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Published VersionCC BY V4.0, Open Access
Journal article
Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi
BMC Genomics, Vol.19, pp.1-9
2018
Appears in UniSC Supported Open Access Outputs
Abstract
Background: Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index. Results: The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits (weight, length and condition factor), with the coefficient of determination (R2) from linear regression analysis ranging from 0.49 to 0.71. Conclusions: Collectively our results demonstrated, for the first time in yellowtail kingfish, the potential application of genomic selection for growth-related traits in the future breeding program for this species, S. lalandi. © 2018 The Author(s).
Details
- Title
- Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi
- Authors
- Nguyen Hong Nguyen (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringH K A Premachandra (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringA Kilian (Author) - Diversity Arrays Technology Pty LtdWayne R Knibb (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
- Publication details
- BMC Genomics, Vol.19, pp.1-9
- Publisher
- BioMed Central Ltd.
- Date published
- 2018
- DOI
- 10.1186/s12864-018-4493-4
- ISSN
- 1471-2164
- Copyright note
- Copyright © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Organisation Unit
- School of Science and Engineering - Legacy; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99451356602621
- Output Type
- Journal article
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