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

Playing Musical Chairs in Big Data to Reveal Variables’ Associations

Hugues Aschard, Bjarni Vilhjalmsson, Chirag Patel, David Skurnik, Jimmy Yu, Brian Wolpin, Peter Kraft, Noah Zaitlen
doi: https://doi.org/10.1101/057190
Hugues Aschard
1Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
2Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bjarni Vilhjalmsson
3Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chirag Patel
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David Skurnik
5Division of Infectious Diseases, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jimmy Yu
6Department of Epidemiology and Biostatistics, Institute of Human Genetics, San Francisco, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Brian Wolpin
7Center for Gastrointestinal Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter Kraft
1Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
2Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
8Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Noah Zaitlen
9Department of Medicine, University of California, San Francisco, CA, 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
  • Supplementary material
  • Preview PDF
Loading

Abstract

Testing for associations in big data faces the problem of multiple comparisons, with true signals buried inside the noise of all associations queried. This is particularly true in genetic association studies where a substantial proportion of the variation of human phenotypes is driven by numerous genetic variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. While successful, this approach does not leverage the environmental and genetic factors shared between the multiple phenotypes collected in contemporary cohorts. Here we develop a method that improves the power of detecting associations when a large number of correlated variables have been measured on the same samples. Our analyses over real and simulated data provide direct support that large sets of correlated variables can be leveraged to achieve dramatic increases in statistical power equivalent to a two or even three folds increase in sample size.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted June 05, 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.
Playing Musical Chairs in Big Data to Reveal Variables’ Associations
(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
Playing Musical Chairs in Big Data to Reveal Variables’ Associations
Hugues Aschard, Bjarni Vilhjalmsson, Chirag Patel, David Skurnik, Jimmy Yu, Brian Wolpin, Peter Kraft, Noah Zaitlen
bioRxiv 057190; doi: https://doi.org/10.1101/057190
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Playing Musical Chairs in Big Data to Reveal Variables’ Associations
Hugues Aschard, Bjarni Vilhjalmsson, Chirag Patel, David Skurnik, Jimmy Yu, Brian Wolpin, Peter Kraft, Noah Zaitlen
bioRxiv 057190; doi: https://doi.org/10.1101/057190

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 (4095)
  • Biochemistry (8786)
  • Bioengineering (6493)
  • Bioinformatics (23386)
  • Biophysics (11766)
  • Cancer Biology (9167)
  • Cell Biology (13290)
  • Clinical Trials (138)
  • Developmental Biology (7422)
  • Ecology (11386)
  • Epidemiology (2066)
  • Evolutionary Biology (15119)
  • Genetics (10413)
  • Genomics (14024)
  • Immunology (9145)
  • Microbiology (22108)
  • Molecular Biology (8793)
  • Neuroscience (47445)
  • Paleontology (350)
  • Pathology (1423)
  • Pharmacology and Toxicology (2483)
  • Physiology (3711)
  • Plant Biology (8063)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2215)
  • Systems Biology (6021)
  • Zoology (1251)