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

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

doi:10.3390/genes12020210

Abstract

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.

Highlights

The Portuguese oyster (Crassostrea angulata) is an important aquaculture species around the world, especially in Asian and European countries [1,2,3]
Our estimates of genomic heritability for morphometric traits (h2 = 0.50–0.55) were considerably higher than those reported for the Pacific oyster, Crassostrea gigas (h2 = 0.23–0.26) [22] but were similar to those reported for the Zhikong scallop, Chlamys farreri (h2 = 0.39–0.54) [40]
These results suggest that genetic variance in the morphometric traits was abundant, and there is considerable potential to improve these traits via genomic selection

Summary

Introduction

The Portuguese oyster (Crassostrea angulata) is an important aquaculture species around the world, especially in Asian and European countries [1,2,3]. The parasite Marteilioides chungmuensis causes spots on the soft tissue of the Portuguese oyster; while polychaete “mud-worms” of the genus Polydora cause black blisters on the inner surfaces of the shell [6]. A Portuguese oyster breeding program in Vietnam, using phenotypic selection has commenced to reduce the incidence of these diseases [9,10]. Morphometric traits (shell length, shell width, shell depth, shell weight), sensory traits (tenderness and taste, and moisture content) are of economic importance in the oyster breeding program [9,10]. Advances in genomic technologies have enabled the incorporation of genomic information into breeding programs, which has increased selection accuracy, especially for traits that are difficult or expensive to measure or have low heritability [11,12]

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