Genetic parameters among growth and carcass traits of Canadian Charolais cattle

Transcription

1 Genetic parameters among growth and carcass traits of Canadian Charolais cattle D. H. Crews, Jr. 1,5, M. Lowerison 2, N. Caron 3, and R. A. Kemp 4 1 Agriculture and Agri-Food Canada Research Centre, Lethbridge, Alberta, Canada T1J 4B1; 2 Canadian Charolais Association, Calgary, Alberta, Canada T2E 6W8; 3 The Semex Alliance, St. Hyacinthe, Quebec, Canada J2S 7B8; 4 RAK Genetic Consulting, Ltd., Lethbridge, Alberta, Canada T1K 6A9. AAFC-LRC manuscript number , received 1 April 204, accepted 25 June Crews, D. H., Jr., Lowerison, M., Caron, N. and Kemp, R. A Genetic parameters among growth and carcass traits of Canadian Charolais cattle. Can. J. Anim. Sci. 84: Genetic parameters for three growth and five carcass traits were estimated for Charolais using a combination of carcass progeny test, purebred field performance and pedigree data. Heritabilities and genetic and residual correlations were derived from variance components for birth weight (BWT, n = ), 205-d weaning weight (WT205, n = ), postweaning gain (PWG, n = ), hot carcass weight (HCW, n = 6958), average subcutaneous fat thickness (FAT, n = 6866), longissimus muscle area (REA, n = 6863), marbling score (MAR, n = 6903) and estimated carcass lean yield percentage (PLY, n = 6852) with an animal model (n = ) and restricted maximum likelihood. Breed of dam and contemporary group appropriate to each trait were included as fixed effects in the model, whereas random effects included direct genetic for all traits, maternal genetic for BWT and WT205, and maternal permanent environmental for WT205. Carcass traits were adjusted to a constant harvest age of 425 d. Heritability estimates of 0.53, 0.22, and 0.21 were obtained for direct components of BWT, WT205, and PWG, respectively, and maternal heritabilities were 0.16 and 0.10 for BWT and WT205, respectively. Direct maternal genetic correlations for BWT ( 0.49) and WT205 ( 0.35) were negative. Heritabilities for HCW, FAT, REA, MAR, and PLY were 0.33, 0.39, 0.43, 0.34, and 0.46, respectively. Genetic correlations among direct effects for growth traits were moderately positive and generally uncorrelated with maternal effects across traits. Lean and fat deposition in the carcass generally had negative, unfavorable genetic correlations, although improvement in lean yield and marbling score may not be strongly antagonistic. Genetic correlations of direct and maternal components of growth traits with carcass traits suggested that selection for increased growth rate would not be antagonistic to improvement in carcass yield or meat quality. Key words: Carcass, Charolais, correlation, genetic parameters, growth Crews, D. H., Jr., Lowerison, M., Caron, N. et Kemp, R. A Génétique des paramètres de la croissance et de la carcasses des bovins Charolais canadiens. Can. J. Anim. Sci. 84: Les auteurs ont estimé les valeurs génétiques de trois paramètres de la croissance et de cinq paramètres de la carcasse des bovins Charolais en recourant à un contrôle de la descendance pour la carcasse, au rendement des bovins de race pure sur le terrain et à des données généalogiques. Ils ont calculé l héritabilité ainsi que les corrélations génétiques et résiduelles des composantes de la variance pour le poids à la naissance (BWT, n = ), le poids au sevrage à 205 jours (WT205, n = ), le gain de poids après le sevrage (PWG, n = ), le poids de la carcasse chaude (HCW, n = 6 958), l épaisseur moyenne du gras sous-cutané (FAT, n = 6 866), la surface du longissimus (REA, n = 6 863), le persillé (MAR, n = 6 903) et le rendement estimatif de la carcasse en viande maigre en pourcentage (PLY, n = 6 852) au moyen d un modèle animal (n = ) et du maximum de vraisemblance restreinte. Les chercheurs ont ajouté la race de la mère et un groupe de contemporains approprié à chaque caractère au modèle comme effets fixes; les effets aléatoires comprenant les effets génétiques directs des caractères, les effets génétiques de la mère pour BWT et WT205 et la valeur phénotypique constante de la mère pour WT205. Les caractères de la carcasse ont été corrigés en fonction d un âge constant de 425 jours pour la commercialisation. Les composantes directes de BWT, de WT205 et de PWG ont une héritabilité estimative de 0,53, 0,22 et 0,21, respectivement, alors que l héritabilité maternelle de BWT et de WT205 s établit à 0,16 et 0,10. Les corrélations entre les valeurs génétiques directes et les valeurs maternelles sont négatives pour BWT ( 0,49) et WT205 ( 0,35). Les paramètres HCW, FAT, REA, MAR et PLY donnent une héritabilité respective de 0,33, 0,39, 0,43, 0,34 et 0,46. Les corrélations génétiques entre les effets directs des paramètres de la croissance étaient modérément positifs et ne présentaient généralement aucune corrélation avec les effets maternels. La proportion de viande maigre et le dépôt de graisse dans la carcasse se caractérisent généralement par une corrélation négative, défavorable, bien que l amélioration du rendement en viande maigre et du persillé ne soient pas forcément très 5 Correspondence should be addressed to st Avenue South, ( dcrews@agr.gc.ca). 589 Abbreviations: BWT, adjusted birth weight; CCA, Canadian Charolais Association; CHARM, Charolais Herd and Record Management; CtoC, Conception to Consumer; EPD, expected progeny differences; FAT, adjusted average carcass subcutaneous fat thickness; HCW, adjusted hot carcass weight; MAR, adjusted carcass marbling score; NCE, national cattle evaluation(s); PLY, estimated carcass percent lean yield; PWG, adjusted post-weaning gain; REA, adjusted carcass longissimus muscle area; REML, restricted maximum likelihood; WT205, adjusted 205-d weaning weight.

