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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Meta-analysis for genome-wide association studies using case-control design: application and practice.

Sungryul Shim1, Jiyoung Kim2, Wonguen Jung2

  • 1Institute for Clinical Molecular Biology Research, Soonchunhyang University Hospital, Seoul, Korea.

Epidemiology and Health
|January 18, 2017
PubMed
Summary

This review outlines a five-step process for conducting genome-wide meta-analyses (GWMA) of genetic association studies. It details specific methods for evaluating genetic models and heterogeneity, crucial for robust genetic research.

Keywords:
Genetic modelsGenome-wide association studyMeta-analysisPolymorphismReviews

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) are essential for identifying genetic variants associated with diseases.
  • Systematic reviews and meta-analyses are critical for synthesizing evidence from multiple GWAS.
  • Standard meta-analysis methods require adaptation for the unique challenges of genetic data.

Purpose of the Study:

  • To systematically outline and standardize the process of conducting genome-wide meta-analysis (GWMA).
  • To provide a practical framework for researchers applying GWMA to genetic association studies.
  • To highlight key considerations and statistical approaches specific to GWMA.

Main Methods:

  • A five-step process for GWMA is described: searching and selection, data extraction, validity evaluation, genetic model-based meta-analysis, and heterogeneity assessment.
  • Emphasis is placed on evaluating Hardy-Weinberg equilibrium (HWE) and performing meta-analyses across five genetic models (dominant, recessive, etc.).
  • Statistical software commands in STATA ('genhwcci', 'metan', 'metareg') are recommended for HWE evaluation, effect size calculation, and meta-regression analysis.

Main Results:

  • The review establishes a clear, step-by-step methodology for performing GWMA.
  • It specifies the necessity of evaluating HWE and utilizing multiple genetic models for accurate genetic association analysis.
  • It recommends specific STATA commands for key analytical steps, facilitating practical application.

Conclusions:

  • The outlined process provides a standardized approach to conducting robust genome-wide meta-analyses.
  • Accurate GWMA requires careful consideration of genetic models and heterogeneity assessment.
  • This framework aids researchers in synthesizing evidence from GWAS effectively.