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Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species

Genomic selection can accelerate genetic progress in aquaculture breeding programmes, particularly for traits measured on siblings of selection candidates. However, it is not widely implemented in most aquaculture species, and remains expensive due to high genotyping costs. Genotype imputation is a...

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Autores principales: Kriaridou, Christina, Tsairidou, Smaragda, Fraslin, Clémence, Gorjanc, Gregor, Looseley, Mark E., Johnston, Ian A., Houston, Ross D., Robledo, Diego
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213886/
https://www.ncbi.nlm.nih.gov/pubmed/37252666
http://dx.doi.org/10.3389/fgene.2023.1194266
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author Kriaridou, Christina
Tsairidou, Smaragda
Fraslin, Clémence
Gorjanc, Gregor
Looseley, Mark E.
Johnston, Ian A.
Houston, Ross D.
Robledo, Diego
author_facet Kriaridou, Christina
Tsairidou, Smaragda
Fraslin, Clémence
Gorjanc, Gregor
Looseley, Mark E.
Johnston, Ian A.
Houston, Ross D.
Robledo, Diego
author_sort Kriaridou, Christina
collection PubMed
description Genomic selection can accelerate genetic progress in aquaculture breeding programmes, particularly for traits measured on siblings of selection candidates. However, it is not widely implemented in most aquaculture species, and remains expensive due to high genotyping costs. Genotype imputation is a promising strategy that can reduce genotyping costs and facilitate the broader uptake of genomic selection in aquaculture breeding programmes. Genotype imputation can predict ungenotyped SNPs in populations genotyped at a low-density (LD), using a reference population genotyped at a high-density (HD). In this study, we used datasets of four aquaculture species (Atlantic salmon, turbot, common carp and Pacific oyster), phenotyped for different traits, to investigate the efficacy of genotype imputation for cost-effective genomic selection. The four datasets had been genotyped at HD, and eight LD panels (300–6,000 SNPs) were generated in silico. SNPs were selected to be: i) evenly distributed according to physical position ii) selected to minimise the linkage disequilibrium between adjacent SNPs or iii) randomly selected. Imputation was performed with three different software packages (AlphaImpute2, FImpute v.3 and findhap v.4). The results revealed that FImpute v.3 was faster and achieved higher imputation accuracies. Imputation accuracy increased with increasing panel density for both SNP selection methods, reaching correlations greater than 0.95 in the three fish species and 0.80 in Pacific oyster. In terms of genomic prediction accuracy, the LD and the imputed panels performed similarly, reaching values very close to the HD panels, except in the pacific oyster dataset, where the LD panel performed better than the imputed panel. In the fish species, when LD panels were used for genomic prediction without imputation, selection of markers based on either physical or genetic distance (instead of randomly) resulted in a high prediction accuracy, whereas imputation achieved near maximal prediction accuracy independently of the LD panel, showing higher reliability. Our results suggests that, in fish species, well-selected LD panels may achieve near maximal genomic selection prediction accuracy, and that the addition of imputation will result in maximal accuracy independently of the LD panel. These strategies represent effective and affordable methods to incorporate genomic selection into most aquaculture settings.
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spelling pubmed-102138862023-05-27 Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species Kriaridou, Christina Tsairidou, Smaragda Fraslin, Clémence Gorjanc, Gregor Looseley, Mark E. Johnston, Ian A. Houston, Ross D. Robledo, Diego Front Genet Genetics Genomic selection can accelerate genetic progress in aquaculture breeding programmes, particularly for traits measured on siblings of selection candidates. However, it is not widely implemented in most aquaculture species, and remains expensive due to high genotyping costs. Genotype imputation is a promising strategy that can reduce genotyping costs and facilitate the broader uptake of genomic selection in aquaculture breeding programmes. Genotype imputation can predict ungenotyped SNPs in populations genotyped at a low-density (LD), using a reference population genotyped at a high-density (HD). In this study, we used datasets of four aquaculture species (Atlantic salmon, turbot, common carp and Pacific oyster), phenotyped for different traits, to investigate the efficacy of genotype imputation for cost-effective genomic selection. The four datasets had been genotyped at HD, and eight LD panels (300–6,000 SNPs) were generated in silico. SNPs were selected to be: i) evenly distributed according to physical position ii) selected to minimise the linkage disequilibrium between adjacent SNPs or iii) randomly selected. Imputation was performed with three different software packages (AlphaImpute2, FImpute v.3 and findhap v.4). The results revealed that FImpute v.3 was faster and achieved higher imputation accuracies. Imputation accuracy increased with increasing panel density for both SNP selection methods, reaching correlations greater than 0.95 in the three fish species and 0.80 in Pacific oyster. In terms of genomic prediction accuracy, the LD and the imputed panels performed similarly, reaching values very close to the HD panels, except in the pacific oyster dataset, where the LD panel performed better than the imputed panel. In the fish species, when LD panels were used for genomic prediction without imputation, selection of markers based on either physical or genetic distance (instead of randomly) resulted in a high prediction accuracy, whereas imputation achieved near maximal prediction accuracy independently of the LD panel, showing higher reliability. Our results suggests that, in fish species, well-selected LD panels may achieve near maximal genomic selection prediction accuracy, and that the addition of imputation will result in maximal accuracy independently of the LD panel. These strategies represent effective and affordable methods to incorporate genomic selection into most aquaculture settings. Frontiers Media S.A. 2023-05-11 /pmc/articles/PMC10213886/ /pubmed/37252666 http://dx.doi.org/10.3389/fgene.2023.1194266 Text en Copyright © 2023 Kriaridou, Tsairidou, Fraslin, Gorjanc, Looseley, Johnston, Houston and Robledo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Kriaridou, Christina
Tsairidou, Smaragda
Fraslin, Clémence
Gorjanc, Gregor
Looseley, Mark E.
Johnston, Ian A.
Houston, Ross D.
Robledo, Diego
Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species
title Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species
title_full Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species
title_fullStr Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species
title_full_unstemmed Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species
title_short Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species
title_sort evaluation of low-density snp panels and imputation for cost-effective genomic selection in four aquaculture species
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213886/
https://www.ncbi.nlm.nih.gov/pubmed/37252666
http://dx.doi.org/10.3389/fgene.2023.1194266
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