Skip to main content

Advertisement

SpringerLink
Log in

Selection responses in survival of Macrobrachium rosenbergii after performing five generations of multi-trait selection for growth and survival

  • Published:
Aquaculture International Aims and scope Submit manuscript

Abstract

A selective breeding program was established to improve the growth and survival of the cultured giant freshwater prawn Macrobrachium rosenbergii. The response to selection was estimated for the survival of M. rosenbergii using a fully pedigreed synthetic population formed by three introduced strains. The data included 122,761 progeny from 437 sires and 723 dams in seven generations with a nested mating structure. The genetic parameters and estimated breeding values (EBVs) were estimated using a generalized linear mixed model with the probit link function. The realized response was estimated from the difference in the marginal means of survival for the selection and control populations, while the predicted response was obtained from the difference in the mean retransformed survival rate based on the survival EBVs between generations. The realized genetic gain in survival from the G1 to G6 generation ranged from −1.24 to 2.72 %. The accumulated realized genetic gain (5.02 %) expressed as a percentage was 8.46 %. Across the generations, high heritability (0.401 ± 0.020, Set 1) was obtained when using the model without the c effect and was significantly different from zero (P < 0.05). However, the low heritability and common environment (0.013 ± 0.011 and 0.088 ± 0.007, Set 2) were estimated using the model that included the c effect. The accumulated predicted gains (6.29 and 0.61 %, respectively) from the Set 1 and Set 2 parameters over the five generations of selection expressed as proportions were 9.08 and 0.87 %, respectively. The low genetic gain for survival is most likely caused by a low relative weight in the selection index and reduced genetic variation because of consecutive between-family selection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Abbreviations

EBV:

Estimated breeding value

VIE:

Visible implant elastomer

BLUP:

Best linear unbiased prediction

C:

The common environmental effect

References

  • Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ (2010) Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci 93:743–752

    Article  CAS  PubMed  Google Scholar 

  • Argue BJ, Arce SM, Lotz JM, Moss SM (2002) Selective breeding of Pacific white shrimp (Litopenaeus vannamei) for growth and resistance to Taura Syndrome Virus. Aquaculture 204:447–460

    Article  Google Scholar 

  • Castillo-Juárez H, Casares JCQ, Campos-Montes G, Villela CC, Ortega AM, Montaldo HH (2007) Heritability for body weight at harvest size in the Pacific white shrimp, Penaeus (Litopenaeus) vannamei, from a multi-environment experiment using univariate and multivariate animal models. Aquaculture 273:42–49

    Article  Google Scholar 

  • Cuéllar-Anjel J, White-Noble B, Schofield P, Chamorro R, Lightner DV (2012) Report of significant WSSV-resistance in the Pacific white shrimp, Litopenaeus vannamei, from a Panamanian breeding program. Aquaculture 368–369:36–39

    Article  Google Scholar 

  • Dégremont L, Bédier E, Boudry P (2010) Summer mortality of hatchery-produced Pacific oyster spat (Crassostrea gigas). II. Response to selection for survival and its influence on growth and yield. Aquaculture 299:21–29

    Article  Google Scholar 

  • FAO (2013) FishStatJ, a tool for fishery statistics analysis Release: 2.0.0. http://www.fao.org/fishery/statistics/software/fishstatj/en

  • Fishback AG, Danzmann RG, Ferguson MM, Gibson JP (2002) Estimates of genetic parameters and genotype by environment interactions for growth traits of rainbow trout (Oncorhynchus mykiss) as inferred using molecular pedigrees. Aquaculture 206:137–150

    Article  Google Scholar 

  • Gilmour A, Cullis B, Welham S, Gogel B, Thompson R (2004) An efficient computing strategy for prediction in mixed linear models. Comput Stat Data An 44:571–586

    Article  Google Scholar 

  • Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml User Guide Release 3.0. VSN International Ltd, Hemel Hempstead

