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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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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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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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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 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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Related Experiment Video

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores.

Lin Chen1, Gouri Mukerjee2, Ruslan Dorfman2

  • 1Agent-Based Modelling Laboratory, York University Toronto, ON, Canada.

Frontiers in Genetics
|March 23, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for complex disease risk prediction using Voronoi tessellation network analysis. It quantifies risk by mapping gene-variant associations and identifying disease-specific clusters.

Keywords:
Voronoi tessellationcluster analysisdata analysisdisease risk assessmentgene-variant scores

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Assessing complex disease risk using genetic and protein-protein interaction data is challenging.
  • Current methods struggle with accurate risk prediction for intricate diseases.

Purpose of the Study:

  • To develop a framework for quantifying complex disease risk.
  • To integrate gene and variant association data for improved risk assessment.
  • To identify disease-specific or subtype-contributing clusters.

Main Methods:

  • Utilized Voronoi tessellation network analysis on a gene-variant map.
  • Integrated data from ClinVar, SNPnexus, and DISEASES databases.
  • Defined relative disease risk based on clustered gene-variant association scores.

Main Results:

  • Developed a gene-variant map incorporating disease association scores.
  • Identified autoimmune-associated clusters indicating system-level interactions.
  • The framework successfully clustered data based on Voronoi cell density.

Conclusions:

  • The proposed framework offers a novel approach to complex disease risk prediction.
  • It enables the identification of clusters relevant to specific disease subtypes or multiple subtypes.
  • This method advances the understanding of system-level interactions in complex diseases.