- -

Review. Promises, pitfalls and challenges of genomic selection in breeding program

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Review. Promises, pitfalls and challenges of genomic selection in breeding program

Show simple item record

Files in this item

dc.contributor.author Ibáñez-Escriche, Noelia es_ES
dc.contributor.author Gonzalez-Recio, Oscar es_ES
dc.date.accessioned 2020-01-15T21:01:23Z
dc.date.available 2020-01-15T21:01:23Z
dc.date.issued 2011 es_ES
dc.identifier.issn 1695-971X es_ES
dc.identifier.uri http://hdl.handle.net/10251/134655
dc.description.abstract [EN] The aim of this work was to review the main challenges and pitfalls of the implementation of genomic selection in the breeding programs of different livestock species. Genomic selection is now one of the main challenges in animal breeding and genetics. Its application could considerably increase the genetic gain in traits of interest. However, the success of its practical implementation depends on the selection scheme characteristics, and these must be studied for each particular case. In dairy cattle, especially in Holsteins, genomic selection is a reality. However, in other livestock species (beef cattle, small ruminants, monogastrics and fish) genomic selection has mainly been used experimentally. The main limitation for its implementation in the mentioned livestock species is the high genotyping costs compared to the low selection value of the candidate. Nevertheless, nowadays the possibility of using single-nucleotide polymorphism (SNP) chips of low density to make genomic selection applications economically feasible is under study. Economic studies may optimize the benefits of genomic selection (GS) to include new traits in the breeding goals. It is evident that genomic selection offers great potential; however, a suitable genotyping strategy and recording system for each case is needed in order to properly exploit it. es_ES
dc.description.abstract [ES] El objetivo principal de este trabajo fue revisar las oportunidades y riesgos de la implementación de la selección genómica en las diferentes especies de producción animal. La selección genómica es actualmente uno de los principales retos en mejora genética animal. Su aplicación podría incrementar de forma considerable la tasa de ganancia genética en caracteres de interés. Sin embargo, el éxito de su implementación práctica depende de las particularidades de cada esquema de selección y por tanto debe ser estudiada para cada caso en concreto. En vacuno de leche, especialmente en Holstein, la selección genómica es una realidad. En el resto de especies de producción animal, vacuno carne, pequeños rumiantes, monogástricos y peces, la selección genómica, hasta ahora, se ha utilizado principalmente de manera experimental. El limitante principal para su implementación, común para todas las especies mencionadas, es el alto coste del genotipado en comparación con el bajo valor de los candidatos a la selección. No obstante, se está estudiando actualmente la posibilidad de utilizar chips de baja densidad, de manera que sea económicamente viable su aplicación. Serán necesarios estudios económicos para optimizar las ventajas de la selección genómica a la hora de incluir nuevos caracteres en los objetivos de selección. La selección genómica ofrece muchas posibilidades; sin embargo, para poder aprovecharlas es necesario adecuar la estrategia de genotipado y recolección de datos en cada caso. es_ES
dc.language Inglés es_ES
dc.publisher Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria es_ES
dc.relation.ispartof Spanish Journal of Agricultural Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Breeding scheme es_ES
dc.subject Chip technology es_ES
dc.subject Genotyping es_ES
dc.subject SNP es_ES
dc.subject Esquemas de selección es_ES
dc.subject Genotipado es_ES
dc.subject Tecnología de chip es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title Review. Promises, pitfalls and challenges of genomic selection in breeding program es_ES
dc.title.alternative Revisión. Promesas, peligros y oportunidades de la selección genómica en los programas de mejora genética es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.5424/sjar/20110902-447-10 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 Ibáñez-Escriche, N.; Gonzalez-Recio, O. (2011). Review. Promises, pitfalls and challenges of genomic selection in breeding program. Spanish Journal of Agricultural Research. 9(2):404-413. https://doi.org/10.5424/sjar/20110902-447-10 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.5424/sjar/20110902-447-10 es_ES
dc.description.upvformatpinicio 404 es_ES
dc.description.upvformatpfin 413 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\393273 es_ES
dc.description.references Calus, M. P. L., & Veerkamp, R. F. (2007). Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. Journal of Animal Breeding and Genetics, 124(6), 362-368. doi:10.1111/j.1439-0388.2007.00691.x es_ES
dc.description.references Daetwyler, H. D., Villanueva, B., Bijma, P., & Woolliams, J. A. (2007). Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics, 124(6), 369-376. doi:10.1111/j.1439-0388.2007.00693.x es_ES
dc.description.references Gianola, D., Fernando, R. L., & Stella, A. (2006). Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures. Genetics, 173(3), 1761-1776. doi:10.1534/genetics.105.049510 es_ES
dc.description.references Gjerde, B., Gunnes, K., & Gjedrem, T. (1983). Effect of inbreeding on survival and growth in rainbow trout. Aquaculture, 34(3-4), 327-332. doi:10.1016/0044-8486(83)90212-0 es_ES
