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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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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 |
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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 |
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