Beef in Burgundy

A recipe for beef in Burgundy

Recent Developments in Beef Cattle Improvement

Profit from cattle enterprises is influenced by the value of sale animals, less the costs of their production. Ever-changing production and economic circumstances provide both threats and opportunities to cow-calf producers, bull breeders and feedlotters. Feeding strategies and other aspects of management typically require continuous between- and within-year modification in order to optimize margins. Responses to such management changes typically occur immediately. In contrast, genetic improvement is a long-term exercise with within-breed changes from selection seldom exceeding 1-2% per year. During favorable periods in the cow-calf economy, producers may feel that there is little need for genetic improvement. Then, during economic downturns, producers may feel they can’t afford to invest in genetic improvement. Both these behaviors lead to suboptimal rates of genetic improvement

Developing a Reduced SNP Panel for Low-cost Genotyping in Beef Cattle

The objective of this study was to develop a low-density reduced SNP panel (RP) that could capture most of the predictive ability of a 50K panel for six important traits (birth, weaning and yearling weights; calving ease direct; marbling; and rib eye area) in beef cattle. More than 15,000 animals from six cattle breeds genotyped with 50K were used to select markers highly associated with target traits. Accuracies of direct genomic breeding values (DGV) were calculated for 3 independent validation populations using either 50K or RP. Accuracies of DGV obtained from RP were comparable with those obtained from 50K (\u3e75% predictive ability of 50K) while the size of RP i

Accuracies of Genomic Prediction in Beef Cattle

The objective of this study was to derive and evaluate the accuracies of molecular breeding values (MBV) for economically relevant traits for commercial implementation in several beef cattle breeds. We developed MBV forHereford, Red Angus and Simmental breeds. Accuracies of MBV ranged from 0.18 to 0.45 in Hereford, 0.37 to 0.85 in Red Angus, and from 0.29 to 0.65 in Simmental using within breed genomic predictions. Single breed genomic predictions had no utility when applied to other breeds. However, the accuracies of MBV improved for some breeds when predictions were derived using multi-breed reference populations. These results have now been implemented as routine predictions for breeders of American Hereford, American Red Angus and American Simmental beef cattle. Similar findings will soon be extended to other breeds

Development and implementation of genomic predictions in beef cattle

peer-reviewedBeef production represents a considerable contribution to local and global economies and food security but also the environmental footprint of agricultural production systems. The development of accurate genomic evaluations in beef populations are more difficult than in dairy populations for reasons including the presence of multiple breeds, poor extent of phenotyping, lack of artificial insemination, and beef systems being generally a lower-margin business of poorer adopters of technology. Several options exist to minimize or overcome the limitations of developing accurate genomic evaluations for beef cattle

Genetic Difference of Five Beef Cattle Breeds Characterized by Genome-wide SNPs and Haplotypes

The objective of this study was to characterize breed differences using SNP available on commonly-used marker panels compared to using genome-wide SNP haplotypes derived from the same markers. The percentage of breed-specific segregating haplotypes was much higher than that of SNPs. Principal components of haplotypes characterized breed difference better than SNP genotypes. Results indicate that haplotypes characterize breed differences more adequately than SNP genotypes and hence offer promise to improve genomic prediction and fine mapping in multi breed population

Improved Accuracy of Across-breed Genomic Prediction Using Haplotypes in Beef Cattle Populations

Genomic prediction uses a reference population of animals with SNP genotypes and phenotypes to predict the merit of selection candidates that typically do not have observed phenotypes. Accuracy of genomic prediction from models that fitted 50K SNP genotypes was low when selection candidates were from a breed only distantly related with the breeds in the reference population. That accuracy was not improved by increasing SNP density from 50K to a ten-fold higher density using imputation. This indicates that the accuracy of genomic prediction mainly came from family-wise co-segregation information. In this study, a genomic prediction model that fitted genome-wide 100 kilo-bp (Kbp) haplotypes improved accuracy for breeds that were not in the reference population. The haplotype model is a more accurate alternative to the SNP model for genomic prediction when animals of the same breed as the prediction candidates are not available for the reference population

Genome-Wide Association Study of Feed Efficiency in Beef Cattle

Feed costs comprise the majority of beef production costs and feed intake has long been recognized as an economically relevant trait for beef cattle. Residual feed intake (RFI) is a function of feed intake and performance and reflects whether animals eat more or less than expected for a given level of production. The objectives of this study were to map quantitative trait loci (QTL) associated with feedlot RFI in 4 different beef cattle populations. A total of 13 significant QTL over 10 different chromosomes were detected. The identified QTL had no overlap across 4 beef cattle populations reflecting different genetic makeup of RFI across different beef populations. Further genotyping and statistical analyses are needed to find the casual mutations. Once found, knowledge of such mutations would create new opportunities for the selection of more efficient animals

GenSim: Simulation of Descendants from Sequenced Ancestors Data

High-density real or imputed SNP genotypes are now routinely used for genomic prediction and genome-wide association studies. This is extending to the use of actual or imputed next generation sequence data in these activities. Simulation studies are useful to mimic these complex scenarios and test different analytical methods. We have developed a software tool GenSim to simulate sequence data in descendants. In this software, a crossover position and origin simulation (CPOS) algorithm is implemented to efficiently drop down sequence data from founders in complex pedigrees. Parallel programming techniques are used to reduce computing time