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Estimation of breed...
Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models
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- Felleki, Majbritt (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Högskolan Dalarna,Statistik,Komplexa system - mikrodataanalys,Institutionen för husdjursgenetik (HGEN),Department of Animal Breeding and Genetics
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- Lee, Dongwhan (författare)
- Department of Statistics, Seoul National University, Seoul 151-747, Korea
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- Lee, Youngjo (författare)
- Department of Statistics, Seoul National University, Seoul 151-747, Korea
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- Gilmour, Arthur R. (författare)
- School of Mathematics and Applied Statistics, Faculty of Informatics, University of Wollongong, Wollongong, NSW 2522, Australia
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- Rönnegård, Lars (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Högskolan Dalarna,Statistik,Komplexa system - mikrodataanalys,Institutionen för husdjursgenetik (HGEN),Department of Animal Breeding and Genetics
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(creator_code:org_t)
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- Cambridge University Press, 2012
- 2012
- Engelska.
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Ingår i: Genetics Research. - : Cambridge University Press. - 0016-6723 .- 1469-5073. ; 94:6, s. 307-317
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://res.slu.se/i...
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Abstract
Ämnesord
Stäng
- The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being −0·52 for IRWLS and −0·62 in Sorensen & Waagepetersen (2003).
Ämnesord
- LANTBRUKSVETENSKAPER -- Husdjursvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Animal and Dairy Sience (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- LANTBRUKSVETENSKAPER -- Bioteknologi med applikationer på växter och djur -- Genetik och förädling inom lantbruksvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agricultural Biotechnology -- Genetics and Breeding in Agricultural Sciences (hsv//eng)
Nyckelord
- genetic heterogeneity
- environmental variation
- hierarchical likelihood
- DHGLM
- Complex Systems – Microdata Analysis
- Komplexa system - mikrodataanalys
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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