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2. Current status and potential of genomic selection to improve selective breeding in the main aquaculture species of International Council for the Exploration of the Sea (ICES) member countries

Author

Boudry, Pierre, Allal, François, Aslam, Muhammad L., Bargelloni, Luca, Bean, Tim P., Brard-Fudulea, Sophie, Brieuc, Marine S.O., Calboli, Federico C.F., Gilbey, John, Haffray, Pierrick, Lamy, Jean-Baptiste, Morvezen, Romain, Purcell, Catherine, Prodöhl, Paulo A., Vandeputte, Marc, Waldbieser, Geoffrey C., Sonesson, Anna K., and Houston, Ross D.

Published
2021
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3. Genome-wide association studies for resistance to viral nervous necrosis in three populations of European sea bass (Dicentrarchus labrax) using a novel 57k SNP array DlabChip

Author

Griot, Ronan, Allal, François, Phocas, Florence, Brard-Fudulea, Sophie, Morvezen, Romain, Bestin, Anastasia, Haffray, Pierrick, François, Yoannah, Morin, Thierry, Poncet, Charles, Vergnet, Alain, Cariou, Sophie, Brunier, Joseph, Bruant, Jean-Sébastien, Peyrou, Bruno, Gagnaire, Pierre-Alexandre, and Vandeputte, Marc

Published
2021
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5. Additional file 2 of Managing genetic diversity in breeding programs of small populations: the case of French local chicken breeds

Author

Restoux, Gwendal, Rognon, Xavier, Vieaud, Agathe, Guemene, Daniel, Petitjean, Florence, Rouger, Romuald, Brard-Fudulea, Sophie, Lubac-Paye, Sophie, Chiron, Geoffrey, and Tixier-Boichard, Michèle

Abstract

Additional file 2: Table S2. Marginal estimated means of each diversity index per group of populations. The letters and grey color stand for significant pairwise differences between groups (Tukey correction for multiple tests).

Published
2022
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6. Additional file 3 of Managing genetic diversity in breeding programs of small populations: the case of French local chicken breeds

Author

Restoux, Gwendal, Rognon, Xavier, Vieaud, Agathe, Guemene, Daniel, Petitjean, Florence, Rouger, Romuald, Brard-Fudulea, Sophie, Lubac-Paye, Sophie, Chiron, Geoffrey, and Tixier-Boichard, Michèle

Abstract

Additional file 3: Figure S1. Correlation matrix of population molecular or demographic diversity indices for breeds involved in a management program (Group 1). Values correspond to Pearson’s correlation coefficients. Colored cells stand for significant correlations (p < 0.05) either positive (blue) or negative (red), the intensity depending on the strength of the correlation between estimates. White cells represent non-significant correlations. Nb.Generation stands for the number of generations since the start of the management program and Fixed for the proportion of fixed alleles.

Published
2022
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7. Additional file 5 of Managing genetic diversity in breeding programs of small populations: the case of French local chicken breeds

Author

Restoux, Gwendal, Rognon, Xavier, Vieaud, Agathe, Guemene, Daniel, Petitjean, Florence, Rouger, Romuald, Brard-Fudulea, Sophie, Lubac-Paye, Sophie, Chiron, Geoffrey, and Tixier-Boichard, Michèle

Abstract

Additional file 5: Figure S3. Mean assignment probability of populations to each of the two clusters. The Asian and European clusters are colored in red and blue, respectively.

Published
2022
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8. Additional file 4 of Managing genetic diversity in breeding programs of small populations: the case of French local chicken breeds

Author

Restoux, Gwendal, Rognon, Xavier, Vieaud, Agathe, Guemene, Daniel, Petitjean, Florence, Rouger, Romuald, Brard-Fudulea, Sophie, Lubac-Paye, Sophie, Chiron, Geoffrey, and Tixier-Boichard, Michèle

Abstract

Additional file 4: Figure S2. Matrix of pairwise Fst between populations. Darker blue indicates a stronger pairwise Fst.

