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Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data.

Arnaud Quelin1,2, Frédéric Austerlitz3, Flora Jay4

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Summary
This summary is machine-generated.

Machine learning methods, particularly the multilayer perceptron (MLP), effectively infer population demographic history from genomic data. These advanced techniques outperform traditional approximate Bayesian computation (ABC) by utilizing comprehensive summary statistics.

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Area of Science:

  • Population genetics
  • Computational biology
  • Evolutionary genomics

Background:

  • High-throughput DNA sequencing generates vast genomic data, advancing population evolutionary and demographic history studies.
  • Demographic inference methods, including simulation-based approximate Bayesian computation (ABC), leverage genomic data but often use partial information.
  • Machine learning (ML) offers potential for integrating more comprehensive genomic information for improved demographic inference.

Purpose of the Study:

  • To evaluate simulation-based supervised machine learning methods for inferring demographic parameters in connected populations.
  • To compare the performance of multilayer perceptron (MLP), random forest (RF), and XGBoost (XGB) against traditional ABC algorithms.
  • To investigate the contribution of various summary statistics to demographic inference using explainable artificial intelligence (XAI).

Main Methods:

  • Applied three supervised machine learning methods (MLP, RF, XGBoost) to genomic data.
  • Utilized a wide range of summary statistics under isolation with migration and secondary contact models.
  • Employed SHAP (SHapley Additive exPlanations) for model interpretability and feature importance analysis.

Main Results:

  • The multilayer perceptron (MLP) demonstrated superior performance compared to random forest (RF) and XGBoost (XGB).
  • MLP predictions incorporated a broader combination of summary statistics, as indicated by permutation feature importance.
  • All tested machine learning methods outperformed three approximate Bayesian computation (ABC) algorithms in demographic inference.

Conclusions:

  • Simulation-based supervised machine learning, especially MLP, is highly effective for inferring complex population demographic histories from genomic data.
  • MLP's ability to integrate diverse summary statistics enhances its accuracy in demographic inference.
  • Explainable AI methods like SHAP provide valuable insights into the feature contributions within machine learning models for population genetics.