Computing simplifications for non-additive genetic models

A limiting factor in the analysis of non‐additive genetic models has been the ability to compute the inverses of non‐additive genetic covariance matrices for large populations. Also, the order of the equations was equal to the number of animals times the number of non‐additive genetic effects that were included in the model. This paper describes a computing algorithm that avoids the inverses of the non‐additive genetic covariance matrices and keeps the size of the equations to be the same as any animal model with only additive genetic effects. Quadratic forms for the non‐additive genetic variances could also be computed without the inverses of the non‐additive genetic covariance matrices.     

Animal model for genetic evaluation of multibreed data.

Extension of beef cattle genetic evaluation procedures to multibreed data sets is proposed as a way to allow inclusion of crossbred animals into current analyses and to provide comparisons between purebred animals of different breeds. Previous papers dealing with multibreed BLUP have proposed sire or sire-maternal grandsire models. Because current models used in the beef industry are predominantly of the reduced animal model form, models were developed for animal model and reduced animal model mixed-model evaluations that would account for fixed and random additive genetic effects, along with fixed and random nonadditive genetic effects for populations with heterogeneous means and variances.     

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 and phenotypic covariance functions for postweaning growth and mature weight of beef cows

Annual weights of cows from 19 to 119 months of age in two herds were analysed fitting a random regression model, regressing on orthogonal polynomials of age in months. Estimates of covariances between random regression coefficients were obtained by restricted maximum likelihood, and the resulting estimates of covariance functions were used to construct covariance matrices for all ages in the data. Analyses were carried out fitting regression coefficients corresponding to overall animal effects only and fitting regressions for animals' additive genetic and permanent, environmental effects. Different definitions of fixed effects subclasses were examined. Models were compared using likelihood ratio tests and estimated standard deviations for the ages in the data. Cubic regressions were sufficient to model both population trajectories and individual growth curves. Random regression coefficients were highly correlated, so that estimation forcing their covariance matrices to have reduced rank (2 or 3) did not reduce likelihoods significantly, allowing parsimonious modelling. Results showed that records were clearly not repeated measurements of a single trait with constant variances. As cows grew up to about 5 years of age, variances. As cows grew up to about 5 years of age, variances increased. Estimates of genetic correlations between 3-year-old and older cows were close to unity in one herd but more erratic in the other. For both herds, genetic correlations between weights on 2-year-old cows and older animals were clearly less than unity.     

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.     

Genetic implications of a simulation model of litter size in swine based on ovulation rate, potential embryonic viability and uterine capacity: I. Genetic theory

A simulation model of litter size in swine based on ovulation rate, uterine capacity and potential embryo viability was compared to three genetic models to clarify its genetic characteristics. The simulation model is equivalent to independent culling based on fixed levels of potentially viable embryos and uterine capacity. Litter size also can be described by a combination of additive, additive x additive, mean environment x additive, random environment and additive x random environment effects. A third genetic model that can describe the simulation model is the associative effects model, in which litter size is the result of grouping two genotypes. The fixed independent culling levels model predicts that genetic parameters will change as the component means change. This genetic model also predicts that selection on an index of ovulation rate and uterine capacity would improve selection response for litter size. This genetic model predicts asymmetry of correlated responses in ovulation rate and uterine capacity when selecting for high and low litter size. The nonadditive genetic model predicts covariances among relatives that are different from their additive relationships; however, simulated results did not detect any differences. The nonadditive genetic model also predicts that heterosis for litter size will differ among crosses based on the mean environment and on additive x additive genetic interaction. The associative effects model predicts that selection for litter size will always lead to a positive response in litter size.     

