Comparative study between phenotypic and genomic analyses aimed at choosing parents for hybridization purposes

Palavras-chave: genome-wide selection; genetic diversity; diallel analysis.

Resumo

The development of superior cultivars involves parents with superiority for the traits of interest and wide genetic variability. Efficient plant breeding and selection strategies that allow for the identification of superior genotypes are essential in breeding programs. This work aims to carry out a comparative study between several strategies for choosing parents, for hybridization purposes, based on phenotypic analysis and molecular information. To obtain the phenotypic and genotypic information of the parents, data simulation was used. For genotyping, 2000 single nucleotide polymorphism markers were used, and from all possible gametes to be formed (22000), 5000 were randomly sampled to form each of the 100 individuals of the population of recombinant inbred strains. To obtain the phenotypic information, five characteristics with different levels of complexity were simulated. The comparative study was carried out using data referring to simulated genotypic values of hybrids and parents. Then, aiming to choose the parents destined for hybridization, different traditional selection strategies based on phenotypic analysis and the genome-wide selection methodology were approached. The genomic information resulted in the choice of the best lines and in obtaining superior hybrids when compared with traditional methodologies. The inclusion of the genomic genetic values of the parents in determining the crosses to be carried out increases the probability of generating phenotypically superior hybrids. Thus, the traditional methods of choosing parents for hybridization purposes are effective, but when incorporating the information from genome-wide selection, the choice of parents provides superior and promising results.

Downloads

Não há dados estatísticos.

Referências

Ahmar, S., Gill, R. A., Jung, K. H., Faheem, A., Qasim, M. U., Mubeen, M., & Zhou, W. (2020). Conventional and molecular techniques from simple breeding to speed breeding in crop plants: recent advances and future outlook. International Journal of Molecular Sciences, 21(7), 1-24. DOI: https://doi.org/10.3390/ijms21072590

Alkimim, E.R., Caixeta, E.T., Sousa, T.V., Resende, M. D. V., Silva, F. L., Sakiyama, N. S., & Zambolin, L. (2020). Selective efficiency of genome-wide selection in Coffea canephora breeding. Tree Genetics & Genomes, 16(3), 1-11. DOI: https://doi.org/10.1007/s11295-020-01433-3

Almeida, C. P., Carvalho Paulino, J. F., Carbonell, S. A. M., Chiorato, A. F., Song, Q., Di Vittori, V., ... Benchimol-Reis, L. L. (2020). Genetic diversity, population structure, and Andean introgression in Brazilian common bean cultivars after half a century of genetic breeding. Genes, 11(11), 1-22. DOI: https://doi.org/10.3390/genes11111298

Barbosa, I. D. P., Silva, M. J., Costa, W. G., Castro Sant'Anna, I., Nascimento, M., & Cruz, C. D. (2021). Genome-enabled prediction through machine learning methods considering different levels of trait complexity. Crop Science, 61(3),1890-1902. DOI: https://doi.org/10.1002/csc2.20488

Barros, L. M., Prochnow, D., Oliveira, V. F., Silva, Oliveira, A. C., & Maia, A. C. (2019). Characterization of open-pollinated maize varieties from Rio Grande do Sul State. Journal of Crop Science and Biotechnology, 22(1), 31-36. DOI: https://doi.org/10.1007/s12892-018-0051-0

Bhandari, H. R., Bhanu, A. N., Srivastava, K., Singh, M. N., Shreya, & Hemantaranjan, A. (2017). Assessment of genetic diversity in crop plants - an overview. Advances in Plants & Agriculture Research, 7(3), 279-286. DOI: https://doi.org/10.15406/apar.2017.07.00255

Bohar, R., Chitkineni, A., & Varshney, R. K. (2020). Genetic molecular markers to accelerate genetic gains in crops. BioTechniques, 69(3), 158-160. DOI: https://doi.org/10.2144/btn-2020-0066

Coelho, I. F., Alves, R. S., Peixoto, M. A., Teodoro, L. P. R., Teodoro, P. E., Pinto, J. F. N., ... Bhering, L. L. (2020). Multi-trait multi-environment diallel analyses for maize breeding. Euphytica, 216(9), 1-17. DOI: https://doi.org/10.1007/s10681-020-02677-9

Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-Lopez, O., Jarquín, D., Los Campos, G., ... Varshney, R. K. (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends in Plant Science, 22(11), 961-975. DOI: https://doi.org/10.1016/j.tplants.2017.08.011

