The DOI matches its Crossref record; 119 of the 119 references it cites were found in a registry or an index.
Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
Open accessAbstract
Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.
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Cites
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Cited by
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
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
Self-citationEfficient Bayesian mixed-model analysis increases association power in large cohorts
Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507
Computationally efficient whole-genome regression for quantitative and binary traits
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Genomic selection: genome-wide prediction in plant improvement
Desta, Z. A., & Ortiz, R. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2014.05.006 · Cited by 688
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
Metadata
- Authors
- de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., & Calus, M. P. L.
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-02-01
- Volume
- 193
- Issue
- 2
- Pages
- 327-345
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W2128343509
- Cited by
- 893
- References
- 119
- FWCI
- 24.80
- Type
- journal article
- Language
- en
- Open access
- bronze
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- de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., & Calus, M. P. L. (2013). Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding. Genetics. https://doi.org/10.1534/genetics.112.143313
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