Genetic correlation of traits measured by ultrasound at yearling and 18 months of age in Nellore beef cattle

Genetic Parameter Estimates for Carcass and Meat Quality Traits and Their Genetic Associations With Sexual Precocity Indicator Traits in Nellore Cattle

For developing beef cattle breeding programmes, it is essential to understand the genetic basis of economically relevant traits, such as carcass, meat quality and female sexual precocity. However, the direct selection of most of these traits is a challenge for producers because of the high cost and measurement difficulty. Genetic correlation estimates between carcass and meat quality traits obtained after slaughter and sexual precocity indicator traits in Nellore are limited in the literature. Thus, this study aimed to estimate genetic parameters for longissimus muscle area (LMA), backfat thickness (BF), hot carcass weight (HCW), shear force tenderness (SF), marbling score (MARB), intramuscular fat content (IMF), age at first calving (AFC), heifer pregnancy (HP) and scrotal circumference (SC) in Nellore cattle, using pedigree and genomic information. For this, data from 6910 young bulls with phenotypic information for carcass and meat traits, 230,682 for sexual precocity indicator traits, and 17,850 animals genotyped with or imputed to the Illumina Bovine HD BeadChip were used. The (co)variance components and genetic parameters were estimated considering BLUP and single-step GBLUP (ssGBLUP) models via Bayesian inference using the GIBBSF90+ software. The multi-trait animal model included additive and residual genetic effects as random; the fixed effects of contemporary group (for all traits) and date of analysis as classes (for BF, SF and MARB); and the linear effects of age at slaughter (all carcass and meat traits) and age at yearling (YW and SC) as covariates. Heritability estimates ranged from 0.13 to 0.34 for carcass and meat quality traits, and for SC, AFC and HP, were 0.33, 0.07 and 0.29, respectively. Favourable genetic correlations were estimated between YW-HCW (0.79 ± 0.03), YW-LMA (0.28 ± 0.05), YW-SC (0.35 ± 0.03), HCW-LMA (0.44 ± 0.05), HCW-SF (-0.22 ± 0.09), HCW-SC (0.19 ± 0.05), MARB-IMF (0.90 ± 0.07), SF-IMF (-0.20 ± 0.11), BF-MARB (0.29 ± 0.08), BF-IMF (0.22 ± 0.09), BF-AFC (-0.21 ± 0.07) and BF-HP (0.24 ± 0.10). In general, the correlations between carcass traits and those of meat quality were low to moderate. Additionally, carcass and meat quality traits did not exhibit strong genetic correlations with female precocity indicators. So, to achieve significant genetic advances in female sexual indicator traits, carcass composition and meat quality, these traits must compose selection indices for Nellore cattle.

Estimation of genetic correlations and genomic prediction accuracy for reproductive and carcass traits in Hanwoo cows

This study estimated the heritabilities (h2) and genetic and phenotypic correlations between reproductive traits, including calving interval (CI), age at first calving (AFC), gestation length (GL), number of artificial inseminations per conception (NAIPC), and carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) in Korean Hanwoo cows. In addition, the accuracy of genomic predictions of breeding values was evaluated by applying the genomic best linear unbiased prediction (GBLUP) and the weighted GBLUP (WGBLUP) method. The phenotypic data for reproductive and carcass traits were collected from 1,544 Hanwoo cows, and all animals were genotyped using Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The genetic parameters were estimated using a multi-trait animal model using the MTG2 program. The estimated h2 for CI, AFC, GL, NAIPC, CWT, EMA, BF, and MS were 0.10, 0.13, 0.17, 0.11, 0.37, 0.35, 0.27, and 0.45, respectively, according to the GBLUP model. The GBLUP accuracy estimates ranged from 0.51 to 0.74, while the WGBLUP accuracy estimates for the traits under study ranged from 0.51 to 0.79. Strong and favorable genetic correlations were observed between GL and NAIPC (0.61), CWT and EMA (0.60), NAIPC and CWT (0.49), AFC and CWT (0.48), CI and GL (0.36), BF and MS (0.35), NAIPC and EMA (0.35), CI and BF (0.30), EMA and MS (0.28), CI and AFC (0.26), AFC and EMA (0.24), and AFC and BF (0.21). The present study identified low to moderate positive genetic correlations between reproductive and CWT traits, suggesting that a heavier body weight may lead to a longer CI, AFC, GL, and NAIPC. The moderately positive genetic correlation between CWT and AFC, and NAIPC, with a phenotypic correlation of nearly zero, suggesting that the genotype-environment interactions are more likely to be responsible for the phenotypic manifestation of these traits. As a result, the inclusion of these traits by breeders as selection criteria may present a good opportunity for developing a selection index to increase the response to the selection and identification of candidate animals, which can result in significantly increased profitability of production systems.

