Análisis BLUP (Best Linear Unbiased Predictors) en etapas avanzadas de selección en papa criolla (Solanum tuberosum Grupo Phureja)
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Pacheco, J. E., Urquijo, J. S., Darghan, A. E., & Rodríguez, L. E. (2020). Análisis BLUP (Best Linear Unbiased Predictors) en etapas avanzadas de selección en papa criolla (Solanum tuberosum Grupo Phureja). Revista Colombiana De Ciencias Hortícolas, 14(1). https://doi.org/10.17584/rcch.2020v14i1.8138
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José E. Pacheco
https://orcid.org/0000-0003-0119-8285
Johan Sebastian Urquijo
https://orcid.org/0000-0002-3854-9280
Aquiles Enrique Darghan
https://orcid.org/0000-0001-5790-1684
Luis Ernesto Rodríguez
https://orcid.org/0000-0002-9058-8404
Resumen
Los programas de mejoramiento en papa utilizan procesos de selección rigurosos en etapas tempranas, mientras en etapas avanzadas la interacción genotipo por ambiente (IGA) dificulta la selección, y reduce la ganancia genética (GA). Para hacer más eficiente el proceso de selección se busca minimizar el efecto ambiental y reducir la presencia de valores extremos que permita la identificación de genotipos superiores. Los Predictores BLUP se presentan como alternativa para el estrechamiento de valores sesgados; se basa en las varianzas por genotipo para determinar su valor respuesta. Como complemento, el índice de selección (IS) ESIM (Eigenvalue Selection Index) permite seleccionar genotipos basados en dos o más variables o caracteres de selección siempre y cuando la matriz económica posea los valores adecuados para resaltar la variable respuesta deseada. Se evaluaron tres etapas de selección en un programa avanzado de mejoramiento de papa diploide, se obtuvieron los valores BLUP para las variables rendimiento y gravedad específica, y a partir de ellos los parámetros genéticos y el IS. La ganancia genética para rendimiento correspondió a 1.228 kg.planta-1 con una heredabilidad (H2)=0,82, mientras que la GA para GE fue 0.02 con una H2= 0,935. El IS a partir de los valores BLUP seleccionó en las etapas finales tres nuevos cultivares (Criolla Dorada, Criolla Ocarina y Criolla Sua Pa) que fueron registradas ante el Instituto Colombiano Agropecuario ICA. Aunque BLUE y BLUP están altamente correlacionadas, el análisis BLUP/ESIM da ventaja al predictor pues reduce las respuestas con efecto ambiental, seleccionando eficientemente genotipos con alto potencial varietal.
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Referencias
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Barbosa, M.H.P., A. Ferreira, L.A. Peixoto, M.D.V. Resende, M. Nascimento, and F.F. Silva. 2014. Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah. Genet. Mol. Res. 13(1), 1619-1626. Doi: https://doi.org/10.4238/2014.March.12.14
Benavente, C.A.T., C.A.B.P. Pinto, I.C.R. Figueiredo, and G.H.M.R. Ribeiro. 2011. Repeatability of family means in early generations of potato under heat stress. Crop Breed. Appl. Biotechnol. 11, 330-337. Doi: https://doi.org/10.1590/S1984-70332011000400006
Bernardo, R. 1995. Best linear unbiased prediction of maize single-cross performance. Crop Sci. 36, 50-56. Doi: https://doi.org/10.2135/cropsci1996.0011183X003600010009x
Bernardo, R. 1996. Testcross additive and dominance effects in best linear unbiased prediction of maize single-cross performance. Theor. Appl. Genet. 93(7), 1098-1102. Doi: https://doi.org/10.1007/BF00230131
Bonierbale, M., W. Amoros, E. Espinoza, E. Mihovilovich, W. Roca, and R. Gómez. 2004. Recursos genéticos de la papa: don del pasado, legado para el futuro. Rev. Latinoam. Papa 12 (Suppl.) 3-14.
