Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T12:11:33.065Z Has data issue: false hasContentIssue false

Selection of parents and estimation of genetic parameters using BLUP and molecular methods for lentil (Lens culinaris Medik.) breeding program in Argentina

Published online by Cambridge University Press:  10 April 2019

Carolina Bermejo*
Affiliation:
Instituto de Investigaciones en Ciencias Agrarias de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IICAR-CONICET), Facultad de Ciencias Agrarias, Universidad Nacional de Rosario (UNR), CC 14, S2125ZAA Zavalla, Santa Fe, Argentina
Federico Cazzola
Affiliation:
Cátedra de Mejoramiento Vegetal y Producción de Semillas, Facultad de Ciencias Agrarias, UNR, CC 14, S2125ZAA Zavalla, Santa Fe, Argentina
Fernando Maglia
Affiliation:
Cátedra de Mejoramiento Vegetal y Producción de Semillas, Facultad de Ciencias Agrarias, UNR, CC 14, S2125ZAA Zavalla, Santa Fe, Argentina
Enrique Cointry
Affiliation:
Instituto de Investigaciones en Ciencias Agrarias de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas (IICAR-CONICET), Facultad de Ciencias Agrarias, Universidad Nacional de Rosario (UNR), CC 14, S2125ZAA Zavalla, Santa Fe, Argentina
*
*Corresponding author. Email: [email protected]

Abstract

The most important objective of lentil breeding programs is to develop new genotypes that are genetically more productive. Besides, it is necessary that the varieties obtained have short flowering cycles to allow the later sowing of summer crops. Selection is based through phenotypic means; however, we argue it should be based on genetic or breeding values because quantitative traits are often influenced by environments and genotype–environment interactions. The objectives of this study were to: (i) identify genotypes with the highest merit; (ii) estimate genetic parameters to know the genetic control of morphological traits in macrosperma and microsperma lentil types using best linear unbiased prediction (BLUP). Twenty-five recombinant inbred lines (RILs) from six F4 families selected on the basis of precocity and high yields were tested in four environments for important quantitative traits. The analysis of variance showed significant differences between genotypes, environments, and genotype–environment interactions for all the traits. Seven macrosperma- and two microsperma-type RILs were selected. Based on average ranking from breeding values and molecular data obtained with sequence-related amplified polymorphism (SRAP), the same genotypes were selected. Genotypic coefficients of variation, heritability across and by environment, and genetic correlation coefficients using BLUP were obtained. According to our results BLUP could replace molecular analysis methods because the selection process was simpler, more cost-effective, and more accurate. The breeding value of parents would give a better ranking of their genetic value than would their phenotypic value; therefore, the selection efficiency would be enhanced and the genetic gain would be more predictable. The selected genotypes could become potential commercial varieties or be used as parental lines in future hybridization programs.

