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

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Autores

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

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.

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References

Araujo, F.F., M.N.S. Santos, N.O. Araújo, T.P. Silva, L.C. Costa, and F.L. Finger. 2020. Growth and dry matter partitioning of potato influenced by paclobutrazol applied to seed tuber. Rev. Colomb. Cienc. Hortic. 14(1). Doi: 10.17584/rcch.2020v14i1.10357

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: 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: 10.1590/S1984-70332011000400006

Bernardo, R. 1995. Best linear unbiased prediction of maize single-cross performance. Crop Sci. 36, 50-56. Doi: 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: 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: 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: 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: 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: 10.1534/genetics.108.087387

Cerón-Rojas, J.J., J. Crossa, J. Sahagún-Castellanos, F. Castillo-González, and A. Santacruz-Varela, 2006. A selection index method based on eigenanalysis. Crop Sci. 46(4), 1711-1721. Doi: 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: 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: 10.2307/3109756

Federer, W. and D. Raghavarao. 1975. On augmented designs. Biometrics 31(1), 29-35. Doi: 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: 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: 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: 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: 10.1371/journal.pone.0024606

Henderson, C. 1953. Estimation of variance and covariance components. Biometrics 9(2), 226-252. Doi: 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: 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: 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: 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: 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: 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: 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: 10.1038/nature10158

Poehlman, J. and D. Allen. 2003. Mejoramiento genético de las cosechas. 2nd ed. Limusa, Mexico, DF.

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: 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: 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: 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: 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: 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: 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: 10.1371/journal.pone.0215315

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