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

Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions

Sarah M. Urbut, Gao Wang, Peter Carbonetto, Matthew Stephens
doi: https://doi.org/10.1101/096552
Sarah M. Urbut
1Pritzker School of Medicine, Growth & Development Training Program, University of Chicago, Chicago, IL, USA.
2Department of Human Genetics, University of Chicago, Chicago, IL, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gao Wang
2Department of Human Genetics, University of Chicago, Chicago, IL, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Carbonetto
2Department of Human Genetics, University of Chicago, Chicago, IL, USA.
4Research Computing Center, University of Chicago, Chicago, IL, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew Stephens
2Department of Human Genetics, University of Chicago, Chicago, IL, USA.
3Department of Statistics, University of Chicago, Chicago, IL, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

We introduce new statistical methods for analyzing genomic datasets that measure many effects in many conditions (e.g., gene expression changes under many treatments). These new methods improve on existing methods by allowing for arbitrary correlations in effect sizes among conditions. This flexible approach increases power, improves effect estimates, and allows for more quantitative assessments of effect-size heterogeneity compared to simple “shared/condition-specific” assessments. We illustrate these features through an analysis of locally-acting variants associated with gene expression (“cis eQTLs”) in 44 human tissues. Our analysis identifies more eQTLs than existing approaches, consistent with improved power. We show that while genetic effects on expression are extensively shared among tissues, effect sizes can still vary greatly among tissues. Some shared eQTLs show stronger effects in subsets of biologically related tissues (e.g., brain-related tissues), or in only one tissue (e.g., testis). Our methods are widely applicable, computationally tractable for many conditions, and available online.

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-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted September 21, 2018.
Download PDF
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.
Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions
(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
Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions
Sarah M. Urbut, Gao Wang, Peter Carbonetto, Matthew Stephens
bioRxiv 096552; doi: https://doi.org/10.1101/096552
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions
Sarah M. Urbut, Gao Wang, Peter Carbonetto, Matthew Stephens
bioRxiv 096552; doi: https://doi.org/10.1101/096552

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 (4239)
  • Biochemistry (9172)
  • Bioengineering (6804)
  • Bioinformatics (24064)
  • Biophysics (12155)
  • Cancer Biology (9564)
  • Cell Biology (13825)
  • Clinical Trials (138)
  • Developmental Biology (7658)
  • Ecology (11737)
  • Epidemiology (2066)
  • Evolutionary Biology (15541)
  • Genetics (10672)
  • Genomics (14359)
  • Immunology (9511)
  • Microbiology (22901)
  • Molecular Biology (9129)
  • Neuroscience (49113)
  • Paleontology (357)
  • Pathology (1487)
  • Pharmacology and Toxicology (2583)
  • Physiology (3851)
  • Plant Biology (8351)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2301)
  • Systems Biology (6205)
  • Zoology (1302)