scholarly journals Genetic prediction for first-service conception rate in Angus heifers using a random regression model

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. S43-S47
Author(s):  
Miguel A Sánchez-Castro ◽  
Milton G Thomas ◽  
R Mark Enns ◽  
Scott E Speidel
Keyword(s):  
Regression Model ◽  
Conception Rate ◽  
Random Regression ◽  
Genetic Prediction ◽  
Random Regression Model ◽  
Service Conception ◽  
Angus Heifers

scholarly journals 260 Genetic prediction for first-service conception rate in Angus heifers using a random regression model

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 197-198
Author(s):  
Miguel A Sánchez-Castro ◽  
Milt Thomas ◽  
Mark Enns ◽  
Scott Speidel
Keyword(s):  
Conception Rate ◽  
Random Regression ◽  
State University ◽  
Base Function ◽  
Genetic Prediction ◽  
Colorado State University ◽  
Random Regression Model ◽  
Service Conception ◽  
Available Information ◽  
Angus Heifers

Abstract First-service conception rate (FSCR) can be defined as the probability of a heifer conceiving in response to her first artificial insemination (AI). Given the binary nature of its phenotypes, FSCR has been typically evaluated using animal threshold models (ATM). However, susceptibility of these models to the extreme-category problem (ECP) limits their ability to use all available information to calculate Expected Progeny Differences (EPD). Random regression models (RRM) represent an alternative method to evaluate binary traits, and they are not affected by ECP. Nevertheless, RRM were originally developed to analyze longitudinal traits, so their usefulness to evaluate traits with singly observed phenotypes remains unclear. Therefore, objectives herein were to evaluate the feasibility of a RRM genetic prediction for heifer FSCR by comparing its resulting EPD and genetic parameters to those obtained with a traditional ATM. Breeding and ultrasound records of 4,334 Angus heifers (progeny of 354 sires and 1,626 dams) collected between 1992 to 2019 at the Colorado State University Beef Improvement Center were utilized. Observations for FSCR (1, successful; 0, unsuccessful) were defined by fetal age at pregnancy inspections performed approximately 130 d post-AI. Traditional FSCR evaluation was performed using a univariate BLUP threshold animal model, whereas an alternative evaluation was performed by regressing FSCR on age at AI using a linear RRM with Legendre Polynomials as the base function. Heritability estimates were 0.03 ± 0.02 for the ATM and 0.005 ± 0.001 for the average age at AI with the RRM, respectively. Pearson and rank correlations between EPD obtained with each method were 0.63 and 0.60, respectively. The regression coefficient of RRM predictions on those obtained with the ATM was 0.095. In conclusion, these results suggested that although a RRM genetic prediction for FSCR was feasible, a considerable degree of re-ranking occurred between the two methodologies.

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

scholarly journals Test-day or 305-day milk yield for genetic evaluation of Gir cattle

2019 ◽  
Vol 54 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Mário Luiz Santana Junior ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Random Regression Model ◽  
The Mean ◽  
Yield Record ◽  
Genetic Evaluations ◽  
Dairy Breed

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

scholarly journals Analysis of longitudinal data of Nellore cattle from performance test at pasture using random regression model

SpringerPlus ◽  
2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Fernando Brito Lopes ◽  
Cláudio Ulhôa Magnabosco ◽  
Fernanda Paulini ◽  
Marcelo Corrêa da Silva ◽  
Eliane Sayuri Miyagi ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Performance Test ◽  
Random Regression ◽  
Random Regression Model ◽  
Nellore Cattle

scholarly journals A Bayesian generalized random regression model for estimating heritability using overdispersed count data

2015 ◽  
Vol 47 (1) ◽  
Author(s):  
Colette Mair ◽  
Michael Stear ◽  
Paul Johnson ◽  
Matthew Denwood ◽  
Joaquin Prada Jimenez de Cisneros ◽  
...  
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Random Regression ◽  
Random Regression Model
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