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AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.

Zhen Yang1, Mei Song1, Xianggeng Huang1

  • 1School of Mathematics and Statistics, Ludong University, Yantai, 264025, Shandong, China.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|August 7, 2025
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Summary
This summary is machine-generated.

We developed adaptiveGS, a novel genomic selection framework, to improve prediction accuracy by adaptively selecting machine learning models. This approach enhances trait prediction and identifies significant SNPs for molecular breeding.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning in Breeding

Background:

  • Genomic selection (GS) is crucial for modern molecular breeding.
  • Stacking ensemble learning (SEL) enhances GS by combining multiple models.
  • A unified framework for selecting base learners (BLs) in SEL is currently lacking.

Purpose of the Study:

  • To develop an adaptive and unified framework for base learner selection in stacking genomic selection.
  • To improve prediction accuracy and model interpretability in genomic selection.
  • To identify significant single nucleotide polymorphisms (SNPs) associated with target traits.

Main Methods:

  • Developed adaptiveGS, a data-driven strategy for selecting optimal BLs based on the PR index (PCC and NRMSE).
  • Implemented SHapley Additive exPlanations (SHAP) for model interpretation and SNP identification.
  • Compared adaptiveGS against 13 other GS algorithms across 21 traits from 4 species.

Main Results:

  • adaptiveGS demonstrated superior predictive accuracy and robustness, outperforming 13 other GS models on most traits.
  • Achieved an average prediction accuracy (PCC) of 0.703, with an average improvement of 14.4%.
  • Successfully identified significant SNPs and potential interaction effects influencing trait variations through SHAP analysis.

Conclusions:

  • adaptiveGS offers an operable and unified solution for stacking GS, enhancing prediction accuracy in plant and animal breeding.
  • The framework provides valuable insights into the genetic architecture of complex traits.
  • The adaptiveGS package is publicly available for broader application in the breeding field.