Advertisement

Zebrafish breeding program: genetic parameters estimates for growth traits

  • Vanessa LewandowskiEmail author
  • Cesar Sary
  • Jaisa Casetta
  • André Luiz Seccatto Garcia
  • Carlos Antonio Lopes de Oliveira
  • Ricardo Pereira Ribeiro
  • Lauro Daniel Vargas Mendez
Animal Genetics • Original Paper
  • 12 Downloads

Abstract

The objective of this study was to evaluate the genetic parameters of two generations of zebrafish breeding program. The base population was formed by crossing individuals of six commercial stocks of zebrafish, resulting in a nucleus with 60 families. Two generations were evaluated, with a total of 780 and 781 individuals for the first and second generation, respectively. The selection was made based on the mean genetic value of each family, followed by mass selection of the breeders. Mathematical models that considered the fixed (age, density in the larval stage, sex, and generation) and random (animal additive genetics, common to full-sibs, and residual) effects were evaluated using BLUPF90 program family. Weight and total length were used as response variables. Total length was the best selection criterion because it had a higher heritability (0.30) than weight (0.22). There was a high common to full-sib effect, especially in the first generation of animals. For second-generation data, the heritability was 0.26 for total length, as well as a lower common to full-sib effect for length. The best model obtained for this evaluation was considering all effects, being age and density as first and second polynomial, respectively. The genetic and phenotypic correlations for weight and length were 0.87 and 0.75, respectively. These results indicate that genetic breeding using total length as the selection criterion may produce a larger and heavier zebrafish strain.

Keywords

Components of variance Danio rerio Total length Heritability Weight 

Notes

Statement of author contributions

Vanessa Lewandowski: Project execution, statistical analysis, and paper writing

Cesar Sary: Project execution and data collect

Jaisa Casetta: Project execution and data collect

André Luiz Seccato Garcia: Statistical analysis

Carlos Antonio Lopes de Oliveira: Experimental design and statistical analysis

Ricardo Pereira Ribeiro: Experimental design

Lauro Daniel Vargas Mendez: Paper revision

Funding information

The authors are grateful to CAPES for the financial support.

Compliance with ethical standard

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution of practice at which the studies were conducted.

Informed consent

Informed consent was obtained from all individual participant included in the study.

