Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata

Sang V Vu; Wayne Knibb; Cedric Gondro; Sankar Subramanian; Ngoc T H Nguyen; Mobashwer Alam; Michael Dove; Arthur R Gilmour; In Van Vu; Salma Bhyan; Rick Tearle; Le Duy Khuong; Tuan Son Le; Wayne O'Connor

doi:10.3389/fgene.2021.661276

Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata

Front Genet. 2021 Jul 8:12:661276. doi: 10.3389/fgene.2021.661276. eCollection 2021.

Authors

Sang V Vu^{1

2

3}, Wayne Knibb¹, Cedric Gondro⁴, Sankar Subramanian^{1

2}, Ngoc T H Nguyen³, Mobashwer Alam⁵, Michael Dove⁶, Arthur R Gilmour⁷, In Van Vu³, Salma Bhyan^{1

2}, Rick Tearle⁸, Le Duy Khuong⁹, Tuan Son Le¹⁰, Wayne O'Connor^{1

2

6}

Affiliations

¹ GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
² School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia.
³ Northern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, Vietnam.
⁴ Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI, United States.
⁵ Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Saint Lucia, QLD, Australia.
⁶ NSW Department of Primary Industries, Port Stephens Fisheries Institute, Taylors Beach, NSW, Australia.
⁷ Consultant, Orange, NSW, Australia.
⁸ School of Animal and Veterinary Science, The University of Adelaide, Adelaide, SA, Australia.
⁹ Faculty of Environment, Ha Long University, Uong Bi, Vietnam.
¹⁰ Research Institute for Marine Fisheries, Ngo Quyen, Hai Phong, Vietnam.

Abstract

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.

Keywords: genetic improvement; genetic parameters; genomic selection; high density SNP markers; prediction accuracy.