A reaction norm model for genomic selection using high-dimensional genomic and environmental data
University of Nebraska–Lincoln · University of Alabama at Birmingham · +7 more institutions
Abstract
New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high…
Citation impact
- FWCI
- 23.05
- Percentile
- 100%
- References
- 53
Authors
12- DJDiego JarquínCorresponding
University of Nebraska–Lincoln, University of Alabama at Birmingham
- JCJosé Crossa
Centro Internacional de Mejoramiento de Maíz Y Trigo
- XLXavier Lacaze
Arvalis - Institut du Végétal, Inter 3
- PDPhilippe du Cheyron
Université Paris-Sud, Arvalis - Institut du Végétal
- JDJoëlle Daucourt
Université Paris-Sud, Arvalis - Institut du Végétal
Topics & keywords
- Covariate
- Gene–environment interaction
- Biology
- Grain yield
- Plant biochemistry
- Covariance
- Environmental data
- Statistics