  • Gitterle T, Salte R, Gjerde B, Cock J, Johansen H, Salazar M, Lozano C, Rye M (2005a) Genetic (co)variation in resistance to White Spot Syndrome Virus (WSSV) and harvest weight in Penaeus (Litopenaeus) vannamei. Aquaculture 246:139–149

    Article  Google Scholar 

  • Gitterle T, Rye M, Salte R, Cock J, Johansen H, Lozano C, Suárez JA, Gjerde B (2005b) Genetic (co)variation in harvest body weight and survival in Penaeus (Litopenaeus) vannamei under standard commercial conditions. Aquaculture 243:83–92

    Article  Google Scholar 

  • Gitterle T, Ødegård J, Gjerde B, Rye M, Salte R (2006) Genetic parameters and accuracy of selection for resistance to White Spot Syndrome Virus (WSSV) in Penaeus (Litopenaeus) vannamei using different statistical models. Aquaculture 251:210–218

    Article  Google Scholar 

  • Gitterle T, Johansen H, Erazo C, Lozano C, Cock J, Salazar M, Rye M (2007) Response to multi-trait selection for harvest weight, overall survival, and resistance to white spot syndrome virus (WSSV) in Penaeus (Litopenaeus) vannamei. Aquaculture 272:S262

    Article  Google Scholar 

  • Gjedrem T, Olesen I (2005) Basic statistical parameters. In: Gjedrem T (ed) Selection and breeding programs in aquaculture,1rd edn. Springer, Dordrecht

    Chapter  Google Scholar 

  • Gjedrem T, Robinson N, Rye M (2012) The importance of selective breeding in aquaculture to meet future demands for animal protein: a review. Aquaculture 350–353:117–129

    Article  Google Scholar 

  • Huang YC, Yin ZX, Weng SP, He JG, Li SD (2012) Selective breeding and preliminary commercial performance of Penaeus vannamei for resistance to white spot syndrome virus (WSSV). Aquaculture 364–365:111–117

    Article  Google Scholar 

  • Kenway M, Macbeth M, Salmon M, McPhee C, Benzie J, Wilson K, Knibb W (2006) Heritability and genetic correlations of growth and survival in black tiger prawn Penaeus monodon reared in tanks. Aquaculture 259:138–145

    Article  Google Scholar 

  • Krishna G, Gopikrishna G, Gopal C, Jahageerdar S, Ravichandran P, Kannappan S, Pillai SM, Paulpandi S, Kiran RP, Saraswati R, Venugopal G, Kumar D, Gitterle T, Lozano C, Rye M, Hayes B (2011) Genetic parameters for growth and survival in Penaeus monodon cultured in India. Aquaculture 318:74–78

    Article  Google Scholar 

  • Luan S, Yang GL, Wang JY, Luo K, Zhang YF, Gao Q, Hu HL, Kong J (2012) Genetic parameters and response to selection for harvest body weight of the giant freshwater prawn Macrobrachium rosenbergii. Aquaculture 362–363:88–96

    Article  Google Scholar 

  • Luo MF, Boettcher PJ, Schaeffer LR, Dekkers J (2001) Bayesian inference for categorical traits with an application to variance component estimation. J Dairy Sci 84:694–704

    Article  CAS  PubMed  Google Scholar 

  • Martìnez V, Neira R, Gall GAE (1999) Estimation of genetic parameters from pedigreed populations: lessons from analysis of alevin weight in Coho salmon (Oncorhynchus kisutch). Aquaculture 180:223–236

    Article  Google Scholar 

  • Moss SM, Doyle RW (2005) Lightner DV (2005) Breeding shrimp for disease resistance: challenges and opportunities for improvement. In: Lester R (ed) Walker P. Manila, Diseases in Asian Aquaculture

    Google Scholar 

  • Nguyen NH, Khaw HL, Ponzoni RW, Hamzah A, Kamaruzzaman N (2007) Can sexual dimorphism and body shape be altered in Nile tilapia (Oreochromis niloticus) by genetic means? Aquaculture 272:S38–S46