2 590 CANADIAN JOURNAL OF ANIMAL SCIENCE antagonistes. Les corrélations génétiques entre les composantes directes et maternelles des caractères de la croissance et de la carcasse laissent croire que sélectionner les animaux pour un meilleur taux de croissance n empêcherait pas une amélioration du rendement de la carcasse ou de la qualité de la viande. Mots clés: Carcasse, Charolais, corrélation, paramètres génétiques, croissance As one of several breed associations conducting annual carcass merit NCE, CCA annually publishes EPD for nine traits including phenotypes related to early (i.e., birth to weaning) growth, post-weaning growth, four component carcass traits, and one predicted yield trait using a multivariate animal model. Estimates of heritabilities and genetic and residual correlations are population-specific parameters required to predict EPD and should be periodically updated to account for their potential change over time due to selection and management (Koots et al. 1994a, b). Genetic parameters specific to the CCA carcass merit NCE have not been estimated recently (Johnston et al. 1992; Caron and Kemp 1999). Growth from birth to weaning, from weaning to yearling, and carcass traits constitute groups of economically important phenotypes in the design of improvement programs. Genetic correlations among these groups of traits would indicate potential antagonisms to multiple trait selection and allow for prediction of correlated response in some traits due to selection for other traits. Data for prediction of carcass merit EPD are primarily derived from the CCA CtoC progeny test. Inclusion of records from the CCA CHARM growth performance database in this NCE reduces potential selection bias resulting from progeny-tested sires being highly selected for growth potential. Estimates of genetic correlations of carcass traits with direct and maternal components of growth traits are generally lacking in the recent literature (Splan et al. 1998, 2002; Crews and Kemp 1999), especially with respect to large field populations. The objectives of this study were to estimate genetic parameters, including heritabilities and genetic and residual correlations, among growth and carcass traits in CCA field data. MATERIALS AND METHODS Data The CCA CtoC carcass database contained carcass records on steers and heifers (n = 6968) harvested over 22 yr between 1975 and Market progeny were sired by 349 Charolais bulls mated to commercial and purebred cows (n = 4063) representing 48 distinct breed types. Sires of calves with carcass data were identified and used to extract birth, weaning, and yearling weights and (or) pedigree data from the separate CHARM database corresponding to (1) their own record, (2) their male and female contemporaries born in the same year and herd, (3) their parents and grandparents, and (4) all available progeny. Records extracted from the CHARM database represented purebred Charolais cattle. The combined growth and carcass data set contained 2122 sires and dams, and was a subset of the data used in the 2004 CCA growth and carcass NCE to evaluate animals for the eight traits analyzed as described below. All data used in this study were from CCA, and animals were cared for according to standard industry practices. The annual CCA carcass merit and twice annual growth NCE include the standard growth traits BWT, WT205 and PWG. Both BWT and WT205 were adjusted for age of dam and sex of calf (BIF 2002). To characterize growth following weaning, PWG was defined as the non-negative difference between adjusted 365-d weight and WT205. Postweaning gain is preferable to actual 365-d weight due to the part-whole relationship of the latter with WT205; most growth NCE predict yearling weight EPD as the sum of direct EPD for WT205 and PWG (E. J. Pollak, personal communication). Component carcass traits including HCW, REA, FAT, and MAR are routinely collected 24 to 48 h postmortem and EPD for these traits have been predicted as part of the CCA carcass merit NCE for approximately 5 yr. Estimated PLY was computed using the formula: PLY = HCW REA FAT (Caron and Kemp 1999). Records for PLY were computed using age-adjusted component carcass traits as described below. Breed of Dam Breed of dam was defined by the concatenation of breed of sire, maternal grand sire, and maternal grand dam, as provided by commercial CtoC cooperator. An average of 98, 92, 5, and 145 records were available per breed of dam level for BWT, WT205, PWG, and carcass traits, respectively. Insufficient cross-classification existed between contemporary group and breed of dam and, therefore, the final model for all traits included breed of dam and contemporary group as separate fixed effects. All cattle with data from CHARM were assigned the Charolais breed of dam code. Adjustment for Age at Measurement As described previously, WT205 was pre-adjusted to a weaning age of 205 d and PWG was adjusted to a postweaning period of 160 d. For carcass traits, the mean age at harvest for all animals with carcass data was approximately 425 d (SD = 33.5 d, range = 344 to 594 d). Therefore, carcass traits were adjusted to 425 d of age using the following formula: y * ij = y ij + βj (425 A i ) where y * ij was the age-adjusted phenotype for trait j, y ij was the unadjusted phenotype for trait j, and A i was age at slaughter for animal i, respectively. The regression coefficient, β j, was estimated for each of HCW ( ), REA ( ), FAT ( ) and MAR ( ) (Caron and Kemp 1999). The regression for MAR was negative because marbling score was measured using an inverse 100-point scale where, for

