Evaluation of feed efficiency traits in different Hereford populations and their effect on variance component estimation

Context Residual feed intake is a relevant trait for beef cattle, given the positive impact on reducing feeding costs and greenhouse gas emissions. The lack of large databases is a restriction when estimating accurate genetic parameters for dry matter intake (DMI) and residual feed intake (RFI), and combining different data sets could be an alternative to increase the amount of data and achieve better estimations. Aim The main objective was to compare Uruguayan data (URY; 780 bulls) and Canadian data (CAN; 1597 bulls), and to assess the adequacy of pooling both data sets (ALL) for the estimation of genetic parameters for DMI and RFI. Methods Feed intake and growth traits phenotypes in both data sets were measured following the same protocols established by the Beef Improvement Federation. Pedigree connections among data sets existed, but were weak. Performance data were analysed for each data set, and individual partial regression coefficients for each energy sink on DMI were obtained and compared. Univariate and multivariate variance components were estimated by the restricted maximum likelihood (REML) for DMI, RFI and their energy sinks traits (average daily gain, metabolic mid weight and back fat thickness). Key results There were some differences in phenotypic performance among data (P < 0.01); however, no differences (P > 0.1) were observed for phenotypic values of RFI between sets. Heritability estimates for DMI were 0.42 (URY), 0.41 (CAN) and 0.45 for ALL data, whereas heritability estimates for RFI were 0.34 (URY), 0.20 (CAN) and 0.25 for ALL data. The results obtained indicate selection on reducing RFI could lead to a decrease in DMI, without compromising other performance traits, as genetic correlations between RFI, growth and liveweight were low or close to 0 (−0.12–0.07). Conclusions As genetic parameters were similar between national data sets (URY, CAN), pooling data (ALL) provided more accurate parameter estimations, as they presented smaller standard deviations, especially in multivariate analysis. Implications Parameters estimated here may be used in international or national genetic evaluation programs.

Genomic Breed Composition of Selection Signatures in Brangus Beef Cattle

Cattle breeding routinely uses crossbreeding between subspecies (Bos taurus taurus and Bos taurus indicus) to form composite breeds, such as Brangus. These composite breeds provide an opportunity to identify recent selection signatures formed in the new population and evaluate the genomic composition of these regions of the genome. Using high-density genotyping, we first identified runs of homozygosity (ROH) and calculated genomic inbreeding. Then, we evaluated the genomic composition of the regions identified as selected (selective sweeps) using a chromosome painting approach. The genomic inbreeding increased at approximately 1% per generation after composite breed formation, showing the need of inbreeding control even in composite breeds. Three selected regions in Brangus were also identified as Angus selection signatures. Two regions (chromosomes 14 and 21) were identified as signatures of selection in Brangus and both founder breeds. Five of the 10 homozygous regions in Brangus were predominantly Angus in origin (probability >80%), and the other five regions had a mixed origin but always with Brahman contributing less than 50%. Therefore, genetic events, such as drift, selection, and complementarity, are likely shaping the genetic composition of founder breeds in specific genomic regions. Such findings highlight a variety of opportunities to better control the selection process and explore heterosis and complementarity at the genomic level in composite breeds.

The impact of selection using residual average daily gain and marbling EPDs on growth, performance, and carcass traits in Angus steers1

One hundred ninety-one Angus steers (age = 546 d ± 33.5; BW = 36.4 kg ± 4.2), sired by bulls divergently selected for feed efficiency over a 3-yr period, were used to compare growth, efficiency, body composition, and carcass characteristics. Selected Angus sires were either high (Hi) or low (Lo) for residual average daily gain (RADG) expected progeny differences (EPDs) and either high (Hi) or low (AVG; breed average) for marbling (MARB) EPDs. Steer weight and body composition, via ultrasound, were measured at weaning (205 d) and at 1 yr (365 d) of age. Steers entered the feedlot at 454 d of age and completed a 70-d GROWSAFE BEEF System evaluation to determine DMI, ADG, and residual feed intake (RFI). Steers were then slaughtered as they reached a backfat thickness of 1.3 cm. Carcasses were chilled for 48 h at 2 °C, ribbed, and USDA yield and quality grade data were collected. The right side of the carcass was fabricated and primal and subprimal weights were collected. A 2.5-cm longissimus steak was removed, vacuum-packaged, aged for 14 d, and frozen for slice shear force determination. Additionally, a 1.3-cm longissimus steak was removed from year 3 steers for proximate analysis. The GLM procedure of SAS was used and the main effects of RADG and MARB and their interaction were tested by the error term, SIRE(RADG*MARB). Year was evaluated as a replicate. Weight, ultrasound backfat, and REA were increased (P ≤ 0.05) at weaning in the Hi compared with AVG MARB steers. Feed efficiency, measured by RFI, was improved (P = 0.05) in the Hi RADG steers compared with Lo RADG steers. Slaughter weight and HCW were heavier (P ≤ 0.03) in the Hi RADG steers compared with Lo RADG steers. An interaction (P = 0.05) between RADG and MARB selection was found for marbling score, steers selected for Lo RADG and Hi MARB had greater marbling scores than all other groups. Longissimus proximate composition from year 3 showed that lipid content was greater (P < 0.01) in the Hi MARB and Lo RADG groups compared with the AVG MARB and Hi RADG groups, respectively. These findings suggest that selection using RADG or MARB EPDs has minimal impact on carcass yield. However, positive selection pressure placed on these breeding values can potentially improve efficiency and carcass quality. Lastly, it appears that improvements in feed efficiency can be obtained without negatively affecting beef carcass merit, especially USDA quality grade.

