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Updated: Aug 29, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Workshop proceedings: GWAS summary statistics standards and sharing.

Jacqueline A L MacArthur1,2, Annalisa Buniello1, Laura W Harris1

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.

Cell Genomics
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

Sharing genome-wide association study (GWAS) summary statistics (SumStats) is crucial for advancing genomic medicine. This initiative proposes standards and infrastructure to improve GWAS SumStats accessibility and utility for larger meta-analyses.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • Genome-wide association studies (GWASs) are vital for mapping complex human traits.
  • Sharing GWAS summary statistics (SumStats) is essential for powerful meta-analyses but faces barriers.
  • Limited accessibility and lack of standardized formats hinder the full potential of GWAS data.

Purpose of the Study:

  • To address challenges in sharing GWAS SumStats and improve their availability, quality, and utility.
  • To develop an action plan for making SumStats and metadata findable, accessible, interoperable, and reusable (FAIR).
  • To promote community standards and incentives for broader data sharing in genomic research.

Main Methods:

  • Convened a community workshop involving experts in GWAS and data sharing.
  • Evaluated technological and sociological barriers to SumStats sharing.
  • Developed recommendations for data standards, infrastructure, and incentives.

Main Results:

  • Identified key barriers to the open sharing of GWAS SumStats.
  • Proposed an action plan focused on FAIR data principles.
  • Recommended the NHGRI-EBI GWAS Catalog as a central repository for SumStats deposition.

Conclusions:

  • Establishing clear standards and infrastructure is critical for enhancing GWAS SumStats sharing.
  • Incentivizing data sharing and promoting FAIR principles will accelerate discoveries in genomic medicine.
  • Community collaboration is essential for advancing the accessibility and utility of large-scale genetic association data.