Genetic associations between mature size and condition score of Nelore cows, and weight, subjective scores and carcass traits as yearlings
Viviane V. de Lacerda A F , Gabriel S. Campos A , Daniel D. Silveira A , Vanerlei M. Roso B , Mario L. SantanaA Departamento de Zootecnia, Universidade Federal de Pelotas, Pelotas, RS, Brazil, CEP 96160-000.
B GenSys Consultores Associados S/S Ltda., Porto Alegre, RS, Brazil.
C Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Mato Grosso, Rondonópolis, MT, Brazil, CEP 78735-901.
D Instituto de Biologia, Universidade Federal de Pelotas, Pelotas, RS, Brazil, CEP 96160-000.
E Conselho Nacional de Desenvolvimento Científico e Tecnológico.
F Corresponding author. Email: vivianelacerda88@hotmail.com
Animal Production Science 59(7) 1209-1217 https://doi.org/10.1071/AN17873
Submitted: 23 December 2017 Accepted: 20 June 2018 Published: 1 August 2018
Abstract
The size and body condition of female livestock is critical for improving production efficiency. However, we know little about how height and body condition score in mature beef cattle are genetically related to traits observed when the animals are younger. In the present study, we used data from 321 650 Nelore cattle, first, to compare genetic parameters and breeding values on the basis of different models employing weight (MW), height (MH) and body condition score (BCS) of mature cows (3–17 years old). Next, we estimated the genetic correlations between the three traits and assorted yearling traits (YW, weight; YC; conformation score; YP, precocity score; YM, muscling score; YN, navel score; LMA, longissimus muscle area; BF, back fat thickness). Finally, we obtained the expected direct responses to selection for MW, MH and BCS of cows and correlated responses for these traits when the selection was applied to yearling traits. For MW and MH, single-trait Bayesian analyses were used to evaluate the effects of including BCS when defining contemporary groups (BCS included, CG1; BCS not included, CG2). For BCS trait, linear and threshold animal models were compared. After, bi-trait analyses that included MW, MH or BCS with yearling traits were performed. The CG1 scenario resulted in a higher heritability for MW (0.45 ± 0.02) than did CG2 (0.39 ± 0.02). Both scenarios yielded the same heritability estimates for MH (0.35 ± 0.02). Sires’ rank correlations between predicted breeding values under CG1 and CG2 were 0.60–0.92 for MW and 0.90–0.98 for MH, considering different selection intensities. Thus, only for MW genetic evaluations, the incorporation of BCS in the definition of the contemporary groups is indicated. For BCS trait, the same sires were selected regardless of the model (linear or threshold). Genetic correlations between MW and five yearling traits (YW, YC, YP, YM and YN) ranged from 0.18 ± 0.03 to 0.84 ± 0.01. The MH had a higher and positive genetic association with YW (0.64 ± 0.02) and YC (0.54 ± 0.03), than with YN (0.18 ± 0.03). However, MH was negatively and lowly genetically correlated with YP (–0.08 ± 0.03) and YM (–0.14 ± 0.03). The BCS had positive genetic associations with all yearling traits, particularly with YP (0.61 ± 0.06) and YM (0.60 ± 0.07). Mature size and carcass traits exhibited a low to moderate negative genetic correlations. However, BCS had positive genetic associations with LMA (0.38 ± 0.12) and BF (0.32 ± 0.14). Despite a shorter generation interval, selection at the yearling stage will result in a slower genetic progress per generation than does direct selection for cow MW, MH or BCS. Moreover, using YW and YC as selection criteria will increase cattle size at maturity without altering BCS. Last, LMA or BF-based selection will reduce mature size, while improving BCS, as a correlated response.
Additional keywords: back fat thickness, Bayesian analyses, beef cattle, mature height, mature weight, longissimus muscle area.
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