Random regression models for the prediction of days to weight, ultrasound rib eye area, and ultrasound back fat depth in beef cattle.

Genetic evaluation research designed to reduce the required days to a specified end point has received very little attention in pertinent scientific literature, given that its economic importance was first discussed in 1957. There are many production scenarios in today's beef industry, making a prediction for the required number of days to a single end point a suboptimal option. Random regression is an attractive alternative to calculate days to weight (DTW), days to ultrasound back fat (DTUBF), and days to ultrasound rib eye area (DTUREA) genetic predictions that could overcome weaknesses of a single end point prediction. The objective of this study was to develop random regression approaches for the prediction of the DTW, DTUREA, and DTUBF. Data were obtained from the Agriculture and Agri-Food Canada Research Centre, Lethbridge, AB, Canada. Data consisted of records on 1,324 feedlot cattle spanning 1999 to 2007. Individual animals averaged 5.77 observations with weights, ultrasound rib eye area (UREA), ultrasound back fat depth (UBF), and ages ranging from 293 to 863 kg, 73.39 to 129.54 cm, 1.53 to 30.47 mm, and 276 to 519 d, respectively. Random regression models using Legendre polynomials were used to regress age of the individual on weight, UREA, and UBF. Fixed effects in the model included an overall fixed regression of age on end point (weight, UREA, and UBF) nested within breed to account for the mean relationship between age and weight as well as a contemporary group effect consisting of breed of the animal (Angus, Charolais, and Charolais sired), feedlot pen, and year of measure. Likelihood ratio tests were used to determine the appropriate random polynomial order. Use of the quadratic polynomial did not account for any additional genetic variation in days for DTW ( > 0.11), for DTUREA ( > 0.18), and for DTUBF ( > 0.20) when compared with the linear random polynomial. Heritability estimates from the linear random regression for DTW ranged from 0.54 to 0.74, corresponding to end points of 293 and 863 kg, respectively. Heritability for DTUREA ranged from 0.51 to 0.34 and for DTUBF ranged from 0.55 to 0.37. These estimates correspond to UREA end points of 35 and 125 cm and UBF end points of 1.53 and 30 mm, respectively. This range of heritability shows DTW, DTUREA, and DTUBF to be highly heritable and indicates that selection pressure aimed at reducing the number of days to reach a finish weight end point can result in genetic change given sufficient data.

Genetic and phenotypic correlations among feed efficiency, production and selected conformation traits in dairy cows.

The difficulties and costs of measuring individual feed intake in dairy cattle are the primary factors limiting the genetic study of feed intake and utilisation, and hence the potential of their subsequent industry-wide applications. However, indirect selection based on heritable, easily measurable, and genetically correlated traits, such as conformation traits, may be an alternative approach to improve feed efficiency. The aim of this study was to estimate genetic and phenotypic correlations among feed intake, production, and feed efficiency traits (particularly residual feed intake; RFI) with routinely recorded conformation traits. A total of 496 repeated records from 260 Holstein dairy cows in different lactations (260, 159 and 77 from first, second and third lactation, respectively) were considered in this study. Individual daily feed intake and monthly BW and body condition scores of these animals were recorded from 5 to 305 days in milk within each lactation from June 2007 to July 2013. Milk yield and composition data of all animals within each lactation were retrieved, and the first lactation conformation traits for primiparous animals were extracted from databases. Individual RFI over 301 days was estimated using linear regression of total 301 days actual energy intake on a total of 301 days estimated traits of metabolic BW, milk production energy requirement, and empty BW change. Pair-wise bivariate animal models were used to estimate genetic and phenotypic parameters among the studied traits. Estimated heritabilities of total intake and production traits ranged from 0.27±0.07 for lactation actual energy intake to 0.45±0.08 for average body condition score over 301 days of the lactation period. RFI showed a moderate heritability estimate (0.20±0.03) and non-significant phenotypic and genetic correlations with lactation 3.5 % fat-corrected milk and average BW over lactation. Among the conformation traits, dairy strength, stature, rear attachment width, chest width and pin width had significant (P<0.05) moderate to strong genetic correlations with RFI. Combinations of these conformation traits could be used as RFI indicators in the dairy genetic improvement programmes to increase the accuracy of the genetic evaluation of feed intake and utilisation included in the index.

Phenotypic and genetic relationships of feeding behavior with feed intake, growth performance, feed efficiency, and carcass merit traits in Angus and Charolais steers.

