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Related Concept Videos

Genome Copying Errors02:46

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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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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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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.
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Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A spatial haplotype copying model with applications to genotype imputation.

Wen-Yun Yang1, Farhad Hormozdiari, Eleazar Eskin

  • 11 Department of Computer Science, University of California , Los Angeles, California.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 20, 2014
PubMed
Summary

This study introduces a spatial-aware haplotype copy model that improves genotype imputation accuracy by considering geographic location. The new model enhances genetic variation analysis and enables precise individual localization on genetic-geographic maps.

Keywords:
1000 Genomesexpectation maximization (EM) algorithmgenotype imputationlinkage disequilibriumpolymorphismsingle nucleotidespatial geneticsstochastic gradient descent

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Haplotype copy models are crucial for analyzing human genetic variation, including ancestry inference and genotype imputation.
  • Current models assume equal a priori contribution from all chromosomes, neglecting geographic influences.
  • Recent advancements explore genetic variation across geographic continua, prompting new modeling approaches.

Purpose of the Study:

  • To develop a novel spatial-aware haplotype copy model that integrates geographic information into the haplotype copying process.
  • To enhance the accuracy of genotype imputation by prioritizing geographically proximate haplotypes.
  • To explore the model's utility in personalized reference panel selection and individual genetic-geographic localization.

Main Methods:

  • Extended hidden Markov models to incorporate spatial awareness into haplotype diversity modeling.
  • Developed a model where geographically closer haplotypes have a higher a priori probability of contributing to the copying process.
  • Utilized simulations based on the 1000 Genomes data for model evaluation.

Main Results:

  • The proposed spatial-aware model demonstrated superior accuracy in genotype imputation compared to standard spatial-unaware models.
  • The model facilitated the selection of smaller, personalized reference panels, improving imputation accuracy and reducing computational runtime.
  • The model successfully localized individuals on a genetic-geographic map using genotype data.

Conclusions:

  • Integrating geographic information into haplotype copy models significantly improves genotype imputation performance.
  • The spatial-aware model offers a more accurate and computationally efficient approach for genetic variation analysis.
  • This model has potential applications in population genetics, personalized medicine, and forensic science.