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  3. A Likelihood-based Framework For Demographic Inference From Genealogical Trees.
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  3. A Likelihood-based Framework For Demographic Inference From Genealogical Trees.

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A likelihood-based framework for demographic inference from genealogical trees.

Caoqi Fan1,2, Jordan L Cahoon3,4, Bryan L Dinh5,3

  • 1Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. fcq1116@gmail.com.

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|March 21, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We introduce genealogical likelihood (gLike), a new method to infer population history from genetic data. gLike accurately estimates demographic parameters, especially for admixed populations, outperforming existing methods.

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Area of Science:

  • Population genetics
  • Computational biology
  • Evolutionary genetics

Background:

  • Demographic history shapes genetic variation within populations.
  • Gene-genealogical trees encode crucial information about population relationships and past events.
  • Accurate inference of complex demographic histories is essential for understanding evolutionary processes.

Purpose of the Study:

  • To develop a novel framework, genealogical likelihood (gLike), for inferring population demographic history.
  • To leverage genealogical information for more accurate demographic inference, particularly in admixed populations.
  • To provide a sensitive and accurate method for estimating demographic parameters.

Main Methods:

  • Developed a graph-based structure to represent relationships among lineages in gene-genealogical trees.
  • Derived the full likelihood across trees under a parameterized demographic model.
  • Utilized simulations and empirical data to validate the gLike framework.

    • Main Results:

    • gLike accurately estimates numerous demographic parameters, including ancestral population sizes, admixture timing, and proportions.
    • The method demonstrates superior performance compared to conventional site frequency spectrum-based methods for admixed populations.
    • gLike shows high sensitivity and accuracy in inferring complex demographic histories.

    Conclusions:

    • The genealogical likelihood (gLike) framework offers a powerful new tool for demographic inference.
    • gLike effectively harnesses genealogical information, providing deeper insights into population histories.
    • This method has broad applicability for studying the demography of diverse species, including humans.