Feeding behavior traits including daily feeding duration (FD), daily feeding head down time (HD), average feeding duration per feeding event (FD_AVE), average feeding head down time per feeding event (HD_AVE), feeding frequency (FF), and meal eating rate (ER) were analyzed to estimate their phenotypic and genetic correlations with feed intake, growth performance, residual feed intake (RFI), ultrasound, and carcass merit traits in Angus and Charolais finishing steers. Heritability estimates for FD, HD, FD_AVE, HD_AVE, FF, and ER were 0.27 ± 0.09 (SE), 0.25 ± 0.09, 0.19 ± 0.06, 0.11 ± 0.05, 0.24 ± 0.08, and 0.38 ± 0.10, respectively, in the Angus population and 0.49 ± 0.12, 0.38 ± 0.11, 0.31 ± 0.09, 0.29 ± 0.10, 0.43 ± 0.11, and 0.56 ± 0.13, respectively, in the Charolais population. In both the Angus and Charolais steer populations, FD and HD had relatively stronger phenotypic (0.17 ± 0.06 to 0.32 ± 0.04) and genetic (0.29 ± 0.17 to 0.54 ± 0.18) correlations with RFI in comparison to other feeding behavior traits investigated, suggesting the potential of FD and HD as indicators in assessing variation of RFI. In general, feeding behavior traits had weak phenotypic correlations with most of the ultrasound and carcass merit traits; however, estimated genetic correlations of the feeding behavior traits with some fat deposition related traits were moderate to moderately strong but differed in magnitude or sign between the Angus and Charolais steer populations, likely reflecting their different biological types. Genetic parameter estimation studies involving feeding behavior traits in beef cattle are lacking and more research is needed to better characterize the relationships between feeding behavior and feed intake, growth, feed utilization, and carcass merit traits, in particular with respect to different biological types of cattle.

Cluster and meta-analyses of genetic parameters for feed intake traits in growing beef cattle.

A data set based on 50 studies including feed intake and utilization traits was used to perform a meta-analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain (ADG); residual feed intake (RFI); metabolic body weight (MBW); feed conversion ratio (FCR); and daily dry matter intake (DMI) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta-analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta-analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG, DMI, RFI, MBW and FCR were 0.32 ± 0.04, 0.39 ± 0.03, 0.31 ± 0.02, 0.31 ± 0.03 and 0.26 ± 0.03, respectively. Pooled genetic correlation estimates ranged from -0.15 to 0.67 among ADG, DMI, RFI, MBW and FCR. These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.

Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle.

In beef cattle, phenotypic data that are difficult and/or costly to measure, such as feed efficiency, and DNA marker genotypes are usually available on a small number of animals of different breeds or populations. To achieve a maximal accuracy of genomic prediction using the phenotype and genotype data, strategies for forming a training population to predict genomic breeding values (GEBV) of the selection candidates need to be evaluated. In this study, we examined the accuracy of predicting GEBV for residual feed intake (RFI) based on 522 Angus and 395 Charolais steers genotyped on SNP with the Illumina Bovine SNP50 Beadchip for 3 training population forming strategies: within breed, across breed, and by pooling data from the 2 breeds (i.e., combined). Two other scenarios with the training and validation data split by birth year and by sire family within a breed were also investigated to assess the impact of genetic relationships on the accuracy of genomic prediction. Three statistical methods including the best linear unbiased prediction with the relationship matrix defined based on the pedigree (PBLUP), based on the SNP genotypes (GBLUP), and a Bayesian method (BayesB) were used to predict the GEBV. The results showed that the accuracy of the GEBV prediction was the highest when the prediction was within breed and when the validation population had greater genetic relationships with the training population, with a maximum of 0.58 for Angus and 0.64 for Charolais. The within-breed prediction accuracies dropped to 0.29 and 0.38, respectively, when the validation populations had a minimal pedigree link with the training population. When the training population of a different breed was used to predict the GEBV of the validation population, that is, across-breed genomic prediction, the accuracies were further reduced to 0.10 to 0.22, depending on the prediction method used. Pooling data from the 2 breeds to form the training population resulted in accuracies increased to 0.31 and 0.43, respectively, for the Angus and Charolais validation populations. The results suggested that the genetic relationship of selection candidates with the training population has a greater impact on the accuracy of GEBV using the Illumina Bovine SNP50 Beadchip. Pooling data from different breeds to form the training population will improve the accuracy of across breed genomic prediction for RFI in beef cattle.

Phenotypic and genetic relationships of feed efficiency with growth performance, ultrasound, and carcass merit traits in Angus and Charolais steers.

