Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi
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 Un...
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doaj-739c73a55f044c4aaac7c7e6256d437a2020-11-24T23:54:40ZengBMCBMC Genomics1471-21642018-01-011911910.1186/s12864-018-4493-4Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandiNguyen H. Nguyen0H. K. A. Premachandra1Andrzej Kilian2Wayne Knibb3The University of the Sunshine CoastThe University of the Sunshine CoastDiversity Arrays Technology Pty LtdThe University of the Sunshine CoastAbstract 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.http://link.springer.com/article/10.1186/s12864-018-4493-4KingfishGenetic improvementGenomic predictionGenomic selection and genotype by sequencing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nguyen H. Nguyen H. K. A. Premachandra Andrzej Kilian Wayne Knibb |
spellingShingle |
Nguyen H. Nguyen H. K. A. Premachandra Andrzej Kilian Wayne Knibb Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi BMC Genomics Kingfish Genetic improvement Genomic prediction Genomic selection and genotype by sequencing |
author_facet |
Nguyen H. Nguyen H. K. A. Premachandra Andrzej Kilian Wayne Knibb |
author_sort |
Nguyen H. Nguyen |
title |
Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi |
title_short |
Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi |
title_full |
Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi |
title_fullStr |
Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi |
title_full_unstemmed |
Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi |
title_sort |
genomic prediction using dart-seq technology for yellowtail kingfish seriola lalandi |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2018-01-01 |
description |
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. |
topic |
Kingfish Genetic improvement Genomic prediction Genomic selection and genotype by sequencing |
url |
http://link.springer.com/article/10.1186/s12864-018-4493-4 |
work_keys_str_mv |
AT nguyenhnguyen genomicpredictionusingdartseqtechnologyforyellowtailkingfishseriolalalandi AT hkapremachandra genomicpredictionusingdartseqtechnologyforyellowtailkingfishseriolalalandi AT andrzejkilian genomicpredictionusingdartseqtechnologyforyellowtailkingfishseriolalalandi AT wayneknibb genomicpredictionusingdartseqtechnologyforyellowtailkingfishseriolalalandi |
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