scholarly journals An alternative derivation method of mixed model equations from best linear unbiased prediction (BLUP) and restricted BLUP of breeding values not using maximum likelihood

2018 ◽  
Vol 89 (6) ◽  
pp. 876-879 ◽  
Author(s):  
Masahiro Satoh
Keyword(s):  
Maximum Likelihood ◽  
Mixed Model ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Alternative Derivation ◽  
Best Linear Unbiased ◽  
Derivation Method ◽  
Model Equations ◽  
Unbiased Prediction

scholarly journals Is it reasonable to use of a kinship matrix for best linear unbiased prediction?

2019 ◽  
Author(s):  
Bongsong Kim
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Kinship Matrix ◽  
Combining Abilities ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

AbstractThe linear mixed model (LMM) is characterized to account for the variance-covariance among entities in a population toward calculating the best linear unbiased prediction (BLUP). Animal and plant breeders widely use the LMM because it is perceived that the a BLUP estimate informs an estimated breeding value (EBV), so to speak a combining ability as a parent, obtained by relating each entity to his/her relatives using the variance-covariance. The LMM practice routinely substitutes an external kinship matrix for the variance-covariance. The challenge relevant to the LMM practice is the fact that it is unrealistic to validate the EBVs because the real breeding values are not measurable but conceptual. This unreality actually means that the EBVs are vague. Although some previous studies measured correlations between the EBVs and empirical combining abilities, they are not sufficient to remove the vagueness of EBVs because uncontrollable environmental factors might interfere with phenotypic observations for measuring the combining abilities. To overcome the challenge, this study scrutinized the soundness of the routine LMM practice from the mathematical perspective. As a result, it was demonstrated that the BLUP estimates resulting from the routine LMM practice mislead the breeding values. The genuine BLUP represents the arithmetic means of multiple phenotypic observations per each entity, given all phenotypic observations adjusted to the mean of zero.

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 Prediction of Rates of Inbreeding in Populations Selected on Best Linear Unbiased Prediction of Breeding Value

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 361-373
Author(s):  
Piter Bijma ◽  
John A Woolliams
Keyword(s):  
Linear Model ◽  
Goodness Of Fit ◽  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Quadratic Model ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Wide Range ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Abstract Predictions for the rate of inbreeding (ΔF) in populations with discrete generations undergoing selection on best linear unbiased prediction (BLUP) of breeding value were developed. Predictions were based on the concept of long-term genetic contributions using a recently established relationship between expected contributions and rates of inbreeding and a known procedure for predicting expected contributions. Expected contributions of individuals were predicted using a linear model, μi(x) = α βsi, where si denotes the selective advantage as a deviation from the contemporaries, which was the sum of the breeding values of the individual and the breeding values of its mates. The accuracy of predictions was evaluated for a wide range of population and genetic parameters. Accurate predictions were obtained for populations of 5–20 sires. For 20–80 sires, systematic underprediction of on average 11% was found, which was shown to be related to the goodness of fit of the linear model. Using simulation, it was shown that a quadratic model would give accurate predictions for those schemes. Furthermore, it was shown that, contrary to random selection, ΔF less than halved when the number of parents was doubled and that in specific cases ΔF may increase with the number of dams.

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