000135185 001__ 135185
000135185 005__ 20240516095249.0
000135185 0247_ $$2doi$$a10.1186/s12711-024-00901-x
000135185 0248_ $$2sideral$$a138544
000135185 037__ $$aART-2024-138544
000135185 041__ $$aeng
000135185 100__ $$0(orcid)0000-0001-6256-5478$$aVarona, Luis$$uUniversidad de Zaragoza
000135185 245__ $$aEquivalence of variance components between standard and recursive genetic models using LDL' transformations
000135185 260__ $$c2024
000135185 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135185 5203_ $$aBackground
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.
000135185 536__ $$9info:eu-repo/grantAgreement/EC/H2020/801586/EU/International Doctoral Programme for Talent Attraction to the Campus of International Excellence of the Ebro Valley/IberusTalent$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 801586-IberusTalent
000135185 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135185 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135185 700__ $$aLópez-Carbonell, David$$uUniversidad de Zaragoza
000135185 700__ $$aSrihi, Houssemeddine
000135185 700__ $$aHervás-Rivero, Carlos$$uUniversidad de Zaragoza
000135185 700__ $$aGonzález-Recio, Óscar
000135185 700__ $$0(orcid)0000-0002-3042-2250$$aAltarriba, Juan$$uUniversidad de Zaragoza
000135185 7102_ $$11001$$2420$$aUniversidad de Zaragoza$$bDpto. Anatom.,Embri.Genét.Ani.$$cÁrea Genética
000135185 773__ $$g56, 33 (2024), 10$$pGenet. sel. evol.$$tGenetics Selection Evolution$$x0999-193X
000135185 8564_ $$s1713637$$uhttps://zaguan.unizar.es/record/135185/files/texto_completo.pdf$$yVersión publicada
000135185 8564_ $$s2460855$$uhttps://zaguan.unizar.es/record/135185/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135185 909CO $$ooai:zaguan.unizar.es:135185$$particulos$$pdriver
000135185 951__ $$a2024-05-16-08:53:56
000135185 980__ $$aARTICLE