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Related Concept Videos

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.
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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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Principles of Pharmacogenetics: Types of Genetic Variants01:27

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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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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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Human Genetics01:28

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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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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Related Experiment Video

Updated: Mar 9, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Predicting Severity of Disease-Causing Variants.

Abhishek Niroula1, Mauno Vihinen1

  • 1Department of Experimental Medical Science, Lund University, Lund, SE-22184, Sweden.

Human Mutation
|January 11, 2017
PubMed
Summary
This summary is machine-generated.

Predicting disease severity from genetic variants is crucial for diagnosis and treatment. A new machine-learning tool, PON-PS, shows promise in classifying variant severity, outperforming existing methods.

Keywords:
genotype-phenotype correlationmutation severityphenotype predictionphenotypic severityseverity prediction

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Medical Genetics

Background:

  • Disease severity often dictates clinical intervention strategies.
  • Accurate prediction of genetic variant impact on disease severity is currently lacking.
  • Existing computational tools struggle to differentiate variants causing severe versus less severe phenotypes.

Purpose of the Study:

  • To develop and validate a novel machine-learning tool for predicting disease severity associated with genetic variants.
  • To assess the performance of existing variation impact predictors in distinguishing severe from less severe phenotypes.
  • To provide a tool that aids in diagnosis, prognosis, and clinical decision-making for genetic diseases.

Main Methods:

  • Manual curation of a dataset of variants linked to severe and less severe phenotypes.
  • Evaluation of existing variation impact predictors on the curated dataset.
  • Development of a machine-learning model (PON-PS) for classifying amino acid substitutions.
  • Independent testing of PON-PS using a separate dataset and variants from four additional proteins.

Main Results:

  • Existing variation impact predictors were unable to effectively separate variants associated with severe and less severe phenotypes.
  • The novel machine-learning method, PON-PS, demonstrated superior performance in distinguishing severe from non-severe variants.
  • PON-PS achieved 61% accuracy on an independent test dataset, outperforming current tolerance prediction methods.

Conclusions:

  • PON-PS is the first generic tool specifically designed for predicting disease severity based on genetic variants.
  • This tool can enhance diagnostic accuracy, prognosis, and guide preventive and clinical strategies.
  • PON-PS serves as a valuable addition to existing evidence for genetic variant interpretation.