Direct and maternal genetic effects due to the introduction of Bos taurus alleles into Brahman cattle in Florida: II. Preweaning growth traits

Records of birth weight (BW), weaning weight (WW) and condition score (CS) from 1,467 Brahman and Brahman X Angus crossbred calves from Brahman and crossbred Brahman sires and Brahman, crossbred Brahman and Angus dams were collected at the Subtropical Agricultural Research Station, Brooksville, Florida, from 1971 to 1982. Best linear unbiased estimates (BLUE) of Brahman sire and dam group additive genetic effects (as deviations from Angus) and Brahman X Angus dam and calf group nonadditive (intralocus) genetic effects (as deviations from intralocus group genetic effects in the parental breeds) were obtained. Linear combinations of these were used to compute direct and maternal Brahman additive and Brahman X Angus nonadditive (intralocus) group genetic effects. The respective BLUE of these four effects were 5.99 +/- 2.08, -5.70 +/- 1.91, .52 +/- 1.81 and 2.85 +/- .72 kg for BW; 9.60 +/- 10.29, 8.76 +/- 9.47, 9.47 +/- 8.96 and 20.95 +/- 3.56 kg for WW; and -1.10 +/- .55, 1.64 +/- .50, 1.47 +/- .47 and .05 +/- .19 units for CS. Linear combinations of the BLUE of sire, dam and calf group genetic effects can be used to predict the genetic worth of crossbred groups composed of any combination of Brahman and Angus breeding. Nonadditive maternal group genetic effects were the most important factor for BW and WW, whereas nonadditive direct group genetic effects were the most important for CS.     

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.     

Genetic effects on preweaning weight gain of Nelore-Hereford calves according to different models and estimation methods

Additive and nonadditive genetic effects on preweaning weight gain (PWG) of a commercial crossbred population were estimated using different genetic models and estimation methods. The data set consisted of 103,445 records on purebred and crossbred Nelore-Hereford calves raised under pasture conditions on farms located in south, southeast, and middle west Brazilian regions. In addition to breed additive and dominance effects, the models including different epistasis covariables were tested. Models considering joint additive and environment (latitude) by genetic effects interactions were also applied. In a first step, analyses were carried out under animal models. In a second step, preadjusted records were analyzed using ordinary least squares (OLS) and ridge regression (RR). The results reinforced evidence that breed additive and dominance effects are not sufficient to explain the observed variability in preweaning traits of Bos taurus x Bos indicus calves, and that genotype x environment interaction plays an important role in the evaluation of crossbred calves. Data were ill-conditioned to estimate the effects of genotype x environment interactions. Models including these effects presented multicolinearity problems. In this case, RR seemed to be a powerful tool for obtaining more plausible and stable estimates. Estimated prediction error variances and variance inflation factors were drastically reduced, and many effects that were not significant under ordinary least squares became significant under RR. Predictions of PWG based on RR estimates were more acceptable from a biological perspective. In temperate and subtropical regions, calves with intermediate genetic compositions (close to 1/2 Nelore) exhibited greater predicted PWG. In the tropics, predicted PWG increased linearly as genotype got closer to Nelore.     

The crossbred sire: experimental results for sheep

Crossbreeding of sheep is practiced to exploit simultaneously the use of additive and nonadditive genetic effects. The goal is to achieve optimal levels of performance appropriate for defined systems of sheep production and marketing. Although the beneficial effects of individual and maternal heterosis on sheep production have been well documented and widely implemented, considerably less is known about the effects of paternal heterosis. Limited evidence suggests that crossbred rams are more sexually aggressive and exhibit greater testicular growth than do purebred rams. Average estimates of paternal heterosis effects were 1.4, -.7 and 2.3% for seasonal fertility, prolificacy and preweaning survival, respectively. The average effect of paternal heterosis on fertility during spring breeding was 29.5%. Progeny of crossbred and purebred sires were similar in birth weight, weaning weight and postweaning growth rate and in phenotypic variation for these growth traits. However, favorable paternal heterosis effects need not exist to warrant the use of crossbred sires. Composite or F1 sires can be used as an effective method to manage the composition of additive breed effects. For example, varying proportions of germ plasm from highly prolific breeds such as the Finnsheep and Romanov can be realized through the use of crossbred sires to set reproductive rates at desired levels. Crossbred sires may be used to a greater extent to optimize additive breed effects than to exploit effects of paternal heterosis. The role of composite breeds in managing both additive and nonadditive effects is discussed.     

