Equivalence of variance components between standard and recursive genetic models using LDL' transformations
Varona, Luis (Universidad de Zaragoza) ; López-Carbonell, David (Universidad de Zaragoza) ; Srihi, Houssemeddine ; Hervás-Rivero, Carlos (Universidad de Zaragoza) ; González-Recio, Óscar ; Altarriba, Juan (Universidad de Zaragoza)
Resumen: Background
Recursive models are a category of structural equation models that propose a causal relationship between traits. These models are more parameterized than multiple trait models, and they require imposing restrictions on the parameter space to ensure statistical identification. Nevertheless, in certain situations, the likelihood of recursive models and multiple trait models are equivalent. Consequently, the estimates of variance components derived from the multiple trait mixed model can be converted into estimates under several recursive models through LDL′ or block-LDL′ transformations.
Results
The procedure was employed on a dataset comprising five traits (birth weight—BW, weight at 90 days—W90, weight at 210 days—W210, cold carcass weight—CCW and conformation—CON) from the Pirenaica beef cattle breed. These phenotypic records were unequally distributed among 149,029 individuals and had a high percentage of missing data. The pedigree used consisted of 343,753 individuals. A Bayesian approach involving a multiple-trait mixed model was applied using a Gibbs sampler. The variance components obtained at each iteration of the Gibbs sampler were subsequently used to estimate the variance components within three distinct recursive models.
Conclusions
The LDL′ or block-LDL′ transformations applied to the variance component estimates achieved from a multiple trait mixed model enabled inference across multiple sets of recursive models, with the sole prerequisite of being likelihood equivalent. Furthermore, the aforementioned transformations simplify the handling of missing data when conducting inference within the realm of recursive models.
Idioma: Inglés
DOI: 10.1186/s12711-024-00901-x
Año: 2024
Publicado en: Genetics Selection Evolution 56, 33 (2024), 10
ISSN: 0999-193X
Financiación: info:eu-repo/grantAgreement/EC/H2020/801586/EU/International Doctoral Programme for Talent Attraction to the Campus of International Excellence of the Ebro Valley/IberusTalent
Tipo y forma: Article (Published version)
Área (Departamento): Área Genética (Dpto. Anatom.,Embri.Genét.Ani.)
Exportado de SIDERAL (2024-05-16-08:53:56)
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Recursive models are a category of structural equation models that propose a causal relationship between traits. These models are more parameterized than multiple trait models, and they require imposing restrictions on the parameter space to ensure statistical identification. Nevertheless, in certain situations, the likelihood of recursive models and multiple trait models are equivalent. Consequently, the estimates of variance components derived from the multiple trait mixed model can be converted into estimates under several recursive models through LDL′ or block-LDL′ transformations.
Results
The procedure was employed on a dataset comprising five traits (birth weight—BW, weight at 90 days—W90, weight at 210 days—W210, cold carcass weight—CCW and conformation—CON) from the Pirenaica beef cattle breed. These phenotypic records were unequally distributed among 149,029 individuals and had a high percentage of missing data. The pedigree used consisted of 343,753 individuals. A Bayesian approach involving a multiple-trait mixed model was applied using a Gibbs sampler. The variance components obtained at each iteration of the Gibbs sampler were subsequently used to estimate the variance components within three distinct recursive models.
Conclusions
The LDL′ or block-LDL′ transformations applied to the variance component estimates achieved from a multiple trait mixed model enabled inference across multiple sets of recursive models, with the sole prerequisite of being likelihood equivalent. Furthermore, the aforementioned transformations simplify the handling of missing data when conducting inference within the realm of recursive models.
Idioma: Inglés
DOI: 10.1186/s12711-024-00901-x
Año: 2024
Publicado en: Genetics Selection Evolution 56, 33 (2024), 10
ISSN: 0999-193X
Financiación: info:eu-repo/grantAgreement/EC/H2020/801586/EU/International Doctoral Programme for Talent Attraction to the Campus of International Excellence of the Ebro Valley/IberusTalent
Tipo y forma: Article (Published version)
Área (Departamento): Área Genética (Dpto. Anatom.,Embri.Genét.Ani.)
Exportado de SIDERAL (2024-05-16-08:53:56)
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Notice créée le 2024-05-16, modifiée le 2024-05-16