Contributions of preweaning growth information and maternal effects for prediction of carcass trait breeding values among crossbred beef cattle

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1 Contributions of preweaning growth information and maternal effects for prediction of carcass trait breeding values among crossbred beef cattle D. H. Crews, Jr. and R. A. Kemp Livestock Sciences Section, Research Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1, Contribution number , received 30 April 1998, accepted 3 December Crews, D. H., Jr. and Kemp, R. A Contributions of preweaning growth information and maternal effects for prediction of carcass trait breeding values among crossbred beef cattle. Can. J. Anim. Sci. 79: Preweaning and carcass trait records from crossbred steers (n = 1015) and heifers (n = 957) were used to estimate genetic parameters and to investigate the efficacy of maternal effects and preweaning growth information for improving estimation of EBV for carcass traits for crossbred beef cattle. Dams (n = 775) representing three F 1 and twelve back-cross combinations involving the Charolais, Hereford, Angus, Simmental and Shorthorn breeds were mated over six years to Limousin bulls (n = 36) at two locations in western Canada. Four animal models, involving from zero to three maternal (co)variances were used to analyze four carcass traits. Rank and simple correlations indicated that maternal effects were relatively unimportant for estimation of direct carcass trait breeding values. Direct heritabilities were 0.8, 0.1 and 0.16 for birth weight, preweaning daily gain and weaning weight, and were 0.0, 0.35, 0.50 and 0.38 for hot carcass weight, fat thickness, ribeye area and percent lean yield, respectively. Maternal heritabilities were 0.1, 0. and 0.40 for birth weight, preweaning daily gain and weaning weight, respectively. Estimated genetic correlations between percent lean yield and hot carcass weight, fat thickness and ribeye area were 0.05, 0.85 and 0.39, respectively, and 0.30 between hot carcass weight and ribeye area. Direct genetic effects for birth weight had moderate (0.51 to 0.54) correlations with direct effects for carcass weight, ribeye area and percent lean yield. Direct genetic effects for fat thickness were negatively correlated with direct effects for birth weight ( 0.44), preweaning daily gain ( 0.15) and weaning weight ( 0.5). Maternal genetic effects for preweaning traits had near-zero correlations with direct genetic effects for fat thickness and percent lean yield. Adding preweaning growth information to genetic evaluations for carcass traits slightly decreased prediction error variances for breeding values and would be recommended when information on carcass traits is limited. Key words: Genetic evaluation, carcass traits, beef cattle Crews, D. H., Jr. et Kemp, R. A Utilité des données sur la croissance en présevrage et des effets maternels pour la prédiction des valeurs d élevage des caractères de carcasse parmi des bovins à viande croisés. Can. J. Anim. Sci. 79: Dans le but d améliorer l estimation de la valeur d élevage espérée des caractères de carcasse de bovins à viande croisés, les relevés provenant de bouvillons et de 957 génisses croisés ont été utilisés pour estimer les paramètres génétiques et scruter l efficacité des effets maternels et des relevés de croissance en présevrage. Des vaches (n = 775) représentant trois F 1 et 1 combinaisons de rétrocroisements impliquant les races Charolais, Hereford, Angus, Simmental et Shorthorn, étaient accouplées pendant six ans à des taureaux Limousins (n = 36) à deux emplacements de l ouest canadien. Quatre modèles animaux comportant de 0 à 3 co(variances) d effets maternels ont servi à analyser quatre caractères de carcasse. Les corrélations simples et les corrélations de rang révélaient que les effets maternels n ont que relativement peu d importance pour estimer les valeurs d élevage directes concernant les caractères de carcasse. L héritabilité des effets directs était, respectivement, de 0,8, 0,1 et 0,16 pour le poids à la naissance, le GMQ en présevrage et le poids au sevrage, et de 0,0, 0,35, 0,50 et 0,38 pour le poids de carcasse chaude, l épaisseur du gras de couverture, la surface de la noix de côte et le rendement relatif en maigre. Les valeurs d héritabilité des effets maternels s établissaient, dans l ordre, à 0,1, 0, et 0,40 pour le poids à la naissance, le GMQ en présevrage et le rendement relatif en maigre. Les corrélations génétiques calculées entre le rendement relatif en maigre, d une part, et le poids de carcasse chaude, l épaisseur du gras de couverture et la surface de la noix de côte, d autre part, étaient, respectivement, de -0,05, -0,85 et 0,39. En outre il y avait une corrélation entre le poids de carcasse chaude et la surface de la noix de côte. Les effets génétiques directs pour le poids à la naissance produisaient des corrélations modestes (0,51 à 0,54) avec ceux concernant le poids de carcasse, la surface de la noix de côte et le rendement relatif en maigre. Les effets génétiques directs pour l épaisseur du gras de couverture étaient négativement reliés avec ceux affectant le poids à la naissance ( 0,44), le GMQ en présevrage ( 0,15) et le poids au sevrage ( 0,5). Les effets génétiques maternels pour les caractères en présevrage avaient des corrélations quasiment nulles avec les effets directs pour l épaisseur du gras de couverture et pour le pourcentage de maigre. En ajoutant les informations sur la croissance en pré- Abbreviations: BV, breeding value; BWT, birth weight; FAT, average back fat thickness; HCW, hot carcass weight; LRTS, likelihood ratio test statistics; PDG, average daily gain from 0 to 00 d of age; PE, permanent environmental; PEV, prediction error variance; PLY, estimated carcass percent lean yield; REA, area of M. Longissimus at the 1th 13th rib interface; WWT, weaning weight adjusted to 00 d of age 17

