Estimation of genetic and phenotypic parameters for ultrasound and carcass merit traits in crossbred beef cattle

Ultrasound measurements of 852 crossbred steers along with carcass merit measurements on 756 of them were used to examine their genetic and phenotypic parameters. Traits including ultrasound backfat thickness (UBF), ultrasound ribeye area (UREA), ultrasound marbling (UMAR), carcass weight (CWT), carcass grade fat (CGF), carcass average backfat thickness (CABF), carcass ribeye area (CREA), carcass marbling score (CMAR), and carcass lean meat yield (CLMY) were measured on 6 yr of residual feed intake trials from 2003 to 2008. Pairwise bivariate animal models were performed for each combination of traits using ASReml software to estimate heritability, phenotypic and genetic correlations among the traits. Significant fixed effects (contemporary group, and sire breed), covariates (age of dam, slaughter weight, and start test age of animal), and random additive effect were fitted in the models. The heritability estimates for UBF, UREA, UMAR, CWT, CGF, CABF, CREA, CMAR, and CLMY were 0.31, 0.17, 0.37, 0.40, 0.22, 0.25, 0.24, 0.38, and 0.28, respectively. Most of the phenotypic correlations were significant (

Phenotypic and genetic relationships among feeding behavior traits, feed intake, and residual feed intake in steers fed grower and finisher diets

Data from a 3-yr feeding trial of crossbred steers (n = 331) were used to examine the relationship between feeding behavior traits and feed efficiency in steers fed grower and finisher diets, successively. There were 2 feeding periods each year whereby the steers were fed a grower diet in the first feeding period (P1) and a finisher diet in the second feeding period (P2). Each feeding period lasted for a minimum of 10 wk, ad libitum. In addition to feed intake, records on 3 measures of feeding behavior [feeding duration (FD), head-down time (HDT), and feeding frequency (FF)] were collected using the GrowSafe feeding system. Residual feed intake (RFI) was calculated by regression, after which the steers were classified as low (0.5 SD) from the mean. The steers had greater (P 0.90) were found for FR among the RFI classes. For the growing period and finishing period, respectively, FD had phenotypic correlations with HDT (0.79, 0.83), FF (0.14, 0.55), DMI (0.38, 0.34), and FR (-0.34, -0.21). Heritability estimates in P1 and P2 for FD, HDT, and FF were 0.25 ± 0.16, 0.14 ± 0.11; 0.14 ± 0.15, 0.09 ± 0.10; and 0.56 ± 0.19, 0.59 ± 0.18, respectively. Genetic correlations between P1 and P2 were 0.91 ± 0.26, 0.93 ± 0.37, and 0.94 ± 0.11 for FD, HDT, and FF, respectively. The results suggest that it may be appropriate to include feeding behavior traits as covariates to indicate measure(s) of animal activity in the calculation of RFI. Feeding behavior phenotypes were greater during the grower-fed period than the finisher-fed period. During these feeding periods, efficient steers exhibited fewer FF, shorter FD, and shorter HDT than inefficient steers

Genetic parameters and genotype x environment interaction for feed efficiency traits in steers fed grower and finisher diets

The objective of this study was to examine the genetic parameters and genetic correlations of feed efficiency traits in steers (n = 490) fed grower or finisher diets in 2 feeding periods. A bivariate model was used to estimate phenotypic and genetic parameters using steers that received the grower and finisher diets in successive feeding periods, whereas a repeated animal model was used to estimate the permanent environmental effects. Genetic correlations between the grower-fed and finisher-fed regimens were 0.50 ± 0.48 and 0.78 ± 0.43 for residual feed intake (RFI) and G:F, respectively. The moderate genetic correlation between the 2 feeding regimens may indicate the presence of a genotype × environment interaction for RFI. Permanent environmental effects (expressed in percentage of phenotypic variance) were detected in the grower-fed steers for ADG (38%), DMI (30%), RFI (18%), and G:F (40%) and also in the finisher-fed steers for ADG (28%), DMI (35%), metabolic mid-weight (23%), and RFI (10%). Heritability estimates were 0.08 ± 0.10 and 0.14 ± 0.15 for the grower-fed steers and 0.42 ± 0.16 and 0.40 ± 17 for the finisher-fed steers for RFI and G:F, respectively. The dependency of the RFI on the feeding regimen may have serious implications when selecting animals in the beef industry. Because of the higher cost of grains, feed efficiency in the feedlot might be overemphasized, whereas efficiency in the cow herd and the backgrounding segments may have less emphasis. These results may also favor the retention (for subsequent breeding) of cows whose steers were efficient in the feedlot sector. Therefore, comprehensive feeding trials may be necessary to provide more insight into the mechanisms surrounding genotype × environment interaction in steers

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved

Associations of marker panel scores with feed intake and efficiency traits in beef cattle using preselected single nucleotide polymorphisms

Because of the moderate heritability and the expense associated with collecting feed intake data, effective selection for residual feed intake would be enhanced if marker-assisted evaluation were used for accurate estimation of genetic merit. In this study, a suite of genetic markers predictive of residual feed intake, DMI, and ADG were preselected using singlemarker regression analysis, and the top 100 SNP were analyzed further to provide prediction equations for the traits. The data used consisted of 728 spring-born beef steers, offspring of a cross between a composite dam line and Angus, Charolais, or University of Alberta hybrid bulls. Feed intake data were collected over a 5-yr period, with 2 groups (fall-winter and winter-spring) tested every year. Training and validation data sets were obtained by splitting the data into 2 distinct sets, by randomly splitting the data into training and testing sets based on sire family (split 1) in 5 replicates or by retaining all animals with no known pedigree relationships as the validation set (split 2). A total of 37,959 SNP were analyzed by single-marker regression, of which only the top 100 that corresponded to a Pvalu