Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Gene Flow02:39

Gene Flow

35.6K
Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
35.6K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.4K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.4K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

59.5K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
59.5K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.1K
Genetic Drift03:33

Genetic Drift

40.7K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
40.7K
Speciation Rates01:07

Speciation Rates

21.4K
Overview
21.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An annotated, chromosome-level genome for the spotted turtle, Clemmys guttata.

The Journal of heredity·2026
Same author

WITHDRAWN: GhostHunter: A Multi-Test Framework for Detecting Ghost Introgression.

bioRxiv : the preprint server for biology·2026
Same author

The missing data problem in population genomics and statistical methods to address them.

G3 (Bethesda, Md.)·2026
Same author

<i>SaVor</i> - A Reproducible Structural Variant Calling and Benchmarking Platform from Short-Read Data.

bioRxiv : the preprint server for biology·2025
Same author

A novel machine learning approach for tumor detection based on telomeric signatures.

Biology methods & protocols·2025
Same author

Genomic profiles of <i>Pyricularia oryzae</i> in Sub-Saharan Africa: exploring population differences and their evolutionary implications in the region.

Frontiers in plant science·2025

Related Experiment Video

Updated: Sep 11, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.0K

Accounting for gene flow from unsampled ghost populations while estimating evolutionary history.

Arun Sethuraman1, Melissa Lynch2, Margaret Wanjiku1

  • 1Department of Biology, San Diego State University, San Diego, California 92182, United States.

G3 (Bethesda, Md.)
|August 12, 2025
PubMed
Summary

Gene flow from ghost populations can bias evolutionary history estimates. Accounting for these unsampled populations is crucial for accurate population genetics and demographic modeling.

Keywords:
BiasEvolutionary HistoryGhost PopulationsPopulations GeneticsTheory

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.0K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

2.3K

Related Experiment Videos

Last Updated: Sep 11, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.0K
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.0K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

2.3K

Area of Science:

  • Population Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Gene flow from unsampled or extinct ('ghost') populations can introduce significant biases into population genomic data.
  • These biases can lead to misinterpretations of evolutionary history, affecting estimates of population structure and demographic parameters.
  • Existing methods may not adequately account for the impact of such 'ghost' populations on genomic analyses.

Purpose of the Study:

  • To theoretically establish and systematically assess the biases introduced by gene flow from ghost populations.
  • To evaluate the impact of ghost populations on population genetics summary statistics (e.g., π, FST, Tajima's D) and demographic history inference (e.g., effective population sizes, divergence times, migration rates).
  • To investigate the utility of the Isolation with Migration (IM) model in capturing these biases, particularly in contexts relevant to recent human evolution.

Main Methods:

  • Development of theoretical expectations for biases caused by ghost population gene flow.
  • Extensive simulations under various ghost topologies to assess biases in summary statistics and demographic models.
  • Analysis of a 355-locus dataset from African Hunter-Gatherer populations to examine real-world implications.

Main Results:

  • Gene flow from ghost populations consistently leads to under-estimated divergence times, over-estimated effective population sizes, and under-estimated migration rates between sampled populations.
  • Summary statistics like FST are under-estimated, while π is over-estimated when ghost population gene flow is not accounted for.
  • Biases are not fully captured by recent IM models, even when using model-based demographic history estimation, highlighting the need for explicit ghost population modeling.

Conclusions:

  • Failure to account for gene flow from unsampled ghost populations can lead to substantial inaccuracies in evolutionary history reconstructions.
  • Accurate demographic inference requires explicit consideration of potential contributions from extinct or unsampled ancestral populations.
  • The findings have implications for understanding human evolution, particularly the relationships between archaic and modern human populations.