The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Marker‐Assisted Selection in Plant Breeding: From Publications to Practice
Abstract
The volume of publications on the development and to a lesser extent the application of molecular markers in plant breeding has increased dramatically during the last decade. However, most of the publications result from investments from donors with a strategic science quality or biotech advocacy mandate leading to insufficient emphasis on applied value in plant breeding. Converting promising publications into practical applications requires the resolution of many logistical and genetical constraints that are rarely addressed in journal publications. This results in a high proportion of published markers failing at one or more of the translation steps from research arena to application domain. The rate of success is likely to increase due to developments in gene‐based marker development, more efficient quantitative trait locus (QTL) mapping procedures, and lower cost genotyping systems. However, some fundamental issues remain to be resolved, particularly regarding complex traits, before marker‐assisted selection realizes its full potential in public sector breeding programs. These include the development of high throughput precision phenotyping systems for QTL mapping, improved understanding of genotype by environment interaction and epistasis, and development of publicly available computational tools tailored to the needs of molecular breeding programs.
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Citations
Cites
Genetics of global gene expression
Rockman, M. V., & Kruglyak, L. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1964 · Cited by 666 · cited 1×
“Dissecting the architecture of quantitative traits in this way connects DNA sequence variation with phenotypic variation, and is improving our understanding of transcriptional regulation and regulatory variation (Rockman and Kruglyak, 2006).”
BackgroundMapping As You Go: An Effective Approach for Marker‐Assisted Selection of Complex Traits
Podlich, D. W., Winkler, C. R., & Cooper, M. (2004) · Crop Science · doi:10.2135/cropsci2004.1560 · Cited by 169
Mixed models including environmental covariables for studying QTL by environment interaction
Malosetti, M., Voltas, J., Romagosa, I., Ullrich, S. E., & van Eeuwijk, F. A. (2004) · Euphytica · doi:10.1023/b:euph.0000040511.46388.ef · Cited by 167
Cited by
Quantitative Trait Loci and Crop Performance under Abiotic Stress: Where Do We Stand?: Table I.
Collins, N. C., Tardieu, F., & Tuberosa, R. (2008) · PLANT PHYSIOLOGY · doi:10.1104/pp.108.118117 · Cited by 559
Atta, K., Mondal, S., Gorai, S., Singh, A. P., Kumari, A., Ghosh, T., Roy, A., Hembram, S., Gaikwad, D. J., Mondal, S., et al. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1241736 · Cited by 523
A deep convolutional neural network approach for predicting phenotypes from genotypes
Ma, W., Qiu, Z., Song, J., Li, J., Cheng, Q., Zhai, J., & Ma, C. (2018) · Planta · doi:10.1007/s00425-018-2976-9 · Cited by 341
Enhancing 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-citationTahmasebi, S., Heidari, B., Pakniyat, H., & McIntyre, C. L. (2017) · Genome · doi:10.1139/gen-2016-0017 · Cited by 120
Metadata
- Authors
- Xu, Y., & Crouch, J. H.
- Venue
- Crop Science
- Publisher
- Wiley
- Published
- 2008-03
- Volume
- 48
- Issue
- 2
- Pages
- 391-407
- ISSN
- 0011-183X (ISSN-L)
- 1435-0653
- OpenAlex
- W2143950081
- Cited by
- 967
- References
- 3
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- closed
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