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Popfinder: A Highly Effective Artificial Neural Network Package for Genetic Population Assignment.

K Birchard1, C Boccia1, H Lounder1

  • 1Department of Biology, Queen's University, Kingston, Ontario, Canada.

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|March 7, 2025
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Summary

Popfinder, a new artificial neural network tool, accurately assigns individuals to their genetic populations, even with low genetic differences. This method aids wildlife conservation and epidemiology by improving population assignment accuracy.

Keywords:
bycatchdisease trackinggenetic stock identificationmachine learningpopulation impact assessmentwildlife tracking

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Accurate genetic population assignment is crucial for ecology, conservation, and epidemiology.
  • Low genetic differentiation poses challenges for traditional population assignment methods.
  • Artificial neural networks show promise for genomic data-based population assignment.

Purpose of the Study:

  • Introduce popfinder, a user-friendly Python pipeline utilizing artificial neural networks for genetic population assignment.
  • Evaluate popfinder's performance on simulated and empirical data with varying genetic structures.
  • Compare popfinder's accuracy and speed against existing population assignment software.

Main Methods:

  • Developed a Python-based artificial neural network pipeline named popfinder.
  • Tested popfinder with simulated genetic data under different gene flow scenarios.
  • Applied popfinder to reduced-representation sequence data from three seabird species with weak population structure.

Main Results:

  • Popfinder achieved high accuracy, precision, and recall in assigning individuals to source populations for most simulated and empirical datasets.
  • Performance was robust even with low genetic differentiation, except in cases of extremely weak structure where comparators also struggled.
  • Popfinder includes a perturbation ranking method for optimizing single nucleotide polymorphism (SNP) panels.

Conclusions:

  • Popfinder is an effective and user-friendly tool for genetic population assignment, particularly valuable when genetic differentiation is low.
  • The pipeline demonstrates high accuracy across diverse datasets, offering improvements over existing methods.
  • Users should be mindful of potential issues like data leakage during model training to ensure reliable results.