Categorical Trait Sire Evaluation by Best Linear Unbiased Prediction of Future Progeny Category Frequencies

Biometrics ◽  
1980 ◽  
Vol 36 (1) ◽  
pp. 117 ◽  
Author(s):  
R. L. Quaas ◽  
L. D. Van Vleck
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Sire Evaluation ◽  
Categorical Trait ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Comparative efficiency of different multiple linear regression prediction equations of first lactation 305-day milk yield for sire evaluation in Murrah buffaloes

2018 ◽  
Author(s):  
Saroj Kumar Sahoo ◽  
Avtar Singh ◽  
G.S. Ambhore ◽  
S.K. Dash ◽  
P.P. Dubey
Keyword(s):  
Least Squares ◽  
Milk Yield ◽  
Best Linear Unbiased Prediction ◽  
Regression Equations ◽  
Linear Unbiased Prediction ◽  
Murrah Buffaloes ◽  
Sire Evaluation ◽  
Best Linear Unbiased ◽  
Animal Model Blup ◽  
Unbiased Prediction

In this study, first lactation 39059 weekly test-day milk yield records of 961 Murrah buffaloes were used to predict first lactation 305-day milk yield (FL305DMY) by stepwise backward regression method. The best single, two, three and four test day combinations were selected for prediction of FL305DMY based on adjusted R2 and RMSE values. The sires were evaluated for 305-day actual and predicted first lactation milk yield based on derived multiple regression equations using four methods viz. least squares (LSQ), simple regressed least squares (SRLS), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) methods. The effectiveness of different sire evaluation methods were judged by error variance, coefficient of determination, coefficient of variation and spearman’s rank correlation. The accuracy of prediction of FL305DMY from weekly test day milk yields were observed to be best for TD-7 (48th day) and TD-22 (153rd day) combination with BLUP-AM as the most efficient method for sire evaluation. It was concluded that the FL305DMY can be predicted as early as 153rd day of lactation and further can be used for early genetic evaluation of Murrah sires.

GENETIC TRENDS FOR MILK AND FAT PRODUCTION IN DAIRY CATTLE

1979 ◽  
Vol 59 (1) ◽  
pp. 203-206 ◽  
Author(s):  
T. R. BATRA
Keyword(s):  
Dairy Cattle ◽  
Prediction Method ◽  
Weighted Average ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Sire Evaluation ◽  
Best Linear Unbiased ◽  
Jersey Cows ◽  
Genetic Trends ◽  
Unbiased Prediction

The Best Linear Unbiased Prediction method is used to evaluate dairy sires in Canada. Milk and fat production records of 2-yr-old Ayrshire, Guernsey, Holstein and Jersey cows calved from 1958 through 1975 were used in the sire evaluation done in November 1976. Genetic trends were estimated as twice the change in weighted average of sire proofs per year. Genetic trends for milk and fat production were 1.32 and 1.62 BCA for Ayrshire; 1.50 and.88 BCA for Guernsey;.72 and.80 BCA for Holstein; and.60 and.54 BCA for Jersey, respectively.

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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