Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

Abstract

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.

Keywords: animal breeding; aquaculture; genetic improvement; genomic selection; oyster and shrimp.

Figure 1

Schematic representation of genomic selection approach in aquaculture. Implementation consists of optimizing prediction equations in a reference population (i.e., farm breeding population), with large numbers of individuals, which have genotype and phenotype information. The prediction equations are then validated on independent test animals (i.e., related generations to reference population). Once the prediction equations are fully optimized, the prediction method is applied to selection candidates to select superior replacement broodstock.

Figure 2

Comparison and correlations of SNP-based kinship estimates (rG) (A) 96 versus 7,500 SNPs, (B) 384 versus 7,500 SNPs, and (C) 3,000 versus 7,500 SNPs calculated on 1,000 L. vannamei samples.

Figure 3

Different stages of predicting pearl oyster size through MVS and machine learning algorithm. Pearl oysters in net are placed on a table while being cleaned, and visual image is taken from above. (A) Oyster net image depicts one of the most challenging visual capture situations in pearl oyster commercial environment. (B) Oysters and net have low contrast from the background, and lighting is variable. (C) Sliding window CNNs correctly identified and measured oyster size with >96% accuracy. (Photo image by Preston Toole).