2 18 CANADIAN JOURNAL OF ANIMAL SCIENCE sevrage aux évaluations génétiques des caractères de carcasse, on obtenait des variances légèrement moins fortes de l erreur de prédiction des valeurs d élevage espérées, de sorte que cette solution est à conseiller lorsque l information relative aux caractères de carcasse est restreinte. Mots clés: Évaluation génétique, caractères de carcasse, bovin à viande It has been suggested that increasing demand for lean meat implies that beef cattle improvement objectives should be broadened to include carcass traits (Mrode 1988). As the North American beef cattle industry and improvement programs place increasing importance on carcass merit, methods of genetic evaluation that may increase accuracy of prediction for carcass traits should be investigated. Carcass trait evaluations may be improved by using information on genetically correlated early-life traits, such as preweaning growth traits, which are commonly measured and included in beef cattle genetic evaluations. Further, models that do not account for important sources of (co)variance, such as maternal effects, are likely to result in cattle evaluations with decreased accuracy. Carcass traits are generally assumed to have moderate to high heritability (Koots et al. 1994a; Marshall 1994), but information on the importance of maternal effects on carcass traits is limited. Selection for preweaning growth traits is expected to result in a correlated response in traits such as carcass yield and its components (Koots et al. 1994b). In addition, genetic correlations between maternal components of preweaning traits and carcass traits are generally unavailable. The objectives of this study were: 1) to investigate the importance of maternal effects and preweaning growth traits for carcass trait breeding value prediction, and ) to estimate genetic parameters among preweaning and carcass traits. MATERIALS AND METHODS Data for this study consisted of preweaning and carcass trait records from steers (n = 1015) and heifers (n = 957) born from 1981 to 1986 at either the Brandon, Manitoba or Onefour, Alberta research (sub)station of Agriculture and Agri-Food Canada. Calves were sired by Limousin bulls (n = 36) and out of crossbred dams (n = 775) representing 15 combinations involving the Angus, Hereford, Charolais, Simmental and Shorthorn breeds. The dam breeds included three F 1 s: Hereford Angus (HA), Charolais Shorthorn (CN) and Simmental Shorthorn (SN), and 1 backcrosses: HCH, ACA, NCN, CCH, CCA, CCN, HSH, ASA, NSN, SSH, SSA and SSN (e.g. HCH = Hereford sire Charolais- Hereford F 1 dam). Details of the development and management of the crossbred cow populations were given by Rahnefeld et al. (1990). Calves were born between March and May in each of the 6 yr and were weighed and identified at birth and male calves were castrated prior to 75 d of age. Steer and heifer calves were weaned during the first week in October at an age of 5 to 6.5 mo. All animals involved in this study were cared for in accordance with the standards set by the Canadian Council on Animal Care (1993). Preweaning traits of interest included birth weight (BWT, kg), preweaning daily gain from 0 to 00 d (PDG, kg d 1 ), and weaning weight, adjusted to 00 d of age (WWT, kg). Calves were fed following weaning under standard feedlot conditions until designated for slaughter when unshrunk liveweight reached at least 400 kg (heifers) or 450 kg (steers). Following routine slaughter, hot carcasses were weighed (HCW, kg), chilled for 4 h, then ribbed and graded. The ribbing site was between the 11th 1th rib, the 1th 13th rib, or both, depending on year of birth (Newman et al. 1994). Regression procedures were used to adjust 11th 1th rib measures to 1th 13th rib-equivalent measures where appropriate, based on those carcasses ribbed at both sites (n = 465). The area of M. Longissimus or ribeye (REA, cm ), and the average of three equally spaced subcutaneous fat thickness measures over the ribeye (FAT, mm) were taken at the ribbing site(s). Percent lean yield of the carcass (PLY, %) was calculated using the formula: (0.03 HCW) + (0.1 REA) (0.681 FAT) (S.D.M. Jones, personal communication). Three records were removed from the original data set because all carcass traits of interest were missing, and five were removed due to missing parentage (sire and dam) information. The final data set consisted of preweaning and carcass trait records from a maximum of 197 animals. Initially, the GLM procedure (SAS Institute, Inc. 1985) was used to test the significance of fixed effects, including calf year of birth, dam breed, sex of calf, location of birthfeeding and all possible interactions. Resulting F statistics corresponding to each effect in the initial models were used as criteria to remove those effects and/or interactions which did not account for important (P < 0.01) portions of observed variation. For all traits, -, 3- and 4-way interactions among fixed effects were not significant, therefore, only main fixed effects were included in the models. Julian birth date and slaughter age were included as covariates in the fixed portion of the final models for preweaning and carcass traits, respectively. Age of dam