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  3. Trait Association For Flowering Time In Lentil From Global Multi-environment Data Using Gwas And Machine Learning.
  1. Home
  3. Trait Association For Flowering Time In Lentil From Global Multi-environment Data Using Gwas And Machine Learning.

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Trait Association for Flowering Time in Lentil from Global Multi-Environment Data Using GWAS and Machine Learning.

Shriprabha R Upadhyaya1,2, Hawlader A Al-Mamun1,2,3, Monica F Danilevicz4

  • 1Centre for Applied Bioinformatics, The University of Western Australia, Perth, WA 6009, Australia.

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View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models complement Genome-Wide Association Studies (GWAS) for identifying genetic markers linked to plant flowering time. These advanced methods improve trait prediction by capturing complex gene interactions and environmental influences.

Keywords:
SHAPXAIepistasissingle nucleotide polymorphisms (SNPs)

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Area of Science:

  • Plant genetics
  • Agricultural science
  • Computational biology

Background:

  • Flowering time is a crucial plant trait influenced by genes and environment.
  • Genome-Wide Association Studies (GWAS) identify genetic markers but often miss complex interactions.
  • Machine Learning (ML) can model these interactions and improve trait prediction.

Purpose of the Study:

  • To identify genetic markers associated with flowering time in lentil (Lens culinaris Medik.).
  • To compare the effectiveness of GWAS and ML approaches in detecting flowering time-associated loci.
  • To leverage Explainable AI (XAI) for enhanced model interpretability.

Main Methods:

  • Analysis of multi-environment lentil data using GWAS.
  • Application of Random Forest and XGBoost machine learning models.
  • Utilisation of SHapley Additive exPlanations (SHAP) for model interpretability.

    • Main Results:

    • GWAS identified eight significant loci, with the top SNP at Chr2_530433205.
    • ML approaches detected nine markers, with the top SNP at Chr7_523220088.
    • Most identified markers were associated with known flowering time genes; ML also suggested potential epistasis.

    Conclusions:

    • Machine learning serves as a powerful complementary tool to GWAS for trait association studies.
    • ML models enhance the discovery of genetic architecture underlying complex traits like flowering time.
    • This study provides valuable genetic insights for developing improved lentil varieties.