Hair coat score in Angus cattle: comparison of linear and threshold models, genetic gain and correlations with growth, morphological and reproductive traits

2021 ◽  
pp. 104512
Author(s):  
Daniel Duarte da Silveira ◽  
Juan Salvador Andrade Tineo ◽  
Patrícia Iana Schmidt ◽  
Gabriel Soares Campos ◽  
Fabio Ricardo Pablos de Souza ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Reproductive Traits ◽  
Threshold Models ◽  
Hair Coat ◽  
Angus Cattle

scholarly journals Whole genome analysis of infectious bovine keratoconjunctivitis in Angus cattle using Bayesian threshold models

2011 ◽  
Vol 5 (Suppl 4) ◽  
pp. S22 ◽  
Author(s):  
Kadir Kizilkaya ◽  
Richard G Tait ◽  
Dorian J Garrick ◽  
Rohan L Fernando ◽  
James M Reecy
Keyword(s):  
Genome Analysis ◽  
Whole Genome ◽  
Threshold Models ◽  
Whole Genome Analysis ◽  
Infectious Bovine Keratoconjunctivitis ◽  
Angus Cattle

scholarly journals Threshold models for the determination of genetic variability among reproductive traits in Nellore cattle

2020 ◽  
Vol 55 ◽  
Author(s):  
Leonardo Martin Nieto ◽  
Luiz Otávio Campos da Silva ◽  
Antônio do Nascimento Ferreira Rosa
Keyword(s):  
Genetic Variability ◽  
Threshold Model ◽  
Reproductive Traits ◽  
Threshold Models ◽  
Multiple Trait ◽  
Bayesian Procedures ◽  
Nellore Cattle ◽  
Heritability Estimates ◽  
Maternal Grandsire

Abstract: The objective of this work was to evaluate the potential of different threshold models to determine the genetic variability in Nellore cattle, with basis on the heritability estimates for the traits stayability (STA) and first calving probability at 36 months of age (CP36). Data came from the Nellore herds participating in the animal breeding program of the Embrapa-Geneplus partnership. Binomial and multi-threshold models were defined for the STA and CP36 traits. Heritability estimates were obtained following Bayesian procedures in the Multiple-trait Gibbs Sampler for Animal Models (MTGSAM) software, using a sire-maternal grandsire model. The heritability estimates, provided by the binary and alternative models, were, respectively, 0.08 and 0.12 for STA and 0.17 and 0.12 for CP36. The multi-threshold model can efficiently detect the genetic variability for stayability, but not for probability of calving for 36-month-old cows.

A genome scan for quantitative trait loci influencing carcass, post-natal growth and reproductive traits in commercial Angus cattle

2010 ◽  
Vol 41 (6) ◽  
pp. 597-607 ◽  
Author(s):  
M. C. McClure ◽  
N. S. Morsci ◽  
R. D. Schnabel ◽  
J. W. Kim ◽  
P. Yao ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Reproductive Traits ◽  
Genome Scan ◽  
Angus Cattle ◽  
A Genome ◽  
Trait Loci

080 Evaluation of Angus Cattle Hair Coat Length and Its Associations with Tolerance to Fescue Toxicosis

2016 ◽  
Vol 95 (suppl_1) ◽  
pp. 40-40 ◽  
Author(s):  
K. Mayberry ◽  
T. L. Devine ◽  
M. H. Poore ◽  
N. V. Serão ◽  
D. H. Poole
Keyword(s):  
Fescue Toxicosis ◽  
Hair Coat ◽  
Angus Cattle

Bayesian estimation of genetic parameters of litter size at birth in crossbred Mule ewes using repeatability and multiple trait threshold models

2007 ◽  
Vol 2007 ◽  
pp. 66-66
Author(s):  
W. Mekkawy ◽  
R. Roehe ◽  
R. M. Lewis ◽  
M. H. Davies ◽  
A. M. Van Heelsum ◽  
...  
Keyword(s):  
Litter Size ◽  
Reproductive Performance ◽  
Genetic Parameters ◽  
Reproductive Traits ◽  
Threshold Models ◽  
Multiple Trait ◽  
Repeated Observations ◽  
Improvement Programs ◽  
Size At Birth ◽  
Estimation Of Genetic Parameters

The reproductive performance of ewes is a major factor influencing the economic success of lowland flocks. Even so reproductive traits have not often been included in sheep genetic improvement programs because of their low heritabilities compared with other performance traits like body weight and carcass merit. Litter size is one of the most important traits affecting reproductive performance. There are two important factors to be considered in the genetic analysis of litter size. First, several authors have suggested that litter size in different parities is controlled in part by different genes, and therefore should be treated as different traits. Second, ignoring the categorical nature of litter size and analysing it using a linear model does not account for its non-normal distribution. As a consequence, threshold models are likely to be more appropriate for the analysis of categorical traits (Gianola and Foulley, 1983). Therefore, the objective of the current study was to estimate the genetic parameters for litter size of Mule ewes using both repeatability and a multiple trait threshold models for repeated observations of litter size across four different parities.

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