was considered a fixed effect for preweaning traits only. To test the importance of maternal effects on carcass traits, four animal models, which included from zero to three maternal (co)variance components were fit to the response variables HCW, FAT, REA and PLY (Crews and Kemp 1998). The basic model for fitting maternal effects was: y = µ + (fixed effects) + (random effects) + e Fixed effects in all models included the overall mean, the main effects of dam breed, sex, location of birth-feeding, year of birth and a linear covariate for slaughter age, plus random residual. Age of dam was not an important (P > 0.05) source of variation for carcass traits. Random effects, differing for each model, were as follows: Model one: D x = direct (d) genetic effect of animal x, Model two: D x + M x = direct and maternal (m) genetic effects of animal x with σ d,m = 0,

3 CREWS AND KEMP CARCASS TRAIT EBV MODELS FOR CROSSBRED BEEF CATTLE 19 Model three: D x + M x = direct and maternal genetic effects of animal x with σ d,m 0, Model four: D x + M x + PE x = direct and maternal genetic and permanent environmental effects of animal x with σ d,m 0. Expected values for model parameters were: y Xb D 0 E M = 0 PE 0 e 0 Variances for random effects were assumed to be: D Aσd M V = PE e Symmetric Aσdm Aσm I qσ pe 0 In i σe where A is the additive relationship matrix among animals, I is the diagonal identity matrix, σ d is direct genetic variance, σ m is the maternal genetic variance, σ pe is the permanent environmental variance, σ e is the residual variance and σ dm is the direct by maternal covariance. The identity matrix was of order q = number of dams for PE effects and n i = numbers of records within each trait for residual effects. The models differed, as described above, as to which of these components were estimated. Stepwise comparison of models (one vs. two, two vs. three and three vs. four) was made by likelihood ratio test. Likelihood ratio test statistics were computed for each comparison as the difference between minus two times the log-likelihood of the model with more parameters and the model with fewer parameters. Probabilities satisfying the expression Pr (χ 1 > LRTS H o is true) were calculated using the PROBCHI function (SAS Institute, Inc. 1985). The null hypothesis (H o ) in each comparison was that the model with more parameters did not improve fit to the data. Estimated breeding values were obtained from each model and the RANK and CORR procedures (SAS Institute, Inc. 1985) were used to compute simple and rank correlations among breeding values from each model. As discussed later, maternal effects did not appear to affect estimates of breeding values, therefore, maternal components of the models were removed, and model one was used in all subsequent analyses of carcass traits: y ijklm = µ + B i + S j + L k + Y l + G m + β X + e ijklm where y ijklm is the HCW, FAT, REA or PLY observation on animal m, µ is the overall mean, B i is the effect of the ith dam breed, S j is the effect of the jth sex, L k is the effect of the kth location of birth-feeding, Y l is the effect of the lth year, G m is the random genetic effect of animal m, β is the regression parameter for slaughter age, X is slaughter age, and e ijklm is the random residual unique to animal m. To estimate heritabilities and genetic correlations among carcass traits, a four-trait animal model was used. In matrix notation, the mixed model equations for this model can be summarized as: y = Xb + Zu + e where y is the vector of observations for the four carcass traits, b,u are vectors of fixed and random effects, respectively, X,Z are design matrices relating fixed and random effects to observations, respectively, and e is an unobservable vector of random residuals. First and second moments of the model were assumed to be: and and y Xb E u = 0 e 0 u1 Aσd Aσd d Aσd d Aσ d1d4 u d dd3 dd4 V Aσ Aσ Aσ = u 3 d3 d3d4 Aσ Aσ u 4 Symmetric Aσ d4 e1 Iσe Iσe e Iσe e Iσ e1e4 e e e e e e V Iσ Iσ Iσ = 3 4 e 3 e3 e3e Iσ Iσ 4 e4 Symmetric Iσ e4 where A is the additive relationship matrix among animals and I is the diagonal identity matrix. The order of the matrix I is equal to the order of the residual matrix for each trait or trait-pair. The terms σ i and σ ij (i, j = d 1 (e 1 ),..., d 4 (e 4 ) for i j) refer to variances and covariances (genetic (d) or residual (e)) for trait i and between traits i and j, respectively. Variance component estimates from the four trait model were used to calculate heritabilities and genetic correlations among carcass traits. Numerous studies have shown the importance of maternal effects on preweaning traits (Koots et al. 1994a), therefore, the model for BWT, PDG and WWT was assumed to be: y ijklmn = µ + B i + S j + L k + Y l + A m + D n + M n + PE n + βx + e ijklmn where y ijklmn is the BWT, PDG or WWT observation on animal n, µ is the overall mean, B i is the effect of the ith dam breed, S j is the effect of the jth sex, L k is the effect of the kth location of birth-feeding, Y l is the effect of the lth year, A m is the effect of the mth age of dam, G n is the direct genetic effect of animal n, D n is the maternal genetic effect of the dam on animal n, PE n is the permanent environmental effect of the dam on animal n, β is the regression parameter for