Breeding objectives of Brangus cattle in Brazil

Our goal was to define a breeding objective for Brangus beef cattle in Brazil. Bioeconomic models were produced and used to estimate economic values (EVs). The scenarios simulated were typical full-cycle beef production systems that are used in tropical and subtropical regions. The breeding objective contained pregnancy rate (PR), warm carcass weight (WCW), mature cow weight (MCW), number of nematode eggs per gram of faeces (EPG) and tick count (TICK). Two models were used in series to estimate the EV. A deterministic model was used to simulate effects of PR, WCW and MCW on profitability with a constant parasite load. Subsequently, stochastic models were used to estimate economic values for TICK and EPG as consequences of their environmental effects on weight gains, mortality and health costs. The EV of PR, WCW, MCW, EPG and TICK, was US$1.59, US$2.11, -US$0.24, -US$5.35 and -US$20.88, respectively. Results indicate positive emphasis should be placed on PR (12.49%) and WCW (65.07%) with negative emphasis on MCW (13.92%), EPG (2.77%) and TICK (5.75%). In comparison with the indexes usually used, these results suggest a reformulation in the selection indexes of the beef production system in tropical and subtropical regions in order to obtain greater profitability.

Reducing the period of data collection for intake and gain to improve response to selection for feed efficiency in beef cattle

Shortening the period of recording individual feed intake may improve selection response for feed efficiency by increasing the number of cattle that can be recorded given facilities of fixed capacity. Individual DMI and ADG records of 3,462 steers and 2,869 heifers over the entire intake recording period (range 62 to 154 d; mean 83 d; DMI83 and ADG83, respectively), DMI and ADG for the first 42 d of the recording period (DMI42 and ADG42, respectively), and postweaning ADG based on the difference between weaning and yearling weights (PADG) were analyzed. Genetic correlations among DMI42 and DMI83, ADG42 and ADG83, ADG42 and PADG, and ADG83 and PADG were 0.995, 0.962, 0.852, and 0.822, respectively. Four objective functions [feed:gain ratio in steers (FGS) and heifers (FGH); residual gain (RG); and residual feed intake (RFI)] based on DMI83 and ADG83 were considered. Indices using DMI42 and ADG42 (I42); DMI42 and PADG (IPW); and DMI42, ADG42, and PADG (IALL) were developed. Accuracy of the 5 EBV, 4 objectives, and 12 objective × index combinations were computed for all 12,033 animals in the pedigree. Accuracies of indices (IA) were summarized for animals with accuracies for objectives (OA) of 0.25, 0.5, 0.75, and 1. For the RG objective and animals with OA of 0.75, indices I42, IPW, and IALL had IA of 0.63, 0.55, and 0.67, respectively. Differences in IA increased with increased emphasis on ADG83 in the objective. Differences in IA between I42 and IPW usually increased with OA. Relative efficiency (RE) of selection on 42-d tests compared with 83 d was computed based on differences in IA and selection intensities of 5%, 25%, 50%, and 75% under the 83-d scenario, assuming 65% more animals could be tested for 42 d. For 25% selected for the RG objective, and animals with OA of 0.75, indices I42, IPW, and IALL had RE of 1.02, 0.90, and 1.10, respectively. As % selected, OA, and emphasis on DMI increased, RE increased. Relative efficiency varied considerably according to assumptions. One-half of the scenarios considered had RE > 1.15 with a maximum of 2.02 and 77% RE > 1.0. A shorter period of recording DMI can improve selection response for feed efficiency. Selection for the efficiency objectives would not affect PADG. It will be most effective if ADG over the period coinciding with intake recording and ADG over a much longer period of time are simultaneously included in a multiple-trait genetic evaluation with DMI and used in a selection index for efficiency.

