EVALUATION OF BEEF SIRES ACROSS BREEDS FOR CALVING EASE

1977 ◽  
Vol 57 (4) ◽  
pp. 635-645 ◽  
Author(s):  
L. R. SCHAEFFER ◽  
J. W. WILTON
Keyword(s):  
Maximum Likelihood ◽  
Scoring System ◽  
Variance Components ◽  
Error Variance ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Calving Ease ◽  
Best Linear Unbiased ◽  
Maximum Likelihood Prediction ◽  
Unbiased Prediction

Agriculture Canada and Alberta Record of Performance calving ease records on 54,139 calves from 3,338 sires of 18 breeds were used to evaluate sires by comparisons across breeds of sire. An objective scoring system was applied to the calving ease codes to derive appropriate weights for each category rather than using percentage of unassisted births or assuming equal intervals between categories. Common sire and error variance components were assumed for all breeds of sire. Heritability of calving ease under the model used was estimated to be.10 by maximum likelihood. Prediction of sire values for calving ease scores of future calves were calculated by best linear unbiased prediction procedures. Shorthorn, Hereford, and Angus sires caused relatively few calving difficulties, while Maine-Anjou sires caused more difficulties. Age of dam and sex of calf differences were also important. The range of sire evaluations for calving ease was narrow, but the bulls in either extreme could be identified.

scholarly journals Parentesco na seleção para produtividade e teores de óleo e proteína em soja via modelos mistos

2013 ◽  
Vol 48 (9) ◽  
pp. 1246-1253 ◽  
Author(s):  
Larissa Correia de Melo Pinheiro ◽  
Pedro Ivo Vieira Good God ◽  
Vinícius Ribeiro Faria ◽  
Ane Gabrielle Oliveira ◽  
Aline Akemi Hasui ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Best Linear Unbiased Prediction ◽  
Restricted Maximum Likelihood ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

O objetivo deste trabalho foi avaliar influência da informação de parentesco na seleção de progênies de soja quanto à produtividade e aos teores de óleo e proteína, com base no uso de modelos mistos de predição dos valores genéticos. Novecentas progênies F4:6 e 200 progênies F4:7 de soja foram avaliadas nas safras 2010/2011 e 2011/2012, respectivamente. As progênies foram obtidas de cruzamentos múltiplos a partir de 57 progenitores. Os dados foram analisados por meio de modelos aleatórios (quadrados mínimos) e mistos BLUP/REML ("best linear unbiased prediction/restricted maximum likelihood"). Os maiores valores de ganhos preditos foram obtidos com o BLUP/REML. Os valores genéticos preditos com o método BLUP/REML, sem informação de parentesco, apresentaram alta correlação com aqueles obtidos com o modelo aleatório, além de detectada alta coincidência das progênies selecionadas. A inclusão da matriz de parentesco resultou na seleção de progênies diferentes e em maior acurácia na predição dos valores genéticos.

scholarly journals Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments

2012 ◽  
Vol 151 (3) ◽  
pp. 381-395 ◽  
Author(s):  
J. FORKMAN ◽  
H-P. PIEPHO
Keyword(s):  
Variance Components ◽  
Best Linear Unbiased Prediction ◽  
Prediction Intervals ◽  
Unbiased Estimation ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased Estimation ◽  
Credible Intervals ◽  
Best Linear Unbiased ◽  
Unbiased Prediction ◽  
Normally Distributed

SUMMARYThe model for analysis of randomized complete block (RCB) experiments usually includes two factors: block and treatment. If treatment is modelled as fixed, best linear unbiased estimation (BLUE) is used, and treatment means estimate expected means. If treatment is modelled as random, best linear unbiased prediction (BLUP) shrinks the treatment means towards the overall mean, which results in smaller root-mean-square error (RMSE) in prediction of means. This theoretical result holds provided the variance components are known, but in practice the variance components are estimated. BLUP using estimated variance components is called empirical best linear unbiased prediction (EBLUP). In small experiments, estimates can be unreliable and the usefulness of EBLUP is uncertain. The present paper investigates, through simulation, the performance of EBLUP in small RCB experiments with normally as well as non-normally distributed random effects. The methods of Satterthwaite (1946) and of Kenward & Roger (1997, 2009), as implemented in the SAS System, were studied. Performance was measured by RMSE, in prediction of means, and coverage of prediction intervals. In addition, a Bayesian approach was used for prediction of treatment differences and computation of credible intervals. EBLUP performed better than BLUE with regard to RMSE, also when the number of treatments was small and when the treatment effects were non-normally distributed. The methods of Satterthwaite and of Kenward & Roger usually produced approximately correct coverage of prediction intervals. The Bayesian method gave the smallest RMSE and usually more accurate coverage of intervals than the other methods.

scholarly journals Estimação de parâmetros genéticos e predição de valor genético aditivo de trigo utilizando modelos mistos

2014 ◽  
Vol 49 (11) ◽  
pp. 882-890 ◽  
Author(s):  
Adérico Júnior Badaró Pimentel ◽  
João Filipi Rodrigues Guimarães ◽  
Moacil Alves de Souza ◽  
Marcos Deon Vilela de Resende ◽  
Lisandra Magna Moura ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Best Linear Unbiased Prediction ◽  
Restricted Maximum Likelihood ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

O objetivo deste trabalho foi estimar os parâmetros genéticos e predizer o valor genético de populações e indivíduos oriundos de populações segregantes de trigo, com o uso da metodologia de modelos mistos ("restricted maximum likelihood"/"best linear unbiased prediction", REML/BLUP). Trinta e seis populações segregantes de trigo e quatro controles foram avaliados na geração F3, em delineamento de blocos ao acaso, com informações de indivíduo retiradas de dentro das parcelas. Os caracteres avaliados foram: produção de grãos, índice de colheita, número de perfilhos e altura de planta. Observou-se a existência de variabilidade genética entre populações em todos os caracteres avaliados. A herdabilidade média variou de 39,15 a 92,78%, e a acurácia, de 62,57 a 96,32%, na seleção de populações. A herdabilidade individual no sentido restrito foi baixa dentro das populações, em todos os caracteres. A acurácia na seleção individual apresentou magnitude média, quanto ao caráter altura de plantas, e baixa quanto aos demais caracteres. A herdabilidade individual contribui para maior ganho nos caracteres altura de planta e índice de colheita com o uso do BLUP individual, em comparação ao BLUP de populações. As populações segregantes Embrapa22/BRS207, Embrapa22/VI98053, Embrapa22/IVI01041, BRS254/BRS207, BRS254/VI98053, BRS254/UFVT1Pioneiro e BRS264/BRS207 destacam-se por apresentar valor genético aditivo elevado em dois ou mais caracteres.

scholarly journals Correction: Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle. Genes 2019, 10, 1019

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1013
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle ◽  
Unbiased Prediction

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

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

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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