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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.

Joseph D Romano1,2,3, Liang Mei4, Jonathan Senn4

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Summary
This summary is machine-generated.

Artificial intelligence and adverse outcome pathways reveal new genetic risk factors for liver cancer. This study identifies novel gene variations in AHR and ABCB11 as potential contributors to toxicity-mediated liver cancer.

Keywords:
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Area of Science:

  • Toxicology
  • Genetics
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Adverse outcome pathways (AOPs) elucidate biological signaling in toxicity-induced diseases.
  • Understanding genetic mechanisms in toxicity-mediated liver cancer is clinically significant.
  • Existing AOP frameworks can be enhanced with AI for novel insights.

Purpose of the Study:

  • To integrate the AOP framework with AI methods to uncover genetic drivers of toxicity-related liver cancer.
  • To identify novel genetic risk factors for liver cancer using a combination of AOP data and real-world genetic information.
  • To apply generative and graph machine learning to AOP and genetic datasets.

Main Methods:

  • Utilized the Adverse Outcome Pathway Database (AOP-DB) for disease-specific AOPs and graph neural network construction.
  • Employed UK Biobank genetic data (SNP data) and phenotype cohorts (liver cancer cases/controls).
  • Applied automated machine learning, genetic algorithms, and graph machine learning, with propensity score matching for covariate balancing.

Main Results:

  • Developed a novel approach combining AOPs and AI for toxicity-genetics research.
  • Identified a new putative risk factor for liver cancer involving genetic variations in the aryl-hydrocarbon receptor (AHR) and ATP binding cassette subfamily B member 11 (ABCB11) genes.
  • Successfully integrated diverse data sources including AOP-DB and UK Biobank.

Conclusions:

  • The combined AOP and AI framework offers powerful insights into toxicity-mediated adverse health outcomes.
  • Genetic variations in AHR and ABCB11 represent a novel potential risk factor for liver cancer.
  • This research paves the way for more precise understanding and prediction of chemically induced diseases.