Genetic evaluation and genetic trend of growth traits of Zandi sheep in semi-arid Iran using random regression models

2013 ◽  
Vol 114 (2-3) ◽  
pp. 195-201 ◽  
Author(s):  
M. Bohlouli ◽  
H. Mohammadi ◽  
S. Alijani
2018 ◽  
Vol 63 (No. 6) ◽  
pp. 212-221 ◽  
Author(s):  
B.B. Teixeira ◽  
R.R. Mota ◽  
R.B. Lôbo ◽  
L.P. Silva ◽  
A.P. Souza Carneiro ◽  
...  

We aimed to evaluate different orders of fixed and random effects in random regression models (RRM) based on Legendre orthogonal polynomials as well as to verify the feasibility of these models to describe growth curves in Nellore cattle. The proposed RRM were also compared to multi-trait models (MTM). Variance components and genetic parameters estimates were performed via REML for all models. Twelve RRM were compared through Akaike (AIC) and Bayesian (BIC) information criteria. The model of order three for the fixed curve and four for all random effects (direct genetic, maternal genetic, permanent environment, and maternal permanent environment) fits best. Estimates of direct genetic, maternal genetic, maternal permanent environment, permanent environment, phenotypic and residual variances were similar between MTM and RRM. Heritability estimates were higher via RRM. We presented perspectives for the use of RRM for genetic evaluation of growth traits in Brazilian Nellore cattle. In general, moderate heritability estimates were obtained for the majority of studied traits when using RRM. Additionally, the precision of these estimates was higher when using RRM instead of MTM. However, concerns about the variance components estimates in advanced ages via Legendre polynomial must be taken into account in future studies.


2010 ◽  
Vol 93 (2-3) ◽  
pp. 126-134 ◽  
Author(s):  
C.M. Kariuki ◽  
E.D. Ilatsia ◽  
C.B. Wasike ◽  
I.S. Kosgey ◽  
A.K. Kahi

2016 ◽  
Vol 58 (1) ◽  
pp. 13-18 ◽  
Author(s):  
K. Karami ◽  
S. Zerehdaran ◽  
M. Tahmoorespur ◽  
B. Barzanooni ◽  
E. Lotfi

animal ◽  
2018 ◽  
Vol 12 (4) ◽  
pp. 667-674 ◽  
Author(s):  
L.F.M. Mota ◽  
P.G.M.A. Martins ◽  
T.O. Littiere ◽  
L.R.A. Abreu ◽  
M.A. Silva ◽  
...  

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 261-261
Author(s):  
Hinayah R Oliveira ◽  
Stephen P Miller ◽  
Luiz F Brito ◽  
Flavio S Schenkel

Abstract A recent study showed that longevity based on different culling reasons should be considered as different traits in genetic evaluations. However, it is still necessary to create a pipeline that avoid including/excluding animals culled for different reasons in every genetic evaluation run. This study aimed to: 1) perform a genetic evaluation of the longevity of cows culled due to fertility-related problems including records of animals culled for other reasons (i.e., age, structural problems, disease, and performance) as censored records; and, 2) identify the impact of censored data in the genetic parameters and breeding values estimated. Two longevity indicators were evaluated: traditional (TL; time from first calving to culling) and functional (FL; time period in which the cow was alive and also calving after its first calving) longevity. Both TL and FL were evaluated from 2 to 15 years-old, and codified as binary traits for each age (0 = culled and 1 = alive/calved). Both trait definitions were analyzed using a Bayesian random regression linear model. Animals culled for reasons other than fertility were either excluded from the data (standard) or had their records censored after the culling date reported in the dataset (censored). After the quality control, 154,419 and 450,124 animals had uncensored and censored records, respectively. Heritabilities estimated for TL over the ages ranged from 0.02 to 0.13 for standard, and from 0.01 to 0.12 for censored datasets. Heritabilities estimated for FL ranged from 0.01 to 0.14 (standard), and from 0.01 to 0.13 (censored). Average (SD) correlation of breeding values predicted over all ages, using the standard and censored datasets, was 0.77 (0.16) for TL, and 0.83 (0.11) for FL. Our findings suggest that including censored data in the analyses might impact the genomic evaluations and further work is need to determine the optimal predictive approach.


2014 ◽  
Vol 13 (4) ◽  
pp. 10943-10951
Author(s):  
F.G. Silva ◽  
R.A. Torres ◽  
L.P. Silva ◽  
H.T. Ventura ◽  
F.F. Silva ◽  
...  

2013 ◽  
Vol 12 (3) ◽  
pp. 2465-2480 ◽  
Author(s):  
R.R. Mota ◽  
L.F.A. Marques ◽  
P.S. Lopes ◽  
L.P. da Silva ◽  
F.R.A. Neto ◽  
...  

2014 ◽  
Vol 86 (7) ◽  
pp. 655-660 ◽  
Author(s):  
Mongkol Thepparat ◽  
Wuttigrai Boonkum ◽  
Monchai Duangjinda ◽  
Sornthep Tumwasorn ◽  
Sansak Nakavisut ◽  
...  

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