Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids
- 1.1k Downloads
A new genomic model that incorporates genotype × environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids.
The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype × environment interaction (G × E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G × E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G × E in the prediction of untested maize hybrids increases the accuracy of genomic models.
KeywordsPrediction Accuracy General Combine Ability Specific Combine Ability Dent Line Genomic Prediction
The authors appreciate the positive and detailed comments from two anonymous reviewers and the time invested by the Associated Editor handling the manuscript. These contributions significantly improved the quality and clarity of the article.
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
- Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, WoodburyGoogle Scholar
- de los Campos G, Pérez-Rodríguez P (2016) BGLR: Bayesian generalized linear regression. R package v. 1.0.5Google Scholar
- Duvick DN, Smith JSC, Cooper M (2004) Long-term selection in a commercial hybrid maize breeding program. Plant Breed Rev Part 2(24):109–152Google Scholar
- Golob GH, Van Loan CF (1996) Matrix computations, 3rd edn. Johns Hopkins University Press, BaltimoreGoogle Scholar
- López-Cruz M, Crossa J, Bonnet D, Dreisigacker S, Poland J, Jannink L-L, Singh RP, Autrique E, de los Campos G (2015) Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3 Genes Genom Genet. doi: 10.1534/g3.114.016097 Google Scholar
- R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org. Accessed 4 Apr 2017