Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Sang V Vu; Cedric Gondro; Ngoc T H Nguyen; Arthur R Gilmour; Rick Tearle; Wayne Knibb; Michael Dove; Le Duy Khuong; Wayne O'Connor

doi:10.3390/genes12020210

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Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

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Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Sang V Vu, Cedric Gondro, Ngoc T H Nguyen, Arthur R Gilmour, Rick Tearle, Wayne Knibb, Michael Dove, Le Duy Khuong and Wayne O'Connor

Genes, Vol.12(2), pp.1-14

2021

DOI: https://doi.org/10.3390/genes12020210

PMID: 33535381

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Abstract

SNP marker density

genomic selection

analysis methods

prediction accuracy

genomic parameters

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

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Title: Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology
Authors: Sang V Vu (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - Legacy
Cedric Gondro (Author) - Michigan State University
Ngoc T H Nguyen (Author) - Research Institute for Aquaculture No1
Arthur R Gilmour (Author)
Rick Tearle (Author) - The University of Adelaide
Wayne Knibb (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - Legacy
Michael Dove (Author) - New South Wales Department of Primary Industries
Le Duy Khuong (Author) - Ha Long University (Vietnam)
Wayne O'Connor (Author) - University of the Sunshine Coast, Queensland, GeneCology Research Centre - Legacy
Publication details: Genes, Vol.12(2), pp.1-14
Publisher: MDPI AG
Date published: 2021
DOI: 10.3390/genes12020210
ISSN: 2073-4425
PMID: 33535381
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; GeneCology Research Centre - Legacy; School of Science, Technology and Engineering
Language: English
Record Identifier: 99509508602621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Genetics & Heredity

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