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Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Genotype imputation to improve the cost-efficiency of genomic selection in farmed Atlantic salmon
Author(s): Tsai, Hsin Y
Matika, Oswald
Edwards, Stefan McKinnon
Antolin-Sanchez, Roberto
Hamilton, Alastair
Guy, Derrick R
Tinch, Alan E
Gharbi, Karim
Stear, Michael
Taggart, John
Bron, James
Hickey, John M
Houston, Ross D
Keywords: aquaculture
disease resistance
Genomic selection
Shared Data Resources
Issue Date: Apr-2017
Date Deposited: 8-May-2017
Citation: Tsai HY, Matika O, Edwards SM, Antolin-Sanchez R, Hamilton A, Guy DR, Tinch AE, Gharbi K, Stear M, Taggart J, Bron J, Hickey JM & Houston RD (2017) Genotype imputation to improve the cost-efficiency of genomic selection in farmed Atlantic salmon. G3: Genes Genomes Genetics, 7 (4), pp. 1377-1383.
Abstract: 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 programs, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programs, 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 program 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 program. 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 (∼250SNPs). 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 programs to improve economically important traits in aquaculture.
DOI Link: 10.1534/g3.117.040717
Rights: Copyright © 2017 Tsai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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