Borges, V., P.V. Ferreira, L. Soares, G.M. Santos, and A.M.M. Santos. 2010. Seleção de clones de batata-doce pelo procedimento REML/BLUP. Acta Sci. Agron. 32(4), 643-649. Doi: https://doi.org/10.4025/actasciagron.v32i4.4837
Burgos, G., W. Amoros, M. Morote, J. Stangoulis, and M. Bonierbale. 2007. Iron and zinc concentration of native Andean potato cultivars from a human nutrition perspective. J. Sci. Food Agric. 87, 668-675. Doi: https://doi.org/10.1002/jsfa.2765
Ceballos, H., J.C. Pérez, O. Joaqui Barandica, J.I. Lenis, N. Morante, F. Calle, and C.H. Hershey 2016. Cassava breeding I: the value of breeding value. Front. Plant Sci. 7, 1227. Doi: https://doi.org/10.3389/fpls.2016.01227
Cerón-Rojas, J.J., F. Castillo-González, J. Sahagún-Castellanos, A. Santacruz-Varela, I. Benítez-Riquelme, and J. Crossa. 2008. A molecular selection index method based on eigen analysis. Genetics 180(1), 547-557. Doi: https://doi.org/10.1534/genetics.108.087387
Cerón-Rojas, J.J., J. Crossa, J. Sahagún-Castellanos, F. Castillo-González y A. Santacruz-Varela, 2006. A selection index method based on eigenanalysis. Crop Sci. 46(4), 1711-1721. Doi: https://doi.org/10.2135/cropsci2005.11-0420
Cerón-Rojas, J.J., J. Crossa, F.H. Toledo, and J. Sahagún-Castellanos. 2016. A predetermined proportional gains eigen selection index method. Crop Sci. 56(5), 2436-2447. Doi: https://doi.org/10.2135/cropsci2015.11.0718
Cotes, J.M., C.E. Ñustez, R. Martínez, and N. Estrada. 2000. Análisis de la interacción genotipo por ambiente en papa (Solanum tuberosum spp. andigena), a través de una metodología no paramétrica. Agron. Colomb. 17, 43-56.
Federer, W.T. 1998. Recovery of interblock, intergradient, and intervariety information in incomplete block and lattice rectangle. Des. Exp. 54(2), 471-481. Doi: https://doi.org/10.2307/3109756
Federer, W. and D. Raghavarao. 1975. On augmented designs. Biometrics 31(1), 29-35. Doi: https://doi.org/10.2307/2529707
Ferreira, A.D.C., R. Fritsche Neto, and I.O. Geraldi. 2008. Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares. Crop Breed. Appl. Biotechnol. 8(3), 219-224. Doi: https://doi.org/10.12702/1984-7033.v08n03a06
Flori, A.R.P.A. and L.B.S. Hamon. 2001. Prediction of oil palm (Elaeis guineensis, Jacq.) agronomic performances using the best linear unbiased predictor (BLUP), 787-792. Doi: https://doi.org/10.1007/s001220051711
Francis, T.R. and L.W. Kannenberg. 1978. Yield stability studies in short-season maize. I. A descriptive method for grouping genotypes. Can. J. Plant Sci. 62(I), 105-111.
Gómez, M.I., H. Restrepo, L.E. Rodríguez, S. Magnitskiy, L. Manrique, and A. Garzón. 2018. Abiotic stress caused by foliar applications of boron to the yellow diploid potato (Solanum tuberosum, Group Phureja) cultivar Criolla Galeras. Rev. Colomb. Cienc. Hortic. 12(3), 582-591. Doi: https://doi.org/10.17584/rcch.2018v12i3.9520
Hammond, J.P., M.R. Broadley, H.C. Bowen, W.P. Spracklen, R.M. Hayden, and P.J. White. 2011. Gene expression changes in phosphorus deficient potato (Solanum tuberosum L.) leaves and the potential for diagnostic gene expression markers. PLoS ONE 6(9). e24606. Doi: https://doi.org/10.1371/journal.pone.0024606
Henderson, C. 1953. Estimation of variance and covariance components. Biometrics 9(2), 226-252. Doi: https://doi.org/10.2307/3001853
Henderson, C. 1984. Applications of linear models in animal breeding models. Univesity of Guelph, Guelph, Ontario, Canada.
