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Genomic Selection in Multi-environment Crop Trials.

Helena Oakey1, Brian Cullis2, Robin Thompson3

  • 1Division of Plant Sciences, University of Dundee at the James Hutton Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK.

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Summary
This summary is machine-generated.

This study introduces a new genomic selection model for crop breeding that improves prediction accuracy by accounting for field trial complexities. The enhanced model increases confidence in selecting superior crop lines and can reduce costs by using fewer genetic markers.

Keywords:
GEBVGenPredbarleygenomic selectionmulti-environment trialrandom ridge regressionshared data resource

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Area of Science:

  • Agricultural Science
  • Genetics
  • Biotechnology

Background:

  • Genomic selection (GS) in crop breeding faces unique challenges compared to animal studies.
  • These include managing replicate plants, spatial field variations, line-environment interactions, and nonadditive genetic effects.

Purpose of the Study:

  • To propose a flexible single-stage genomic selection approach to address these challenges.
  • To improve the accuracy and efficiency of predicting genetic values in crop breeding programs.

Main Methods:

  • Developed a linear mixed model incorporating spatial and design terms for field trials.
  • Utilized ridge regression best linear unbiased prediction (RR-BLUP) for marker and marker-by-environment interactions.
  • Partitioned line genetic variation into marker (additive) and nonmarker (nonadditive) effects using raw replicate data.

Main Results:

  • The proposed model significantly improved predictive ability in barley height trials.
  • Single-trial analysis showed up to 5.7% improvement; multiple environment trial (MET) analysis improved predictive ability by up to 11.4%.
  • Achieved similar predictive ability with fewer markers (500-1000) compared to traditional models using 3490 markers.

Conclusions:

  • The new genomic selection approach enhances prediction accuracy and confidence in identifying elite crop lines.
  • This method offers potential cost reductions in breeding by enabling the use of fewer markers without sacrificing predictive power.