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Related Concept Videos

Frequency-dependent Selection01:21

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.Positive Frequency-Dependent SelectionIn positive...
Genetic Drift03:33

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.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
Mutation, Gene Flow, and Genetic Drift01:09

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Detecting selective sweeps: a new approach based on hidden markov models.

Simon Boitard1, Christian Schlötterer, Andreas Futschik

  • 1Institute of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria. simon.boitard@toulouse.inra.fr

Genetics
|February 11, 2009
PubMed
Summary

We introduce hidden Markov models (HMMs) for detecting selective sweeps using SNP data. Our HMMs improve accuracy, especially during population bottlenecks, by modeling linked site correlations.

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Area of Science:

  • Population genetics
  • Genomics
  • Bioinformatics

Background:

  • Detecting selective sweeps is crucial for understanding evolutionary adaptation.
  • Single nucleotide polymorphism (SNP) data is commonly used for this purpose.
  • Existing methods often overlook correlations between linked genetic sites.

Purpose of the Study:

  • To develop and evaluate a novel hidden Markov model (HMM) approach for detecting and localizing selective sweeps.
  • To assess the performance of HMMs in identifying selection in DNA sequences, considering linked site correlations.

Main Methods:

  • Utilized hidden Markov models (HMMs) to analyze SNP data for selective sweep detection.
  • Incorporated site frequency spectrum and spatial diversity patterns.
  • Explicitly modeled the correlation structure between linked genetic sites.

Main Results:

  • HMMs demonstrated comparable detection power and localization accuracy to existing methods in constant-size populations.
  • HMMs significantly reduced false positives in scenarios involving population bottlenecks.
  • The explicit modeling of linked site correlations improved performance under demographic challenges.

Conclusions:

  • Hidden Markov models offer a robust framework for detecting selective sweeps.
  • This HMM approach provides improved accuracy and reduced false positives, particularly during population bottlenecks.
  • The method advances the analysis of selection in population genetics studies.