Henderson, C.R. 2012. Best linear unbiased prediction (BLUP) of random effects in the normal linear mixed effects model. Statistics, Iwoa State University, Ames, IA.
Huamán, Z. and D.M. Spooner. 2002. Reclassification of landrace populations of cultivated potatoes (Solanum sect. Petota). Am. J. Bot. 89(6), 947-965. Doi: https://doi.org/10.3732/ajb.89.6.947
Littell, R.C., G.A. Milliken, W.W. Stroup, R.D. Wolfinger, and O. Schabenberger. 2006. SAS for mixed models. 2nd ed. SAS Press, Cary, NC.
Olivoto, T., M. Nardino, I.R. Carvalho, D.N. Follmann, M. Ferrari, V.J. Szareski de Pelegrin, and V.Q. de Souza. 2017. REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits. Genet. Mol. Res. 16(1), gmr16019525. Doi: https://doi.org/10.4238/gmr16019525
Patterson, H. and R. Thompson. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58(3), 545-554. Doi: https://doi.org/10.1093/biomet/58.3.545
Peña, C., L.-P. Restrepo-Sánchez, A. Kushalappa, L.-E. Rodríguez-Molano, T. Mosquera, and C.-E. Narváez-Cuenca. 2015. Nutritional contents of advanced breeding clones of Solanum tuberosum group Phureja. LWT - Food Sci. Technol. 62(1), 76-82. Doi: https://doi.org/10.1016/j.lwt.2015.01.038
Piepho, H.P. 1994. Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theor. Appl. Genet. 89(5). Doi: https://doi.org/10.1007/BF00222462
Piepho, H.P., J. Möhring, A.E. Melchinger, and A. Büchse. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1-2), 209-228. Doi: https://doi.org/10.1007/s10681-007-9449-8
PGSC, Potato Genome Sequencing Consortium. 2011. Genome sequence and analysis of the tuber crop potato. Nature 475, 189-195. Doi: https://doi.org/10.1038/nature10158
Poehlman, J. and D. Allen. 2003. Mejoramiento genético de las cosechas. 2nd ed. Limusa, Mexico D.F.
Rivadeneira, J., D. Ortega, V. Morales, C. Monteros, and X. Cuesta. 2016. Efecto de la interacción genotipo por ambiente sobre los contenidos de hierro, zinc y vitamina C en genotipos de papa (Solanum sp.). Rev. Latinoam. Papa 20(1), 32-45.
Rivera, J.E., A.O. Herrera, and L.E. Rodríguez. 2011. Assessment of the processing profile of six "creole potato" genotypes (Solanum tuberosum Phureja Group). Agron. Colomb. 29(1), 73-81.
Robinson, G.K. 1991. That BLUP is a good thing: the estimation of random effects. Stat. Sci. 6(1), 15-32. Doi: https://doi.org/10.1214/ss/1177011926
Rodríguez, L.E. 2013. Análisis genético y molecular para rendimiento y período de reposo de tubérculo en papa a nivel diploide (S. bukasovii x S. tuberosum grupo Phureja). PhD thesis. Universidad Nacional de Colombia, Bogota.
Rodríguez-Pérez, L. 2010. Ecofisiología del cultivo de la papa (Solanum tuberosum L.). Rev. Colomb. Cienc. Hortic. 4(1), 97-108. Doi: https://doi.org/10.17584/rcch.2010v4i1.1229
Slater, A.T., G.M. Wilson, N.O.I. Cogan, J.W. Forster, and B.J. Hayes. 2014. Improving the analysis of low heritability complex traits for enhanced genetic gain in potato. Theor. Appl. Genet. 127(4), 809-820. Doi: https://doi.org/10.1007/s00122-013-2258-7
Smith, H.F. 1936. A discriminant function for plant selection. pp. 466-476. In: Papers on Quantitative Genetics and Related Topics. Department of Genetics, North Carolina State College, Releigh, CN.
