Association mapping from sequencing reads using k-mers

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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

1. 1000 Genomes Project Consortium
(2012) 1000 Genomes : A Deep Catalog of Human Genetic Variation
http://www.internationalgenome.org/data.

http://www.internationalgenome.org

Article and author information

Author details

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.

"This ORCID iD identifies the author of this article:" 0000-0003-1805-3971
Ingileif Hallgrímsdóttir

Department of Statistics, University of California, Berkeley, Berkeley, United States

Competing interests
The authors declare that no competing interests exist.
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.

"This ORCID iD identifies the author of this article:" 0000-0002-7528-738X
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.

Copyright

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.