Feed efficiency is of particular importance to the beef industry, as feed costs represent the single largest variable cost in beef production systems. Selection for more efficient cattle will lead to reduction of feed related costs, but should not have adverse impacts on quality of the carcass. In this study, we evaluated phenotypic and genetic correlations of residual feed intake (RFI), RFI adjusted for end-of-test ultrasound backfat thickness (RFIf), and RFI adjusted for ultrasound backfat thickness and LM area (RFIfr) with growth, ultrasound, and carcass merit traits in an Angus population of 551 steers and in a Charolais population of 417 steers. In the Angus steer population, the phenotypic and genetic correlation of RFI with carcass merit traits including HCW, carcass backfat, carcass LM area, lean meat yield, and carcass marbling were not significant or weak with correlations coefficients ranging from -0.0007 ± 0.05 to 0.18 ± 0.21. In the Charolais steer population, the phenotypic and genetic correlations of RFI with the carcass merit traits were also weak, with correlation coefficients ranging from -0.07 ± 0.06 to 0.19 ± 0.18, except for the genetic correlation with carcass average backfat, which was moderate with a magnitude of 0.42 ± 0.29. Inclusion of ultrasound backfat thickness in the model to predict the expected daily DMI for maintenance explained on average an additional 0.5% variation of DMI in the Angus steers and 2.3% variation of DMI in the Charolais steer population. Inclusion of both the ultrasound backfat and LM area in the model explained only 0.7% additional variance in DMI in the Angus steer population and only 0.6% in the Charolais steer population on top of the RFIf model. We concluded that RFIf adjusted for ultrasound backfat at the end of the test will lead to decreases of both the phenotypic and genetic correlations with carcass backfat and marbling score to a greater extent for late-maturing beef breeds such as Charolais than for early-maturing beef breeds such as Angus. However, further inclusion of ultrasound LM area on top of the final ultrasound backfat in the model of calculating RFI had little effect in reducing the correlations of RFI with the carcass merit traits.

Relationships among performance, residual feed intake, and product quality of progeny from Red Angus sires divergent for maintenance energy EPD.

Energy expenditure is a physiological process that may be closely associated with residual feed intake (RFI). The maintenance energy (ME(M)) EPD was developed by the Red Angus Association of America (RAAA) and is used as an indicator of energy expenditure. The objectives of this study were to evaluate and quantify the following relationships using progeny of Red Angus (RA) sires divergent for ME(M) EPD: 1) postweaning RFI and finishing phase feed efficiency (FE), 2) postweaning RFI and end-product quality, and 3) postweaning RFI and sire ME(M) EPD. A total of 12 RA sires divergent for ME(M) EPD were chosen using the RAAA-generated ME(M) EPD values and were partitioned into 2 groups: high ME(M) EPD (≥4 Mcal/mo) and low ME(M) EPD (<4 Mcal/mo), based on the breed average of 4 Mcal/mo. Commercial crossbred cows were inseminated to produce 3 cohorts of progeny, which were tested for postweaning RFI (cohorts 1, 2, and 3) and finishing phase FE (cohorts 1 and 3). Results indicate that postweaning RFI and finishing phase FE of steer progeny tended to be positively correlated (r = 0.38; P = 0.06) in cohort 1 and were positively correlated (r = 0.50; P = 0.001) in cohort 3. In addition, postweaning RFI was not phenotypically correlated (P > 0.05) with any carcass traits or end-product quality measurements. Sire ME(M) EPD was phenotypically correlated (P < 0.05) with carcass traits in cohort 1 (HCW, LM area, KPH, fat thickness, and yield grade) and cohort 2 (KPH and fat thickness). Since variation in measured LM area was not explained by the genetic potential of rib eye area EPD, and therefore, the observed correlation between sire ME(M) EPD and measured LM area may suggest an association between ME(M) EPD and LM area. A correlation (r = 0.24; P = 0.02) was observed between postweaning RFI and ultrasound intramuscular fat percentage in cohort 2 but was not detected in cohorts 1 or 3. In addition, no phenotypic relationship was observed (P > 0.05) between progeny postweaning RFI and sire ME(M) EPD. Therefore, results suggest 1) RFI measured during the postweaning growth phase is indicative of FE status in the finishing phase, 2) neither RFI nor sire ME(M) EPD negatively affected carcass or end-product quality, and 3) RFI and sire ME(M) EPD are not phenotypically associated.

Genetic associations between feed efficiency measured in a performance test station and performance of growing cattle in commercial beef herds.

