THE EVALUATION OF BEEF BULLS FROM AN ORGANIZED PROGENY TEST PROGRAM

1980 ◽  
Vol 60 (3) ◽  
pp. 621-626 ◽  
Author(s):  
L. R. SCHAEFFER ◽  
HOON SONG ◽  
J. W. WILTON

Three methods of evaluating beef sires for weaning weight with data obtained from an organized young sire progeny testing program were compared. Information from Agriculture Canada’s National Beef Sire Monitoring Program was utilized along with computational procedures based on best linear unbiased prediction. The methods were applied to data from the Canadian Simmental Association as an illustration of the methods. A model which incorporates the proofs of the reference sires into the comparisons with test bulls was considered more appropriate than the other two models compared. The results also showed that even in an organized progeny test program, test bulls are not truly mated to cows of equal merit or across equal herd environments.

1983 ◽  
Vol 63 (1) ◽  
pp. 17-26 ◽  
Author(s):  
J. L. FOULLEY ◽  
L. R. SCHAEFFER ◽  
H. SONG ◽  
J. W. WILTON

A numerical procedure was utilized to optimize the number of progeny for young bulls and reference sires in a beef sire progeny testing program. Optimization was based on attaining a desired level of accuracy on the types of comparisons that were to be made after the test was completed. As heritability increased, the total number of progeny required decreased, but the percentage of reference sire progeny remained the same. There were near optimum progeny distributions with smaller total number of progeny and only slightly less accurate than the optimum solutions which could also be considered for application. Key words: Progeny testing, beef cattle, optimum designs, reference sires


BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ainhoa Calleja-Rodriguez ◽  
Jin Pan ◽  
Tomas Funda ◽  
Zhiqiang Chen ◽  
John Baison ◽  
...  

Abstract Background Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. Conclusions Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.


Author(s):  
Ângela Martins ◽  
Virgínia Santos ◽  
Mário Silvestre

ResumoA história do melhoramento genético animal acompanha a história da Humanidade, começando com a domesticação do primeiro animal, que terá sido o cão, ao qual se seguiram os bovinos, ovinos e todas as outras espécies que deram origem às raças domésticas da atualidade. Inicialmente a seleção de reprodutores era efetuada de forma empírica. No século XVIII R. Bakewell foi pioneiro na utilização de registos produtivos e testes de descendência. No final deste século começaram a ser estabelecidos os livros genealógicos de diversas raças. No século XIX, os avanços científicos protagonizados por C. Darwin e G. Mendel são fundamentais para que, na primeira metade do século XX se desenvolva a maior parte da teoria do melhoramento animal, com o contributo de vários investigadores (R. Fisher, S. Wright, J. Haldane). Jay Lush ficou conhecido como o pai do melhoramento animal moderno. Defendeu que em vez da aparência subjetiva, o melhoramento animal deve-se basear em conhecimentos da genética quantitativa e da estatística. Charles Henderson apresentou o método Best Linear Unbiased Prediction (BLUP) para a estimativa do valor genético aditivo e sugeriu a integração da genealogia completa da população para incluir as relações genéticas entre os indivíduos. A evolução dos computadores permitiu a implementação generalizada do BLUP no final da década de 1980. Nos últimos tempos T. Meuwissen e M. Goddard desenvolveram a forma de incorporar informação do ADN em grande escala no modelo animal para estimar os valores genómicos. Palavras-chave: genética, melhoramento animal Abstract The history of animal breeding follows the history of humanity, beginning with the domestication of the first animal, which was the dog, followed by the cattle, sheep and all other species that gave rise to the domestic breed of the present time. Initially the selection of breeders was carried out empirically. In the eighteenth century R. Bakewell pioneered the use of records of performance of animals and progeny testing. At the end of this century herdbooks of various breeds began to be established. In the 19th century, the scientific advances made by Darwin and Mendel are fundamental for the, in the first half of the 20th century, development of most animal breeding theory with the contribution of several researchers (R. Fisher, S. Wright, J. Haldane). Jay Lush became known as the father of modern animal breeding. He argued that instead of subjective appearance, animal breeding should be bas 


Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1013
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  

The authors wish to make the following corrections to this paper [...]


Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  

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.


Author(s):  
B Grundy ◽  
WG Hill

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.


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