3 CREWS ET AL. BEEF GROWTH AND CARCASS GENETIC PARAMETERS 591 example, to corresponded to a small degree of marbling, and to corresponded to a slight degree of marbling. Therefore, carcasses with less intramuscular fat received higher marbling scores. Data Edits and Contemporary Groups Records were retained in the final data set according to the presence of complete fixed and random effects per phenotype. Breed of dam was required for all traits. For BWT and WT205, records were required to have birth (year of birth herd of origin sex, n = 831) or weaning (year of birth herd of origin sex weaning management group, n = 564) contemporary group, respectively, and maternal parent identified. All PWG records were required to be non-negative and have yearling contemporary group (year of birth herd of origin sex yearling management code, n = 259) identified. Harvest contemporary group (herd of origin sex year of harvest harvest group, n = 112) was assumed equal for all carcass traits. The average numbers of records per contemporary group were 65.3, 55.7, 74.9, and 62.2 for BWT, WT205, PWG, and carcass traits, respectively. No contemporary group had fewer than five records, and the maximum numbers of records per contemporary group ranged from 162 to 283. With respect to BWT and WT205, wherein models included maternal effects as described below, dams had an average of 2.19 calves (range 1 to 13) with BWT records, and an average of 1.96 calves (range 1 to 11) with WT205 records, however, approximately onehalf of calves with BWT and(or) WT205 records were out of dams with more than one progeny with data for these traits. For PWG and carcass traits, maternal effects were not included in the random portion of the model. Variance Components Models All traits were initially subjected to univariate analyses with the animal model: y = Xb + Zu + e where X and Z were design matrices relating vectors of unknown fixed (b) and random (u) effects to observations (y) and e was a vector of random residuals, unique to animals. Effects in this model had first and second moments assumed to be: y Xb E u = 0 e 0 and V u g e = 2 Aσ Iσe where A was the additive relationship matrix (n = ), I was an identity matrix of order equal to numbers of animals with observations, σ 2 g was additive (direct) genetic variance, and σ 2 e was residual variance. A summary of published heritability estimates for beef production traits (Koots et al. 1994a) indicated that BWT and WT205 have non-zero maternal genetic variances in most studies, and in the case of WT205, an additional component due to maternal permanent environmental effects. Therefore, the univariate models for BWT and WT205 were augmented to include maternal genetic effects with null mean and random effect (co)variances described as ud 2 Aσg Aσg, m V u 2 m = Aσgm, Aσm e Iσ e where all components were as before with the addition of σ 2 m defined as maternal genetic variance and σ g,m defined as the direct maternal genetic covariance. The model for WT205 was further augmented to include a maternal permanent environmental effect. The vector of random WT205 maternal permanent environmental effects were assumed to have null mean [i.e., E(pe) = 0], variance defined as V(pe) = Iσ 2 pe, and be independent from other random effects in the model. The identity matrix used to disperse WT205 maternal permanent environmental effects was of order equal to the number of dams with progeny with WT205 records (n = ). As described later, maternal genetic and permanent environmental effects for growth traits measured up to weaning had non-zero variances, precluding the use of reduced models (i.e., including only direct genetic effects). The univariate models for PWG and carcass traits included random direct genetic effects only as traits measured at 1 yr of age or later were assumed to have no maternal genetic component (Crews and Kemp 1999). Multiple trait models were used to estimate genetic and residual covariances; however, computing limitations prevented fitting a single variance components model for all eight growth and carcass traits. Extension of the univariate models described previously to the three-trait model used for growth is straightforward. An attempt was made to fit a five-trait model to the carcass traits; however, poor convergence resulted due to the linear dependency of PLY with the component carcass traits used in its prediction (i.e., HCW, FAT, and REA). This dependency apparently caused genetic and residual (co)variance matrices to not be positive definite, resulting in convergence failure. Therefore, genetic and residual covariances involving PLY were estimated using bivariate models with each of the seven other traits. Genetic and residual parameters of growth with carcass traits were estimated using a series of bivariate models fit for each growth trait with each carcass trait in pairs. Models were fit, (co)variance components estimated and genetic parameters computed with the ASREML software package (Gilmour et al. 2002), which employs REML with an average information algorithm. Standard errors associated with (co)variance components and genetic parameters were also computed by the software and used to evaluate their relative significance. RESULTS AND DISCUSSION Summary statistics for growth and carcass traits are reported in Table 1. The largest number of records was available

