Marine Biotechnology

, Volume 21, Issue 5, pp 707–717 | Cite as

First Genome-wide Association Analysis for Growth Traits in the Largest Coral Reef-Dwelling Bony Fishes, the Giant Grouper (Epinephelus lanceolatus)

  • Lina Wu
  • Yang Yang
  • Bijun Li
  • Wenhua Huang
  • Xi Wang
  • Xiaochun Liu
  • Zining MengEmail author
  • Junhong Xia
Original Article


The giant grouper, Epinephelus lanceolatus, is the largest coral reef-dwelling bony fish species. However, despite extremely fast growth performance and the considerable economic importance in this species, its genetic regulation of growth remains unknown. Here, we performed the first genome-wide association study (GWAS) for five growth traits in 289 giant groupers using 42,323 single nucleotide polymorphisms (SNPs) obtained by genotyping-by-sequencing (GBS). We identified a total of 36 growth-related SNPs, of which 11 SNPs reached a genome-wide significance level. The phenotypic variance explained by these SNPs varied from 7.09% for body height to 18.42% for body length. Moreover, 22 quantitative trait loci (QTLs) for growth traits, including nine significant QTLs and 13 suggestive QTLs, were found on multiple chromosomes. Interestingly, the QTL (LG17: 6934451) was shared between body weight and body height, while two significant QTLs (LG7: 22596399 and LG15: 11877836) for body length were consistent with the associated regions of total length at the genome-wide suggestive level. Eight potential candidate genes close to the associated SNPs were selected for expression analysis, of which four genes (phosphatidylinositol transfer protein cytoplasmic 1, protein tyrosine phosphatase receptor type E, alpha/beta hydrolase domain-containing protein 17C, and vascular endothelial growth factor A-A) were differentially expressed and involved in metabolism, development, response stress, etc. This study improves our understanding of the complex genetic architecture of growth in the giant grouper. The results contribute to the selective breeding of grouper species and the conservation of coral reef fishes.


GWAS QTL Growth GBS Giant grouper 



We would like to thank the referees and editor for their valuable comments and suggestions, as well as careful corrections of our manuscript. We thank Dr. Leyun Zheng (Fisheries Research Institute of Fujian) for providing fish samples.

Sequence Data Accession

The sequencing data have been deposited into the DDBJ sequence read archive (DRA) with BioProject no. PRJDB8251.

Author Contributions

L.W. and Z.M. designed the study. X.W. and W.H. collected the samples. L.W. and Z.M. performed the laboratory work. L.W., Y.Y., and B.L. performed the analyses. J.X. contributed technical assistance. L.W. and Z.M. drafted the paper. X.L. and Z.M. contributed reagents/materials/analysis tools.

Funding Information

This research was funded by the Science and Technology Planning Project of Guangzhou (201804020013), the Special Fund for Agro-scientific Research in the Public Interest (201403008), the National Natural Science Foundation of China (31872572), and the Modern Agriculture Talents Support Program (2016–2020).

Compliance with Ethical Standards

All experiments in the present study were approved by the Animal Care and Use Committee in the Life Sciences School of Sun Yet-Sen University.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Supplementary material

10126_2019_9916_Fig4_ESM.png (130 kb)
Fig. S1

Frequency of SNPs in different structural attributes of genic and intergenic regions (PNG 130 kb)

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High Resolution Image (TIF 212 kb)
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Fig. S2

Frequency distribution of different traits in giant grouper (PNG 90 kb)

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High Resolution Image (TIF 377 kb)
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Fig. S3

Correlation heatmap between different characteristics (PNG 170 kb)

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High Resolution Image (TIF 733 kb)
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Fig. S4

Population genetic relatedness among the 289 fishes. The genetic relatedness showed that most of the values are distributed at the levels of 0~0.5 (blue region) (PNG 558 kb)

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High Resolution Image (TIF 2001 kb)
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Fig. S5

