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Related Experiment Video

Updated: Jun 7, 2025

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

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Stochastic simulation to optimize rice breeding at IRRI.

Fallou Seck1,2, Parthiban Thathapalli Prakash1, Giovanny Covarrubias-Pazaran1

  • 1Rice Breeding Platform, International Rice Research Institute, Metro Manila, Philippines.

Frontiers in Plant Science
|November 18, 2024
PubMed
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Accelerating rice breeding with genomic selection (GS) and reduced cycle times significantly boosts genetic gain. Shorter breeding cycles enhance yield potential, crucial for meeting global demand despite climate change challenges.

Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Genetics

Background:

  • Global rice demand is rising, necessitating enhanced breeding strategies to overcome climate change impacts.
  • Traditional rice breeding faces limitations in achieving the required genetic gain for key traits.
  • The International Rice Research Institute (IRRI) employs advanced techniques like rapid generation advance (RGA) and genomic selection (GS).

Purpose of the Study:

  • To compare different genomic selection schemes and their impact on medium- and long-term genetic gain in rice.
  • To evaluate the effectiveness of reduced breeding cycle times on genetic improvement.
  • To optimize rice breeding programs for increased genetic gain.

Main Methods:

  • Stochastic simulations were used to compare breeding schemes with varying cycle times.
Keywords:
breeding strategygenetic gaingenomic selectionricestochastic simulations

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  • Four genomic selection schemes were simulated: 5-year, 3-year (current), and two 2-year recycling options.
  • The study assessed genetic gain under different genotype-by-environment interaction (GEI) contexts.

    • Main Results:

    • A 2-year within-cohort prediction scheme demonstrated a significant medium-term increase in genetic gain (22-27%) across varying GEI levels.
    • A 2-year between-cohort prediction scheme showed long-term efficiency, particularly without GEI.
    • Shorter breeding cycles led to increased genetic gain but also faster depletion of genetic variance compared to current methods.

    Conclusions:

    • Reducing breeding cycle time and optimizing the target population of environments can increase genetic gain rates.
    • Further research into crossing strategies is needed to optimally utilize genetic variance.
    • These findings support data-driven optimization for efficient rice breeding programs.