RELATIONSHIP BETWEEN PEDIGREE INDEX AND PROGENY PERFORMANCE FOR CANADIAN DAIRY BULLS

1976 ◽  
Vol 56 (4) ◽  
pp. 715-719 ◽  
Author(s):  
H. M. STEWART ◽  
E. B. BURNSIDE ◽  
G. B. WEAVER ◽  
M. G. FREEMAN
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Regression Coefficients ◽  
Linear Unbiased Prediction ◽  
Point Score ◽  
Body Conformation ◽  
Class Average ◽  
Holstein Bulls ◽  
Best Linear Unbiased ◽  
Dairy Bulls ◽  
Progeny Performance

Progeny performance was related to pedigree indexes for 216 Holstein bulls which entered Canadian Artificial Insemination units from 1962 to 1970. Progeny performance for body conformation (average point score) was related to pedigree index for point score, the sire’s point score proof, and an approximate index based on sire and material grandsire proofs at the time the bull entered the stud. Regression coefficients were 0.34 (±.07), 0.17 (±.14), and 0.32 (±.07), respectively. Regressions of progeny performance for Breed Class Average (BCA) milk (Herdmate Comparison) on pedigree index for BCA milk (Herdmate Comparison), the sire’s HMC index, and the approximate HMC index yielded coefficients of.70 (±.11),.44 (±.08), and.94 (±.15), respectively. In both cases, the pedigree index was the best indicator of progeny performance, followed by the approximate index and the sire’s index, respectively. As no Best Linear Unbiased Prediction (BLUP) pedigree index for BCA milk was available, progeny performance for BLUP proofs was related to pedigree index for milk (HMC), the sire’s BLUP proof and an approximate index based on the sire’s and maternal grandsire’s BLUP milk proofs. Regression estimates were.70 (±.10),.56 (±.08), and 1.16 (±.13). The approximate index was a better predictor of progeny performance (BLUP) than the pedigree index based on HMC milk proofs.

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 [...]

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.

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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