Skip to main navigation menu Skip to main content Skip to site footer

Vol. 14 No. 1 (2020)

Vegetable section

BLUP (Best Linear Unbiased Predictors) analysis for the selection of superior yellow diploid potato genotypes (Solanum tuberosum group Phureja)

https://doi.org/10.17584/rcch.2020v14i1.8138

Published 2020-06-12

  • José E. Pacheco
    • ,
  • Johan Sebastian Urquijo
    • ,
  • Aquiles Enrique Darghan
    • ,
  • Luis Ernesto Rodríguez

José E. Pacheco
Universidad Pedagógica y Tecnológica de Colombia, Grupo de Investigación BIOPLASMA-UPTC, Tunja

Johan Sebastian Urquijo
Universidad Nacional de Colombia, Facultad de Ciencias Agrarias, Bogota

Aquiles Enrique Darghan
Universidad Nacional de Colombia, Facultad de Ciencias Agrarias, Bogota

Luis Ernesto Rodríguez
Universidad Nacional de Colombia, Facultad de Ciencias Agrarias, Bogota

Evaluation field of promising genotypes of yellow diploid potato. Photo: L.E. Rodríguez.

Abstract

One of the major challenges that breeders face is the differential response of genotypes from one environment to another, known as the genotype × environmental interaction (GxE). The optimal procedure with the restricted maximum likelihood/best linear unbiased predictor (REML/BLUP) allows simultaneous estimation of genetic parameters and prediction of genotypic values. BLUP predictors are an alternative to the narrowing of biased values, which are based on variances of genotype to determine the response value, as a complement to the selection index (SI). The ESIM (Eigenvalue Selection Index) selects genotypes based on two or more variables or selection characters as long as the economic matrix possesses the appropriate values for highlighting the desired response variable. Three stages of selection were evaluated in an advanced diploid potato improvement program. BLUP values were obtained for the yield and specific gravity variables, used to determine the genetic parameters and the SI. The genetic gain for yield corresponded to 1.228 kg/plant with a heritability (H2) = 0.82, while the GA for GE was 0.02 with an H2 = 0.935. The SI from the BLUP values selected in the final stages of the three new cultivars (Criolla Dorada, Criolla Ocarina and Criolla Sua Pa) was registered at the Instituto Colombiano Agropecuario (ICA). Although BLUE and BLUP are highly correlated, the BLUP/ESIM analysis has an advantage as a predictor because it reduces responses to the environmental effect, efficiently selecting genotypes with a high agronomic potential.