4 0 CANADIAN JOURNAL OF ANIMAL SCIENCE julian birth date, X is julian birth date, and e ijklmn is the random residual unique to animal n. The animal model equations for preweaning traits can be summarized in matrix notation as: y = Xb + Z d u d + Z m u m + Pw + e where y is the vector of preweaning trait observations, b is the vector of fixed effects, u d, u m are vectors of direct and maternal genetic effects, respectively, w is the vector of permanent environmental effects, X is the design matrix relating fixed effects to observations, Z d, Z m are design matrices relating direct and maternal genetic effects to observations, respectively, P is a matrix relating permanent environmental effects to observations, and e is the vector of random residuals. The first and second moments of preweaning model parameters were assumed to be: and y Xb u d 0 Eum = 0 w 0 e 0 ud Aσd Aσ d, m 0 0 u m dm m V Aσ, Aσ 0 0 = w 0 0 I w qσ 0 e In i σe where A and I are the additive relationship and diagonal identity matrices, respectively. The order of the matrices I q and I n(i) are equal to the number of dams and the number of animals with records, respectively. The terms σ d, σ m, σ w, and σ e are direct genetic, maternal genetic, permanent environmental and residual variances, respectively, and σ d,m is the direct by maternal covariance. Variance components were estimated using the MTD- FREML programs of Boldman et al. (1995). All traits were initially run as single traits. Convergence was defined as the point where the variance of the simplex was less than Results from the first run were used as starting values for up to three cold restarts to ensure convergence to the same solutions. The additive relationship matrix included 783 individuals, with 811 base parents. Base parents were defined as animals without parentage information. Sires had an average of 55 calves, and dams had from one to five calves. Solutions for direct genetic and residual variance components resulting from single trait analyses were then used along with literature estimates of genetic correlations (Koots et al. 1994b; Marshall 1994) to provide starting values for the four carcass trait model. Estimates of (co)variance components from the four carcass trait model were then used along with estimates from the single preweaning trait models as starting values for a series of 1 two-trait models, each including one carcass and one preweaning trait to obtain estimates of covariances among direct effects for carcass traits and direct and maternal effects for preweaning traits. For each carcass trait, five models were used to generate sire and dam within-breed breeding values and corresponding prediction error variances. Model one included the carcass trait as a single trait. Model two was the four carcass trait model. Models three, four, and five included the carcass trait of interest plus BWT, PDG, and WWT, respectively. From these results, four ratios of prediction error variance (PEV) were computed. For each ratio, the denominator was the PEV from model one. Further, for ratios 1 4, the numerator was the PEV from models two five, respectively. A comparison of these ratios was made using the MEANS and GLM procedures (SAS Institute, Inc. 1985). RESULTS Summary statistics for preweaning and carcass traits are given in Table 1. Results from maternal models one and two are presented in Table. Likelihoods of models three and four were not significantly (P > 0.0) better than for model two for any trait analyzed. Therefore, only results from models one and two will be presented. Addition of maternal components to model one did not improve the likelihood of HCW models (P > 0.34). The correlations among breeding values for all four HCW models were between 0.99 and Mean rank change of animals based on HCW breeding values was negligible, indicating that model one ranked animals similarly to models two, three and four. With model one, the estimated direct heritability for FAT was However, the estimate of direct heritability with model two for FAT was 0.5 and the maternal heritability estimate was The model two likelihood was improved for FAT compared to the likelihood for model one. Correlations were high (0.97 to 0.99) among breeding values from the four models for FAT although rank changes were large for some animals. Model two tended (P < 0.10) to have a better likelihood for REA than did model one. Also, comparison of variance components between these two models indicated that the reduction in direct heritability from 0.39 with Model one to 0.4 with model two was due to a change in the estimate of additive genetic variance. Maternal heritability for REA estimated using model two was Similar to FAT, breeding values for REA were highly correlated (0.98 to 0.99) among all models. Model two also had an improved (P < 0.05) likelihood for PLY than did model one. The direct heritability estimate of 0.38 from model one was greater than the corresponding estimate from model two (0.5), due to a reduction in additive genetic variance, and an increase in residual variance. A reduction in additive genetic variance and an increase in residual variance was observed with model two vs. model one for REA and PLY as well. Maternal heritability was estimated to be 0.08 for PLY in model two. Likelihoods for models three and four were not significantly better than model two for FAT, REA or PLY indicating that direct by maternal covariances and permanent environmental effects were not different from zero for carcass traits. Table 3 contains estimates of heritabilities and genetic and phenotypic correlations from the four carcass trait

