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3 results on '"Helies, Virginie"'

1. Breeding for general disease resistance: a selection experiment in rabbits

Author

Gunia, Mélanie, Ruesche, Julien, Aymard, Patrick, Gillet, Emilie, Herbert, Caroline, Helies, Virginie, Savietto, Davi, Robert, Raphaël, Warin, Laura, Gilbert, Hélène, Garreau, Hervé, and Gunia, Mélanie

Subjects
Heritability, Disease resistance, Genetics, Rabbit, [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, Selection
Abstract

We performed a selection experiment to investigate the possibility to improve resistance to non-specific diseases in rabbits. The selection criterion was a binary trait (healthy versus sick or dead) obtained from clinical signs of diseases occurring naturally on farm. A herd of 116 to 132 does and 30 bucks was bred based on this selection criterion. The heritability of the trait was 0.035 ± 0.009. After four generations of selection, the genetic progress was 1.5 genetic standard deviation, indicating that breeding for general disease resistance is feasible.

Published
2022

2. MACHINE LEARNING ALGORITHMS FOR THE PREDICTION OF FEED EFFICIENCY BASED ON CAECAL MICROBIOTA

Author

Piles, Miriam, Tusell, Llibertat, Velasco-Galilea, Maria, Helies, Virginie, Drouilhet, Laurence, Zemb, Olivier, Sánchez Juan, Pablo, Garreau, Hervé, Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), World Rabbit Science Association, and European Project: 633531,H2020,H2020-SFS-2014-2,Feed-a-Gene(2015)

Subjects
feed efficiency machine learning caecal microbiota prediction selection, machine learning, [SDV]Life Sciences [q-bio], feed efficiency, selection, prediction, caecal microbiota
Abstract

International audience; This study aimed at predicting feed conversion ratio (FCR) of young rabbits from abundances of amplicon sequence variants (ASVs) to improve this trait by selecting animals with the most favorable microbiota and identifying the most relevant microorganisms involved in feed efficiency. Data come from two rabbit populations coming from paternal INRA 1001 line (the G10, selected for 10 generations for decreased residual feed intake and the G0 control produced from frozen embryos of the common ancestor line). There were 296 and 292 FCR data from G10 and G0 individuals, respectively. Phenotypic data were pre-corrected for the systematic effects of group, batch, litter size and sex and the random litter effect. Sequence quality control and chimera removal were performed with the DADA2 pipeline. Samples with less than 5,000 final sequence counts and doubleton ASV were removed. The ASV counts of the final table (including 918 ASVs) were centered log-ratio transformed and corrected for batch effects with a surrogate variable analysis. Nested resampling for hyper-parameter tuning and prediction validation was implemented leading to 25 pairs of training/test sets. Bayesian regression models (Bayesian Lasso, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces) and machine learning algorithms (Support vector machine and Elastic net) were fitted to all ASVs leading to an almost null prediction accuracy in all cases. Then, ASVs were ranked for their prediction importance using the permutation accuracy importance score in a Random Forest algorithm based on conditional inference and, different subsets of increasing size (50, 100, 150, 200, 300, 400, 500, All) of the most important ASVs and surrogate variables were used as predictors in the machine learning algorithms. The best performance and the most stable results were obtained with machine learning using the 100 most important ASVs being most of them assigned to order Clostridiales. The medians of the Spearman correlation (interquartile range) were 0.33 (0.09) and 0.32 (0.06) for SVM and ENET, respectively.

Published
2021

3. Divergent selection for longevity in breeding does: indirect response for energy balance and fat stores

Author

Garreau, Herve, Ducrocq, Vincent, Ruesche, Julien, Tudela, Francois, Saleil, Georges, Juin, Herve, Debrusse, Anne-Marie, Lamothe, Eddie, Helies, Virginie, Duzert, Roger, Lamothe, Laurence, Larzul, Catherine, Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, UE 1322 UE PEA Cunicole Toulousain, Institut National de la Recherche Agronomique (INRA)-Génétique animale (G.A.)-Physiologie Animale et Systèmes d'Elevage (PHASE), Institut National de la Recherche Agronomique (INRA)-UE PEA Cunicole Toulousain (PECTOUL), Elevage Alternatif et Santé des Monogastriques (UE EASM), Tissus animaux, nutrition, digestion, écosystème et métabolisme (TANDEM), 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-INP. Ecole Nationale Supérieure Agronomique de Toulouse, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]

Subjects
SELECTION, ComputingMilieux_THECOMPUTINGPROFESSION, MORTALITY, [SDV]Life Sciences [q-bio], ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, GENETIC, IMMUNITY DEFENSE, InformationSystems_MODELSANDPRINCIPLES, RABBIT, LONGEVITY, FERTILITY, NUTRITION, ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS, InformationSystems_MISCELLANEOUS
Abstract

ID 446; absent

Published
2010

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