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Subset testing and analysis of multiple phenotypes.

Andriy Derkach1, Ruth M Pfeiffer1

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland.

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|March 29, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new method, STAMP (subset testing and analysis of multiple phenotypes), for region-based meta-analysis of genome-wide association studies (GWAS). STAMP effectively identifies associated phenotypes and distinguishes true associations from study heterogeneity.

Keywords:
gene-based testheterogeneitymeta-analysismixture model

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding complex traits.
  • Meta-analysis of multiple GWAS enhances the detection of genetic associations.
  • Distinguishing true biological signals from between-study heterogeneity in meta-analyses remains a challenge.

Purpose of the Study:

  • To develop a flexible region-based meta-analysis method for multiple phenotypes across different GWAS.
  • To introduce a statistical framework that accounts for phenotype subsets and study heterogeneity.
  • To improve the power of detecting genetic associations with complex traits.

Main Methods:

  • Developed the Subset Testing and Analysis of Multiple Phenotypes (STAMP) procedure.
  • Employed mixture models for region-based meta-analysis of diverse phenotypes and GWAS data.
  • Computed posterior probabilities to quantify the likelihood of true association for each phenotype.

Main Results:

  • STAMP demonstrates superior power compared to standard meta-analysis approaches when 25-50% of outcomes are truly associated.
  • For other proportions of associated outcomes, STAMP's power is comparable to existing methods.
  • The method was successfully applied to analyze chromosome 9p21 associations with 14 cancers and cis-regulatory element associations in The Cancer Genome Atlas.

Conclusions:

  • STAMP provides a robust framework for multi-phenotype, multi-study GWAS meta-analysis.
  • The method effectively identifies subsets of associated phenotypes and mitigates heterogeneity.
  • STAMP enhances the discovery of genetic associations underlying complex traits and diseases.