QQ plots of the p values in GWAS analysis using GLM and MLM models for (a) total length (TL), (b) body length (BL), (c) body height (BH), (d) body thickness (BT), (e) body weight (BW) (PNG 204 kb)

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High Resolution Image (TIF 1343 kb)
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Fig. S6

QQ plots of the p values in GWAS analysis using the MLM model for (a) total length (TL), (b) body length (BL), (c) body height (BH), (d) body thickness (BT), (e) body weight (BW) (TIF 1127 kb) (PNG 159 kb)

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  1. Ahmad F, Azevedo JL, Cortright R, Dohm GL, Goldstein BJ (1997) Alterations in skeletal muscle protein-tyrosine phosphatase activity and expression in insulin-resistant human obesity and diabetes. J Clin Invest 100:449–458CrossRefPubMedPubMedCentralGoogle Scholar
  2. Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bak A, Jacob AI, Aga-Mizrachi S, Brutman-Barazani T, Sampson SR, Elson A (2008) Cytosolic protein tyrosine phosphatase-ε is a negative regulator of insulin signaling in skeletal muscle. Endocrinology 149:605–614CrossRefPubMedGoogle Scholar
  4. Baker KD, Ramel MC, Lekven AC (2010) A direct role for Wnt8 in ventrolateral mesoderm patterning. Dev Dyn 239:2828–2836Google Scholar
  5. Barría A, Christensen KA, Yoshida GM, Correa K, Jedlicki A, Lhorente JP, Davidson WS, Yáñez JM (2018) Genomic predictions and genome-wide association study of resistance against Piscirickettsia salmonis in coho salmon (Oncorhynchus kisutch) using ddRAD sequencing. G3 (Bethesda) 8:1183–1194CrossRefGoogle Scholar
  6. Bharadwaj MS, Zhou Y, Molina AJ, Criswell T, Lu B (2014) Examination of bioenergetic function in the inner mitochondrial membrane peptidase 2-like (Immp2l) mutant mice. Redox Biol 2:1008–1015CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bonferroni C (1936) Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze 8:3–62Google Scholar
  8. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635CrossRefPubMedGoogle Scholar
  9. Campbell NR, Lapatra SE, Overturf K, Towner R, Narum SR (2014) Association mapping of disease resistance traits in rainbow trout using restriction site associated DNA sequencing. G3 (Bethesda) 4:2473–2481CrossRefGoogle Scholar
  10. Chan PY, Han X, Zheng B, Deran M, Yu J, Jarugumilli GK, Deng H, Pan D, Luo X, Wu X (2016) Autopalmitoylation of TEAD proteins regulates transcriptional output of the Hippo pathway. Nat Chem Biol 12:282–289CrossRefPubMedPubMedCentralGoogle Scholar
  11. Christodoulides C, Lagathu C, Sethi JK, Vidal-Puig A (2009) Adipogenesis and WNT signalling. Trends Endocrin Met 20:16–24CrossRefGoogle Scholar
  12. Cockcroft S (2007) Trafficking of phosphatidylinositol by phosphatidylinositol transfer proteins. Biochem Soc Symp 74:259–271CrossRefGoogle Scholar
  13. Craig MT, Sadovy de Mitcheson YJ, Heemstra PC (2011) Groupers of the world: a field and market guide. Grahamstown, South AfricaGoogle Scholar
  14. Dong L, Xiao S, Chen J, Wan L, Wang Z (2016) Genomic selection using extreme phenotypes and pre-selection of SNPs in large yellow croaker (Larimichthys crocea). Mar Biotechnol (NY) 18:575–583CrossRefGoogle Scholar
  15. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379CrossRefPubMedPubMedCentralGoogle Scholar
