Association mapping from sequencing reads using k-mers
- University of California, Berkeley, United States
- 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.
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1000 Genomes : A Deep Catalog of Human Genetic Variationhttp://www.internationalgenome.org/data.
Article and author information
Author details
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
- Jonathan Flint, University of California, Los Angeles, United States
Version history
- Received: October 18, 2017
- Accepted: June 8, 2018
- Accepted Manuscript published: June 13, 2018 (version 1)
- 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.
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Association mapping from sequencing reads using k-mers
eLife 7:e32920.
https://doi.org/10.7554/eLife.32920
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