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Genomic inference using diffusion models and the allele frequency spectrum.

Aaron P Ragsdale1, Claudia Moreau2, Simon Gravel1

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Population variation is shaped by evolutionary and demographic forces. Analyzing allele frequencies helps infer past human population history and detect selection, crucial for accurate genetic research.

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Human genomics

Background:

  • Evolutionary, biological, and demographic processes jointly influence population variation.
  • Understanding these influences is key to inferring past population demography and selection.
  • Diffusion approximation models are valuable for inference using allele frequency distributions.

Purpose of the Study:

  • To discuss computational advancements in reconstructing human demographic history.
  • To assess the impact of selection on inferred demographic parameters and evolutionary rates.
  • To evaluate the relevance of classical evolutionary models in contemporary genetic studies.

Main Methods:

  • Utilized whole-genome sequence data from 797 French Canadian individuals.
  • Employed forward-time diffusion approximation models for demographic inference.
  • Assessed the neutrality of synonymous variants to detect selection.

Main Results:

  • Synonymous variants can be subject to selection, biasing demographic inferences.
  • Selection impacts inferred mutation rates and distributions of fitness effects.
  • Computational developments enhance the reconstruction of human demographic history.

Conclusions:

  • Selection significantly biases inferences of demographic history, mutation rates, and fitness effects.
  • Classical evolutionary models remain relevant for modern genetic research.
  • Accurate demographic inference requires accounting for selection on variants.