Ticona-Benavente, C.A. and C.A.B.P. Pinto. 2012. Selection intensities of families and clones in potato breeding. Ciênc. Agrotecnol. 36(1), 60-68. Doi: https://doi.org/10.1590/S1413-70542012000100008
Ticona-Benavente, C.A. and D.F. da Silva Filho 2015. Comparison of BLUE and BLUP/REML in the selection of clones and families of potato (Solanum tuberosum). Genet. Mol. Res. 14(4), 18421-18430. Doi: https://doi.org/10.4238/2015.December.23.30
Vittorazzi, C.A.T., A.G. Amaral Junior, A.P. Guimarães, F.H.L. Viana, G.F. Silva Pena, R.F. Daher, I.F.S. Gerhardt, G.H.F. Oliveira, and M.G. Pereira. 2017. Indices estimated using REML/BLUP and introduction of a super-trait for the selection of progenies in popcorn. Genet. Mol. Res. 16(3), gmr16039769. Doi: https://doi.org/10.4238/gmr16039769
Volpato, L., R.S. Alves, P.E. Teodoro, M.D. Vilela de Resende, M. Nascimento, A.C.C. Nascimento, W.H. Ludke, F. Lopes da Silva, and A. Borém. 2019. Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. PLoS One. 14(4), e0215315. Doi: https://doi.org/10.1371/journal.pone.0215315
Barbosa, M.H.P., A. Ferreira, L.A. Peixoto, M.D.V. Resende, M. Nascimento, and F.F. Silva. 2014. Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah. Genet. Mol. Res. 13(1), 1619-1626. Doi: https://doi.org/10.4238/2014.March.12.14
Benavente, C.A.T., C.A.B.P. Pinto, I.C.R. Figueiredo, and G.H.M.R. Ribeiro. 2011. Repeatability of family means in early generations of potato under heat stress. Crop Breed. Appl. Biotechnol. 11, 330-337. Doi: https://doi.org/10.1590/S1984-70332011000400006
Bernardo, R. 1995. Best linear unbiased prediction of maize single-cross performance. Crop Sci. 36, 50-56. Doi: https://doi.org/10.2135/cropsci1996.0011183X003600010009x
Bernardo, R. 1996. Testcross additive and dominance effects in best linear unbiased prediction of maize single-cross performance. Theor. Appl. Genet. 93(7), 1098-1102. Doi: https://doi.org/10.1007/BF00230131
Bonierbale, M., W. Amoros, E. Espinoza, E. Mihovilovich, W. Roca, and R. Gómez. 2004. Recursos genéticos de la papa: don del pasado, legado para el futuro. Rev. Latinoam. Papa 12 (Suppl.) 3-14.
Borges, V., P.V. Ferreira, L. Soares, G.M. Santos, and A.M.M. Santos. 2010. Seleção de clones de batata-doce pelo procedimento REML/BLUP. Acta Sci. Agron. 32(4), 643-649. Doi: https://doi.org/10.4025/actasciagron.v32i4.4837
Burgos, G., W. Amoros, M. Morote, J. Stangoulis, and M. Bonierbale. 2007. Iron and zinc concentration of native Andean potato cultivars from a human nutrition perspective. J. Sci. Food Agric. 87, 668-675. Doi: https://doi.org/10.1002/jsfa.2765
Ceballos, H., J.C. Pérez, O. Joaqui Barandica, J.I. Lenis, N. Morante, F. Calle, and C.H. Hershey 2016. Cassava breeding I: the value of breeding value. Front. Plant Sci. 7, 1227. Doi: https://doi.org/10.3389/fpls.2016.01227
Cerón-Rojas, J.J., F. Castillo-González, J. Sahagún-Castellanos, A. Santacruz-Varela, I. Benítez-Riquelme, and J. Crossa. 2008. A molecular selection index method based on eigen analysis. Genetics 180(1), 547-557. Doi: https://doi.org/10.1534/genetics.108.087387
Cerón-Rojas, J.J., J. Crossa, J. Sahagún-Castellanos, F. Castillo-González y A. Santacruz-Varela, 2006. A selection index method based on eigenanalysis. Crop Sci. 46(4), 1711-1721. Doi: https://doi.org/10.2135/cropsci2005.11-0420
Cerón-Rojas, J.J., J. Crossa, F.H. Toledo, and J. Sahagún-Castellanos. 2016. A predetermined proportional gains eigen selection index method. Crop Sci. 56(5), 2436-2447. Doi: https://doi.org/10.2135/cropsci2015.11.0718
Cotes, J.M., C.E. Ñustez, R. Martínez, and N. Estrada. 2000. Análisis de la interacción genotipo por ambiente en papa (Solanum tuberosum spp. andigena), a través de una metodología no paramétrica. Agron. Colomb. 17, 43-56.