Cruz, C.D. (2013). Genes Software para análise de dados em estatística experimental e em genética quantitativa. Acta Scientiarum. Agronomy, 35(3), 271-276. DOI: https://doi.org/10.4025/actasciagron.v35i3.21251

Cruz, C. D. (2016). Genes Software – extended and integrated with the R, Matlab and Selegen. Acta Scientiarum. Agronomy, 38(4), 547-552. DOI: http://dx.doi.org/10.4025/actasciagron.v38i4.32629

Cruz, C. D., Carneiro, P. C. S., & Bhering, L. L. (2021). Biometry in plant breeding. Crop Breeding and Applied Biotechnology, 21(S), 1-11. DOI: http://dx.doi.org/10.1590/1984- 70332021v21Sa18

Cruz, C. D., Ferreira, F. M., & Pessoni, L. A. (2011). Biometria aplicada ao estudo da diversidade genética. Viçosa, MG: UFV.

Cruz, C. D., Salgado, C. C., & Bhering, L. L. (2013). Genômica aplicada. Viçosa, MG: UFV.

Falconer, D. S. (1981). Introdução à genética quantitativa. Viçosa, MG: UFV.

Ferreira, D. F., Oliveira, A. C., Santos, M. X., & Ramalho, M. A. P. (1995). Métodos de avaliação da divergência genética em milho e suas relações com os cruzamentos dialélicos. Pesquisa Agropecuária Brasileira, 30(9), 1189–1194.

Fonseca, J. S., & Martins, G. A. (1996). Curso de estatística (6. ed.). São Paulo, SP: Atlas.

Friske, É., Schuelter, A. R., Schuster, I., Marcolin, J., & Silva, M. F. (2018). Genetic diversity of maize lines for traits related to maturity and yield components. Australian Journal of Crop Science, 12(12), 1820-1828. DOI: https://doi.org/10.21475/ajcs.18.12.12.p1005

Garrido-Cardenas, J. A., Mesa-Valle, C., & Manzano-Agugliaro, F. (2017). Trends in plant research using molecular markers. Planta, 247, 543-557. DOI: https://doi.org/10.1007/s00425-017-2829-y

Gupta, P. K., Kumar, J., Mir, R. R., & Kumar, A. (2010). Marker-assisted selection as a component of conventional plant breeding. In J. Janick (Ed.), Plant breeding reviews. Hoboken, NJ: John Wiley & Sons. DOI: https://doi.org/10.1002/9780470535486.ch4

Hayman, B. I. (1954). The analysis of variance of diallel tables. Biometrics, 10(2), 235-244. DOI: http://dx.doi.org/10.2307/3001877

Hill, W. G. (2012). Quantitative genetics in the genomics era. Current Genomics, 13(3), 196-206. DOI: https://doi.org/10.2174/138920212800543110

Hongyu, K., Sandanielo, V. L. M., & Junior, G. J. O. (2016). Análise de Componentes Principais: resumo teórico, aplicação e interpretação. Engineering and Science, 5(1), 83-90. DOI: https://doi.org/10.18607/ES201653398

Khaki, S., Khalilzadeh, Z., & Wang, L. (2020). Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PLoS ONE, 15(15), 1-13. DOI: https://doi.org/10.1371/journal.pone.0233382

Kulka, V. P., Silva, T. A., Contreras-Soto, R. I., Maldonado, C., Mora, F., & Scapim, C. A. (2018). Diallel analysis and genetic differentiation of tropical and temperate maize inbred lines. Crop Breeding and Applied Biotechnology, 18(1), 31-38. DOI: https://doi.org/10.1590/1984-70332018v18n1a5

Lenaerts, B., Collard, B. C., & Demont, M. (2018). Global survey of rice breeders to investigate characteristics and willingness to adopt alternative breeding methods. Agriculture & Food Security, 7(40), 1-15. DOI: https://doi.org/10.1186/s40066-018-0191-3

Lyzenga, W. J., Pozniak, C. J., & Kagale, S. (2021). Advanced domestication: Harnessing the precision of gene editing in crop breeding. Plant Biotechnology Journal, 19(4), 660-670. DOI: https://doi.org/10.1111/pbi.13576

Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829. DOI: https://doi.org/10.1093/genetics/157.4.1819

Moura, L. M., Anjos, R. S. R., Batista, R. O., Vale, N. M., Cruz, C. D., Carneiro, J. E. S., ... Carneiro, P. C. S. (2018). Combining ability of common bean parents in different seasons, locations and generations. Euphytica, 214(181), 1-13. DOI: https://doi.org/10.1007/s10681-018-2259-3

