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Gait visual scores are widely applied to horse breeding because they are a fast and easy phenotyping strategy, allowing the numeric interpretation of a complex biological process such as gait quality. However, they may suffer from subjectivity or high environmental influence. We aimed to investigate potential causal relationships among six visual gait scores in Campolina horses. The data included 5475 horses with records for at least one of the following traits: Dissociation (Di), Comfort (C), Style (S), Regularity (R), Development (De), and Gait total Scores (GtS). The pedigree comprised three generations with 14,079 horses in the additive relationship matrix. Under a Bayesian framework, (co)variance components were estimated through a multitrait animal model (MTM). Then, the inductive causation algorithm (IC) was applied to the residual (co)variance matrix samples. The resulting undirected graph from IC was directed in 6 possible causal structures, each fitted by a structural equation model. The final causal structure was chosen based on deviance information criteria (DIC). It was found that S significantly impacts the causal network of gait, directly and indirectly affecting C. The indirect causal effect of S on C was through the direct effect of S on De, then the direct effect of De on R, and finally, the direct effect of R on C. Di was caused by S, which is the reason for the genetic correlation between Di and GtS, due to causal effects being added to the model, they absorb the genetic correlation between Di and GtS. Those paths have biological meaning to horse movements and can help breeders and researchers better understand horses' complex causal network of gait., (© 2024 The Author(s). Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

Multitrait models can increase the accuracy of breeding value prediction and reduce bias due to selection by using traits measured before and after it has occurred. However, as the number of traits grows, a similar trend is expected for the number of parameters to be estimated, which directly affects the computing power and the amount of data required. The aim of the present study was to apply reduced rank (principal components model-PCM) and factor analytical models (FAM), to estimate (co)variance components for nineteen traits, jointly evaluated in a single analysis in Campolina horses. A total of 18 morphometric traits (MT) and one gait visual score (GtS), along with genealogical records of 48,806 horses, were analysed under a restricted maximum likelihood framework. Nine PCM, nine FAM and one standard multitrait model (MTM) were fitted to the data and compared to find the best suitable model. Based on Bayesian information criterion, the best model was the FAM option, considering five common factors (FAM5). After performing an intraclass analysis, none of MT were genetically negatively correlated, whereas GtS was negatively related to all MT, except for the genetic correlations among GtS and BLL, and between GtS and BLLBL (0.01 and 0.10 respectively). From all MT, two traits were derived computing ratios involving other traits, those had negative correlations with others MT, but all favourable for selection. Similar patterns were observed between the genetic parameters obtained from MTM and FAM5 respectively. The heritability estimates ranged from 0.09 (head width) to 0.47 (height at withers). Our results indicated that FAM was efficient to reduce the multitrait analysis dimensionality, and therefore, traits can be combined based on the first three eigenvectors from the additive genetic (co)variance matrix. In addition, there was sufficient genetic variation for selection, benefiting its potential implementation in a breeding program., (© 2021 Wiley-VCH GmbH.)