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1.
Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33 155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85 118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.  相似文献   

2.
This study compared the accuracy of several models for obtaining genetic evaluations of calving difficulty. The models were univariate threshold animal (TAM), threshold sire-maternal grandsire (TSM), linear animal (LAM), and linear sire-maternal grandsire (LSM) models and bivariate threshold-linear animal (TLAM), threshold-linear sire-maternal grandsire (TLSM), linear-linear animal (LLAM), and linear-linear sire-maternal grandsire (LLSM) models for calving difficulty and birth weight. Data were obtained from the American Gelbvieh Association and included 84,420 first-parity records of both calving difficulty and birth weight. Calving difficulty scores were distributed as 73.4% in the first category (no assistance), 18.7% in the second, 6.3% in the third, and 1.6% in the fourth. Included in the animal models were fixed sex of calf by age of dam subclasses, random herd-year-season effects, and random animal direct and maternal breeding values. Sire-maternal grandsire models were similar to the animal models, with animal and maternal effects replaced by sire and maternal grandsire effects. Models were compared using a data splitting technique based on the correlation of estimated breeding values from two samples, with one-half of the calving difficulty records discarded randomly in the first sample and the remaining calving difficulty records discarded in the second sample. Reported correlations are averages of 10 replicates. The results obtained using animal models confirmed the slight advantage of TAM over LAM (0.69 vs 0.63) and TLAM over LLAM (0.90 vs 0.86). Bivariate analyses greatly improved the accuracy of genetic prediction of direct effects on calving difficulty relative to univariate analyses. Similar ranking of the models was found for maternal effects, but smaller correlations were obtained for bivariate models. For sire-maternal grandsire models, no differences between sire or maternal grandsire correlations were observed for TLSM compared to LLSM, and small differences were observed between TSM and LSM. The threshold model offered advantages over the linear model in animal models but not in sire-maternal grandsire models. For genetic evaluation of calving difficulty in beef cattle, the threshold-linear animal model seems to be the best choice for predicting both direct and maternal effects.  相似文献   

3.
Breeding value evaluation for UK Limousin beef cattle data was carried out by multiple-trait linear–threshold animal model with variance components assumed to be known. Polychotomous calving ease with five categories was analysed with two continuous traits: birth weight and gestation length. Field data consisted of 220,799 animals with observations with every possible combination of traits, and 270,035 animals in the pedigree. The threshold model was solved either with Newton Raphson or Expectation Maximisation algorithm, and solutions were compared to evaluation by a linear model with original and normalised scores. There were insignificant differences in solutions between the two algorithms for threshold model analyses. Furthermore, solutions of the continuous traits were similar by the threshold and linear models. For the categorical trait, correlations for random effects from the threshold and linear models were high. In case of normalised scores (original scores case in brackets) correlations with solutions from the threshold and linear model were 0.97 (0.94) and 0.97 (0.93) for direct and maternal genetic effects and 0.95 (0.89) for permanent maternal effects. Even so, at least one third of the top 1% ranking of bulls differed between the linear and the threshold models. Predictive abilities as correlations between estimated breeding values and pedigree indices were almost equal between the linear and threshold models for both continuous and categorical traits. In conclusion, despite the higher computational demand, the linear–threshold animal model can be seen worthwhile in the genetic evaluation of the national UK beef cattle data set.  相似文献   

4.
Best linear unbiased predictors (BLUP) of breeding values for additive direct and additive maternal genetic effects were estimated from 3,944 purebred Yorkshire and Landrace first-parity litters recorded on the Quebec Record of Performance Sow Productivity Program and born between 1977 and 1987. Breeding values for gilts, dams, and sires were estimated using an individual animal model for measures of litter size of total number born (NOBN), number born alive (NOBA), and number weaned (NOWN). Environmental trends were estimated from average herd-year solutions, and genetic trends were estimated by regression of estimated breeding value on year of birth. Environmental trends were positive for all traits in both breeds but were significant only for NOWN in Landrace (.051 +/- .021 pigs/yr). Genetic trends were very small but were mainly negative for direct breeding value and combined direct and maternal breeding value. Significant estimates of genetic trends (P less than .05) were observed only within the Yorkshire breed, and these ranged from -.012 +/- .004 to .004 +/- .002 pigs/yr.  相似文献   