4 592 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 1. Summary statistics and univariate variance components and genetic parameters (± SE) for growth and carcass traits Item z BWT WT205 PWG HCW FAT REA MAR y PLY n Mean Min w v Max SD σ 2 g σ g,m σ 2 m σ 2 pe x σ 2 e σ 2 P h 2 d 0.53 ± ± ± ± ± ± ± ± 0.04 r g,m 0.49 ± ± 0.07 h 2 m 0.16 ± ± 0.02 c ± 0.01 z σ 2 g = additive (direct) genetic variance, σ g,m = direct maternal genetic covariance, σ 2 m = maternal genetic variance, σ 2 pe = maternal permanent environmental variance, σ 2 e = residual variance, σ 2 P = phenotypic variance, h 2 d = direct heritability, r g,m = direct maternal genetic correlation, h 2 m = maternal heritability, and c 2 = proportion of phenotypic variance attributed to maternal permanent environmental effects. y Marbling score: to = traces, to = slight, to = small. x Parameter not estimated. w Calculation as adjusted 365-d weight minus WT205 resulted in 33 PWG phenotypes equal to zero. Growth records were edited such that unadjusted yearling weight was required to be larger than weaning weight prior to calculation of PWG. v Adjustment to 425 d harvest age resulted in 14 FAT phenotypes equal to zero. Unadjusted subcutaneous fat thickness minimum value was 1 mm. for BWT, with fewer records available for WT205 and PWG. Even though CCA has adopted cow-based (i.e., whole herd) reporting, only 36% of calves with BWT records also had records for PWG. Zero values were included in the final data set for PWG and FAT. In the initial data edits, postweaning growth data were used only if unadjusted yearling weight was larger than unadjusted weaning weight. Therefore, the 33 PWG records that were each set equal to zero resulted from adjusted 365 d weight and WT205 being equal. In the case of FAT, the minimum unadjusted average subcutaneous carcass fat thickness was 1 mm, and adjustment to 425 d harvest age resulted in 14 FAT records equal to zero. Table 1 shows that the average carcass in the CCA database adjusted to 425 d of age weighed kg had mm of subcutaneous fat, a longissimus muscle area of cm 2 and estimated PLY of 59.58%. The average marbling score was 76.30, corresponding to the lower portion of the AA Canadian quality grade. Among the 6,903 carcasses with marbling score, 31% had sufficient scores to receive the AAA quality grade, whereas approximately 8% had less than a slight degree of marbling. Single Trait Variance Components and Genetic Parameters As described previously, all traits were initially subjected to univariate genetic models to obtain initial estimates of variance components and subsequently provide starting values for multiple trait models (Table 1). Variances of 11.02, 3.29 and 9.60 kg 2 were obtained for direct genetic, maternal genetic, and residual variance components of BWT, respectively, with a negative covariance of 2.95 kg 2 between direct and maternal effects. For WT205, estimates of , 83.67, , and kg 2 were obtained for direct genetic, maternal genetic, maternal permanent environmental, and residual variance components, respectively. Similar to BWT, a negative covariance ( kg 2 ) was estimated between direct and maternal effects on WT205. For BWT and WT205, the parsimony of the final model was judged beginning with univariate analyses. The likelihood of the models presented in Table 1 was higher than for models with no maternal components or with the direct maternal covariance constrained to zero, although model likelihood comparisons will not be presented here. It is alternatively sufficient that direct and maternal heritability estimates, as well as the direct maternal genetic correlation for both BWT and WT205 were significantly (P < 0.001) different from zero to warrant inclusion of the relevant maternal (co)variances in the final model. Direct heritability estimates for BWT (0.53 ± 0.02) and WT205 (0.23 ± 0.02) were high and moderate, respectively. The estimate for direct BWT was considerably higher than the weighted mean estimate of 0.31 in the summary of Koots et al. (1994a) and the estimate for direct WT205 was equal (0.24) to the weighted mean heritability in that summary. Maternal heritability estimates were 0.16 ± 0.01 and 0.10 ± 0.02 for BWT and WT205, respectively, which were very comparable to the low mean heritabilities of 0.14 for maternal BWT and 0.13 for maternal WT205 in the summary of Koots et al. (1994a). Single trait results for growth up to weaning indicate that in the Charolais breed, direct effects account for considerably more phenotypic variance in early growth than maternal effects. Further, for both BWT ( 0.49 ± 0.03) and WT205 ( 0.38 ± 0.07), direct maternal covariances and correlations were negative and moderate to high in magnitude. Numerous studies have similarly reported negative direct maternal correlations for WT205 (Koots et al. 1994b) indicating an apparent antagonism between direct and maternal components of early growth. Thirteen percent of

