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1

Genetic parameters for somatic cell score for Polish Black-and-White cattle estimated with a random regression model

100%
Ptak E., Brzozowski P., Jagusiak W., Zdziarski K.
Journal of Animal and Feed Sciences
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2007
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tom 16
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nr 3
357-369
2
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Application of the covariance function approach with an iterative two-stage algorithm to the estimation of parameters of a random regression test day model for dairy production traits

100%
Szyda J.
Journal of Applied Genetics
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2001
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tom 42
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nr 2
3

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

86%
Wolc A., Szwaczkowski T.
Journal of Applied Genetics
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2009
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tom 50
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nr 1
41-46
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.
4

Dissecting an interplay between genomic and pedigree sources of information to estimate breeding values for milk yield in Polish Holstein-Friesian dairy cattle in a one-step approach based on a random regression test day model

72%
Suchocki T., Liu Z., Zarnecki A., Szyda J.
Animal Science Papers and Reports
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2017
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tom 35
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nr 2
In the future an approach incorporating cows’ measured phenotypes and marker genotypes of cows and bulls within a single model can be applied. The most important advantage of such a model is the simultaneous use of pedigree and marker-based genomic relationship data. Such a solution allows the use of both genotyped and non-genotyped animals in the prediction procedure. This pilot study is aimed towards implementation of a one-step approach in a random regression test day model context for the Polish Holstein Friesian population, considering various ways of adjusting the relationship matrix. Data consisted of 890 animals (10 genotyped bulls, 100 cows with phenotypic data and 780 ancestors without genotypes or phenotypes). Random regression test day models with a polygenic effect on milk yield modeled by second order Legendre polynomials for the estimation of variance-covariance parameters and were used for prediction of genomically enhanced breeding values (GEBV). In this model, a matrix combining pedigree and marker-based information was used instead of a traditional numerator relationship matrix. In this matrix the proportions of information coming from pedigree and markers were defined by weighting parameters w and 1-w for pedigree and marker-based information matrices, respectively. Various weights of the two sources of information were considered. The accuracy of GEBV both for genotyped bulls and for cows with phenotypes was highest for weighting parameter w=0 and lowest for w=l. Incorporating genomic information into a conventional genetic evaluation improves reliabilities of breeding value prediction, however, pedigree information is important to maintain the stability of evaluation for non-genotyped animals. Implementation of the single-step approach in a random regression test day model framework is very attractive for genomic prediction in dairy cattle, since it allows to incorporate genomic information directly into a conventional genetic evaluation. However, for accurate predictions it is essential to achieve the right balance between the numerator relationship and markers-based relationship information.
5

Adjustments for heterogenous herd-year variances in a random regression model for genetic evaluations of Polish Black-and-White cattle

72%
Strabel T., Jankowski T., Jamrozik J.
Journal of Applied Genetics
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2006
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tom 47
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nr 2
The study investigated the existence of heterogeneous variance in first-lactation daily milk yield of Polish Black-and-White cows across herds in different years. Bayesian Information Criterion was used to show that the model with unequal residual variances for different herd-years was more plausible than the model assuming equal variances. A method of adjusting phenotypic records was developed to account for unequal variability in herd-years. Factors used for the data adjustment considered variation of general residuals and residuals for specific herd-years. The size of herd-year was also taken into account. Varied power of corrections was used to analyze the effect of adjustment on estimated breeding values. The method was applied to daily milk records of 817 165 primiparous cows. The effectiveness of the data adjustment was evaluated by the analysis of differences between each bull’s breeding value and its parental index. Data correction reduced the average difference and variance of differences between breeding values and parental indices. Accounting for the size of herd-year classes in correction factors improved the efficiency of heterogeneous variance adjustment.
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