Estimation of segregation variance for birth weight in beef cattle

Birchmeier, A. N.; Cantet, Rodolfo Juan Carlos; Fernando, Rohan L.; Morris, C. A.; Holgado, Fernando Daniel; Jara, Alejandro; Cristal, M. Santos

doi:10.1016/s0301-6226(02)00013-1

Cited by 13 publications

(22 citation statements)

References 19 publications

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“…Modal estimates of variance components for gestation length were 0.67, 1.16 and 3.45 for additive genetic, permanent environmental and residual variances, respectively. Unfortunately, data from F 0 generation were not available for the discrimination between additive variances from the parental populations and the segregation variance (Birchmeier et al, 2002) and, in addition, genetic components related to dominance and linkage disequilibrium between loci can be absorbed by the additive genetic variance in an F 2 design. Within this context, we must be cautious with the heritability provided by the current analysis (h 2 50.13).…”

Section: Resultsmentioning

confidence: 99%

Empirical Bayes factor analyses of quantitative trait loci for gestation length in Iberian × Meishan F2 sows

Casellas

Varona²,

Muñoz³

et al. 2008

Animal

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The aim of this study was to investigate chromosomal regions affecting gestation length in sows. An experimental F 2 cross between Iberian and Meishan pig breeds was used for this purpose and we genotyped 119 markers covering the 18 porcine autosomal chromosomes. Within this context, we have developed a new empirical Bayes factor (BF) approach to compare between nested models, with and without the quantitative trait loci (QTL) effect, and after including the location of the QTL as an unknown parameter in the model. This empirical BF can be easily calculated from the output of a Markov chain Monte Carlo sampling by averaging conditional densities at the null QTL effects. Linkage analyses were performed in each chromosome using an animal model to account for infinitesimal genetic effects. Initially, three QTL were detected at chromosomes 6, 8 and 11 although, after correcting for multiple testing, only the additive QTL located in cM 110 of chromosome 8 remained. For this QTL, the allelic effect of substitution of the Iberian allele increased gestation length in 0.521 days, with a highest posterior density region at 95% ranged between 0.121 and 0.972 days. Although future studies are necessary to confirm if detected QTL is relevant and segregating in commercial pig populations, a hot-spot on the genetic regulation of gestation length in pigs seems to be located in chromosome 8.

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Section: Resultsmentioning

confidence: 99%

Empirical Bayes factor analyses of quantitative trait loci for gestation length in Iberian × Meishan F2 sows

Casellas

Varona²,

Muñoz³

et al. 2008

Animal

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show abstract

“…The model proposed in this paper is equivalent to the current model of choice [2][3][4]17] for genetic evaluations in crossbred populations, including the segregation components. When discarding the segregation terms, the proposed model can also be used as an equivalent model to that proposed by Elzo's group [6][7][8].…”

Section: Discussionmentioning

confidence: 99%

“…When using the Gibbs sampler, the variance components will have the well known scaled inverted chi-square full conditional distributions, avoiding the use of the non standard forms described in Cardoso and Tempelman [4]. Although not implemented in this paper, restricted maximum likelihood estimates of the variance components via the EM algorithm (details in [2]) will also be simpler when based on the quadratic forms of the partial additive values.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, restricted maximum likelihood estimates for multibreed models became more complicated than in a conventional animal model; the details can be found in Elzo [7] and Birchmeier et al [2]. On the contrary, Bayesian inferences applied on this model resulted in non standard full conditionals for the variance components, making a Metropolis-Hastings implementation involving the repeated evaluation of the determinants of G necessary [4].…”

Section: Introductionmentioning

confidence: 99%

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Multibreed analysis by splitting the breeding values

García-Cortés

Toro²

2006

Genet Sel Evol

100

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-An equivalent model for multibreed variance covariance estimation is presented. It considers the additive case including or not the segregation variances. The model is based on splitting the additive genetic values in several independent parts depending on their genetic origin. For each part, it expresses the covariance between relatives as a partial numerator relationship matrix times the corresponding variance component. Estimation of fixed effects, random effects or variance components provided by the model are as simple as any model including several random factors. We present a small example describing the mixed model equations for genetic evaluations and two simulated examples to illustrate the Bayesian variance component estimation.segregation variance / multibreed population / genetic evaluation / mixed model equations / Gibbs sampler

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“…Such data are not common; thus, GGPS has mostly been studied using computer simulations, Lo et al (1993) presented a theory for estimating the covariance between crossbred animals for an additive model, and Gantet and Fernando (1995) showcnl how to use the theory to predict breeding values by BLUP, However, the genetic variance components for the multibreed models were difficult to estimate with REML (Elzo, 1994;Birchmeier et al, 2002) or Bayesian (Gardoso and Tempelman, 2004) methods. For these reasons, various approximations have been used.…”

Section: Introductionmentioning

confidence: 99%

Genetic evaluation combining purebred and crossbred data in a pig breeding scheme1

Ibáñez‐Escriche

Reixach²,

Lleonart³

et al. 2011

Journal of Animal Science

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Genetic evaluations using purebred data alone and combined purebred and crossbred information were performed for lean meat percentage in a pig breeding scheme. One purebred (PB) model and 2 crossbred models (CCPS1 and CCPS2) were used in the analyses. Data were obtained from the Selección Batallé S.A. Company (Riudarenes, Spain) and spanned a period of 4 yr (2006 to 2009). The data corresponded to 3 nuclei of purebred populations, Landrace (LD), Duroc (DU), and Pietrain (PI); 1 multiplying farm with animals from a 2-way cross (TB1; DU × LD); and commercial farms with animals from a 3-way cross (TB2; TB1 × PI). Genetic parameters were similar across the models, with the exception of purebred PI. The DU and LD purebreds presented large heritabilities (0.5 to 0.6) for lean meat percentage, whereas the PI purebred showed a lower heritability (approximately 0.1) for the PB model and moderate heritability for the CCPS1 and CCPS2 models (0.2 to 0.3). The mean reliability of the predicted purebred breeding values was clearly increased when the CCPS1 and CCPS2 models were used. Moreover, a reranking of the animals with important changes in the selection decisions was observed in the PI purebred. In a simulation study, the CCPS1 model achieved a greater response to selection than the PB model for the PI purebred. On another hand, between the CCPS1 and CCPS2 models, CCPS1 was slightly superior in terms of predictive ability, exhibiting a greater robustness. These results illustrate the usefulness of using crossbred models to evaluate lean meat percentage in this pig breeding scheme.

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Estimation of segregation variance for birth weight in beef cattle

Cited by 13 publications

References 19 publications

Empirical Bayes factor analyses of quantitative trait loci for gestation length in Iberian × Meishan F2 sows

Empirical Bayes factor analyses of quantitative trait loci for gestation length in Iberian × Meishan F2 sows

Multibreed analysis by splitting the breeding values

Genetic evaluation combining purebred and crossbred data in a pig breeding scheme1

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