  16. Farcy E, Serpentini A, Fievet B, Lebel J (2007) Identification of cDNAs encoding HSP70 and HSP90 in the abalone Haliotis tuberculata: transcriptional induction in response to thermal stress in hemocyte primary culture. Comp Biochem Physiol B Biochem Mol Biol 146:540–550CrossRefPubMedGoogle Scholar
  17. Geng X, Sha J, Liu S, Bao L, Zhang J, Wang R, Yao J, Li C, Feng J, Sun F, Sun L, Jiang C, Zhang Y, Chen A, Dunham R, Zhi D, Liu Z (2015) A genome-wide association study in catfish reveals the presence of functional hubs of related genes within QTLs for columnaris disease resistance. BMC Genomics 16:196CrossRefPubMedPubMedCentralGoogle Scholar
  18. Geng X, Liu S, Yao J, Bao L, Zhang J, Li C, Wang R, Sha J, Zeng P, Zhi D (2016) A genome-wide association study identifies multiple regions associated with head size in catfish. G3 (Bethesda) 6:3389–3398CrossRefGoogle Scholar
  19. Geng X, Liu S, Yuan Z, Jiang Y, Zhi D, Liu Z (2017) A genome-wide association study reveals that genes with functions for bone development are associated with body conformation in catfish. Mar Biotechnol 19:570–578CrossRefPubMedGoogle Scholar
  20. Gjedrem T (2012) Genetic improvement for the development of efficient global aquaculture: a personal opinion review. Aquaculture 344-349:12–22CrossRefGoogle Scholar
  21. Gonzalez-Pena D, Gao G, Baranski M, Moen T, Cleveland BM, Kenney PB, Vallejo RL, Palti Y, Leeds TD (2016) Genome-wide association study for identifying loci that affect fillet yield, carcass, and body weight traits in rainbow trout (Oncorhynchus mykiss). Front Genet 7:203CrossRefPubMedPubMedCentralGoogle Scholar
  22. Gu XH, Jiang DL, Huang Y, Li BJ, Chen CH, Lin HR, Xia JH (2018) Identifying a major QTL associated with salinity tolerance in Nile tilapia using QTL-Seq. Mar Biotechnol 20:98–107CrossRefPubMedGoogle Scholar
  23. Hardy OJ, Vekemans X (2002) spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620CrossRefGoogle Scholar
  24. Helyar SJ, Hemmer-Hansen J, Bekkevold D, Taylor M, Ogden R, Limborg M, Cariani A, Maes G, Diopere E, Carvalho G (2011) Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol Ecol Resour 11:123–136CrossRefPubMedGoogle Scholar
  25. Huang YZ, Zou Y, Lin Q, He H, Zheng L, Zhang ZJ, Dang YL, Lei CZ, Lan XY, Qi XS, Chen H (2017) Effects of genetic variants of the bovine WNT8A gene on nine important growth traits in beef cattle. J Genet 96:535–544CrossRefPubMedGoogle Scholar
  26. Jiang DL, Gu XH, Li BJ, Zhu ZX, Qin H, Meng ZN, Lin HR, Xia JH (2019) Identifying a long QTL cluster across chrLG18 associated with salt tolerance in tilapia using GWAS and QTL-seq. Mar Biotechnol.
  27. Jin Y, Zhou T, Geng X, Liu S, Chen A, Yao J, Jiang C, Tan S, Su B, Liu Z (2017) A genome-wide association study of heat stress-associated SNPs in catfish. Anim Genet 48:233–236CrossRefPubMedGoogle Scholar
  28. Kessuwan K, Kubota S, Liu Q, Sano M, Okamoto N, Sakamoto T, Yamashita H, Nakamura Y, Ozaki A (2016) Detection of growth-related quantitative trait loci and high-resolution genetic linkage maps using simple sequence repeat markers in the kelp grouper (Epinephelus bruneus). Mar Biotechnol 18:57–84CrossRefPubMedGoogle Scholar
