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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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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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Related Experiment Video

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PathVar: A Customisable NGS Variant Calling Algorithm Implicates Novel Candidate Genes and Pathways in Hemiplegic

Mohammed M Alfayyadh1, Neven Maksemous1, Heidi G Sutherland1

  • 1Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, Australia.

Clinical Genetics
|October 12, 2024
PubMed
Summary

PathVar, a new bioinformatics tool, aids in discovering genetic causes of hemiplegic migraine (HM) by analyzing next-generation sequencing data. It identifies potential pathogenic variants, advancing research into HM and other complex genetic disorders.

Keywords:
PathVarannotationbioinformaticsgenetic variantshemiplegic migraine

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Area of Science:

  • Genomics
  • Bioinformatics
  • Neurology

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data, necessitating advanced bioinformatics tools for disease gene discovery.
  • Hemiplegic migraine (HM) is a severe neurological disorder with a known genetic component, yet many patients lack identified pathogenic variants in known genes.
  • The genetic heterogeneity of HM highlights the need for novel approaches to identify causative genetic factors.

Purpose of the Study:

  • To develop and validate PathVar, a novel bioinformatics algorithm for identifying pathogenic variants in NGS data.
  • To apply PathVar to whole exome sequencing data from hemiplegic migraine patients to uncover potential genetic causes.
  • To identify novel candidate genes associated with hemiplegic migraine.

Main Methods:

  • Development of PathVar, integrating tools like GATK HaplotypeCaller, VEP, ANNOVAR, and TAPES for variant calling, annotation, and pathogenicity assignment.
  • Application of PathVar to whole exome sequencing data from 184 hemiplegic migraine patients.
  • Analysis of identified variants for pathogenicity and association with HM.

Main Results:

  • PathVar successfully identified 648 probably pathogenic variants across multiple patients in the HM cohort.
  • Several novel candidate genes associated with hemiplegic migraine were identified.
  • A significant number of these candidate genes are involved in the Rho GTPases pathway.

Conclusions:

  • PathVar is an effective bioinformatics tool for discovering candidate pathogenic variants in NGS data for complex diseases like HM.
  • The findings suggest novel genetic pathways, including the Rho GTPases pathway, may contribute to hemiplegic migraine pathogenesis.
  • PathVar can facilitate future research into the genetic basis of HM and other genetically heterogeneous disorders.