References

  1. Acosta D d S, Danielle NM, Altenhofen S et al (2016) Copper at low levels impairs memory of adult zebrafish (Danio rerio) and affects swimming performance of larvae. Comp Biochem Physiol Part C Toxicol Pharmacol 185–186:122–130.  https://doi.org/10.1016/j.cbpc.2016.03.008 CrossRefGoogle Scholar
  2. Bentsen HB, Gjerde B, Nguyen NH et al (2012) Genetic improvement of farmed tilapias: genetic parameters for body weight at harvest in Nile tilapia (Oreochromis niloticus) during five generations of testing in multiple environments. Aquaculture 338–341:56–65.  https://doi.org/10.1016/j.aquaculture.2012.01.027 CrossRefGoogle Scholar
  3. Eknath AE, Bentsen HB, Ponzoni RW et al (2007) Genetic improvement of farmed tilapias: composition and genetic parameters of a synthetic base population of Oreochromis niloticus for selective breeding. Aquaculture 273:1–14.  https://doi.org/10.1016/j.aquaculture.2007.09.015 CrossRefGoogle Scholar
  4. Falconer D (1987) Introdução à genética quantitativa. UFV, ViçosaGoogle Scholar
  5. Garcia ALS, de Oliveira CAL, Karim HM et al (2017) Genetic parameters for growth performance, fillet traits, and fat percentage of male Nile tilapia (Oreochromis niloticus). J Appl Genet 58:527–533.  https://doi.org/10.1007/s13353-017-0413-6 CrossRefGoogle Scholar
  6. Gjedrem T (1985) Improvement of productivity through breeding schemes. GeoJournal 10:233–241CrossRefGoogle Scholar
  7. Grunwald DJ, Eisen JS (2002) Headwaters of the zebrafish — emergence of a new model vertebrate. Nat Rev Genet 3:717–724.  https://doi.org/10.1038/nrg892 CrossRefGoogle Scholar
  8. Guan J, Hu Y, Wang M et al (2016) Estimating genetic parameters and genotype-by-environment interactions in body traits of turbot in two different rearing environments. Aquaculture 450:321–327.  https://doi.org/10.1016/j.aquaculture.2015.08.014 CrossRefGoogle Scholar
  9. He J-H, Gao J-M, Huang C-J, Li C-Q (2014) Zebrafish models for assessing developmental and reproductive toxicity. Neurotoxicol Teratol 42:35–42.  https://doi.org/10.1016/j.ntt.2014.01.006 CrossRefGoogle Scholar
  10. Heildelberger P, Welch P (1983) Simulation run length control in the presence of an initial transient. Oper Res 31:1109–1144CrossRefGoogle Scholar
  11. Janhunen M, Kause A, Vehviläinen H et al (2014) Correcting within-family pre-selection in genetic evaluation of growth—a simulation study on rainbow trout. Aquaculture 434:220–226.  https://doi.org/10.1016/j.aquaculture.2014.08.020 CrossRefGoogle Scholar
  12. Kalueff AV, Echevarria DJ, Stewart AM (2014) Gaining translational momentum: more zebrafish models for neuroscience research. Prog Neuro-Psychopharmacol Biol Psychiatry 55:1–6.  https://doi.org/10.1016/j.pnpbp.2014.01.022 CrossRefGoogle Scholar
  13. Khaw HL, Ponzoni RW, Yee HY et al (2016) Genetic and non-genetic indirect effects for harvest weight in the GIFT strain of Nile tilapia (Oreochromis niloticus). Aquaculture 450:154–161.  https://doi.org/10.1016/j.aquaculture.2015.07.033 CrossRefGoogle Scholar
  14. Lawrence C (2016) New frontiers for zebrafish management. In: Detrich HW III (Ed) The Zebrafish: Genetics, genomics and transcriptomics, 3rd edn.  Elsevier, Amsterdam, pp 483–508Google Scholar
  15. Lawrence C, Mason T (2012) Zebrafish housing systems: a review of basic operating principles and considerations for design and functionality. ILAR J 53:179–191.   https://doi.org/10.1093/ilar.53.2.179
  16. Lawrence C, Ebersole JP, Kesseli RV (2007) Rapid growth and out-crossing promote female development in zebrafish (Danio rerio). Environ Biol Fish 81:239–246.  https://doi.org/10.1007/s10641-007-9195-8 CrossRefGoogle Scholar
  17. Lawrence C, Best J, James A, Maloney K (2012) The effects of feeding frequency on growth and reproduction in zebrafish (Danio rerio). Aquaculture 368–369:103–108.  https://doi.org/10.1016/j.aquaculture.2012.09.022 CrossRefGoogle Scholar
  18. Lyu D, Wang W, Luan S et al (2017) Estimating genetic parameters for growth traits with molecular relatedness in turbot (Scophthalmus maximus, Linnaeus). Aquaculture 468:149–155.  https://doi.org/10.1016/j.aquaculture.2016.09.049 CrossRefGoogle Scholar