Interest in selection for improved feed efficiency is increasing, but before any steps are taken toward selecting for feed efficiency, correlations with other economically important traits must first be quantified. The objective of this study was to quantify the genetic associations between feed efficiency measured during performance testing and linear type traits, BW, live animal value, and carcass traits recorded in commercial herds. Feed efficiency data were available on 2,605 bulls from 1 performance test station. There were between 10,384 and 93,442 performance records on type traits, BW, animal value, or carcass traits from 17,225 commercial herds. (Co)variance components were estimated using linear mixed animal models. Genetic correlations between the muscular type traits in commercial animals and feed conversion ratio (-0.33 to -0.25), residual feed intake (RFI; -0.33 to -0.22), and residual BW gain (RG; 0.24 to 0.27) suggest that selection for improved feed efficiency should increase muscling. This is further evidenced by the genetic correlations between carcass conformation of commercial animals and feed conversion ratio (-0.46), RFI (-0.37), and residual BW gain (0.35) measured in performance-tested animals. Furthermore, the genetic correlations between RFI and both ultrasonic fat depth and carcass fat score (0.39 and 0.33, respectively) indicated that selection for improved RFI will result in leaner animals. It can be concluded from the genetic correlations estimated in this study that selection for feed efficiency will have no unfavorable effects on the performance traits measured in this study and will actually lead to an improvement in performance for some traits, such as muscularity, animal price, and carcass conformation. Conversely, this suggests that genetic selection for traits such as carcass quality, muscling traits, and animal value might also be indirectly selecting for more efficient animals.

Genetic relationships between feed efficiency in growing males and beef cow performance.

Most studies on feed efficiency in beef cattle have focused on performance in young animals despite the contribution of the cow herd to overall profitability of beef production systems. The objective of this study was to quantify, using a large data set, the genetic covariances between feed efficiency in growing animals measured in a performance-test station, and beef cow performance including fertility, survival, calving traits, BW, maternal weaning weight, cow price, and cull cow carcass characteristics in commercial herds. Feed efficiency data were available on 2,605 purebred bulls from 1 test station. Records on cow performance were available on up to 94,936 crossbred beef cows. Genetic covariances were estimated using animal and animal-dam linear mixed models. Results showed that selection for feed efficiency, defined as feed conversion ratio (FCR) or residual BW gain (RG), improved maternal weaning weight as evidenced by the respective genetic correlations of -0.61 and 0.57. Despite residual feed intake (RFI) being phenotypically independent of BW, a negative genetic correlation existed between RFI and cow BW (-0.23; although the SE of 0.31 was large). None of the feed efficiency traits were correlated with fertility, calving difficulty, or perinatal mortality. However, genetic correlations estimated between age at first calving and FCR (-0.55 ± 0.14), Kleiber ratio (0.33 ± 0.15), RFI (-0.29 ± 0.14), residual BW gain (0.36 ± 0.15), and relative growth rate (0.37 ± 0.15) all suggest that selection for improved efficiency may delay the age at first calving, and we speculate, using information from other studies, that this may be due to a delay in the onset of puberty. Results from this study, based on the estimated genetic correlations, suggest that selection for improved feed efficiency will have no deleterious effect on cow performance traits with the exception of delaying the age at first calving.

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 single-marker 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 P-value <0.002 were retained. The 100 SNP were then analyzed using random regression BLUP, and only SNP that were jointly significant (P < 0.05) were included in the final marker panels. The marker effects from the selected panels were used to derive the molecular breeding values, which were calculated as a weighted sum of the number of copies of the more frequent allele at each SNP locus, with the weights being the allele substitution effects. The correlation between molecular breeding value and phenotype represented the accuracy of prediction. For all traits evaluated, accuracy across breeds was low, ranging between 0.007 and 0.414. Accuracy was least in data split 2, where the validation individuals had no pedigree relationship with animals in the training data. Given the low predictive ability observed, a large number of individuals may be needed for prediction when using such an admixed population. Further, these results suggest that breed composition of the target population in which the marker panels are likely to be used should be an important consideration when developing prediction equations across breeds, especially where an admixed population is used as the training data set.

Grass silage intake, rumen and blood variables, ultrasonic and body measurements, feeding behavior, and activity in pregnant beef heifers differing in phenotypic residual feed intake.