Müller, B. S. F., Pappas, G. J., Valdisser, P. A. M. R., Coelho, G. R. C., Menezes, I. P. P., Abreu, A. G., ... Vianello, R. P. (2015). An operational SNP panel integrated to SSR marker for the assessment of genetic diversity and population structure of the common bean. Plant Molecular Biology Reporter, 33, 1697-1711. DOI: https://doi.org/10.1007s11105-015-0866-x

Nadeem, M. A., Nawaz, M. A., Shahid, M. Q., Doğan, Y., Comertpay, G., Yıldız, M., ... Labhane, N. (2018). DNA molecular markers in plant breeding: Current status and recent advancements in genomic selection and genome editing. Biotechnology & Biotechnological Equipment, 32(2), 261-285. DOI: https://doi.org/10.1080/13102818.2017.1400401

Phuke, R. M., Anuradha, K., Radhika, K., Jabeen, F., Anuradha, G., Ramesh, T., ... Kumar, A. A. (2017). Genetic variability, genotype × environment interaction, correlation, and GGE biplot analysis for grain iron and zinc concentration and other agronomic traits in RIL population of sorghum (Sorghum bicolor L. Moench). Frontiers in Plant Science, 8(712), 1-13. DOI: https://doi.org/10.3389/fpls.2017.00712

Pimentel, A. J. B., Ribeiro, G., Souza, M. A., Moura, L. M., Assis, J. C., & Machado, J. C. (2013). Comparação de métodos de seleção de genitores e populações segregantes aplicados ao melhoramento de trigo. Bragantia, 72(2), 113-121. DOI: https://doi.org/10.1590/S0006-87052013005000026

R Core Team. (2019). R: A language and environment for statistical computing. Vienna, AT: R Foundation for Statistical Computing. Retrieved on Aug. 10, 2021 from https://www.R-project.org/

Rasmussen, S. K. (2020). Molecular genetics, genomics, and biotechnology in crop plant breeding. Agronomy, 10(3), 1-5. DOI: https://doi.org/10.3390/agronomy10030439

Sousa, I. C., Nascimento, M., Silva, G. N., Nascimento, A. C. C., Cruz, C. D., Silva, F. F. E, ... Caixeta, E. T. (2021). Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Scientia Agricola, 78(4), 1-8. DOI: https://doi.org/10.1590/1678-992X-2020-0021.

Swarup, S., Cargill, E. J., Crosby, K., Flagel, L., Kniskern, J., & Glenn, K.C. (2021). Genetic diversity is indispensable for plant breeding to improve crops. Crop Science, 61(2), 839-852. DOI: https://doi.org/10.1002/csc2.20377

Vasconcelos, E. S., Cruz, C. D., Bhering, L. L., & Resende Junior, M. F. R. (2007). Método alternativo para análise de agrupamento. Pesquisa Agropecuária Brasileira, 42(10), 1421-1428.

Wang, J., Li, H., & Zhang, L. (2020). Genetic mapping and breeding design (2nd ed.). Beijing, CH: Science Press.

Whang, X., Zhang, Z., Xu, Y., Li, P., Zhang, X., & Xu C. (2019). Using genomic data to improve the estimation of general combining ability based on sparse partial diallel cross designs in maize. The Crop Journal, 8(5), 819-829. DOI: https://doi.org/10.1016/j.cj.2020.04.012

Werle, A. J. K., Ferreira, F. R. A., Pinto, R. J. B., Mangolin, C. A., Scapim, C. A., & Gonçalves, L. S. A. (2014). Diallel analysis of maize inbred lines for grain yield, oil and protein content. Crop Breeding and Applied Biotechnology, 14(1), 23-28. DOI: https://doi.org/10.1590/S1984-70332014000100004

Publicado
2023-08-22
Como Citar
Chagas, F. E. de O., Silva, M. J. da, Silva Júnior , A. C. da, Rosado, R. D. S., & Cruz, C. D. (2023). Comparative study between phenotypic and genomic analyses aimed at choosing parents for hybridization purposes. Acta Scientiarum. Agronomy, 45(1), e61550. https://doi.org/10.4025/actasciagron.v45i1.61550
Seção
Melhoramento Vegetal

 

2.0
2019CiteScore
 
 
60th percentile
Powered by  Scopus

 

2.0
2019CiteScore
 
 
60th percentile
Powered by  Scopus