dc.description.references Goddard, M. (2008). Genomic selection: prediction of accuracy and maximisation of long term response. Genetica, 136(2), 245-257. doi:10.1007/s10709-008-9308-0 es_ES
dc.description.references González-Recio, O., & Forni, S. (2011). Genome-wide prediction of discrete traits using bayesian regressions and machine learning. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-7 es_ES
dc.description.references González-Recio, O., Gianola, D., Long, N., Weigel, K. A., Rosa, G. J. M., & Avendaño, S. (2008). Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers. Genetics, 178(4), 2305-2313. doi:10.1534/genetics.107.084293 es_ES
dc.description.references GONZÁLEZ-RECIO, O., WEIGEL, K. A., GIANOLA, D., NAYA, H., & ROSA, G. J. M. (2010). L2-Boosting algorithm applied to high-dimensional problems in genomic selection. Genetics Research, 92(3), 227-237. doi:10.1017/s0016672310000261 es_ES
dc.description.references Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2009). Genomic Selection Using Low-Density Marker Panels. Genetics, 182(1), 343-353. doi:10.1534/genetics.108.100289 es_ES
dc.description.references Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K., & Goddard, M. E. (2009). Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 41(1). doi:10.1186/1297-9686-41-51 es_ES
dc.description.references Ibánẽz-Escriche, N., Fernando, R. L., Toosi, A., & Dekkers, J. C. (2009). Genomic selection of purebreds for crossbred performance. Genetics Selection Evolution, 41(1), 12. doi:10.1186/1297-9686-41-12 es_ES
dc.description.references Ibáñez-Escriche, N., & Blasco, A. (2011). Modifying growth curve parameters by multitrait genomic selection1. Journal of Animal Science, 89(3), 661-668. doi:10.2527/jas.2010-2984 es_ES
dc.description.references Kizilkaya, K., Fernando, R. L., & Garrick, D. J. (2010). Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes1. Journal of Animal Science, 88(2), 544-551. doi:10.2527/jas.2009-2064 es_ES
dc.description.references König, S., Simianer, H., & Willam, A. (2009). Economic evaluation of genomic breeding programs. Journal of Dairy Science, 92(1), 382-391. doi:10.3168/jds.2008-1310 es_ES
dc.description.references Long, N., Gianola, D., Rosa, G. J. M., Weigel, K. A., & Avendaño, S. (2007). Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. Journal of Animal Breeding and Genetics, 124(6), 377-389. doi:10.1111/j.1439-0388.2007.00694.x es_ES
dc.description.references Muir, W. M. (2007). Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics, 124(6), 342-355. doi:10.1111/j.1439-0388.2007.00700.x es_ES
dc.description.references Nielsen, H. M., Sonesson, A. K., Yazdi, H., & Meuwissen, T. H. E. (2009). Comparison of accuracy of genome-wide and BLUP breeding value estimates in sib based aquaculture breeding schemes. Aquaculture, 289(3-4), 259-264. doi:10.1016/j.aquaculture.2009.01.027 es_ES
dc.description.references Ødegård, J., Sonesson, A. K., Yazdi, M. H., & Meuwissen, T. H. (2009). Introgression of a major QTL from an inferior into a superior population using genomic selection. Genetics Selection Evolution, 41(1). doi:10.1186/1297-9686-41-38 es_ES
dc.description.references Ødegård, J., Yazdi, M. H., Sonesson, A. K., & Meuwissen, T. H. E. (2008). Incorporating Desirable Genetic Characteristics From an Inferior Into a Superior Population Using Genomic Selection. Genetics, 181(2), 737-745. doi:10.1534/genetics.108.098160 es_ES
dc.description.references Pedersen, L. D., Sørensen, A. C., & Berg, P. (2009). Marker-assisted selection can reduce true as well as pedigree-estimated inbreeding. Journal of Dairy Science, 92(5), 2214-2223. doi:10.3168/jds.2008-1616 es_ES
dc.description.references Pedersen, L. D., Sørensen, A. C., & Berg, P. (2009). Marker-assisted selection reduces expected inbreeding but can result in large effects of hitchhiking. Journal of Animal Breeding and Genetics, 127(3), 189-198. doi:10.1111/j.1439-0388.2009.00834.x es_ES
dc.description.references Sonesson, A. K., & Meuwissen, T. H. (2009). Testing strategies for genomic selection in aquaculture breeding programs. Genetics Selection Evolution, 41(1). doi:10.1186/1297-9686-41-37 es_ES
dc.description.references Su, G.-S., Liljedahl, L.-E., & Gall, G. A. E. (1996). Effects of inbreeding on growth and reproductive traits in rainbow trout (Oncorhynchus mykiss). Aquaculture, 142(3-4), 139-148. doi:10.1016/0044-8486(96)01255-0 es_ES
dc.description.references Toro, M. A., & Varona, L. (2010). A note on mate allocation for dominance handling in genomic selection. Genetics Selection Evolution, 42(1). doi:10.1186/1297-9686-42-33 es_ES
dc.description.references VanRaden, P. M., & Sullivan, P. G. (2010). International genomic evaluation methods for dairy cattle. Genetics Selection Evolution, 42(1). doi:10.1186/1297-9686-42-7 es_ES
dc.description.references VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F., & Schenkel, F. S. (2009). Invited Review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science, 92(1), 16-24. doi:10.3168/jds.2008-1514 es_ES
dc.description.references Weigel, K. A., de los Campos, G., González-Recio, O., Naya, H., Wu, X. L., Long, N., … Gianola, D. (2009). Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science, 92(10), 5248-5257. doi:10.3168/jds.2009-2092 es_ES
dc.description.references Weigel, K. A., Van Tassell, C. P., O’Connell, J. R., VanRaden, P. M., & Wiggans, G. R. (2010). Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science, 93(5), 2229-2238. doi:10.3168/jds.2009-2849 es_ES


This item appears in the following Collection(s)

Show simple item record