135185 20260217205602.0 doi 10.1186/s12711-024-00901-x sideral 138544 ART-2024-138544 eng Varona, Luis Universidad de Zaragoza (orcid)0000-0001-6256-5478 Equivalence of variance components between standard and recursive genetic models using LDL' transformations 2024 Access copy available to the general public Unrestricted 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. 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 This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 801586-IberusTalent info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 3.1 2024 AGRICULTURE, DAIRY & ANIMAL SCIENCE 12 / 86 = 0.14 2024 Q1 T1 GENETICS & HEREDITY 69 / 192 = 0.359 2024 Q2 T2 0.903 2024 Animal Science and Zoology 2024 Q1 Medicine (miscellaneous) 2024 Q1 Ecology, Evolution, Behavior and Systematics 2024 Q1 Genetics 2024 Q2 6.4 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion López-Carbonell, David Universidad de Zaragoza Srihi, Houssemeddine Hervás-Rivero, Carlos Universidad de Zaragoza González-Recio, Óscar Altarriba, Juan Universidad de Zaragoza (orcid)0000-0002-3042-2250 1001 420 Universidad de Zaragoza Dpto. Anatom.,Embri.Genét.Ani. Área Genética 56, 33 (2024), 10 Genet. sel. evol. Genetics Selection Evolution 0999-193X 1713637 http://zaguan.unizar.es/record/135185/files/texto_completo.pdf Versión publicada 2460855 http://zaguan.unizar.es/record/135185/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135185 articulos driver 2026-02-17-20:44:59 ARTICLE