5 CREWS AND KEMP CARCASS TRAIT EBV MODELS FOR CROSSBRED BEEF CATTLE 1 Table 1. Summary statistics for preweaning and carcass traits Trait N Mean SD CV Minimum Maximum Preweaning BWT (kg) PDG (kg d 1 ) WWT (kg) Carcass HCW (kg) FAT (mm) REA (cm ) PLY (%) Table. Summary of variance components estimates from direct and maternal carcass trait models z Model 1 Model Trait σ d h d σ e σ d h d σ m h m σ e HCW FAT REA PLY z Model 1 included only direct genetic effects. Model two also included maternal genetic effects. See text. analysis. Estimates of heritabilities for HCW (0.0), FAT (0.35) and PLY (0.38) were in the moderate range, while the heritability estimate for REA (0.50) was high. Genetic correlations among carcass traits ranged from high and negative to moderate and positive. Genetic correlations involving percent lean yield indicated a moderate genetic correlation between PLY and REA (0.39), and a strongly negative correlation between PLY and FAT ( 0.85), but a genetic correlation of near zero ( 0.05) between PLY and HCW. Direct and maternal heritability estimates along with permanent environmental proportions of phenotypic variance for preweaning traits are given in Table 4. Direct heritabilities were low to moderate, with estimates of 0.8, 0.1 and 0.16 for BWT, PDG and WWT, respectively. Maternal heritability estimates were 0.1, 0. and 0.40 for BWT, PDG and WWT. Estimated genetic correlations between direct and maternal effects for preweaning traits were moderate to high and negative ( 0.37 to 0.95). These genetic correlations reflect an antagonism between direct and maternal effects for preweaning traits. Of particular interest in this study were correlations between direct and maternal effects of preweaning traits and direct effects of carcass traits (Table 5). Estimated correlations between direct effects for BWT and direct effects for HCW, REA and PLY were above Similarly, the BWT by FAT direct genetic correlation estimate was moderate, but negative ( 0.44). Preweaning daily gain direct by carcass trait direct effect correlation estimates ( 0.15 to 0.6) were smaller in magnitude than corresponding correlations involving BWT. Ribeye area and HCW direct effects had moderate and positive correlation estimates with WWT direct effects (0.34 and 0.8, respectively). Fat thickness direct effects had a moderately negative estimated correlation with WWT direct effects ( 0.5). The estimated WWT- PLY direct correlation was low (0.15). Correlations between maternal effects of preweaning traits and direct effects of carcass traits were also estimated (Table 5). The absolute values of these correlations were generally small (<0.18). However, maternal effects of BWT and WWT had larger correlations with direct effects of HCW (0.44 and 0.64, respectively). The estimated correlation between maternal effects for BWT and direct effects for REA was negative ( 0.3). Table 6 contains a summary of the ratios of PEV for the different models for sires and dams by carcass trait. For HCW, FAT and PLY, ratios of PEV were significantly (P < 0.05) decreased (from one) by the addition of either the remaining three carcass traits or any of the three preweaning traits to the analyses. For REA, a two-trait model including PDG did not significantly (P > 0.10) improve PEV compared to the single trait model. The average ratio for sires (0.976) was higher (P < 0.01) than the average ratio for dams (0.958). DISCUSSION Although addition of maternal components did not significantly improve the likelihood for HCW models, non-zero maternal variances were detected for FAT, REA and PLY. For traits where the addition of maternal components improved the likelihood, estimates of maternal heritability were 0.09 or less. When maternal variances appeared to be important (model two), additive genetic variance estimates were reduced and residual variance estimates increased compared to the results of model one, resulting in lower direct heritability with model two. This result shows that model two was not simply repartitioning direct genetic variance only. Although model three, which allowed for nonzero direct by maternal covariance, provided no better fit than model two for any of the traits, estimates of the direct by maternal covariance from model three tended to be negative. These results suggest that maternal effects may exist

6 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 3. Phenotypic variance estimates, heritabilities, genetic and phenotypic correlations (± SE) among carcass traits z,y Trait σ p x HCW FAT REA PLY HCW ± FAT ± ± REA ± ± ± PLY ± ± ± ± 0.10 z Heritabilities are on the diagonal, genetic correlations below the diagonal and phenotypic correlations (P < 0.03) above the diagonal. y All parameter estimates derived from the four carcass trait model. x Phenotypic variance. Table 4. Genetic parameters for preweaning traits z Trait σ p h d h m pe r am BWT ± ± PDG ± ± WWT ± ± z Genetic parameters: σ p = phenotypic variance, h d = direct heritability, h m = maternal heritability, pe = permanent environmental proportion of phenotypic variance, r am = direct by maternal genetic correlation. Parameters are from single trait models. for carcass traits, but selection for these maternal effects may be antagonistic to direct selection for carcass yield and its components. Including PE effects with model four, however, did not significantly (P > 0.15) improve the likelihood in agreement that PE effects would not be expected to be significant for traits measured at ages beyond 1 yr. With changes in estimates of direct genetic variance with different models, estimates of breeding values would also be expected to change. Therefore, simple and rank correlations were obtained to compare rankings of animals based on the four