Genetic evaluation of Angus cattle for carcass marbling using ultrasound and genomic indicators

Animal board invited review: Practical applications of genomic information in livestock

Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal's (and by extension its progeny's) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application.

Estimation of direct and maternal genetic effects and annotation of potential candidate genes for weight and meat quality traits in a genotyped outdoor dual-purpose cattle breed

With regard to potential applications of genomic selection in small numbered breeds, we evaluated genomic models and focused on potential candidate gene annotations for weight and meat quality traits in the local Rotes Höhenvieh (RHV) breed. Traits included 6,003 birth weights (BWT), 5,719 200 d-weights (200dw), 4,594 365 d-weights (365dw), and 547 records for intramuscular fat content (IMF). A total of 581,304 SNP from 370 genotyped cattle with phenotypic records were included in genomic analyses. Model evaluations focused on single- and multiple-trait models with direct and with direct and maternal genetic effects. Genetic relationship matrices were based on pedigree (A-matrix), SNP markers (G-matrix), or both (H-matrix). Genome-wide association studies (GWASs) were carried out using linear mixed models to identify potential candidate genes for the traits of interest. De-regressed proofs (DRP) for direct and maternal genetic components were used as pseudo-phenotypes in the GWAS. Accuracies of direct breeding values were higher from models based on G or on H compared to A. Highest accuracies (> 0.89) were obtained for IMF with multiple-trait models using the G-matrix. Direct heritabilities with maternal genetic effects ranged from 0.62 to 0.66 for BWT, from 0.45 to 0.55 for 200dW, from 0.40 to 0.44 for 365dW, and from 0.48 to 0.75 for IMF. Maternal heritabilities for BWT, 200dW, and 365dW were in a narrow range from 0.21 to 0.24, 0.24 to 0.27, and 0.21 to 0.25, respectively, and from 0.25 to 0.65 for IMF. Direct genetic correlations among body weight traits were positive and favorable, and very similar from different models but showed a stronger variation with 0.31 (A), −0.13 (G), and 0.45 (H) between BWT and IMF. In gene annotations, we identified 6, 3, 1, and 6 potential candidate genes for direct genetic effect on BWT, 200dW, 365dW, and IMF traits, respectively. Regarding maternal genetic effects, four (SHROOM3, ZNF609, PECAM1, and TEX2) and two (TMEM182 and SEC11A) genes were detected as potential candidate genes for BWT and 365dW, respectively. Potential candidate genes for maternal effect on IMF were GRHL2, FGA, FGB, and CTNNA3. As the most important finding from a practical breeding perspective, a small number of genotyped RHV cattle enabled accurate breeding values for high heritability IMF.

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.

Genetic Analysis of Major Carcass Traits of Korean Hanwoo Males Raised for Thirty Months

Simple Summary

Generally, Korean Hanwoo males produced under a 24-month production cycle (PROD24) are evaluated as a part of the progeny test program. However, there is little information on other males outside the PROD24, such as those raised under a 30-month production cycle (PROD30) for higher profits. Therefore, we investigated PROD30 males for important carcass traits (carcass weight, eye muscle area, backfat thickness, and marbling score) using a reasonably large dataset to understand their genetic merit. To do so, we estimated the genetic parameters of traits using animal model. Our analysis revealed moderate to high heritability values for the studied traits. The marbling score was found to be highly heritable at 0.56. The genetic correlation between traits was mostly moderate to low, and the backfat thickness was poorly correlated with the marbling score. These results are consistent with many previous reports on PROD24. Our study suggests that PROD30 and PROD24 males might have somewhat similar genetic potential, as well as similar genetic backgrounds. Thus, it could be concluded that there is further scope for PROD30 males to improve Hanwoo males’ overall prediction accuracy, especially under a genomic selection program, together with PROD24 males.

Abstract

Understanding animals’ genetic potential for carcass traits is the key to genetic improvements in any beef cattle. In this study, we investigated the genetic merits of carcass traits using Hanwoo males raised in a 30-month production system (PROD30). We achieved this using a dataset comprising 6092 Hanwoo males born between 2005 and 2017 and measures of four carcass traits (carcass weight, CWT; eye muscle area, EMA; backfat thickness, BFT; and marbling score, MS). Genetic parameters were estimated using a multiple-trait animal model through the AIREMLF90 program. According to the multiple-trait model, the h² of CWT, EMA, BFT, and MS were 0.35 ± 0.04, 0.43 ± 0.05, 0.48 ± 0.05, and 0.56 ± 0.05, respectively. The strongest genetic correlation (r_g) was obtained between CWT and EMA (0.49 ± 0.07), whereas it was negligible between CWT and BFT. EMA and MS were also moderately correlated, whereas there was a relatively low negative correlation between EMA and BFT (−0.26 ± 0.08). Our study revealed a consistent indirect genetic improvement in animals from 2005 onwards. Although Hanwoo improvement has mainly focused on males under a 24-month production cycle, we observed PROD30 males to have somewhat similar genetic potential. Our results provide useful insights into the genetic merits of PROD30 males for the first time, which may facilitate future studies on them and their integration into the Hanwoo National Evaluation for genomic selection.