The objectives of this study were to quantify the phenotypic variation in residual feed intake (RFI) in pregnant beef heifers offered a grass silage diet and to characterize their productivity. Seventy-three pregnant (mean gestation d 198, SD = 27 d) Simmental and Simmental × Holstein-Friesian heifers (mean initial BW 548, SD = 47.5 kg) were offered grass silage ad libitum. Heifer DMI, BW, BCS, skeletal measurements, ultrasonic fat and muscle depth, visual muscularity score, rumen fermentation, total tract digestibility, blood metabolite and hematology variables, feeding, and activity behavior were measured during an 84-d feed intake study. After parturition calf birth weight, calving difficulty, cow serum IgG, hematology variables, and calf humoral immune status were measured. In a subset of cows (n = 28), DMI, milk yield and various body composition variables were also measured approximately 3 wk postpartum. Phenotypic RFI was calculated for each animal as the difference between actual DMI and expected DMI. Expected DMI was computed for each animal by regressing average daily DMI on conceptus-adjusted mean BW(0.75) and conceptus-adjusted ADG over an 84-d period. Within breed, heifers were ranked by RFI into low (efficient), medium, and high (inefficient) groups by dividing them into thirds. Heifers with high RFI had 8.8 and 17.1% greater (P < 0.001) DMI than medium and low RFI groups, respectively. The RFI groups did not differ in ADG or BW (P > 0.05). Residual feed intake was positively correlated with DMI (r = 0.85) but not with feed conversion ratio, ADG, or BW. The RFI groups did not differ (P > 0.05) in skeletal size, BCS, ultrasonic fat depth, total tract digestibility, calf birth weight, calving difficulty, serum IgG concentrations, or milk yield. Visual muscularity scores, initial test and postpartum ultrasonic muscle depth were negatively correlated with RFI (P < 0.05). Including mean ultrasonic muscle depth into the base RFI regression model increased its R(2) (0.29 to 0.38). Pearson rank correlation between RFI and muscle-adjusted RFI was 0.93. The results show that efficient RFI heifers consumed less feed without any compromise in growth, body composition, or maternal traits measured.

Relationship between feeding behavior and performance of feedlot steers fed barley-based diets.

The relationship between feeding behavior and performance of 274 feedlot cattle was evaluated using Charolais cross steers from 2 consecutive years averaging 293 ± 41 kg for yr 1 (n = 115) and 349 ± 41 for yr 2 (n = 159). Steers were blocked by BW and assigned to 3 (yr 1) or 4 (yr 2) feedlot pens equipped with a radio frequency identification system (GrowSafe Systems). Each pen contained 5 feeding stalls that allowed individual animal access to a feed tub suspended on load cells. The system recorded animal identification, duration, and frequency of feedings as well as the amount of feed consumed during each visit. Daily variation in DMI (DVI), calculated as the absolute difference in DMI from one day to the next, as well as eating rate were determined for each steer. Barley-based diets were delivered to meet steer ad libitum intake over the 213- and 181-d feeding periods for yr 1 and 2 of the study, respectively. The backgrounding periods included the first 85 and 56 d of yr 1 and 2, respectively, in which steers were fed a 14 to 30% concentrate diet, whereas the finishing periods included the last 116 and 101 d of feeding in yr 1 and 2, respectively, with the diet consisting of 77.9% concentrate. Steers were weighed individually every 14 d. To relate feeding behavior to performance, steers were grouped by ADG and G:F and categorized as high, average, or low (based on 1 SD greater than and less than the mean). In the backgrounding and finishing periods of both years of the study, steers classified as having high ADG exhibited greater (P < 0.001) DVI than steers classified as having average or low ADG. Total daily DMI was also greater (P < 0.001) for steers in the high ADG group than those in the low ADG group. Overall, those steers with the greatest G:F also tended (P = 0.15) to have greater DVI than average or low G:F steers. Compared with average or low G:F steers, DMI by high G:F steers in both years of the study was less during backgrounding, finishing, and overall (P = 0.02). Bunk visits and bunk attendance duration were less frequent and shorter (P ≤ 0.01) overall for high compared with low G:F steers. In this study, steers with more variable eating patterns exhibited greater ADG and tended to have greater G:F, a finding that is contrary to industry perception.

Genetic parameters for intramuscular fat percentage, marbling score, scrotal circumference, and heifer pregnancy in Red Angus cattle.

Selection criteria for yearling bulls commonly include indicators of fertility and carcass merit, such as scrotal circumference (SC) and intramuscular fat percentage (IMF). Genetic correlation estimates between ultrasound traits such as IMF and carcass marbling score (MS) with fertility traits SC and heifer pregnancy (HP) have not been reported. Therefore, the objective of this study was to estimate the genetic parameters among the indicator traits IMF and SC, and the economically relevant traits MS and HP. Records for IMF (n=73,051), MS (n=15,260), SC (n=43,487), and HP (n=37,802) were obtained from the Red Angus Association of America, and a 4-generation ancestral pedigree (n=10,460) was constructed from the 8,915 sires represented in the data. (Co)variance components were estimated using a multivariate sire model and average information REML to obtain estimates of heritability and genetic correlations. Fixed effects included contemporary group and the linear effect of age at measurement for all traits, and an additional effect of age of dam for both HP and SC. The random effect of sire was included to estimate additive genetic effects, which were assumed to be continuous for IMF, MS, and SC, but a probit threshold link function was fitted for HP. Generally moderate heritability estimates of 0.29 ± 0.01, 0.35 ± 0.06, 0.32 ± 0.02, and 0.17 ± 0.01 were obtained for IMF, MS, SC, and HP on the underlying scale, respectively. The confidence interval for the estimated genetic correlation between MS and HP (0.10 ± 0.15) included zero, suggesting a negligible genetic association. The genetic correlation between MS and IMF was high (0.80 ± 0.05), but the estimate for HP and SC (0.05 ± 0.09) was near zero, as were the estimated genetic correlations of SC with MS (0.01 ± 0.08) and IMF (0.05 ± 0.06), and for HP with IMF (0.13 ± 0.09). These results suggest that concomitant selection for increased fertility and carcass merit would not be antagonistic.