models. In the case of HCW, correlations ranged from 0.9 to 1.0, which was expected because of the small differences in direct genetic variance for model two vs. model one. Corresponding simple and rank correlations for FAT, REA and PLY were high (> 0.9), indicating near perfect correspondence among breeding values, regardless of whether maternal effects were included in the model. These results suggest that, although maternal variance components may be non-zero for carcass traits, animals will tend to be ranked similarly when the model included only direct vs. direct plus maternal genetic effects. If maternal effects are included, even with relatively small variances, the reduction in direct genetic variance may tend to reduce the range of breeding values. Researchers have generally stated that carcass traits have moderate to high direct heritability, and therefore would be expected to respond to direct selection (Marshall 1994). Reviews of published genetic parameter estimates provide for comparison with the current results (Koots et al. 1994a,b; Marshall 1994). Estimates of heritabilities and genetic correlations from the four carcass trait analyses were generally similar to estimates reported in these reviews. Koots et al. (1994a) reported weighted average heritabilities of 0.3, 0.44, 0.4 and 0.55 for HCW, FAT, REA and PLY, respectively. The present estimate of direct heritability for PLY was less than the average reported by Koots and coworkers. Differences in cattle populations and the method for calculating PLY may have contributed to the difference. The remaining direct heritability estimates in this study were similar to the weighted, age-adjusted averages. Estimates of phenotypic correlations among carcass traits indicated a strongly negative association between FAT and PLY ( 0.87), and small association between FAT and REA (0.09) and between FAT and HCW (0.14). Other studies have shown similar results for the correlation between FAT and PLY (Marshall 1994). Estimates of phenotypic correlations between FAT and HCW were greater in the study of Koch (1978), but similar in the reports of Koch et al. (198) and Lamb et al. (1990). The part whole relationship between HCW and FAT would be expected to result in positive phenotypic correlations. The phenotypic correlation between HCW and REA was 0.10, indicating that increases in REA were associated with increased HCW, which probably is due to the part whole relationship between muscle size and carcass weight, but is not as large as with FAT due to the nonlinear relationship of REA and HCW at heavier weights. In other studies (Lamb et al. 1990; Wilson et al. 1993; Koots et al. 1994b), this association ranged from 0.43 to The phenotypic correlation between PLY and REA (0.40) reflects the relative weight of REA in the PLY equation and the relationship between increased muscle size and carcass yield. Similarly, Koch et al. (198) reported a phenotypic correlation of 0.7 between REA and PLY. Genetic correlations among carcass traits ranged from high and negative to moderate and positive. Similar to the phenotypic correlations, genetic correlations between FAT with HCW and REA were low (0.13 and 0.14, respectively). The average genetic correlation between FAT and REA reported by Koots et al. (1994b) was 0.08, which represented a difference in direction from the current results, but both are small. The genetic correlation between FAT and REA is probably either negative or near zero, as reflected by the results of the present study and the review of Koots et al. (1994b). The estimate of 0.30 for the genetic correlation between REA and HCW was similar to the average of Koots et al. (1994b), but about 0.15 less than estimates reported by Koch et al. (198), Lamb et al. (1990), Johnson et al. (199) and Wilson et al. (1993). Genetic correlations involving

7 CREWS AND KEMP CARCASS TRAIT EBV MODELS FOR CROSSBRED BEEF CATTLE 3 Table 5. Genetic correlations (± SE) between direct and maternal effects for preweaning traits and direct effects for carcass traits Traits Preweaning Carcass r d(p), d(c) z r m(p), d(c) y BWT HCW 0.5 ± ± 0.3 FAT 0.44 ± ± 0.6 REA 0.54 ± ± 0.4 PLY 0.51 ± ± 0.6 PDG HCW 0.15 ± ± 0.7 FAT 0.15 ± ± 0.5 REA 0.6 ± ± 0.4 PLY 0.06 ± ± 0.6 WWT HCW 0.8 ± ± 0.1 FAT 0.5 ± ± 0.19 REA 0.34 ± ± 0.18 PLY 0.15 ± ± 0.19 z Correlation between direct preweaning and direct carcass effects. y Correlation between maternal preweaning and direct carcass effects. Table 6. Ratios of prediction error variance for sires and dams by carcass trait Ratio Numerator PEV z HCW FAT REA PLY Sires (n = 36) 1 4-trait model With BWT With PDG y With WWT Dams (n = 775) 1 4-trait model With BWT With PDG With WWT z PEV denominator for all ratios were from model one (single carcass trait), PEV numerators for ratios 1 4 were from models 5, respectively. See text for model explanation. y Mean ratio not different (P > 0.10) from All other mean ratios differ (P < 0.05) from PLY were of particular interest in this study. Results indicated a moderate genetic correlation between PLY and REA (0.38), and a strongly negative correlation between PLY and FAT ( 0.85). Selection for PLY, therefore, would be expected to result in carcasses of similar weight with less subcutaneous fat and larger REA. The summary of Koots et al. (1994b) included no studies that estimated genetic correlations involving PLY and the other carcass traits in this study. The summary by Marshall (1994) cited studies reporting genetic correlations between PLY and FAT and REA of 0.74 and 0.53, respectively, which largely