Genome-wide association and genomic prediction of growth traits in the European flat oyster (Ostrea edulis)

Abstract

The European flat oyster (Ostrea edulis) is a bivalve mollusc that was once widely distributed in Europe and represented an important food resource for humans for centuries. Populations of O. edulis experienced a severe decline across their biogeographic range mainly due to anthropogenic activities and disease outbreaks. To restore the economic and ecological benefits of European flat oyster populations, extensive protection and restoration efforts are in place within Europe. In line with the increasing interest in supporting restoration and oyster farming through the breeding of stocks with enhanced performance, the present study aimed to evaluate the potential of genomic selection for improving growth traits in a European flat oyster population obtained from successive mass-spawning events. Four growth-related traits were evaluated: total weight (TW), shell height (SH), shell width (SW) and shell length (SL). The heritability of the growth traits was moderate-low, with estimates of 0.45, 0.37, 0.22, and 0.32 for TW, SH, SW and SL, respectively. A genome-wide association analysis revealed a largely polygenic genetic architecture for the four growth traits, with two distinct QTLs detected on chromosome 4. To investigate whether genomic selection can be implemented in flat oyster breeding at a reduced cost, the utility of low-density SNP panels (down to 100 SNPs) was assessed. Genomic prediction accuracies using the full density panel were high (>0.83 for all traits). The evaluation of the effect of reducing the number of markers used to predict genomic breeding values revealed that similar selection accuracies could be achieved for all traits with 2K SNPs as for a full panel containing 4,577 SNPs. Only slight reductions in accuracies were observed at the lowest SNP density tested (i.e. 100 SNPs), likely due to a high relatedness between individuals being included in the training and validation sets during cross-validation. Overall, our results suggest that the genetic improvement of growth traits in oysters is feasible. Nevertheless, and although low-density SNP panels appear as a promising strategy for applying GS at a reduced cost, additional populations with different degrees of genetic relationship should be assessed to derive estimates of prediction accuracies to be expected in practical breeding programmes.

Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss)

“…With the panels containing 300 or 500 SNPs, the accuracy was within the range of those obtained with PBLUP, with a non-significant decrease in accuracy by 3.3% or 1.6% for the 300-SNP panels for RandLD and EquaLD, respectively (see Additional file 1: Table S3). Those values are within the range of what has been reported in several other aquaculture species for various traits [17,18,20,26,29,32,[52][53][54][55].…”

“…Two in silico studies on five fish species (common carp, turbot, sea bass, rainbow trout and Atlantic salmon) [17,18] compared LD panels to HD panels with a density ranging between 12 to 40K and found that LD panels containing between 3000 and 10,000 SNPs are sufficient to obtain near maximum accuracy. Recently similar prediction accuracies with 2000 SNPs and 4500 SNPs were obtained in flat oyster [20]. For European sea bass and sea bream populations, which were initially genotyped with about 60K SNPs, the use of a panel with only 6000 SNPs achieved accuracies that reached 90% of the accuracy of the HD panel [19].…”

“…Depending on the species and the trait (reviewed in Song et al [11]), such LD panels are sufficient to achieve an accuracy of genomic prediction similar to that obtained with a medium-or high-density (HD) panel. In those studies, further reduction of the density to hundreds of SNPs resulted in a significant drop in accuracy [17,20]. This issue could potentially be resolved via the use of imputation.…”

Chromosome level reference genome for European flat oyster (Ostrea edulis L.)

“…In this regard, the genome reported here is proving useful already, with a recent study revealing that SNP markers previously associated with Bonamia resistance (Vera et al, 2019) are located in high linkage-disequilibrium across a large region of super-scaffold 8, which contains many candidate immune genes (Martinez et al 2022). Another recent study from has mapped variants genotyped with an existing medium density SNP array (Gutierrez et al, 2017) against our new O. edulis genome, identifying QTLs underpinning variation in growth traits on super-scaffold 4 (Peñaloza et al, 2022).…”

The genetics and breeding of the Portuguese oyster, Crassostrea angulata: lessons, experiences, and challenges in Vietnam

“…The ML algorithms learn the details using the training dataset, and its prediction efficiency is evaluated using the validation dataset. A derivation of this method called fivefold cross-validation analysis is now increasingly used in genomic prediction analyses of Portuguese oysters (Vu et al, 2021c), European flat oysters (Peñaloza et al, 2022), and Pacific oysters (Kriaridou et al, 2023). Typically, the genotype data is divided into 80% training and 20% validation datasets.…”