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Related Concept Videos

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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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A gene-based association method for mapping traits using reference transcriptome data.

Eric R Gamazon1,2, Heather E Wheeler3, Kaanan P Shah1

  • 1Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA.

Nature Genetics
|August 11, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces PrediXcan, a novel gene-based method to uncover biological mechanisms linking genetic variants to complex traits. PrediXcan identifies genes influencing disease by analyzing genetically regulated gene expression, aiding in understanding trait etiology.

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Area of Science:

  • Genetics
  • Genomics
  • Molecular Biology

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic variants linked to complex traits.
  • The underlying biological mechanisms connecting these variants to phenotypes remain largely unknown.

Purpose of the Study:

  • To propose and validate PrediXcan, a gene-based association method to directly test molecular mechanisms of genetic variation's effect on phenotype.
  • To identify genes involved in the etiology of complex traits by analyzing genetically regulated gene expression.

Main Methods:

  • PrediXcan estimates gene expression based on an individual's genetic profile using tissue-specific prediction models trained on transcriptome data.
  • It correlates imputed gene expression with phenotype data to identify associated genes.
  • This gene-based approach reduces the multiple-testing burden compared to variant-based methods.

Main Results:

  • PrediXcan successfully detected known genes associated with complex disease traits.
  • The method also identified novel genes implicated in disease etiology.
  • Results provide insights into the molecular mechanisms underlying genetic associations with phenotypes.

Conclusions:

  • PrediXcan offers a powerful approach to unravel the biological basis of GWAS findings.
  • The method facilitates the identification of genes and pathways involved in complex trait development.
  • PrediXcan aids in designing more focused follow-up experiments for mechanistic studies.