Current status of genomic evaluation

Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient for an accurate estimation of SNP effects. Genomic estimated breeding values (GEBV) were composed of an index with parent average, direct genomic value, and deduction of a parental index to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but had minimal to no impact with large data sets. Efforts to include potentially causative SNP derived from sequence data or high-density chips showed limited or no gain in accuracy. After the implementation of genomic selection, EBV by BLUP became biased because of genomic preselection and DRP computed based on EBV required adjustments, and the creation of DRP for females is hard and subject to double counting. Genomic selection was greatly simplified by single-step genomic BLUP (ssGBLUP). This method based on combining genomic and pedigree relationships automatically creates an index with all sources of information, can use any combination of male and female genotypes, and accounts for preselection. To avoid biases, especially under strong selection, ssGBLUP requires that pedigree and genomic relationships are compatible. Because the inversion of the genomic relationship matrix (G) becomes costly with more than 100k genotyped animals, large data computations in ssGBLUP were solved by exploiting limited dimensionality of genomic data due to limited effective population size. With such dimensionality ranging from 4k in chickens to about 15k in cattle, the inverse of G can be created directly (e.g., by the algorithm for proven and young) at a linear cost. Due to its simplicity and accuracy, ssGBLUP is routinely used for genomic selection by the major chicken, pig, and beef industries. Single step can be used to derive SNP effects for indirect prediction and for genome-wide association studies, including computations of the P-values. Alternative single-step formulations exist that use SNP effects for genotyped or for all animals. Although genomics is the new standard in breeding and genetics, there are still some problems that need to be solved. This involves new validation procedures that are unaffected by selection, parameter estimation that accounts for all the genomic data used in selection, and strategies to address reduction in genetic variances after genomic selection was implemented.

Genome-Wide Association Study of Meat Quality Traits in Hanwoo Beef Cattle Using Imputed Whole-Genome Sequence Data

The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately.

Evaluation of a genomic-enhanced sorting system for feeder cattle1

This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield (LMY) groups. A discovery set of 2,609 animals with genotypes and carcass phenotypes was used to predict MBVs for LMY and marbling score (MBS) for 299 Angus, 181 Charolais, and 638 Kinsella Composite steers using genomic best linear unbiased prediction. Steers were sorted in silico into four MBV groups namely Quality (with MBVs greater than the mean for LMY and MBS), Lean (with MBVs greater than the mean for LMY but less than or equal to the mean for MBS), Marbling (with MBVs greater than the mean for MBS but less than or equal to the mean for LMY), and Other (with MBVs lower than the mean for LMY and MBS). Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups using descriptive statistics and least square analysis. Accuracy of MBV predictions was assessed by Pearson's correlation between predicted MBV and adjusted phenotype divided by the square root of trait heritability. Genomic breed compositions were predicted for all steers to correct for possible population stratification and breed effects in the test model. The number of steers that met the expected carcass outcome was counted to produce actual percentages for each MBV group. Results showed that on average, Quality and Marbling groups had greater back-fat and more marbling across the three populations while Lean group had leaner carcasses. Carcass weights were similar across MBV groups. Within MBV groups, decreases in variability were observed for most traits suggesting improvement in carcass uniformity. Greater than 70% of the steers in Quality, Lean, and Marbling groups met the Quality Grid and Y1-LMY target for Angus and Charolais but not for Kinsella composite. The accuracy of MBV prediction ranged from 0.43 to 0.59 indicating that up to 35% of the observed carcass trait variability can be predicted, which suggests utility of MBV as a marker-assisted management tool to sort feeder cattle into uniform carcass endpoint groups under similar environmental and management conditions. Further investigation is warranted to evaluate the performance of feeder cattle sorted based on MBV and managed for different carcass endpoints as well as the cost-benefit implications for feedlot operations.

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

Comparison of economic returns among genetic evaluation strategies in a 2-tiered Charolais-sired beef cattle production system1,2

The objective of this study was to estimate economic returns and costs associated with 4 scenarios of genetic evaluation that combine genotypes, phenotypes, and pedigree information from a vertically integrated purebred (PB) and commercial (CM) beef cattle system. Inference was to a genetic evaluation for a production system producing Charolais terminal sires for 10,000 CM cows. The first genetic evaluation scenario, denoted PB_A, modeled a genetic evaluation in which pedigree information and phenotypes are available for PB seedstock animals. Scenario PB_H contained the same information as PB_A with the addition of 25K density (GeneSeek Genomic Profiler LD) single nucleotide polymorphism (SNP) genotypes from PB animals. Scenario PBCM_A contained pedigree records and phenotypes from PB and CM cattle. Scenario PBCM_H contained phenotypes, pedigree, and genotypes from the PB and CM animals. Estimates of prediction error variance, (co)variance, and selection index parameters were used to estimate accuracy of selection candidates (r_TI) and genetic gain resulting from selection on an economic index in US dollars (ΔG). Annual costs and incomes were used to determine the 30-yr cumulative net present value (CNPV) per CM calf resulting from selection in these genetic evaluation scenarios. Adding genotypes and CM production phenotypes to genetic evaluation increased the r_TI of selection candidates and ΔG across all 4 scenarios. Scenario PBCM_H produced the highest annual ΔG in the PB herd at US$11.91 per head. Including CM phenotypes and parentage testing in the genetic evaluation increased the time to breakeven from 12 yr in PB_A to 19 years in PBCM_A after accounting for the cost of that information. Adding CM phenotypes and genotypes increased the breakeven time from 12 yr in PB_H to 18 yr in PBCM_H. Scenario PB_H produced the highest 30-yr CNPV per slaughtered CM calf at US$371.16. These results using field data indicate that economically relevant r_TI and ΔG can be realized by adding 25K SNP genotypes and CM phenotypes to genetic evaluation, but the additional cost of that data significantly delays the economic return to the enterprise.

Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle - A review

Intramuscular fat (IMF) content in skeletal muscle including the longissimus dorsi muscle (LM), also known as marbling fat, is one of the most important factors determining beef quality in several countries including Korea, Japan, Australia, and the United States. Genetics and breed, management, and nutrition affect IMF deposition. Japanese Black cattle breed has the highest IMF content in the world, and Korean cattle (also called Hanwoo) the second highest. Here, we review results of research on genetic factors (breed and sex differences and heritability) that affect IMF deposition. Cattle management factors are also important for IMF deposition. Castration of bulls increases IMF deposition in most cattle breeds. The effects of several management factors, including weaning age, castration, slaughter weight and age, and environmental conditions on IMF deposition are also reviewed. Nutritional factors, including fat metabolism, digestion and absorption of feed, glucose/starch availability, and vitamin A, D, and C levels are important for IMF deposition. Manipulating IMF deposition through developmental programming via metabolic imprinting is a recently proposed nutritional method to change potential IMF deposition during the fetal and neonatal periods in rodents and domestic animals. Application of fetal nutritional programming to increase IMF deposition of progeny in later life is reviewed. The coordination of several factors affects IMF deposition. Thus, a combination of several strategies may be needed to manipulate IMF deposition, depending on the consumer’s beef preference. In particular, stage-specific feeding programs with concentrate-based diets developed by Japan and Korea are described in this article.

Phenotyping for Genetic Improvement of Feed Efficiency in Fish: Lessons From Pig Breeding

Feed incurs most of the cost of aquaculture production, so feed efficiency (FE) improvement is of great importance. Our aim is to use work done in pigs to formulate a logical framework for assessing the most useful component traits influencing feed intake (FI) and efficiency in farmed fish - either to identify traits that can together be used for genetic improvement of FE, or as substitute traits for FI recording. Improvement of gross FE in growing fish can be accomplished by selection for increased growth rate. However, the correlation of growth with FE is typically only modest, and hence there is room for further improvement of FE through methods other than growth selection. Based on a literature review we propose that the most effective additional methods are selection for reduced body lipid content and for reduced residual FI (RFI). Both methods require more or less sophisticated recording equipment; in particular, the estimation of RFI requires recording of FI which is a challenge. In mammals and birds, both these approaches have been effective, and despite the high costs of FI recording, the RFI approach can be cost-efficient because maintenance requirements are high and therefore RFI variation covers a large part of FI variance. Maintenance requirements of fish are lower and therefore RFI variation covers a smaller part of FI variance. Moreover, accurate high-volume routine individual FI recording is much more challenging in fish than in mammals or birds. It follows that selection for reduced body fat content is likely a more effective (and certainly more cost-efficient) way to improve feed conversion ratio in fish than selection for reduced RFI. As long as body fat content is dealt with as an explicit selection criterion, the only valid reason for FI recording would be the requirement of RFI reduction. So, if RFI reduction is not required, there would be no need for the expense and effort of individual FI recording - and in fish breeding that would be a very desirable situation. Solid evidence for these propositions is still scarce, and their generality still needs to be confirmed.

Genetic parameters of carcass and meat quality traits in different muscles (longissimus dorsi and semimembranosus) of Hanwoo (Korean cattle)

We estimated heritability () and genetic and phenotypic correlations for carcass and meat quality traits of longissimus dorsi (LD) and semimembranosus (SM) muscles in 30-mo-old Hanwoo steers. Variance and covariance components were estimated using REML procedures under univariate and bivariate models. The mean carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS) were 428.20 ± 46.30 kg, 87.38 ± 8.54 cm2, 13.00 ± 5.14 mm, and 5.21 ± 1.56, respectively. The mean CIE reflectance of meat lightness (L*), redness (a*), and yellowness (b*) were 40.01 ± 2.73, 22.37 ± 2.18, and 10.35 ± 1.46, respectively, in LD muscles and 36.33 ± 2.44, 22.91 ± 2.43, and 10.25 ± 1.65, respectively, in SM muscles. The mean Warner-Bratzler shear force (WBSF), intramuscular fat content (IMF), water-holding capacity (WHC), and protein and ash content in LD and SM muscles were 3.84 ± 0.96 and 6.52 ± 1.21 kg, 15.91 ± 4.39 and 5.10 ± 1.94%, 62.07 ± 3.38 and 71.61 ± 2.06%, 20.01 ± 1.39 and 21.34 ± 0.89%, and 0.80 ± 0.10 and 0.93 ± 0.07, respectively. The estimates of CWT, EMA, BFT, and MS were 0.51 ± 0.13, 0.45 ± 0.13, 0.29 ± 0.09, and 0.22 ± 0.08, respectively. The estimates were moderate for meat quality traits and were 0.37 ± 0.12, 0.40 ± 0.12, 0.33 ± 0.10, 0.33 ± 0.10, 0.30 ± 0.11, and 0.24 ± 0.09 for L*, WBSF, IMF, WHC, and protein and ash content, respectively, in LD muscle; estimates from SM muscle were comparatively low (0.08 ± 0.06 to 0.25 ± 0.09). Estimates of for a* and b* were also low (0.08 ± 0.06 to 0.13 ± 0.07). Carcass weight had a moderate, positive genetic correlation with EMA (0.60 ± 0.13) and a weak correlation with MS and BFT. The genetic correlations among the 3 colorimeter variants were strong and positive within and between muscles. Intramuscular fat content had moderate to strong and negative genetic correlations with WBSF (-0.49 ± 0.18), WHC (-0.99 ± 0.01), and protein (-0.93 ± 0.04) and ash content (-0.98 ± 0.06) in LD muscle, whereas the associations were less pronounced in SM muscle. In general, CWT and EMA had low genetic and phenotypic correlations with meat quality traits, which suggests that the traits are independent and have distinct genetic contributions in each muscle. Conversely, with few exceptions, meat quality traits had genetic and phenotypic correlations with MS and BFT. In conclusion, the estimated genetic parameters for carcass and meat quality traits could be used for genetic evaluation and breeding programs in Korean Hanwoo cattle populations.