Genetic variance and covariance and breed differences for feed intake and average daily gain to improve feed efficiency in growing cattle

Feed costs are a major economic expense in finishing and developing cattle; however, collection of feed intake data is costly. Examining relationships among measures of growth and intake, including breed differences, could facilitate selection for efficient cattle. Objectives of this study were to estimate genetic parameters for growth and intake traits and compare indices for feed efficiency to accelerate selection response. On-test ADFI and on-test ADG (TESTADG) and postweaning ADG (PWADG) records for 5,606 finishing steers and growing heifers were collected at the U.S. Meat Animal Research Center in Clay Center, NE. On-test ADFI and ADG data were recorded over testing periods that ranged from 62 to 148 d. Individual quadratic regressions were fitted for BW on time, and TESTADG was predicted from the resulting equations. We included PWADG in the model to improve estimates of growth and intake parameters; PWADG was derived by dividing gain from weaning weight to yearling weight by the number of days between the weights. Genetic parameters were estimated using multiple-trait REML animal models with TESTADG, ADFI, and PWADG for both sexes as dependent variables. Fixed contemporary groups were cohorts of calves simultaneously tested, and covariates included age on test, age of dam, direct and maternal heterosis, and breed composition. Genetic correlations (SE) between steer TESTADG and ADFI, PWADG and ADFI, and TESTADG and PWADG were 0.33 (0.10), 0.59 (0.06), and 0.50 (0.09), respectively, and corresponding estimates for heifers were 0.66 (0.073), 0.77 (0.05), and 0.88 (0.05), respectively. Indices combining EBV for ADFI with EBV for ADG were developed and evaluated. Greater improvement in feed efficiency can be expected using an unrestricted index versus a restricted index. Heterosis significantly affected each trait contributing to greater ADFI and TESTADG. Breed additive effects were estimated for ADFI, TESTADG, and the efficiency indices.

Genetic correlations between female fertility and postweaning growth and feed efficiency traits in multibreed beef cattle

With selection in beef cattle now incorporating feed efficiency, knowing the relationship with other traits is needed. Genetic relationships were estimated with an animal model in ASReml with a three-generation pedigree inclusive of 2882 animals. Multibreed data from two Ontario beef research farms with fertility traits were available on 1366 females and postweaning traits, including feed efficiency on 1297 individuals. Estimates of heritability for fertility traits were low to moderate ranging from 0.03 ± 0.01 for pregnancy rate to 0.21 ± 0.02 for gestation length, and postweaning traits were moderate to high with feed conversion ratio at 0.22 ± 0.06 to mid-metabolic weight at 0.89 ± 0.01. Both dry matter intake and mid-metabolic weight were genetically correlated with most fertility traits from −0.52 to 0.34. The genetic correlation between average daily gain and days to calving was moderately negative (–0.33 ± 0.16) as was residual feed intake with days to calving (–0.34 ± 0.17). Bigger cows with more feed intake and faster growth were more fertile, and residual feed intake had an unfavorable genetic correlation with days to calving, indicating that programs to select for feed efficiency should include fertility simultaneously in a selection index.

Genetic parameter estimation and evaluation of Duroc boars for feed efficiency and component traits

MacNeil, M. D. and Kemp, R. A. 2015. Genetic parameter estimation and evaluation of Duroc boars for feed efficiency and component traits. Can. J. Anim. Sci. 95: 155–159. The objective of this research was to produce a genetic evaluation for traits related to feed efficiency of Duroc boars. Meeting this objective required partitioning phenotypic (co)variance into additive genetic and environmental components for feed intake and traits indicative of growth and body composition. Boars (N=3291) were housed in group pens of 22 to 24 animals with two electronic feeders per pen and feed intake was recorded for 8 to 14 wk. Body weight was recorded for each boar at the start and end of test, at approximately 100 kg and at up to three times during the test. The pedigree used contained sire and dam of each boar with at least one recorded phenotype (N=4651) and their maternal and paternal grandsires. Variance components were estimated by restricted maximum likelihood for animal models in a series of uni-variate and bi-variate analyses. Two multiple trait genetic evaluations were conducted to predict estimated breeding value for feed intake using animal models. The first evaluation included feed intake (h2=0.33±0.05), age at 100 kg (h2=0.31±0.04), and subcutaneous fat depth (h2=0.47±0.05). The second genetic evaluation included feed intake, average daily gain (h2=0.27±0.04), mid-test weight (h2=0.33±0.05), and subcutaneous fat depth. Genetic correlations of feed intake with age at 100 kg and fat depth were –0.80±0.05 and 0.57±0.08, respectively. Estimated breeding values for measures of feed efficiency (residual feed intake and residual gain) were calculated from the results of the second analysis and the associated additive genetic (co)variance components.

