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

“Gap hunting” to characterize clustered probe signals in Illumina methylation array data

Shan V. Andrews, Christine Ladd-Acosta, Andrew P. Feinberg, Kasper D. Hansen, M. Daniele Fallin
doi: https://doi.org/10.1101/059659
Shan V. Andrews
1Department of Epidemiology, 615 N. Wolfe Street, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
2Wendy Klag Center for Autism and Developmental Disabilities, 615 N. Wolfe Street, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christine Ladd-Acosta
1Department of Epidemiology, 615 N. Wolfe Street, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
2Wendy Klag Center for Autism and Developmental Disabilities, 615 N. Wolfe Street, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
3Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Baltimore, MD 21205
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew P. Feinberg
3Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Baltimore, MD 21205
4Department of Medicine, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Baltimore, MD, 21205
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kasper D. Hansen
3Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Baltimore, MD 21205
5Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA
6McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, 1800 Orleans Street, Baltimore, MD 21287, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
M. Daniele Fallin
2Wendy Klag Center for Autism and Developmental Disabilities, 615 N. Wolfe Street, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
3Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Baltimore, MD 21205
7Department of Mental Health, Johns Hopkins School of Public Health, 624 N. Broadway, Baltimore, MD 21205
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: dfallin@jhu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Background The Illumina 450K array has been widely used in epigenetic association studies. Current quality-control (QC) pipelines typically remove certain sets of probes, such as those containing a SNP or with multiple mapping locations. An additional set of potentially problematic probes are those with DNA methylation (DNAm) distributions characterized by two or more distinct clusters separated by gaps. Data-driven identification of such probes may offer additional insights for downstream analyses.

Results We developed a procedure, termed “gap hunting”, to identify probes showing clustered distributions. Among 590 peripheral blood samples from the Study to Explore Early Development, we identified 11,007 “gap probes”. The vast majority (9,199) are likely attributed to an underlying SNP(s) or other variant in the probe, although SNP-affected probes exist that do not produce a gap signals. Specific factors predict which SNPs lead to gap signals, including type of nucleotide change, probe type, DNA strand, and overall methylation state. These expected effects are demonstrated in paired genotype and 450k data on the same samples. Gap probes can also serve as a surrogate for the local genetic sequence on a haplotype scale and can be used to adjust for population stratification.

Conclusions The characteristics of gap probes reflect potentially informative biology. QC pipelines may benefit from an efficient data-driven approach that “flags” gap probes, rather than filtering such probes, followed by careful interpretation of downstream association analyses. Our results should translate directly to the recently released Illumina 850K EPIC array given the similar chemistry and content design.

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 November 17, 2016.
Download PDF

Supplementary Material

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.
“Gap hunting” to characterize clustered probe signals in Illumina methylation array data
(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
“Gap hunting” to characterize clustered probe signals in Illumina methylation array data
Shan V. Andrews, Christine Ladd-Acosta, Andrew P. Feinberg, Kasper D. Hansen, M. Daniele Fallin
bioRxiv 059659; doi: https://doi.org/10.1101/059659
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
“Gap hunting” to characterize clustered probe signals in Illumina methylation array data
Shan V. Andrews, Christine Ladd-Acosta, Andrew P. Feinberg, Kasper D. Hansen, M. Daniele Fallin
bioRxiv 059659; doi: https://doi.org/10.1101/059659

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

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4087)
  • Biochemistry (8762)
  • Bioengineering (6479)
  • Bioinformatics (23341)
  • Biophysics (11750)
  • Cancer Biology (9149)
  • Cell Biology (13248)
  • Clinical Trials (138)
  • Developmental Biology (7417)
  • Ecology (11369)
  • Epidemiology (2066)
  • Evolutionary Biology (15087)
  • Genetics (10399)
  • Genomics (14009)
  • Immunology (9121)
  • Microbiology (22040)
  • Molecular Biology (8779)
  • Neuroscience (47368)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8050)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2208)
  • Systems Biology (6016)
  • Zoology (1249)