Genotype imputation to improve the cost-efficiency of genomic selection in farmed Atlantic salmon

• Published in: G3 Genes|Genomes|Genetics
• Main Authors: Tsai, Hsin-Yuan, Matika, Oswald, Edwards, Stefan McKinnon, Antolín-Sánchez, Roberto, Hamilton, Alastair, Guy, Derrick R., Tinch, Alan E., Gharbi, Karim, Stear, Michael J., Taggart, John B., Bron, James E., Hickey, John M., Houston, Ross D.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Genetics Society of America 2017
• Subjects: Atlantic salmon
• Online Access: http://eprints.gla.ac.uk/138189/; http://eprints.gla.ac.uk/138189/7/138189.pdf

Genomic selection uses genome-wide marker information to predict breeding values for traits of economic interest, and is more accurate than pedigree-based methods. The development of high density SNP arrays for Atlantic salmon has enabled genomic selection in selective breeding programmes, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programmes, trait records are available on many thousands of fish with close relationships to the selection candidates. Therefore, routine high density SNP genotyping may be prohibitively expensive. One means to reducing genotyping cost is the use of genotype imputation, where selected key animals (e.g. breeding programme parents) are genotyped at high density, and the majority of individuals (e.g. performance tested fish and selection candidates) are 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 the context of a salmon breeding programme. The specific aims were: (i) to measure the accuracy of genotype imputation 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; and (ii) to assess the efficacy of imputed genotype data in genomic prediction of key performance traits (sea lice resistance and body weight). Imputation accuracies of up to 0.90 were observed using the simple two-generation pedigree dataset, and moderately high accuracy (0.83) was possible even with very low density SNP data (~250 SNPs). 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 genome-wide SNP data for genomic prediction in Atlantic salmon. Genotype imputation approaches are likely to form a critical component of cost-efficient genomic selection programmes to improve economically important traits in aquaculture.