Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30572
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon
Author(s): Tsairidou, Smaragda
Hamilton, Alastair
Robledo, Diego
Bron, James E
Houston, Ross D
Keywords: Salmon Breeding
Genotype Imputation
Aquaculture
Sea Lice Resistance
Genomic Prediction
Issue Date: Dec-2019
Citation: Tsairidou S, Hamilton A, Robledo D, Bron JE & Houston RD (2019) Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon. G3: Genes Genomes Genetics, 10 (2), pp. 581-590. https://doi.org/10.1534/g3.119.400800
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 characterised by large full-sibling families has yet to be fully assessed. The aim of this study was to optimise 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.
DOI Link: 10.1534/g3.119.400800
Rights: Copyright © 2020 Tsairidou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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