1. Deterministic modelling of a poultry breeding scheme
- Author
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François, Yoannah, Alnahhas, N., Brard-Fudulea, S., Varenne, A., Burlot, T., Chapuis, Herve, Elsen, Jean Michel, Le Roy, Pascale, ProdInra, Migration, Syndicat des Sélectionneurs Avicoles et Aquacoles Français (SYSAAF), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-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, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)
- Subjects
[SDV] Life Sciences [q-bio] ,[SDV.GEN]Life Sciences [q-bio]/Genetics ,[SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics ,[SDV]Life Sciences [q-bio] ,[INFO]Computer Science [cs] ,[SDV.GEN] Life Sciences [q-bio]/Genetics ,[SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics ,[INFO] Computer Science [cs] ,poultry selection scheme ,deterministic modelling ,genomic selection - Abstract
International audience; Deterministic modelling of a poultry breeding scheme Deterministic modeling of selection schemes consists of calculating the expected genetic gain after a selection process. Consequences of selection on the genetic trend are calculated using equations based on estimated breeding values and on parameters describing the population and the selection process. Professionals could use this tool to compare different strategies for the improvement of their selection schemes. In this study, the model is based on a layer chicken breeding scheme. Breeding goal includes numerous traits such as laying performances, egg quality and body weight. This is a two steps model combining a familial selection process and an individual one. Within selected families, candidates from the good families are undergoing a mass selection whereas candidates from medium families are undergoing a within family selection of a fixed number of candidates. The model requires a large number of parameters: number of candidates, proportions of selected families and selected candidates per family, accuracy of breeding values, genetic correlations between traits, weights of traits in the selection index. The variability of the parameters composing the model enables comparing a large diversity of scenarios, including the use of genomic evaluations. In order to illustrate the usefulness of the model, we simulated 3 selection strategies using classic breeding values and/or genomic breeding values. The genetic gain predictions obtained were then compared. The model included two traits T1 (h² = 0.13) and T2 (h² = 0.33), with a genetic correlation varying between 0 and 0.70 and a weight in the index of 40% and 60% respectively. Results show an increase of predicted gain for both traits with genomic evaluation. Improvement is constant for T2, whereas for T1 the increase of predicted gain is much better when genetic correlation between T1 and T2 is high. The genomic breeding scheme model seems more sensitive than the classic breeding scheme to parameters like the between traits correlation and their weight in the index.
- Published
- 2018