Federer, W.T. 1998. Recovery of interblock, intergradient, and intervariety information in incomplete block and lattice rectangle. Des. Exp. 54(2), 471-481. Doi: https://doi.org/10.2307/3109756
Federer, W. and D. Raghavarao. 1975. On augmented designs. Biometrics 31(1), 29-35. Doi: https://doi.org/10.2307/2529707
Ferreira, A.D.C., R. Fritsche Neto, and I.O. Geraldi. 2008. Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares. Crop Breed. Appl. Biotechnol. 8(3), 219-224. Doi: https://doi.org/10.12702/1984-7033.v08n03a06
Flori, A.R.P.A. and L.B.S. Hamon. 2001. Prediction of oil palm (Elaeis guineensis, Jacq.) agronomic performances using the best linear unbiased predictor (BLUP), 787-792. Doi: https://doi.org/10.1007/s001220051711
Francis, T.R. and L.W. Kannenberg. 1978. Yield stability studies in short-season maize. I. A descriptive method for grouping genotypes. Can. J. Plant Sci. 62(I), 105-111.
Gómez, M.I., H. Restrepo, L.E. Rodríguez, S. Magnitskiy, L. Manrique, and A. Garzón. 2018. Abiotic stress caused by foliar applications of boron to the yellow diploid potato (Solanum tuberosum, Group Phureja) cultivar Criolla Galeras. Rev. Colomb. Cienc. Hortic. 12(3), 582-591. Doi: https://doi.org/10.17584/rcch.2018v12i3.9520
Hammond, J.P., M.R. Broadley, H.C. Bowen, W.P. Spracklen, R.M. Hayden, and P.J. White. 2011. Gene expression changes in phosphorus deficient potato (Solanum tuberosum L.) leaves and the potential for diagnostic gene expression markers. PLoS ONE 6(9). e24606. Doi: https://doi.org/10.1371/journal.pone.0024606
Henderson, C. 1953. Estimation of variance and covariance components. Biometrics 9(2), 226-252. Doi: https://doi.org/10.2307/3001853
Henderson, C. 1984. Applications of linear models in animal breeding models. Univesity of Guelph, Guelph, Ontario, Canada.
Henderson, C.R. 2012. Best linear unbiased prediction (BLUP) of random effects in the normal linear mixed effects model. Statistics, Iwoa State University, Ames, IA.
Huamán, Z. and D.M. Spooner. 2002. Reclassification of landrace populations of cultivated potatoes (Solanum sect. Petota). Am. J. Bot. 89(6), 947-965. Doi: https://doi.org/10.3732/ajb.89.6.947
Littell, R.C., G.A. Milliken, W.W. Stroup, R.D. Wolfinger, and O. Schabenberger. 2006. SAS for mixed models. 2nd ed. SAS Press, Cary, NC.
