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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
134
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
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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Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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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).
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Single nucleotide variations: biological impact and theoretical interpretation.

Panagiotis Katsonis1, Amanda Koire, Stephen Joseph Wilson

  • 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.

Protein Science : a Publication of the Protein Society
|September 20, 2014
PubMed
Summary

Identifying disease-causing genomic variations is key. This review covers computational methods for predicting how non-synonymous single nucleotide variations affect protein function, aiding genetic research.

Keywords:
disease causing SNV (single nucleotide variation)functional impact prediction methodsmissense variant classificationnon-synonymous protein mutationssingle nucleotide polymorphism prioritization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate vast genomic variant data.
  • A significant challenge is pinpointing variations that cause disease or influence phenotypic traits.
  • Most disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), necessitating functional impact assessment.

Purpose of the Study:

  • To review computational methods for predicting the functional impact of nsSNVs.
  • To discuss the principles, advantages, and limitations of these prediction methods.
  • To explore their comparative performance and synergistic potential.

Main Methods:

  • Review of existing literature on nsSNV impact prediction.
  • Analysis of computational approaches including statistical methods, machine learning, and protein evolution models.
  • Examination of sequence homology and structural information in impact prediction.

Main Results:

  • Computational prediction of nsSNV impact relies on sequence homology and structural data.
  • Various methods (statistical, machine learning, evolutionary models) exist with distinct strengths and weaknesses.
  • These methods offer complementary insights into variant pathogenicity.

Conclusions:

  • Computational nsSNV impact prediction is crucial for interpreting genomic data in disease research.
  • Understanding these methods aids in prioritizing variants for further investigation in medical genetics.
  • Future directions involve refining prediction accuracy and expanding applications in biological and clinical settings.