1. Epidemiology and Global Health
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Association mapping from sequencing reads using k-mers

  1. Atif Rahman  Is a corresponding author
  2. Ingileif Hallgrímsdóttir
  3. Michael Eisen
  4. Lior Pachter  Is a corresponding author
  1. University of California, Berkeley, United States
  2. Howard Hughes Medical Institute, University of California, Berkeley, United States
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Cite as: eLife 2018;7:e32920 doi: 10.7554/eLife.32920

Abstract

Genome wide association studies (GWAS) rely on microarrays, or more recently mapping of sequencing reads, to genotype individuals. The reliance on prior sequencing of a reference genome limits the scope of association studies, and also precludes mapping associations outside of the reference. We present an alignment free method for association studies of categorical phenotypes based on counting k-mers in whole-genome sequencing reads, testing for associations directly between k-mers and the trait of interest, and local assembly of the statistically significant k-mers to identify sequence differences. An analysis of the 1000 genomes data show that sequences identified by our method largely agree with results obtained using the standard approach. However, unlike standard GWAS, our method identifies associations with structural variations and sites not present in the reference genome. We also demonstrate that population stratification can be inferred from k-mers. Finally, application to an E.coli dataset on ampicillin resistance validates the approach.

Article and author information

Author details

  1. Atif Rahman

    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, United States
    For correspondence
    atif@cse.buet.ac.bd
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0003-1805-3971
  2. Ingileif Hallgrímsdóttir

    Department of Statistics, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Michael Eisen

    Department of Molecular and Cell Biology, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon 0000-0002-7528-738X
  4. Lior Pachter

    Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, United States
    For correspondence
    lpachter@caltech.edu
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (NIH R21 HG006583)

  • Atif Rahman
  • Ingileif Hallgrímsdóttir
  • Michael Eisen
  • Lior Pachter

Fulbright Science and Technology Fellowship (15093630)

  • Atif Rahman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Jonathan Flint, Reviewing Editor, University of California, Los Angeles, United States

Publication history

  1. Received: October 18, 2017
  2. Accepted: June 8, 2018
  3. Accepted Manuscript published: June 13, 2018 (version 1)

Copyright

© 2018, Rahman et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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