Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon
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 h...
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ftlatrobeunivfig:oai:figshare.com:article/17205902 2023-05-15T15:31:13+02:00 Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon Hsin-Yuan Tsai Oswald Matika Stefan McKinnon Edwards Roberto Antolín–Sánchez Alastair Hamilton Derrick R Guy Alan E Tinch Karim Gharbi Michael Stear John B Taggart James E Bron John M Hickey Ross D Houston 2022-01-19T23:37:46Z https://doi.org/10.26181/17205902.v1 https://figshare.com/articles/journal_contribution/Genotype_Imputation_to_Improve_the_Cost-Efficiency_of_Genomic_Selection_in_Farmed_Atlantic_Salmon/17205902 unknown doi:10.26181/17205902.v1 https://figshare.com/articles/journal_contribution/Genotype_Imputation_to_Improve_the_Cost-Efficiency_of_Genomic_Selection_in_Farmed_Atlantic_Salmon/17205902 In Copyright Uncategorized aquaculture disease resistance Genomic Selection imputation GenPred Shared Data Resources farmed Atlantic salmon Text Journal contribution 2022 ftlatrobeunivfig https://doi.org/10.26181/17205902.v1 2022-01-20T00:02:47Z 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 ... Other Non-Article Part of Journal/Newspaper Atlantic salmon La Trobe University (Melbourne): Figshare |
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Open Polar |
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La Trobe University (Melbourne): Figshare |
op_collection_id |
ftlatrobeunivfig |
language |
unknown |
topic |
Uncategorized aquaculture disease resistance Genomic Selection imputation GenPred Shared Data Resources farmed Atlantic salmon |
spellingShingle |
Uncategorized aquaculture disease resistance Genomic Selection imputation GenPred Shared Data Resources farmed Atlantic salmon Hsin-Yuan Tsai Oswald Matika Stefan McKinnon Edwards Roberto Antolín–Sánchez Alastair Hamilton Derrick R Guy Alan E Tinch Karim Gharbi Michael Stear John B Taggart James E Bron John M Hickey Ross D Houston Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon |
topic_facet |
Uncategorized aquaculture disease resistance Genomic Selection imputation GenPred Shared Data Resources farmed Atlantic salmon |
description |
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 ... |
format |
Other Non-Article Part of Journal/Newspaper |
author |
Hsin-Yuan Tsai Oswald Matika Stefan McKinnon Edwards Roberto Antolín–Sánchez Alastair Hamilton Derrick R Guy Alan E Tinch Karim Gharbi Michael Stear John B Taggart James E Bron John M Hickey Ross D Houston |
author_facet |
Hsin-Yuan Tsai Oswald Matika Stefan McKinnon Edwards Roberto Antolín–Sánchez Alastair Hamilton Derrick R Guy Alan E Tinch Karim Gharbi Michael Stear John B Taggart James E Bron John M Hickey Ross D Houston |
author_sort |
Hsin-Yuan Tsai |
title |
Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon |
title_short |
Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon |
title_full |
Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon |
title_fullStr |
Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon |
title_full_unstemmed |
Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon |
title_sort |
genotype imputation to improve the cost-efficiency of genomic selection in farmed atlantic salmon |
publishDate |
2022 |
url |
https://doi.org/10.26181/17205902.v1 https://figshare.com/articles/journal_contribution/Genotype_Imputation_to_Improve_the_Cost-Efficiency_of_Genomic_Selection_in_Farmed_Atlantic_Salmon/17205902 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_relation |
doi:10.26181/17205902.v1 https://figshare.com/articles/journal_contribution/Genotype_Imputation_to_Improve_the_Cost-Efficiency_of_Genomic_Selection_in_Farmed_Atlantic_Salmon/17205902 |
op_rights |
In Copyright |
op_doi |
https://doi.org/10.26181/17205902.v1 |
_version_ |
1766361705816784896 |