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Updated: Jul 12, 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^1,3, Bryan L Dinh^1,2

¹Department of Quantitative and Computational Biology, University of Southern California.

Biorxiv : the Preprint Server for Biology

|October 24, 2023

Summary

This summary is machine-generated.

We developed a new method, genealogical likelihood (gLike), to infer population history from genetic data. gLike accurately estimates demographic parameters and outperforms existing methods by using more genealogical information.

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Area of Science:

Population Genetics
Computational Biology
Genomic History

Background:

Demographic history shapes genetic variation, encoded in gene-genealogical trees.
Current methods often use limited genetic data summaries, like allele frequency spectra.

Approach:

Introduced the genealogical likelihood (gLike) framework for inferring demographic history.
Utilized a graph-based structure to compute exact marginal probabilities under demographic models.
Summarized lineage relationships and population trajectories through time.

Key Points:

gLike accurately estimates numerous demographic parameters (e.g., population sizes, admixture timing/proportions).
Outperformed conventional methods using allele frequency spectra in simulations and real-world data.
Enabled tracing ancestral histories without needing source population samples.

Conclusions:

gLike harnesses underutilized genealogical information for sensitive and accurate inference of complex population demographies.
Applicable to humans and other species, especially with improving genome-wide genealogy estimation.
Provides insights into historical demography for admixed populations like Latino Americans and Native Hawaiians.