Genetic evaluation for the beef industry in Australia

2005 ◽  
Vol 45 (8) ◽  
pp. 913 ◽  
Author(s):  
H-U. Graser ◽  
B. Tier ◽  
D. J. Johnston ◽  
S. A. Barwick
Keyword(s):  
Beef Cattle ◽  
Evaluation System ◽  
Carcass Characteristics ◽  
Genetic Evaluation ◽  
Beef Industry ◽  
Linear Unbiased Prediction ◽  
Cattle Industry ◽  
Evaluation Procedures ◽  
Complex Models ◽  
Best Linear Unbiased

Genetic evaluation for beef cattle in Australia has been performed using an animal model with best linear unbiased prediction since 1984. The evaluation procedures have evolved from simple to more complex models and from few to a large number of traits, including traits for reproduction, growth and carcass characteristics. This paper describes in detail the current beef cattle genetic evaluation system ‘BREEDPLAN’ used for the Australian beef cattle industry, the traits analysed and underlying models, and presents a short overview of the challenges and planned developments of coming years.

New approaches to genetic evaluation of beef cattle

1988 ◽  
Vol 12 ◽  
pp. 99-110
Author(s):  
E. John Pollak
Keyword(s):  
Animal Model ◽  
Beef Cattle ◽  
Mixed Model ◽  
The United States ◽  
Genetic Evaluation ◽  
Beef Industry ◽  
Widespread Application ◽  
Cattle Industry ◽  
Reduced Animal Model ◽  
Genetic Evaluations

The beef cattle industry in the United States has undergone dramatic changes over the past decade with the adoption of genetic evaluation programs. The method of choice has been Henderson's mixed model methodology for best linear unbiased prediction (BLUP). The most prevalently used model is the animal model (Henderson and Quaas, 1976) computed by the equivalent reduced animal model (Quaas and Pollak, 1980).Neither the methodology or the models being used are particularly new. What is new in this industry is the widespread application of these techniques to the analysis of the data banks maintained by the breed organizations. Today many breed associations publish a national sire evaluation, and most of these have published their first in the last three years. This rapid proliferation of published evaluations has coincided with an attitude in the industry of promoting specification beef and predictable performance. Genetic evaluations provide information not only to achieve goals in selection but as well for merchandizing cattle based on quantifiable potential. The enthusiasm for genetic evaluations right now in the U.S. beef industry is high.

scholarly journals Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1890
Author(s):  
Ling Xu ◽  
Qunhao Niu ◽  
Yan Chen ◽  
Zezhao Wang ◽  
Lei Xu ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Prediction Accuracy ◽  
Imputation Accuracy ◽  
Predictive Performance ◽  
Cost Effective ◽  
Low Density ◽  
Beef Industry ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Snp Panel

Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits.

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

An empirical check on best linear unbiased prediction genetic evaluation using pig field recording data

1997 ◽  
Vol 77 (2) ◽  
pp. 211-216 ◽  
Author(s):  
V. M. Quinton ◽  
C. Smith
Keyword(s):  
Large Body ◽  
Best Linear Unbiased Prediction ◽  
Genetic Evaluation ◽  
Linear Unbiased Prediction ◽  
Field Recording ◽  
Best Linear Unbiased ◽  
Simulation Results ◽  
Pig Performance ◽  
Computer Simulation Studies ◽  
Unbiased Prediction

The theory and use of best linear unbiased prediction in genetic evaluation are well developed. However, there has been little empirical checking of its efficacy in practice. The objective here was to use a large body of Canadian pig performance records to check on the predicted benefits of BLUP in genetic evaluation. Phenotype records were available on fat depth and on days to 100 kg on some 65 000 progeny born in 1994 and 1995 from parents evaluated before 1994. Rank correlations between parent and progeny in data were calculated within herd-year-season to avoid effects due to differences in these factors. Computer simulation studies were also run to check on the predicted results. The simulation results confirmed the expectations on the higher correlation of mid-parental EBV than of mid-parental phenotype with progeny genotype and a regression (of progeny phenotype on mid-parental EBV) of unity when all relevant pedigree and performance data were used. In the data analysis, the (rank) correlations with progeny phenotype were consistently higher (36 and 27%) for mid-parental BLUP genetic evaluation than for mid-parental phenotypes, confirming the superiority of the BLUP evaluations over phenotypes. However, the regression of progeny phenotype on mid-parent BLUP EBV was usually less than the predicted value of unity. Simulation results suggest that either the base population heritability was lower than that used in the evaluation or that the information used was incomplete. Key words: Best linear unbiased prediction, EBV, pigs, performance, selection

scholarly journals Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1886
Author(s):  
Masoumeh Naserkheil ◽  
Hossein Mehrban ◽  
Deukmin Lee ◽  
Mi Na Park
Keyword(s):  
Prediction Accuracy ◽  
Single Step ◽  
Linear Regression Method ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Cattle Industry ◽  
A Value ◽  
Starting Point ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle

There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits—bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round—in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice.

ACCURACY OF SELECTION INDEX AND BEST LINEAR UNBIASED PREDICTION FOR WITHIN-HERD SELECTION WITH ASSORTATIVE MATING OF BEEF CATTLE

1989 ◽  
Vol 69 (2) ◽  
pp. 315-322 ◽  
Author(s):  
J. A. B. ROBINSON ◽  
J. W. WILTON ◽  
L. R. SCHAEFFER
Keyword(s):  
Beef Cattle ◽  
Assortative Mating ◽  
Index System ◽  
Selection Index ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Net Worth ◽  
Best Linear Unbiased ◽  
Mating Scheme ◽  
Unbiased Prediction

A simulation of a selection and mating scheme for beef herds was conducted to compare the genetic progress achieved over 20 generations through evaluation of the animals by best linear unbiased prediction and by a selection index. For comparison, the same selection and mating scheme was applied to the herd using the true genetic values of each animal. Traits considered in the simulation were direct maternal genetic calving ease, birth weight, weaning weight and yearling weight. The analysis was replicated 100 times for each method of evaluation. In general, the best linear unbiased prediction system achieved greater genetic response than the selection index system. The BLUP system gave 18.7% better genetic improvement in total net worth than the selection index system. However, the selection index system gave only 42.7% and the BLUP system gave 50.6% of the response from selection on true net worth values. Keywords: Beef cattle, selection index, assortative mating.

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