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Genealogical histories in structured populations.

Seiji Kumagai1, Marcy K Uyenoyama1

  • 1Department of Biology, Box 90338, Duke University, Durham, NC 27708-0338, USA.

Theoretical Population Biology
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This study models gene genealogies in structured populations. We found that migration and population structure significantly impact coalescence times and the geographical location of common ancestors.

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Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Computational Biology

Background:

  • Gene genealogies are crucial for understanding population history.
  • In structured populations, migration and coalescence dynamics are complex.
  • Previous models often simplified geographical subdivision.

Purpose of the Study:

  • To analyze gene genealogies in a two-deme population model.
  • To derive exact distributions for key genealogical statistics.
  • To investigate factors influencing ancestral location and tree structure.

Main Methods:

  • Utilized generating functions for precise mathematical derivations.
  • Modeled migration between two distinct population demes (sub-populations).
  • Calculated moments and densities for coalescence time, mutation number, and tree length.

Main Results:

  • Obtained exact densities and moments for genealogical properties.
  • Identified factors influencing the geographical origin of the most recent common ancestor.
  • Characterized deviations from exponential distributions in internode lengths.

Conclusions:

  • Genealogical structure is highly sensitive to population subdivision and migration rates.
  • Mathematical models provide powerful tools for dissecting complex evolutionary processes.
  • Findings offer insights into inferring population history from genetic data.