Genome-Wide Association and Genomic Prediction of Growth Traits in the European Flat Oyster (Ostrea edulis) - Peñaloza, Carolina; Barria, Agustin; Papadopoulou, Athina; Hooper, Chantelle; Preston, Joanne; Green, Matthew; Helmer, Luke; Kean-Hammerson, Jacob; Nascimento-Schulze, Jennifer C.; Minardi, Diana; Gundappa, Manu Kumar; Macqueen, Daniel J.; et al. (Frontiers in Genetics, 2022)
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: body weight (BW), 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 BW, 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 (100-4.5K 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. However, 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.