    Article  Google Scholar 

  • Ødegård J, Meuwissen TH, Heringstad B, Madsen P (2010) A simple algorithm to estimate genetic variance in an animal threshold model using Bayesian inference. Genet Sel Evol 42:29–35

    Article  PubMed Central  PubMed  Google Scholar 

  • Ødegård J, Baranski M, Gjerde B, Gjedrem T (2011) Methodology for genetic evaluation of disease resistance in aquaculture species: challenges and future prospects. Aquac Res 42:103–114

    Article  Google Scholar 

  • Pante MJR, Gjerde B, McMillan I, Misztal I (2002) Estimation of additive and dominance genetic variances for body weight at harvest in rainbow trout, Oncorhynchus mykiss. Aquaculture 204:383–392

    Article  Google Scholar 

  • Rutten MJM, Komen H, Bovenhuis H (2005) Longitudinal genetic analysis of Nile tilapia (Oreochromis niloticus L.) body weight using a random regression model. Aquaculture 246:101–113

    Article  Google Scholar 

  • Rye M, Lillevik KM, Gjerde B (1990) Survival in early life of Atlantic salmon and rainbow trout: estimates of heritabilities and genetic correlations. Aquaculture 89:209–216

    Article  Google Scholar 

  • Shaw FH, Shaw RG, Wilkinson GS, Turelli M (1995) Changes in genetic variances and covariances: G whiz! Evolution 49:1260–1267

    Article  Google Scholar 

  • Vehviläinen H, Kause A, Quinton C, Koskinen H, Paananen T (2008) Survival of the currently fittest: genetics of rainbow trout survival across time and space. Genetics 180:507–516

    Article  PubMed Central  PubMed  Google Scholar 

  • Wahab MA, Ahmad Al Nahid S, Ahmed N, Haque MM, Karim M (2012) Current status and prospects of farming the giant river prawn Macrobrachium rosenbergii (De Man) in Bangladesh. Aquac Res 43:970–983

    Article  Google Scholar 

  • Welham S, Cullis B, Gogel B, Gilmour A, Thompson R (2004) Prediction in linear mixed models. Aust Nz J Stat 46:325–347

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by grants from the Special Fund for Agro-scientific Research in the Public Interest (200903045), and the National Key Technology R&D Program (2012BAD26B04). The authors would also like to thank Drs Arthur Richard Gilmour (VSN International), Bjarne Gjerde (Nofima), Raul W. Ponzoni (The WorldFish Center), Jørgen Ødegård (Nofima), and Yuxi Zhang (Qingdao Agriculture University) for their constructive suggestions in the data analysis and revision of this manuscript.

Author information

Authors and Affiliations

  1. Key Laboratory for Sustainable Utilization of Marine Fisheries Resources, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Nanjing Road 106, Qingdao, 266071, China

    Sheng Luan, Kun Luo & Jie Kong

  2. Zhejiang Institute of Freshwater Fisheries, Hangchangqiao South Road 999, Huzhou, 313001, China

    Guoliang Yang, Junyi Wang, Xuefeng Chen & Qiang Gao

  3. Zhejiang South Tailake Freshwater Fish Breeding Co., Ltd., Baique Town, Huzhou, 313001, China

    Guoliang Yang, Junyi Wang, Xuefeng Chen & Qiang Gao

  4. The National Fishery Technical Extension Center, Maizidian Road 18, Beijing, 100026, China

    Honglang Hu

Authors
  1. Sheng Luan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoliang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kun Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuefeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Honglang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jie Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Kong.

Additional information

Sheng Luan and Guoliang Yang have contributed equally to the work.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Luan, S., Yang, G., Wang, J. et al. Selection responses in survival of Macrobrachium rosenbergii after performing five generations of multi-trait selection for growth and survival. Aquacult Int 22, 993–1007 (2014). https://doi.org/10.1007/s10499-013-9722-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10499-013-9722-x

Keywords

Search

Navigation