective number of daughters to give the equivalent variance of the estimates of sire effects. Using random effects models the relative advantage, in terms of a smaller variance of sire effects, increased as the size of herd-period-season subclass decreased.In herd-period-season fixed effects models subclasses with a single of few records, or subclasses with all or almost all records of the same sire, contribute nothing or little to the progeny group comparisons. The random effects models could avoid these losses, and were considered to be useful especially where herds are small, provided sires can be assumed as randomly distributed over environmental subclasses.     

Estimation of variance and covariance components to determine heritabilities and repeatability of weaning weight in American Simmental cattle

Components of (co)variance for weaning weight were estimated from field data provided by the American Simmental Association. These components were obtained for the observational components of variance corresponding to a sire, maternal grandsire, and dam within maternal grandsire model. From these estimates, direct additive genetic variance (Sigma2A), maternal additive genetic variance (Sigma2M), covariance between direct and maternal additive genetic effects (SigmaAM), variance of permanent environment(Sigma2pe) and temporary environment variance(Sigma2te) were determined. A procedure to approximate restricted maximum likelihood (REML) estimates of the observational components of variance based on the expectation-maximization (EM) algorithm is described. From these results, phenotypic variance ( ) of weaning weight was 667.88 kg2. Values forSigma2A, Sigma2M, Sigma2pe and Sigma2te were 79,30,58,38,49.45, and 469.97 kg2, respectively. Genetic correlation between direct and maternal additive genetic effects was .16.     

Estimates of (co)variance components for productive and composite reproductive traits in Iranian Cashmere goats

The objective of this study was to estimate variance and covariance components, in Iranian Cashmere goats, for birth weight (BWT) and weaning weight (WWT) performances of kids and total weight of kids weaned (TWW) per doe joined at first (TWW1), second (TWW2) and third (TWW3) parities by REML procedures using univariate and multivariate animal models. The analysis was based on 2313 records of kids and 940 records of does. Through ignoring or including maternal additive genetic or maternal permanent environmental effects, four different models were fitted for BWT and WWT performances. For TWW performances only two models (without or with service sire effect) were used. Models were compared using likelihood ratio test. Direct additive genetic and maternal permanent environmental effects had significant influence on BWT and WWT performances. These effects accounted for 9.4% and 15.6%, and 13.9% and 6.7% of phenotypic variation, respectively. No significant effect of service sire was observed on TWW. The estimates of heritabilities were 0.072, 0.109 and 0.082 for TWW1, TWW2 and TWW3, respectively. Direct genetic correlations among all performances were positive and low (for BWT with TWW) to high (for BWT with WWT and WWT with TWW). The corresponding estimates for phenotypic and residual correlations were moderate and lower than genetic correlations. The high genetic correlation among WWT and TWW suggests that direct selection on TWW1 or indirect selection on WWT would increase total weight of kids weaned per doe joined.     

Choice of statistical model for estimating genetic parameters using restricted maximum likelihood in swine

Summary Restricted maximum likelihood (REML) was used to determine the choice of statistical model, additive genetic maternal and common litter effects and consequences of ignoring these effects on estimates of variance–covariance components under random and phenotypic selection in swine using computer simulation. Two closed herds of different size and two traits, (i) pre‐weaning average daily gain and (ii) litter size at birth, were considered. Three levels of additive direct and maternal genetic correlations (r_dm) were assumed to each trait. Four mixed models (denoted as GRM1 through GRM4) were used to generate data sets. Model GRM1 included only additive direct genetic effects, GRM2 included only additive direct genetic and common litter effects, GRM3 included only additive direct and maternal genetic effects and GRM4 included all the random effects. Four mixed animal models (defined as EPM1 through EPM4) were defined for estimating genetic parameters similar to GRM. Data from each GRM were fitted with EPM1 through EPM4. The largest biased estimates of additive genetic variance were obtained when EPM1 was fitted to data generated assuming the presence of either additive maternal genetic, common litter effects or a combination thereof. The bias of estimated additive direct genetic variance (VA_d) increased and those of recidual variance (VE) decreased with an increase in level of r_dm when GRM3 was used. EPM1, EPM2 and EPM3 resulted in biased estimation of the direct genetic variances. EPM4 was the most accurate in each GRM. Phenotypic selection substantially increased bias of estimated additive direct genetic effect and its mean square error in trait 1, but decreased those in trait 2 when ignored in the statistical model. For trait 2, estimates under phenotypic selection were more biased than those under random selection. It was concluded that statistical models for estimating variance components should include all random effects considered to avoid bias.     