Type
Research Article
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alvarado, G., López, M., Vargas, M., Pacheco, A., Rodríguez, F., Burgueño, J. and Crossa, J. (2016). META-R (Multi Environment Trail Analysis with R for Windows) Version 6.01. hdl:11529/10201, CIMMYT Research Data & Software Repository Network, V20, México.Google Scholar
Avola, G., Riggi, E., Gresta, F., Sortino, O. and Onofri, A. (2018). Random effects models, BLUPs and redundancy analyses for grain legume crops in semi-arid environments. European Journal of Agronomy 93, 1826.CrossRefGoogle Scholar
Balzarini, M. and Di Rienzo, J. (2003). Infogen: Software for Statistical Analysis of Genetic Markers. Córdoba: National University of Córdoba (in Spanish).Google Scholar
Bartlett, M.S. (1937). Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London. Series A-Mathematical and Physical Sciences 160(901), 268282.Google Scholar
Barulina, E.I. (1930). The lentils of the USSR and other countries. Bulletin of Applied Botany, Genetic and Selection 40(Suppl.), 1319 [in Russian] Cambridge.Google Scholar
Bertoldo, J.G., Nodari, R.O., Coimbra, J.L.M., Guidolin, A.F., Toaldo, D., Pinho de Morais, P.P. and Elias, H.T. (2014). Genetic progress of black bean (Phaseolus vulgaris L.) over seven years. Interciencia 39(1), 2431.Google Scholar
Ceccarelli, S. (2015). Efficiency of plant breeding. Crop Science 55(1), 8797.CrossRefGoogle Scholar
Chahota, R.K., Kishore, N., Dhiman, K.C., Sharma, T.R. and Sharma, S.K. (2007). Predicting transgressive segregants in early generation using single seed descent method-derived micro-macrosperma genepool of lentil (Lens culinaris Medikus). Euphytica 156(3), 305310.CrossRefGoogle Scholar
Chiorato, A.F., Carbonell, S.A.M., Dias, L.A.S. and Resende, M.D.V. (2008). Prediction of genotypic values and estimation of genetic parameters in common bean. Brazilian Archives of Biology and Technology 51, 465472.CrossRefGoogle Scholar
Erskine, W., Sarker, A. and Kumar, S. (2016) Lentil: Breeding. In: Wrigley, C., Corke, H., Seetharaman, K. and Faubion, J. (eds), Encyclopedia of Food Grains, 2nd Edition. Oxford: Academic Press, 317324.CrossRefGoogle Scholar
Ganjeali, A., Khormizi, A.B., Lahouti, M. and Shafieian, H. (2015). Ameliorative effects of Ca2+ on deleterious effects of salinity on nutrients uptake and some morphological traits of two genotypes of lentil (Lens culinaris M.). Journal of Science and Technology 6(22), 84.Google Scholar
Henderson, C.R. (1985). Best linear unbiased prediction of nonadditive genetic merits in non-inbred populations. Journal of Animal Science 60, 111117.CrossRefGoogle Scholar
Lado, B., Battenfield, S., Guzmán, C., Quincke, M., Singh, R.P., Dreisigacker, S., Peña, J.R., Fritz, A., Silva, P., Polland, J. and Gutiérrez, L. (2017). Strategies for selecting crosses using genomic prediction in two wheat breeding programs. The Plant Genome 10(2), 112.CrossRefGoogle ScholarPubMed
Materne, M. and McNeil, D.L. (2007). Breeding methods and achievements. In Yadav, S.S., McNeil, D.L. and Stevenson, P.C. (eds), Lentil. Dordrecht, The Netherlands: Springer, 241253.CrossRefGoogle Scholar
Paliya, S., Saxena, A., Tikle, A.N., Singh, M. and Tilwari, A. (2015). Genetic divergence and character association of seed yield and component traits of lentil (Lens culinaris M.). Advances in Bioresearch 6(2), 5359.Google Scholar
Pereira, F.C., Bruzi, A.T., de Matos, J.W., Rezende, B.A., Prado, L.C. and Nunes, J.A.R. (2017). Implications of the population effect in the selection of soybean progeny. Plant Breeding 136(5), 679687.CrossRefGoogle Scholar
Piepho, H.P., Möhring, J., Melchinger, A.E. and Büchse, A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161, 209228.CrossRefGoogle Scholar
Rahimi, M.H., Houshmand, S., Khodambashi, M., Shiran, B. and Mohammady, S. (2016). Effect of drought stress on agro-morphological traits of lentil (Lens culinaris Medik.) recombinant inbred lines. Bangladesh Journal of Agricultural Research 41(2), 207219.CrossRefGoogle Scholar
Resende, M.D.V. (2002). Genética biométrica e estatística no melhoramento de plantas perenes. Brasília: Embrapa.Google Scholar
Resende, M.D. V., Ramalho, M.A.P., Nunes, J.A.R., da Silva, F.L. and Carneiro, P.C.S. (2017). BLUP in the genetic evaluation of parents, generations, populations, and progenies. In da Silva, F.L., Borém, A., Sediyama, T. and Ludke, W.H. (eds), Soybean Breeding. Cham, Switzerland: Springer International Publishing AG, pp. 229252.CrossRefGoogle Scholar
Ruiz Corral, J.A., Medina, G., González, A., Flores, L., Ramírez, O., Ortiz, T., Byerly, M. and Martínez, P. (2013). Agroecological requirements of crops, 2nd Edn. Technical Book No. 3. INIFAP. Tepatitlán de Morelos, Jalisco, México: National Institute of Agricultural and Cattle Forestry Research-CIRPAC-Experimental Center Altos de Jalisco, 564 p.Google Scholar
Shapiro, S.S. and Wilk, M.B. (1965). An analysis of variance test for normality (complete samples). Biometrika 52, 591611.CrossRefGoogle Scholar
Smýkal, P., Horacek, J., Dostalova, R. and Hybl, M. (2008). Variety discrimination in pea (Pisum sativum L.) by molecular, biochemical and morphological markers. Theorethical and Applied Genetics 49(2), 155166.CrossRefGoogle ScholarPubMed
Soh, A.C. (1994). Ranking parents by best linear unbiased prediction (BLUP) breeding values in oil palm. Euphytica 76, 1321.CrossRefGoogle Scholar
Świeca, M., Gawlik-Dziki, U., Kowalczyk, D. and Złotek, U. (2012). Impact of germination time and type of illumination on the antioxidant compounds and antioxidant capacity of Lens culinaris sprouts. Scientia Horticulturae 140, 8795.CrossRefGoogle Scholar
Tabti, D., Laouar, M., Rajendran, K., Kumar, S. and Abdelguerfi, A. (2018). Identification of desirable mutants in quantitative traits of lentil at early (M2) generation. Journal of Environmental Biology 39(2), 137142.CrossRefGoogle Scholar
Thavarajah, D., Thavarajah, P., Vial, E., Gebhardt, M., Lacher, C., Kumar, S. and Combs, G.F. (2015). Will selenium increase lentil (Lens culinaris Medik) yield and seed quality? Frontiers in Plant Science 6, 356.CrossRefGoogle ScholarPubMed
Vargas, M., Combs, E., Alvarado, G., Atlin, G., Mathews, K. and Crossa, J. (2013). META: a suite of SAS programs to analyze multienvironment breeding trials. Agronomy Journal, 105(1), 1119.CrossRefGoogle Scholar