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Breeding for robustness: investigating the genotypebyenvironment interaction and microenvironmental sensitivity of Genetically Improved Farmed Tilapia (Oreochromis niloticus)

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Breeding for robustness: investigating the genotypebyenvironment interaction and microenvironmental sensitivity of Genetically Improved Farmed Tilapia (Oreochromis niloticus)

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dc.contributor.author Agha, S. es_ES
dc.contributor.author Mekkawy, W. es_ES
dc.contributor.author Ibañez Escriche, Noelia es_ES
dc.contributor.author Lind, C.E. es_ES
dc.contributor.author Kumar, J. es_ES
dc.contributor.author Mandal, A. es_ES
dc.contributor.author Benzie, J.A.H. es_ES
dc.contributor.author Doeschl-Willson, A. es_ES
dc.date.accessioned 2020-06-05T03:32:28Z
dc.date.available 2020-06-05T03:32:28Z
dc.date.issued 2018-10 es_ES
dc.identifier.issn 0268-9146 es_ES
dc.identifier.uri http://hdl.handle.net/10251/145400
dc.description.abstract [EN] Robustness has become a highly desirable breeding goal in the globalized agricultural market. Both genotype-by-environment interaction (G x E) and micro-environmental sensitivity are important robustness components of aquaculture production, in which breeding stock is often disseminated to different environments. The objectives of this study were (i) to quantify the degree of G x E by assessing the growth performance of Genetically Improved Farmed Tilapia (GIFT) across three countries (Malaysia, India and China) and (ii) to quantify the genetic heterogeneity of environmental variance for body weight at harvest (BW) in GIFT as a measure of micro-environmental sensitivity. Selection for BW was carried out for 13 generations in Malaysia. Subsets of 60 full-sib families from Malaysia were sent to China and India after five and nine generations respectively. First, a multi-trait animal model was used to analyse the BW in different countries as different traits. The results indicate a strong G x E. Second, a genetically structured environmental variance model, implemented using Bayesian inference, was used to analyse micro-environmental sensitivity of BW in each country. The analysis revealed the presence of genetic heterogeneity of both BW and its environmental variance in all environments. The presence of genetic variation in residual variance of BW implies that the residual variance can be modified by selection. Incorporating both G x E and micro-environmental sensitivity information may help in selecting robust genotypes with high performance across environments and resilience to environmental fluctuations. es_ES
dc.description.sponsorship Dr. Agha was funded by Newton Grant, British Research Council. Dr. Doeschl-Wilson's contribution was funded by the BBSRC Institute Strategic Programme Grants [BB/J004235/1 (ISP1) and BBS/E/D/30002276 (ISP3)]. Dr. Ibanez-Escriche's contribution was funded by the Marie-Curie Fellowships (H2020-MSCA-IF2014_ST-653216). es_ES
dc.language Inglés es_ES
dc.publisher Blackwell Publishing es_ES
dc.relation.ispartof Animal Genetics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Aquaculture breeding es_ES
dc.subject Genetic heterogeneity of environmental variance es_ES
dc.subject Nile tilapia es_ES
dc.subject Resilience es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title Breeding for robustness: investigating the genotypebyenvironment interaction and microenvironmental sensitivity of Genetically Improved Farmed Tilapia (Oreochromis niloticus) es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1111/age.12680 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/653216/EU/Understanding how selection for body weight in mouse operates at the RNA level/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//BB%2FJ004235%2F1/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//BBS%2FE%2FD%2F30002276/GB/Complex phenotypes and genotype x environment interactions/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal es_ES
dc.description.bibliographicCitation Agha, S.; Mekkawy, W.; Ibañez Escriche, N.; Lind, C.; Kumar, J.; Mandal, A.; Benzie, J.... (2018). Breeding for robustness: investigating the genotypebyenvironment interaction and microenvironmental sensitivity of Genetically Improved Farmed Tilapia (Oreochromis niloticus). Animal Genetics. 49(5):421-427. https://doi.org/10.1111/age.12680 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1111/age.12680 es_ES
dc.description.upvformatpinicio 421 es_ES
dc.description.upvformatpfin 427 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 49 es_ES
dc.description.issue 5 es_ES
dc.identifier.pmid 30058152 es_ES
dc.identifier.pmcid PMC6175454 es_ES
dc.relation.pasarela S\368116 es_ES
dc.contributor.funder British Research Council es_ES
dc.contributor.funder UK Research and Innovation es_ES
dc.contributor.funder Biotechnology and Biological Sciences Research Council, Reino Unido es_ES
dc.description.references Garreau, H., Bolet, G., Larzul, C., Robert-Granié, C., Saleil, G., SanCristobal, M., & Bodin, L. (2008). Results of four generations of a canalising selection for rabbit birth weight. Livestock Science, 119(1-3), 55-62. doi:10.1016/j.livsci.2008.02.009 es_ES