5.
The objective of this study was to estimate the genetic parameters, genetic trends and breeding values using linear model (LM) and threshold model (TM) for the development of hip dysplasia (HD) in Labrador Retrievers in the Czech Republic (n = 3151). The right and left hip joints were evaluated separately using the Fédération Cynologique Internationale scoring system. Four linear and four TMs were tested for the correct estimation of genetic parameters. All the tested models utilized fixed effects of sex, assessor, year of birth, regression of age at evaluation, random direct genetic effects and the effect of the animals' permanent environments. The models differed in the inclusion of the following effects: fixed effects of regression of inbreeding coefficient, random maternal effect and random effect of the maternal permanent environment. Compared to the TM, the LM provided lower coefficients of direct (0.25-0.29 versus 0.26-0.35) and maternal heritability (0.01-0.02 versus 0.03-0.05), repeatability (0.76-0.77 versus 0.78-0.83) and of the correlation between direct and maternal effects (-0.55 to -0.21 versus -0.80 to -0.27). In the tested models, no statistical significance was found for fixed regression of inbreeding coefficients or for the random effect of the permanent maternal environment. In spite of the similarity of the LM and TM results, the TM is recommended as the more suitable model for estimating genetic parameters and subsequent breeding values for HD in Labrador Retrievers in the Czech Republic.  相似文献   

6.
Several models were evaluated in terms of predictive ability for calving difficulty. Data included birth weight and calving difficulty scores provided by the American Gelbvieh Association from 26,006 calves born to first-parity cows and five simulated populations of 6,200 animals each. Included in the model were fixed age of dam x sex interaction effects, random herd-year-season effects, and random animal direct and maternal effects. Bivariate linear-threshold and linear-linear models for birth weight/calving ease and univariate threshold and linear models for calving ease were applied to the data sets. For each data set and model, one-half of calving ease records were randomly discarded. Predictive ability of the different models was defined with the mean square error (MSE) for the difference between a deleted calving ease score and its prediction obtained from the remaining data. In terms of correlation between simulated and predicted breeding values, the threshold models had a 1% advantage for direct genetic effects and 3% for maternal genetic effects. In simulation, the average MSE was .29 for linear-threshold, .32 for linear-linear, .37 for threshold, and .39 for linear model. For the field data set, the MSE was .31, .33, .39, and .40, respectively. Although the bivariate models for calving ease/birth weight were more accurate than univariate models, the threshold models showed a greater advantage under the bivariate model. For the purpose of genetic evaluation for calving difficulty in beef cattle, the use of the linear-threshold model seems justified. In dairy cattle, the evaluation for calving ease can benefit from recording birth weight.  相似文献   

7.
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.  相似文献   

8.
Genetic parameters and genetic trends for birth weight (BW), weaning weight (WW), 6-month weight (6MW), and yearling weight (YW) traits were estimated by using records of 5,634 Makooei lambs, descendants of 289 sires and 1,726 dams, born between 1996 and 2009 at the Makooei sheep breeding station, West Azerbaijan, Iran. The (co)variance components were estimated with different animal models using a restricted maximum likelihood procedure and the most appropriate model for each trait was determined by Akaike’s Information Criterion. Breeding values of animals were predicted with best linear unbiased prediction methodology under multi-trait animal models and genetic trends were estimated by regression mean breeding values on birth year. The most appropriate model for BW was a model including direct and maternal genetic effects, regardless of their covariance. The model for WW and 6MW included direct additive genetic effects. The model for YW included direct genetic effects only. Direct heritabilities based on the best model were estimated 0.15?±?0.04, 0.16?±?0.03, 0.21?±?0.04, and 0.22?±?0.06 for BW, WW, 6MW, and YW, respectively, and maternal heritability obtained 0.08?±?0.02 for BW. Genetic correlations among the traits were positive and varied from 0.28 for BW–YW to 0.66 for BW–WW and phenotypic correlations were generally lower than the genetic correlations. Genetic trends were 8.1?±?2, 67.4?±?5, 38.7?±?4, and 47.6?±?6 g per year for BW, WW, 6MW, and YW, respectively.  相似文献   