Weighted genomic prediction for growth and carcass-related traits in Nelore cattle

This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass-related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n = 7769) were genotyped with the low-density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single-step genome-wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass-related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass-related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass-related traits in young animals.

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

Simple Summary

Tropical pasture-based beef production systems play a vital role in global food security. The importance of promoting sustainable intensification of such systems has been debated worldwide. Demand for beef is growing together with concerns over the impact of its production on the environment. Implementing sustainable livestock intensification programs relies on animal genetic improvement. In tropical areas, the lack of sexual precocity is a bottleneck for cattle efficiency, directly impacting the sustainability of production systems. In the present review we present and discuss the state of the art of genetic evaluation for sexual precocity in Bos indicus beef cattle, covering the definition of measurable traits, genetic parameter estimates, genomic analyses, and a case study of selection for sexual precocity in Nellore breeding programs.

Abstract

Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.

Genetic analysis of growth, visual scores, height, and carcass traits in Nelore cattle

Covariance components were estimated for growth traits (BW, birth weight; WW, weaning weight; YW, yearling weight), visual scores (BQ, breed quality; CS, conformation; MS, muscling; NS, navel; PS, finishing precocity), hip height (HH), and carcass traits (BF, backfat thickness; LMA, longissimus muscle area) measured at yearling. Genetic gains were obtained and validation models on direct and maternal effects for BW and WW were fitted. Genetic correlations of growth traits with CS, PS, MS, and HH ranged from 0.20 ± 0.01 to 0.94 ± 0.01 and were positive and low with NS (0.11 ± 0.01 to 0.20 ± 0.01) and favorable with BQ (0.14 ± 0.02 to 0.37 ± 0.02). Null to moderate genetic correlations were obtained between growth and carcass traits. Genetic gains were positive and significant, except for BW. An increase of 0.76 and 0.72 kg is expected for BW and WW, respectively, per unit increase in estimated breeding value (EBV) for direct effect and an additional 0.74 and 1.43, respectively, kg per unit increase in EBV for the maternal effect. Monitoring genetic gains for HH and NS is relevant to maintain an adequate body size and a navel morphological correction, if necessary. Simultaneous selection for growth, morphological, and carcass traits in line with improve maternal performance is a feasible strategy to increase herd productivity.

Random-effect meta-analysis of genetic parameter estimates for carcass and meat quality traits in beef cattle

Considerable variability of genetic parameter estimates is observed among different studies for the same trait, which is associated with the distinct effects included in the statistical model, population breed, and sample sizes. The random-effect meta-analysis summarizes genetic parameters considering the heterogeneity among studies. Therefore, the aim of this study was to perform a random-effect meta-analysis of heritability and genetic correlation estimates for carcass and meat quality traits in beef cattle. A total of 152 estimates of heritability and 83 genetic correlations for longissimus muscle area (LMA), back fat thickness (BFT), and marbling score (MRB) were used. High heterogeneity among published studies was observed for all traits, indicating the need of a random-effects model to perform the analysis. Estimates of heritability through the meta-analysis using the random-effects model were high (0.30 to 0.34), indicating that fast genetic progress can be obtained for these traits. However, genetic correlations had low magnitude (lower than 0.25), which suggested that all three traits should be included in the selection scheme.

Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo

There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.

Estimation of Genetic Parameters and Correlation between Yearling Ultrasound Measurements and Carcass Traits in Hanwoo Cattle

Simple Summary

Knowledge of genetic parameters is essential to obtain breeding values in order to increase the response to selection and incorporate novel traits in designing a breeding program. There is a growing demand for the genetic improvement of carcass traits in the Korean beef industry. The use of yearling ultrasound measurements as indicator traits can be an efficient way to evaluate carcass traits. To date, the assessment of genetic parameters for ultrasound measurements in Hanwoo cattle is still limited. Therefore, this study was conducted to estimate the heritability, and the genetic and phenotypic correlations of yearling ultrasonic and carcass traits in Hanwoo cattle. The results revealed moderate to high heritability estimates for the traits of interest, which indicate a probable increase in the response to selection for these traits. Moreover, high and favorable genetic correlations were observed between carcass traits and their corresponding ultrasound measurements. Our findings suggest that the inclusion of yearling ultrasound data on potential replacements would be suitable as a selection tool for genetic improvement of carcass traits in Hanwoo breeding programs.