绵羊生长性状母本效应方差组分、遗传参数估计的研究

本文利用公畜母畜模型和公畜外祖父模型估计了初生重、断奶重的直接加性遗传方差、母本遗传方差和遗传参数,得出初生重的直接加性遗传效应、母本遗传效应和总的加性遗传效应的遗传力分别为:0.164、0.101、0.103;断奶重相应的各遗传力为:0.076、0.108、0.081。初生重和断奶重二性状加性遗传效应和母本遗传效应间的遗传相关为:-0.57和-0.36。     

Adjusting for heterogeneity of experimental data in genetic evaluation of dry matter intake in dairy cattle

The objectives of the present study were (i) to find the best fitted model for repeatedly measured daily dry matter intake (DMI) data obtained from different herds and experiments across lactations and (ii) to get better estimates of the genetic parameters and better genetic evaluations. After editing, there were 572,512 daily DMI records of 3,495 animals (Holstein cows) from 11 different herds across 13 lactations and the animals were under 110 different nutritional experiments. The fitted model for this data set was a univariate repeated‐measure animal model (called model 1) in which additive genetic and permanent environmental (within and across lactations) effects were fitted as random. Model 1 was fitted as two distinct models (called models 2 and 3) based on alternative fixed effect corrections. For unscaled data, each model (models 2 and 3) was fitted as a homoscedastic (HOM) model first and then as a heteroscedastic (HET) model. Then, data were scaled by multiplying with particular herd‐scaling factors, which were calculated by accounting for heterogeneity of phenotypic within‐herd variances. Models were selected based on cross‐validation and prediction accuracy results. Scaling factors were re‐estimated to determine the effectiveness of accounting for herd heterogeneity. Variance components and respective heritability and repeatability were estimated based on a pedigree‐based relationship matrix. Results indicated that the model fitted for scaled data showed better fit than the models (HOM or HET) fitted for unscaled data. The heritability estimates of the models 2 and 3 fitted for scaled data were 0.30 and 0.08, respectively. The repeatability estimates of the model fitted for scaled data ranged from 0.51 to 0.63. The re‐estimated scaling factor after accounting for heterogeneity of residual variances was close to 1.0, indicating the stabilization of residual variances and herd accounted for most of the heterogeneity. The rank correlation of EBVs between scaled and unscaled data ranged from 0.96 to 0.97.     

Effect of including relationships in the estimation of genetic parameters of beef calves.

Variances and covariances for birth weight, gain from birth to weaning (ADG), and 205-d weight were obtained from a sire-dam model and a sire-maternal grandsire model for a herd of Angus and a herd of Hereford cattle. Estimates of direct additive genetic variance (sigma 2A), maternal additive genetic variance (sigma 2M), covariance between direct and maternal additive genetic effects (sigma AM), permanent environmental variance (sigma 2PE), and residual variance (sigma 2e) were obtained both with and without the inverse of the numerator relationship matrix (A-1) included. Estimates of heritability for direct genetic effects (h2A), maternal genetic effects (h2M), and the correlation between direct and maternal effects (rAM) for birth weight were .37, .18, and -.01 in Angus and .53, .23, and -.19 in Herefords, respectively, for the analyses without A-1. For the analyses with A-1, estimates of h2A, h2M, and rAM were .42, .22, and -.12 for Angus and .58, .22, and -.13 for Herefords, respectively. Estimates of h2A, h2M, and rAM for ADG were .43, .15, and -.44 in Angus and .52, .38, and -.03 in Herefords, respectively, without A-1. With A-1, estimates of h2A, h2M, and rAM were .57, .15, and -.32 for Angus and .58, .39, and -.05 for Herefords, respectively. Estimates of h2A, h2M, and rAM for 205-d weight were .49, .15, and -.46 for Angus and .58, .43, and -.06 for Herefords, respectively, without A-1. With A-1, estimates of h2A, h2M, and rAM were .63, .16, and -.36 for Angus and .66, .43, and -.08 for Herefords, respectively. Estimates of h2A were higher with A-1 than without A-1, but estimates of h2M were similar. Using variances and covariances obtained from analyses including A-1 generally gave higher estimates of direct breeding values than using variances and covariances obtained from analyses not including A-1. Both Pearson product-moment and Spearman rank correlations were high (.99) between estimates of breeding values from the two analyses, although some changes in rank did occur.     

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.     

