Genetic improvement of silage maize: predicting genetic gain using selection
            indexes and best linear unbiased prediction

Abstract This study aimed to estimate genetic parameters, including genomic data, for feeding behavior, feed efficiency, and growth traits in Nellore cattle. The following feeding behavior traits were studied (861 animals with records): time spent at the feed bunk (TF), duration of one feeding event (FD), frequency of visits to the bunk (FF), feeding rate (FR), and dry matter intake (DMI) per visit (DMIv). The feed efficiency traits (1,543 animals with records) included residual feed intake (RFI), residual weight gain (RWG), and feed conversion (FC). The growth traits studied were average daily gain (ADG, n = 1,543 animals) and selection (postweaning) weight (WSel, n = 9,549 animals). The (co)variance components were estimated by the maximum restricted likelihood method, fitting animal models that did (single-step genomic best linear unbiased prediction) or did not include (best linear unbiased prediction) genomic information in two-trait analyses. The direct responses to selection were calculated for the feed efficiency traits, ADG, and WSel, as well as the correlated responses in feed efficiency and growth by direct selection for shorter TF. The estimated heritabilities were 0.51 ± 0.06, 0.35 ± 0.06, 0.27 ± 0.07, 0.34 ± 0.06, and 0.33 ± 0.06 for TF, FD, FF, FR, and DMIv, respectively. In general, TF and FD showed positive genetic correlations with all feed efficiency traits (RFI, RWG, and FC), ADG, DMI, and WSel. Additionally, TF showed high and positive genetic and phenotypic correlations with RFI (0.71 ± 0.10 and 0.46 ± 0.02, respectively) and DMI (0.56 ± 0.09 and 0.48 ± 0.03), and medium to weak genetic correlations with growth (0.32 ± 0.11 with ADG and 0.14 ± 0.09 with WSel). The results suggest that TF is a strong indicator trait of feed efficiency, which exhibits high heritability and a weak positive genetic correlation with growth. In a context of a selection index, the inclusion of TF to select animals for shorter TF may accelerate the genetic gain in feed efficiency by reducing RFI but with zero or slightly negative genetic gain in growth traits.

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Best linear unbiased prediction of clonal breeding values and genetic values from full-sib mating designs

Canadian Journal of Forest Research ◽

10.1139/x03-118 ◽

2003 ◽

Vol 33 (10) ◽

pp. 2036-2043 ◽

Cited By ~ 8

Author(s):

Bin Xiang ◽

Bailian Li

Keyword(s):

Genetic Gain ◽

Best Linear Unbiased Prediction ◽

Breeding Value ◽

Improvement Program ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Sib Mating ◽

Best Linear Unbiased ◽

Progeny Tests ◽

Unbiased Prediction

Full-sib progeny tests with clonal replicates may provide better breeding value estimates and the greatest genetic gain in a tree improvement program. Clonal breeding values (CBV) that combine the family and within-family breeding values due to additive genetic effects can maximize the genetic gain for advanced generation breeding. Clonal genetic values (CGV) that further incorporate full-sib family specific combining ability due to nonadditive genetic effect can maximize gain for a deployment program with clonal propagation techniques. The best linear unbiased prediction (BLUP) is the best statistical method for estimating both CBV and CGV because of its desirable statistical properties compared with the heritability-based gain calculation. A BLUP method for determining both the CBV and CGV for full-sib clonal progeny tests was proposed in this paper. The formulas for CBV and CGV were derived using general BLUP methodology, and formulas were derived for the calculations of their standard errors. An analytical method by using a standard statistical package (SAS PROC MIXED) was presented for CBV and CGV calculations from any full-sib mating designs.

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

Genes ◽

10.3390/genes11091013 ◽

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 [...]

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A method for reducing inbreeding with Best Linear Unbiased Prediction

Proceedings of the British Society of Animal Production (1972) ◽

10.1017/s030822960002362x ◽

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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