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Genomic prediction in wheat breeding benefits from sparse testing designs. Model M3, incorporating genomic by environment interaction (GE), consistently improves prediction accuracy across various line allocations and population sizes.

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Area of Science:

  • Plant Breeding
  • Genomics
  • Agricultural Science

Background:

  • Genome-enabled prediction is crucial for accelerating plant breeding.
  • Sparse testing designs, involving overlapping and non-overlapping line allocations across environments, are employed to optimize resource allocation.
  • Understanding the impact of these designs on prediction accuracy is essential for efficient breeding programs.

Purpose of the Study:

  • To evaluate the impact of sparse testing allocation designs on genome-enabled prediction accuracy in wheat breeding.
  • To compare the performance of different genome-enabled prediction models, including those with and without genomic by environment interaction (GE).
  • To determine optimal strategies for line allocation and testing population size to maximize prediction accuracy.

Main Methods:

  • Three wheat datasets (W1-W3) were utilized.
  • Three genome-enabled prediction models were applied: M1 (main effects), M2 (main effects + genomic effects), and M3 (main effects + genomic effects + GE).
  • Various sparse testing allocation designs (non-overlapping, overlapping, and mixed) and testing population sizes were systematically analyzed.

Main Results:

  • The genomic by environment interaction (GE) component in model M3 captured greater genetic variability compared to main genomic effects in M1 and M2.
  • Model M3 consistently yielded higher prediction accuracy than models M1 and M2 across all allocation designs and population sizes.
  • Overlapping sets of 30-50 lines in all environments ensured stable prediction accuracy.
  • Reducing testing population size decreased prediction accuracy, which was regained by increasing the number of lines tested across environments.

Conclusions:

  • Model M3, incorporating GE, is superior for genome-enabled prediction in wheat breeding under sparse testing scenarios.
  • Optimal sparse testing designs, particularly those with overlapping lines, can maintain high prediction accuracy.
  • Model M3 provides flexibility to maintain prediction accuracy even with extreme allocation designs (all non-overlapping or all overlapping lines).