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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 |