Genetic parameters for body weight, hip height, and the ratio of weight to hip height from random regression analyses of Brahman feedlot cattle12

2007 ◽  
Vol 85 (1) ◽  
pp. 42-52 ◽  
Author(s):  
D. G. Riley ◽  
S. W. Coleman ◽  
C. C. Chase ◽  
T. A. Olson ◽  
A. C. Hammond
Keyword(s):  
Body Weight ◽  
Genetic Parameters ◽  
Random Regression ◽  
Regression Analyses

Estimation of genetic parameters for body weight of the Goettingen minipig with random regression models1

2007 ◽  
Vol 85 (10) ◽  
pp. 2423-2428 ◽  
Author(s):  
F. Köhn ◽  
A. R. Sharifi ◽  
Š. Malovrh ◽  
H. Simianer
Keyword(s):  
Body Weight ◽  
Genetic Parameters ◽  
Random Regression ◽  
Estimation Of Genetic Parameters

Genetic parameters for body weight in buffaloes (Bubalus bubalis) in Colombia using random regression models

2013 ◽  
Vol 158 (1-3) ◽  
pp. 40-49 ◽  
Author(s):  
D.M. Bolívar ◽  
M.F. Cerón-Muñoz ◽  
A.A. Boligon ◽  
M.A. Elzo ◽  
A.C. Herrera
Keyword(s):  
Body Weight ◽  
Regression Models ◽  
Genetic Parameters ◽  
Bubalus Bubalis ◽  
Random Regression

Genetic parameters of body weight in sheep estimated via random regression and multi-trait animal models

2011 ◽  
Vol 100 (1) ◽  
pp. 15-18 ◽  
Author(s):  
A. Wolc ◽  
E. Barczak ◽  
J. Wójtowski ◽  
P. Ślósarz ◽  
T. Szwaczkowski
Keyword(s):  
Body Weight ◽  
Animal Models ◽  
Genetic Parameters ◽  
Random Regression

scholarly journals Estimation of (co)variance components and genetic parameters for weights of red-winged tinamou using random regression models

2011 ◽  
Vol 40 (4) ◽  
pp. 781-787 ◽  
Author(s):  
P. Tholon ◽  
S.A. Queiroz
Keyword(s):  
Body Weight ◽  
Environmental Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Genetic Effect ◽  
Information Criterion ◽  
Random Regression ◽  
Additive Genetic Effect ◽  
First Eigenvalue ◽  
Permanent Effect

The objective of this work was to determine genetic parameters for body weight of tinamou in captivity. It was used random regression models in analyses of data by considering the direct additive genetic (DA) and permanent environmental effects of the animal (PE) as random effects. Residual variances were modeled by using a fifth-order variance function. The mean population growth curve was fitted by sixth-order Legendre orthogonal polynomials. Direct additive genetic effects and animal environmental permanent effect were modeled by using Legendre polynomials of order two to nine. The best results were obtained by models with orders of fit of 6 for direct additive genetic effect and of order 3 for permanent effect by Akaike information criterion and of order 3 for both additive genetic effect and permanent effect by Schwarz Bayesian information criterion and likelihood ratio test. Heritability estimates ranged from 0.02 to 0.57. The first eigenvalue explained 94% and 90% of the variation from additive direct and permant environmental effects, respectively. Selection of tinamou for body weight is more effective after 112 days of age.

scholarly journals Random regression models with B-splines to estimate genetic parameters for body weight of young bulls in performance tests

2018 ◽  
Vol 47 (0) ◽  
Author(s):  
Daiane Cristina Becker Scalez ◽  
Breno de Oliveira Fragomeni ◽  
Dalinne Chrystian Carvalho dos Santos ◽  
Tiago Luciano Passafaro ◽  
Maurício Mello de Alencar ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Models ◽  
Genetic Parameters ◽  
Random Regression ◽  
Performance Tests ◽  
B Splines

scholarly journals Estimation of genetic parameters for body weight traits in Mazandaran native breeder hens by random regression models

Genetika ◽  
2019 ◽  
Vol 51 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Yousef Naderi
Keyword(s):  
Body Weight ◽  
Genetic Correlation ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
The Body ◽  
Random Regression ◽  
Likelihood Method ◽  
Primary Concern ◽  
Environmental Correlations

The primary concern of this study is to investigate appropriate random regression model for estimate genetic parameters body weight at hatch (BW1), eight (BW8), twelve (BW12) and thirty two (BW32) weeks of ages by the restricted maximum likelihood method. The body weight records set included 39872 during 16 generations of hens kept at the Mazandaran Breeding Center of Iran. Random regression were modelled using generation-hatch as a fixed effect and additive genetic and permanent environmental effects as random effects Residual variances were modeled through a step function with 1 and 3 classes. The model was considered to be the most appropriate with the highest significant log likelihood ratio test (LRT) and the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). Heritability values increased from 0.21 for BW1, to 0.40 for BW32. Genetic correlations of body weight at different record keeping were often higher than permanent environmental correlations. Genetic correlations between pairs of body weight measures were moderate to high with a range from 0.25 to 0.97. The largest genetic correlation, as well as permanent environmental correlation, was observed between BW12and BW32. High and moderate broad sense heritability values for all studied traits shows that these traits are less influenced by residual effects which make them effectively transmitted to the progeny. Findings show that genetic improvement for body weight can be achieved by selection. The Heritability of body weight at thirty two weeks of ages and its relatively high genetic correlation with all other ages showed that it could be the most appropriate period for selection. Also, the genetic trend estimates for body weight traits showed that selection decisions made during the breeding program effectively improved the growth performance.

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