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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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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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Genome-wide Association Studies-GWAS01:11

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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.
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Pharmacogenomics: Identification of New Drug Targets01:29

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Comparing Copy Number Variations and SNPs02:26

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

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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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Related Experiment Video

Updated: Apr 27, 2026

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Ranking non-synonymous single nucleotide polymorphisms based on disease concepts.

Hashem A Shihab, Julian Gough, Matthew Mort

  • 1Bristol Centre for Systems Biomedicine and MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK. Tom.Gaunt@bristol.ac.uk.

Human Genomics
|July 2, 2014
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Summary
This summary is machine-generated.

This study enhances variant prioritization by adapting the Functional Analysis through Hidden Markov Models (FATHMM) algorithm with disease-specific information. This approach significantly reduces false positives in identifying functional mutations for specific diseases.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The increasing volume of non-synonymous single nucleotide polymorphisms (nsSNPs) from sequencing necessitates accurate computational tools for variant prioritization.
  • Existing 'disease-agnostic' algorithms predict deleterious mutations but lack specificity for clinical applications.
  • A disease-specific approach could improve the accuracy of variant prioritization in clinical and research settings, reducing false positives.

Purpose of the Study:

  • To develop and evaluate a disease-specific functional prediction algorithm by integrating a disease-specific weighting scheme into FATHMM.
  • To assess the efficacy of this disease-specific approach in reducing false positive candidate mutations compared to traditional methods.

Main Methods:

  • Incorporation of a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm.
  • Testing the modified FATHMM algorithm across 17 distinct disease concepts/categories.
  • Comparison of the disease-specific FATHMM predictions against traditional, disease-agnostic prediction algorithms.

Main Results:

  • The disease-specific FATHMM approach demonstrated a reduction in false positives compared to traditional prediction algorithms.
  • This improvement was observed across a diverse set of 17 disease concepts/categories.
  • The findings support the utility of disease-specific functional predictions for prioritizing candidate variants.

Conclusions:

  • Disease-specific functional prediction significantly enhances the accuracy of variant prioritization in whole-exome/whole-genome sequencing data.
  • This approach offers a valuable tool for researchers and clinicians aiming to identify disease-relevant mutations more efficiently.
  • A web-based implementation of the disease-specific FATHMM algorithm is publicly available for use.