Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations
Abstract Background Sea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to...
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ftbiomed:oai:biomedcentral.com:s12711-016-0226-9 2023-05-15T15:30:54+02:00 Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations Tsai, Hsin-Yuan Hamilton, Alastair Tinch, Alan Guy, Derrick Bron, James Taggart, John Gharbi, Karim Stear, Michael Matika, Oswald Pong-Wong, Ricardo Bishop, Steve Houston, Ross 2016-06-29 http://www.gsejournal.org/content/48/1/47 en eng BioMed Central Ltd. http://www.gsejournal.org/content/48/1/47 Copyright 2016 The Author(s) Research Article 2016 ftbiomed 2016-07-03T00:00:23Z Abstract Background Sea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to predict breeding values, and can achieve markedly higher accuracy than pedigree-based methods. Our aim was to assess the genetic architecture of host resistance to sea lice, and test the utility of genomic prediction of breeding values. Individual lice counts were measured in challenge experiments using two large Atlantic salmon post-smolt populations from a commercial breeding programme, which had genotypes for ~33 K single nucleotide polymorphisms (SNPs). The specific objectives were to: (i) estimate the heritability of host resistance; (ii) assess its genetic architecture by performing a genome-wide association study (GWAS); (iii) assess the accuracy of predicted breeding values using varying SNP densities (0.5 to 33 K) and compare it to that of pedigree-based prediction; and (iv) evaluate the accuracy of prediction in closely and distantly related animals. Results Heritability of host resistance was significant (0.22 to 0.33) in both populations using either pedigree or genomic relationship matrices. The GWAS suggested that lice resistance is a polygenic trait, and no genome-wide significant quantitative trait loci were identified. Based on cross-validation analysis, genomic predictions were more accurate than pedigree-based predictions for both populations. Although prediction accuracies were highest when closely-related animals were used in the training and validation sets, the benefit of having genomic-versus pedigree-based predictions within a population increased as the relationships between training and validation sets decreased. Prediction accuracy reached an asymptote with a SNP density of ~5 K within populations, although higher SNP density was advantageous for cross-population prediction. Conclusions Host resistance to sea lice in farmed Atlantic salmon has a significant genetic component. Phenotypes relating to host resistance can be predicted with moderate to high accuracy within populations, with a major advantage of genomic over pedigree-based methods, even at relatively sparse SNP densities. Prediction accuracies across populations were low, but improved with higher marker densities. Genomic selection can contribute to lice control in salmon farming. Article in Journal/Newspaper Atlantic salmon BioMed Central |
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Open Polar |
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BioMed Central |
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ftbiomed |
language |
English |
description |
Abstract Background Sea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to predict breeding values, and can achieve markedly higher accuracy than pedigree-based methods. Our aim was to assess the genetic architecture of host resistance to sea lice, and test the utility of genomic prediction of breeding values. Individual lice counts were measured in challenge experiments using two large Atlantic salmon post-smolt populations from a commercial breeding programme, which had genotypes for ~33 K single nucleotide polymorphisms (SNPs). The specific objectives were to: (i) estimate the heritability of host resistance; (ii) assess its genetic architecture by performing a genome-wide association study (GWAS); (iii) assess the accuracy of predicted breeding values using varying SNP densities (0.5 to 33 K) and compare it to that of pedigree-based prediction; and (iv) evaluate the accuracy of prediction in closely and distantly related animals. Results Heritability of host resistance was significant (0.22 to 0.33) in both populations using either pedigree or genomic relationship matrices. The GWAS suggested that lice resistance is a polygenic trait, and no genome-wide significant quantitative trait loci were identified. Based on cross-validation analysis, genomic predictions were more accurate than pedigree-based predictions for both populations. Although prediction accuracies were highest when closely-related animals were used in the training and validation sets, the benefit of having genomic-versus pedigree-based predictions within a population increased as the relationships between training and validation sets decreased. Prediction accuracy reached an asymptote with a SNP density of ~5 K within populations, although higher SNP density was advantageous for cross-population prediction. Conclusions Host resistance to sea lice in farmed Atlantic salmon has a significant genetic component. Phenotypes relating to host resistance can be predicted with moderate to high accuracy within populations, with a major advantage of genomic over pedigree-based methods, even at relatively sparse SNP densities. Prediction accuracies across populations were low, but improved with higher marker densities. Genomic selection can contribute to lice control in salmon farming. |
format |
Article in Journal/Newspaper |
author |
Tsai, Hsin-Yuan Hamilton, Alastair Tinch, Alan Guy, Derrick Bron, James Taggart, John Gharbi, Karim Stear, Michael Matika, Oswald Pong-Wong, Ricardo Bishop, Steve Houston, Ross |
spellingShingle |
Tsai, Hsin-Yuan Hamilton, Alastair Tinch, Alan Guy, Derrick Bron, James Taggart, John Gharbi, Karim Stear, Michael Matika, Oswald Pong-Wong, Ricardo Bishop, Steve Houston, Ross Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations |
author_facet |
Tsai, Hsin-Yuan Hamilton, Alastair Tinch, Alan Guy, Derrick Bron, James Taggart, John Gharbi, Karim Stear, Michael Matika, Oswald Pong-Wong, Ricardo Bishop, Steve Houston, Ross |
author_sort |
Tsai, Hsin-Yuan |
title |
Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations |
title_short |
Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations |
title_full |
Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations |
title_fullStr |
Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations |
title_full_unstemmed |
Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations |
title_sort |
genomic prediction of host resistance to sea lice in farmed atlantic salmon populations |
publisher |
BioMed Central Ltd. |
publishDate |
2016 |
url |
http://www.gsejournal.org/content/48/1/47 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_relation |
http://www.gsejournal.org/content/48/1/47 |
op_rights |
Copyright 2016 The Author(s) |
_version_ |
1766361367679336448 |