Estimating maternal genetic effects in livestock

This study investigates the estimation of direct and maternal genetic (co)variances, accounting for environmental covariances between direct and maternal effects. Estimated genetic correlations between direct and maternal effects presented in the literature have often been strongly negative, and their validity has been questioned. Explanations of extreme estimates have focused on the existence of environmental covariances between dam and offspring. As a solution, models including a regression on dam-phenotype have been proposed, but have yielded biased estimates. The performance of models that implement the variance structure arising from the classical model of Willham, however, has not been evaluated. This study investigated the covariance structure of the parts of the residual term that arise from Willham's model. Results show that a correlation between the residual of the record of an individual and that of its dam is a direct consequence of combining Willham's model with the usual assumption that phenotypic covariances between different traits are the sum of additive genetic and environmental covariances. Stochastic simulations show that fitting this structure yields unbiased estimates of the genetic (co)variances. When correlated residuals were ignored in the cases investigated, the bias in the estimated genetic correlations was approximately equal to the value of the environmental correlation. In contrast to models including a regression on dam-phenotype, there were no difficulties with interpretation of results, and the approach was consistent with standard quantitative genetic theory. The use of Willham's model while accounting for correlated residuals is conceptually appealing and yields unbiased results, with no need for regression on dam phenotype. Inclusion of the ability to fit the residual variance structure required for maternal effects into existing software packages would be helpful to animal breeders.     

Effects of sire misidentification on estimates of genetic parameters for birth and weaning weights in Hereford cattle

Birth weights (4,155) and weaning weights (3,884) of Line 1 Herefords collected at the Fort Keogh Livestock and Range Research Laboratory in Miles City, MT, between the years of 1935 to 1989 were available. To study the effect of misidentification on estimates of genetic parameters, the sire identification of calf was randomly replaced by the identification of another sire based on the fraction of progeny each sire contributed to a yearly calf crop. Misidentification rates ranged from 5 to 50% with increments of 5%. For each rate of misidentification, 100 replicates were obtained and analyzed with single-trait and two-trait analyses with a restricted maximum likelihood (REML) algorithm. Two different models were used. Both models contained year x sex combinations and ages of dam as fixed effects, calendar birth date as a fixed covariate, and random animal and maternal genetic effects and maternal permanent environment effects. Model 2 also included sire x year combinations as random effects. As the rate of misidentification increased, estimates of the direct-maternal genetic correlation increased for both traits, with both models, for all analyses. With singletrait analyses, estimates of the fraction of variance that were due to sire x year interaction effects increased slightly for birth weight (near zero) and decreased slightly (0.015 to 0.004) for weaning weight as misidentification increased. With two-trait analyses, estimates of fraction of variance that were due to sire x year effects gradually decreased for weaning weight as misidentification increased. With the two-trait analyses, and with both models, as the level of sire misidentification increased, estimates of the genetic correlation between direct effects gradually increased, and estimates of the correlation between maternal effects gradually decreased. Estimates of the direct-maternal genetic correlation were more positive with Model 2 than with Model 1 for all levels of misidentification. Results of this study indicate that misidentification of sires would severely bias estimates of genetic parameters and would reduce genetic gain from selection.     

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。     

Bayesian analysis of birth weight and litter size in Baluchi sheep using Gibbs sampling

Variance and covariance components for birth weight (BWT), as a lamb trait, and litter size measured on ewes in the first, second, and third parities (LS1 through LS3) were estimated using a Bayesian application of the Gibbs sampler. Data came from Baluchi sheep born between 1966 and 1989 at the Abbasabad sheep breeding station, located northeast of Mashhad, Iran. There were 10,406 records of BWT recorded for all ewe lambs and for ram lambs that later became sires or maternal grandsires. All lambs that later became dams had records of LS1 through LS3. Separate bivariate analyses were done for each combination of BWT and one of the three variables LS1 through LS3. The Gibbs sampler with data augmentation was used to draw samples from the marginal posterior distribution for sire, maternal grandsire, and residual variances and the covariance between the sire and maternal grandsire for BWT, variances for the sire and residual variances for the litter size traits, and the covariances between sire effects for different trait combinations, sire and maternal grandsire effects for different combinations of BWT and LS1 through LS3, and the residual covariations between traits. Although most of the densities of estimates were slightly skewed, they seemed to fit the normal distribution well, because the mean, mode, and median were similar. Direct and maternal heritabilities for BWT were relatively high with marginal posterior modes of .14 and .13, respectively. The average of the three direct-maternal genetic correlation estimates for BWT was low, .10, but had a high standard deviation. Heritability increased from LS1 to LS3 and was relatively high, .29 to .37. Direct genetic correlations between BWT and LS1 and between BWT and LS3 were negative, -.32 and -.43, respectively. Otherwise, the same correlation between BWT and LS2 was positive and low, .06. Genetic correlations between maternal effects for BWT and direct effects for LS1 through LS3 were all highly negative and consistent for all parities, circa -.75. Environmental correlations between BWT and LS1 through LS3 were relatively low and ranged from .18 to .29 and had high standard errors.     

Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis

The aim of this study was to compare correlation matrices between direct genomic predictions for 31 traits at the genomic and chromosomal levels in US Holstein bulls. Multivariate factor analysis carried out at the genome level identified seven factors associated with conformation, longevity, yield, feet and legs, fat and protein content traits. Some differences were found at the chromosome level; variations in covariance structure on BTA 6, 14, 18 and 20 were interpreted as evidence of segregating QTL for different groups of traits. For example, milk yield and composition tended to join in a single factor on BTA 14, which is known to harbour the DGAT1 locus that affects these traits. Another example was on BTA 18, where a factor strongly correlated with sire calving ease and conformation traits was identified. It is known that in US Holstein, there is a segregating QTL on BTA18 influencing these traits. Moreover, a possible candidate gene for daughter pregnancy rate was suggested for BTA28. The methodology proposed in this study could be used to identify individual chromosomes, which have covariance structures that differ from the overall (whole genome) covariance structure. Such differences can be difficult to detect when a large number of traits are evaluated, and covariances may be affected by QTL that do not have large allele substitution effects.     

Covariance functions and random regression models for cow weight in beef cattle

Data from the first four cycles of the Germplasm Evaluation program at the U.S. Meat Animal Research Center were used to evaluate weights of Angus, Hereford, and F1 cows produced by crosses of 22 sire and 2 dam (Angus and Hereford) breeds. Four weights per year were available for cows from 2 through 8 yr of age (AY) with age in months (AM). Weights (n = 61,798) were analyzed with REML using covariance function-random regression models (CF-RRM), with regression on orthogonal (Legendre) polynomials of AM. Models included fixed regression on AM and effects of cow line, age in years, season of measurement, and their interactions; year of birth; and pregnancy-lactation codes. Random parts of the models fitted RRM coefficients for additive (a) and permanent environmental (c) effects. Estimates of CF were used to estimate covariances among all ages. Temporary environmental effects were modeled to account for heterogeneity of variance by AY. Quadratic fixed regression was sufficient to model population trajectory and was fitted in all analyses. Other models varied order of fit and rank of coefficients for a and c. A parsimonious model included linear and quartic regression coefficients for a and c, respectively. A reduced cubic order sufficed for c. Estimates of all variances increased with age. Estimates for older ages disagreed with estimates using traditional bivariate models. Plots of covariances for c were smooth for intermediate, but erratic for extreme ages. Heritability estimates ranged from 0.38 (36 mo) to 0.78 (94 mo), with fluctuations especially for extreme ages. Estimates of genetic correlations were high for most pairs of ages, with the lowest estimate (0.70) between extreme ages (19 and 103 mo). Results suggest that although cow weights do not fit a repeatability model with constant variances as well as CF-RRM, a repeatability model might be an acceptable approximation for prediction of additive genetic effects.     

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.     

Genetic and environmental factors affecting serum macrominerals and weights in an Angus-Brahman multibreed herd: I. Additive and nonadditive group genetic effects of serum calcium, phosphorus, and magnesium and weight at weaning.