Selection for feed efficiency traits and correlated genetic responses in feed intake and weight gain of Nellore cattle

The objectives of this study were to estimate genetic parameters for indicator traits of feed efficiency and to recommend traits that would result in better responses to selection for increased weaning weight (weaning weight adjusted to 210 d of age [W210]), ADG, and metabolic BW (BW(0.75)) and lower DMI. Records of W210 from 8,004 Nellore animals born between 1978 and 2011 and postweaning performance test records from 678 males and females born between 2004 and 2011 were used. The following feed efficiency traits were evaluated: G:F, partial efficiency of growth (PEG), relative growth rate (RGR), Kleiber's ratio (KR), residual feed intake (RFI), residual weight gain (RWG), and residual intake and gain (RIG). Covariance and variance components were estimated by the restricted maximum likelihood method using multitrait analysis under an animal model. Estimates of genetic gain and correlated responses were obtained considering single-stage and 2-stage selection. Heritability estimates were 0.22 ± 0.03 (W210), 0.60 ± 0.08 (DMI), 0.42 ± 0.08 (ADG), 0.56 ± 0.06 (BW(0.75)), 0.19 ± 0.07 (G:F), 0.25 ± 0.09 (PEG), 0.19 ± 0.07 (RGR), 0.22 ± 0.07 (KR), 0.33 ± 0.10 (RFI), 0.13 ± 0.07 (RWG), and 0.19 ± 0.08 (RIG). The genetic correlations of DMI with W210 (0.64 ± 0.10), ADG (0.87 ± 0.06), and BW(0.75) (0.84 ± 0.05) were high. The only efficiency traits showing favorable responses to selection for lower DMI were G:F, PEG, RFI, and RIG. However, the use of G:F, PEG, or RFI as a selection criterion results in unfavorable correlated responses in some growth traits. The linear combination of RFI and RWG through RIG is the best selection criterion to obtain favorable responses in postweaning growth and feed intake of Nellore cattle in single-stage selection. Genetic gains in feed efficiency are expected even after preselection for W210 and subsequent feed efficiency testing of the preselected animals.

Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models

Different methods for genomic evaluation were compared for accuracy and feasibility of evaluation using phenotypic, pedigree, and genomic information for a trait influenced by a maternal effect. A simulated population was constructed that included 15,800 animals in 5 generations. Genotypes from 45,000 SNP were available for 1,500 animals in the last 3 generations. Genotyped animals in the last generation had no phenotypes. Weaning weight data were simulated using an animal model with direct and maternal effects. Additive direct and maternal effects were considered either noncorrelated (formula in text) or negatively correlated (formula in text). Methods of analysis were traditional BLUP, BayesC using phenotypes and ignoring maternal effects (BayesCPR), BayesC using deregressed EBV (BayesCDEBV), and single-step genomic BLUP (ssGBLUP). Whereas BayesCPR can be used when phenotypes of only genotyped animals are available, BayesCDEBV can be used when BLUP EBV of genotyped animals are available, and ssGBLUP is suitable when genotypes, phenotypes, and pedigrees are jointly available. For all genotyped and young genotyped animals, mean accuracies from BayesCPR and BayesCDEBV were lower than accuracies from BLUP for direct and maternal effects. The differences in mean accuracy were greater when genetic correlation was negative. Gains in accuracy were observed when ssGBLUP was compared with BLUP; for the direct (maternal) effect the average gain was 0.01 (0.02) for all genotyped animals and 0.03 (0.02) for young genotyped animals without phenotypes. Similar gains were observed for 0 and negative genetic correlation. Accuracy with BayesCPR was affected by ignoring phenotypes of nongenotyped animals and maternal effect and by not accounting for parent average. Accuracy with BayesCDEBV was affected by approximations needed for deregression, not accounting for parent average, and sequential rather than simultaneous fitting of genomic and nongenomic information. Whereas BayesCDEBV presented a considerable bias, especially for maternal effect, ssGBLUP was unbiased for both effects. The computing time was 1 s for BLUP, 44 s for ssGBLUP, and over 2,000 s for BayesC. Greatest computational efficiency and accuracy of genomic prediction for a maternally affected trait was obtained when information from all nongenotyped but related individuals was included and phenotypes, pedigree, and genotypes were available and considered jointly. Increasing the gain in accuracy of genomic predictions obtained by ssGBLUP over BLUP may require an increase in the number of genotyped animals.