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Association mapping from sequencing reads using k-mers

Rahman, Atif and Hallgrímsdóttir, Ingileif and Eisen, Michael and Pachter, Lior (2018) Association mapping from sequencing reads using k-mers. eLife, 7 . Art. No. e32920. ISSN 2050-084X. PMCID PMC6044908. https://resolver.caltech.edu/CaltechAUTHORS:20190503-134759852

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.7554/elife.32920DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044908PubMed CentralArticle
https://www.biorxiv.org/content/10.1101/141267v1DOIDiscussion Paper
https://doi.org/10.7554/eLife.32920.012DOITransparent reporting form
ORCID:
AuthorORCID
Rahman, Atif0000-0003-1805-3971
Eisen, Michael0000-0002-7528-738X
Pachter, Lior0000-0002-9164-6231
Additional Information:© 2018 Rahman et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Received: 18 October 2017; Accepted: 08 June 2018; Published: 13 June 2018. We thank Faraz Tavakoli, Harold Pimentel, Brielin Brown and Nicolas Bray for helpful conversations in the development of the method for association mapping from sequencing reads using k-mers. AR, IH, MBE and LP were funded in part by NIH R21 HG006583. AR was funded in part by Fulbright Science and Technology Fellowship 15093630. The authors declare that no competing interests exist. Author contributions: Atif Rahman, Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing; Ingi leif Hallgrímsdóttir, Validation, Methodology, Writing—review and editing; Michael Eisen, Conceptu alization, Supervision, Funding acquisition, Methodology, Project administration, Writing—review and editing; Lior Pachter, Conceptualization, Formal analysis, Supervision, Funding acquisition, Vali dation, Methodology, Project administration, Writing—review and editing. 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.
Funders:
Funding AgencyGrant Number
NIHR21 HG006583
Fulbright Foundation15093630
PubMed Central ID:PMC6044908
Record Number:CaltechAUTHORS:20190503-134759852
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190503-134759852
Official Citation:Rahman et al. eLife 2018;7:e32920. DOI: https://doi.org/10.7554/eLife.32920
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95211
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 May 2019 21:19
Last Modified:03 Oct 2019 21:11

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