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)
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 mai...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.665920/full |
| _version_ | 1818681921457094656 |
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| author | Ronan Griot Ronan Griot Ronan Griot François Allal Florence Phocas Sophie Brard-Fudulea Romain Morvezen Pierrick Haffray Yoannah François Thierry Morin Anastasia Bestin Jean-Sébastien Bruant Sophie Cariou Bruno Peyrou Joseph Brunier Marc Vandeputte Marc Vandeputte |
| author_facet | Ronan Griot Ronan Griot Ronan Griot François Allal Florence Phocas Sophie Brard-Fudulea Romain Morvezen Pierrick Haffray Yoannah François Thierry Morin Anastasia Bestin Jean-Sébastien Bruant Sophie Cariou Bruno Peyrou Joseph Brunier Marc Vandeputte Marc Vandeputte |
| author_sort | Ronan Griot |
| collection | DOAJ |
| description | 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 effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream. |
| first_indexed | 2024-12-17T10:10:38Z |
| format | Article |
| id | doaj.art-9a55a24bf6af4eafa5d938a0b93f7a82 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T10:10:38Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-9a55a24bf6af4eafa5d938a0b93f7a822022-12-21T21:53:02ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-07-011210.3389/fgene.2021.665920665920Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)Ronan Griot0Ronan Griot1Ronan Griot2François Allal3Florence Phocas4Sophie Brard-Fudulea5Romain Morvezen6Pierrick Haffray7Yoannah François8Thierry Morin9Anastasia Bestin10Jean-Sébastien Bruant11Sophie Cariou12Bruno Peyrou13Joseph Brunier14Marc Vandeputte15Marc Vandeputte16SYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, FranceUniversité Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, FranceMARBEC, Univ. Montpellier, Ifremer, CNRS, IRD, Palavas-les-Flots, FranceMARBEC, Univ. Montpellier, Ifremer, CNRS, IRD, Palavas-les-Flots, FranceUniversité Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, FranceSYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, FranceSYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, FranceSYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, FranceSYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, FranceANSES, Ploufragan-Plouzané-Niort Laboratory, Viral Fish Diseases Unit, National Reference Laboratory for Regulated Fish Diseases, Technopôle Brest-Iroise, Plouzané, FranceSYSAAF, Station LPGP/INRAE, Campus de Beaulieu, Rennes, FranceFerme Marine du Douhet, La Brée Les Bains, FranceFerme Marine du Douhet, La Brée Les Bains, FranceEcloserie Marine de Gravelines-Ichtus, Gravelines, FranceEcloserie Marine de Gravelines-Ichtus, Gravelines, FranceUniversité Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, FranceMARBEC, Univ. Montpellier, Ifremer, CNRS, IRD, Palavas-les-Flots, FranceDisease 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 effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream.https://www.frontiersin.org/articles/10.3389/fgene.2021.665920/fullgenomic selectiondicentrarchus labraxSparus auratadisease resistanceaquaculture |
| spellingShingle | Ronan Griot Ronan Griot Ronan Griot François Allal Florence Phocas Sophie Brard-Fudulea Romain Morvezen Pierrick Haffray Yoannah François Thierry Morin Anastasia Bestin Jean-Sébastien Bruant Sophie Cariou Bruno Peyrou Joseph Brunier Marc Vandeputte Marc Vandeputte 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) Frontiers in Genetics genomic selection dicentrarchus labrax Sparus aurata disease resistance aquaculture |
| title | 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) |
| title_full | 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) |
| title_fullStr | 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) |
| title_full_unstemmed | 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) |
| title_short | 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) |
| title_sort | 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 |
| topic | genomic selection dicentrarchus labrax Sparus aurata disease resistance aquaculture |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.665920/full |
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