The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi‐parental Maize Populations under Drought Stress
Open accessAbstract
Genomic selection incorporates all the available marker information into a model to predict genetic values of breeding progenies for selection. The objective of this study was to estimate genetic gains in grain yield from genomic selection (GS) in eight bi‐parental maize populations under managed drought stress environments. In each population, 148 to 300 F 2:3 (C 0) progenies were derived and crossed to a single‐cross tester from a complementary heterotic group. The resulting testcrosses of each population were evaluated under two to four managed drought stress and three to four well‐watered conditions in different locations and genotyped with 191 to 286 single nucleotide polymorphism (SNP) markers. The top 10% families were selected from C 0 using a phenotypic selection index and were intermated to form C 1. Selections both at C 1 and C 2 were based on genomic estimated breeding values (GEBVs). The best lines from C 0 were also advanced using a pedigree selection scheme. For genetic gain studies, a total of 55 entries representing the eight populations were crossed to a single‐cross tester, and evaluated in four managed drought stress environments. Each population was represented by bulk seed containing equal amounts of seed of C 0, C 1, C 2, C 3, parents, F 1 s, and lines developed via pedigree selection. Five commercial checks were included for comparison. The average gain from genomic selection per cycle across eight populations was 0.086 Mg ha–1. The average grain yield of C 3–derived hybrids was significantly higher than that of hybrids derived from C 0. Hybrids derived from C 3 produced 7.3% (0.176 Mg ha–1) higher grain yield than those developed through the conventional pedigree breeding method. The study demonstrated that genomic selection is more effective than pedigree‐based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.
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Cites
Efficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J., Piraux, F., Guerreiro, L., Pérez, P., Calus, M., et al. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2243-1 · Cited by 687
Self-citationEvaluation of genome-wide selection efficiency in maize nested association mapping populations
Guo, Z., Tucker, D. M., Lu, J., Kishore, V., & Gay, G. (2012) · Theoretical and Applied Genetics · doi:10.1007/s00122-011-1702-9 · Cited by 127
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Self-citationEnhancing genetic gain in the era of molecular breeding
Xu, Y., Li, P., Zou, C., Lu, Y., Xie, C., Zhang, X., Prasanna, B. M., & Olsen, M. S. (2017) · Journal of Experimental Botany · doi:10.1093/jxb/erx135 · Cited by 336
Self-citationDias, K. O. D. G., Gezan, S. A., Guimarães, C. T., Nazarian, A., da Costa e Silva, L., Parentoni, S. N., de Oliveira Guimarães, P. E., de Oliveira Anoni, C., Pádua, J. M. V., de Oliveira Pinto, M., et al. (2018) · Heredity · doi:10.1038/s41437-018-0053-6 · Cited by 115
Crossa, J., Martini, J. W. R., Gianola, D., Pérez-Rodríguez, P., Jarquin, D., Juliana, P., Montesinos-López, O., & Cuevas, J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01168 · Cited by 79
Self-citationMultimodal deep learning methods enhance genomic prediction of wheat breeding
Montesinos-López, A., Rivera, C., Pinto, F., Piñera, F., Gonzalez, D., Reynolds, M., Pérez-Rodríguez, P., Li, H., Montesinos-López, O. A., & Crossa, J. (2023) · G3: Genes, Genomes, Genetics · doi:10.1093/g3journal/jkad045 · Cited by 41
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Metadata
- Authors
- Beyene, Y., Semagn, K., Mugo, S., Tarekegne, A., Babu, R., Meisel, B., Sehabiague, P., Makumbi, D., Magorokosho, C., Oikeh, S., Gakunga, J., Vargas, M., Olsen, M., Prasanna, B. M., Banziger, M., & Crossa, J.
- Venue
- Crop Science
- Publisher
- Wiley
- Published
- 2015-01
- Volume
- 55
- Issue
- 1
- Pages
- 154-163
- ISSN
- 0011-183X (ISSN-L)
- 1435-0653
- OpenAlex
- W2063021966
- Cited by
- 302
- References
- 3
- FWCI
- 11.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Beyene, Y., Semagn, K., Mugo, S., Tarekegne, A., Babu, R., Meisel, B., Sehabiague, P., Makumbi, D., Magorokosho, C., Oikeh, S., Gakunga, J., Vargas, M., Olsen, M., Prasanna, B. M., Banziger, M., & Crossa, J. (2015). Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi‐parental Maize Populations under Drought Stress. Crop Science. https://doi.org/10.2135/cropsci2014.07.0460
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