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

Joint estimation of contamination, error and demography for nuclear DNA from ancient humans

Fernando Racimo, Gabriel Renaud, Montgomery Slatkin
doi: https://doi.org/10.1101/022285
Fernando Racimo
aDepartment of Integrative Biology, University of California, Berkeley, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gabriel Renaud
bDepartment of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Montgomery Slatkin
aDepartment of Integrative Biology, University of California, Berkeley, 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
  • Preview PDF
Loading

Abstract

When sequencing an ancient DNA sample from a hominin fossil, DNA from present-day humans involved in excavation and extraction will be sequenced along with the endogenous material. This type of contamination is problematic for downstream analyses as it will introduce a bias towards the population of the contaminating individual(s). Quantifying the extent of contamination is a crucial step as it allows researchers to account for possible biases that may arise in downstream genetic analyses. Here, we present an MCMC algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters – including drift times and admixture rates – for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. We assume we have a large panel representing the putative contaminant population (e.g. European, East Asian or African). The method is implemented in a C++ program called ’Demographic Inference with Contamination and Error’ (DICE). We applied it to simulations and genome data from ancient Neanderthals and modern humans. With reasonable levels of genome sequence coverage (> 3X), we find we can recover accurate estimates of all these parameters, even when the contamination rate is as high as 50%.

Footnotes

  • Email address: fernandoracimo{at}gmail.com (Fernando Racimo)

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 January 19, 2016.
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.
Joint estimation of contamination, error and demography for nuclear DNA from ancient humans
(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
Joint estimation of contamination, error and demography for nuclear DNA from ancient humans
Fernando Racimo, Gabriel Renaud, Montgomery Slatkin
bioRxiv 022285; doi: https://doi.org/10.1101/022285
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Joint estimation of contamination, error and demography for nuclear DNA from ancient humans
Fernando Racimo, Gabriel Renaud, Montgomery Slatkin
bioRxiv 022285; doi: https://doi.org/10.1101/022285

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 (2643)
  • Biochemistry (5245)
  • Bioengineering (3659)
  • Bioinformatics (15764)
  • Biophysics (7231)
  • Cancer Biology (5607)
  • Cell Biology (8073)
  • Clinical Trials (138)
  • Developmental Biology (4755)
  • Ecology (7489)
  • Epidemiology (2059)
  • Evolutionary Biology (10546)
  • Genetics (7711)
  • Genomics (10102)
  • Immunology (5172)
  • Microbiology (13865)
  • Molecular Biology (5370)
  • Neuroscience (30684)
  • Paleontology (214)
  • Pathology (874)
  • Pharmacology and Toxicology (1522)
  • Physiology (2243)
  • Plant Biology (5001)
  • Scientific Communication and Education (1039)
  • Synthetic Biology (1383)
  • Systems Biology (4139)
  • Zoology (810)