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Related Experiment Video

Updated: Sep 8, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building.

S Yadav1, S Dillon2, M McNeil3

  • 1Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, Australia. seema.yadav@uq.edu.au.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|September 6, 2025
PubMed
Summary

Optimal Haplotype Selection (OHS) using metaheuristic algorithms improves crop breeding by identifying superior parent sets. This genomic-assisted breeding strategy enhances long-term genetic gain and maintains diversity for sustainable crop improvement.

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Area of Science:

  • Plant breeding and genetics
  • Computational biology
  • Bioinformatics

Background:

  • Genomic-assisted breeding aims to develop superior crop genotypes by stacking desirable haplotypes.
  • Optimal Haplotype Selection (OHS) faces challenges in identifying optimal parent sets due to combinatorial complexity.
  • Metaheuristic search algorithms (MSAs) offer potential solutions for complex optimisation problems in parent selection.

Purpose of the Study:

  • To evaluate the performance of MSAs (GA, DE, PSO, SA) for parent selection in genotype building (GB).
  • To compare two GB objectives: OHS and Optimal Population Value (OPV).
  • To assess the long-term impact of OHS and OPV on genetic gain and diversity.

Main Methods:

  • Utilised a wheat population (583 lines) genotyped for 29,972 SNPs and phenotyped for stripe rust.
  • Applied GA, DE, PSO, and SA to optimise parent selection for OHS and OPV.
  • Simulated 100 breeding cycles to evaluate long-term genetic gain and diversity retention.

Main Results:

  • Genetic Algorithm (GA) demonstrated high fitness and rapid convergence.
  • Optimal Haplotype Selection (OHS) outperformed OPV and Genomic Estimated Breeding Value (GEBV)-based selection in long-term genetic gain and diversity.
  • OHS maintained heterozygosity and additive variance, crucial for sustainable crop improvement.

Conclusions:

  • Metaheuristic algorithms effectively optimise parent selection for genotype building.
  • OHS is a promising strategy for enhancing long-term genetic gain and maintaining diversity in crop breeding programs.
  • Prioritising collective parent set performance over individual ranking improves selection outcomes in genomic-assisted breeding.