Amounts of serum calcium, phosphorus, and magnesium at weaning (WCa, WP, and WMg, respectively) and weaning weights (WW) were obtained from 380 Angus (A), Brahman (B), and A x B calves of various expected A and B fractions reared at the Pine Acres Research Station of the University of Florida, Citra from 1989 to 1990. Calves were produced by mating A, .75A, .25B, .5A .5B, .25A .75B, B, and Brangus (.625A .375B) sires to dams of the same expected breed fractions, except for .25A .75B dams. Best linear unbiased estimates (BLUE) of genetic effects, expressed as regression coefficients, were 1) -15.07 +/- 13.65 mg of WCa, -11.21 +/- 12.07 mg of WP, -1.23 +/- 2.99 mg of WMg, and .66 +/- 1.18 kg of WW for the difference between A and B additive direct; 2) 9.79 +/- 6.94 mg of WCa, -5.72 +/- 6.14 mg of WP, 1.64 +/- 1.52 mg of WMg, and .52 +/- .60 kg of WW for the difference between A and B additive maternal; 3) 242.21 +/- 51.56 mg of WCa, 66.67 +/- 45.62 mg of WP, 52.16 +/- 11.27 mg of WMg, and 22.61 +/- 4.44 kg of WW for A x B nonadditive direct; and 4) 373.63 +/- 38.44 mg of WCa, 93.96 +/- 34.02 mg of WP, 69.90 +/- 8.41 mg of WMg, and 36.83 +/- 3.31 kg of WW for A x B nonadditive maternal. Nonadditive (A x B) effects were the main factors affecting total (sum of additive plus nonadditive) genetic effects in this multibreed population. Total genetic effects were used to rank breed group of sire x breed group of dam combinations.(ABSTRACT TRUNCATED AT 250 WORDS)     

Genetic variances due to imprinted genes in cattle

The effect of paternally expressed, i.e. maternally imprinted, genes on slaughter records from 2744 German Gelbvieh finishing bulls were estimated. Significant effects of paternal gametes were found for two fatness traits and an estimate of meat content. Paternally expressed genes explained 14 and 16% of the phenotypic variances for pelvic fat and kidney fat, respectively. Ignoring paternal gametic effects resulted in inflated estimates of the additive genetic variances. The heritabilities of pelvic and kidney fat dropped from 0.31 to 0.16 and from 0.59 to 0.28, respectively, when paternal gametes were fitted. A 15% influence of paternally expressed genes and a reduction in heritability of 20% were also found for estimated meat content. Simulation studies demonstrated that the uncorrelated random effect of the sire is a useful indicator for the presence of paternal gametic effects in variance component estimations. The presented results correspond well with findings in swine, where a paternally expressed QTL at the Igf2 gene influences similar trait complexes. A viable speculation could therefore be that an imprinted bovine Igf2 gene caused the effects described here.     

Relationship of sire expected progeny differences to milk yield in Brangus cows

Milk yield from 160 Brangus cows sired by 65 Brangus bulls was measured over a 3-yr period with a single-cow milking machine to estimate the relationship of actual milk yield of daughters and their calves' BW with cow sire EPD for milk during the preweaning period. Milk yield was measured six times per year at an average 49, 78, 109, 138, 168, and 198 d postpartum. The regression of daughters' milk yield on sire milk EPD was quadratic (P < 0.01), and the initial linear portion of the curve differed among months (P < 0.05) at an average cow BW. Similarly, the regression of 6-mo average 24-h milk yield on sire milk EPD was curvilinear (P < 0.05). When cow BW was fitted as a covariate in the regression of 6-mo average 24-h milk yield on sire milk EPD, there was an interaction of cow BW with linear sire milk EPD and quadratic sire milk EPD (P < 0.10). The associated response surface suggested that the regression was primarily linear in cows weighing < or = 520 kg and curvilinear in cows weighing >520 kg. A trend existed for the regression of calf 205-d weight on grandsire milk EPD to be curvilinear (P < 0.21); however, the regression of calf 205-d weight on milk yield of their dam was linear (P < 0.01). Results from these data suggest that genetic potential for milk yield, and possibly the associated effects on calf BW transmitted through the grandsire, may have a practical maximum because of nutritional limitations that prevent the expression of genetic potential beyond that level, particularly in heavier cows, which suggests the need to match sire milk EPD and cow BW with production environment.