5 CREWS ET AL. BEEF GROWTH AND CARCASS GENETIC PARAMETERS 593 phenotypic variance for WT205 was attributed to maternal permanent environmental variance. The heritability of PWG (0.20 ± 0.02) was moderate, but lower than the weighted mean value (0.31) reported by Koots et al. (1994a). Because PWG was analyzed rather than adjusted yearling weight, maternal genetic components were assumed to be zero. Koots et al. (1994a) reported an unweighted mean maternal heritability for yearling weight of 0.11 based on six published studies, which is far fewer than the numbers of studies reporting direct heritabilities for most early-life growth traits, or maternal heritabilities for growth traits measured earlier in life. It is reasonable to assume that maternal effects have diminished if not negligible importance for traits measured near or after yearling (Crews and Kemp 1999; Mwansa et al. 2000). Further, non-zero maternal genetic variances for such traits may be more related to maternal effects carried over from preweaning growth, expressed because yearling weight and WT205 share a significant part-whole relationship. Univariate variance components and genetic parameters for carcass traits are also reported in Table 1. Heritability estimates were 0.32 ± 0.04, 0.38 ± 0.04, 0.43 ± 0.04, 0.34 ± 0.04, and 0.45 ± 0.04 for HCW, FAT, REA, MAR, and PLY, respectively, and would be classified as moderate to high. Heritability estimates generally compared favorably with those summarized by Koots et al. (1994a), where weighted average heritabilities were equal to or greater than those of the present study with the exception of HCW, which was higher here than in that summary (0.23). The moderate heritability for HCW in the present study compares more favorably to the estimate of 0.31 reported by Wilson et al. (1992) using Angus field data, but was lower than the estimates of 0.48 and 0.61 by Benyshek (1981) and Meyer et al. (2004), respectively, using Hereford field data. Experimental data from the Meat Animal Research Center in Nebraska yielded HCW heritability estimates of greater than 0.44 in three studies (MacNeil et al. 1984; Splan et al. 1998, 2002). The estimate of 0.09 reported by Johnston et al. (1992), based on a subset of the present data, was low, most likely due to adjustment of carcass weight records to a constant subcutaneous fat depth of 8.9 mm. The heritability for FAT reported here (0.38 ± 0.04) was in the lower portion of the range reported in recent studies, where estimates ranged from 0.35 (Crews and Kemp 1999) to 0.66 (Splan et al. 1998). The estimate of 0.26 in Angus by Wilson et al. (1992) was lower than other published estimates, as was the estimate of 0.20 reported by Meyer et al. (2004). The heritability estimate for REA (0.43 ± 0.04) was very similar to the weighted average estimate of 0.42 reported by Koots et al. (1994a) as well as estimates reported elsewhere [0.40, Benyshek (1981), 0.38, Johnston et al. (1992)], but was lower than estimates of 0.58 to 0.61 reported by Splan et al. (1998, 2002) and of 0.68 reported by Meyer et al. (2004). The moderate heritability estimate of 0.34 ± 0.04 for MAR was higher than comparable field data estimates ranging from 0.23 to 0.26 (Johnston et al. 1992; Wilson et al. 1992; Woodward et al. 1992) in Angus, Charolais and Simmental, but was similar to the estimate of 0.36 reported by Meyer et al. (2004) using Hereford field data in Australia. Higher heritability estimates have been reported from Hereford field data (Benyshek 1981) and experimental crossbred populations (Splan et al. 1998; Splan et al. 2002). The heritability of PLY (0.45 ± 0.04) was lower than the weighted mean derived for lean percentage by Koots et al. (1994a), although the unweighted mean heritability in that summary was Crews and Kemp (1999) reported a heritability of 0.39 for percent lean yield defined slightly differently than in the present study. Multivariate Growth Trait Heritability and Genetic Correlations Table 2 summarizes genetic parameters obtained from the multivariate growth trait model, including heritabilities reported for comparison with the univariate results in Table 1. Heritability estimates from the multivariate model were within 0.01 of those from univariate models. Similarly, direct maternal genetic correlations for BWT and WT205 were within 0.03 of corresponding parameters from the single trait results. Direct effects for BWT were moderately and positively correlated (0.29 to 0.33) with direct effects for growth traits measured later (i.e., WT205 and PWG). Koots et al. (1994b) summarized more than 65 studies estimating the genetic correlation of direct BWT with direct WT205 and PWG and reported moderate to high and positive estimates of 0.47 and 0.39, respectively. The genetic correlation of direct WT205 with direct PWG (0.39 ± 0.06) was likewise positive and moderate. Direct BWT and maternal WT205 had a low and negative genetic correlation ( 0.16 ± 0.06), whereas maternal BWT and maternal WT205 had a moderately positive genetic correlation (0.33 ± 0.07). Direct effects for WT205 and PWG had low, near zero genetic correlations with maternal BWT. From these results, it appears that direct and maternal effects were negatively correlated within trait; however, in general, direct and maternal effects were not correlated across traits. The apparent antagonism, therefore, between direct and maternal effects appears to be restricted within traits such as BWT and WT205 and selection for maternal WT205, for example, may not be antagonistic to selection for increased growth rate in general. Residual correlations among growth traits, also reported in Table 2 (footnote), were different (P < 0.05) from zero but decreased in magnitude as traits were measured farther apart in early life. It is interesting to note that the residual correlation between WT205 and PWG was 0.09, indicating that non-genetic effects resulting in increased WT205 had decreasing effects on PWG. Genetic correlations shown in Table 2, being similar (i.e., within a confidence range predicted from parameter standard errors) to those in other studies (e.g., Johnston et al. 1992; Koots et al. 1994b), may support the concept of a generalized genetic potential for growth, which could be used to reduce the dimensionality of multiple trait NCE with a method such as random regression. Multivariate Carcass Trait Heritability and Correlations Heritability estimates for carcass traits from the multivariate carcass model (Table 3) were essentially equal to corresponding univariate results, with estimates of 0.33 ± 0.04, 0.39 ± 0.04, 0.43 ± 0.05, 0.34 ± 0.04, and 0.46 ± 0.05 for