Keywords

Potato breeding, Selection index, ESIM, Genetic parameters

PDF

References

  1. Alvarado, G., M. López, M. Vargas, Á. Pacheco, F. Rodríguez, J. Burgueño y J. Crossa. 2015. META-R: Multi Environment Trail Analysis with R for Windows V19. International Maize and Wheat Improvement Center. En http://hdl.handle.net/11529/10201; Consultado: marzo de 2017.
  2. Barbosa, M.H.P., A. Ferreira, L.A. Peixoto, M.D.V. Resende, M. Nascimento y 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.
  3. Bernardo, R. 1995. Best linear unbiased prediction of maize single-cross performance. Crop Sci. 36, 50-56.
  4. 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.
  5. Bonierbale, M., W. Amoros, E. Espinoza, E. Mihovilovich, W. Roca y R. Gómez. 2004. Recursos Genéticos de la papa: don del pasado, legado para el futuro. Rev. Latinoam. Papa 12 (suplemento).
  6. Borges, V., P.V. Ferreira, L. Soares, G.M. Santos y A.M.M. Santos. 2010. Seleção de clones de batata-doce pelo procedimento REML/BLUP. Acta Sci. – Agron. 32(4), 643-649.
  7. Burgos, G., W. Amoros, M. Morote, J. Stangoulis y M. Bonierbale. 2007. Iron and zinc concentration of native Andean potato cultivars from a human nutrition perspective. J. Sci. Food Agric. 87, 668-675.
  8. Ceballos, H., N. Morante, F. Calle, J.I. Lenis, G. Jaramillo y C. Pérez. 2002. Mejoramiento Genético de la yuca. pp. 295-325. En: Ospina, P., H. Ceballos, E. Alvarez, A. Bellotti, L. Calvert, B. Arias, … M.I. Cuervo (eds.), La yuca en el tercer milenio: sistemas modernos de producción, procesamiento, utilización y comercialización. Centro Internacional de Agricultura Tropical (CIAT); Consorcio Latinoamericano para la Investigación y el Desarrollo de la Yuca, Cali, Colombia.
  9. Ceballos, H., J.C. Pérez, O. Joaqui Barandica, J.I. Lenis, N. Morante, F. Calle, … C.H. Hershey, 2016. Cassava breeding i: the value of breeding value. Front. Plant Sci. 7(September).
  10. Cerón-Rojas, J.J., F. Castillo-González, J. Sahagún-Castellanos, A. Santacruz-Varela, I. Benítez-Riquelme y J. Crossa. 2008. A molecular selection index method based on eigenanalysis. Genetics 180(1), 547-557.
  11. 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.
  12. Cerón-Rojas, J.J., J. Crossa, F.H. Toledo, y J. Sahagún-Castellanos. 2016. A predetermined proportional gains eigen selection index method. Crop Sci. 56(5), 2436-2447.
  13. Cotes, J.M., C.E. Ñustez, R. Martínez, y 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.
  14. Federer, W. y D. Raghavarao. 1975. On augmented designs. Biometrics 31(1), 29-35.
  15. Federer, W.T. 1998. Recovery of interblock, intergradient, and intervariety information in incomplete block and lattice rectangle. Des. Exp. 54(2), 471-481.
  16. Ferreira de Carvalho, A.D., R. Fritsche Neto y 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.
  17. Flori, A.R.P.A. y L.B.S. Hamon. 2001. Prediction of oil palm (Elaeis guineensis, Jacq.) agronomic performances using the best linear unbiased predictor ( BLUP ), 787–792.
  18. Francis, T.R. y 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.
  19. Hammond, J.P., M.R. Broadley, H.C. Bowen, W.P. Spracklen, R.M. Hayden y 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).
  20. Henderson, C. 1953. Estimation of variance and covariance components. Biometrics 9(2), 226-252.
  21. Henderson, C. 1984. Applications of linear models in animal breeding models. Univesity of Guelph, Guelph, Ontario, Canada.
  22. Henderson, C.R. 2012. Best linear unbiased prediction (BLUP) of random effects in the normal linear mixed effects model. Aaps.
  23. Huamán, Z. y D.M. Spooner. 2002. Reclassification of landrace populations of cultivated potatoes (Solanum sect. Petota). Am. J. Bot. 89(6), 947-965.
  24. Littell, R.C., G.A. Milliken, W.W. Stroup, R.D. Wolfinger y O. Schabenberger. 2006. SAS for mixed models. 2a ed. SAS Press, Cary, NC.
  25. Patterson, H. y R. Thompson. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58(3), 545-554.
  26. Peña, C., L.-P. Restrepo-Sánchez, A. Kushalappa, L.-E. Rodríguez-Molano, T. Mosquera y C.-E. Narváez-Cuenca. 2015. Nutritional contents of advanced breeding clones of Solanum tuberosum group Phureja. LWT - Food Sci. Technol. 62(1), 76-82.
  27. 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).
  28. Piepho, H.P., J. Möhring, A.E. Melchinger y A. Büchse. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1–2), 209-228.
  29. Poehlman, J. y D. Allen. 2003. Mejoramiento genético de las cosechas. 2a ed. Limusa, Mexico D.F.
  30. PGSC, Potato Genome Sequencing Consortium. 2011. Genome sequence and analysis of the tuber crop potato. Nature 475, 189-195.
  31. Rivadeneira, J., D. Ortega, V. Morales, C. Monteros, y 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.
  32. Rivera, J.E., A.O. Herrera y L.E. Rodríguez. 2011. Assessment of the processing profile of six “creole potato” genotypes (Solanum tuberosum Phureja Group). Agron. Colomb. 29(1), 73-81.
  33. Robinson, G.K. 1991. That BLUP is a good thing: the estimation of random effects. Stat. Sci. 6(1), 15-32.
  34. Rodríguez M., L.E. 2013. Análisis genético y molecular para rendimiento y período de reposo de tubérculo en papa a nivel diploide (S. bukasovvi x S. tuberosum grupo Phureja). Universidad Nacional de Colombia, en http://www.bdigital.unal.edu.co/44373/; consultado: octubre de 2017.
  35. Slater, A.T., G.M. Wilson, N.O.I. Cogan, J.W. Forster y 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.
  36. Smith, H.F. 1936. A discriminant function for plant selection. Papers on Quantitative Genetics and Related Topics, 466–476.
  37. Ticona-Benavente, C.A., C.A. Brasil Pereira Pinto, I.C. Rodrigues de Figueiredo y G.H. Martins Rodrigues Ribeiro. 2011. Repeatability of family means in early generations of potato under heat stress. Crop Breed. Appl. Biotechnol. 11, 330-337.
  38. Ticona-Benavente, C. A. y 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.
  39. Ticona-Benavente, C.A. y C.A.B.P. Pinto. 2012. Selection intensities of families and clones in potato breeding. Ciênc. Agrotecnol. 36(1), 60-68.

Downloads

Download data is not yet available.

Similar Articles

1 2 3 4 > >> 

You may also start an advanced similarity search for this article.