The DOI matches its Crossref record; 204 of the 204 references it cites were found in a registry or an index.
Enhancing genetic gain in the era of molecular breeding
Open accessAbstract
As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial selection. To develop pro ducts that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders' demonstration trials and the actual yield in farmers' fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary approaches, team breeding, will be required to address the challenge of maintaining a plentiful and safe food supply for future generations. New opportunities for enhancing genetic gain, a high efficiency breeding pipeline, and broad-sense genetic gain are also discussed prospectively.
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Cites
Introduction to Quantitative Genetics.
Edwards, A. W. F., & Falconer, D. S. (1982) · Biometrics · doi:10.2307/2529912 · Cited by 21,447
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
RNA-Guided Human Genome Engineering via Cas9
Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., Norville, J. E., & Church, G. M. (2013) · Science · doi:10.1126/science.1232033 · Cited by 9,210
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917
Global food demand and the sustainable intensification of agriculture
Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116437108 · Cited by 7,692
Cpf1 Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas System
Zetsche, B., Gootenberg, J. S., Abudayyeh, O. O., Slaymaker, I. M., Makarova, K. S., Essletzbichler, P., Volz, S. E., Joung, J., van der Oost, J., Regev, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.038 · Cited by 4,761
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637
Integrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
Seed Banks and Molecular Maps: Unlocking Genetic Potential from the Wild
Tanksley, S. D., & McCouch, S. R. (1997) · Science · doi:10.1126/science.277.5329.1063 · Cited by 2,353
C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector
Abudayyeh, O. O., Gootenberg, J. S., Konermann, S., Joung, J., Slaymaker, I. M., Cox, D. B. T., Shmakov, S., Makarova, K. S., Semenova, E., Minakhin, L., et al. (2016) · Science · doi:10.1126/science.aaf5573 · Cited by 2,336
Cited by
Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants
Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., Prasanna, B. M., Olsen, M. S., Wang, G., & Zhang, A. (2020) · Plant Communications · doi:10.1016/j.xplc.2019.100005 · Cited by 281
Self-citationSharma, K. K., Palakolanu, S. R., Bhattacharya, J., Shankhapal, A. R., & Bhatnagar-Mathur, P. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.999207 · Cited by 24
Metadata
- Authors
- Xu, Y., Li, P., Zou, C., Lu, Y., Xie, C., Zhang, X., Prasanna, B. M., & Olsen, M. S.
- Venue
- Journal of Experimental Botany
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-10
- Volume
- 68
- Issue
- 11
- Pages
- 2641-2666
- ISSN
- 0022-0957 (ISSN-L)
- 1460-2431
- OpenAlex
- W2613078054
- Cited by
- 336
- References
- 204
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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