Symmetric differences squared and analysis of variance procedures for estimating genetic and environmental variances and covariances for beef cattle weaning weight: II. Estimates from a data set

Analysis of variance (ANOVA) and symmetric differences squared (SDS) methods were used to estimate additive genetic and environmental variances and covariances associated with weaning weight. The two methods were applied to 503 beef records collected over 19 yr from a relatively unselected university Angus herd. The SDS methodology was used with four models. The first model included direct (g) and maternal (gm) additive genetic effects, the genetic covariance between direct and maternal additive genetic effects (sigma ggm), permanent maternal environmental effects (m) and temporary environmental effects (e). The second model also allowed for a nonzero environmental covariance (sigma mem) between dam and offspring weaning weights. Models 3 and 4 were models 1 and 2, respectively, expanded to include a grandmaternal genetic effect (gn) and covariances sigma ggn and sigma gmgn. Two ANOVA solution sets for the parameters of model 4 were based on sire, dam, maternal grandsire, maternal grandam and phenotypic variances and offspring-dam (covOD), offspring-sire (covOS), offspring-grandam (covOGD) and offspring-maternal half-aunt or uncle (covOMH) covariances. Four ANOVA solution sets for the parameters of model 2 were based on sire, dam, within dam and maternal grandsire variances, covOD and either covOS or covOGD. Symmetric differences squared estimates of h2g and h2gm averaged .30 and .16, respectively. All SDS estimates of rho ggm (correlation between direct and maternal genetic effects) were less than -1. Estimates of sigma mem were positive. Both SDS estimates and one of the two ANOVA estimates of the grandmaternal variance were negative. The ANOVA model 4 estimates of h2g were .33. The estimates of h2gm were .44 and .39, while the estimates for rho ggm were -.88 and -.80. Both estimates of sigma mem were positive. The four ANOVA model 2 estimates of h2g and h2gm averaged .33 and .48, respectively. Three of the four estimates of rho ggm were less than -.97; the fourth was .35. Three of the four estimates of sigma mem were positive. Expectations show the extent to which SDS and ANOVA estimators were biased by nonzero grandmaternal components that were not accounted for. The extent to which dominance components bias the ANOVA estimators also is shown. Nonzero grandmaternal effects need to be taken into account in either SDS or ANOVA solution sets, or important biases occur with most of the estimators. More numerous, and generally more severe, biases occur with ANOVA estimators than with SDS estimators in solution sets that do not account for grandmaternal effects.     

Partitioning of herd,year and season variation in milk production

Variances in milk and fat yields due to herd-period-season effects (period either 6 or 12 months, and season either 1, 2 or 4 months) were partitioned fitting a three nested effects model with herd, period within herd and season within period within herd as random nested effects, sire a fixed effect and linear and quadratic regressions on age at calving. The overall effects of period and season of calving were regarded as fixed effects and were removed by precorrecting records using least squares constants estimated from the same data. Environmental correlations within herd, herd-period and herd-period-season were also estimated for all period and season groupings. Records of first lactation milk and fat yields on 25 158 progeny of 69 widely used proven Friesian-Holstein sires in 832 herds in England and Wales were used.The variance components for the effects of herd, period within herd, season within period within herd and residual accounted for 31, 6, 5 and 58% of the total variance in milk yield, and 35, 8, 7 and 50% of the variance in fat yield, respectively, using a period of 12 months and a season of 4 months. Differences amongst correlations within the same herd-period-season, fitting seasons of different lengths, were small. It was therefore concluded that shorter seasons in a herd-period-season fixed effect model of sire evaluation would be of no advantage.     

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.     

The effect of ignoring individual heterogeneity in Weibull log-normal sire frailty models

The objective of this study was, by means of simulation, to quantify the effect of ignoring individual heterogeneity in Weibull sire frailty models on parameter estimates and to address the consequences for genetic inferences. Three simulation studies were evaluated, which included 3 levels of individual heterogeneity combined with 4 levels of censoring (0, 25, 50, or 75%). Data were simulated according to balanced half-sib designs using Weibull log-normal animal frailty models with a normally distributed residual effect on the log-frailty scale. The 12 data sets were analyzed with 2 models: the sire model, equivalent to the animal model used to generate the data (complete sire model), and a corresponding model in which individual heterogeneity in log-frailty was neglected (incomplete sire model). Parameter estimates were obtained from a Bayesian analysis using Gibbs sampling, and also from the software Survival Kit for the incomplete sire model. For the incomplete sire model, the Monte Carlo and Survival Kit parameter estimates were similar. This study established that when unobserved individual heterogeneity was ignored, the parameter estimates that included sire effects were biased toward zero by an amount that depended in magnitude on the level of censoring and the size of the ignored individual heterogeneity. Despite the biased parameter estimates, the ranking of sires, measured by the rank correlations between true and estimated sire effects, was unaffected. In comparison, parameter estimates obtained using complete sire models were consistent with the true values used to simulate the data. Thus, in this study, several issues of concern were demonstrated for the incomplete sire model.     