6 594 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 2. Multivariate heritability and genetic correlation (± SE) estimates for growth traits z Trait BWTd y WT205d y PWG BWTm x WT205m x WT205pe w BWTd 0.53 ± 0.02 WT205d 0.33 ± ± 0.02 PWG 0.29 ± ± ± 0.02 BWTm 0.49 ± ± ± ± 0.01 WT205m 0.16 ± ± ± ± ± 0.01 WT205pe v 0.12 ± 0.01 z Residual correlations were 0.21 ± 0.02 (BWT WT205), 0.06 ± 0.02 (BWT PWG), and 0.09 ± 0.02 (WT205 PWG). Heritability estimates are on the diagonal in bold, and genetic correlations are below the diagonal y Additive (direct) genetic effects. x Maternal genetic effects. w Maternal permanent environmental effects. v Maternal permanent environmental effects were independent of genetic effects by model definition. Table 3. Multivariate heritability and genetic correlation z (± SE) estimates for carcass traits Trait HCW FAT REA MAR PLY y HCW 0.33 ± ± ± ± ± 0.04 FAT 0.18 ± ± ± ± ± 0.01 REA 0.47 ± ± ± ± ± 0.03 MAR 0.21 ± ± ± ± ± 0.03 PLY 0.05 ± ± ± ± ± 0.04 z Heritability estimates are on the diagonal in bold, genetic correlations below the diagonal, and residual correlations above the diagonal. y Genetic and residual correlations involving PLY were obtained from bivariate models due to poor convergence properties of solutions with a five-trait model for carcass traits. Estimated heritability for PLY was equal across all bivariate carcass models. HCW, FAT, REA, MAR, and PLY, respectively. The genetic correlation of HCW with REA was high and positive (0.47 ± 0.07), equal to the mean of five estimates reported in the summary of Koots et al. (1994b) and the estimate of 0.45 reported by Johnston et al. (1992) based on a subset of the present data. Genetic correlations of HCW with FAT (0.18 ± 0.08) and PLY 0.05 ± 0.08) were small and should be judged near zero. Koots et al. (1994b) reported a mean genetic correlation of 0.38 between HCW and FAT, which was similar in sign but larger in magnitude than in the present study. Similarly, positive and small to moderate genetic correlations between HCW and FAT have been reported with Angus field data (0.38, Wilson et al. 1992) and experimental crossbred data (0.13, Crews and Kemp 1999). Although the point estimate for the correlation between direct genetic effects of HCW with MAR was negative ( 0.21 ± 0.09), this correlation would not be considered antagonistic because the method for assigning MAR to carcasses resulted in carcasses with higher degrees of marbling receiving lower marbling scores. A similarly favorable, positive correlation between genetic effects for HCW and MAR of 0.16 was reported by Koots et al. (1994b), based on two studies published prior to Although the estimate of 0.31 reported by Johnston et al. (1992) was opposite in sign (i.e., unfavorable) compared to the present study, they analyzed a subset of the present data with carcass traits adjusted to a constant fat end point of 8.9 mm rather than a constant age end point. This comparison suggests that choice of end point adjustment has an effect on sign and magnitude of genetic correlations among carcass traits. The genetic correlation of 0.06 between HCW and MAR reported by Wilson et al. (1992) using Angus field data was near zero. The genetic correlation of HCW with PLY ( 0.05 ± 0.08) was essentially zero, a result similar to that reported by Crews and Kemp (1999), indicating that selection for PLY could be implemented independent of carcass weight. A low genetic correlation of 0.19 ± 0.08 indicated that deposition of FAT was not strongly associated with deposition of marbling, although the sign of the correlation would be considered unfavorable. Other studies have shown stronger genetic associations between subcutaneous and intramuscular fat (Koots et al. 1994b; Meyer et al. 2004). It is reasonable that, as separate measures of lipid deposition, FAT and MAR should be positively correlated; however, the estimated genetic correlation found here suggests that FAT and MAR deposition are not equivalent biological processes. It is also reasonable, therefore, that selection for increased MAR may be accomplished without increases in FAT, which would be desirable considering that increases in MAR (i.e., quality grade) are associated with increased carcass value whereas increases in FAT (i.e., decreased yield) would more likely be associated with carcass value discounts. Genetic correlations of FAT with REA ( 0.35 ± 0.07) and PLY ( 0.88 ± 0.02) were negative and strong. The summary of Koots et al. (1994b) reported a mean genetic correlation of 0.08 between REA and FAT based on seven studies, which supported work with Angus field data reported by Wilson et al. (1992). Few recent studies include estimates involving PLY as defined here, although Crews and Kemp (1999) reported a genetic correlation between carcass lean yield and FAT of 0.85 ± Strongly negative genetic correlations between FAT and PLY would be predicted due to the antagonism between fat deposition and fat-free lean, whereas genetic correlations between FAT and measures of retail yield or cutability may be less strong. For example, a moderately negative genetic correlation ( 0.34) between carcass rib fat and retail beef yield was reported by Meyer et al. (2004) from Australian Hereford field data.

