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5Gs for crop genetic improvement
Open accessAbstract
Here we propose a 5G breeding approach for bringing much-needed disruptive changes to crop improvement. These 5Gs are Genome assembly, Germplasm characterization, Gene function identification, Genomic breeding (GB), and Gene editing (GE). In our view, it is important to have genome assemblies available for each crop and a deep collection of germplasm characterized at sequencing and agronomic levels for identification of marker-trait associations and superior haplotypes. Systems biology and sequencing-based mapping approaches can be used to identify genes involved in pathways leading to the expression of a trait, thereby providing diagnostic markers for target traits. These genes, markers, haplotypes, and genome-wide sequencing data may be utilized in GB and GE methodologies in combination with a rapid cycle breeding strategy.
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Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917
Michelmore, R. W., Paran, I., & Kesseli, R. V. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.21.9828 · Cited by 4,535
Temperature increase reduces global yields of major crops in four independent estimates
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang, M., Yao, Y., Bassu, S., Ciais, P., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1701762114 · Cited by 3,311
Genome-wide association studies of 14 agronomic traits in rice landraces
Huang, X., Wei, X., Sang, T., Zhao, Q., Feng, Q., Zhao, Y., Li, C., Zhu, C., Lu, T., Zhang, Z., et al. (2010) · Nature Genetics · doi:10.1038/ng.695 · Cited by 2,148
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Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., Burgueño, J., González-Camacho, J. M., Pérez-Elizalde, S., Beyene, Y., et al. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2017.08.011 · Cited by 1,839
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Wang, W., Mauleon, R., Hu, Z., Chebotarov, D., Tai, S., Wu, Z., Li, M., Zheng, T., Fuentes, R. R., Zhang, F., et al. (2018) · Nature · doi:10.1038/s41586-018-0063-9 · Cited by 1,748
Takagi, H., Abe, A., Yoshida, K., Kosugi, S., Natsume, S., Mitsuoka, C., Uemura, A., Utsushi, H., Tamiru, M., Takuno, S., et al. (2013) · The Plant Journal · doi:10.1111/tpj.12105 · Cited by 1,490
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Abe, A., Kosugi, S., Yoshida, K., Natsume, S., Takagi, H., Kanzaki, H., Matsumura, H., Yoshida, K., Mitsuoka, C., Tamiru, M., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2095 · Cited by 1,363
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Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., Iyer, R., Schatz, M. C., Sinha, S., & Robinson, G. E. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002195 · Cited by 1,340
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Watson, A., Ghosh, S., Williams, M. J., Cuddy, W. S., Simmonds, J., Rey, M.-D., Asyraf Md Hatta, M., Hinchliffe, A., Steed, A., Reynolds, D., et al. (2018) · Nature Plants · doi:10.1038/s41477-017-0083-8 · Cited by 1,228
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Metadata
- Authors
- Varshney, R. K., Sinha, P., Singh, V. K., Kumar, A., Zhang, Q., & Bennetzen, J. L.
- Venue
- Current Opinion in Plant Biology
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 56
- Pages
- 190-196
- ISSN
- 1369-5266 (ISSN-L)
- OpenAlex
- W3003662894
- Cited by
- 257
- References
- 60
- FWCI
- 5.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
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