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Longitudinal analyses of correlated response efficiencies of fillet traits in Nile tilapia

Published online by Cambridge University Press:  20 July 2017

E.M. Turra ,
A. F. A. Fernandes ,
E. R. de Alvarenga ,
E. A. Teixeira ,
G. F. O. Alves ,
L. G. Manduca ,
T. W. Murphy  and
M. A. Silva
E.M. Turra*
Affiliation:
Departamento de Zootecnia, Escola de Veterinária da Universidade Federal de Minas Gerais, Av. Antônio Carlos, no 6627, Caixa Postal 567, Campus da UFMG, CEP 30123-970, Belo Horizonte, MG –Brazil
A. F. A. Fernandes
Affiliation:
Animal Science Department, University of Wisconsin-Madison, 470 Animal Science Building 1675, Observatory Dr, Madison, WI 53706, USA
E. R. de Alvarenga
Affiliation:
Departamento de Zootecnia, Escola de Veterinária da Universidade Federal de Minas Gerais, Av. Antônio Carlos, no 6627, Caixa Postal 567, Campus da UFMG, CEP 30123-970, Belo Horizonte, MG –Brazil
E. A. Teixeira
Affiliation:
Departamento de Zootecnia, Escola de Veterinária da Universidade Federal de Minas Gerais, Av. Antônio Carlos, no 6627, Caixa Postal 567, Campus da UFMG, CEP 30123-970, Belo Horizonte, MG –Brazil
G. F. O. Alves
Affiliation:
Departamento de Zootecnia, Escola de Veterinária da Universidade Federal de Minas Gerais, Av. Antônio Carlos, no 6627, Caixa Postal 567, Campus da UFMG, CEP 30123-970, Belo Horizonte, MG –Brazil
L. G. Manduca
Affiliation:
Departamento de Zootecnia, Escola de Veterinária da Universidade Federal de Minas Gerais, Av. Antônio Carlos, no 6627, Caixa Postal 567, Campus da UFMG, CEP 30123-970, Belo Horizonte, MG –Brazil
T. W. Murphy
Affiliation:
Animal Science Department, University of Wisconsin-Madison, 470 Animal Science Building 1675, Observatory Dr, Madison, WI 53706, USA
M. A. Silva
Affiliation:
Departamento de Zootecnia, Escola de Veterinária da Universidade Federal de Minas Gerais, Av. Antônio Carlos, no 6627, Caixa Postal 567, Campus da UFMG, CEP 30123-970, Belo Horizonte, MG –Brazil
*
†E-mail: eduardoturra@yahoo.com.br
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Abstract

Recent studies with Nile tilapia have shown divergent results regarding the possibility of selecting on morphometric measurements to promote indirect genetic gains in fillet yield (FY). The use of indirect selection for fillet traits is important as these traits are only measurable after harvesting. Random regression models are a powerful tool in association studies to identify the best time point to measure and select animals. Random regression models can also be applied in a multiple trait approach to analyze indirect response to selection, which would avoid the need to sacrifice candidate fish. Therefore, the aim of this study was to investigate the genetic relationships between several body measurements, weight and fillet traits throughout the growth period and to evaluate the possibility of indirect selection for fillet traits in Nile tilapia. Data were collected from 2042 fish and was divided into two subsets. The first subset was used to estimate genetic parameters, including the permanent environmental effect for BW and body measurements (8758 records for each body measurement, as each fish was individually weighed and measured a maximum of six times). The second subset (2042 records for each trait) was used to estimate genetic correlations and heritabilities, which enabled the calculation of correlated response efficiencies between body measurements and the fillet traits. Heritability estimates across ages ranged from 0.05 to 0.5 for height, 0.02 to 0.48 for corrected length (CL), 0.05 to 0.68 for width, 0.08 to 0.57 for fillet weight (FW) and 0.12 to 0.42 for FY. All genetic correlation estimates between body measurements and FW were positive and strong (0.64 to 0.98). The estimates of genetic correlation between body measurements and FY were positive (except for CL at some ages), but weak to moderate (−0.08 to 0.68). These estimates resulted in strong and favorable correlated response efficiencies for FW and positive, but moderate for FY. These results indicate the possibility of achieving indirect genetic gains for FW and by selecting for morphometric traits, but low efficiency for FY when compared with direct selection.

Keywords

bivariate analysesgenetic parametersindirect selectionfillet traitsOreochromis niloticus
Type
Research Article
Information
animal , Volume 12 , Issue 3 , March 2018 , pp. 445 - 453
Copyright
© The Animal Consortium 2017 