Evaluation of implant strategies in Angus-sired steers with high or low genetic potential for marbling and gain

Sixty-nine Angus-sired steer calves (332.3 kg initial BW) were used to determine the effects of single or double implant strategies on steers of high or low genetic potential (GP) determined by the GeneMax (Zoetis, Florham Park, NJ) genetic profiling test. Steers were assigned to treatments in a 2 × 2 factorial design with factors of 1) composite GP score (high, mean GP score of 86.5 [HI]; low, mean GP score of 25.3[LO]) and 2) implant strategy (single, steers implanted on d 70 [1X], or double, steers implanted d 0 and 70 [2X]). All steers were given the same implant (Revalor-S; Merck Animal Health, Summit, NJ), with the 2X group implanted on d 0 and 70 and the 1X group implanted only on d 70. A diet containing 1.38 Mcal NEg/kg DM was fed ad libitum, once daily. Ultrasound was used to measure body composition characteristics on d 0 and 70. Steers were harvested after 140 d on feed. At both the d-0 and d-70 ultrasound, HI steers had greater ( < 0.001) percent intramuscular fat (IMF) than LO steers, but no differences ( ≥ 0.24) were observed in LM area (LMA), rib fat thickness (RF), or rump fat thickness (RMFT). Steers in the 2X group had larger ( = 0.02) LMA and less ( = 0.03) IMF on d 70 than 1X steers and no differences ( ≥ 0.50) in RF or RMFT were observed. From d 0 to 70, HI steers had ADG, DMI, and G:F ( ≥ 0.60) similar to LO steers; however, 2X steers had greater ( < 0.001) ADG and were more ( < 0.001) feed efficient compared with 1X steers during the same interval. Over the entire 140-d feeding period, there were no differences ( ≥ 0.6) in BW, ADG, DMI, or G:F between GP groups; however, 2X steers had greater ( = 0.03) ADG compared with 1X steers and still had similar ( ≥ 0.12) DMI and G:F. Upon slaughter, marbling score tended to be impacted by a GP × implant interaction (499.9 ± 18.5, 545.6 ± 18.5, 487.1 ± 18.5, and 469.8 ± 18.5 for HI and 2X, HI and 1X, LO and 2X, and LO and 1X, respectively; = 0.06). No differences ( ≥ 0.7) were observed between GP groups for HCW, LMA, RF, KPH, or yield grade (YG). Steers in the 1X group had less ( = 0.003) RF than 2X steers but similar ( ≥ 0.14) HCW, marbling, LMA, KPH, and YG. A greater proportion ( = 0.03) of HI steers had choice carcasses (100 ± 0.0%) compared with LO steers (87.8 ± 3.9%). Results of this study indicate that the GP test used in the current study predicted differences in IMF, carcass marbling, and percent carcasses graded as choice.

Genomic prediction for tick resistance in Braford and Hereford cattle

One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for -means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breed-specific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.

Genetic parameters for yearling weight, carcass traits, and primal-cut yields of Hanwoo cattle

Genetic parameters associated with yearling weight, carcass traits, and primal-cut yields of male Hanwoo cattle were investigated using univariate and bivariate animal models. The mean yearling weight (YWT), carcass weight (CWT), longissimus muscle area (LMA), backfat thickness (BFT), and marbling score (MS) were 352.47 ± 0.40 kg, 337.39 ± 0.64 kg, 78.28 ± 0.13 cm2, 8.45 ± 0.05 mm, and 3.25 ± 0.03, respectively. Total primal-cut yield (TPC) was 78.95 ± 0.10% of CWT, of which 42.3% was contributed by the forequarters (chuck, CHK; shoulder, SLD; ribs, RIB; and brisket and flank, BAF). Loins, top round (TRND), and round (RND) were associated with yields of 13.57%, 5.45 ± 0.01%, and 8.87 ± 0.02%, respectively. The largest cut studied was ribs (15.67 ± 0.03%). The estimated heritabilities (h2) of YWT, CWT, LMA, BFT, and MS were 0.18 ± 0.02, 0.29 ± 0.04, 0.38 ± 0.05, 0.45 ± 0.05, and 0.62 ± 0.07, respectively. Shoulder yield was highly heritable in Hanwoo steers (0.83 ± 0.13), followed by the yields of round (0.66 ± 0.12), striploin (0.64 ± 0.12), top round (0.62 ± 0.12), sirloin (0.60 ± 0.12), and total primal-cut yield (0.52 ± 0.11). The h2 values of CHK, BAF, RIB, and tenderloin (TLN) ranged from 0.19 ± 0.09 to 0.41 ± 0.11. Generally, the genetic CV was low for most traits (2.33%-6.15%), except for CHK, BFT, and MS. The genetic correlation (rg) was strong between YWT and CWT (0.77 ± 0.06). The greatest positive and negative rg among carcass traits were those between LMA and CWT (0.52 ± 0.08) and between LMA and BFT (-0.30 ± 0.09), respectively. The correlation between CHK and SLD (0.81 ± 0.14), and those between SLD, TLN, TRND, and RND, were mostly strong (0.77-0.87), but the rg between RIB and other traits were strongly negative. The TPC yield showed moderate to high rg with most primal cuts. The YWT, CWT, and LMA correlated notably with CHK, SLD, and loin yields, especially LMA. However, BFT and MS were negatively correlated with many primal cuts but RIB. Those rg estimates were also opposite of that of LMA and CWT with primal cuts. Phenotypic correlations (rp) were generally weaker than rg estimates. The rp of YWT, CWT, and LMA were either zero or moderately negative compared to those of the BFT and MS with primal cuts. Most primal cuts yielded positive rp estimates among them, except for RIB. Our results suggest that direct selection for YWT, various carcass traits, and primal-cut yields may increase the carcass value of Hanwoo males.

