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1

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.
|
|
issue 2
177-191
EN
The covariance function approach with an iterative two-stage algorithm of Liu et al. (2000) was applied to estimate parameters for the Polish Black-and-White dairy population based on a sample of 338 808 test day records for milk, fat, and protein yields. A multiple trait sire model was used to estimate covariances of lactation stages. A third-order Legendre polynomial was subsequently fitted to the estimated (co)variances to derive (co)variances of random regression coefficients for both additive genetic and permanent environment effects. Daily and 305-day heritability estimates obtained are consistent with several studies which used both fixed and random regression test day models. Genetic correlations between any two days in milk (DIM) of the same lactation as well as genetic correlations between the same DIM of two lactations were within a biologically acceptable range. It was shown that the applied estimation procedure can utilise very large data sets and give plausible estimates of (co)variance components.
2

Modelling test day data from dairy cattle

63%
Szyda J., Liu Z.
Journal of Applied Genetics
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1999
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vol. 40
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issue 2
103-116
EN
Four statistical models for genetic evaluations utilising dairy test day data are considered. These are: the fixed regression model, the random regression model, the autoregressive model and a multiple trait model. The emphasis is put on the comparison of these models in terms of their assumed covariance structure, modelling and prediction of breeding values and parameterisation. In the future one of the models should be used for a routine genetic evaluation of the Polish Black-and-White dairy cattle. Therefore, characteristics of test day data from the Polish population are given. In conclusion, it appears that thanks to its flexibility in handling heterogeneous variances during lactation, variable autocorrelation and non-uniform spacing between tests, the random regression model forms the most suitable approach.
3

Additive effects of 19 porcine SNPs on growth rate, meat content and selection index

51%
Kaminski S., Help H., Suchocki T., Szyda J.
Journal of Applied Genetics
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2009
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vol. 50
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issue 3
235-243
EN
A total of 306 boars (108 Large White and 198 Landrace) were genotyped for 52 candidate SNPs to determine which of the polymorphisms influence growth rate, meat content and selection index. The effects of SNPs were estimated by a mixed linear model including a random additive polygenic animal effect, fixed effects of SNPs including additive, and pairwise additive-by-additive epistases, year*season of birth, breed and RYR1 genotype. In order to estimate all possible pairwise SNP combinations without overparameterising the model a stochastic approach was adopted. A total of 1 350 replications of the model were generated, each containing five randomly selected SNPs. The final estimates of the fixed effects of the model equaled an average out of the replications. The hypothesis of a nonzero effect of SNP was tested by the Wald test. Among 4 257 estimates calculated, many significant (P<0.01), but mostly minor effects (below 1 phenotypic standard deviation) were recorded. The selected SNPs will be further investigated to determine which may be used in MAS.
4

Practical application of daughter yield deviations in dairy cattle breeding

51%
Szyda J., Ptak E., Komisarek J., Zarnecki A.
Journal of Applied Genetics
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2008
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vol. 49
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issue 2
183-191
EN
Daughter yield deviations (DYDs) of bulls and yield deviations (YDs) of cows, besides estimated breeding values (EBVs), are standard measures of animals' genetic merits in routine genetic evaluations worldwide. In this contribution, we first point out differences and similarities between DYDs and EBVs calculated for milk, fat and protein yields. While the latter measure represents the additive polygenic value of an animal, the former consists of both the additive polygenic and residual components. Then, a summary of DYDs and YDs calculated for the Polish population of dairy cattle is presented. The estimated correlations between DYDs and EBVs are generally high, but vary considerably depending on the minimum number of daughters used for calculation of DYDs and on the accuracy of calculated DYDs. Using DYDs estimated for each production year for 16 452 bulls, we demonstrate how to use DYDs for the validation of genetic trend estimated in the model used for genetic evaluation. Based on genotypic data of 252 bulls, we show that DYDs can be used for the estimation of candidate gene effects. For each of the yield traits, the within-bull genetic trend was relatively high, ranging between 1.39% of genetic standard deviation per production year for milk and 7.67% of genetic standard deviation per production year for fat, both in the 2nd lactation. Out of 8 polymorphisms tested, 5 showed a significant correlation with DYD, with the highest effect attributed to the polymorphism within the leptin receptor gene, whose additive effect was estimated as 247.33 kg of milk at 2nd parity.
5

Application of a mixed inheritance model to the detection of quantitative trait loci in swine

51%
Szyda J., Liu Z., Grindflek E., Lien S.
Journal of Applied Genetics
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2002
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vol. 43
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issue 1
69-83
EN
The primary goal of this study was to investigate statistical properties of a mixed inheritance model for the localization of quantitative trait loci (QTL). This is based on the analysis of phenotypic data for the amount of intramuscular fat (IMF) scored on 305 individuals originating from a cross between Duroc and Norwegian Landrace breeds. Marker genotype information is available for F1 and F2 generations. Statistical procedures compared involve i) the interval mapping, ii) the composite interval mapping, iii) a regression method, and iv) a mixed inheritance model accounting for a random animal additive genetic effect and relationships between individuals. The basic statistical properties of the latter approach are then assessed using Monte Carlo simulations showing slight unconservativeness as compared to and reasonable power to detect QTL of moderate effects. In the analysis of IMF data, the significant evidence for the existing QTL is detected on chromosome 6. A chromosomal region recommended for a second-step fine mapping analysis is identified between markers SW1823 and S0228, based on three types of confidence intervals derived by using: i) the Jackknife algorithm, ii) the numerical variance approximation, and iii) the LOD score approach. The Jackknife algorithm was additionally used to quantify each family?s contribution to the test statistic and to the estimate of QTL position.
6

Statistical aspects of genetic association testing in small samples, based on selective DNA pooling data in the arctic fox

45%
Szyda J., Liu Z., Zaton-Dobrowolska M., Wierzbicki H., Rzasa A.
Journal of Applied Genetics
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2008
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vol. 49
|
issue 1
81-92
EN
We analysed data from a selective DNA pooling experiment with 130 individuals of the arctic fox (Alopex lagopus), which originated from 2 different types regarding body size. The association between alleles of 6 selected unlinked molecular markers and body size was tested by using univariate and multinomial logistic regression models, applying odds ratio and test statistics from the power divergence family. Due to the small sample size and the resulting sparseness of the data table, in hypothesis testing we could not rely on the asymptotic distributions of the tests. Instead, we tried to account for data sparseness by (i) modifying confidence intervals of odds ratio; (ii) using a normal approximation of the asymptotic distribution of the power divergence tests with different approaches for calculating moments of the statistics; and (iii) assessing P values empirically, based on bootstrap samples. As a result, a significant association was observed for 3 markers. Furthermore, we used simulations to assess the validity of the normal approximation of the asymptotic distribution of the test statistics under the conditions of small and sparse samples.
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