Evidence map›Paper›PMID 29382299›Full record

ArticleBMC genomics2018

Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi.

Nguyen H Nguyen, H K A Premachandra, Andrzej Kilian, Wayne Knibb

Abstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024
    Review
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  5. Review
  6. Novel Alleles fromInternational journal of molecular sciences · 2024
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  7. Article
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  9. Genomic Selection in Aquaculture Species.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Genetic Connectivity of the Sky Emperor,Frontiers in genetics · 2019
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nguyen H NguyenThe University of the Sunshine Coast, Maroochydore DC, QLD, 4558, Australia. NNguyen@usc.edu.au.ORCID 0000-0002-4143-955X
H K A PremachandraThe University of the Sunshine Coast, Maroochydore DC, QLD, 4558, Australia.
Andrzej KilianDiversity Arrays Technology Pty Ltd, Kirinari St, Bruce, ACT, 2617, Australia.
Wayne KnibbThe University of the Sunshine Coast, Maroochydore DC, QLD, 4558, Australia.

Funding

The Australian Seafood CRC Project No. 2013/700
6 · The paper itself

Abstract

backgroundGenomic 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.

resultsThe 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 (R

conclusionsCollectively 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.

Indexed as

GenomicsAnimalsBody WeightFishesGenotyping TechniquesPhenotypeGenetic improvementGenomic predictionGenomic selection and genotype by sequencingKingfish

Identifiers

PMID29382299
PMCPMC5791361

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.