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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).

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

Chi-square Analysis

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

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Related Experiment Video

Updated: Feb 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

Estimating Allele Frequencies.

Indra Adrianto¹, Courtney Montgomery²

¹Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK, USA.

Methods in Molecular Biology (Clifton, N.J.)

|October 6, 2017

Summary

This summary is machine-generated.

This chapter details methods for estimating allele frequencies in both unrelated and related individuals using natural estimators and maximum likelihood estimation (MLE). It also covers factors influencing these frequencies in populations.

Keywords:

ABO blood group Allele Disease research Expectation-maximization algorithm Families Founder Genetic drift Genotype Hardy–Weinberg equilibrium Log-likelihood Maximum likelihood estimation Migration Mutation Natural estimator Natural selection Nonfounder Nonrandom mating Pedigree Phenotype Population genetics Related individuals Relatives Unrelated individuals

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Area of Science:

Population Genetics
Statistical Genetics

Background:

Accurate estimation of allele frequencies is fundamental to population genetics and genetic epidemiology.
Understanding allele frequency estimation methods is crucial for analyzing genetic data from diverse populations.

Purpose of the Study:

To describe methods for estimating allele frequencies from genetic data.
To explain techniques applicable to both unrelated and related individuals.
To discuss factors influencing allele frequencies in populations.

Main Methods:

Utilizing natural estimators for allele frequency calculation in samples of unrelated individuals with codominant markers.
Applying maximum likelihood estimation (MLE) for computing allele frequencies in genetic data from related individuals.
Explaining various factors that impact allele frequencies within populations.

Main Results:

Natural estimators are suitable for allele frequency estimation in unrelated individuals with simple codominant markers.
Maximum likelihood estimation (MLE) provides a robust method for allele frequency computation in related individuals.
Population-specific factors significantly influence observed allele frequencies.

Conclusions:

The chapter provides a comprehensive overview of allele frequency estimation techniques.
Both unrelated and related individuals' data can be effectively analyzed using appropriate statistical methods.
Factors influencing allele frequencies are essential considerations in genetic studies.