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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...

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A two-step multiple-marker strategy for genome-wide association studies.

Hugues Aschard1,2, Mickaël Guedj3,4, Florence Demenais1,2

  • 1INSERM, U794, Tour Evry 2, 523 Place des Terrasses de l'Agora, 91034, Evry, France.

BMC Proceedings
|May 10, 2008
PubMed
Summary

This study introduces a two-step genome-wide association study strategy using multiple markers to efficiently identify trait loci. This approach reduces genotyping costs and minimizes false positives compared to single-marker methods.

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Published on: November 3, 2010

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) face challenges in marker selection and multiple testing.
  • Reducing genotyped markers is crucial for cost-effectiveness and statistical power in GWAS.
  • Efficiently identifying trait loci requires optimized analytical strategies.

Purpose of the Study:

  • To propose and evaluate a novel two-step, multiple-marker strategy for genome-wide association studies.
  • To reduce the number of markers genotyped without compromising the efficiency of trait loci identification.
  • To minimize the multiple testing burden and false-positive rates in genetic association analyses.

Main Methods:

  • A two-step strategy employing local score for candidate region screening (Step 1).
  • Utilized FBAT-LC (Family-Based Association Test - Local Composite) for significance testing of marker sets (Step 2).
  • Evaluated performance on simulated data from Genetic Analysis Workshop 15 (GAW15) Problem 3.

Main Results:

  • The proposed multiple-marker strategy successfully detected seven out of nine simulated trait loci in at least 87% of replicates.
  • The framework effectively handled association with disease presence and disease severity.
  • Comparison with single-marker approaches demonstrated reduced multiple testing and fewer false positives.

Conclusions:

  • The developed two-step, multiple-marker strategy offers an efficient and robust approach for genome-wide association studies.
  • This method effectively balances cost reduction with the accurate identification of genetic loci.
  • Considering marker regions over single markers significantly improves statistical rigor in GWAS.