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

Evaluating the use of ABBA-BABA statistics to locate introgressed loci

Simon H. Martin, John W. Davey, Chris D. Jiggins
doi: https://doi.org/10.1101/001347
Simon H. Martin
1Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: shm45@cam.ac.uk
John W. Davey
1Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chris D. Jiggins
1Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
  • 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

Several methods have been proposed to test for introgression across genomes. One method tests for a genome-wide excess of shared derived alleles between taxa using Patterson’s D statistic, but does not establish which loci show such an excess or whether the excess is due to introgression or ancestral population structure. Several recent studies have extended the use of D by applying the statistic to small genomic regions, rather than genome-wide. Here, we use simulations and whole genome data from Heliconius butterflies to investigate the behavior of D in small genomic regions. We find that D is unreliable in this situation as it gives inflated values when effective population size is low, causing D outliers to cluster in genomic regions of reduced diversity. As an alternative, we propose a related statistic Embedded Image a modified version of a statistic originally developed to estimate the genome-wide fraction of admixture. Embedded Image is not subject to the same biases as D, and is better at identifying introgressed loci. Finally, we show that both D and Embedded Image outliers tend to cluster in regions of low absolute divergence (dXY), which can confound a recently proposed test for differentiating introgression from shared ancestral variation at individual loci.

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 4.0 International license.
Back to top
PreviousNext
Posted August 20, 2014.
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.
Evaluating the use of ABBA-BABA statistics to locate introgressed loci
(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
Evaluating the use of ABBA-BABA statistics to locate introgressed loci
Simon H. Martin, John W. Davey, Chris D. Jiggins
bioRxiv 001347; doi: https://doi.org/10.1101/001347
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Evaluating the use of ABBA-BABA statistics to locate introgressed loci
Simon H. Martin, John W. Davey, Chris D. Jiggins
bioRxiv 001347; doi: https://doi.org/10.1101/001347

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

  • Evolutionary Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4087)
  • Biochemistry (8766)
  • Bioengineering (6480)
  • Bioinformatics (23346)
  • Biophysics (11751)
  • Cancer Biology (9150)
  • Cell Biology (13255)
  • Clinical Trials (138)
  • Developmental Biology (7417)
  • Ecology (11370)
  • Epidemiology (2066)
  • Evolutionary Biology (15088)
  • Genetics (10402)
  • Genomics (14012)
  • Immunology (9122)
  • Microbiology (22050)
  • Molecular Biology (8780)
  • Neuroscience (47375)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8050)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2209)
  • Systems Biology (6016)
  • Zoology (1250)