Symmetric differences squared and analysis of variance procedures for estimating genetic and environmental variances and covariances for beef cattle weaning weight: I. Comparison via simulation

Analysis of variance (ANOVA) and symmetric differences squared (SDS) methods for estimating genetic and environmental variances and covariances associated with beef cattle weaning weight were compared via simulation. Simulation was based on the pedigree and record structure of 503 beef weaning weights collected over 19 yr from a university herd. The SDS methodology was used with four models. The simplest model included direct (g) and maternal (gm) additive genetic effects, genetic covariance between direct and maternal additive genetic effects (sigma ggm), permanent maternal environmental effects (m) and temporary environmental effects (e). The second model also allowed for a nonzero environmental covariance (sigma mem) between dam and offspring weaning weights. Models 3 and 4 were models 1 and 2, respectively, expanded to include a grandmaternal genetic effect (gn) and covariances sigma ggn and sigma gmgn. Two ANOVA solution sets for the parameters of model 4 were obtained using sire, dam, maternal grandsire, maternal grandam and phenotypic variances and offspring-dam (covOD), offspring-sire (covOS), offspring-grandam (covOGD), and offspring-maternal half-aunt or uncle (covOMH) covariances. Four ANOVA solution sets for the parameters of model 2 were obtained using sire, dam, within dam and maternal grandsire variances, covOD and either covOS or covOGD. Two sets of 1,000 replicates of the data were simulated. These data were used to compare precision and accuracy of SDS and ANOVA estimators, to estimate correlations among SDS and ANOVA estimators, and to study the importance of taking inbreeding into account with SDS methodology. All ANOVA estimators for rho ggm were biased downward. The SDS procedure had a clear advantage over ANOVA. Averages of SDS estimates were closer to parameter values used to simulate the data and their standard deviations were generally smaller. The standard deviations of both SDS and ANOVA estimates of rho ggm were very large. It is important to allow for a nonzero sigma mem (at least when it is negative) when using SDS methods; otherwise estimators of sigma 2gm and sigma ggm are biased upward and downward, respectively.     

Estimates of genetic correlations between days to calving and reproductive and weight traits in Nelore cattle

Data comprising 53,181 calving records were analyzed to estimate the genetic correlation between days to calving (DC), and days to first calving (DFC), and the following traits: scrotal circumference (SC), age at first calving (AFC), and weight adjusted for 550 d of age (W550) in a Nelore herd. (Co)variance components were estimated using the REML method fitting bivariate animal models. The fixed effects considered for DC were contemporary group, month of last calving, and age at breeding season (linear and quadratic effects). Contemporary groups were composed by herd, year, season, and management group at birth; herd and management group at weaning; herd, season, and management group at mating; and sex of calf and mating type (multiple sires, single sire, or AI). In DFC analysis, the same fixed effects were considered excluding the month of last calving. For DC, a repeatability animal model was applied. Noncalvers were not considered in analyses because an attempt to include them, attributing a penalty, did not improve the identification of genetic differences between animals. Heritability estimates ranged from 0.04 to 0.06 for DC, from 0.06 to 0.13 for DFC, from 0.42 to 0.44 for SC, from 0.06 to 0.08 for AFC, and was 0.30 for W550. The genetic correlation estimated between DC and SC was low and negative (-0.10), between DC and AFC was high and positive (0.76), and between DC and W550 was almost null (0.07). Similar results were found for genetic correlation estimates between DFC and SC (-0.14), AFC (0.94), and W550 (-0.02). The genetic correlation estimates indicate that the use of DC in the selection of beef cattle may promote favorable correlated responses to age at first mating and, consequently, higher gains in sexual precocity can be expected.     

Estimation of genetic parameters for 13 female fertility indices in Holstein dairy cows

This study was aimed to assess genetic parameters for 13 traits in heifers and first-parity Holstein dairy cows. Data consisted of calving and insemination dates of 14,707 Holstein dairy cows in Isfahan province of Iran. Reproductive traits included age at first service (AFS), first service to conception (FSTC), gestation length (GL), age at first calving (AFC), calving to first service (CTFS), days open (DO), calving interval (CI), number of services per conception (NS), and non-return rate at 56 days (NRR). Model equations were optimized using GLM procedure in SAS package following genetic analysis using animal models in ASREML software. Minimum and maximum departure from normal distribution for phenotypic records belonged to AFS, NRR, GL, DO, CI and AFC, NS, FSTC, CTFS, respectively. Estimated heritability varied from 0.002 (NRR) to 0.184 (GL) in heifers and from 0.003 (NRR) to 0.153 (GL) in first-parity cows. AFS, CTFS, and GL were noticeably heritable compared to other assessed traits. Estimated absolute additive genetic correlations were in the range of 0.01 (NRR and AFS) and 0.99 (NRR and NS) in heifers and 0.07 (GL and CI) to 1 (FSTC and CI) in cows. Additive genetic correlations were antagonistic between AFS and other traits, except AFC. Interestingly, NRR which has been included in sire catalogs had the highest average absolute genetic associations with other traits.