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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).

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Genetic Drift

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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.

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.

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Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

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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...

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Related Experiment Video

Updated: Jun 5, 2025

Measuring Microbial Mutation Rates with the Fluctuation Assay

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

Stochastic offspring distributions amplify selection bias in mutation accumulation experiments.

Mojgan Ezadian¹, Lindi M Wahl¹

¹Mathematics, Western University, London, Canada.

Theoretical Population Biology

|December 13, 2024

Summary

Mutation accumulation experiments reveal stronger selection bias than previously thought. A new stochastic model, accounting for genetic drift and lineage loss, accurately corrects for these effects in evolutionary studies.

Keywords:

Bacteria Fitness effects of mutations Mathematical model Mutation accumulation

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Last Updated: Jun 5, 2025

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Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Published on: October 16, 2014

Area of Science:

Evolutionary Biology
Microbial Genetics
Population Genetics

Background:

Mutation accumulation (MA) experiments are crucial for understanding evolutionary processes.
Previous MA studies often used deterministic models, potentially underestimating selection effects.
Microbial MA experiments typically involve growth phases followed by severe bottlenecks.

Purpose of the Study:

To develop a fully stochastic model for microbial mutation accumulation experiments.
To investigate the impact of realistic offspring distributions on selection bias.
To provide accurate methods for correcting selection effects in MA data.

Main Methods:

Developed a stochastic model incorporating genetic drift and lineage loss.
Utilized analytical and numerical approaches for bias correction.
Applied methods to simulated MA data for validation.

Main Results:

Stochastic offspring distributions significantly amplify selection bias in MA experiments.
Selection bias is stronger than predicted by previous deterministic models.
The developed methods accurately correct for selection effects on fitness distributions.

Conclusions:

Stochasticity is critical for accurately modeling selection in MA experiments.
The new correction methods improve the analysis of evolutionary trajectories.
This work enhances the interpretation of fitness effect distributions derived from MA studies.