Olivoto, T., M. Nardino, I.R. Carvalho, D.N. Follmann, M. Ferrari, V.J. Szareski de Pelegrin, and V.Q. de Souza. 2017. REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits. Genet. Mol. Res. 16(1), gmr16019525. Doi: https://doi.org/10.4238/gmr16019525
Patterson, H. and R. Thompson. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58(3), 545-554. Doi: https://doi.org/10.1093/biomet/58.3.545
Peña, C., L.-P. Restrepo-Sánchez, A. Kushalappa, L.-E. Rodríguez-Molano, T. Mosquera, and C.-E. Narváez-Cuenca. 2015. Nutritional contents of advanced breeding clones of Solanum tuberosum group Phureja. LWT - Food Sci. Technol. 62(1), 76-82. Doi: https://doi.org/10.1016/j.lwt.2015.01.038
Piepho, H.P. 1994. Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theor. Appl. Genet. 89(5). Doi: https://doi.org/10.1007/BF00222462
Piepho, H.P., J. Möhring, A.E. Melchinger, and A. Büchse. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1-2), 209-228. Doi: https://doi.org/10.1007/s10681-007-9449-8
PGSC, Potato Genome Sequencing Consortium. 2011. Genome sequence and analysis of the tuber crop potato. Nature 475, 189-195. Doi: https://doi.org/10.1038/nature10158
Poehlman, J. and D. Allen. 2003. Mejoramiento genético de las cosechas. 2nd ed. Limusa, Mexico D.F.
Rivadeneira, J., D. Ortega, V. Morales, C. Monteros, and X. Cuesta. 2016. Efecto de la interacción genotipo por ambiente sobre los contenidos de hierro, zinc y vitamina C en genotipos de papa (Solanum sp.). Rev. Latinoam. Papa 20(1), 32-45.
Rivera, J.E., A.O. Herrera, and L.E. Rodríguez. 2011. Assessment of the processing profile of six "creole potato" genotypes (Solanum tuberosum Phureja Group). Agron. Colomb. 29(1), 73-81.
Robinson, G.K. 1991. That BLUP is a good thing: the estimation of random effects. Stat. Sci. 6(1), 15-32. Doi: https://doi.org/10.1214/ss/1177011926
Rodríguez, L.E. 2013. Análisis genético y molecular para rendimiento y período de reposo de tubérculo en papa a nivel diploide (S. bukasovii x S. tuberosum grupo Phureja). PhD thesis. Universidad Nacional de Colombia, Bogota.
Rodríguez-Pérez, L. 2010. Ecofisiología del cultivo de la papa (Solanum tuberosum L.). Rev. Colomb. Cienc. Hortic. 4(1), 97-108. Doi: https://doi.org/10.17584/rcch.2010v4i1.1229
Slater, A.T., G.M. Wilson, N.O.I. Cogan, J.W. Forster, and B.J. Hayes. 2014. Improving the analysis of low heritability complex traits for enhanced genetic gain in potato. Theor. Appl. Genet. 127(4), 809-820. Doi: https://doi.org/10.1007/s00122-013-2258-7
Smith, H.F. 1936. A discriminant function for plant selection. pp. 466-476. In: Papers on Quantitative Genetics and Related Topics. Department of Genetics, North Carolina State College, Releigh, CN.
Ticona-Benavente, C.A. and C.A.B.P. Pinto. 2012. Selection intensities of families and clones in potato breeding. Ciênc. Agrotecnol. 36(1), 60-68. Doi: https://doi.org/10.1590/S1413-70542012000100008
Ticona-Benavente, C.A. and D.F. da Silva Filho 2015. Comparison of BLUE and BLUP/REML in the selection of clones and families of potato (Solanum tuberosum). Genet. Mol. Res. 14(4), 18421-18430. Doi: https://doi.org/10.4238/2015.December.23.30
Vittorazzi, C.A.T., A.G. Amaral Junior, A.P. Guimarães, F.H.L. Viana, G.F. Silva Pena, R.F. Daher, I.F.S. Gerhardt, G.H.F. Oliveira, and M.G. Pereira. 2017. Indices estimated using REML/BLUP and introduction of a super-trait for the selection of progenies in popcorn. Genet. Mol. Res. 16(3), gmr16039769. Doi: https://doi.org/10.4238/gmr16039769
Volpato, L., R.S. Alves, P.E. Teodoro, M.D. Vilela de Resende, M. Nascimento, A.C.C. Nascimento, W.H. Ludke, F. Lopes da Silva, and A. Borém. 2019. Multi-trait multi-environment models in the genetic selection of segregating soybean progeny. PLoS One. 14(4), e0215315. Doi: https://doi.org/10.1371/journal.pone.0215315
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