Economic indices using breeding values predicted by BLUP

Schneeberger, M.; Barwick, Stephen; Crow, G. H.; Hammond, Keith

doi:10.1111/j.1439-0388.1992.tb00395.x

Cited by 97 publications

(85 citation statements)

References 4 publications

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“…EBVs for androstenone, skatole, indole or other compounds can be weighted accordingly to model individual contributions to the aggregate breeding objective (in this case, HNS). A method such as that presented by Schneeberger et al (1992) could be used to calculate appropriate weights for EBVs. Using this method, weights depend only on genetic variances and covariances among the individual boar taint components and the HNS, and on the economic values of these traits.…”

Section: Selection Indexmentioning

confidence: 99%

A performance test for boar taint compounds in live boars

Baes

Mattei

Luther

et al. 2013

Animal

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Genetically reducing boar taint using low-taint lines is considered the most sustainable and economic long-term alternative to surgical castration of male pigs. Owing to the high heritability of the main boar taint components (androstenone, skatole and indole), breeding is an excellent tool for reducing the number of tainted carcasses. To incorporate boar taint into breeding programmes, standardized performance testing is required. The objective of this study was to develop and formally present a performance test for the main boar taint compounds on live breeding candidates. First, a standardized performance test for boar taint was established. A biopsy device was developed to extract small tissue samples (200 to 300 mg) from breeding candidates. Quantification of boar taint components from these small samples using specialized chemical extraction methods proved accurate and repeatable (r 5 0.938). Following establishment of the method, biopsy samples of 516 live boars (100 to 130 kg live weight) were collected in the second step. Various mixed linear models were tested for each boar taint compound; models were ranked in terms of their information content. Pedigree information of 2245 ancestors of biopsied animals was included, and genetic parameters were estimated using univariate and multivariate models. Androstenone (in mg/g liquid fat (LF): mean 5 0.578, s 5 0.527), skatole (in mg/g LF: mean 5 0.033, s 5 0.002) and indole (in mg/g LF: mean 5 0.032, s 5 0.002) levels obtained by biopsy were plausible. Heritability estimates for androstenone calculated with univariate (0.453) and multivariate (0.452) analyses were comparable to those in the literature. Heritabilities for skatole (0.495) and indole (0.550) were higher than that for androstenone. Genetic and phenotypic correlations were similar to those published previously. Our results show that data on boar taint compounds from small adipose samples obtained by biopsy provide similar genetic parameters as that described in the literature for larger samples and are therefore a reliable performance test for boar taint in live breeding candidates.

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Section: Selection Indexmentioning

confidence: 99%

A performance test for boar taint compounds in live boars

Baes

Mattei

Luther

et al. 2013

Animal

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“…criteria and traits were identical. However, it was later generalized to distinct criteria and traits (Binet, 1965;Lin, 1978), to all types of information on relatives (see examples in Van Vleck, 1993) and to breeding values predicted using BLUP as selection criteria (Schneeberger et al, 1992). Coefficients for the latter are calculated as:…”

Section: Indirect Selection Correlated Responsementioning

confidence: 99%

Sélection très précoce pour les propriétés du bois massif : détection précoce des individus les plus performants

Apiolaza

2009

Ann. For. Sci.

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Abstract• This article reviews the theoretical basis for indirect selection -where early selection is an example -and how correlated response is calculated.• The review is followed by a description of issues as to the choice of selection criteria that could explain the lack of substantial progress on breeding for wood quality. These include: the autoregressive nature of selection criteria, overemphasizing the importance of basic density as selection criterion, ignoring age-related trends of wood properties, using rotation age rather than technical thresholds as objective traits and ignoring that not all grades have identical marginal economic value.• Three data sets are either analyzed for the first time or reanalyzed under different assumptions, to explore the importance of these criticisms.• Finally, the use of critical value thresholds as very early selection criteria is suggested and discussed in the context of improving intrinsic corewood quality. Mots-clés :rigidité / sélection précoce / caractère seuil / Pinus radiata Résumé -Sélection très précoce pour les propriétés du bois massif : détection précoce des individus les plus performants.• Cet article passe en revue les bases théoriques des méthodes de sélection indirecte -parmi lesquelles figure la sélection précoce -et la manière dont le gain corrélé est calculé.• Cette revue est suivie d'une description des problèmes liés au choix des critères de sélection et qui pourraient expliquer le manque de progrès substantiel suite à l'amélioration pour les propriétés du bois. Ceux-ci incluent l'auto-corrélation des critères de sélection, l'exagération de l'importance de la densité comme critère de sélection, l'oubli de l'évolution des propriétés du bois avec l'âge cambial, l'utilisation de l'âge de révolution comme caractère-cible plutôt que des seuils techniques et l'oubli que toutes les classes de produits n'ont pas la même valeur économique marginale.• Trois jeux de données ont été soit analysés pour la première fois soit ré-analysés sous différentes hypothèses pour explorer l'importance de ces critiques.• Enfin, l'utilisation de seuils critiques comme critères de sélection très précoce est suggérée et discutée dans le cadre de l'amélioration intrinsèque de la qualité du bois juvénile.

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“…Not only is this more practical, but literature supports the argument that index coefficients applied to EBV are more accurate (Schneeberger et al, 1992;Bourdon, 1998). From a practicality standpoint, phenotypic measures will rarely be available for all animals on all traits included in the selection criteria.…”

Section: Calculating Selection Index Coefficientsmentioning

confidence: 87%