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The look ahead trace back optimizer for genomic selection under transparent and opaque simulators.

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The new look ahead trace back algorithm accelerates genetic gain in genomic selection (GS). Opaque simulators were developed to realistically evaluate GS algorithms, improving in silico predictions for plant breeding.

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Area of Science:

  • Quantitative Genetics
  • Plant Breeding
  • Computational Biology

Background:

  • Genomic selection (GS) accuracy and selection strategies are crucial for genetic gain.
  • The look ahead selection algorithm outperforms truncation selection by focusing on potential elite progeny.
  • Imperfect genomic prediction necessitates advanced selection strategies.

Purpose of the Study:

  • Introduce the look ahead trace back algorithm, a variant of look ahead selection.
  • Develop and utilize opaque simulators for more realistic evaluation of GS algorithms.
  • Compare GS algorithm performance across simulators with varying levels of opacity.

Main Methods:

  • Designed partially observable, opaque simulators capturing additive/non-additive genetic effects and uncertain recombination.
  • Implemented and compared various GS algorithms, including the new look ahead trace back algorithm.
  • Conducted computational experiments using a maize dataset across four distinct simulators.

Main Results:

  • The look ahead trace back algorithm shows potential for accelerated genetic gain, especially with imperfect predictions.
  • GS algorithm performance varied significantly depending on the simulator's level of opacity.
  • Transparent simulators may provide an unrealistic advantage to GS algorithms compared to real-world breeding.

Conclusions:

  • Opaque simulators are essential for accurately assessing GS algorithms' real-world performance.
  • Realistic simulation environments are critical to bridge the gap between in silico and in planta results.
  • Further research into sophisticated, opaque simulation is needed for advancing genomic selection.