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Full likelihood inference from the site frequency spectrum based on the optimal tree resolution.

Raazesh Sainudiin1, Amandine Véber2

  • 1Department of Mathematics, Uppsala University, Uppsala, Sweden.

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PubMed
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We developed a novel importance sampler to compute population genetic likelihoods from site frequency spectra (SFS). This method efficiently handles demographic changes and population structure, reducing computational complexity for evolutionary inference.

Keywords:
Controlled Markov process on hidden genealogical treesImportance samplerOptimal tree resolutionSemi-parametric estimation

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Area of Science:

  • Population Genetics
  • Computational Evolutionary Biology
  • Statistical Inference

Background:

  • Accurate inference of population genetic scenarios requires computing likelihood functions based on observed genetic data, such as the site frequency spectrum (SFS).
  • Traditional methods can be computationally intensive, especially when dealing with complex demographic histories and population structures.
  • Existing approaches often struggle with the high dimensionality of the genealogical state space.

Purpose of the Study:

  • To develop a novel importance sampler for efficiently computing the full likelihood function of demographic and structural scenarios.
  • To reduce the computational burden by utilizing only the necessary information from genealogical trees for SFS likelihood calculation.
  • To accommodate models of demographic change and non-panmictic population structure.

Main Methods:

  • Developed a novel importance sampler that represents genealogies using minimal information required for SFS likelihood.
  • Employed a controlled Markov process to generate compatible SFS histories ('particles').
  • Utilized Aldous' Beta-splitting model for prior distributions on genealogical topologies and parametric priors for demographic models (e.g., exponential growth, bottlenecks).

Main Results:

  • The importance sampler significantly reduces the state space complexity for likelihood computation.
  • The method effectively integrates demographic changes and population structure into the likelihood framework.
  • Demonstrated capability to estimate population scenario likelihoods using independent SFS data and distinguish between different population models.

Conclusions:

  • The novel importance sampler provides an efficient and flexible tool for population genetic inference.
  • This approach enhances the ability to model complex demographic histories and population structures from SFS data.
  • The method offers a significant advancement in computational population genetics, enabling more robust evolutionary analyses.