Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata
Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these tr...
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| Format: | Article |
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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.2021.661276/full |
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| author | Sang V. Vu Sang V. Vu Sang V. Vu Wayne Knibb Cedric Gondro Sankar Subramanian Sankar Subramanian Ngoc T. H. Nguyen Mobashwer Alam Michael Dove Arthur R. Gilmour In Van Vu Salma Bhyan Salma Bhyan Rick Tearle Le Duy Khuong Tuan Son Le Wayne O’Connor Wayne O’Connor Wayne O’Connor |
| author_facet | Sang V. Vu Sang V. Vu Sang V. Vu Wayne Knibb Cedric Gondro Sankar Subramanian Sankar Subramanian Ngoc T. H. Nguyen Mobashwer Alam Michael Dove Arthur R. Gilmour In Van Vu Salma Bhyan Salma Bhyan Rick Tearle Le Duy Khuong Tuan Son Le Wayne O’Connor Wayne O’Connor Wayne O’Connor |
| author_sort | Sang V. Vu |
| collection | DOAJ |
| description | Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12–15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38–0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population. |
| first_indexed | 2024-12-17T12:55:43Z |
| format | Article |
| id | doaj.art-97c438e780244524a1842f3ddf8ebad2 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-17T12:55:43Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj.art-97c438e780244524a1842f3ddf8ebad22022-12-21T21:47:29ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-07-011210.3389/fgene.2021.661276661276Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulataSang V. Vu0Sang V. Vu1Sang V. Vu2Wayne Knibb3Cedric Gondro4Sankar Subramanian5Sankar Subramanian6Ngoc T. H. Nguyen7Mobashwer Alam8Michael Dove9Arthur R. Gilmour10In Van Vu11Salma Bhyan12Salma Bhyan13Rick Tearle14Le Duy Khuong15Tuan Son Le16Wayne O’Connor17Wayne O’Connor18Wayne O’Connor19GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaSchool of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaNorthern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, VietnamGeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaDepartment of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI, United StatesGeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaSchool of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaNorthern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, VietnamQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, Saint Lucia, QLD, AustraliaNSW Department of Primary Industries, Port Stephens Fisheries Institute, Taylors Beach, NSW, AustraliaConsultant, Orange, NSW, AustraliaNorthern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, VietnamGeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaSchool of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaSchool of Animal and Veterinary Science, The University of Adelaide, Adelaide, SA, AustraliaFaculty of Environment, Ha Long University, Uong Bi, Vietnam0Research Institute for Marine Fisheries, Ngo Quyen, Hai Phong, VietnamGeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaSchool of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, AustraliaNSW Department of Primary Industries, Port Stephens Fisheries Institute, Taylors Beach, NSW, AustraliaGenetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12–15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38–0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.https://www.frontiersin.org/articles/10.3389/fgene.2021.661276/fullgenetic improvementgenomic selectionhigh density SNP markersprediction accuracygenetic parameters |
| spellingShingle | Sang V. Vu Sang V. Vu Sang V. Vu Wayne Knibb Cedric Gondro Sankar Subramanian Sankar Subramanian Ngoc T. H. Nguyen Mobashwer Alam Michael Dove Arthur R. Gilmour In Van Vu Salma Bhyan Salma Bhyan Rick Tearle Le Duy Khuong Tuan Son Le Wayne O’Connor Wayne O’Connor Wayne O’Connor Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata Frontiers in Genetics genetic improvement genomic selection high density SNP markers prediction accuracy genetic parameters |
| title | Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata |
| title_full | Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata |
| title_fullStr | Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata |
| title_full_unstemmed | Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata |
| title_short | Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata |
| title_sort | genomic prediction for whole weight body shape meat yield and color traits in the portuguese oyster crassostrea angulata |
| topic | genetic improvement genomic selection high density SNP markers prediction accuracy genetic parameters |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.661276/full |
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