Marine Biotechnology

, Volume 21, Issue 5, pp 643–654 | Cite as

Mapping and Validating QTL for Fatty Acid Compositions and Growth Traits in Asian Seabass

  • Le Wang
  • Elaine Chua
  • Fei Sun
  • Zi Yi Wan
  • Baoqing Ye
  • Hongyan Pang
  • Yanfei Wen
  • Gen Hua YueEmail author
Original Article


Asian seabass is an important food fish species. While improving growth, increasing the nutritional value is important, omega-3 fatty acids are indispensable to human health. Identifying and validating DNA markers associated with traits is the first step towards marker-assisted selection (MAS). We quantified 13 different fatty acids and three growth traits in 213 F2 Asian seabass from a family at the age 270 days post hatch, and screened QTL for these traits. The content of total fatty acids in 100 g flesh was 2.57 ± 0.80 g, while the proportions of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were 16.96 ± 2.20% and 5.42 ± 0.90%, respectively. A linkage map with 2424 SNPs was constructed and used for QTL mapping. For fatty acid compositions, 14 significant QTL were identified on three linkage groups (LG5, LG11 and LG14), with phenotypic variance explained (PVE) from 12.8 to 24.6%. Thirty-nine suggestive QTL were detected on 16 LGs. Two significant QTL for EPA were identified on LG5 and LG14, with PVE of 15.2% and 15.1%, respectively. No significant QTL was identified for DHA. For growth traits, six significant and 13 suggestive QTL were identified on two and seven LGs, respectively. Only a few significant QTL for fatty acids overlapped with previously mapped QTL for these traits, suggesting that most QTL detected in a family are family-specific and could only be used in MAS in the family per se. To facilitate population-wide molecular breeding, more powerful methods (e.g. GWAS) should be used to identify SNPs for genomic selection.


Asian seabass Selective breeding Fatty acid Growth Genetic map QTL 



This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its Competitive Research Program (CRP Award No. NRF-CRP7-2010-01) and TLL’s Innovation Support Fund (2016-54-0026).

Authors’ Contributions

LW and GHY designed the experiments. EC and FS analysed fatty acid compositions. LW performed GBS and QTL analysis. LW, ZYW, FS, BY, HP and YW collected phenotypic data. LW and GHY drafted the manuscript. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

10126_2019_9909_MOESM1_ESM.docx (22 kb)
Supplementary Table 1 (DOCX 22 kb)
10126_2019_9909_MOESM2_ESM.xlsx (294 kb)
Supplementary Table 2 (XLSX 294 kb)


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Molecular Population Genetics and Breeding Group, Temasek Life Sciences Laboratory, 1 Research LinkNational University of SingaporeSingaporeSingapore
  2. 2.School of Biological SciencesNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Biological SciencesNational University of SingaporeSingaporeSingapore

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