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Related Concept Videos

Genome Copying Errors02:46

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.
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,...
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Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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...
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Hardy-Weinberg Principle

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Related Experiment Video

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Genotype error detection using Hidden Markov Models of haplotype diversity.

Justin Kennedy1, Ion Măndoiu, Bogdan Paşaniuc

  • 1Computer Science and Engineering Department, University of Connecticut, Storrs, Connecticut 06269-2155, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 1, 2008
PubMed
Summary

Genotyping errors can impact genetic studies. This research introduces improved methods for detecting these errors in trio genotype data using likelihood ratios and Hidden Markov Models, enhancing accuracy and scalability.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • Genotyping errors compromise genetic association and linkage analyses, especially haplotype-based methods.
  • Existing error detection methods may lack accuracy or scalability for large datasets.

Purpose of the Study:

  • To develop and evaluate enhanced methods for detecting genotyping errors in trio data.
  • To improve the accuracy and computational efficiency of error detection in genetic studies.

Main Methods:

  • Utilized a likelihood ratio test approach combined with efficient likelihood computations.
  • Employed a Hidden Markov Model (HMM) to capture population haplotype diversity.
  • Tested methods on both simulated and real genetic datasets.

Main Results:

  • The proposed methods demonstrate significantly improved genotype error detection accuracy.
  • Achieved highly scalable running times, suitable for large-scale genetic analyses.
  • Outperformed previous error detection techniques in experimental evaluations.

Conclusions:

  • The enhanced likelihood ratio test approach with HMMs provides a robust and efficient solution for genotyping error detection.
  • These improved methods are crucial for ensuring the reliability of genetic association and linkage studies.