Cargando…

Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model

BACKGROUND: Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective w...

Descripción completa

Detalles Bibliográficos
Autores principales: Arnal, Mathieu, Larroque, Hélène, Leclerc, Hélène, Ducrocq, Vincent, Robert-Granié, Christèle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693143/
https://www.ncbi.nlm.nih.gov/pubmed/31409294
http://dx.doi.org/10.1186/s12711-019-0485-3
_version_ 1783443651977805824
author Arnal, Mathieu
Larroque, Hélène
Leclerc, Hélène
Ducrocq, Vincent
Robert-Granié, Christèle
author_facet Arnal, Mathieu
Larroque, Hélène
Leclerc, Hélène
Ducrocq, Vincent
Robert-Granié, Christèle
author_sort Arnal, Mathieu
collection PubMed
description BACKGROUND: Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats. RESULTS: The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them. CONCLUSIONS: A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12711-019-0485-3) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6693143
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-66931432019-08-16 Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model Arnal, Mathieu Larroque, Hélène Leclerc, Hélène Ducrocq, Vincent Robert-Granié, Christèle Genet Sel Evol Research Article BACKGROUND: Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats. RESULTS: The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them. CONCLUSIONS: A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12711-019-0485-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-13 /pmc/articles/PMC6693143/ /pubmed/31409294 http://dx.doi.org/10.1186/s12711-019-0485-3 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Arnal, Mathieu
Larroque, Hélène
Leclerc, Hélène
Ducrocq, Vincent
Robert-Granié, Christèle
Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model
title Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model
title_full Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model
title_fullStr Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model
title_full_unstemmed Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model
title_short Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model
title_sort genetic parameters for first lactation dairy traits in the alpine and saanen goat breeds using a random regression test-day model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693143/
https://www.ncbi.nlm.nih.gov/pubmed/31409294
http://dx.doi.org/10.1186/s12711-019-0485-3
work_keys_str_mv AT arnalmathieu geneticparametersforfirstlactationdairytraitsinthealpineandsaanengoatbreedsusingarandomregressiontestdaymodel
AT larroquehelene geneticparametersforfirstlactationdairytraitsinthealpineandsaanengoatbreedsusingarandomregressiontestdaymodel
AT leclerchelene geneticparametersforfirstlactationdairytraitsinthealpineandsaanengoatbreedsusingarandomregressiontestdaymodel
AT ducrocqvincent geneticparametersforfirstlactationdairytraitsinthealpineandsaanengoatbreedsusingarandomregressiontestdaymodel
AT robertgraniechristele geneticparametersforfirstlactationdairytraitsinthealpineandsaanengoatbreedsusingarandomregressiontestdaymodel