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

Predicting quantitative traits from genome and phenome with near perfect accuracy

Kaspar Märtens, Johan Hallin, Jonas Warringer, Gianni Liti, Leopold Parts
doi: https://doi.org/10.1101/029868
Kaspar Märtens
1Institute of Computer Science, University of Tartu, Tartu, Estonia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Johan Hallin
2Institute of Research on Cancer and Aging, University of Sophia Antipolis, Nice, France
3Department of Chemistry and Molecular Biology, Gothenburg University, Gothenburg, Sweden
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonas Warringer
3Department of Chemistry and Molecular Biology, Gothenburg University, Gothenburg, Sweden
4Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, As, Norway
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: leopold.parts@sanger.ac.uk gianni.liti@unice.fr jonas.warringer@cmb.se
Gianni Liti
2Institute of Research on Cancer and Aging, University of Sophia Antipolis, Nice, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: leopold.parts@sanger.ac.uk gianni.liti@unice.fr jonas.warringer@cmb.se
Leopold Parts
1Institute of Computer Science, University of Tartu, Tartu, Estonia
5Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: leopold.parts@sanger.ac.uk gianni.liti@unice.fr jonas.warringer@cmb.se
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

In spite of decades of linkage and association studies and its potential impact on human health1, reliable prediction of an individual's risk for heritable disease remains difficult2-4. Large numbers of mapped loci do not explain substantial fractions of the heritable variation, leaving an open question of whether accurate complex trait predictions can be achieved in practice5,6. Here, we use a full genome sequenced population of 7396 yeast strains of varying relatedness, and predict growth traits from family information, effects of segregating genetic variants, and growth measurements in other environments with an average coefficient of determination R2 of 0.91. This accuracy exceeds narrow-sense heritability, approaches limits imposed by measurement repeatability, and is higher than achieved with a single replicate assay in the lab. We find that both relatedness and variant-based predictions are greatly aided by availability of closer relatives, while information from a large number of more distant relatives does not improve predictive performance when close relatives can be used. Our results prove that very accurate prediction of heritable traits is possible, and recommend prioritizing collection of deeper family-based data over large reference cohorts.

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 4.0 International license.
Back to top
PreviousNext
Posted October 26, 2015.
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.
Predicting quantitative traits from genome and phenome with near perfect accuracy
(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
Predicting quantitative traits from genome and phenome with near perfect accuracy
Kaspar Märtens, Johan Hallin, Jonas Warringer, Gianni Liti, Leopold Parts
bioRxiv 029868; doi: https://doi.org/10.1101/029868
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Predicting quantitative traits from genome and phenome with near perfect accuracy
Kaspar Märtens, Johan Hallin, Jonas Warringer, Gianni Liti, Leopold Parts
bioRxiv 029868; doi: https://doi.org/10.1101/029868

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 (3609)
  • Biochemistry (7584)
  • Bioengineering (5533)
  • Bioinformatics (20816)
  • Biophysics (10341)
  • Cancer Biology (7992)
  • Cell Biology (11651)
  • Clinical Trials (138)
  • Developmental Biology (6616)
  • Ecology (10222)
  • Epidemiology (2065)
  • Evolutionary Biology (13639)
  • Genetics (9553)
  • Genomics (12856)
  • Immunology (7928)
  • Microbiology (19561)
  • Molecular Biology (7673)
  • Neuroscience (42165)
  • Paleontology (308)
  • Pathology (1259)
  • Pharmacology and Toxicology (2204)
  • Physiology (3271)
  • Plant Biology (7052)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1953)
  • Systems Biology (5431)
  • Zoology (1119)