agree with the current results. Caron and Kemp (1998) reported genetic correlations of 0.01, 0.8 and 0.83 between PLY and HCW, REA and FAT, respectively. The estimated genetic correlation of near zero ( 0.05) between HCW and PLY was also similar to the estimate of 0.11 by Koch et al. (198). Cutability at a constant age end point was reported by Koots et al. (1994b) to have genetic correlations with HCW, FAT and REA of 0.1, 0.33 and 0.6, respectively. At a constant HCW, PLY and cutability would be assumed to be similar traits. In that case, the results of the present study appear to be in support of those reports summarized by Koots et al. (1994b). A large number of studies in the literature have reported direct heritabilities for preweaning traits. The estimates for BWT and WWT from Johnson et al. (199) were 0.5 and 0.09, which were similar to our results. The average weighted, age-adjusted direct heritabilities for BWT, PDG and WWT reported by Koots et al. (1994a) were 0.31, 0.9 and 0.4, respectively. While the present estimates agree with regard to BWT, the averages reported by Koots et al. (1994a) are greater for PDG and WWT. Although unclear, it is possible that the differences arise from either modeling or sampling differences. Maternal heritabilities were 0.1, 0. and 0.40 for BWT, PDG and WWT, respectively. Koots et al. (1994a) reported weighted average maternal heritabilities of 0.14, 0.4 and 0.13 for these same traits. The largest discrepancy with these results occurs for WWT. Estimated correlations between direct and maternal effects for preweaning traits were moderate to high and negative ( 0.55, 0.95 and 0.37 for BWT, PDG and WWT, respectively). Koots et al. (1994b) reported averages from 0.7 to 0.30 for these same correlations. The results of the present study were in agreement in direction, but tended to be stronger in magnitude. Mrode (1988) stated that when negative environmental covariances were avoided as a source of bias, estimates

8 4 CANADIAN JOURNAL OF ANIMAL SCIENCE of the maternal by direct genetic correlation for WWT ranged from 0.05 to 0.8. Therefore, the genetic association would be expected to be generally antagonistic between direct and maternal effects for preweaning traits. Permanent environmental proportions of phenotypic variance were low for BWT (0.09) and WWT (0.0), but moderate for PDG (0.8). Direct-by-direct correlations between preweaning and carcass traits would contribute added information for carcass trait evaluations. The correlations between direct BWT and direct carcass traits were generally the highest. These correlations were positive with HCW, REA and PLY (0.5, 0.54 and 0.51, respectively). The correlation between direct BWT and direct FAT was also high but negative ( 0.44). Similarly, Koots et al. (1994b) reported averages of the genetic correlations between direct BWT and HCW and REA of 0.60 and 0.31, respectively. In contrast to the present results, Koots et al. (1994b) reported an average genetic correlation of 0.3 between direct BWT and PLY. It would be expected that direct effects for weights at different ages would be positively correlated, which would also hold for REA, since REA and HCW are generally positively correlated. In this study, the positive correlation between PLY and direct BWT may be due to the fact that calves with higher PLY are also leaner and may also have been heavier, larger-framed calves at birth. A negative genetic correlation between direct BWT and PLY, as reported by Koots et al. (1994b) seems to suggest that calves with higher PLY are those that are lighter at birth, but perhaps grow at faster rates and put on fat at a slower rate. The difference in these estimates, therefore, may be due to differences in biological types used to estimate the parameters across the studies. Genetic correlations between direct WWT and carcass traits tended to follow the same pattern as with BWT. Other studies have similarly reported positive genetic correlations between direct WWT and HCW and REA, as well as negative genetic correlations between direct WWT and FAT (Koots et al. 1994b). The results of the present study show a positive genetic correlation between direct PDG and REA, which is in agreement with the findings of Koch (1978), Koch et al. (198) and Lamb et al. (1990), as well as the summary of Koots et al. (1994b). Also, a positive, but nearzero genetic correlation (0.06) between direct PDG and PLY was estimated in the present study. Woodward et al. (199) estimated this correlation to be 0.0, but the estimate in the summary of Koots et al. (1994b) was 0.3. Again, the estimated genetic correlation in the present study between direct PDG and FAT was negative ( 0.15), but small, which may be different from other studies, which have shown this correlation to be positive (Koots et al. 1994b). To the extent that PDG is related to fattening in the preweaning period, a positive correlation between PDG and FAT may be expected. But, in lower nutritional environments, a negative or near-zero correlation between these traits tends to follow the correlation between PDG and other measures of lean growth, such as REA and PLY. Presently, there is insufficient evidence to conclude that genetic correlations involving direct PDG are different from zero, therefore, definite conclusions are not possible. The current analysis indicates that direct selection for increased HCW, REA or PLY, or for decreased FAT, would result in a correlated increase in direct BWT. Since it is generally assumed that increases in BWT are related to increased incidence