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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
- 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.
- Subjects :
- Male
Saanen goat
test-day record
lcsh:QH426-470
[SDV]Life Sciences [q-bio]
biology.animal_breed
Biology
Genetic correlation
milk-yield
Lactation
fat
Genetic variation
Statistics
persistency
Genetics
medicine
Animals
Genetic variability
restricted maximum-likelihood
somatic-cell score
protein ratio
curve
Ecology, Evolution, Behavior and Systematics
ComputingMilieux_MISCELLANEOUS
lcsh:SF1-1100
2. Zero hunger
Models, Statistical
Models, Genetic
Covariance matrix
Goats
0402 animal and dairy science
Regression analysis
04 agricultural and veterinary sciences
General Medicine
040201 dairy & animal science
Dairying
lcsh:Genetics
[SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics
Milk
medicine.anatomical_structure
Principal component analysis
Regression Analysis
Female
Animal Science and Zoology
lcsh:Animal culture
Research Article
Autre (Sciences du Vivant)
Subjects
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
- Full Text :
- https://doi.org/10.1186/s12711-019-0485-3⟩