  29. Kubota S, Longloy A, Singhabun A, Khammee W, Kessuwan K, Bunlipatanon P, Ozaki A, Silapajarn K, Tanasomwang V, Okamoto N, Sakamoto T (2017) Quantitative trait locus mapping of growth-related traits in inter-specific F1 hybrid grouper (Epinephelus fuscoguttatus × E. lanceolatus) in a tropical climate. Aquac Res 48:5913–5927CrossRefGoogle Scholar
  30. Lalitha S (2000) Primer Premier 5. Biotech Software, Internet Report 1:270–272CrossRefGoogle Scholar
  31. Lavides MN, Polunin NVC, Stead SM, Tabaranza DG, Comeros MT, Dongallo JR (2009) Finfish disappearances around Bohol, Philippines inferred from traditional ecological knowledge. Environ Conserv 36:235–244CrossRefGoogle Scholar
  32. Lavides MN, Molina EP, Jr DLRG, Mill AC, Rushton SP, Stead SM, Polunin NV (2016) Patterns of coral-reef finfish species disappearances inferred from fishers’ knowledge in global epicentre of marine shorefish diversity. PLoS One 11:e0155752CrossRefPubMedPubMedCentralGoogle Scholar
  33. Legnini I, Di Timoteo G, Rossi F, Morlando M, Briganti F, Sthandier O, Fatica A, Santini T, Andronache A, Wade M, Laneve P, Rajewsky N, Bozzoni I (2017) Circ-ZNF609 is a circular RNA that can be translated and functions in myogenesis. Mol Cell 66:22–37CrossRefPubMedPubMedCentralGoogle Scholar
  34. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760CrossRefPubMedPubMedCentralGoogle Scholar
  35. Li N, Zhou T, Geng X, Jin Y, Wang X, Liu S, Xu X, Gao D, Li Q, Liu Z (2018) Identification of novel genes significantly affecting growth in catfish through GWAS analysis. Mol Genet Genomics 293:1–13CrossRefGoogle Scholar
  36. Li BJ, Zhu ZX, Gu XH, Lin HR, Xia JH (2019) QTL mapping for red blotches in Malaysia red tilapia (Oreochromis spp.). Mar Biotechnol.
  37. Liang D, Chang JR, Chin AJ, Smith A, Kelly C, Weinberg ES, Ge R (2001) The role of vascular endothelial growth factor (VEGF) in vasculogenesis, angiogenesis, and hematopoiesis in zebrafish development. Mech Dev 108:29–43CrossRefPubMedGoogle Scholar
  38. Liu ZJ, Cordes J (2004) DNA marker technologies and their applications in aquaculture genetics. Aquaculture 238:1–37CrossRefGoogle Scholar
  39. Loiselle BA, Sork VL, Nason J, Graham C (1995) Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am J Bot 82:1420–1425CrossRefGoogle Scholar
  40. Mackay TFC, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10:565–577CrossRefPubMedGoogle Scholar
  41. Mckenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303CrossRefPubMedPubMedCentralGoogle Scholar
  42. Nathan B, Katsutoshi G, Carsten S, Gerhard W, Jocelyn L, Schafer CA, Berman SS, Michael K, Zon LI (2007) Duplicate VegfA genes and orthologues of the KDR receptor tyrosine kinase family mediate vascular development in the zebrafish. Blood 110:3627–3636CrossRefGoogle Scholar
  43. Nguyen NH, Rastas PMA, Premachandra HKA, Knibb W (2018) First high-density linkage map and single nucleotide polymorphisms significantly associated with traits of economic importance in yellowtail kingfish Seriola lalandi. Front Genet 9:693Google Scholar
  44. Otrock ZK, Mahfouz RR, Makarem JA, Shamseddine AI (2007) Understanding the biology of angiogenesis: review of the most important molecular mechanisms. Blood Cells Mol Dis 39:212–220CrossRefPubMedGoogle Scholar
  45. Palaiokostas C, Kocour M, Prchal M, Houston RD (2018) Accuracy of genomic evaluations of juvenile growth rate in common carp (Cyprinus carpio) using genotyping by sequencing. Front Genet 9:82CrossRefPubMedPubMedCentralGoogle Scholar