7 CREWS ET AL. BEEF GROWTH AND CARCASS GENETIC PARAMETERS 595 Genetic correlations of REA with MAR (0.10 ± 0.08) and PLY (0.72 ± 0.04) were positive, although the genetic association of REA with MAR was weak. Similar to that with FAT, therefore, selection for increased MAR would not be considered antagonistic to maintaining or increasing muscle size. Because of the PLY definition, the strongly positive genetic correlation between REA and PLY would be expected. These results suggest, in general, that increases in weight and muscling were positively associated, but not strongly associated with deposition of fat, either in subcutaneous or intramuscular depots. Muscle growth and subcutaneous fat deposition were negatively correlated, although muscle growth tended to be less related to deposition of intramuscular fat. Genetic improvement in carcass lean yield could be accomplished nearly independent of genetic change in either body size (e.g., HCW) or quality grade (e.g., MAR). Residual correlations among carcass traits were similar in sign and magnitude to genetic correlations for all trait pairs with the possible exception of FAT and REA. The residual correlation between REA and FAT ( 0.10 ± 0.04) was not as strong as the corresponding genetic correlation, suggesting that non-genetic effects on muscle area and subcutaneous fat were less antagonistic than genetic effects, wherein the non-genetic effects may be related to growth in general as opposed to genetic effects, which may be related more to partitioning growth into fat versus lean depots. Genetic Correlations of Growth with Carcass Traits Table 4 summarizes genetic correlations of direct and maternal components of growth with carcass traits. A total of 4805 animals in the final data set had both growth and carcass traits, forcing genetic correlations of growth with carcass traits to be estimated somewhat through additive relationships because not all animals with carcass records had growth data. Carcass data were largely derived from commercial calves in CtoC cooperator herds where recording of early growth phenotypes is less common than in purebred herds. In the more complete databases, where contemporary groups are required to have a minimum of two records, 6974 animals had both growth and carcass data available. Therefore, standard errors associated with genetic correlations in Table 4 reflected the reduced number of animals with appropriate pairings of growth and carcass phenotypes, potentially reducing the number of correlations that would be judged significantly different from zero. Direct effects on BWT were positively correlated with HCW (0.39 ± 0.08) and REA (0.21 ± 0.08) but negatively correlated with FAT ( 0.21 ± 0.08) suggesting that direct BWT was a reasonable predictor of lean growth, also evidenced by a moderately positive genetic correlation with PLY (0.21 ± 0.08). Generally strong and positive genetic correlations between BWT and HCW have been reported in several studies (MacNeil et al. 1984; Johnston et al. 1992; Crews and Kemp 1999) as well as in the summary of published genetic correlations by Koots et al. (1994b). Also, MacNeil et al. (1984), Koots et al. (1994b) and Crews and Kemp (1999) found negative genetic correlations ranging from 0.07 to 0.44 between BWT and FAT, which support the results of the present study. The genetic correlation found here between direct BWT and REA was similar to the mean (0.31) reported by Koots et al. (1994b), but lower than the estimate of 0.54 reported by Crews and Kemp (1999). Direct WT205 had a stronger positive genetic correlation (0.79 ± 0.05) with HCW than either direct BWT or PWG. Other studies of both experimental and field data have reported similarly high genetic correlations between direct weaning weight and HCW, ranging from 0.70 (Splan et al. 2002) to 0.92 (Splan et al. 1998). Corresponding genetic correlations reported by Johnston et al. (1992) and Crews and Kemp (1999) were also positive, but lower in magnitude. Direct WT205 had a moderately positive genetic correlation with FAT (0.30 ± 0.10), but a lower positive genetic correlation with REA (0.19 ± 0.10). These associations led to a genetic correlation of 0.25 ± 0.10 of direct WT205 with PLY. Recent studies (Splan et al. 1998, 2002) have shown moderately positive genetic correlations of 0.26 to 0.30 between direct WT205 and FAT, which are supported by the results of this study. Koots et al. (1994b) summarized eight studies where the mean genetic correlation of direct WT205 with FAT was low but positive (0.07). Most studies have shown moderate to high (i.e., 0.29 to 0.34) genetic correlations between direct WT205 and REA (Splan et al. 1998; Crews and Kemp 1999; Splan et al. 2002), which were similar to mean of four studies summarized by Koots et al. (1994b) but were higher than estimated in the present study (0.19 ± 0.10). Overall, direct WT205 appeared to be less related to lean components of the carcass than was direct BWT. Few studies in the recent literature have reported genetic correlations of PWG with carcass traits. The genetic correlation of 0.49 ± 0.13 between PWG and HCW was strong and positive, which is in general agreement with results by Johnston et al. (1992) and Splan et al. (1998) who both reported strongly positive genetic correlations of HCW with yearling weight. Koots et al. (1994b) summarized a single study reporting a genetic correlation between PWG and HCW of Genetic correlations of PWG were moderate and positive with both FAT (0.21 ± 0.14) and REA (0.38 ± 0.13), but near zero (0.06 ± 0.14) with PLY. A single study summarized by Koots et al. (1994b) reported a genetic correlation of 0.83 between PWG and carcass lean percentage, but that phenotype may be different than PLY as used in this study. Splan et al. (1998) reported genetic correlations of yearling weight with FAT (0.34) and REA (0.29) that were moderate and similar to the present results involving PWG. Direct components of BWT and WT205 tended to have positive and therefore unfavorable genetic correlations with MAR, however, the genetic correlation between PWG and MAR ( 0.38 ± 0.15) was negative and therefore favorable. The standard errors associated with these genetic correlations were relatively large and therefore do not support any strong conclusions. The recent literature similarly provides no clear indication of the genetic association of growth with MAR across experimental and field populations. For example, Woodward et al. (1992) reported a near-zero genetic correlation (0.05) between BWT and MAR whereas marbling adjusted to a constant fat end point in Johnston et al. (1992) had an unfavorable genetic correlation of 0.26 with BWT. Further,

