The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers
Open accessAbstract
Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker‐based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes (“newly” developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confirmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefit greatly from using multienvironment GS models.
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Cites
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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Prospects for Genomewide Selection for Quantitative Traits in Maize
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Self-citationNon-invasive genetic sampling and individual identification
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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-citationImpact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review
Raza, A., Razzaq, A., Mehmood, S. S., Zou, X., Zhang, X., Lv, Y., & Xu, J. (2019) · Plants · doi:10.3390/plants8020034 · Cited by 1,762
“Burgueño et al. (2012) proposed the first statistical design by applying a linear mixed model to G × E model [157].”
MethodGenomic 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
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-citationGenomic prediction in CIMMYT maize and wheat breeding programs
Crossa, J., Pérez, P., Hickey, J., Burgueño, J., Ornella, L., Cerón-Rojas, J., Zhang, X., Dreisigacker, S., Babu, R., Li, Y., et al. (2014) · Heredity · doi:10.1038/hdy.2013.16 · Cited by 451
Self-citation
Metadata
- Authors
- Burgueño, J., de los Campos, G., Weigel, K., & Crossa, J.
- Venue
- Crop Science
- Publisher
- Wiley
- Published
- 2012-03
- Volume
- 52
- Issue
- 2
- Pages
- 707-719
- ISSN
- 0011-183X (ISSN-L)
- 1435-0653
- OpenAlex
- W2151391832
- Cited by
- 601
- References
- 38
- FWCI
- 10.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- National Association of Animal Breeders
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- Burgueño, J., de los Campos, G., Weigel, K., & Crossa, J. (2012). Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers. Crop Science. https://doi.org/10.2135/cropsci2011.06.0299
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