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

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.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 5.

The following previously published data sets were used

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 "This ORCID iD identifies the author of this article:" 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 "This ORCID iD identifies the author of this article:" 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, University of California, Los Angeles, United States

Version history

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

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.

Metrics

  • 13,436
    views
  • 1,129
    downloads
  • 87
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Atif Rahman
  2. Ingileif Hallgrímsdóttir
  3. Michael Eisen
  4. Lior Pachter
(2018)
Association mapping from sequencing reads using k-mers
eLife 7:e32920.
https://doi.org/10.7554/eLife.32920

Share this article

https://doi.org/10.7554/eLife.32920

Further reading

    1. Ecology
    2. Epidemiology and Global Health
    Emilia Johnson, Reuben Sunil Kumar Sharma ... Kimberly Fornace
    Research Article

    Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Clara Akpan
    Insight

    Systematically tracking and analysing reproductive loss in livestock helps with efforts to safeguard the health and productivity of food animals by identifying causes and high-risk areas.