Genotype Imputation to Improve the Efficiency of Genomic Selection in Atlantic Salmon

High density SNP arrays have enabled genomic selection in salmon selective breeding programmes. However, in sibling testing schemes, trait records are available on many thousands of fish closely related to the selection candidates. Therefore, routine high density SNP genotyping can be prohibitively expensive. To reduce genotyping costs, selected key animals (e.g. breeding programme parents) can be genotyped at high density, and the majority of individuals (e.g.performance tested fish and selection candidates) genotyped at much lower density, followed by imputation to high density.

The main objectives of the current study were to assess the feasibility and accuracy of genotype imputation in salmon breeding. The accuracy of genotype imputation was assessed using medium (25 K) and high (78 K) density mapped SNP panels, by masking varying proportions of the genotypes and assessing the correlation between the imputed genotypes and the true genotypes. The efficacy of imputed genotype data for genomic prediction of key performance traits (sea lice resistance and body weight) was evaluated.

Imputation accuracies of up to 0.9 were achieved using the simple two-generation pedigree dataset, and this was possible even with low density SNP data for the offspring. The performance of genomic prediction using imputed genotype data was comparable to using true genotype data, and both were superior to pedigree-based prediction. These results demonstrate that the genotype imputation approach used in this study can provide a cost-effective method for generating robust high density genome-wide SNP data for Atlantic salmon. Genotype imputation approaches are likely to form a critical component of efficient genomic selection programmes to improve economically important traits in aquaculture.