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Theoretical and Applied Genetics

, Volume 130, Issue 7, pp 1431–1440 | Cite as

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids

  • Rocío Acosta-Pech
  • José Crossa
  • Gustavo de los Campos
  • Simon Teyssèdre
  • Bruno Claustres
  • Sergio Pérez-Elizalde
  • Paulino Pérez-RodríguezEmail author
Original Article

Abstract

Key message

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.

Abstract

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.

Keywords

Prediction Accuracy General Combine Ability Specific Combine Ability Dent Line Genomic Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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.

Supplementary material

122_2017_2898_MOESM1_ESM.docx (41 kb)
Supplementary material 1 (DOCX 45 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Rocío Acosta-Pech
    • 1
  • José Crossa
    • 2
  • Gustavo de los Campos
    • 3
  • Simon Teyssèdre
    • 4
  • Bruno Claustres
    • 4
  • Sergio Pérez-Elizalde
    • 1
  • Paulino Pérez-Rodríguez
    • 1
    Email author
  1. 1.Colegio de Postgraduados, CP 56230MontecillosMéxico
  2. 2.Biometrics and Statistics Unit of the International Maize and Wheat Improvement Center (CIMMYT)México DFMéxico
  3. 3.Department of Epidemiology and BiostatisticsMichigan State UniversityEast LansingUSA
  4. 4.RAGT 2n, Analytics Research TeamDruelleFrance

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