4.6 Article

Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp

期刊

FRONTIERS IN GENETICS
卷 10, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2019.00543

关键词

KHVD; carp; RAD-seq; genomic selection; aquaculture breeding

资金

  1. European Union's Seventh Framework Programme (FP7 2007-2013) [613611]
  2. Institute Strategic Funding Grants [BBS/E/D/20002172, BBS/E/D/30002275, BBS/E/D/10002070]
  3. project, Biodiverzita under the Ministry of Education, Youth and Sports of the Czech Republic [CZ.02.1.01/0.0/0.0/16_025/0007370]
  4. project PROFISH under the Ministry of Education, Youth and Sports of the Czech Republic [CZ.02.1.01/0.0/0.0/16_019/0000869]
  5. Ministry of Agriculture of the Czech Republic [MZE-RO 0518]

向作者/读者索取更多资源

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.

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