Bayesian analysis of genetic backgrounds
of twinning rate in Thoroughbred horses

Skotarczak, Ewa; Moliński, Krzysztof; Szwaczkowski, T.; Dobek, A.

doi:10.22358/jafs/66812/2007

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…It can also be determined by real genotypic effects and their frequencies as well as numerical properties of applied algorithms. This paper is a continuation of an earlier study by Molinski et al (2003) and Skotarczak et al (2007Skotarczak et al ( , 2008 concerning Bayesian detection of single loci under threshold animal model. No literature on statistical effectiveness of proposed tools is available.…”

Section: Introductionsupporting

confidence: 58%

Bayesian analysis of ordinal categorical data under a mixed inheritance model

et al. 2011

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The effectiveness of proposed Gibbs sampling (GS) algorithm to detect single loci determining livestock threshold traits under a different hypothetical breeding and statistical modeling scenarios was examined. The following factors were included into the analysis: the presence of fixed effects, knowledge of one threshold, the size of the population (1 212 and 3 070 pedigreed individuals, respectively) and proportions of individuals in three genotypic classes. Five threshold and one linear unitrait animal model were employed to analysis of these datasets. The GS algorithm was applied to estimate fixed effects (optionally), additive polygenic variance, single allele frequencies, genotypic effects and one threshold (optionally). For each case, 2 000 000 rounds of GS were conducted. The first 1 000 000 steps were discarded as a burn-in-period. The results were collected from every 20th iteration. In general, the accuracy of parameter estimates is not satisfactory. However, taking into account the scant amount of information provided by the ordinal categorical data, it seems that such an analysis is a good first approach. Except for one case in which the estimate was very close to the true value, in all the other cases the estimated gene effect was smaller than the true effect. In general, the algorithm proposed does not provide overestimated effects of single locus. Keywords

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Section: Introductionsupporting

confidence: 58%

Bayesian analysis of ordinal categorical data under a mixed inheritance model

et al. 2011

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“…First, statistical analysis of categorical data is difficult since the majority of classical statistical tools apply to continuous variables (traits) with a normal distribution. Second, genetic evaluation requires specific modelling, thus data transformation or nonlinear (especially threshold) models are recommended (Skotarczak et al, 2007). Furthermore, simultaneous estimation or prediction of effects for many categorical traits is computationally very demanding.…”

Section: Introductionmentioning

confidence: 99%

Entropy analysis of performance test results of warmblood stallions

Dobek¹,

Borowska²,

Moliński³

et al. 2012

J. Anim. Feed Sci.

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The objective of the present study was to evaluate relationships among recorded traits in a 100-day stationary performance test in warmblood stallions in Poland. The possibility of reducing the number of recorded traits was assessed as well. The data are from 582 warmblood stallions undergoing a 100-day test in 2002-2010 at two Polish training centres. The stallions were pre-selected on nine single conformation and movement traits and then inspected on nineteen performance traits scored by the head of the training centre, judging commission, and test riders. To establish the relationships within the data, entropy analysis was used. Entropy and conditional entropy in relation to the final assessment, as well as joint entropy, mutual information, and their quotient were estimated. High relationships between traits depend on their source of variability (head of training centre, judging commission, test rider). Variability over time of ranks for many traits is influenced by the differences in the genetic structure of the stallions tested in the last ten years. These estimates of conditional entropy can be helpful in derivation of weights for the recorded conformation, movement, and performance traits.

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