Genetic parameters for docility, weaning weight, yearling weight, and intramuscular fat percentage in Hereford cattle

Cattle behavior, including measures of docility, is important to beef cattle producers not only from a human safety perspective but also due to potential correlations to economically relevant traits. Field data from the American Hereford Association was used to estimate genetic parameters for chute score (CS; = 25,037), weaning weight (WW; = 24,908), yearling weight (YW; = 23,978), and intramuscular fat percentage (IMF; = 12,566). Single-trait and bivariate animal models were used to estimate heritabilities and genetic correlations. All models included fixed effects of sex and contemporary group, defined as herd-year-season, and direct genetic and residual components were included as random effects. For CS and WW, additional random effects of maternal genetic and maternal permanent environment were also fitted. For CS, WW, YW, and IMF, heritability estimates were 0.27 ± 0.02, 0.35 ± 0.03, 0.36 ± 0.02, and 0.27 ± 0.02, respectively. Genetic correlations between CS and WW, CS and YW, CS and IMF, WW and YW, WW and IMF, and YW and IMF were -0.12 ± 0.06, -0.10 ± 0.05, -0.08 ± 0.06, 0.47 ± 0.05, -0.19 ± 0.09, and -0.41 ± 0.05, respectively. Heritability estimates for all traits suggest that they would respond favorably to selection and that selection for increased WW or YW could decrease marbling. Genetic correlations between CS and WW, YW, and IMF were all favorable but weak, suggesting that selection for improved docility will not have negative consequences on growth or carcass quality. Furthermore, maternal additive and maternal permanent environmental variances for CS were near 0, suggesting that their inclusion in National Cattle Evaluations is not warranted.

Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions

Cattle body composition is difficult to model because several factors affect the composition of the average daily gain (ADG) of growing animals. The objective of this study was to identify commercial single nucleotide polymorphism (SNP) panels that could improve the predictability of days on feed (DOF) to reach a target United States Department of Agriculture (USDA) grade given animal, diet, and environmental information under feedyard conditions. The data for this study was comprised of crossbred heifers (n = 681) and steers (n = 836) from commercial feedyards. Eleven molecular breeding value (MBV) scores derived from SNP panels of candidate gene polymorphisms and two-leptin gene SNP (UASMS2 and E2FB) were evaluated. The empty body fat (EBF) and the shrunk body weight (SBW) at 28% EBF (AFSBW) were computed by the Cattle Value Discovery System (CVDS) model using hip height (EBF_HH and AFSBW_HH) or carcass traits (EBF_CT and AFSBW_CT) of the animals. The DOF_HH was calculated when AFSBW_HH and ADG_HH were used and DOF_CT was calculated when AFSBW_CT and ADG_CT were used. The CVDS estimates dry matter required (DMR) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBW_CT was assumed more accurate than the AFSBW_HH because it was computed using carcass traits. The difference between AFSBW_CT and AFSBW_HH, DOF_CT and DOF_HH, and DMR and dry matter intake (DMI) were regressed on the MBV scores and leptin gene SNP to explain the variation. Our results indicate quite a large range of correlations among MBV scores and model input and output variables, but MBV ribeye area was the most strongly correlated with the differences in DOF, AFSBW, and DMI by explaining 8, 13.2 and 6.5%, respectively, of the variation. This suggests that specific MBV scores might explain additional variation of input and output variables used by nutritional models in predicting individual animal performance.

Genetic parameters for sensory traits in longissimus muscle and their associations with tenderness, marbling score, and intramuscular fat in Angus cattle

The objective of this study was to estimate heritabilities for sensory traits and genetic correlations among sensory traits and with marbling score (MS), Warner-Bratzler shear force (WBSF), and intramuscular fat content (IMFC). Samples of LM from 2,285 Angus cattle were obtained and fabricated into steaks for laboratory analysis and 1,720 steaks were analyzed by a trained sensory panel. Restricted maximum likelihood procedures were used to obtain estimates of variance and covariance components under a multitrait animal model. Estimates of heritability for MS, IMFC, WBSF, tenderness, juiciness, and connective tissue traits were 0.67, 0.38, 0.19, 0.18, 0.06, and 0.25, respectively. The genetic correlations of MS with tenderness, juiciness, and connective tissue were estimated to be 0.57 ± 0.14, 1.00 ± 0.17, and 0.49 ± 0.13, all positive and strong. Estimated genetic correlations of IMFC with tenderness, juiciness, and connective tissue were 0.56 ± 0.16, 1.00 ± 0.21, and 0.50 ± 0.15, respectively. The genetic correlations of WBSF with tenderness, juiciness, and connective tissue were all favorable and estimated to be -0.99 ± 0.08, -0.33 ± 0.30 and -0.99 ± 0.07, respectively. Strong and positive genetic correlations were estimated between tenderness and juiciness (0.54 ± 0.28) and between connective tissue and juiciness (0.58 ± 0.26). In general, genetic correlations were large and favorable, which indicated that strong relationships exist and similar gene and gene networks may control MS, IMFC, and juiciness or WBSF, panel tenderness, and connective tissue. The results from this study confirm that MS currently used in selection breeding programs has positive genetic correlations with tenderness and juiciness and, therefore, is an effective indicator trait for the improvement of tenderness and juiciness in beef. This study also indicated that a more objective measure, particularly WBSF, a trait not easy to improve through phenotypic selection, is an excellent candidate trait for genomic selection aimed at improving eating satisfaction.

Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle

Background

The major obstacles for the implementation of genomic selection in Australian beef cattle are the variety of breeds and in general, small numbers of genotyped and phenotyped individuals per breed. The Australian Beef Cooperative Research Center (Beef CRC) investigated these issues by deriving genomic prediction equations (PE) from a training set of animals that covers a range of breeds and crosses including Angus, Murray Grey, Shorthorn, Hereford, Brahman, Belmont Red, Santa Gertrudis and Tropical Composite. This paper presents accuracies of genomically estimated breeding values (GEBV) that were calculated from these PE in the commercial pure-breed beef cattle seed stock sector.

Methods

PE derived by the Beef CRC from multi-breed and pure-breed training populations were applied to genotyped Angus, Limousin and Brahman sires and young animals, but with no pure-breed Limousin in the training population. The accuracy of the resulting GEBV was assessed by their genetic correlation to their phenotypic target trait in a bi-variate REML approach that models GEBV as trait observations.

Results

Accuracies of most GEBV for Angus and Brahman were between 0.1 and 0.4, with accuracies for abattoir carcass traits generally greater than for live animal body composition traits and reproduction traits. Estimated accuracies greater than 0.5 were only observed for Brahman abattoir carcass traits and for Angus carcass rib fat. Averaged across traits within breeds, accuracies of GEBV were highest when PE from the pooled across-breed training population were used. However, for the Angus and Brahman breeds the difference in accuracy from using pure-breed PE was small. For the Limousin breed no reasonable results could be achieved for any trait.

Conclusion

Although accuracies were generally low compared to published accuracies estimated within breeds, they are in line with those derived in other multi-breed populations. Thus PE developed by the Beef CRC can contribute to the implementation of genomic selection in Australian beef cattle breeding.

Use of robust multivariate linear mixed models for estimation of genetic parameters for carcass traits in beef cattle

Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers, but heavy-tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting multivariate Normal (MN) or heavy-tail distributions (multivariate Student's t and multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th rib fat (FAT) traits in beef cattle using 2475 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favoured MSt over MS and MN models, respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.89 ± 0.90 (4.35, 7.86) and 2.04 ± 0.18 (1.70, 2.41), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the data set. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN, respectively. Posterior inferences on genetic variance were, however, comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt and MS models indicated similar and moderate heritability comparable with the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT, which showed an antagonistic relationship. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.

Comparison of molecular breeding values based on within- and across-breed training in beef cattle

Background

Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported.

Methods

Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype.

Results

With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero.

Conclusions

Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.

Accuracies of direct genomic breeding values in Hereford beef cattle using national or international training populations

The objective of this study was to estimate accuracies of direct genomic breeding values (DGV) for nationally evaluated traits of 1,081 American (AMH), 100 Argentine (ARH), 75 Canadian (CAH), and 395 Uruguayan (URH) Hereford animals genotyped using the Illumina BovineSNP50 BeadChip. Deregressed EBV (DEBV) were used as observations in a weighted analysis to derive DGV using BayesB and BayesC methods. The AMH animals were clustered into 4 groups, using either K-means or random clustering. Cross validation was performed with the group not used in training providing validation of the accuracies of estimated DGV. Genomic predictions were also evaluated for AMH animals by training on older animals and validating on younger animals. Bivariate animal models were used for each trait to estimate genetic correlations between DEBV and DGV. Genomic predictions were separately evaluated for foreign animals from each country using marker estimates from training on AMH or pooled international data. Pedigree estimated breeding values were developed for AMH animals, using traditional, pedigree-based BLUP (PBLUP) for comparison purposes. Using BayesB (BayesC) method, the average simple correlations between DGV and DEBV in AMH animals was 0.24 (0.21), 0.39 (0.36), and 0.32 (0.30) when training and validation sets were formed by K-means clustering, random allocation or year of birth of the animals, respectively. Genetic correlations between DEBV and DGV ranged from 0.20 (0.18) to 0.52 (0.45) in AMH animals. The DGV from BayesB were more accurate than from BayesC for most traits in AMH animals. Genomic predictions for foreign animals were less accurate than those obtained in AMH animals. Among foreign animals, genomic predictions were more accurate for CAH animals, which reflect the greater use of AMH sires in CAH in comparison with ARH and URH populations. Small changes in accuracies of DGV were observed for foreign animals by using admixed training populations. On average, genomic predictions across countries were more accurate for CAH and URH animals using BayesB. On average, accuracies of genomic predictions using BayesB (BayesC) method were 66% (55%) greater than those obtained from PBLUP. These results demonstrate the feasibility of developing DGV for American Hereford beef cattle. However, foreign breeders, especially South American Hereford breeders, need to genotype more animals to obtain more accurate genomic predictions.

Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle

BACKGROUND

In national evaluations, direct genomic breeding values can be considered as correlated traits to those for which phenotypes are available for traditional estimation of breeding values. For this purpose, estimates of the accuracy of direct genomic breeding values expressed as genetic correlations between traits and their respective direct genomic breeding values are required.

METHODS

We derived direct genomic breeding values for 2239 registered Limousin and 2703 registered Simmental beef cattle genotyped with either the Illumina BovineSNP50 BeadChip or the Illumina BovineHD BeadChip. For the 264 Simmental animals that were genotyped with the BovineHD BeadChip, genotypes for markers present on the BovineSNP50 BeadChip were extracted. Deregressed estimated breeding values were used as observations in weighted analyses that estimated marker effects to derive direct genomic breeding values for each breed. For each breed, genotyped individuals were clustered into five groups using K-means clustering, with the aim of increasing within-group and decreasing between-group pedigree relationships. Cross-validation was performed five times for each breed, using four groups for training and the fifth group for validation. For each trait, we then applied a weighted bivariate analysis of the direct genomic breeding values of genotyped animals from all five validation sets and their corresponding deregressed estimated breeding values to estimate variance and covariance components.