Repeatability of feed efficiency, carcass ultrasound, feeding behavior, and blood metabolic variables in finishing heifers divergently selected for residual feed intake.

This study examined the relationship between feed efficiency and performance, and feeding behavior, blood metabolic variables, and various ultrasonic measurements in finishing beef heifers. Within-animal repeatability estimates of feed intake and behavior, performance, feed efficiency, ultrasonic body measures, and plasma analytes across the growing and finishing stages of the lifespan of the animal were also calculated. Fifty heifers previously ranked as yearlings on phenotypic residual feed intake (RFI) were used. Animals [initial BW = 418 (SD = 31.5) kg] were offered a TMR diet consisting of 70:30 concentrate and corn silage on a DM basis (ME 10.7 MJ/kg of DM; DM 530 g/kg) for 84 d. Feeding duration (min/d) and feeding frequency (events/d) were calculated for each animal on a daily basis using a computerized feeding system. Ultrasonic kidney fat and lumbar and rump fat and muscle depths were recorded on 3 equally spaced occasions during the experimental period. Blood samples were collected by jugular venipuncture on 4 occasions during the experimental period and analyzed for plasma concentrations of IGF-I, insulin, and various metabolites. Phenotypic RFI was calculated for all animals as the residuals from a regression model regressing DMI on ADG and midtest BW(0.75). Repeatability was calculated for several traits both within and between production phase using intraclass correlation and Pearson correlation coefficients as appropriate. Overall ADG, DMI, G:F, and RFI were 1.17 kg/d (SD = 0.19), 10.81 kg/d (SD = 1.02), 0.11 kg of BW gain/kg of DM (SD = 0.02), and 0.00 kg of DM/d (SD 0.59). Daily feeding events and eating rate tended to be positively correlated (P = 0.08) with RFI. Ultrasonic kidney fat depth tended to be related to G:F (r = -0.28; P = 0.07), and kidney fat accretion tended to be related to RFI (r = 0.29; P = 0.08). Plasma urea (r = 0.38; P < 0.01), ß-hydroxybutyrate (r = 0.40; P < 0.01), and insulin (r = 0.23; P = 0.07) concentrations were correlated with RFI. Plasma glucose (r = -0.25; P = 0.07), glucose:insulin (r = 0.33; P < 0.05), and insulin (r = -0.30; P < 0.05) were associated with G:F. However, systemic IGF-I was unrelated (P > 0.10) to any measure of feed efficiency. Repeatability estimates within the finishing period for DMI, feeding duration, feeding events, feed intake/feeding event, and eating rate were 0.34, 0.37, 0.60, 0.62, and 0.56, respectively. Repeatability estimates (P < 0.001) between the growing and finishing phases for DMI, G:F, and RFI were r = 0.61, r = 0.37, and r = 0.62, respectively. Moderate to strong repeatability values (ranging from r = 0.40 to 0.76; P < 0.001) were obtained for feeding behavior traits between the yearling and finishing phases. We conclude that RFI and feeding behavior are repeatable traits and that some plasma analytes may be potential indicators of RFI in beef cattle.

Genetic analysis of longitudinal data in beef cattle: a review.

Currently, many different data types are collected by beef cattle breed associations for the purpose of genetic evaluation. These data points are all biological characteristics of individual animals that can be measured multiple times over an animal's lifetime. Some traits can only be measured once on an individual animal, whereas others, such as the body weight of an animal as it grows, can be measured many times. Data such as growth has been often referred to as "longitudinal" or "infinite-dimensional" since it is theoretically possible to observe the trait an infinite number of times over the life span of a given individual. Analysis of such data is not without its challenges, and as a result many different methods have been or are beginning to be implemented in the genetic analysis of beef cattle data, each an improvement over its predecessor. These methods of analysis range from the classic repeated measures to the more contemporary suite of random regressions that use covariance functions or even splines as their base function. Each of the approaches has both strengths and weaknesses in the analysis of longitudinal data. Here we summarize past and current genetic evaluation technology for analyzing this type of data and review some emerging technologies beginning to be implemented in national cattle evaluation schemes, along with their potential implications for the beef industry.

Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance-tested beef bulls.

No genetic parameters for performance and feed efficiency traits are available for Irish performance-tested bulls. The objective of this study was to determine the phenotypic and genetic variation for feed intake, BW, ADG, and measures of feed efficiency including feed conversion ratio (FCR), relative growth rate, Kleiber ratio, residual BW gain (RG), and residual feed intake (RFI). Observations were available on up to 2,605 bulls for each trait from one test station across 24 yr; breeds included in the analyses were Aberdeen Angus (AN), Charolais (CH), Hereford, Limousin (LI), and Simmental. The test period was at least 70 d. Bulls were individually offered concentrates ad libitum, with a restricted forage allowance. Differences in performance and feed efficiency existed among breeds. For example, AN, on average, ate 0.04 kg of DM/d more than CH but had ADG of 0.14 kg/d less over the 70-d test period. Results showed LI and CH were the most efficient breeds when efficiency was defined as FCR or RFI. When animals were partitioned into groups based on high, medium, or low RFI, the low RFI (i.e., most efficient) group were also the more efficient as defined by RG and FCR. The low RFI group had the same ADG as the medium group and a greater ADG (P < 0.01) than the high group (1.67 vs. 1.66 and 1.63 kg/d); yet they ate 0.67 kg of DM/d less (P < 0.001) than the medium RFI group and 1.22 kg of DM/d less (P < 0.001) than the high RFI (i.e., least efficient) group. Genetic parameters for all performance and efficiency measures were estimated across breeds using linear animal mixed models; heritability estimates for feed efficiency traits ranged from 0.28 +/- 0.06 (RG) to 0.45 +/- 0.06 (RFI). An additional series of analyses included a maternal component in the model; maternal heritability estimates for feed efficiency traits ranged from 0.05 +/- 0.03 (RG) to 0.11 +/- 0.05 (relative growth rate). Genetic correlations between most of the different feed efficiency measures were strong. Results from this study indicate significant genetic differences in performance and some measures of feed efficiency among performance-tested beef bulls.

Effect of divergence in residual feed intake on feeding behavior, blood metabolic variables, and body composition traits in growing beef heifers.

This study examined the relationship of feed efficiency and performance with feeding behavior, blood metabolic variables, and various body composition measurements in growing beef heifers. Individual DMI and growth were measured in yearling Limousin x Holstein-Friesian heifers [n = 86; initial BW = 191.8 (SD = 37) kg] fed a TMR diet comprising 70:30 concentrate:corn silage on a DM basis (ME of 2.65 Mcal/kg of DM; DM of 580 g/kg) for 82 d. Meal duration (min/d) and meal frequency (events/d) were calculated for each animal on a daily basis using an Insentec computerized feeding system. Physical measurements as well as ultrasonic fat and muscle depths were recorded on 3 equally spaced occasions during the experimental period. Blood samples were collected by jugular venipuncture on 4 equally spaced occasions and analyzed for plasma concentrations of IGF-I, insulin, leptin, and various metabolites. Phenotypic residual feed intake (RFI) was calculated for all animals as the residuals from a multiple regression model regressing DMI on ADG and midtest BW(0.75). Overall, ADG, DMI, feed conversion ratio (FCR), and RFI were 1.51 (SD = 0.13), 6.74 (SD = 0.99), 4.48 (SD = 0.65), and 0.00 (SD = 0.48) kg/d, respectively. Residual feed intake was positively correlated with DMI (r = 0.47) and FCR (r = 0.46), but not with ADG or midtest BW. Positive correlations (ranging from r = 0.27 to r = 0.63) were estimated between ultrasonic measures of final lumbar fat and lumbar fat accretion over the test period and DMI, FCR, and RFI. The inclusion of gain in lumbar fat to the base RFI model increased R(2) (0.77 vs. 0.80) value for the degree of variation in DMI not explained by midtest BW and ADG alone. The Pearson rank correlation between RFI and carcass-adjusted RFI (RFI(c)) was high (r = 0.93). From the plasma analytes measured, NEFA (r = -0.21; P < 0.05) and beta-hydroxybutyrate (r = 0.37; P < 0.05) concentrations were correlated with RFI. Plasma leptin (r = 0.48), glucose:insulin (r = -0.23), NEFA (r = -0.32), and beta-hydroxybutyrate (r = 0.25) were associated with FCR. However, systemic IGF-I and insulin were unrelated (P > 0.05) to any measure of feed efficiency. The feeding behavior traits of eating rate, daily feeding events, and nonfeeding events were positively correlated (P < 0.05) with RFI and RFI(c). This multifactorial study provides new information on some of the biological processes responsible for variation in feed efficiency in beef cattle.

