title: Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon.
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 (∼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 programs to improve economically important traits in aquaculture.
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
authors.forename: Hsin-Yuan
authors.initials: HY
authors.lastname: Tsai
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
authors.forename: Oswald
authors.initials: O
authors.lastname: Matika
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
authors.forename: Stefan McKinnon
authors.initials: SM
authors.lastname: Edwards
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
authors.forename: Roberto
authors.initials: R
authors.lastname: Antolín-Sánchez
authors.affiliation: Hendrix Genetics Aquaculture BV/ NetherlandsVilla 'de Körver', Spoorstraat 695831 CK BoxmeerThe Netherlands.
authors.forename: Alastair
authors.initials: A
authors.lastname: Hamilton
authors.affiliation: Hendrix Genetics Aquaculture BV/ NetherlandsVilla 'de Körver', Spoorstraat 695831 CK BoxmeerThe Netherlands.
authors.forename: Derrick R
authors.initials: DR
authors.lastname: Guy
authors.affiliation: Hendrix Genetics Aquaculture BV/ NetherlandsVilla 'de Körver', Spoorstraat 695831 CK BoxmeerThe Netherlands.
authors.forename: Alan E
authors.initials: AE
authors.lastname: Tinch
authors.affiliation: Edinburgh Genomics, Ashworth Laboratories, University of Edinburgh, EH9 3JT, United Kingdom.
authors.forename: Karim
authors.initials: K
authors.lastname: Gharbi
authors.affiliation: Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, G61 1QH, United Kingdom.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Stear
authors.affiliation: Institute of Aquaculture, University of Stirling, FK9 4LA, United Kingdom.
authors.forename: John B
authors.initials: JB
authors.lastname: Taggart
authors.affiliation: Institute of Aquaculture, University of Stirling, FK9 4LA, United Kingdom.
authors.forename: James E
authors.initials: JE
authors.lastname: Bron
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
authors.forename: John M
authors.initials: JM
authors.lastname: Hickey
authors.affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom ross.houston@roslin.ed.ac.uk.
authors.forename: Ross D
authors.initials: RD
authors.lastname: Houston
mesh_terms: D000818:Animals; D017756:Aquaculture / Q000191:economics* / Q000379:methods*; D001947:Breeding; D003362:Cost-Benefit Analysis; D005787:Gene Frequency / Q000235:genetics; D023281:Genomics; D005838:Genotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D020317:Salmo salar / Q000235:genetics* / Q000254:growth & development*
keywords: GenPred; Genomic Selection; Shared Data Resources; aquaculture; disease resistance; imputation