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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.

In-vitro Mutagenesis

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.

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.

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.

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Related Experiment Video

Updated: Jul 2, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Predicting thresholds for population replacement gene drives.

Anna Janzen^1,2, Ratnasri Pothula^1,2, Adam Sychla^1,2

¹Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, 55455, MN, USA.

|February 18, 2024

Summary

Engineered Genetic Incompatibility (EGI) can act as a threshold-dependent gene drive (TDGD). This study reveals unexpected performance differences between EGI agent genotypes and a surprising temperature effect on population replacement thresholds.

Keywords:

Drosophila melanogaster Gene drive Population replacement

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Published on: September 15, 2023

Area of Science:

Population genetics
Genetic engineering
Synthetic biology

Background:

Threshold-dependent gene drives (TDGDs) offer controllable trait propagation.
Engineered Genetic Incompatibility (EGI) is an extreme underdominance system functioning as a TDGD in Drosophila melanogaster.

Purpose of the Study:

Compare fecundity, mating preferences, and relative fitness of two EGI agent genotypes.
Investigate temperature-dependent performance and population replacement thresholds for EGI systems.

Main Methods:

Single generation fitness assay in Drosophila melanogaster.
Comparison of EGI agent genotypes against wild-type populations.
Analysis of mating behavior and temperature-dependent fitness.

Main Results:

Significant, unpredictable differences in EGI agent behavior and performance were observed.
A temperature-dependent shift in the population replacement threshold was identified for a pyramus-expressing EGI agent.
Empirical data revealed genotype-specific variations not predicted by genetic design.

Conclusions:

A single-generation fitness assay can accelerate threshold estimation for TDGD strategies with inviable hybrids.
Empirical characterization of multiple engineered lines is crucial due to unpredictable genotypic variations.
Understanding genotype-environment interactions is key for EGI system development and control.