Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

The variant call format provides efficient and robust storage of GWAS summary statistics

View ORCID ProfileMatthew Lyon, View ORCID ProfileShea J Andrews, View ORCID ProfileBen Elsworth, View ORCID ProfileTom R Gaunt, View ORCID ProfileGibran Hemani, View ORCID ProfileEdoardo Marcora
doi: https://doi.org/10.1101/2020.05.29.115824
Matthew Lyon
1National Institute for Health Research Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
2Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Matthew Lyon
  • For correspondence: matt.lyon@bristol.ac.uk
Shea J Andrews
3Ronald M. Loeb Center for Alzheimer’s disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Shea J Andrews
Ben Elsworth
2Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ben Elsworth
Tom R Gaunt
1National Institute for Health Research Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
2Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tom R Gaunt
Gibran Hemani
2Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gibran Hemani
Edoardo Marcora
3Ronald M. Loeb Center for Alzheimer’s disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Edoardo Marcora
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Genome-wide association study (GWAS) summary statistics are a fundamental resource for a variety of research applications 1–6. Yet despite their widespread utility, no common storage format has been widely adopted, hindering tool development and data sharing, analysis and integration. Existing tabular formats 7,8 often ambiguously or incompletely store information about genetic variants and their associations, and also lack essential metadata increasing the possibility of errors in data interpretation and post-GWAS analyses. Additionally, data in these formats are typically not indexed, requiring the whole file to be read which is computationally inefficient. To address these issues, we propose an adaptation of the variant call format9 (GWAS-VCF) and have produced a suite of open-source tools for using this format in downstream analyses. Simulation studies determine GWAS-VCF is 9-46x faster than tabular alternatives when extracting variant(s) by genomic position. Our results demonstrate the GWAS-VCF provides a robust and performant solution for sharing, analysis and integration of GWAS data. We provide open access to over 10,000 complete GWAS summary datasets converted to this format (available from: https://gwas.mrcieu.ac.uk).

Competing Interest Statement

TRG receives funding from GlaxoSmithKline and Biogen for unrelated research.

Footnotes

  • https://gwas.mrcieu.ac.uk

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted May 30, 2020.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
The variant call format provides efficient and robust storage of GWAS summary statistics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The variant call format provides efficient and robust storage of GWAS summary statistics
Matthew Lyon, Shea J Andrews, Ben Elsworth, Tom R Gaunt, Gibran Hemani, Edoardo Marcora
bioRxiv 2020.05.29.115824; doi: https://doi.org/10.1101/2020.05.29.115824
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
The variant call format provides efficient and robust storage of GWAS summary statistics
Matthew Lyon, Shea J Andrews, Ben Elsworth, Tom R Gaunt, Gibran Hemani, Edoardo Marcora
bioRxiv 2020.05.29.115824; doi: https://doi.org/10.1101/2020.05.29.115824

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4122)
  • Biochemistry (8831)
  • Bioengineering (6536)
  • Bioinformatics (23493)
  • Biophysics (11818)
  • Cancer Biology (9235)
  • Cell Biology (13350)
  • Clinical Trials (138)
  • Developmental Biology (7453)
  • Ecology (11431)
  • Epidemiology (2066)
  • Evolutionary Biology (15183)
  • Genetics (10458)
  • Genomics (14057)
  • Immunology (9193)
  • Microbiology (22221)
  • Molecular Biology (8833)
  • Neuroscience (47670)
  • Paleontology (352)
  • Pathology (1432)
  • Pharmacology and Toxicology (2493)
  • Physiology (3741)
  • Plant Biology (8097)
  • Scientific Communication and Education (1438)
  • Synthetic Biology (2226)
  • Systems Biology (6046)
  • Zoology (1258)