Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

Abstract

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

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

“…(parents) results in selection accuracies similar to those obtained with 75K SNP panels for sea lice resistance in Atlantic salmon (Tsairidou et al, 2019). In aquaculture, studies of imputation for genomic selection have been limited to salmonid species to date; however, it shows great potential and is likely to be a staple component of modern aquaculture breeding programs.…”

“…However, in empirical studies of aquaculture species to date this decrease in accuracy seems to be relatively small and only observable once SNP densities drop to a few hundred markers [e.g. (Tsai et al, 2016;Correa et al, 2017;Gutierrez et al, 2018;Robledo et al, 2018;Vallejo et al, 2018;Yoshida et al, 2018a;Palaiokostas et al, 2019;Tsairidou et al, 2019)], which is likely a consequence of the large full sibling family sizes, such that long haplotypes are shared between many individuals in the reference and test population.…”

Harnessing genomics to fast-track genetic improvement in aquaculture

“…Lower-density SNP panels, potentially typed using targeted GBS techniques (for example, GT-Seq 78 ) or fluorescence-based assays, tend to be cheaper than SNP arrays. Low-density genotyping can be integrated with genotype imputation [G] to increase the accuracy of genomic selection to levels approaching those obtained with high-density genotyping [79][80][81] .…”

Development and Evaluation of a High-Throughput Single-Nucleotide Polymorphism Array for Large Yellow Croaker (Larimichthys crocea)

“…Genotype imputation from very low to medium density has been proved to be a cost-effective tool for GS in Atlantic salmon breeding programs, and reducing SNP density had little impact on prediction accuracy until 5,000 SNPs (Tsairidou et al, 2020). In the near future, a second-generation SNP array named NingXin-II with 50K SNPs for large yellow breeding will be developed.…”