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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

Authors :
H. Larroque
H. Leclerc
Vincent Ducrocq
Mathieu Arnal
Christèle Robert-Granié
Génétique Physiologie et Systèmes d'Elevage (GenPhySE )
Ecole Nationale Vétérinaire de Toulouse (ENVT)
Institut National Polytechnique (Toulouse) (Toulouse INP)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Institut de l'élevage (IDELE)
Génétique Animale et Biologie Intégrative (GABI)
Université Paris-Saclay-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Source :
Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2019, 51 (1), pp.43. ⟨10.1186/s12711-019-0485-3⟩, Genetics Selection Evolution 1 (51), Non paginé. (2019), Genetics Selection Evolution, BioMed Central, 2019, 51 (1), ⟨10.1186/s12711-019-0485-3⟩, Genetics Selection Evolution, Vol 51, Iss 1, Pp 1-15 (2019), Genetics, Selection, Evolution : GSE
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

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.

Details

Language :
English
ISSN :
0999193X and 12979686
Database :
OpenAIRE
Journal :
Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2019, 51 (1), pp.43. ⟨10.1186/s12711-019-0485-3⟩, Genetics Selection Evolution 1 (51), Non paginé. (2019), Genetics Selection Evolution, BioMed Central, 2019, 51 (1), ⟨10.1186/s12711-019-0485-3⟩, Genetics Selection Evolution, Vol 51, Iss 1, Pp 1-15 (2019), Genetics, Selection, Evolution : GSE
Accession number :
edsair.doi.dedup.....839bad2704f9cc4b97ca2a023d6ae81f