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1

Statistical modelling of growth using a mixed model with orthogonal polynomials

100%
Suchocki T., Szyda J.
Journal of Applied Genetics
|
2011
|
tom 52
|
nr 1
In statistical modelling, the effects of singlenucleotide polymorphisms (SNPs) are often regarded as time-independent. However, for traits recorded repeatedly, it is very interesting to investigate the behaviour of gene effects over time. In the analysis, simulated data from the 13th QTL-MAS Workshop (Wageningen, The Netherlands, April 2009) was used and the major goal was the modelling of genetic effects as time-dependent. For this purpose, a mixed model which describes each effect using the thirdorder Legendre orthogonal polynomials, in order to account for the correlation between consecutive measurements, is fitted. In this model, SNPs are modelled as fixed, while the environment is modelled as random effects. The maximum likelihood estimates of model parameters are obtained by the expectation–maximisation (EM) algorithm and the significance of the additive SNP effects is based on the likelihood ratio test, with p-values corrected for multiple testing. For each significant SNP, the percentage of the total variance contributed by this SNP is calculated. Moreover, by using a model which simultaneously incorporates effects of all of the SNPs, the prediction of future yields is conducted. As a result, 179 from the total of 453 SNPs covering 16 out of 18 true quantitative trait loci (QTL) were selected. The correlation between predicted and true breeding values was 0.73 for the data set with all SNPs and 0.84 for the data set with selected SNPs. In conclusion, we showed that a longitudinal approach allows for estimating changes of the variance contributed by each SNP over time and demonstrated that, for prediction, the pre-selection of SNPs plays an important role.
2

Utilization of information from gene networks towards a better understanding of functional similarities between complex traits: a dairy cattle model

81%
Fraszczak M., Suchocki T., Szyda J.
Journal of Applied Genetics
|
2016
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tom 57
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nr 1
3

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

81%
Suchocki T., Liu Z., Zarnecki A., Szyda J.
Animal Science Papers and Reports
|
2017
|
tom 35
|
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.
4

Inconsistency of associations between growth hormone receptor gene polymorphism and milk performance traits in Polish Holstein-Friesian cows and bulls

81%
Olenski K., Suchocki T., Kaminski S.
Animal Science Papers and Reports
|
2010
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tom 28
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nr 3
The aim of the study was to evaluate the significance of associations between missense mutation S555G in bovine GHR gene and two sets of data: milk performance data of cows and breeding values of bulls. To generate genotypes the polymorphic region of GHR exon 10 (S555G) was amplified and genotyped using PCR-RFLP method. 395 Polish Holstein-Friesian and 477 Polish Holstein-Friesian bulls were screened giving the following frequencies of alleles: A – 0.832 and 0.891 and G – 0.168 and 0.109 for cows and bulls, respectively. With the use of the Linear Mixed Model analysis it was show that A allele has positive effect on milk performance traits in cows and breeding value of bulls. The A allele is significantly related to fat yield (by 18.554±5.24 kg; P<0.0005), protein yield (by 9.072±3.643 kg; P<0.01) and fat content (by 0.1±0.05%; P<0.05). The A allele significantly increases bulls’ breeding value for protein content (by 0.044%±0.011, P<0.0002). The results show inconsistency of associations between cow and bull data signalling that careful consideration has to be undertaken before final approval of SNP as effective marker used in dairy cattle selection.
5

