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

Explaining Missing Heritability Using Gaussian Process Regression

Kevin Sharp, Wim Wiegerinck, Alejandro Arias-Vasquez, Barbara Franke, Jonathan Marchini, Cornelis A. Albers, Hilbert J. Kappen
doi: https://doi.org/10.1101/040576
Kevin Sharp
aDepartment of Biophysics, Radboud University Nijmegen, The Netherlands.
bDepartment of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
dDepartment of Statistics, University of Oxford, Oxford, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wim Wiegerinck
aDepartment of Biophysics, Radboud University Nijmegen, The Netherlands.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alejandro Arias-Vasquez
bDepartment of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
cDepartment of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Barbara Franke
bDepartment of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
cDepartment of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonathan Marchini
dDepartment of Statistics, University of Oxford, Oxford, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cornelis A. Albers
bDepartment of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
cDepartment of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hilbert J. Kappen
aDepartment of Biophysics, Radboud University Nijmegen, The Netherlands.
  • 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

For many traits and common human diseases, causal loci uncovered by genetic association studies account for little of the known heritable variation. Such ‘missing heritability’ may be due to the effect of non-additive interactions between multiple loci, but this has been little explored and difficult to test using existing parametric approaches. We propose a Bayesian non-parametric Gaussian Process Regression model, for identifying associated loci in the presence of interactions of arbitrary order. We analysed 46 quantitative yeast phenotypes and found that over 70% of the total known missing heritability could be explained using common genetic variants, many without significant marginal effects. Additional analysis of an immunological rat phenotype identified a three SNP interaction model providing a significantly better fit (p-value 9.0e-11) than the null model incorporating only the single marginally significant SNP. This new approach, called GPMM, represents a significant advance in approaches to understanding the missing heritability problem with potentially important implications for studies of complex, quantitative traits.

Footnotes

  • ↵* These authors were primary supervisors of this work at various stages.

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 February 22, 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.
Explaining Missing Heritability Using Gaussian Process Regression
(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
Explaining Missing Heritability Using Gaussian Process Regression
Kevin Sharp, Wim Wiegerinck, Alejandro Arias-Vasquez, Barbara Franke, Jonathan Marchini, Cornelis A. Albers, Hilbert J. Kappen
bioRxiv 040576; doi: https://doi.org/10.1101/040576
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Explaining Missing Heritability Using Gaussian Process Regression
Kevin Sharp, Wim Wiegerinck, Alejandro Arias-Vasquez, Barbara Franke, Jonathan Marchini, Cornelis A. Albers, Hilbert J. Kappen
bioRxiv 040576; doi: https://doi.org/10.1101/040576

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 (3586)
  • Biochemistry (7545)
  • Bioengineering (5495)
  • Bioinformatics (20732)
  • Biophysics (10294)
  • Cancer Biology (7951)
  • Cell Biology (11610)
  • Clinical Trials (138)
  • Developmental Biology (6586)
  • Ecology (10168)
  • Epidemiology (2065)
  • Evolutionary Biology (13578)
  • Genetics (9520)
  • Genomics (12817)
  • Immunology (7906)
  • Microbiology (19503)
  • Molecular Biology (7641)
  • Neuroscience (41982)
  • Paleontology (307)
  • Pathology (1254)
  • Pharmacology and Toxicology (2192)
  • Physiology (3259)
  • Plant Biology (7018)
  • Scientific Communication and Education (1293)
  • Synthetic Biology (1947)
  • Systems Biology (5418)
  • Zoology (1113)