Abstract - In animal breeding, Mark

Abstract - In animal breeding, Markov chain Monte Carlo algorithms are increasingly
used to draw statistical inferences about marginal posterior distributions of parameters
in genetic models. The Gibbs sampling algorithm is most commonly used and requires
full conditional densities to be of a standard form. In this study, we describe a Bayesian
method for the statistical mapping of quantitative trait loci ((aTL), where the application
of a reduced animal model leads to non-standard densities for dispersion parameters.
The Metropolis Hastings algorithm is used to obtain samples from these non-standard
densities. The flexibility of the Metropolis Hastings algorithm also allows us change the
parameterization of the genetic model. Alternatively to the usual variance components,
we use one variance component (= residual) and two ratios of variance components, i.e.
heritability and proportion of genetic variance due to the (aTL, to parameterize the genetic
model. Prior knowledge on ratios can more easily be implemented, partly by absence of
scale effects. Three sets of simulated data are used to study performance of the reduced
animal model, parameterization of the genetic model, and testing the presence of the QTL
at a fixed position. © Inra/Elsevier, Paris
reduced animal model / dispersion parameters / Markov chain Monte Carlo /
quantitative trait loci

ผลลัพธ์ (อังกฤษ) 1:

abstract - in animal breeding, markov chain monte carlo algorithms are increasingly
used to draw statistical inferences about marginal posterior distributions of parameters
in genetic models. the gibbs sampling algorithm is most commonly used and requires
full conditional densities to be of a standard form. in this study, we describe a bayesian
.method for the statistical mapping of quantitative trait loci ((atl), where the application
of a reduced animal model leads to non-standard densities for dispersion parameters.
the metropolis hastings algorithm is used to obtain samples from these non-standard
densities. . the flexibility of the metropolis hastings algorithm also allows us change the
parameterization of the genetic model.alternatively to the usual variance components,
we use one variance component (= residual) and two ratios of variance components, ie
heritability and proportion of genetic variance due to the (atl, to parameterize the genetic
model. prior knowledge on ratios can. more easily be implemented, partly by absence of
scale effects.three sets of simulated data are used to study performance of the reduced
animal model, parameterization of the genetic model, and testing the presence of the qtl
at a fixed position. & copy; inra / elsevier, paris
reduced animal model / dispersion parameters / markov chain monte carlo /
quantitative trait loci.

ผลลัพธ์ (อังกฤษ) 3:

Abstract - In animal breeding, chain Markov Monte Carlo algorithms are increasingly
used to draw statistical inferences about marginal posterior distributions of parameters in
genetic models. The Gibbs sampling algorithm is most commonly used and requires
full conditional densities to be of a standard form.In this study, we describe a Bayesian
statistical method for the mapping of quantitative trait loci (aTL), where the application of a reduced
animal model leads to non-standard densities for dispersion parameters.
The Metropolis Hastings algorithm is used to obtain samples from these non-standard
densities. The Metropolis Hastings flexibility of the algorithm also allows us
change the parameterization of the genetic model.The usual Alternatively to variance components, we use one
variance Component (= residual) and two ratios of variance components, I. e.
heritability and proportion of genetic variance due to the (aTL, to parameterize the model genetic
. Prior knowledge on ratios can be more easily implemented, partly by
absence of scale effects.Three sets of simulated data are used to study the performance of reduced
animal model, parameterization of the genetic model, and testing the presence of the QTL
at a fixed position. & copy; Inra/Elsevier, Paris
reduced animal model/dispersion parameters/Markov chain Monte Carlo/
quantitative trait loci