000126363 001__ 126363
000126363 005__ 20241125101153.0
000126363 0247_ $$2doi$$a10.3168/jds.2022-22578
000126363 0248_ $$2sideral$$a133752
000126363 037__ $$aART-2023-133752
000126363 041__ $$aeng
000126363 100__ $$0(orcid)0000-0001-6256-5478$$aVarona, L.$$uUniversidad de Zaragoza
000126363 245__ $$aInvited review: Recursive models in animal breeding: Interpretation, limitations, and extensions
000126363 260__ $$c2023
000126363 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126363 5203_ $$aStructural equation models allow causal effects between 2 or more variables to be considered and can postulate unidirectional (recursive models; RM) or bidirectional (simultaneous models) causality between variables. This review evaluated the properties of RM in animal breeding and how to interpret the genetic parameters and the corresponding estimated breeding values. In many cases, RM and mixed multitrait models (MTM) are statistically equivalent, although subject to the assumption of variance-covariance matrices and restrictions imposed for achieving model identification. Inference under RM requires imposing some restrictions on the (co)variance matrix or on the location parameters. The estimates of the variance components and the breeding values can be transformed from RM to MTM, although the biological interpretation differs. In the MTM, the breeding values predict the full influence of the additive genetic effects on the traits and should be used for breeding purposes. In contrast, the RM breeding values express the additive genetic effect while holding the causal traits constant. The differences between the additive genetic effect in RM and MTM can be used to identify the genomic regions that affect the additive genetic variation of traits directly or causally mediated for another trait or traits. Furthermore, we presented some extensions of the RM that are useful for modeling quantitative traits with alternative assumptions. The equivalence of RM and MTM can be used to infer causal effects on sequentially expressed traits by manipulating the residual (co)variance matrix under the MTM. Further, RM can be implemented to analyze causality between traits that might differ among subgroups or within the parametric space of the independent traits. In addition, RM can be expanded to create models that introduce some degree of regularization in the recursive structure that aims to estimate a large number of recursive parameters. Finally, RM can be used in some cases for operational reasons, although there is no causality between traits.
000126363 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126363 590__ $$a3.7$$b2023
000126363 592__ $$a1.219$$b2023
000126363 591__ $$aAGRICULTURE, DAIRY & ANIMAL SCIENCE$$b7 / 80 = 0.087$$c2023$$dQ1$$eT1
000126363 593__ $$aAnimal Science and Zoology$$c2023$$dQ1
000126363 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b57 / 173 = 0.329$$c2023$$dQ2$$eT1
000126363 593__ $$aGenetics$$c2023$$dQ1
000126363 593__ $$aFood Science$$c2023$$dQ1
000126363 594__ $$a7.9$$b2023
000126363 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126363 700__ $$aGonzález-Recio, O.
000126363 7102_ $$11001$$2420$$aUniversidad de Zaragoza$$bDpto. Anatom.,Embri.Genét.Ani.$$cÁrea Genética
000126363 773__ $$g106, 4 (2023), 2198-2212$$pJ. dairy sci.$$tJournal of Dairy Science$$x0022-0302
000126363 8564_ $$s1141886$$uhttps://zaguan.unizar.es/record/126363/files/texto_completo.pdf$$yVersión publicada
000126363 8564_ $$s3245248$$uhttps://zaguan.unizar.es/record/126363/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126363 909CO $$ooai:zaguan.unizar.es:126363$$particulos$$pdriver
000126363 951__ $$a2024-11-22-12:07:47
000126363 980__ $$aARTICLE