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GWAS Meta-Analysis

Definition
AI-generated

GWAS meta-analysis combines association evidence across multiple studies by pooling per-variant summary statistics (often inverse-variance weighted on the effect scale) under fixed- or random-effects assumptions, after harmonizing alleles, strands, and genome builds.

Topics
GWAS
Related
Effective Sample Size Genome-Wide Association Study (GWAS) GWAS Summary Statistics

Why it matters in GWAS

Larger aggregate sample sizes increase power for polygenic traits and stabilize effect estimates; meta-analysis is the standard route to megabase-scale cohorts without sharing individual-level data.

Example usage

"The primary methods include GWAS Meta-Analysis as part of the association and model-comparison workflow."

Effective Sample Size Genome-Wide Association Study (GWAS) GWAS Summary Statistics Liftover Measure of Effect Multiple Testing Correction Odds Ratio (OR)
Related-term network

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Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Willer CJ, Li Y, Abecasis GR. (2010). METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics.
  • Uffelmann E, et al. (2021). Genome-wide association studies. Nat Rev Methods Primers. https://doi.org/10.1038/s43586-021-00056-9
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