educed rank analysis of morphometric and functional traits in Campolina horse

Autor(es)

Bussiman, Fernando de Oliveira ^[1] ; Bueno Carvalho, Rachel Santos ^[2] ; e Silva, Fabyano Fonseca ^[3] ; Ventura, Ricardo Vieira ^[1] ; Sterman Ferraz, Jose Bento ^[4] ; Mattos, Elisangela Chicaroni ^[4] ; Eler, Joanir Pereira ^[4] ; de Carvalho Balieiro, Julio Cesar ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Coll Vet Med & Anim Sci, Bioinformat & Anim Breeding Lab, Dept Anim Nutr & Prod, BIOMA VNP, FMVZ USP, Av Duque de Cazias Norte 225, BR-13635900 Pirassununga, SP - Brazil

^[2] Univ Sao Paulo, Coll Anim Sci & Food Engn, Dept Basic Sci, ZAB FZEA USP, Pirassununga - Brazil

^[3] Univ Fed Vicosa, Dept Anim Sci, DZO UFV, Vicosa, MG - Brazil

^[4] Univ Silo Paulo, Coll Anim Sci & Food Engn, Dept Vet Med, Grp Anim Breeding & Biotechnol, GMAB ZMV FZEA USP, Pirassununga - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF ANIMAL BREEDING AND GENETICS; v. 139, n. 2 NOV 2021.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 18/26465-3 - Uso de simulação e modelagem como ferramentas de identificação de animais para genotipagem
• Beneficiário: Fernando de Oliveira Bussiman
• Modalidade de apoio: Bolsas no Brasil - Doutorado