Genetic evaluation of pedigree beef cattle in Great Britain

1993 ◽  
Vol 1993 ◽  
pp. 106-106
Author(s):  
R E Crump ◽  
J G E Bryan ◽  
D Nicholson ◽  
R Thompson ◽  
G Simm
Keyword(s):  
Great Britain ◽  
Herd Size ◽  
Best Linear Unbiased Prediction ◽  
Individual Animal ◽  
Genetic Progress ◽  
Linear Unbiased Prediction ◽  
Beef Herds ◽  
Best Linear Unbiased ◽  
Improved Performance ◽  
High Level

In order that genetic progress in British beef breeds could be improved, performance traits have been recorded by the Meat and Livestock Commission for many years. A large number of pedigree beef herds have recorded with the Meat and Livestock Commission during this period. Until recently, these records were only made use of via within herd contemporary comparisons such that the results for animals could not be compared across herds or time.Through the use of Individual Animal Model Best Linear Unbiased Prediction (BLUP), differences between herds and contemporary groups within herds can be accounted for provided there are genetic links between herds and contemporary groups. As a result of the small pedigree herd size in Great Britain, typically less than 20, sires are often chosen from outside the herd in order to reduce inbreeding. This practise has resulted in there being a relatively high level of connectedness between contemporary groups and this enables the BLUP procedure to disentangle management and genetic effects.

The use of Mendelian indices for balancing genetic response and inbreeding

1996 ◽  
Vol 1996 ◽  
pp. 114-114
Author(s):  
B Grundy ◽  
Z W Luo ◽  
B Villanueva ◽  
J A Woolliams
Keyword(s):  
Genetic Gain ◽  
Genetic Variance ◽  
Genetic Relationships ◽  
Best Linear Unbiased Prediction ◽  
Breeding Programme ◽  
Genetic Response ◽  
Genetic Progress ◽  
Linear Unbiased Prediction ◽  
Selection Procedures ◽  
Best Linear Unbiased

The difficulty in designing an optimal breeding programme arises from a conflict between improvement in genetic gain and increase in inbreeding since selection procedures which increase genetic progress are usually associated with increased rates of inbreeding. Best linear unbiased prediction (BLUP) has optimal properties regarding the expected genetic gain after one generation of selection. However, since full genetic relationships are accounted for, selected animals are likely to be more related, leading to a higher rate of inbreeding and a larger decrease in genetic variance than less accurate methods.

DAIRY HERD GENETIC DIFFERENCES FOR LACTATION PRODUCTION

1982 ◽  
Vol 62 (2) ◽  
pp. 323-331 ◽  
Author(s):  
L. R. SCHAEFFER ◽  
A. KERR ◽  
E. B. BURNSIDE
Keyword(s):  
Milk Yield ◽  
Dairy Herd ◽  
Herd Size ◽  
Best Linear Unbiased Prediction ◽  
Genetic Differences ◽  
Phenotypic Variance ◽  
Linear Unbiased Prediction ◽  
Production Program ◽  
Best Linear Unbiased ◽  
Genetic Trends

Cow estimated transmitting abilities (ETA) for milk yield and fat percent derived by best linear unbiased prediction methods were used to compute averages for each herd and year of calving subclass for herds enrolled on the Record of Production program (ROP). Means and variances of herd averages were tabulated according to province, herd size, and year of calving. The genetic trends in herd averages within herd size categories were positive for milk yields and negative for fat percent. The variance of herd averages has increased since 1958 within herd size categories, but has decreased over all herds due to a shift in size of herds since 1958. Herds with fewer than 20 cows represented 31.7% of the ROP herds in Canada in 1977 while in 1958 they represented 72.4%. However, in 1977 the larger herds did not show any genetic advantage over smaller herds. Ontario herds of size 20–9 cows showed greater variability in average ETA for milk and fat percent than herds in other provinces. Correlations among traits on a herd average basis have not changed in the last 10 yr even though herd averages have changed substantially over the same period. Herd genetic differences accounted for only 2.05% of herd phenotypic variance for milk yield and 12.74% for fat percent. Key words: Genetic differences, herds, cow indexing

The use of Mendelian indices for balancing genetic response and inbreeding

1996 ◽  
Vol 1996 ◽  
pp. 114-114
Author(s):  
B Grundy ◽  
Z W Luo ◽  
B Villanueva ◽  
J A Woolliams
Keyword(s):  
Genetic Gain ◽  
Genetic Variance ◽  
Genetic Relationships ◽  
Best Linear Unbiased Prediction ◽  
Breeding Programme ◽  
Genetic Response ◽  
Genetic Progress ◽  
Linear Unbiased Prediction ◽  
Selection Procedures ◽  
Best Linear Unbiased

The difficulty in designing an optimal breeding programme arises from a conflict between improvement in genetic gain and increase in inbreeding since selection procedures which increase genetic progress are usually associated with increased rates of inbreeding. Best linear unbiased prediction (BLUP) has optimal properties regarding the expected genetic gain after one generation of selection. However, since full genetic relationships are accounted for, selected animals are likely to be more related, leading to a higher rate of inbreeding and a larger decrease in genetic variance than less accurate methods.

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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