Abstract

Genetic parameters have a significant role in designing a breeding program and are required to evaluate economically important traits. The objective of this study was to estimate heritability and genetic correlation between yearling ultrasound measurements, such as backfat thickness (UBFT), eye muscle area (UEMA), intramuscular fat content (UIMF), and carcass traits, such as backfat thickness (BFT), carcass weight (CW), eye muscle area (EMA), marbling score (MS) at approximately 24 months of age, as well as yearling weight (YW) in Hanwoo bulls (15,796) and steers (5682). The (co) variance components were estimated using a multi-trait animal model. Moderate to high heritability estimates were obtained and were 0.42, 0.50, 0.56, and 0.59 for CW, EMA, BFT, and MS, respectively. Heritability estimates for yearling measurements of YW, UEMA, UBFT, and UIMF were 0.31, 0.32, 0.30, and 0.19, respectively. Favorable and strong genetic correlations were observed between UIMF and MS (0.78), UBFT and BFT (0.63), and UEMA and EMA (0.65). Moreover, the estimated genetic correlation between YW and CW was high (0.84) and relatively moderate between YW and EMA (0.43). These results suggest that genetic improvement can be achieved for carcass traits when using yearling ultrasound measurements as selection criteria in ongoing Hanwoo breeding programs.

Genetic parameters for performance, feed efficiency, and carcass traits in Senepol heifers

Improving feed efficiency is a key breeding goal in the beef cattle industry. In this study, we estimated the genetic parameters for feed efficiency and carcass traits in Senepol cattle raised in tropical regions. Various indicators of feed efficiency [gain to feed ratio (G:F), feed conversion ratio (FCR), residual weight gain (RG), residual intake and body weight gain (RIG), and residual feed intake (RFI)] as well as growth [final BW, average daily gain (ADG), and DM intake (DMI)], and carcass [rib-eye area (REA), backfat thickness (BF), intramuscular fat score, and carcass conformation score] traits were included in the study. After data editing, records from 1 393 heifers obtained between 2009 and 2018 were used for the analyses. We fitted an animal model that included contemporary group (animals from the same farm that were evaluated in the same test season) as the fixed effect, and a linear effect of animal age at the beginning of the test as a covariate; in addition to random direct additive genetic and residual effects. The (co)variance components were estimated by Bayesian inference in uni- and bivariate analyses. Our results showed that feed efficiency indicators derived from residual variables such as RG, RIG, and RFI can be improved through genetic selection (h² = 0.14 ± 0.06, 0.13 ± 0.06, and 0.20 ± 0.08, respectively). Variables calculated as ratios such as G:F and FCR were more influenced by environmental factors (h² = 0.08 ± 0.05 and 0.09 ± 0.05), and were, therefore, less suitable for use in breeding programs. The traits with the greatest and impact on genetic progress in feed efficiency were ADG, REA, and BF. The traits with the greatest and least impact on growth and carcass traits were RG and RFI, respectively. Selection for feed efficiency will result in distinct overall effects on the growth and carcass traits of Senepol heifers. Direct selection for lower RFI may reduce DMI and increase carcass fatness at the finishing stage, but it might also result in reduced growth and muscle deposition. Residual BW gain is associated with the highest weight gain and zero impact on REA and BF, however, it is linked to higher feed consumption. Thus, the most suitable feed efficiency indicator was RIG, as it promoted the greatest decrease in feed intake concomitant with faster growth, with a similar impact on carcass traits when compared to the other feed efficiency indicators.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm²), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Quantitative study of genetic gain for growth, carcass, and morphological traits of Nelore cattle

The aim of this study was to evaluate the genetic variability, genetic and phenotypic associations, and genetic gains of birth (BW), weaning (WW), and yearling (YW) weights, loin muscle area (LMA), backfat thickness (BF), rump fat thickness (RF), scores of body structure (BS), finishing precocity (FS), and muscling (MS) in Nelore cattle. Genetic parameters were obtained through Bayesian inference using BLUPF90 programs. All studied traits showed genetic variability, with heritability ranging from 0.29 to 0.47. In all studied ages, weights presented positive genetic correlations with LMA (ranging from 0.13 to 0.53), being generally stronger in comparison with the other carcass traits analyzed (BF and RF). Similarly, weights were higher genetic associated with BS (0.47–0.92) than with FS (0.18–0.62) and MS (0.22–0.65), respectively. The BF and RF showed positive and moderate genetic associations with FS and MS (0.31–0.36). Genetic trends were significant (P < 0.05) and favorable for WW, YW, and visual scores. Selection for increasing BW, WW, YW, and LMA will result in modest or no change in BF and RF (correlated response ranging from −0.04 to 0.07 mm per generation). In this population, carcass traits must be included in the selection indexes to obtain genetic gains in carcass quality, if desired.