8 596 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 4. Genetic correlations (± SE) of growth traits with carcass traits Trait HCW FAT REA MAR PLY BWTd z 0.39 ± ± ± ± ± 0.08 WT205d z 0.79 ± ± ± ± ± 0.10 PWG 0.49 ± ± ± ± ± 0.14 BWTm y 0.12 ± ± ± ± ± 0.10 WT205m y 0.27 ± ± ± ± ± 0.14 z Additive (direct) genetic effects. y Maternal genetic effects. Table 5. Residual correlations (± SE) of growth traits with carcass traits Trait HCW FAT REA MAR PLY BWT 0.16 ± ± ± ± ± 0.05 WT ± ± ± ± ± 0.04 PWG 0.43 ± ± ± ± ± 0.09 Woodward et al. (1992) and Splan et al. (1998) reported positive genetic correlations of WT205 with MAR, conflicting with negative estimates of 0.55 and 0.12 reported by Johnston et al. (1992) and Splan et al. (2002), respectively. Five studies summarized by Koots et al. (1994b) had a mean genetic correlation of 0.17 between direct WT205 and MAR, which is unfavorable and supported by the present study. Further, Splan et al. (1998) showed a positive genetic correlation between yearling weight and MAR, which was weaker but similarly favorable to the genetic correlation found in the present study. The genetic correlations of maternal components of BWT and WT205 with carcass traits were low and generally near zero. There was a weak trend for maternal BWT to have a positive genetic correlation with FAT (0.13 ± 0.10) and therefore a negative genetic correlation with PLY ( 0.12 ± 0.10). Genetic correlations of maternal WT205 were near zero with FAT ( 0.02 ± 0.14), MAR ( 0.07 ± 0.15), and PLY (0.13 ± 0.14). Maternal WT205 had a moderately positive genetic correlation with HCW (0.27 ± 0.10), but this correlation was lower than an analogous parameter (0.61) reported by Splan et al. (2002). The study of Crews and Kemp (1999) found a negative genetic correlation of 0.23 between maternal BWT and REA. Splan et al. (2002) found that maternal WT205 had a moderately positive genetic correlation of 0.29 with REA, results generally supported by the present study. The corresponding genetic correlation of maternal WT205 with REA reported by Crews and Kemp (1999) was positive as well. Further, Crews and Kemp (1999) showed no genetic association between PLY and maternal components of BWT or WT205. For completeness, residual correlations among growth and carcass traits are reported in Table 5. Both FAT and PLY had small and generally non-significant residual correlations with growth traits. Residual correlations of HCW with growth traits were uniformly positive, weak with BWT, strong with WT205 and moderate to high with PWG. Similarly, REA had moderately positive residual correlations with WT205 and PWG, but a negligible residual correlation with BWT. Residual correlations involving MAR were non-significant with BWT, but weak, negative and therefore favorable with WT205 and PWG. CONCLUSION Comprehensive genetic improvement programs for beef cattle should consider not only direct and maternal components of growth, but end product traits as well. Heritabilities for growth and carcass traits were moderate to high, suggesting that selection could be used to improve these traits. Genetic correlations among direct components of growth were moderately positive although genetic correlations between direct and maternal effects for growth were either negative (within traits) or near zero (across traits). Genetic correlations of component carcass traits related to muscling with those related to fat were generally negative. Genetic correlations indicated that selection for growth rates or weights would not be strongly antagonistic to improvement in carcass lean yield or meat quality. ACKNOWLEDGMENTS Funding support and access to data from the Canadian Charolais Association, as well as additional funding support from the Agriculture and Agri-Food Canada Matching Investment initiative for this project are gratefully acknowledged. The technical expertise of M. Brooks and R. Crews in database management was extremely valuable. Benyshek, L. L Heritabilities for growth and carcass traits estimated from data on Herefords under commercial conditions. J. Anim. Sci. 53: BIF Guidelines for Uniform beef improvement programs. 8th ed. Animal and Dairy Science Department, University of Georgia, Athens, GA. Caron, N. and Kemp, R. A Development of genetic evaluation, selection and testing procedures for carcass and meat quality traits. Final Report. Canadian Charolais Association and Agriculture and Agri-Food Canada Research Centre, Lethbridge, AB. August pp. 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: Gilmour, A. R., Gogel, B. J., Cullis, B. R., Welham, S. J. and Thompson, R ASReml user guide release VSN International Ltd., Hemel Hempstead, HP1 1ES, UK. Johnston, 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. 72:

9 CREWS ET AL. BEEF GROWTH AND CARCASS GENETIC PARAMETERS 597 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. 62: Koots, K. R., Gibson, J. P. and Wilton, J. W. 1994b. Analyses of published genetic parameter estimates for beef production traits. 2. Phenotypic and genetic correlations. Anim. Breed. Abstr. 62: MacNeil, M. D., Cundiff, L. V., Dinkel, C. A. and Koch, R. M Genetic correlations among sex-limited traits in beef cattle. J. Anim. Sci. 58: Meyer, K., Johnston, D. J. and Graser, H.-U Estimates of the complete genetic covariance matrix for traits in multi-trait genetic evaluation of Australian Hereford cattle. Aust. J. Ag. Res. 55: Mwansa, P. B., Kemp, R. A., Crews, D. H., Jr., Kastelic, J. P., Bailey, D. R. C. and Coulter, G. H Comparison of models for genetic evaluation of scrotal circumference in crossbred bulls. J. Anim. Sci. 78: Splan, R. K., Cundiff, L. V. and Van Vleck, L. D Genetic parameters for sex-specific traits in beef cattle. J. Anim. Sci. 76: Splan, R. K., Cundiff, L. V., Dikeman, M. E. and Van Vleck, L. D Estimates of parameters between direct and maternal genetic effects for weaning weight and direct genetic effects for carcass traits in crossbred cattle. J. Anim. Sci. 80: Wilson, D. E., Willham, R. L., Nothcutt, 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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