Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms

Abstract

Genomic selection increases the rate of genetic gain in breeding programs, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals, which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors, more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9,000 SNPs) to accurately predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). The traits show heritabilities between 0.19-0.49, and genomic prediction accuracies using the full density panel of 0.55-0.87. A consistent pattern of genomic prediction accuracy was observed across species with little or no accuracy reduction until SNP density was reduced below 1,000 SNPs (prediction accuracies of 0.44-0.75). Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93% of maximum accuracy achieved with 1,000 SNPs, 89% with 500 SNPs, and 70% with 100 SNPs). A notable drop in accuracy was observed between 200 SNP panels (0.44-0.75) and 100 SNP panels (0.39-0.66). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.

Harnessing genomics to fast-track genetic improvement in aquaculture

“…Adequate sample size for the genotyped and phenotyped population is key to fully assess the efficacy of genomic selection (for example, >1,000 individuals), but the population structure is equally important, as prediction accuracy is very dependent on the proximity of relationships between animals in the training and validation sets 74 . While several thousand genome-wide markers are also required, it is noteworthy that a reduction in SNP density down to only 1,000 or 2,000 SNPs tends to be sufficient to achieve the asymptote of prediction accuracy where these close relationships exist 66,75 . However, the accuracy drops drastically as the relationship between the reference and test populations becomes more distant, as demonstrated in Atlantic salmon 76 and common carp (Cyprinus carpio) 77 ; therefore, routine trait measurement and genotyping are required each generation to retrain the genomic prediction models.…”

Genetic Dissection of a Precocious Phenotype in Male Tiger Pufferfish (Takifugu rubripes) using Genotyping by Random Amplicon Sequencing, Direct (GRAS-Di)

“…It has been integrated in some aquaculture species, including Atlantic salmon (Ødegård et al 2014 ; Tsai et al 2015 ; Robledo et al 2018b ), Yesso scallop ( Patinopecten yessoensis ) (Dou et al 2016 ), Pacific oyster (Gutierrez et al 2018 ), and coho salmon ( Oncorhynchus kisutch ) (Barría et al 2018 ). Although hundreds of thousands of SNPs are required to conduct GS for livestock animals (Fan et al 2010 ), it appears that a few thousand of SNPs are sufficient to gain high prediction accuracy in aquaculture species where cultured populations often consist of closely related individuals (Zenger et al 2019 ; Kriaridou et al 2020 ). At the population level, we were able to detect 22 K SNP loci using GRAS-Di in our rather small population, and therefore, we expect that GRAS-Di can be applied for GS of aquaculture species.…”

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

“…Fish breeding faces the same problems in this regard since the breeding scheme, the typical farm size and the average individual value are similar to those of pig and chicken. Due to these reasons, only the most affordable approaches (e.g., low density genotyping) can be applied routinely even in high-value fish species (Lillehammer et al, 2013;Kriaridou et al, 2020). Reduction of the density and thus the price of the SNP chip seems to be a key factor for the widespread applicability of GS in aquaculture species.…”

“…The tremendous number of siblings produced from a single spawning can be divided to RP and selection candidates. Huge genomic segments shared between these two closely related populations ensure the reliable genome-wide LD representation by even a low density array (Kriaridou et al, 2020). GS has been applied in Atlantic salmon for growth traits (Tsai et al, 2015), for sea lice resistance (Houston et al, 2014;Tsai et al, 2016); in rainbow trout for bacterial cold water disease resistance (Vallejo et al, 2016(Vallejo et al, , 2017, but examples can be also found among the high-volume species like common carp (Palaiokostas et al, 2018), or channel catfish (Garcia et al, 2018); for reviews see Houston et al (2020) and You et al (2020).…”

“…Later, the use of low-density genotypes and the imputation were optimized (Tsairidou et al, 2020). Finally, the utility of low-to-medium density SNP panels (with the number of markers ranging 100 to 9,000) was tested in four datasets, analyzing five different traits (days to death, log length, weight, amoebic load, and gill score) in four species [salmon, common carp, gilthead seabream and Pacific oyster (Kriaridou et al, 2020)].…”