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Complex genetic admixture histories reconstructed with Approximate Bayesian Computation.

Cesar A Fortes-Lima1,2, Romain Laurent1, Valentin Thouzeau3,4

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Summary
This summary is machine-generated.

We developed a new computational framework, MetHis, to reconstruct complex human admixture histories. This method accurately identifies demographic patterns, revealing decreasing ancestral contributions over time in admixed populations.

Keywords:
Approximate Bayesian Computationadmixtureinferencemachine-learningpopulation genetics

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Admixture is a key evolutionary process shaping genetic diversity across species.
  • Inferring complex admixture histories, especially beyond simple pulse models, is methodologically challenging using traditional maximum-likelihood approaches.
  • Existing methods struggle with intricate admixture scenarios involving multiple or recurring admixture events.

Purpose of the Study:

  • To develop an Approximate Bayesian Computation (ABC) framework for reconstructing complex admixture histories using independent genetic markers.
  • To create a simulation software package, MetHis, capable of modeling diverse admixture scenarios.
  • To apply this novel framework to analyze the admixture history of specific human populations.

Main Methods:

  • Developed the MetHis software package to simulate genetic data under various complex admixture scenarios (multiple pulses, recurring admixture).
  • Utilized independent genetic markers (SNPs, microsatellites) and user-defined prior distributions for simulation parameters.
  • Coupled MetHis with machine-learning ABC algorithms (random forest for scenario choice, neural networks for parameter estimation) to analyze simulated and empirical data.

Main Results:

  • Random forest ABC accurately distinguished between most complex admixture scenarios, with errors primarily in biologically similar, nested parameter spaces.
  • Neural network ABC provided accurate and conservative posterior parameter estimation for complex admixture scenarios.
  • Analysis of African American and Barbadian populations indicated that monotonically decreasing ancestral contributions over time provided a better fit than multiple admixture pulses.

Conclusions:

  • The developed ABC framework and MetHis software offer a powerful tool for reconstructing detailed and complex admixture histories.
  • This approach overcomes limitations of maximum-likelihood methods for intricate demographic scenarios.
  • The findings suggest that decreasing admixture over time is a more accurate model for the demographic history of the studied populations.