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References

Akaike, H 1973. Information theory and an extension of the maximum likelihood principle. In Proceedings of the Second International Symposium on Information Theory (ed. BN Petrov and F Csaki), pp. 267–281. Akademiai Kiado, Budapest, Hungary.Google Scholar
Bosworth, BG, Holland, M and Brazil, BL 2001. Evaluation of ultrasound imagery and body shape to predict carcass and fillet yield in farm-raised catfish. Journal of Animal Science 79, 1483–1490.Google Scholar
Burnham, KP and Anderson, DR 2004. In Model selection and multimodel inference – A Pratical Information – Theoretic Approach. (ed. KP Burnham and DR Anderson), pp. 488. Springer, New York, NY, USA. https://doi.org/10.1007/b97636.Google Scholar
Charo-Karisa, H, Bovenhuis, H, Rezk, MA, Ponzoni, RW, van Arendonk, JAM and Komen, H 2007. Phenotypic and genetic parameters for body measurements, reproductive traits and gut length of Nile tilapia (Oreochromis niloticus) selected for growth in low-input earthen ponds. Aquaculture 273, 15–23, https://doi.org/10.1016/j.aquaculture.2007.09.011.Google Scholar
Cibert, C, Fermon, Y, Vallod, D and Meunier, FJ 1999. Morphological screening of carp Cyprinus carpio: relationship between morphology and fillet yield. Aquatic Living Resources 12, 1–10, https://doi.org/10.1016/S0990-7440(99)80009-6.Google Scholar
Diodatti, FC 2006. Medidas morfométricas no peso e rendimento de componentes corporais de tilápia do Nilo (Oreochromis niloticus). Master of Science dissertation, Federal University of Lavras, Lavras, MG, Brazil.Google Scholar
Food and Agriculture Organization 2016. Fishery and aquaculture statistics. Global fisheries commodities production and trade 1976-2013. FishStatJ (Retrieved on 27 March 2017 from www.fao.org/fishery/statistics/software/fishstatj/en.Google Scholar
Fernandes, AFA, Silva, MA, Alvarenga, ER, Teixeira, EA, Silva, AF, Alves, GFO, Salles, SCM, Manduca, LG and Turra, EM 2015. Morphometric traits as selection criteria for carcass yield and body weight in Nile tilapia (Oreochromis niloticus L.) at five ages. Aquaculture 446, 303–309, https://doi.org/10.1016/j.aquaculture.2015.05.009.Google Scholar
Martínez, V, Neira, R and 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.Google Scholar
Meyer, K 2011. WOMBAT – A program for mixed model analyses by restricted maximum likelihood. User notes, Animal Genetics and Breeding Unit, Armidale, Australia.Google Scholar
Mrode, RA and Thompson, R 2005. Linear models for the prediction of animal breeding values, 2nd edition. CABI Publishing, Cambridge, MA, USA.Google Scholar
Neyman, J and Pearson, ES 1928. On the use and interpretation of certain test criteria for purposes of statistical inference part I. Biometrika 20A, 175–240.Google Scholar
Nguyen, N, Ponzoni, R, Abu-Bakar, K, Hamzah, A, Khaw, H and Yee, H 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.Google Scholar
Pante, MJR, Gjerde, B, McMillan, I and Misztal, I 2002. Estimation of additive and dominance genetic variances for body weight at harvest in rainbow trout, Oncorhynchus mykiss . Aquaculture 204, 383–392.Google Scholar
Rutten, M, Bovenhuis, H and Komen, H 2004. Modeling fillet traits based on body measurements in three Nile tilapia strains (Oreochromis niloticus L.). Aquaculture 231, 113–122, https://doi.org/10.1016/j.aquaculture.2003.11.002.Google Scholar
Rutten, M, Bovenhuis, H and Komen, H 2005a. Genetic parameters for fillet traits and body measurements in Nile tilapia (Oreochromis niloticus L.). Aquaculture 246, 125–132, https://doi.org/10.1016/j.aquaculture.2005.01.006.Google Scholar
Rutten, M, Komen, H and Bovenhuis, H 2005b. Longitudinal genetic analysis of Nile tilapia (Oreochromis niloticus L.) body weight using a random regression model. Aquaculture 246, 101–113, https://doi.org/10.1016/j.aquaculture.2004.12.020.CrossRefGoogle Scholar
Schaeffer, L R 2016. Random regression models. Retrieved on 31 October 2016 from http://www.aps.uoguelph.ca/~lrs/ABModels/NOTES/RRM14a.pdf.Google Scholar
Simm, G, Smith, C and Thompson, R 1987. The use of product traits such as lean growth rate as selection criteria in animal breeding. Animal Production 45, 307–316.Google Scholar
Turra, EM, Oliveira, DAA, Valente, BD, Teixeira, EA, Prado, SA, Alvarenga, ER, Melo, DC, Felipe, VPS, Fernandes, AFA and Silva, MA 2012b. Longitudinal genetic analyses of fillet traits in Nile tilapia Oreochromis niloticus . Aquaculture 356, 381–390, https://doi.org/10.1016/j.aquaculture.2012.04.039.Google Scholar
Turra, EM, Oliveira, DAA, Valente, BD, Teixeira, EA, Prado, SA, Melo, DC, Fernandes, AFA, Alvarenga, ER and Silva, MA 2012a. Estimation of genetic parameters for body weights of Nile tilapia Oreochromis niloticus using random regression models. Aquaculture 354, 31–37, https://doi.org/10.1016/j.aquaculture.2012.04.035.CrossRefGoogle Scholar
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