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Mutations

Mutations

Mutations

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

Mutations

Mutations

Mutations in Microorganisms

Mutations in Microorganisms

Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...

Point and Frameshift Mutations

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...

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.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

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Related Experiment Video

Updated: Mar 20, 2026

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

Stationary mutation models.

Ivar Simonsson¹, Petter Mostad¹

¹Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-41296 Göteborg, Sweden.

Forensic Science International. Genetics

|May 28, 2016

Summary

This summary is machine-generated.

Including untested parents in DNA relationship inference can alter probability calculations due to mutation models. This study addresses this by proposing stationary mutation models to resolve fundamental issues in genetic relationship testing.

Keywords:

DNA relationship inference Familias Mutation matrices STR markers

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Last Updated: Mar 20, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Area of Science:

Forensic Science
Genetics
Biostatistics

Background:

DNA testing is crucial for relationship inference, often using software like Familias.
Incorporating potential mutations in DNA analysis can lead to unexpected changes in results when untested parents are included.

Purpose of the Study:

To investigate the cause of result variations in DNA relationship inference when untested parents are included.
To propose and evaluate stationary mutation models as a solution to these variations.

Main Methods:

Tracing the effect of untested parents to fundamental model-formulation issues.
Developing and presenting methods for deriving stationary mutation matrices from original mutation matrices.
Evaluating the properties of these derived stationary mutation models.

Main Results:

The inclusion of untested parents in DNA relationship inference can introduce slight, problematic changes in calculated probabilities.
Stationary mutation models were found to resolve these fundamental model-formulating issues.
Methods for obtaining stationary mutation models were presented and evaluated.

Conclusions:

Stationary mutation models typically resolve issues in DNA relationship inference caused by including untested parents.
However, for many marker types, achieving stationarity may conflict with biologically realistic mutation matrix requirements.