Genetic analysis of morphological and functional traits in Campolina horses using Bayesian multi-trait model - Bussiman, Fernando de Oliveira; Perez, Bruno da Costa; Ventura, Ricardo Vieira; Silva, Fabyano Fonseca e; Peixoto, Maria Gabriela Campolina Diniz; Vizoná, Rafael Guimarães; Mattos, Elisângela Chicaroni; Ferraz, José Bento Sterman; Eler, Joanir Pereira; Curi, Rogério Abdallah [UNESP]; Balieiro, Júlio Cesar de Carvalho (Elsevier, 2018)
In general, is said that functional traits have a positive genetic correlation with conformation in horses but, this hypothesis has never been investigated in the Brazilian Campolina breed. We aimed to estimate genetic parameters (heritabilities and genetic correlations) for these traits based on genealogical records from 107,951 animals, in which 43,159 were phenotyped. A total of 16 morphological traits (MT); one gaits score (GtS) and two traits related to conformation harmony (CH), were simultaneously analysed under a Bayesian multi-trait model framework. Genetic trends were estimated over the years of birth for animals born between 1951 and 2016. MT were all genetically positively associated (from 0.05 to 0.98). CH traits presented positive and negative genetic correlations, but all favourable to the selection goals. GtS was negatively associated with all MT, except for Chest Width (0.08). CH and GtS presented lower positive genetic correlation (0.10 and 0.01, for the ratio between Height at Withers and Height at Back, and ratio of Back-Loins Length over Body Length, respectively). Observed results indicated the existence of sufficient additive genetic variance (heritability estimates ranged from 0.07 to 0.43) for the studied traits, benefiting the implementation of a breeding program, if the desired is to select animals for morphology or gaits. All genetic trends were favorable despite of the phenotypic selection in the Campolina breed. These trends presented low regression coefficients, but the increase on average predicted breeding values for the investigated period was 137.9%.
Keywords: Bayesian inference; Gaited horse; Genetic correlations; Multi-trait models; Quantitative genetics