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Robust demographic inference from genomic and SNP data.

Laurent Excoffier1, Isabelle Dupanloup, Emilia Huerta-Sánchez

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This study presents a new simulation-based framework for inferring population demography from genomic data. The method accurately reconstructs complex evolutionary histories, outperforming existing tools for intricate demographic models.

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Area of Science:

  • Genomics
  • Population Genetics
  • Computational Biology

Background:

  • Inferring demographic history from genomic data is crucial for understanding evolution.
  • Existing likelihood-based methods struggle with complex evolutionary models and large datasets.

Purpose of the Study:

  • To introduce a flexible and robust simulation-based framework for inferring demographic parameters from the site frequency spectrum (SFS).
  • To enable the study of complex evolutionary models not tractable by current methods.
  • To extend demographic inference to ascertained SNP data.

Main Methods:

  • Developed a composite-likelihood approach using simulation.
  • Applied the framework to non-coding genomic SNP data from four human populations.
  • Extended the methodology to handle ascertained SNP data from SNP chips.

Main Results:

  • The framework accurately infers demographic parameters for complex models, showing favorable accuracy and speed compared to existing methods like ∂a∂i.
  • Demonstrated successful application to human population genomics, including reconstruction of African demography.
  • Inferred ancient divergence (>110 Ky) between Yoruba and San populations using ascertained SNP panels.

Conclusions:

  • The developed framework is a versatile and powerful tool for inferring complex demographic histories from large genomic datasets.
  • It overcomes limitations of previous methods, particularly for ascertained SNP data and multi-population models.
  • The methodology is well-suited for future studies of intricate evolutionary scenarios.