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Updated: May 21, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

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

Caoqi Fan^1,2, Jordan L Cahoon^3,4, Bryan L Dinh^5,3

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.

Nature Genetics

|March 21, 2025

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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Last Updated: May 21, 2025

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Published on: August 14, 2018

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.