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Updated: May 11, 2026
10:36
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
Detecting negative selection on recurrent mutations using gene genealogy
Kiyoshi Ezawa1, Giddy Landan, Dan Graur
1Department of Biology and Biochemistry, University of Houston, Houston, TX 77204-5001, USA. kezawa.ezawa3@gmail.com
BMC Genetics
|May 9, 2013
Summary
Detecting negative selection is challenging, but new statistical tests can identify deleterious recurrent mutations by analyzing their impact on gene genealogies. These methods improve the study of population genetics and genetic disorders.
Area of Science:
- Population Genetics
- Evolutionary Biology
- Genomics
Background:
- Detecting negative selection on mutant alleles is difficult due to their rarity and minimal impact on population dynamics.
- Recurrent mutations, identified in genetic disorder and genome-wide studies, offer a potential signal for negative selection.
- Exploiting the enhanced genealogical signal from recurrent mutations may reveal deleterious alleles.
Purpose of the Study:
- To develop novel statistical tests for detecting negative selection on recurrent mutations.
- To assess the performance of these new tests in identifying deleterious recurrent mutations.
Main Methods:
- Devised two new test statistics based on mutant counts and the size of identical-by-descent mutant classes.
- Utilized simulations of recurrently mutated loci with neutral single nucleotide polymorphisms (SNPs) and no recombination.
- Developed a maximum parsimony algorithm for enumerating mutation histories and resolving genealogies.
Main Results:
- The new tests demonstrated high power in detecting negative selection under constant population size.
- The tests showed moderate power in detecting selection in expanding populations.
- The maximum parsimony algorithm effectively handled incompletely resolved genealogies.
Conclusions:
- The developed neutrality tests possess high power for detecting negative selection.
- These tests offer new approaches for studying the population genetics of recurrent mutations.
- The methods may aid in identifying genetic disorders previously missed by existing techniques.
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