Mixture models in quantitative genetics and applications to animal breeding
Finite mixture models are helpful for uncovering heterogeneity due to hidden structure; for example, unknown major genes. The first part of this article gives examples and reviews quantitative genetics issues of continuous characters having a finite mixture of Gaussian components. The partition of variance in a mixture, the covariance between relatives under the supposition of an additive genetic model and the offspring-parent regression are derived. Formulae for assessing the effect of mass selection operating on a mixture are given. Expressions for the genetic correlation between a mixture and a Gaussian trait are presented. If there is heterogeneity in a population at the genetic or environmental levels, then genetic parameters based on theory treating distributions as homogeneous can lead to misleading interpretations. Subsequently, methods for parameter estimation (e.g., maximum likelihood) are reviewed, and the Bayesian approach is illustrated via an application to somatic cell scores in dairy cattle.
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Sociedade Brasileira de Zootecnia
2007
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oai:scielo:S1516-359820070010000172008-08-05Mixture models in quantitative genetics and applications to animal breedingGianola,DanielBoettcher,Paul J.Ødegård,JørgenHeringstad,Bjørg Bayesian methods dairy cattle maximum likelihood mixture distributions quantitative genetics somatic cell scores Finite mixture models are helpful for uncovering heterogeneity due to hidden structure; for example, unknown major genes. The first part of this article gives examples and reviews quantitative genetics issues of continuous characters having a finite mixture of Gaussian components. The partition of variance in a mixture, the covariance between relatives under the supposition of an additive genetic model and the offspring-parent regression are derived. Formulae for assessing the effect of mass selection operating on a mixture are given. Expressions for the genetic correlation between a mixture and a Gaussian trait are presented. If there is heterogeneity in a population at the genetic or environmental levels, then genetic parameters based on theory treating distributions as homogeneous can lead to misleading interpretations. Subsequently, methods for parameter estimation (e.g., maximum likelihood) are reviewed, and the Bayesian approach is illustrated via an application to somatic cell scores in dairy cattle.info:eu-repo/semantics/openAccessSociedade Brasileira de ZootecniaRevista Brasileira de Zootecnia v.36 suppl.0 20072007-07-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982007001000017en10.1590/S1516-35982007001000017 |
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Gianola,Daniel Boettcher,Paul J. Ødegård,Jørgen Heringstad,Bjørg |
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Gianola,Daniel Boettcher,Paul J. Ødegård,Jørgen Heringstad,Bjørg Mixture models in quantitative genetics and applications to animal breeding |
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Gianola,Daniel Boettcher,Paul J. Ødegård,Jørgen Heringstad,Bjørg |
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Gianola,Daniel |
| title |
Mixture models in quantitative genetics and applications to animal breeding |
| title_short |
Mixture models in quantitative genetics and applications to animal breeding |
| title_full |
Mixture models in quantitative genetics and applications to animal breeding |
| title_fullStr |
Mixture models in quantitative genetics and applications to animal breeding |
| title_full_unstemmed |
Mixture models in quantitative genetics and applications to animal breeding |
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mixture models in quantitative genetics and applications to animal breeding |
| description |
Finite mixture models are helpful for uncovering heterogeneity due to hidden structure; for example, unknown major genes. The first part of this article gives examples and reviews quantitative genetics issues of continuous characters having a finite mixture of Gaussian components. The partition of variance in a mixture, the covariance between relatives under the supposition of an additive genetic model and the offspring-parent regression are derived. Formulae for assessing the effect of mass selection operating on a mixture are given. Expressions for the genetic correlation between a mixture and a Gaussian trait are presented. If there is heterogeneity in a population at the genetic or environmental levels, then genetic parameters based on theory treating distributions as homogeneous can lead to misleading interpretations. Subsequently, methods for parameter estimation (e.g., maximum likelihood) are reviewed, and the Bayesian approach is illustrated via an application to somatic cell scores in dairy cattle. |
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Sociedade Brasileira de Zootecnia |
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2007 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982007001000017 |
| work_keys_str_mv |
AT gianoladaniel mixturemodelsinquantitativegeneticsandapplicationstoanimalbreeding AT boettcherpaulj mixturemodelsinquantitativegeneticsandapplicationstoanimalbreeding AT ødegardjørgen mixturemodelsinquantitativegeneticsandapplicationstoanimalbreeding AT heringstadbjørg mixturemodelsinquantitativegeneticsandapplicationstoanimalbreeding |
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