Phenotypic and genetic relationships of residual feed intake with performance and ultrasound carcass traits in Brangus heifers.

The objective of this study was to characterize residual feed intake (RFI) and to estimate phenotypic and genetic correlations with performance and ultrasound carcass traits in growing heifers. Four postweaning feed efficiency trials were conducted using 468 Brangus heifers. The complete Brangus pedigree file from Camp Cooley Ranch (Franklin, TX), which included 31,215 animals, was used to generate genetic parameter estimates. The heifer progeny from 223 dams were sired by 36 bulls, whereas the complete pedigree file contained 1,710 sires and 8,191 dams. Heifers were individually fed a roughage-based diet (ME = 1.98 Mcal/kg of DM) using Calan gate feeders for 70 d. Heifer BW was recorded weekly and ultrasound measures of 12th- to 13th-rib fat thickness (BF) and LM area (LMA) obtained at d 0 and 70. Residual feed intake (RFIp) was computed as actual minus predicted DMI, with predicted DMI determined by linear regression of DMI on mid-test BW(0.75) (MBW) and ADG with trial, trial x MBW, and trial x ADG as random effects. Overall means for ADG, DMI, and RFI were 1.01 (SD = 0.15), 9.51 (SD = 1.02), and 0.00 (SD = 0.71) kg/d, respectively. Stepwise regression analysis revealed that inclusion of gain in BF and final LMA into the base model increased the R(2) (0.578 vs. 0.534) and accounted for 9% of the variation in DMI not explained by MBW and ADG (RFIp). Residual feed intake and carcass-adjusted RFI (RFIc) were strongly correlated phenotypically and genetically with DMI and FCR, but not with ADG or MBW. Gain in BF was phenotypically correlated (P < 0.05) with RFIp (0.22), but not with FCR or RFIc; however, final BF was genetically correlated (P < 0.05) with RFIp (0.36) and RFIc (0.39). Gain in LMA was weakly phenotypically correlated with FCR, but not with RFIp or RFIc; however, gain in LMA was strongly genetically correlated with RFIp (0.55) and RFIc (0.77). The Spearman rank correlation between RFIp and RFIc was high (0.96). These results suggest that adjusting RFI for ultrasound carcass composition traits will facilitate selection phenotypically independent of growth, body size, and carcass composition; however, genetic relationships may still exist between RFI and carcass composition.

Genetic parameters for calving ease, gestation length, and birth weight in Charolais cattle.

In this study, a 3-trait linear model was used to obtain genetic parameters for direct and maternal components of calving ease (CE), gestation length (GEST), and birth weight (BWT). Calving ease scores were transformed into Snell scores and expressed as percent unassisted calving (SC), ranging from 0 to 100% (least to greatest ease). A total of 40,420 records (n = 14,403 for CE) were obtained from the Canadian Charolais Association field database. The animal model included fixed effects of contemporary group (herd x year of birth combinations), age of heifer, and sex of calf (only for CE), whereas random effects included direct and maternal genetic effects, residual error, and permanent environmental effects (for CE). The BWT and GEST were preadjusted for age of dam and sex of calf effects. Variance components were estimated using REML. Mean SC was 83.31% (SD = 23.30) and ranged from 3.44 to 100%. Mean BWT was 46.54 kg (SD = 4.79), whereas mean GEST was 286.48 d (SD = 4.93). Direct heritability estimates for SC, BWT, and GEST were 0.14 +/- 0.02, 0.46 +/- 0.03, and 0.62 +/- 0.04, respectively, and maternal heritability estimates were 0.06 +/- 0.02, 0.14 +/- 0.02, and 0.10 +/- 0.02, respectively. The permanent environmental effect as a proportion of SC phenotypic variance was 0.35 +/- 0.11, indicating a large influence on CE. Genetic correlations of direct SC with direct BWT and GEST were -0.93 +/- 0.04 and -0.38 +/- 0.08, respectively, whereas maternal correlations were -0.69 +/- 0.14 and -0.49 +/- 0.17, respectively, illustrating the importance of including both traits in CE evaluations. Within trait direct x maternal genetic correlations were substantial and negative. Regression of average direct and average maternal EBV on year of birth yielded significant genetic trends for the direct effects of BWT, GEST, and CE, whereas no trends were detected for maternal effects. Even though CE is routinely analyzed, no study has evaluated transformed CE scores with 2 correlated traits. In these data, the large negative genetic correlation between BWT and CE suggests that increasing SC would also decrease BWT. Genetic improvement programs, therefore, should consider both CE and growth.