  46. Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One 7:e32253CrossRefPubMedPubMedCentralGoogle Scholar
  47. Quinton CD, Mcmillan I, Glebe BD (2005) Development of an Atlantic salmon (Salmo salar) genetic improvement program: genetic parameters of harvest body weight and carcass quality traits estimated with animal models. Aquaculture 247:211–217CrossRefGoogle Scholar
  48. Randall JE (1995) Coastal fishes of Oman. Bathurst, AustraliaGoogle Scholar
  49. Robledo D, Matika O, Hamilton A, Houston RD (2018) Genome-wide association and genomic selection for resistance to amoebic gill disease in Atlantic salmon. G3 (Bethesda) 4:1195–1203CrossRefGoogle Scholar
  50. Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M (2010) Genome-wide association studies in diverse populations. Nat Rev Genet 11:356–366CrossRefPubMedPubMedCentralGoogle Scholar
  51. Salem M, Al-Tobasei R, Ali A, Lourenco D, Gao G, Palti Y, Kenney B, Leeds TD (2018) Genome-wide association analysis with a 50K transcribed gene SNP-chip identifies QTL affecting muscle yield in rainbow trout. Front Genet 9:387CrossRefPubMedPubMedCentralGoogle Scholar
  52. Salisbury BA, Pungliya M, Choi JY, Jiang R, Sun XJ, Stephens JC (2003) SNP and haplotype variation in the human genome. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 526:53–61CrossRefPubMedGoogle Scholar
  53. Sang JW, Kit MCS, Martin BR (2018) Protein depalmitoylases. Crit Rev Biochem Mol Biol 53:83–98CrossRefGoogle Scholar
  54. Schaid DJ, Chen W, Larson NB (2018) From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet 19:491–504CrossRefPubMedPubMedCentralGoogle Scholar
  55. Spinelli M, Fusco S, Grassi C (2018) Nutrient-dependent changes of protein palmitoylation: impact on nuclear enzymes and regulation of gene expression. Int J Mol Sci 19:3820CrossRefPubMedCentralGoogle Scholar
  56. Sun Y, Guo C-Y, Wang D-D, Li XF, Xiao L, Zhang X, You X, Shi Q, Hu G-J, Fang C, Lin H-R, Zhang Y (2016) Transcriptome analysis reveals the molecular mechanisms underlying growth superiority in a novel grouper hybrid (Epinephelus fuscogutatus♀ × E. lanceolatus♂). BMC Genet 17:24CrossRefPubMedPubMedCentralGoogle Scholar
  57. Tsai HY, Hamilton A, Tinch AE, Guy DR, Gharbi K, Stear MJ, Matika O, Bishop SC, Houston RD (2015) Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array. BMC Genomics 16:969CrossRefPubMedPubMedCentralGoogle Scholar
  58. Vaughan LK, Wiener HW, Aslibekyan S, Allison DB, Havel PJ, Stanhope KL, O’brien DM, Hopkins SE, Lemas DJ, Boyer BB (2015) Linkage and association analysis of obesity traits reveals novel loci and interactions with dietary n-3 fatty acids in an Alaska Native (Yup’ik) population. Metab Clin Exp 64:689–697CrossRefPubMedPubMedCentralGoogle Scholar
  59. Visscher PM, Wray NR, Zhang Q, Sklar P, Mccarthy MI, Brown MA, Yang J (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22CrossRefPubMedPubMedCentralGoogle Scholar
  60. Vonnahme KA, Wilson ME, Li Y, Rupnow HL, Phernetton TM, Ford SP, Magness RR (2005) Circulating levels of nitric oxide and vascular endothelial growth factor throughout ovine pregnancy. J Physiol 565:101–109CrossRefPubMedPubMedCentralGoogle Scholar