9.
Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.  相似文献   

10.
Weaning weights from Gelbvieh (GV; n = 82,138) and Limousin (LM; n = 88,639) calves were used to estimate genetic and environmental variance components with models that included different values for the correlation (lambda) between permanent environmental effects of dams and their daughters. Each analysis included fixed discrete effects of contemporary group, sex of calf, age of dam at calving, and month of calving, a fixed continuous effect of age of calf, random direct and maternal additive genetic effects, permanent environmental effects due to dams, and residual effects. The REML procedure was employed with a "grid search," in which the likelihood was computed for a series of values for lambda. For both breeds, models that included a nonzero value for lambda fitted the data significantly better than the model that did not include lambda. The maximum restricted likelihood was obtained for lambda of approximately -0.2 for both breeds. Estimates of residual and direct genetic variances were similar for all values of lambda, including zero; however, estimates of maternal genetic variance and maternal heritability increased slightly, and maternal permanent environmental variance and the proportion of the maternal variance to the total (phenotypic) variance decreased slightly, when the correlated structure for permanent environmental effects was assumed. As the value of lambda became more negative, absolute values of the direct-maternal genetic covariance and direct-maternal correlation estimates were decreased. Pearson and rank correlations for direct genetic, maternal genetic, and maternal environmental effects estimated with and without lambda were very high (>0.99). These results indicated that the linear relationship between maternal permanent environmental effects of dams and their daughters for weaning weight is negative but low in both breeds. Considering this relationship in the operational model did not significantly affect estimated breeding values, and thus, it may not be important in genetic evaluations.  相似文献   

11.
Effects of foster dams can be included in genetic evaluations using animal models with maternal effects in several ways. The alternatives discussed involve minor changes in computing strategies from strategies used with reduced animal models that predict breeding values for direct and maternal effects. The easiest alternative is to assign foster dams to groups by breed and time period and add equations for fixed effects of breed-period. Random and, assumed, independent effects of foster dams can be nested in breed-period groups. If foster dams do not repeat, then those effects can be absorbed into equations for other fixed effects, additive direct breeding value and breed-period effects by slightly modifying least squares contributions to coefficients of those equations. A third alternative for foster dams of the same breed is to add breeding values for foster dams for direct and maternal effects to solution vectors for breeding values. Equations are similar to those without foster dams, except that least squares contributions to coefficient matrix and right-hand sides are to equations for maternal breeding values and nongenetic maternal effects of foster dams rather than biological dams. Relationships and covariance between direct and maternal effects contribute mixed-model coefficients to direct and maternal breeding value equations of biological dams. This alternative basically requires only larger solution vectors for direct and maternal breeding values to accommodate foster dams that might not be included. The fourth alternative includes a vector of maternal breeding values for foster dams of each breed of foster dams and would require using rules of Westell to calculate coefficients due to relationships and fixed maternal genetic groups within each breed of foster dam. These alternatives do not require much additional computational effort compared with full or reduced animal model equations when the transformation to predict breeding values is used with Westell's rules to calculate coefficients due to relationships and genetic group effects due to prior genetic selection.  相似文献   

12.
Genetic parameters for weaning hip height (WHH), weaning weight (WWT), postweaning hip height growth (PHG), and hip height at 18 mo of age (HH18) and their relationships were estimated for Brahman cattle born from 1984 to 1994 at the Subtropical Agricultural Research Station, Brooksville, FL. Records per trait were 889 WHH, 892 WWT, and 684 HH18. (Co)variances were estimated using REML with a derivative-free algorithm and fitting three two-trait animal models (i.e., WHH-WWT, WHH-PHG, and WWT-HH18). Heritability estimates of WHH direct effects were 0.73 and 0.65 for models WHH-WWT and WHH-PHG and were 0.29 and 0.33 for WWT direct for models WHH-WWT and WWT-HH18, respectively. Estimates of heritability for PHG and HH18 direct were 0.13 and 0.87, respectively. Heritability estimates for maternal effects were 0.10 and 0.09 for WHH for models WHH-WWT and WHH-PHG and 0.18 and 0.18 for WWT for models WHH-WWT and WWT-HH18, respectively. Heritability estimates for PHG and HH18 maternal were 0.00 and 0.03. Estimates of the genetic correlation between direct effects for the different traits were moderate and positive; they were also positive between WHH and WWT maternal and WWT and HH18 maternal but negative (-0.19) between WHH and PHG maternal, which may indicate the existence of compensatory growth. Negative genetic correlations existed between direct and maternal effects for WHH, WWT, PHG, and HH18. The correlation between direct and WWT maternal effects was low and negative, moderate and negative between WHH direct and PHG maternal, and high and negative (-0.80) between WWT direct and HH18 maternal. There is a strong genetic relationship between hip height and weight at weaning that also affects hip height at 18 mo of age. Both product-moment and rank correlations between estimated breeding values (EBV) for direct values indicate that almost all of the same animals would be selected for PHG EBV if the selection criterion used was WHH EBV, and that it is possible to accomplish a preliminary selection for HH18 EBV using WHH EBV. Correlations between breeding values for WHH, WWT, and HH18 indicate that it will be possible to identify animals that will reduce, maintain, or increase hip height while weaning weight is increased. Thus, if the breeding objective is to manipulate growth to 18 mo of age, implementation of multiple-trait breeding programs considering hip height and weight at weaning will help to predict hip height at 18 mo of age.  相似文献   