Published
2022
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9. Additional file 6 of Managing genetic diversity in breeding programs of small populations: the case of French local chicken breeds

Author

Restoux, Gwendal, Rognon, Xavier, Vieaud, Agathe, Guemene, Daniel, Petitjean, Florence, Rouger, Romuald, Brard-Fudulea, Sophie, Lubac-Paye, Sophie, Chiron, Geoffrey, and Tixier-Boichard, Michèle

Abstract

Additional file 6: Figure S4. Unrooted neighbor-joining tree of the Hergnie breed. The colors of the surrounding circle represent the different breeders.

Published
2022
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10. Corrigendum: Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, The European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)

Author

Griot, Ronan, primary, Allal, François, additional, Phocas, Florence, additional, Brard-Fudulea, Sophie, additional, Morvezen, Romain, additional, Haffray, Pierrick, additional, François, Yoannah, additional, Morin, Thierry, additional, Bestin, Anastasia, additional, Bruant, Jean-Sébastien, additional, Cariou, Sophie, additional, Peyrou, Bruno, additional, Brunier, Joseph, additional, and Vandeputte, Marc, additional

Published
2021
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11. Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)

Author

Griot, Ronan, primary, Allal, François, additional, Phocas, Florence, additional, Brard-Fudulea, Sophie, additional, Morvezen, Romain, additional, Haffray, Pierrick, additional, François, Yoannah, additional, Morin, Thierry, additional, Bestin, Anastasia, additional, Bruant, Jean-Sébastien, additional, Cariou, Sophie, additional, Peyrou, Bruno, additional, Brunier, Joseph, additional, and Vandeputte, Marc, additional

Published
2021
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12. Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)

Author

Griot, Ronan, Allal, Francois, Phocas, Florence, Brard-fudulea, Sophie, Morvezen, Romain, Haffray, Pierrick, François, Yoannah, Morin, Thierry, Bestin, Anastasia, Bruant, Jean-sébastien, Cariou, Sophie, Peyrou, Bruno, Brunier, Joseph, Vandeputte, Marc, Griot, Ronan, Allal, Francois, Phocas, Florence, Brard-fudulea, Sophie, Morvezen, Romain, Haffray, Pierrick, François, Yoannah, Morin, Thierry, Bestin, Anastasia, Bruant, Jean-sébastien, Cariou, Sophie, Peyrou, Bruno, Brunier, Joseph, and Vandeputte, Marc

Abstract

Disease outbreaks are a major threat to the aquaculture industry, and can be controlled by selective breeding. With the development of high-throughput genotyping technologies, genomic selection may become accessible even in minor species. Training population size and marker density are among the main drivers of the prediction accuracy, which both have a high impact on the cost of genomic selection. In this study, we assessed the impact of training population size as well as marker density on the prediction accuracy of disease resistance traits in European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata). We performed a challenge to nervous necrosis virus (NNV) in two sea bass cohorts, a challenge to Vibrio harveyi in one sea bass cohort and a challenge to Photobacterium damselae subsp. piscicida in one sea bream cohort. Challenged individuals were genotyped on 57K–60K SNP chips. Markers were sampled to design virtual SNP chips of 1K, 3K, 6K, and 10K markers. Similarly, challenged individuals were randomly sampled to vary training population size from 50 to 800 individuals. The accuracy of genomic-based (GBLUP model) and pedigree-based estimated breeding values (EBV) (PBLUP model) was computed for each training population size using Monte-Carlo cross-validation. Genomic-based breeding values were also computed using the virtual chips to study the effect of marker density. For resistance to Viral Nervous Necrosis (VNN), as one major QTL was detected, the opportunity of marker-assisted selection was investigated by adding a QTL effect in both genomic and pedigree prediction models. As training population size increased, accuracy increased to reach values in range of 0.51–0.65 for full density chips. The accuracy could still increase with more individuals in the training population as the accuracy plateau was not reached. When using only the 6K density chip, accuracy reached at least 90% of that obtained with the full density chip. Adding the QTL e