of dystocia, selection schemes for carcass traits should be designed to consider increases in BWT. Correlations between maternal genetic effects for preweaning traits and direct genetic effects for carcass traits were generally low and near zero. Exceptions included the estimated genetic correlation between maternal BWT and HCW (0.44). This estimate supports the average of 0.37 reported by Koots et al. (1994b). A similar correlation was found between maternal WWT and HCW (0.64), for which no other literature estimates could be found. Higher maternal BWT and WWT breeding values would therefore be associated with higher direct HCW breeding values. Improvement in accuracy of carcass trait evaluations by adding preweaning growth information was measured as the decrease in prediction error variance of estimated breeding values resulting from more complete models (Woodward et al. 199). A ratio of one would represent no reduction in PEV as a result of adding (preweaning) traits to the carcass trait evaluation model. For HCW, FAT and PLY, PEV ratios were significantly (P < 0.05) decreased (from one) by the addition of either the other carcass traits (model two) or any of the preweaning traits (models three, four or five) to the analyses. For REA, a bivariate model including PDG did not significantly (P > 0.10) improve PEV compared to the univariate model. The average ratio for sires (0.976) was higher (P < 0.01) than the average ratio for dams (0.958). This result may be expected because more progeny carcass information was available for sires than for dams. The largest PEV decreases were observed for dams with fewer than three calves, although adding preweaning growth information did decrease carcass trait PEV for most parents. All sex of parent by carcass trait mean ratios in this study were above Therefore, relative improvement from multiple vs. single trait evaluations may be outweighed by the added computational cost of multiple trait models. Adding preweaning growth information has been shown to improve carcass trait evaluations, and therefore may be recommended when information on carcass traits is limited. ACKNOWLEDGMENTS The authors wish to acknowledge scientists, technicians and herd managers who originally participated in and contributed to the Foreign Cattle Breeds Evaluation project, which was the source of data for these analyses. Boldman, K. G., Kriese, L. A., Van Vleck, L. D., Van Tassell, C. P. and Kachman, S. D A manual for use of MTD- FREML. A program to obtain estimates of variances and covariances [Draft]. USDA-ARS. Lincoln, NE. Canadian Council on Animal Care Guide to the care and use of experimental animals. Vol 1. E. D. Olfert, B. M. Cross, and A. A. McWilliam, eds. CCAC, Ottawa, ON. Caron, N. and Kemp, R. A Determination of an appropriate selection criteria for growth composition in Charolais. Proc. 6th World Congress on Genetics Applied to Livestock Production, Armidale, NSW, Australia. 3:

9 CREWS AND KEMP CARCASS TRAIT EBV MODELS FOR CROSSBRED BEEF CATTLE 5 Crews, Jr., D. H. and Kemp, R. A Maternal (co)variance components for carcass trait breeding values among crossbred beef cattle. Proc. 6th World Congress on Genetics Applied to Livestock Production, Armidale, NSW, Australia. 3: Johnson, D. J., Benyshek, L. L., Bertrand, J. K., Johnson, M. H. and Weiss, G. M Estimates of genetic parameters for growth and carcass traits in Charolais cattle. Can. J. Anim. Sci. 7: Koch, R. M Selection in beef cattle III. Correlated response of carcass traits to selection for weaning weight, yearling weight and muscle score in cattle. J. Anim. Sci. 47: Koch, R. M., Cundiff, L. V. and Gregory, K. E Heritabilities and genetic, environmental and phenotypic correlations of carcass traits in a population of diverse biological types and their implications in selection programs. J. Anim. Sci. 55: Koots, K. R., Gibson, J. P., Smith, C. and Wilton, J. W. 1994a. Analyses of published genetic parameter estimates for beef production traits. 1. Heritability. Anim. Breed. Abstr. 6: Koots, K. R., Gibson, J. P. and Wilton, J. W. 1994b. Analyses of published genetic parameter estimates for beef production traits.. Phenotypic and genetic correlations. Anim. Breed. Abstr. 6: Lamb, M. A., Robison, O. W. and Tess, M. W Genetic parameters for carcass traits in Hereford bulls. J. Anim. Sci. 68: 64. Marshall, D. M Breed differences and genetic parameters for body composition traits in beef cattle. J. Anim. Sci. 7: Mrode, R. A Selection experiments in beef cattle. Part : A review of responses and correlated responses. Anim. Breed. Abstr. 56: Newman, J. E., Rahnefeld, G. W., Tong, A. K. W., Jones, S. D. M., Freeden, H. T., Weiss, G. M. and Bailey, D. R. C Slaughter and carcass traits of calves from first-cross and reciprocal back-cross beef cows. Can. J. Anim. Sci. 74: Rahnefeld, G. W., McKay, R. M., Weiss, G. M., Freeden, H. T., Lawson, J. E., Newman, J. E. and Bailey, D. R. C Growth and maternal performance of two-year-old F 1 and reciprocal backcross heifers in two environments. Can. J. Anim. Sci. 70: SAS Institute, Inc SAS user s guide: Statistics. SAS Institute, Inc., Cary, NC. Wilson, D. E., Willham, R. L., Northcutt, S. L. and Rouse, G. H Genetic parameters for carcass traits estimated from Angus field records. J. Anim. Sci. 71: Woodward, B. W., Pollak, E. J. and Quaas, R. L Parameter estimation for carcass traits including growth information of Simmental beef cattle using restricted maximum likelihood with a multiple trait model. J. Anim. Sci. 70:

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