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Accounting for population structure in deep learning models for genomic analysis.

Gabrielle Dagasso1, Matthias Wilms2, Raissa Souza1

  • 1Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.

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|July 7, 2025
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Summary

Deep learning models for genotype analysis may not be significantly impacted by population structure. However, explainable AI shows differences in feature importance, suggesting a need to mitigate shortcut learning by prioritizing ancestry-related variants.

Keywords:
ConfoundersDeep learningGenomic analysisPopulation structureShortcut learning

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Area of Science:

  • Genomics
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Deep learning is increasingly used for genotype analysis.
  • Genetic relatedness is a known confounder in conventional genomic analyses.
  • Many deep learning models overlook genetic relatedness, potentially introducing bias.

Purpose of the Study:

  • To investigate if omitting genetic relatedness in deep learning models causes confounding effects.
  • To determine if ancestry-related variants introduce bias in deep learning genotype analyses.

Main Methods:

  • Developed and utilized a deep learning model for classification tasks.
  • Employed single nucleotide polymorphism (SNP) data from simulated and real-world datasets.
  • Examined potential confounding by population structure and shortcut learning.

Main Results:

  • Population structure did not significantly affect deep learning model performance.
  • Explainable AI highlighted differences in SNP feature importance between confounded and unconfounded models.
  • Models may exhibit shortcut learning by focusing on ancestry-related variants.

Conclusions:

  • While population structure may not heavily impact performance, reducing shortcut learning is crucial.
  • Prioritize ancestry-related variants over other biomarkers in deep learning genomic analyses.
  • Code for analysis is available at https://github.com/notTrivial/populationStructure.