13.
Weaning weights from nine sets of Angus field data from three regions of the United States were analyzed. Six animal models were used to compare two approaches to account for an environmental dam-offspring covariance and to investigate the effects of sire x herd-year interaction on the genetic parameters. Model 1 included random direct and maternal genetic, maternal permanent environmental, and residual effects. Age at weaning was a covariate. Other fixed effects were age of dam and a herd-year-management-sex combination. Possible influence of a dam's phenotype on her daughter's maternal ability was modeled by including a regression on maternal phenotype (fm) (Model 3) or by fitting grandmaternal genetic and grandmaternal permanent environmental effects (Model 5). Models 2, 4, and 6 were based on Models 1, 3, and 5, respectively, and additionally included sire x herd-year (SH) interaction effects. With Model 3, estimates of fm ranged from -.003 to .014, and (co)variance estimates were similar to those from Model 1. With Model 5, grandmaternal heritability estimates ranged from .02 to .07. Estimates of maternal heritability and direct-maternal correlation (r(am)) increased compared with Model 1. With models including SH, estimates of the fraction of phenotypic variance due to SH interaction effects were from .02 to .10. Estimates of direct and maternal heritability were smaller and estimates of r(am) were greater than with models without SH interaction effects. Likelihood values showed that SH interaction effects were more important than fm and grandmaternal effects. The comparisons of models suggest that r(am) may be biased downward if SH interaction and(or) grandmaternal effects are not included in models for weaning weight.  相似文献   

14.
Mixed model techniques were used to evaluate the importance of cytoplasmic genetic effects on beef cattle performance. Birth weight (BWT), preweaning average daily gain (ADG), weaning weight (WT205), postweaning gain (PG), ultrasonic backfat thickness (FAT) and predicted milk yield (MILK) data were collected in two herds of Hereford cattle located at Plymouth and Raleigh, North Carolina. Cytoplasmic lines were determined based on the foundation female in the maternal lineage of each animal. An animal model was used to account for all nuclear genetic variation among animals within herds. Direct breeding values were estimated for all animals with records and their parents for all traits. For MILK, permanent environmental effects were estimated for animals with multiple records. For preweaning traits, maternal breeding values and permanent maternal environmental effects also were estimated. In all analyses, F-tests for cytoplasmic effects were not significant. Probability values approached significance (P = .15 to P = .10) only for PG and FAT at Plymouth. Assumptions regarding the ratios of genetic and environmental variances and covariances had no effect on F-tests. Results contrast with earlier analyses of the same data in which nuclear genetic effects were accounted for by including sire and maternal grandsire in the statistical model. This study failed to show that cytoplasmic genetic effects were important sources of variation in performance; residual additive genetic effects were confounded with cytoplasmic lines for these herds. Because cytoplasmic sources may be regarded as founder effects, further research is needed in other populations.  相似文献   

15.
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.  相似文献   

16.
Rules for forming the mixed-model equations for the reduced animal model with all relationships and including maternal effects have been set out by Quaas and Pollak. They also have shown how to simplify the mixed-model equations when genetic group effects are included in the model with what has become known as the Q-P transformation. Westell has given rules for calculating the coefficients for the Q-P transformed equations that are associated with the inverse of the numerator relationship matrix and genetic group effects. Those rules can be extended to include maternal effects and genetic groups for maternal as well as direct effects. As with the rules of Quaas and Pollak for the equations for the reduced animal model, a similar set of rules can be obtained for the genetic groups model after the Q-P transformation. The rules are derived easily by examining the algebraic results of absorbing the direct and maternal breeding value equations for non-parents into the parent breeding value, group and fixed effects equations. These rules involve Westell's rules and the inverse elements of the genetic (co)variance matrix for direct and maternal additive genetic effects. The rules make calculation of breeding values for parents for models including direct and maternal genetic group effects nearly as easy as for models without genetic group effects. Back solution for direct and maternal breeding values of non-parents similarly is as simple as when genetic group effects are not in the model.  相似文献   

