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

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.

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

“…However, a major impediment to widely using genomic selection in the aquaculture industry is the cost of genotyping and phenotyping the selection candidates. The former can be dealt with by using low-density SNP panels that can balance between prediction accuracy and sequencing costs to achieve the accuracy needed for genomic selection (Lillehammer et al, 2013;Gutierrez et al, 2018;Tsairidou et al, 2020;Boudry et al, 2021;Vu et al, 2021a).…”

“…The SNP development and analysis were as described by Kilian et al (2012) . Briefly, DNA samples were digested with Pst I– Sph I ( Kilian et al, 2012 ; Vu et al, 2021a ) and then ligated using two different adaptors with two different restriction enzyme overhangs. The Pst I-compatible adapter consists of Illumina’s flow-cell attachment, a sequencing primer, and a “staggered” varying-length barcode region ( Elshire et al, 2011 ).…”

Genomic Selection and Genome-wide Association Study for Feed-Efficiency Traits in a Farmed Nile Tilapia (Oreochromis niloticus) Population

“…The impact of SNP density on predicted accuracies has been studied thoroughly for a wide variety of traits and species in aquaculture ( Yoshida et al, 2019 ; Kriaridou et al, 2020 ; Tsairidou et al, 2020 ; Al-Tobasei et al, 2021 ; Vu et al, 2021 ). The results of these studies highlight the potential to significantly reduce SNP density without affecting the accuracy of prediction.…”

Optimizing hatchery practices for genetic improvement of marine bivalves

“…In other cases, selected genotypes outperform in certain conditions, but become poor performers when exposed to a different set of conditionsthat is re-ranking of genotypes (Langdon et al 2003;D egremont et al 2005;Evans & Langdon 2006;Wang et al 2013). In marine bivalves, between-family variance described for traits such as growth, survival and environmental resilience (D egremont et al 2005;D egremont et al 2015a;Scanes et al 2020) indicates the genetic basis of traits associated with performance (Vu et al 2021). The re-ranking of genotypes, in turn, highlights the intrinsic effect of GxE on overall performance of a family or cohort.…”