dc.description.references Gelman, A., & Rubin, D. B. (1992). Inference from Iterative Simulation Using Multiple Sequences. Statistical Science, 7(4), 457-472. doi:10.1214/ss/1177011136 es_ES
dc.description.references Henderson, C. R. (1975). Best Linear Unbiased Estimation and Prediction under a Selection Model. Biometrics, 31(2), 423. doi:10.2307/2529430 es_ES
dc.description.references HILL, W. G., & MULDER, H. A. (2010). Genetic analysis of environmental variation. Genetics Research, 92(5-6), 381-395. doi:10.1017/s0016672310000546 es_ES
dc.description.references Ibáñez-Escriche, N., Varona, L., Sorensen, D., & Noguera, J. L. (2008). A study of heterogeneity of environmental variance for slaughter weight in pigs. animal, 2(1), 19-26. doi:10.1017/s1751731107001000 es_ES
dc.description.references Ibáñez-Escriche, N., Garcia, M., & Sorensen, D. (2009). GSEVM v.2: MCMC software to analyze genetically structured environmental variance models. Journal of Animal Breeding and Genetics, 127(3), 249-251. doi:10.1111/j.1439-0388.2009.00846.x es_ES
dc.description.references Janhunen, M., Kause, A., Vehviläinen, H., & Järvisalo, O. (2012). Genetics of Microenvironmental Sensitivity of Body Weight in Rainbow Trout (Oncorhynchus mykiss) Selected for Improved Growth. PLoS ONE, 7(6), e38766. doi:10.1371/journal.pone.0038766 es_ES
dc.description.references Khaw, H. L., Ponzoni, R. W., Hamzah, A., Abu-Bakar, K. R., & Bijma, P. (2012). Genotype by production environment interaction in the GIFT strain of Nile tilapia (Oreochromis niloticus). Aquaculture, 326-329, 53-60. doi:10.1016/j.aquaculture.2011.11.016 es_ES
dc.description.references Knap, P. W. (2005). Breeding robust pigs. Australian Journal of Experimental Agriculture, 45(8), 763. doi:10.1071/ea05041 es_ES
dc.description.references Marjanovic, J., Mulder, H. A., Khaw, H. L., & Bijma, P. (2016). Genetic parameters for uniformity of harvest weight and body size traits in the GIFT strain of Nile tilapia. Genetics Selection Evolution, 48(1). doi:10.1186/s12711-016-0218-9 es_ES
dc.description.references Misztal I. Tsuruta S. Lourenco D. Aguilar I. Legarra A. Vitezica Z. 2015 Manual for blupf 90 Family of Programs http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=blupf90_all2.pdf es_ES
dc.description.references Mulder, H. A., & Bijma, P. (2005). Effects of genotype × environment interaction on genetic gain in breeding programs1. Journal of Animal Science, 83(1), 49-61. doi:10.2527/2005.83149x es_ES
dc.description.references Mulder, H. A., Veerkamp, R. F., Ducro, B. J., van Arendonk, J. A. M., & Bijma, P. (2006). Optimization of Dairy Cattle Breeding Programs for Different Environments with Genotype by Environment Interaction. Journal of Dairy Science, 89(5), 1740-1752. doi:10.3168/jds.s0022-0302(06)72242-1 es_ES
dc.description.references Mulder, H. A., Bijma, P., & Hill, W. G. (2007). Prediction of Breeding Values and Selection Responses With Genetic Heterogeneity of Environmental Variance. Genetics, 175(4), 1895-1910. doi:10.1534/genetics.106.063743 es_ES
dc.description.references Mulder, H. A., Rönnegård, L., Fikse, W. F., Veerkamp, R. F., & Strandberg, E. (2013). Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models. Genetics Selection Evolution, 45(1). doi:10.1186/1297-9686-45-23 es_ES
dc.description.references Ponzoni, R. W., Nguyen, N. H., Khaw, H. L., Hamzah, A., Bakar, K. R. A., & Yee, H. Y. (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(1), 27-41. doi:10.1111/j.1753-5131.2010.01041.x es_ES
dc.description.references Robertson, A. (1959). The Sampling Variance of the Genetic Correlation Coefficient. Biometrics, 15(3), 469. doi:10.2307/2527750 es_ES
dc.description.references Sae-Lim, P., Kause, A., Janhunen, M., Vehviläinen, H., Koskinen, H., Gjerde, B., … Mulder, H. A. (2015). Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments. Genetics Selection Evolution, 47(1). doi:10.1186/s12711-015-0122-8 es_ES
dc.description.references Sae-Lim, P., Kause, A., Mulder, H. A., Martin, K. E., Barfoot, A. J., Parsons, J. E., … Komen, H. (2013). Genotype-by-environment interaction of growth traits in rainbow trout (Oncorhynchus mykiss): A continental scale study1. Journal of Animal Science, 91(12), 5572-5581. doi:10.2527/jas.2012-5949 es_ES
dc.description.references SanCristobal-Gaudy, M., Elsen, J.-M., Bodin, L., & Chevalet, C. (1998). Prediction of the response to a selection for canalisation of a continuous trait in animal breeding. Genetics Selection Evolution, 30(5), 423. doi:10.1186/1297-9686-30-5-423 es_ES
dc.description.references Sonesson, A. K., Ødegård, J., & Rönnegård, L. (2013). Genetic heterogeneity of within-family variance of body weight in Atlantic salmon (Salmo salar). Genetics Selection Evolution, 45(1), 41. doi:10.1186/1297-9686-45-41 es_ES
dc.description.references SORENSEN, D., & WAAGEPETERSEN, R. (2003). Normal linear models with genetically structured residual variance heterogeneity: a case study. Genetical Research, 82(3), 207-222. doi:10.1017/s0016672303006426 es_ES
dc.description.references Strandberg, E., Felleki, M., Fikse, W. F., Franzén, J., Mulder, H. A., Rönnegård, L., … Windig, J. J. (2013). Statistical tools to select for robustness and milk quality. Advances in Animal Biosciences, 4(3), 606-611. doi:10.1017/s2040470013000162 es_ES


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