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Genomic selection in multi-environment plant breeding trials using a factor analytic linear mixed model.

Daniel J Tolhurst1, Ky L Mathews1, Alison B Smith1

  • 1National Institute for Applied Statistics Research Australia, Centre for Bioinformatics and Biometrics, University of Wollongong, Wollongong, New South Wales, Australia.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
|June 28, 2019
PubMed
Summary

Genomic selection (GS) in plant breeding requires advanced models to handle complex multi-environment trials (METs). This study introduces a factor analytic model to improve genetic gain by accounting for variety by environment interactions.

Keywords:
factor analysisgenomic selectionlinear mixed modelsmulti-environment trialsvariety by environment interaction

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Area of Science:

  • Agricultural Science
  • Genetics
  • Biotechnology

Background:

  • Genomic selection (GS) is a powerful tool for improving genetic gain, widely adopted in animal breeding.
  • Transferring GS to plant breeding presents challenges due to complex multi-environment trial (MET) data and variety by environment interactions (VEI).
  • Existing GS methods may not fully account for the intricate structure and interactions present in plant breeding MET data.

Purpose of the Study:

  • To develop a novel statistical model for genomic selection in plant breeding that effectively handles multi-environment trial (MET) data.
  • To incorporate molecular marker data and model variety by environment interactions (VEI) within a unified framework.
  • To introduce and demonstrate advanced selection tools derived from factor analytic models for practical application in plant breeding.

Main Methods:

  • Development of a single-step factor analytic linear mixed model tailored for plant breeding MET data.
  • Integration of molecular marker data into the mixed model framework.
  • Appropriate modeling of non-genetic sources of variation within trials and the crucial variety by environment interaction (VEI).
  • Application of recently developed factor analytic-based selection tools to a real-world plant breeding dataset.

Main Results:

  • The proposed factor analytic model successfully accommodates complex MET data structures and models VEI.
  • The study demonstrates the effectiveness of the developed selection tools in facilitating genomic selection.
  • The power and versatility of the tools are shown for analyzing variety by environment and marker by environment effects.

Conclusions:

  • The developed single-step factor analytic linear mixed model provides a robust approach for genomic selection in plant breeding.
  • This methodology enhances the ability to model complex interactions, leading to improved genetic gain predictions.
  • The associated selection tools offer practical and powerful solutions for plant breeders utilizing genomic data in METs.