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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).

Genetic Drift

Genetic Drift

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.

Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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Related Experiment Video

Updated: Jul 16, 2025

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

Diverse mutant selection windows shape spatial heterogeneity in evolving populations.

Eshan S King¹, Beck Pierce², Michael Hinczewski³

¹Systems Biology and Bioinformatics Program, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.

Biorxiv : the Preprint Server for Biology

|September 21, 2023

Summary

This summary is machine-generated.

Fitness seascapes offer a robust model for predicting drug resistance by analyzing genotype-specific dose-response data. This approach allows for simultaneous comparisons of multiple mutant selection windows (MSWs), improving therapeutic strategies.

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Area of Science:

Evolutionary medicine
Microbial genetics
Pharmacodynamics

Background:

Mutant selection windows (MSWs) traditionally compare two pathogen subtypes (drug-sensitive vs. drug-resistant) to predict resistance.
Existing models like fitness landscapes (N alleles) allow N-genotype comparisons but lack continuous drug response data.
Clinical drug concentrations vary, necessitating a more comprehensive model for pathogen response to therapy.

Approach:

Introduced fitness seascapes to model genotype-by-environment interactions, encoding genotype-specific dose-response data.
Enabled simultaneous multiple MSW comparisons by analyzing dose-response curves.
Extended MSW modeling to spatial drug diffusion scenarios and agent-based models.

Key Points:

Fitness seascapes facilitate N*2 unique MSW comparisons, offering greater analytical power.
Demonstrated spatially heterogeneous MSWs in drug diffusion models, reflecting real-world selection dynamics.
Showcased how the spatial structure of MSWs influences the evolution of drug resistance.

Conclusions:

Fitness seascapes provide a more robust and detailed framework for understanding pathogen drug resistance.
This model enhances predictions of resistance and informs the design of novel therapeutic strategies.
Highlights the critical role of dose-dependent fitness landscapes in evolutionary medicine and drug development.