RESULTS

After minimizing relationships between training and validation groups, estimated genetic correlations between each trait and its direct genomic breeding values ranged from 0.39 to 0.76 in Limousin and from 0.29 to 0.65 in Simmental. The efficiency of selection based on direct genomic breeding values relative to selection based on parent average information ranged from 0.68 to 1.28 in genotyped Limousin and from 0.51 to 1.44 in genotyped Simmental animals. The efficiencies were higher for 323 non-genotyped young Simmental animals, born after January 2012, and ranged from 0.60 to 2.04.

CONCLUSIONS

Direct genomic breeding values show promise for routine use by Limousin and Simmental breeders to improve the accuracy of predicted genetic merit of their animals at a young age and increase response to selection. Benefits from selecting on direct genomic breeding values are greater for breeders who use natural mating sires in their herds than for those who use artificial insemination sires. Producers with unregistered commercial Limousin and Simmental cattle could also benefit from being able to identify genetically superior animals in their herds, an opportunity that has in the past been limited to seed stock animals.

Effects of a slow-release urea product on performance, carcass characteristics, and nitrogen balance of steers fed steam-flaked corn

Two experiments were conducted to examine the impact of source, urea (U) or Optigen II (OP), and level of dietary NPN on performance (Exp.1) and N balance (Exp. 2) of growing cattle. Sixty Angus crossbred steers (initial BW = 353 ± 13.9 kg) were used to evaluate performance, and fed 1 of 3 steam-flaked corn based diets: U (U(1.2), 1.2% NPN), OP (OP(1.3), 1.3% NPN), or OP without cottonseed meal (OP(3.1), 3.1% NPN). U(1.2)and OP(1.3) contained cottonseed meal and NPN as CP sources, whereas OP(3.1) contained only NPN. For Exp. 1, steers were blocked by postweaning BW and assigned to treatment (TRT) and pen within block. Body weight was collected every 14 d during the 105-d trial. Six steers from each TRT were selected based on residual feed intake (RFI) and harvested. Carcass and organ measurements were obtained. Cumulative animal performance was evaluated in 3 periods (0 to 35, 0 to 70, and 0 to 105 d) using a mixed coefficient model with initial BW as a covariate. For Exp. 2, 5 ruminally cannulated Holstein steers in a 5 × 5 Latin square design were used to evaluate N balance. Steers were fed a steam-flaked corn based diet with either no NPN, 0.75% U or N equivalent OP, or 1.5% U or N equivalent OP. Intake was measured, and feed, orts, urine, and fecal samples were obtained and composited for each steer by period. Data were analyzed using a mixed coefficient model. Orthogonal contrasts were used to evaluate differences between OP and U, and high and low level of NPN. For Exp. 1, there were no differences (P > 0.10) in initial BW, final BW, ADG, or DMI among TRT for any of the periods. However, for period 1 steers on OP(3.1) had reduced F:G than U(1.2) (5.71 kg/kg vs. 7.39 kg/kg; P = 0.03), and steers fed OP(1.3) tended to have less F:G than those fed U(1.2) (6.07 kg/kg vs. 7.39 kg/kg; P = 0.07). In period 2, OP(3.1) had reduced F:G than U(1.2) (5.58 kg/kg vs. 6.56 kg/kg; P = 0.03), but did not differ from OP(1.3) (5.97). For Exp. 2, steers fed OP tended (P = 0.09) to have less apparent N absorption than those fed U. Apparent N absorption differed (P < 0.05) for level of NPN. There were no differences (P > 0.10) in intake or digestibility among source or level of NPN. No major differences (P > 0.10) on performance and carcass composition were observed between U and OP diets. Steers had better initial F:G (Period 1 and 2) when OP was used as the only source of feed N (OP(3.1)), suggesting that OP may replace true protein feeds in finishing cattle diets, minimizing feed use with no impact on carcass quality.

Genomics and the global beef cattle industry

After two decades of developing DNA-based tools for selection, we are at an interesting juncture. Genomic technology has essentially eliminated the potentially large negative impact of spontaneous single-mutation genetic defects as the management of recent examples in beef cattle have demonstrated. We have the ability to perform more accurate selection based on molecular breeding values (MBVs) for animals closely related to the discovery population. Yet the amount of genetic variation explained falls short of expectations held for the technology. Tests are less effective in distant relatives within a breed and are not robust enough for across-breed use. It is hypothesised that ‘larger single-nucleotide polymorphism (SNP) panels’ will help extend the effective use of tests to more distantly related animals and across breeds. Sequencing and imputing sequences across individuals will enable us to discover causative mutations or SNPs in perfect harmony with the mutation. However, the investment to revisit discovery populations will be large. We can ill afford to duplicate genotyping or sequencing activities for prominent individuals. Hence, a global strategy for genotyping and sequencing becomes an attractive proposition as many of our livestock populations are related. As we learned more of the complexities of the genome, the number of animals in discovery populations necessary to achieve high levels of predictability has grown dramatically. No one organisation has the resources to assemble the animals needed, especially for novel, expensive or hard to measure phenotypes. This scenario is fertile ground for increased international collaboration in all livestock species.

