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Genetic parameters and response to selection for growth in tambaqui

Published online by Cambridge University Press:  20 March 2020

E. C. Campos*
Affiliation:
PeixeGen Research Group – Management, Breeding and Molecular Genetics of Freshwater Fish, Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil Graduate Program in Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil
C. A. L. Oliveira
Affiliation:
PeixeGen Research Group – Management, Breeding and Molecular Genetics of Freshwater Fish, Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil
F. C. T. Araújo
Affiliation:
PeixeGen Research Group – Management, Breeding and Molecular Genetics of Freshwater Fish, Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil Graduate Program in Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil
H. Todesco
Affiliation:
PeixeGen Research Group – Management, Breeding and Molecular Genetics of Freshwater Fish, Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil Graduate Program in Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil
F. N. Souza
Affiliation:
Bom Futuro Group, Avenida dos Florais S/N, Cuiabá, Mato Grosso, Brazil
R. M. Rossi
Affiliation:
Department of Statistics, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil
D. C. Fornari
Affiliation:
PeixeGen Research Group – Management, Breeding and Molecular Genetics of Freshwater Fish, Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil Aquamat – Mato Grosso Aquaculture Association, Rua Tiradentes 220, Cuiabá, Mato Grosso, Brazil
R. P. Ribeiro
Affiliation:
PeixeGen Research Group – Management, Breeding and Molecular Genetics of Freshwater Fish, Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil Department of Animal Science, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil
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Abstract

Although the tambaqui (Colossoma macropomum) is the most cultivated native fish species in Brazil, estimated breeding values for growth traits are rarely used for selection of superior individuals in commercial fingerling production. This study aimed to estimate the (co)variance components of growth traits. Body weight, length and width of 2500 tambaqui were determined at tagging and at 6 and 12 months after tagging in a commercial breeding programme in Brazil. Heritability estimates were low for traits measured at tagging (0.10 to 0.19) and moderate to high for traits measured at 6 and 12 months (0.23 to 0.81). Common full-sib effects were high at tagging (>73%), low at 6 months and negligible at 12 months. Positive genetic correlations were found among growth traits at 12 months (0.84 to 0.99) and between growth traits at 6 and 12 months (0.80 to 0.92). These results show that animal selection can be performed at 6 months after tagging. Expected genetic gains for growth traits ranged from 8% to 31%. A simulation of the sex ratio was performed, as individuals did not reach sexual maturity during the experimental period. Because of the sexual dimorphism, more accurate heritability estimates were obtained when considering the female proportion to be 90% in the high-weight group. The findings indicate that it is possible to obtain considerable genetic gains in growth by selecting for growth traits. The development of a tool to determine the sex of animals at early stages can improve the response to selection in tambaqui.

Type
Research Article
Copyright
© The Animal Consortium 2020

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Footnotes

a

Present address: Graduate Program in Animal Science, Center of Agrarian Sciences, State University of Maringá, Avenida Colombo 5790, Maringá, Paraná, Brazil.