Genetic analysis of female reproductive efficiency, scrotal circumference and growth traits in Nelore cattle

This study estimated the genetic variability and correlations between growth and reproductive traits in Nelore cattle. Annual genetic changes in female reproductive traits were also evaluated using repeated measures. Direct heritability was moderate for the growth traits and scrotal circumference (0.235 ± 0.015 to 0.443 ± 0.006), indicating genetic variability in these traits in the population studied. Maternal heritability was low for weaning height (0.042 ± 0.012) and weaning weight (0.112 ± 0.013). Regarding female reproductive traits, gestation length (GL) exhibited a slightly higher heritability (0.179 ± 0.006) than days to calving (DC) and calving interval (CI) (0.123 ± 0.006 and 0.105 ± 0.008, respectively). Repeatability was lower for GL (0.222 ± 0.006) compared to DC (0.318 ± 0.004) and CI (0.406 ± 0.006). Moderate and positive genetic correlations were obtained between the growth traits and scrotal circumference, ranging from 0.193 ± 0.019 to 0.519 ± 0.007. Weaning height showed low and positive genetic associations with GL (0.138 ± 0.007) and CI (0.189 ± 0.016), but was not correlated with DC (-0.016 ± 0.011). Similarly, weaning weight, yearling height or yearling weight was not associated with any of the female reproductive traits. The genetic correlations between scrotal circumference and female reproductive traits were negative and favorable, but low: -0.116 ± 0.020 (GL), -0.084 ± 0.028 (DC), and -0.054 ± 0.038 (CI). Favorable genetic associations were estimated between all female reproductive traits, ranging from 0.170 ± 0.040 to 0.442 ± 0.050. Genetic changes were favorable for GL and CI (-0.02 days/year and -0.11 days/year, respectively) and unfavorable for DC (0.16 days/year). In conclusion, selection for higher growth using the estimated breeding values for height and weight will not affect the reproductive performance of Nelore females. Nonetheless, simultaneous selection for growth and reproductive traits is possible.

Selection of performance-tested young bulls and indirect responses in commercial beef cattle herds on pasture and in feedlots

BACKGROUND

Central testing is used to select young bulls which are likely to contribute to increased net income of the commercial beef cattle herd. We present genetic parameters for growth and reproductive traits on performance-tested young bulls and commercial animals that are raised on pasture and in feedlots.

METHODS

Records on young bulls and heifers in performance tests or commercial herds were used. Genetic parameters for growth and reproductive traits were estimated. Correlated responses for commercial animals when selection was applied on performance-tested young bulls were computed.

RESULTS

The 90% highest posterior density (HPD90) intervals for heritabilities of final weight (FW), average daily gain (ADG) and scrotal circumference (SC) ranged from 0.41 to 0.49, 0.23 to 0.30 and 0.47 to 0.57, respectively, for performance-tested young bulls on pasture, from 0.45 to 0.60, 0.20 to 0.32 and 0.56 to 0.70, respectively, for performance-tested young bulls in feedlots, from 0.29 to 0.33, 0.14 to 0.18 and 0.35 to 0.45, respectively, for commercial animals on pasture, and from 0.24 to 0.44, 0.13 to 0.24 and 0.35 to 0.57 respectively, for commercial animals in feedlots. The HPD90 intervals for genetic correlations of FW, ADG and SC in performance-tested young bulls on pasture (feedlots) with FW, ADG and SC in commercial animals on pasture (feedlots) ranged from 0.86 to 0.96 (0.83 to 0.94), 0.78 to 0.90 (0.40 to 0.79) and from 0.92 to 0.97 (0.50 to 0.83), respectively. Age at first calving was genetically related to ADG (HPD90 interval = -0.48 to -0.06) and SC (HPD90 interval = -0.41 to -0.05) for performance-tested young bulls on pasture, however it was not related to ADG (HPD90 interval = -0.29 to 0.10) and SC (HPD90 interval = -0.35 to 0.13) for performance-tested young bulls in feedlots.

CONCLUSIONS

Heritabilities for growth and SC are higher for performance-tested young bulls than for commercial animals. Evaluating and selecting for increased growth and SC on performance-tested young bulls is efficient to improve growth, SC and age at first calving in commercial animals. Evaluating and selecting performance-tested young bulls is more efficient for young bulls on pasture than in feedlots.