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Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp
Genomic selection (GS) is increasingly applied in breeding programs of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organization for Animal Health and the European Un...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6582704/ https://www.ncbi.nlm.nih.gov/pubmed/31249593 http://dx.doi.org/10.3389/fgene.2019.00543 |
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author | Palaiokostas, Christos Vesely, Tomas Kocour, Martin Prchal, Martin Pokorova, Dagmar Piackova, Veronika Pojezdal, Lubomir Houston, Ross D. |
author_facet | Palaiokostas, Christos Vesely, Tomas Kocour, Martin Prchal, Martin Pokorova, Dagmar Piackova, Veronika Pojezdal, Lubomir Houston, Ross D. |
author_sort | Palaiokostas, Christos |
collection | PubMed |
description | Genomic selection (GS) is increasingly applied in breeding programs of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organization for Animal Health and the European Union, causing major economic losses to carp production. GS has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. GS was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using GS was between 8 and 18% higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives. |
format | Online Article Text |
id | pubmed-6582704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65827042019-06-27 Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp Palaiokostas, Christos Vesely, Tomas Kocour, Martin Prchal, Martin Pokorova, Dagmar Piackova, Veronika Pojezdal, Lubomir Houston, Ross D. Front Genet Genetics Genomic selection (GS) is increasingly applied in breeding programs of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organization for Animal Health and the European Union, causing major economic losses to carp production. GS has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. GS was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using GS was between 8 and 18% higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives. Frontiers Media S.A. 2019-06-12 /pmc/articles/PMC6582704/ /pubmed/31249593 http://dx.doi.org/10.3389/fgene.2019.00543 Text en Copyright © 2019 Palaiokostas, Vesely, Kocour, Prchal, Pokorova, Piackova, Pojezdal and Houston. http://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 Palaiokostas, Christos Vesely, Tomas Kocour, Martin Prchal, Martin Pokorova, Dagmar Piackova, Veronika Pojezdal, Lubomir Houston, Ross D. Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp |
title | Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp |
title_full | Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp |
title_fullStr | Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp |
title_full_unstemmed | Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp |
title_short | Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp |
title_sort | optimizing genomic prediction of host resistance to koi herpesvirus disease in carp |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6582704/ https://www.ncbi.nlm.nih.gov/pubmed/31249593 http://dx.doi.org/10.3389/fgene.2019.00543 |
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