Finishing precocity visual score and genetic associations with growth traits in Angus beef cattle

Everling, Dionéia Magda; Bresolin, Tiago; Rorato, Paulo Roberto Nogara; Araújo, Ronyere Olegário de; Boligon, Arione Augusti; Weber, Tomás; Dornelles, M.A; Campos, Leonardo Talavera

doi:10.4238/2014.september.26.13

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…As reported by Ribeiro et al (2001) and Araujo et al (2014), moderate to high heritability estimates for weaning weight and low maternal heritability for weaning weight indicate less reliance on descendants to get higher weights due to the contribution of their mothers, which shows that the most significant part of total variability is due to genetic additive action. This explains partially the low covariance, and further, the negative covariance between direct and maternal genetic effects (σ a,m ) recorded with different adjusted models (Tables 4 and 5).…”

Section:    mentioning

confidence: 81%

“…Some difficulties can be verified in obtaining Gibbs chain convergence in animal models because the algorithm used is characterized as an iterative process (Faria et al, 2008). Nevertheless, Bayesian inference is recommended to obtain genetic correlations between categorical (survival and visual scores) and continuous traits (weights) through multi-trait analysis (Everling et al, 2014). In this study, the Bayesian approach was performed using the GIBBS3F90 program (Misztal et al, 2015) (assuming linear distribution of data) and THRIGIBBS3F90 program (Misztal et al, 2015) program (assuming threshold distribution of data).…”

Section: Methods Of Restricted Estimated Maximum Likelihood (Reml)mentioning

confidence: 99%

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Preweaning Calf Survival of a Nellore Beef Cattle Population

Júnior¹,

Petrini²,

Mourão³

et al. 2017

JAS

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The preweaning calf survival (SW) is one of the main economic bottlenecks of beef cattle rearing systems, however there is still few quantitative studies approaching this issue. Being a binary trait, genetic parameters for SW can be estimated considering continuous or categorical data under frequentist and Bayesian methods providing support for the selection and mating of animals in breeding programs. Therefore, the objectives in this study were to obtain and compare the variance component estimates for preweaning

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Section:    mentioning

confidence: 81%

Section: Methods Of Restricted Estimated Maximum Likelihood (Reml)mentioning

confidence: 99%

Preweaning Calf Survival of a Nellore Beef Cattle Population

Júnior¹,

Petrini²,

Mourão³

et al. 2017

JAS

View full text Add to dashboard Cite

show abstract

“…Some difficulties can be verified in obtaining Gibbs chain convergence in animal models because the algorithm used is characterized as an iterative process (Faria et al, 2008). Nevertheless, Bayesian inference is recommended to obtain genetic correlations between categorical (survival and visual scores) and continuous traits (weights) through multi-trait analysis (Everling et al, 2014). In this study, the Bayesian approach was performed using the GIBBS3F90 (Misztal et al, 2015) program (assuming linear distribution of data) and THRIGIBBS3F90 (Misztal et al, 2015) program (assuming threshold distribution of data).…”

Section: Estimation Methodsmentioning

confidence: 99%

Categorical Visual Score Traits of a Nellore Beef Cattle Population

Júnior¹,

Petrini²,

Mourão³

et al. 2017

JAS

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Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h 2 ) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

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“…Similarly, the genetic covariance matrix G can be calculated as G = G0 ⊗ I. In the mixed model used, β is considered the vector of solutions for the systemic effects; however, from the Bayesian point of view, it is a vector of random effects in which the initial distribution values have uninformative priors; thus, they do not provide much information about the parameter and, therefore, have a uniform probability distribution (Everling, et al, 2014). This type of probability distribution indicates the same probability of occurrence of each of the possible variable values.…”

Section: Methodsmentioning

confidence: 99%

<b>Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model

et al. 2017

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ABSTRACT. Variance components must be obtained to estimate genetic parameters and predict breeding values. This information can be obtained through Bayesian inference. When multiple traits are evaluated, Bayesian inference can be used in multi-trait models. The objective of this study was to obtain estimates of genetic parameters, gains with selection, and genetic correlations among traits. Likewise, we aim to predict the genetic values and select the best kale genotypes using the Bayesian approach in a multi-trait linear model. The following traits were evaluated: stem diameter, plant height, number of shoots, number of marketable leaves and fresh weight of leaves using Bayesian inference in 22 kale genotypes. The experiment consisted of a randomized block design with three replications and four plants per plot. Genetic effects predominated over environmental effects. The highest correlation estimates were found between the fresh weight of leaves and stem diameter and between the plant height and number of marketable leaves. The following commercial cultivars and genotypes are recommended for cultivation and to integrate into breeding programs: UFLA 11, UFLA 5, UFLA 6, UFVJM 3 and UFVJM 19. The estimates of the gain with selection indicate the potential for improvement of the studied population.Keywords: Brassica oleracea L. var. acephala DC., genetic parameters, crop breeding, statistical modeling, correlations.Parâmetros populacionais e seleção de genótipos de couve por inferência bayesiana em modelo linear multicaracterístico RESUMO. Para selecionar genitores em programas de melhoramento deve-se obter os componentes de variância para estimar parâmetros genéticos e predizer valores genéticos, os quais podem ser obtidos vantajosamente pela inferência bayesiana. Quando várias características são avaliadas a inferência bayesiana pode ser utilizada em modelos multicaracterísticos. Objetivou-se obter estimativas de parâmetros genéticos, ganhos de seleção, conhecer as correlações genéticas entre as características, predizer valores genéticos e selecionar melhores genótipos de couve utilizando a abordagem bayesiana em modelo linear multicaracterístico. Foram avaliados o diâmetro do caule, altura da planta, número de brotações, número de folhas comercializáveis e massa fresca de folhas por inferência bayesiana em 22 genótipos de couve. Foi utilizado o delineamento em blocos casualizados com três repetições e quatro plantas por parcela. Verificou-se a predominância dos efeitos genéticos sobre os ambientais. As maiores estimativas de correlação foram encontradas entre a matéria fresca de folhas e as características diâmetro do caule, altura de plantas e número de folhas comercializáveis. Além das testemunhas comerciais, são indicados para o cultivo e para integrar programas de melhoramento os genótipos UFLA 11, UFLA 5, UFLA 6, UFVJM 3 e UFVJM 19. As estimativas do ganho de seleção indicaram o potencial de melhoramento para a população estudada.

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Finishing precocity visual score and genetic associations with growth traits in Angus beef cattle

Cited by 8 publications

References 16 publications

Preweaning Calf Survival of a Nellore Beef Cattle Population

Preweaning Calf Survival of a Nellore Beef Cattle Population

Categorical Visual Score Traits of a Nellore Beef Cattle Population

<b>Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model

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