Estimation of genetic parameters for monthly egg production in laying hens based on random regression models

2009 ◽  
Vol 50 (1) ◽  
pp. 41-46 ◽  
Author(s):  
A. Wolc ◽  
T. Szwaczkowski
Keyword(s):  
Regression Models ◽  
Egg Production ◽  
Genetic Parameters ◽  
Laying Hens ◽  
Random Regression ◽  
Estimation Of Genetic Parameters

scholarly journals Estimation of genetic parameters for partial egg production periods by means of random regression models

2012 ◽  
Vol 11 (3) ◽  
pp. 1819-1829 ◽  
Author(s):  
G.C. Venturini ◽  
D.A. Grossi ◽  
S.B. Ramos ◽  
V.A.R. Cruz ◽  
C.G. Souza ◽  
...  
Keyword(s):  
Regression Models ◽  
Egg Production ◽  
Genetic Parameters ◽  
Random Regression ◽  
Estimation Of Genetic Parameters

scholarly journals Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

2013 ◽  
Vol 96 (9) ◽  
pp. 5923-5932 ◽  
Author(s):  
Rusbel Raul Aspilcueta Borquis ◽  
Francisco Ribeiro de Araujo Neto ◽  
Fernando Baldi ◽  
Naudin Hurtado-Lugo ◽  
Gregório M.F. de Camargo ◽  
...  
Keyword(s):  
Milk Fat ◽  
Regression Models ◽  
Genetic Parameters ◽  
Protein Yield ◽  
Random Regression ◽  
Multiple Trait ◽  
Estimation Of Genetic Parameters

scholarly journals Genetic evaluation of laying hens under fixed regression animal models

2007 ◽  
Vol 50 (3) ◽  
pp. 279-287
Author(s):  
A. Wolc ◽  
M. Lisowski ◽  
T. Szwaczkowski
Keyword(s):  
Animal Models ◽  
Akaike Information Criterion ◽  
Regression Models ◽  
Egg Production ◽  
Genetic Parameters ◽  
Laying Hens ◽  
Information Criterion ◽  
Genetic Evaluation ◽  
Term Selection

Abstract. Monthly egg production in laying hens was studied under fixed regression models. The data of 37071 birds from three strains under long term selection were analysed. The covariates from four production curves were included in nested and non-nested form. From linearized functions the model of Ali and Schaeffer most adequately described the egg production. Akaike Information Criterion favoured models with nested covariates. The following genetic parameters were estimated: heritability 0.02–0.2, 0.03–0.06, 0.03–0.20 repeatability 0.11–0.23, 0.21–0.30, 0.34–0.43 for A22, A88 and K66 lines, respectively. In conclusion, regression models could be considered in genetic evaluation of laying hens.

scholarly journals Comparison of random regression models, traditional animal model and with the inclusion of molecular markers in the estimation of genetic parameters in Colombian Holstein cattle

2021 ◽  
Vol 42 (3) ◽  
pp. 1303-1322
Author(s):  
Daniel Cardona-Cifuentes ◽  
◽  
Albeiro López-Herrera ◽  
Luis Gabriel González-Herrera ◽  
Mario Fernando Cerón-Muñoz ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Somatic Cell ◽  
Milk Production ◽  
Regression Models ◽  
Genetic Parameters ◽  
Evaluation Model ◽  
Random Regression ◽  
Holstein Cattle ◽  
Protein Percentage ◽  
Estimation Of Genetic Parameters

The use of molecular markers to identify desirable genes in animal production is known as marker-assisted selection. The traditional genetic evaluation model uses the BLUP methodology; when genetic markers are included in the evaluation model, the methodology is known as M-BLUP. In contrast, random regression models (RRM), unlike the models based on production at 305 days, consider factors that change for each animal from one test to another. The objective of this study was to compare variance components, genetic parameters and breeding values for milk production, protein percentage and somatic cell score in Colombian Holstein cattle using BLUP, M-BLUP and RRM. For the estimation of genetic parameters and values, 2003 lactations corresponding to 1417 cows in 55 herds were used, and effects of the order of delivery, herd, and contemporary group were included. The three traits presented greater heritability under the MBLUP model: 0.44 for protein percentage, 0.27 for milk production and 0.28 for somatic cell score. This was because the genetic variance was greater when M-BLUP was used, which allowed a greater accuracy of the breeding value estimation in the three traits. Therefore, the model that includes information on molecular markers is more suitable for genetic evaluation in Colombian Holstein cattle.

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