17.
Data from 32 nucleus and multiplier herds in Germany was used to estimate variance components and breeding values for five maternal behaviour traits in sows. The estimation was performed univariately using an animal threshold model. About 31,000 farrowings recorded from December 2003 until July 2005 were included. The heritability coefficients were 0.07 (0.06) for group behaviour, 0.06 (0.03) for attitude to people, 0.05 (0.01) for maternal ability, 0.03 (0.01) for crushing of piglets and 0.02 (0.02) for savaging of piglets. Additionally, genetic correlations between the behaviour traits and between the behaviour traits and litter size, respectively, were estimated multivariately by REML with a linear model. Low heritability and weak genetic correlation to litter size at birth indicate that it may be difficult to genetically improve the maternal behaviour, and that selection for better mothering ability is not necessarily accompanied by reduced litter size at birth.  相似文献   

18.
Estimates of direct and maternal genetic parameters in beef cattle were obtained with a random regression model with a linear spline function (SFM) and were compared with those obtained by a multitrait model (MTM). Weight data of 18,900 Gelbvieh calves were used, of which 100, 75, and 17% had birth (BWT), weaning (WWT), and yearling (YWT) weights, respectively. The MTM analysis was conducted with a three-trait maternal animal model. The MTM included an overall linear partial fixed regression on age at recording for WWT and YWT, and direct-maternal genetic and maternal permanent environmental effects. The SFM included the same effects as MTM, plus a direct permanent environmental effect and heterogeneous residual variance. Three knots, or breakpoints, were set to 1, 205, and 365 d. (Co)variance components in both models were estimated with a Bayesian implementation via Gibbs sampling using flat priors. Because BWT had no variability of age at recording, there was good agreement between corresponding components of variance estimated from both models. For WWT and YWT, with the exception of the sum of direct permanent environmental and residual variances, there was a general tendency for SFM estimates of variances to be lower than MTM estimates. Direct and maternal heritability estimates with SFM tended to be lower than those estimated with MTM. For example, the direct heritability for YWT was 0.59 with MTM, and 0.48 with SFM. Estimated genetic correlations for direct and maternal effects with SFM were less negative than those with MTM. For example, the direct-maternal correlation for WWT was -0.43 with MTM and -0.33 with SFM. Estimates with SFM may be superior to MTM due to better modeling of age in both fixed and random effects.  相似文献   

19.
A Markov Chain Monte Carlo Bayesian method and BLUP analyses were used on Tunisian dairy cattle data. Data included 92,106 lactation records collected on 37,536 animals over 19 freshening years, from 1983 to 2001. Each record was partitioned into the fixed effects of herd-year, month of calving, and age-parity, a permanent environmental effect, an additive genetic effect, and a residual effect.

Posterior conditional distributions were determined for variance components and model effects. Solutions (BLUE) and posterior means for levels of herd-year, month of calving, and age-parity showed similar patterns. Posterior means of heritability and repeatability were 0.17 ± 18 × 10− 5 and 0.39 ± 8 × 10− 5, respectively. Posterior means of bull's breeding values were compared to bull's BLUP solutions. BLUP solutions were obtained using 0.17 and 0.39, estimated from the data, and 0.25 and 0.40 estimates for heritability and repeatability, respectively. Rank correlations between bull's posterior means and BLUP breeding values were 0.998 and 0.994 using genetic parameters estimated from the data and from the literature, respectively. This correlation coefficient was 0.995 between bull's BLUP solutions using either of the two sets of genetic parameters.  相似文献   


20.
In this study the amount of genotype by environment interaction (G×E) for length of productive life of Swedish Red and White dairy cattle was studied. The environmental variables used were the herd-year averages of number of first parity cows, peak yield, protein yield, and productive life. Data were analysed using multi-trait models (using low and high quartile herds) and reaction norm models (using the whole continuous scale). Considerable G×E was found for the trait productive life between environments (herds) with short or long average productive life, and a genetic correlation of 0.74 was found. For productive life in relation to herd size, G×E was negligible, and the genetic correlation was 0.95. For productive life in relation to herd-year average of peak yield or protein yield some G×E was indicated by the reaction norm model, especially between extreme environments. The G×E between herds with short or long average productive life should be studied further and might need to be considered in breeding programmes for dairy cattle.  相似文献   

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