References

Akaike, H 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716723.CrossRefGoogle Scholar
Almeida, FL, Lopes, JS, Crescencio, R, Izel, ACU, Chagas, EC and Boijink, C 2016. Early puberty of farmed tambaqui (Colossoma macropomum): possible influence of male sexual maturation on harvest weight. Aquaculture 452, 224232.CrossRefGoogle Scholar
Andrade, DR and Yasui, GS 2003. O manejo da reprodução natural e artificial e sua importância na produção de peixes no brasil. Revista Brasileira de Reprodução Animal 27, 166172.Google Scholar
Araujo-Lima, C and Goulding, M 1998. So fruitful a fish: ecology, conservation and aquaculture of the Amazon's tambaqui. Environmental Conservation 25, 279289.Google Scholar
Bentsen, HB, Gjerde, B, Nguyen, NH, Rye, M, Ponzoni, RW, Palada de Vera, MS, Bolivar, HL, Velasco, RR, Danting, JC, Dionisio, EE, Longalong, FM, Reyes, RA, Abella, TA, Tayamen, MM and Eknath, AE 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, 5665.CrossRefGoogle Scholar
Castro-Pereira, VM, Alencar, MM and Barbosa, RT 2007. Estimativas de parâmetros genéticos e de ganhos direto e indireto à seleção para características reprodutivas e de crescimento em um rebanho da raça Canchim. Revista Brasileira de Zootecnia 36, 10291036.CrossRefGoogle Scholar
CEPAL, FAO and IICA 2017. Perspectivas de la agricultura y del desarrollo rural en las américas: una mirada hacia América Latina y el Caribe 2017-2018. Instituto Interamericano de Cooperación para la Agricultura, San José, CR.Google Scholar
Dong, Z, Nguyen, NH and Zhu, W 2015. Genetic evaluation of a selective breeding program for common carp Cyprinus carpio conducted from 2004 to 2014. BMC Genetics 16, 19.CrossRefGoogle ScholarPubMed
Falconer, DS and Mackay, TFC 1996. Introduction to quantitative genetics. Longmans Green, Harlow, EX, UK.Google Scholar
Fu, J, Shen, Y, Xu, X and Li, J 2016. Genetic parameter estimates for growth of grass carp, Ctenopharyngodon idella, at 10 and 18 months of age. Aquaculture 450, 342348.CrossRefGoogle Scholar
Garcia, ALS, Oliveira, CAL, Karim, HM, Sary, C, Todesco, H and Ribeiro, RP 2017. Genetic parameters for growth performance, fillet traits, and fat percentage of male Nile tilapia (Oreochromis niloticus). Journal of Applied Genetics 58, 527533.CrossRefGoogle Scholar
Gjedrem, T 2000. Genetic improvement of cold-water fish species. Aquaculture Research 31, 2533.CrossRefGoogle Scholar
Gjedrem, T 2005. Selection and breeding programs in aquaculture. Springer, Dordrecht, Netherlands.CrossRefGoogle Scholar
Gjedrem, T 2012. Genetic improvement for the development of efficient global aquaculture: a personal opinion review. Aquaculture 344–349, 1222.CrossRefGoogle Scholar
Gjedrem, T and Rye, M 2018. Selection response in fish and shellfish: a review. Reviews in Aquaculture 10, 168179.CrossRefGoogle Scholar
IBGE 2017. Produção pecuária municipal 2017. Instituto Brasileiro de Geografia e Estatística 45, 1–8.Google Scholar
Leeds, TD, Vallejo, RL, Weber, GM, Gonzalez-Pena, D and Silverstein, JT 2016. Response to five generations of selection for growth performance traits in rainbow trout (Oncorhynchus mykiss). Aquaculture 465, 341351.CrossRefGoogle Scholar
Mello, F, Oliveira, CAL, Streit, Jr, D, Resende, EK, Oliveira, SN, Fornari, DC, Barreto, RV, Povh, JA and Ribeiro, RP 2016. Estimation of genetic parameters for body weight and morphometric traits to tambaqui Colossoma macropomum. Journal of FisheriesSciences.com 10, 96100.Google Scholar
Misztal, I, Tsuruta, S, Lourenco, D, Aguilar, I, Legarra, A and Vitezica, Z 2016. Manual for BLUPF90 family of programs. University of Georgia, Athens, GA, USA.Google Scholar
Nguyen, NH 2016. Genetic improvement for important farmed aquaculture species with a reference to carp, tilapia and prawns in Asia: achievements, lessons and challenges. Fish and Fisheries 17, 483506.CrossRefGoogle Scholar
Nguyen, NH, Hamzah, A and Thoa, NP 2017. Effects of genotype by environment interaction on genetic gain and genetic parameter estimates in Red tilapia (Oreochromis spp.). Frontiers in Genetics 8, 82.CrossRefGoogle Scholar
Nguyen, NH, Ponzoni, RW, Abu-Bakar, KR, Hamzah, A, Khaw, HL and Yee, HY 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, 15.CrossRefGoogle Scholar
Ninh, NH, Ponzoni, RW, Nguyen, NH, Woolliams, JA, Taggart, JB, McAndrew, BJ and Penman, DJ 2013. A comparison of communal and separate rearing of families in selective breeding of common carp (Cyprinus carpio): responses to selection. Aquaculture 408–409, 152159.CrossRefGoogle Scholar
Oliveira, CAL, Ribeiro, RP, Yoshida, GM, Kunita, NM and Rizzato, GS 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. Journal of Applied Genetics 57, 487493.10.1007/s13353-016-0338-5CrossRefGoogle Scholar
Oliveira, CAL, Yoshida, GM, de Oliveira, SN, Kunita, NM, dos Santos, AI, Filho, LA and Ribeiro, RP 2015. Avaliação genética de tilápias-do-nilo durante cinco anos de seleção. Pesquisa Agropecuaria Brasileira 50, 871877.CrossRefGoogle Scholar
Plummer, M, Best, N, Cowles, K and Vines, K 2006. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 711.Google Scholar
Ponzoni, RW, Hamzah, A, Tan, S and Kamaruzzaman, N 2005. Genetic parameters and response to selection for live weight in the GIFT strain of Nile Tilapia (Oreochromis niloticus). Aquaculture 247, 203210.CrossRefGoogle Scholar
Ponzoni, RW, Nguyen, NH, Khaw, HL, Hamzah, A, Bakar, KRA and Yee, HY 2011. Genetic improvement of Nile tilapia (Oreochromis niloticus) with special reference to the work conducted by the WorldFish Center with the GIFT strain. Reviews in Aquaculture 3, 2741.10.1111/j.1753-5131.2010.01041.xCrossRefGoogle Scholar
Porto, EP, Oliveira, CAL, Martins, EN, Ribeiro, RP, Conti, ACM, Kunita, NM, Oliveira, SN and Porto, PP 2015. Respostas à seleção de características de desempenho em tilápia-do-nilo. Pesquisa Agropecuaria Brasileira 50, 745752.CrossRefGoogle Scholar
R Development Core Team 2019. R: a language and environment for statistical computing. The R Foundation for Statistical Computing, Vienna, AT.Google Scholar
Trong, TQ, Mulder, HA, van Arendonk, JAM and Komen, H 2013. Heritability and genotype by environment interaction estimates for harvest weight, growth rate, and shape of Nile tilapia (Oreochromis niloticus) grown in river cage and VAC in Vietnam. Aquaculture 384, 119127.CrossRefGoogle Scholar
Woynárovich, A and Van Anrooy, R 2019. Field guide to the culture of tambaqui (Colossoma macropomum. FAO Fisheries and Aquaculture Technical Paper 624. FAO, Rome, IT.Google Scholar
Yoshida, GM, Lopes de Oliveira, CA, Oliveira, SN, Kunita, NM, Resende, EK, Filho, LA and Ribeiro, RP 2013. Associação entre características de desempenho de tilápia-do-nilo ao longo do período de cultivo. Pesquisa Agropecuaria Brasileira 48, 816824.CrossRefGoogle Scholar
Zardin, AMSO, Oliveira, CAL, Oliveira, SN, Yoshida, GM, Albuquerque, DT, Campos, CM and Ribeiro, RP 2019. Growth curves by Gompertz nonlinear regression model for male and female Nile tilapias from different genetic group. Aquaculture 511, 734243.CrossRefGoogle Scholar