Modelling QTL effect on BTA06 using random regression test day models

81%
Suchocki T., Szyda J., Zhang Q.
Journal of Applied Genetics
|
2013
|
tom 54
|
nr 1
In statistical models, a quantitative trait locus (QTL) effect has been incorporated either as a fixed or as a random term, but, up to now, it has been mainly considered as a time-independent variable. However, for traits recorded repeatedly, it is very interesting to investigate the variation of QTL over time. The major goal of this study was to estimate the position and effect of QTL for milk, fat, protein yields and for somatic cell score based on test day records, while testing whether the effects are constant or variable throughout lactation. The analysed data consisted of 23 paternal half-sib families (716 daughters of 23 sires) of Chinese Holstein-Friesian cattle genotyped at 14 microsatellites located in the area of the casein loci on BTA6. A sequence of three models was used: (i) a lactation model, (ii) a random regression model with a QTL constant in time and (iii) a random regression model with a QTL variable in time. The results showed that, for each production trait, at least one significant QTL exists. For milk and protein yields, the QTL effect was variable in time, while for fat yield, each of the three models resulted in a significant QTL effect. When a QTL is incorporated into a model as a constant over time, its effect is averaged over lactation stages and may, thereby, be difficult or even impossible to be detected. Our results showed that, in such a situation, only a longitudinal model is able to identify loci significantly influencing trait variation.
6

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

81%
Kaminski S., Help H., Suchocki T., Szyda J.
Journal of Applied Genetics
|
2009
|
tom 50
|
nr 3
235-243
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.
7

Fitting and validating the genomic evaluation model to Polish Holstein-Friesian cattle

81%
Szyda J., Zarnecki A., Suchocki T., Kaminski S.
Journal of Applied Genetics
|
2011
|
tom 52
|
nr 3
The aim of the study was to fit the genomic evaluation model to Polish Holstein-Friesian dairy cattle. A training data set for the estimation of additive effects of single nucleotide polymorphisms (SNPs) consisted of 1227 Polish Holstein-Friesian bulls. Genotypes were obtained by the use of Illumina BovineSNP50 Genotyping BeadChip. Altogether 29 traits were considered: milk-, fat- and protein- yields, somatic cell score, four female fertility traits, and 21 traits describing conformation. The prediction of direct genomic values was based on a mixed model containing deregressed national proofs as a dependent variable and random SNP effects as independent variables. The correlations between direct genomic values and conventional estimated breeding values estimated for the whole data set were overall very high and varied between 0.98 for production traits and 0.78 for non return rates for cows. For the validation data set of 232 bulls the corresponding correlations were 0.38 for milk-, 0.37 for protein-, and 0.32 for fat yields, while the correlations between genomic enhanced breeding values and conventional estimated breeding values for the four traits were: 0.43, 0.44, 0.31, and 0.35. This model was able to pass the interbull validation criteria for genomic selection, which indicates that it is realistic to implement genomic selection in Polish Holstein-Friesian cattle.
8

Polymorphism in coding and regulatory sequences of beta-casein gene is associated with milk production traits in Holstein-Friesian cattle

71%
Olenski K., Cieslinska A., Suchocki T., Szyda J., Kaminski S.
Animal Science Papers and Reports
|
2012
|
tom 30
|
nr 1
Beta-casein gene (CSN2) is considered a marker of milk production traits in dairy cattle. Allele A2, the most common in Holstein-Friesian breed, is beneficial for milk protein yield. However, within CSN2 other mutations occur which could potentially be involved in shaping the variation of milk production traits. In this study a new SNP (C/T) was found located within a regulatory sequence of CSN2 (position -1578), called enhancer which is thought to play an important role in the amount of the beta-casein mRNA produced. The aim of the study was to verify effects of both SNPs on milk production traits in Polish Holstein-Friesian (HF) dairy cattle. Six-hundred-and-fifty bulls were genotyped with two methods, PCR-ACRS (Amplification-Created Restriction Site, Mph1103 I) and PCR-RFLP (EcoR I), for the A1/A2 polymorphism and the C/T SNP polymorphism (BCE129), respectively. Although both SNPs are located close to each other, their allele frequencies were found significantly different (0.33, 0.67, 0.89 and 0.11 for the A1, A2, C and T, respectively). A mixed linear model was used for testing the association between these polymorphisms and deregressed breeding values for production traits. The analysis revealed that the allele coding the A2 protein variant increases breeding values for milk and milk protein yields while allele C of BCE129 increases the milk fat yield.
9

Assessing the degree of stratification between closely related Holstein-Friesian populations

71%
Szyda J., Suchocki T., Qanbari S., Liu Z., Simianer H.
Journal of Applied Genetics
|
2017
|
tom 58
|
nr 4
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