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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.

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).

Probability Laws

Probability Laws

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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.

Law of Segregation

Law of Segregation

When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.

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Related Experiment Video

Updated: Apr 3, 2026

Measuring Microbial Mutation Rates with the Fluctuation Assay

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

A Bayesian Approach to Inferring Rates of Selfing and Locus-Specific Mutation.

Benjamin D Redelings¹, Seiji Kumagai¹, Andrey Tatarenkov²

¹Department of Biology, Duke University, Durham, North Carolina 27708-0338.

|September 17, 2015

Summary

This study introduces a Bayesian method to analyze genetic variation and characterize population mating systems, including self-fertilization and outcrossing. The novel approach enhances understanding of reproductive strategies in diverse species.

Keywords:

Bayesian Ewens sampling formula MCMC mating system selfing rate

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Published on: July 11, 2025

Area of Science:

Population Genetics
Evolutionary Biology
Genomics

Background:

Understanding population mating systems is crucial for evolutionary biology.
Existing models often simplify complex reproductive strategies like mixed selfing and outcrossing.
The killifish *Kryptolebias marmoratus* presents a unique model for studying self-fertilization.

Purpose of the Study:

To develop a Bayesian statistical method for characterizing population mating systems.
To infer reproductive strategies, including hermaphroditism and gynodioecy, using genomic variation.
To model the mating system of the self-fertilizing killifish *Kryptolebias marmoratus*.

Main Methods:

Extended coalescence model to incorporate self-fertilization and outcrossing.
Utilized the Ewens sampling formula (ESF) under an infinite-alleles mutation model.
Developed a Markov chain Monte Carlo (MCMC) algorithm for parameter estimation, including locus-specific mutation rates.

Main Results:

The method provides joint posterior distributions for mating system parameters.
It estimates population-wide proportion of uniparental individuals and mutation rates.
The approach can infer the number of generations since the last outcrossing event for individuals.

Conclusions:

The Bayesian method offers a robust framework for analyzing complex mating systems.
It successfully characterizes genetic variation related to reproduction in hermaphroditic and gynodioecious populations.
The developed model provides insights into the evolutionary dynamics of self-fertilizing species.