Physiology and Endocrinology Symposium: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females

The promise of genomic selection is accurate prediction of the genetic potential of animals from their genotypes. Simple DNA tests might replace low-accuracy predictions for expensive or lowly heritable measures of puberty and fertility based on performance and pedigree. Knowing with some certainty which DNA variants (e.g., SNP) affect puberty and fertility is the best way to fulfill the promise. Several SNP from the BovineSNP50 assay have tentatively been associated with reproductive traits including age at puberty, antral follicle count, and pregnancy observed on different sets of heifers. However, sample sizes are too small and SNP density is too sparse to definitively determine genomic regions harboring causal variants affecting reproductive success. Additionally, associations between individual SNP and similar phenotypes are inconsistent across data sets, and genomic predictions do not appear to be globally applicable to cattle of different breeds. Discrepancies may be a result of different QTL segregating in the sampled populations, differences in linkage disequilibrium (LD) patterns such that the same SNP are not correlated with the same QTL, and spurious correlations with phenotype. Several approaches can be used independently or in combination to improve detection of genomic factors affecting heifer puberty and fertility. Larger samples and denser SNP will increase power to detect real associations with SNP having more consistent LD with underlying QTL. Meta-analysis combining results from different studies can also be used to effectively increase sample size. High-density genotyping with heifers pooled by pregnancy status or early and late puberty can be a cost-effective means to sample large numbers. Networks of genes, implicated by associations with multiple traits correlated with puberty and fertility, could provide insight into the complex nature of these traits, especially if corroborated by functional annotation, established gene interaction pathways, and transcript expression. Example analyses are provided to demonstrate how integrating information about gene function and regulation with statistical associations from whole-genome SNP genotyping assays might enhance knowledge of genomic mechanisms affecting puberty and fertility, enabling reliable DNA tests to guide heifer selection decisions.

Accounting for genomic pre-selection in national BLUP evaluations in dairy cattle

Background

In future Best Linear Unbiased Prediction (BLUP) evaluations of dairy cattle, genomic selection of young sires will cause evaluation biases and loss of accuracy once the selected ones get progeny.

Methods

To avoid such bias in the estimation of breeding values, we propose to include information on all genotyped bulls, including the culled ones, in BLUP evaluations. Estimated breeding values based on genomic information were converted into genomic pseudo-performances and then analyzed simultaneously with actual performances. Using simulations based on actual data from the French Holstein population, bias and accuracy of BLUP evaluations were computed for young sires undergoing progeny testing or genomic pre-selection. For bulls pre-selected based on their genomic profile, three different types of information can be included in the BLUP evaluations: (1) data from pre-selected genotyped candidate bulls with actual performances on their daughters, (2) data from bulls with both actual and genomic pseudo-performances, or (3) data from all the genotyped candidates with genomic pseudo-performances. The effects of different levels of heritability, genomic pre-selection intensity and accuracy of genomic evaluation were considered.

Results

Including information from all the genotyped candidates, i.e. genomic pseudo-performances for both selected and culled candidates, removed bias from genetic evaluation and increased accuracy. This approach was effective regardless of the magnitude of the initial bias and as long as the accuracy of the genomic evaluations was sufficiently high.

Conclusions

The proposed method can be easily and quickly implemented in BLUP evaluations at the national level, although some improvement is necessary to more accurately propagate genomic information from genotyped to non-genotyped animals. In addition, it is a convenient method to combine direct genomic, phenotypic and pedigree-based information in a multiple-step procedure.

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.

The nature, scope and impact of genomic prediction in beef cattle in the United States

Artificial selection has proven to be effective at altering the performance of animal production systems. Nevertheless, selection based on assessment of the genetic superiority of candidates is suboptimal as a result of errors in the prediction of genetic merit. Conventional breeding programs may extend phenotypic measurements on selection candidates to include correlated indicator traits, or delay selection decisions well beyond puberty so that phenotypic performance can be observed on progeny or other relatives. Extending the generation interval to increase the accuracy of selection reduces annual rates of gain compared to accurate selection and use of parents of the next generation at the immediate time they reach breeding age. Genomic prediction aims at reducing prediction errors at breeding age by exploiting information on the transmission of chromosome fragments from parents to selection candidates, in conjunction with knowledge on the value of every chromosome fragment. For genomic prediction to influence beef cattle breeding programs and the rate or cost of genetic gains, training analyses must be undertaken, and genomic prediction tools made available for breeders and other industry stakeholders. This paper reviews the nature or kind of studies currently underway, the scope or extent of some of those studies, and comments on the likely predictive value of genomic information for beef cattle improvement.

The value of using DNA markers for beef bull selection in the seedstock sector

The objective of this study was to estimate the value derived from using DNA information to increase the accuracy of beef sire selection in a closed seedstock herd. Breeding objectives for commercial production systems targeting 2 diverse markets were examined using multiple-trait selection indexes developed for the Australian cattle industry. Indexes included those for both maternal (self-replacing) and terminal herds targeting either a domestic market, where steers are finished on pasture, or the export market, where steers are finished on concentrate rations in feedlots and marbling has a large value. Selection index theory was used to predict the response to conventional selection based on phenotypic performance records, and this was compared with including information from 2 hypothetical marker panels. In 1 case the marker panel explained a percentage of additive genetic variance equal to the heritability for all traits in the breeding objective and selection criteria, and in the other case to one-half of this amount. Discounted gene flow methodology was used to calculate the value derived from the use of superior bulls selected using DNA test information and performance recording over that derived from conventional selection using performance recording alone. Results were ultimately calculated as discounted returns per DNA test purchased by the seedstock operator. The DNA testing using these hypothetical marker panels increased the selection response between 29 to 158%. The value of this improvement above that obtained using traditional performance recording ranged from $89 to 565 per commercial bull, and $5,332 to 27,910 per stud bull. Assuming that the entire bull calf crop was tested to achieve these gains, the value of the genetic gain derived from DNA testing ranged from $204 to 1,119 per test. All values assumed that the benefits derived from using superior bulls were efficiently transferred along the production chain to the seedstock producer incurring the costs of genotyping. These results suggest that the development of greater-accuracy DNA tests for beef cattle selection could be beneficial from an industry-wide perspective, but the commercial viability will strongly depend on price signaling throughout the production chain.