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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.
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Related Experiment Video

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Infinium Assay for Large-scale SNP Genotyping Applications
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Published on: November 19, 2013

Meta-analysis methods for genome-wide association studies and beyond.

Evangelos Evangelou1, John P A Ioannidis

  • 1Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece.

Nature Reviews. Genetics
|May 10, 2013
PubMed
Summary

This study reviews statistical methods for genome-wide association studies (GWAS) meta-analysis to find genetic risk variants. It offers guidelines for researchers, covering complex data, sequencing, and rare variants, and discusses data sharing and consortia.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic risk variants.
  • Meta-analysis of GWAS data is a widely adopted and powerful approach for increasing statistical power and discovering genetic associations.
  • Challenges exist in interpreting results, assessing heterogeneity, and applying methods to diverse data types.

Purpose of the Study:

  • To provide a comprehensive overview of statistical methods for genome-wide association studies (GWAS) meta-analysis.
  • To discuss the interpretation, heterogeneity assessment, and application of these methods to complex and sequencing data.
  • To offer practical guidelines for researchers and address challenges in data sharing and consortium building.

Main Methods:

  • Review of established and emerging statistical methodologies for GWAS meta-analysis.
  • Discussion of techniques for handling complex genetic data and sequencing information.
  • Exploration of strategies for assessing heterogeneity and ensuring robust interpretation of results.

Main Results:

  • Identification of key statistical methods applicable to GWAS meta-analysis.
  • Elucidation of considerations for complex data, sequencing data, and rare variant analysis.
  • Highlighting best practices for data interpretation and heterogeneity assessment.

Conclusions:

  • Effective application of GWAS meta-analysis requires careful consideration of statistical methods and data characteristics.
  • Guidelines are provided to aid researchers in planning and executing robust meta-analyses.
  • Addressing data accessibility and collaborative efforts through consortia is vital for advancing genetic discovery.