Published
2021
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15. Développement d’un panel de marqueurs SNP pour détecter l’hybridation entre la caille des blés et la caille japonaise

Author

Rouger, R., Brard-Fudulea, Sophie, Thébault, N, Pitel, Frédérique, Vignal, Alain, Bed'Hom, Bertrand, Rognon, Xavier, Lumineau, Sophie, Guemene, Daniel, Biologie des Oiseaux et Aviculture (BOA), Institut National de la Recherche Agronomique (INRA)-Université de Tours, 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, Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, UAR 0558 Département Physiologie Animale et Systèmes d'Elevage, Institut National de la Recherche Agronomique (INRA)-Physiologie Animale et Systèmes d'Elevage (PHASE), and Institut National de la Recherche Agronomique (INRA)-Département Physiologie Animale et Systèmes d'Elevage (DEPT PHASE)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], caille japonaise, caille, génétique, génomique
Published
2019

16. MOESM1 of Genome-wide estimates of genetic diversity, inbreeding and effective size of experimental and commercial rainbow trout lines undergoing selective breeding

Author

D’Ambrosio, Jonathan, Phocas, Florence, Haffray, Pierrick, Bestin, Anastasia, Brard-Fudulea, Sophie, Poncet, Charles, Quillet, Edwige, Dechamp, Nicolas, Fraslin, Clémence, Charles, Mathieu, and Dupont-Nivet, Mathilde

Abstract

Additional file 1. Table S1: Number of SNPs per minimum allele frequency (MAF) category in each line. Table S2: Average r2 ± SD between SNPs according to different distances. Table S3: Sensitivity analysis of ROH estimates to MAF and maximal distance gap between two SNPs and derived inbreeding coefficient. Table S4: Estimates of effective population size (standard errors in brackets) for each line with or without Omy5 and Omy20.

Published
2019
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17. Genome-wide estimates of genetic diversity, inbreeding and effective size of experimental and commercial rainbow trout lines undergoing selective breeding

Author

D'Ambrosio, Jonathan, Haffray, Pierrick, Bestin, Anastasia, Brard-Fudulea, Sophie, Poncet, Charles, Quillet, Edwige, Dechamp, Nicolas, Fraslin, Clémence, Charles, Mathieu, Dupont Nivet, Mathilde, and Phocas, Florence

Subjects
consanguinité, diversité génétique, reproduction animale, Run of homozygosity, Autozygosity, Linkage disequilibrium, Heterozygosity, Inbreeding coefficient, Effective population size, SNP, Fish, truite arc en ciel
Abstract

Background: Selective breeding is a relatively recent practice in aquaculture species compared to terrestrial livestock. Nevertheless, the genetic variability of farmed salmonid lines, which have been selected for several generations, should be assessed. Indeed, a signifcant decrease in genetic variability due to high selection intensity could have occurred, potentially jeopardizing the long-term genetic progress as well as the adaptive capacities of populations facing change(s) in the environment. Thus, it is important to evaluate the impact of selection practices on genetic diversity to limit future inbreeding. The current study presents an analysis of genetic diversity within and between six French rainbow trout (Oncorhynchus mykiss) experimental or commercial lines based on a medium-density single nucleotide polymorphism (SNP) chip and various molecular genetic indicators: fxation index (FST), linkage disequilibrium (LD), efective population size (Ne) and inbreeding coefcient derived from runs of homozygosity (ROH). Results: Our results showed a moderate level of genetic diferentiation between selected lines (FST ranging from 0.08 to 0.15). LD declined rapidly over the frst 100 kb, but then remained quite high at long distances, leading to low estimates of Ne in the last generation ranging from 24 to 68 depending on the line and methodology considered. These results were consistent with inbreeding estimates that varied from 10.0% in an unselected experimental line to 19.5% in a commercial line, and which are clearly higher than corresponding estimates in ruminants or pigs. In addition, strong variations in LD and inbreeding were observed along the genome that may be due to diferences in local rates of recombination or due to key genes that tended to have fxed favorable alleles for domestication or production. Conclusions: This is the frst report on ROH for any aquaculture species. Inbreeding appeared to be moderate to high in the six French rainbow trout lines, due to founder efects at the start of the breeding programs, but also likely to sweepstakes reproductive success in addition to selection for the selected lines. Efcient management of inbreeding is a major goal in breeding programs to ensure that populations can adapt to future breeding objectives and SNP information can be used to manage the rate at which inbreeding builds up in the fsh genome.

