Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Hardy-Weinberg Principle

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

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).

You might also read

Related Articles

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

Sort by

Same author

Modeling the length distribution of gene conversion tracts in humans from the UK Biobank sequence data.

PLoS genetics·2025

Same author

Multiple-testing corrections in selection scans using identity-by-descent segments.

American journal of human genetics·2025

Same author

Estimating gene conversion rates from population data using multi-individual identity by descent.

American journal of human genetics·2025

Same author

Identity-By-Descent Mapping Using Multi-Individual IBD With Genome-Wide Multiple Testing Adjustment.

Genetic epidemiology·2025

Same author

Fast simulation of identity-by-descent segments.

Bulletin of mathematical biology·2025

Same author

Estimating gene conversion rates from population data using multi-individual identity by descent.

bioRxiv : the preprint server for biology·2025

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 21, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

ASAFE: ancestry-specific allele frequency estimation.

Qian S Zhang¹, Brian L Browning², Sharon R Browning³

¹Department of Medicine Department of Biostatistics and.

Bioinformatics (Oxford, England)

|May 7, 2016

Summary

This summary is machine-generated.

We developed a new method, Ancestry Specific Allele Frequency Estimation (ASAFE), to accurately estimate allele frequencies in admixed populations for genome-wide association studies (GWAS). This tool improves replication study design by accounting for complex genetic ancestry.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

Related Experiment Videos

Last Updated: Mar 21, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

Area of Science:

Genetics
Bioinformatics
Population Genetics

Background:

Genome-wide association studies (GWAS) in admixed populations require accurate allele frequency estimation.
Understanding ancestry-specific allele frequencies is crucial for designing effective replication GWAS.

Purpose of the Study:

To develop and validate a novel algorithm for estimating ancestry-specific allele frequencies in 3-way admixed populations.
To provide a tool that can improve the design of replication GWAS in diverse populations.

Main Methods:

Developed an Expectation-Maximization (EM) algorithm to estimate ancestry-specific allele frequencies.
The algorithm handles bi-allelic markers with unknown genotype phasing relative to local ancestries in admixed individuals.

Main Results:

The developed algorithm, named Ancestry Specific Allele Frequency Estimation (ASAFE), demonstrated low error rates on simulated data.
ASAFE provides a robust method for inferring allele frequencies specific to ancestral components.

Conclusions:

ASAFE is a valuable tool for genetic research in admixed populations.
Accurate ancestry-specific allele frequency estimation can enhance the power and efficiency of GWAS.