  19. Meyer BM, Froehlich JM, Galt NJ, Biga PR (2013) Inbred strains of zebrafish exhibit variation in growth performance and myostatin expression following fasting. Comp Biochem Physiol Part A Mol Integr Physiol 164:1–9.  https://doi.org/10.1016/j.cbpa.2012.10.004 CrossRefGoogle Scholar
  20. Misztal I, Tsuruta S, Lourenco DAL et al (2015) Manual for BLUPF90 family programs. University of Georgia, AthensGoogle Scholar
  21. Mizgirev I, Revskoy S (2010) Generation of clonal zebrafish lines and transplantable hepatic tumors. Nat Protoc 5:383–394.  https://doi.org/10.1038/nprot.2010.8 CrossRefGoogle Scholar
  22. Monroe JD, Manning DP, Uribe PM et al (2016) Hearing sensitivity differs between zebrafish lines used in auditory research. Hear Res 341:220–231.  https://doi.org/10.1016/j.heares.2016.09.004 CrossRefGoogle Scholar
  23. Nasiadka A, Clark MD (2012) Zebrafish breeding in the laboratory environment. ILAR J 53:161–168CrossRefGoogle Scholar
  24. Nguyen NH, Ponzoni RW, Abu-Bakar KR et al (2010) Correlated response in fillet weight and yield to selection for increased harvest weight in genetically improved farmed tilapia (GIFT strain), Oreochromis niloticus. Aquaculture 305:1–5.  https://doi.org/10.1016/j.aquaculture.2010.04.007 CrossRefGoogle Scholar
  25. Oliveira CAL, Ribeiro RP, Yoshida GM et al (2016) Correlated changes in body shape after five generations of selection to improve growth rate in a breeding program for Nile tilapia Oreochromis niloticus in Brazil. J Appl Genet 57:487–493.  https://doi.org/10.1007/s13353-016-0338-5
  26. Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6:7–11Google Scholar
  27. Ribas L, Piferrer F (2014) The zebrafish (Danio rerio) as a model organism, with emphasis on applications for finfish aquaculture research. Rev Aquac 6:209–240.  https://doi.org/10.1111/raq.12041 CrossRefGoogle Scholar
  28. Sang NV, Klemetsdal G, Ødegård J, Gjøen HM (2012) Genetic parameters of economically important traits recorded at a given age in striped catfish (Pangasianodon hypophthalmus). Aquaculture 344–349:82–89.  https://doi.org/10.1016/j.aquaculture.2012.03.013 CrossRefGoogle Scholar
  29. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:583–639.  https://doi.org/10.1111/1467-9868.00353 CrossRefGoogle Scholar
  30. Turra EM, de Oliveira DAA, Valente BD et al (2012) Estimation of genetic parameters for body weights of Nile tilapia Oreochromis niloticus using random regression models. Aquaculture 354–355:31–37.  https://doi.org/10.1016/j.aquaculture.2012.04.035 CrossRefGoogle Scholar
  31. Vignet C, Bégout M-L, Péan S et al (2013) Systematic screening of behavioral responses in two zebrafish strains. Zebrafish 10:365–375.  https://doi.org/10.1089/zeb.2013.0871 CrossRefGoogle Scholar
  32. Vilella AJ, Severin J, Ureta-Vidal A et al (2008) EnsemblCompara GeneTrees: complete, duplication-aware phylogenetic trees in vertebrates. Genome Res 19:327–335.  https://doi.org/10.1101/gr.073585.107 CrossRefGoogle Scholar
  33. Xu K, Duan W, Xiao J et al (2015) Development and application of biological technologies in fish genetic breeding. Sci China Life Sci 58:187–201.  https://doi.org/10.1007/s11427-015-4798-3 CrossRefGoogle Scholar
  34. Yakulov TA, Walz G (2015) Zebrafish database: customizable, free, and open-source solution for facility management. Zebrafish 12:462–469.  https://doi.org/10.1089/zeb.2015.1122 CrossRefGoogle Scholar
  35. Yáñez JM, Lhorente JP, Bassini LN et al (2014) Genetic co-variation between resistance against both Caligus rogercresseyi and Piscirickettsia salmonis, and body weight in Atlantic salmon (Salmo salar). Aquaculture 433:295–298.  https://doi.org/10.1016/j.aquaculture.2014.06.026 CrossRefGoogle Scholar

Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2019

Authors and Affiliations

  • Vanessa Lewandowski
    • 1
    Email author
  • Cesar Sary
    • 2
  • Jaisa Casetta
    • 2
  • André Luiz Seccatto Garcia
    • 3
  • Carlos Antonio Lopes de Oliveira
    • 2
  • Ricardo Pereira Ribeiro
    • 2
  • Lauro Daniel Vargas Mendez
    • 2
  1. 1.Faculty of Agrarian SciencesFederal University of Grande Dourados – UFGDDouradosBrazil
  2. 2.Department of Animal ScienceState University of Maringá – UEMMaringáBrazil
  3. 3.Department of Animal and Dairy ScienceUniversity of GeorgiaAthensUSA

Personalised recommendations