  61. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164CrossRefPubMedPubMedCentralGoogle Scholar
  62. Wang H, Misztal I, Aguilar I, Legarra A, Fernando RL, Vitezica Z, Okimoto R, Wing T, Hawken R, Muir WM (2014) Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front Genet 5:134PubMedPubMedCentralGoogle Scholar
  63. Wang X, Liu X, Deng D, Mei Y, Li X (2016) Genetic determinants of pig birth weight variability. BMC Genet 17:S15CrossRefGoogle Scholar
  64. Wang L, Liu P, Huang SQ, Ye BQ, Chua E, Wan ZY, Yue GH (2017a) Genome-wide association study identifies loci associated with resistance to viral nervous necrosis disease in Asian seabass. Mar Biotechnol 19:255–265CrossRefPubMedGoogle Scholar
  65. Wang X, Liu S, Jiang C, Geng X, Zhou T, Li N, Bao L, Li Y, Yao J, Yang Y, Zhong X, Jin Y, Dunham R, Liu Z (2017b) Multiple across-strain and within-strain QTLs suggest highly complex genetic architecture for hypoxia tolerance in channel catfish. Mol Gen Genomics 292:63–76CrossRefGoogle Scholar
  66. Williams KC (2009) A review of feeding practices and nutritional requirements of postlarval groupers. Aquaculture 292:141–152CrossRefGoogle Scholar
  67. Yang S, Wang L, Zhang Y, Liu XC, Lin HR, Meng ZN (2011) Development and characterization of 32 microsatellite loci in the giant grouper Epinephelus lanceolatus (Serranidae). Genet Mol Res 10:4006–4011CrossRefPubMedGoogle Scholar
  68. Yu H, You X, Li J, Liu H, Meng Z, Xiao L, Zhang H, Lin HR, Zhang Y, Shi Q (2016) Genome-wide mapping of growth-related quantitative trait loci in orange-spotted grouper (Epinephelus coioides) using double digest restriction-site associated DNA sequencing (ddRADseq). Int J Mol Sci 17:501CrossRefPubMedPubMedCentralGoogle Scholar
  69. Yu H, You X, Li J, Zhang X, Zhang S, Jiang S, Lin X, Lin HR, Meng Z, Shi Q (2018) A genome-wide association study on growth traits in orange-spotted grouper (Epinephelus coioides) with RAD-seq genotyping. Sci China Life Sci 61:934–946CrossRefPubMedGoogle Scholar
  70. Yue GH (2014) Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish Fish 15:376–396CrossRefGoogle Scholar
  71. Yue G, Wang L (2017) Current status of genome sequencing and its applications in aquaculture. Aquaculture 468:337–347CrossRefGoogle Scholar
  72. Zhong X, Wang X, Zhou T, Jin Y, Tan S, Jiang C, Geng X, Li N, Shi H, Zeng Q, Yang Y, Yuan Z, Bao L, Liu S, Tian C, Peatman E, Li Q, Liu Z (2017) Genome-wide association study reveals multiple novel QTL associated with low oxygen tolerance in hybrid catfish. Mar Biotechnol (NY) 19:379–390CrossRefGoogle Scholar
  73. Zhou Z, Chen L, Dong C, Peng W, Kong S, Sun J, Pu F, Chen B, Feng J, Xu P (2018) Genome-scale association study of abnormal scale pattern in yellow river carp identified previously known causative gene in European mirror carp. Mar Biotechnol 20:573–583CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Lina Wu
    • 1
  • Yang Yang
    • 1
  • Bijun Li
    • 1
  • Wenhua Huang
    • 1
  • Xi Wang
    • 1
  • Xiaochun Liu
    • 1
  • Zining Meng
    • 1
    Email author
  • Junhong Xia
    • 1
  1. 1.State Key Laboratory of Biocontrol, Institute of Aquatic Economic Animals, and the Guangdong Province Key Laboratory for Aquatic Economic Animals, Life Science SchoolSun Yet-Sen UniversityGuangzhouChina

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