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1

Simulation study on the application of Gibbs sampling for major gene detection in a population of laying hens

100%
Szydlowski M., Szwaczkowski T.
Journal of Applied Genetics
|
1998
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vol. 39
|
issue 4
321-330
EN
A method for the detection of segregating major genes based on the analysis of estimated marginal posterior major gene variance density was examined. The properties of the method were investigated using data sets simulated for a real population of laying hens consisting of eleven generations. Marginal posterior densities of model parameters were estimated by the Gibbs sampling approach proposed by JANSS et al. (1995). With the data of about 4000 observations it was possible to detect a major gene responsible for one third of the genetic variance and one tenth of the phenotypic variance, irrespectively of the degree of dominance at the major locus. The inference based on the posterior marginal major gene variance can be sensitive to skewness of the data. It was shown that skewness of 0.2 can lead to a false detection of a major gene. The method is robust against a non-genetic mixture of normal distributions.
2

Estimation of genetic parameters for monthly egg production in laying hens based on random regression models

100%
Wolc A., Szwaczkowski T.
Journal of Applied Genetics
|
2009
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vol. 50
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issue 1
41-46
EN
. Heritability and genetic correlations of monthly egg production under random regression models were estimated. Three layer lines (A22, A88, K66) in six consecutive generations were analysed. A22 (13,770 recorded hens) and A88 (13,950 recorded hens) are maternal lines of Rhode Island White birds selected on egg production and shell colour; K66 (9,351 recorded birds) is a paternal line of Rhode Island Red birds selected on egg weight. Eight models with different orders of Legendre polynomials were applied. Adequacy of the models was checked by the Akaike Information Criterion. According to the most adequate model including second order Legendre polynomials for fixed effects and third order for additive genetic and permanent environmental effects, relatively high heritabilities were estimated in the first (h2=0.3) and final (h2 above 0.3) periods of production with a substantial decrease in heritability during the egg production peak. Methodology based on random regression animal models can be recommended for genetic evaluation of laying hens.
3

Curvilinear inbreeding effects on some performance traits in laying hens

81%
Szwaczkowski T., Cywa-Benko K., Wezyk S.
Journal of Applied Genetics
|
2004
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vol. 45
|
issue 3
341-342
EN
Quadratic partial regression coefficients were estimated for the inbreeding level on five performance traits (body weight, average egg weight, age at first egg, percentage of fertilized eggs, and hatchability of set eggs) of two strains of laying hens. Data on 5631 of H77 layers and 3563 of N88 layers from nine consecutive generations were analysed. Only dams were accounted for. Partial regression coefficients were estimated by REML with a single-trait animal model, which included fixed effects (generation and hatching period) and random effects (additive genetic and error effects). The mean inbreeding level was 0.87% in strain H77 and 1.08% in strain N88. The inbreeding effects were analysed based on the quadratic partial regression equations. A slight inbreeding depression was found for all the traits analysed in N88. In strain H77, negative effects of inbreeding were only noted for body weight and average egg weight. The small inbreeding effects shown here resulted from a relatively low level of homozygosity in the populations studied. The strains were found to differ in the effects of inbreeding. It is worth pointing out that differences were noted both between the inbreeding depression estimated from the partial linear regression equation and the quadratic partial regression equation, as well as different inbreeding levels.
4

Inbreeding effects on lifetime in David?s deer (Elaphurus davidianus, Milne Edwards 1866) population

81%
Sternicki T., Szablewski P., Szwaczkowski T.
Journal of Applied Genetics
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2003
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vol. 44
|
issue 2
175-183
EN
The current population of David?s deer is derived from 18 individuals kept in Woburn Abbey Park (England). The aim of this study is to evaluate the inbreeding rate as well as inbreeding depression in longevity. The recorded data have been extracted from the International Species Information System (ISIS). Complete records of 2042 individuals (born in 1947-2000) from zoological gardens were studied. The following four subsets of data were formed: all individuals, individuals over 31 days of life, sexually mature individuals (above 450 days old) and individuals with identified sex. Two models (including inbreeding coefficient as linear and quadratic covariables, respectively) have been employed. These computations were performed by the use of the DFREML package programs. This study has shown that average levels of inbreeding in the David?s deer are relatively low (no exceed 0.028). The highest level of inbreeding was registered for mature individuals. In general, the average inbreeding on length of life was small for the above mentioned the inbreeding level (from 24 days to 77 days). However, on the basis estimated regression coefficients it can be suggested that an increase of inbreeding could lead to a drastic reduction of longevity. Relationships between inbreeding level and longevity are usually better described by quadratic partial regression (except for the oldest individuals). On the other hand, from a statistical point of view, a relatively low inbreeding level of the population studied is not suitable to derive the slope of the dependencies.
5

Analysis of single gene multitrait effects in livestock by the use of Gibbs sampling

61%
Dobek A., Molinski M. K., Molinski K., Szydlowski S. M., Szydlowski M., Szwaczkowski S. T., Szwaczkowski T.
Journal of Applied Genetics
|
2000
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vol. 41
|
issue 4
275-283
EN
The paper presents a method of multivariate data analysis described by a model which involves fixed effects, additive polygenic individual effects and the effects of a major gene. To find the estimates of model parameters, the maximalisation of likelihood function method is applied. The maximum of likelihood function is computed by the use of the Gibbs sampling approach. In this approach, following the conditional posterior distributions, values of all unknown parameters are generated. On the basis of the obtained samples the marginal posterior densities as well as the estimates of fixed effects, gene frequency, genotypic values, major gene, polygenic and error (co)variances are calculated. A numerical example, supplemented to theoretical considerations, deals with data simulated according to the considered model.
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