Published
2019

18. MOESM2 of Genome-wide estimates of genetic diversity, inbreeding and effective size of experimental and commercial rainbow trout lines undergoing selective breeding

Author

D’Ambrosio, Jonathan, Phocas, Florence, Haffray, Pierrick, Bestin, Anastasia, Brard-Fudulea, Sophie, Poncet, Charles, Quillet, Edwige, Dechamp, Nicolas, Clémence Fraslin, Charles, Mathieu, and Dupont-Nivet, Mathilde

Abstract

Additional file 2. Figure S1: Line mean linkage disequilibrium at 50Â kb for each chromosome. Figure S2: Proportion of individuals per line with a SNP in a ROH along the genome. Figure S3: Proportion of individuals per line with a SNP in a ROH along chromosome 10.

Published
2019
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19. Development and characterization of a 57K Single Nucleotide Polymorphism array for European sea bass

Author

Allal, François, Duranton, Maud, Morvezen, Romain, Brard-Fudulea, Sophie, Poncet, Charles, Belmonte, Elodie, Griot, Ronan, Bruant, Jean-Sébastien, Cariou, Sophie, Bajek, Aline, Peyrou, Bruno, Vandeputte, Marc, Céline, Jaimet, Haffray, Pierrick, Gagnaire, Pierre-Alexandre, MARine Biodiversity Exploitation and Conservation (UMR MARBEC), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226, Syndicat des Sélectionneurs Avicoles et Aquacoles Français (SYSAAF), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Ferme Marine du Douhet, Ecloserie Marine de Gravelines, Aquanord, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), and Vandeputte, Marc

Subjects
[SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV.SA.SPA]Life Sciences [q-bio]/Agricultural sciences/Animal production studies, [SDV.SA.STP]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery, [SDV.SA.STP] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery, [SDV.SA.ZOO]Life Sciences [q-bio]/Agricultural sciences/Zootechny, [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, [SDV.SA.SPA] Life Sciences [q-bio]/Agricultural sciences/Animal production studies, [SDV.SA.ZOO] Life Sciences [q-bio]/Agricultural sciences/Zootechny, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

20. APIS: A new auto-adaptive parentage inference software tolerant to missing parents

Author

Griot, Ronan, Allal, François, Brard-Fudulea, Sophie, Cariou, Sophie, Haffray, Pierrick, Vandeputte, Marc, Syndicat des Sélectionneurs Avicoles et Aquacoles Français (SYSAAF), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), and Ferme Marine du Douhet

Subjects
[SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV.SA.SPA]Life Sciences [q-bio]/Agricultural sciences/Animal production studies, [SDV.SA.STP]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of fishery, [SDV.SA.ZOO]Life Sciences [q-bio]/Agricultural sciences/Zootechny, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

23. APIS: An auto‐adaptive parentage inference software that tolerates missing parents.

Author

Griot, Ronan, Allal, François, Brard‐Fudulea, Sophie, Morvezen, Romain, Haffray, Pierrick, Phocas, Florence, and Vandeputte, Marc

Subjects
FALSE positive error, SINGLE nucleotide polymorphisms, ERROR rates, EUROPEAN seabass, PARENTS, FISH breeding
Abstract

In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood‐based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user‐defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user‐set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (>200) are genotyped. We have demonstrated that APIS is an easy‐to‐use and reliable software for parentage assignment, even when up to 50% of sires are missing. [ABSTRACT FROM AUTHOR]

Published
2020
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