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Association mapping from sequencing reads using k-mers
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 pres...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044908/ https://www.ncbi.nlm.nih.gov/pubmed/29897334 http://dx.doi.org/10.7554/eLife.32920 |
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author | Rahman, Atif Hallgrímsdóttir, Ingileif Eisen, Michael Pachter, Lior |
author_facet | Rahman, Atif Hallgrímsdóttir, Ingileif Eisen, Michael Pachter, Lior |
author_sort | Rahman, Atif |
collection | PubMed |
description | 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 [Formula: see text]-mers in whole-genome sequencing reads, testing for associations directly between [Formula: see text]-mers and the trait of interest, and local assembly of the statistically significant [Formula: see text]-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 [Formula: see text]-mers. Finally, application to an E.coli dataset on ampicillin resistance validates the approach. |
format | Online Article Text |
id | pubmed-6044908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-60449082018-07-16 Association mapping from sequencing reads using k-mers Rahman, Atif Hallgrímsdóttir, Ingileif Eisen, Michael Pachter, Lior eLife Epidemiology and Global Health 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 [Formula: see text]-mers in whole-genome sequencing reads, testing for associations directly between [Formula: see text]-mers and the trait of interest, and local assembly of the statistically significant [Formula: see text]-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 [Formula: see text]-mers. Finally, application to an E.coli dataset on ampicillin resistance validates the approach. eLife Sciences Publications, Ltd 2018-06-13 /pmc/articles/PMC6044908/ /pubmed/29897334 http://dx.doi.org/10.7554/eLife.32920 Text en © 2018, Rahman et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Epidemiology and Global Health Rahman, Atif Hallgrímsdóttir, Ingileif Eisen, Michael Pachter, Lior Association mapping from sequencing reads using k-mers |
title | Association mapping from sequencing reads using k-mers |
title_full | Association mapping from sequencing reads using k-mers |
title_fullStr | Association mapping from sequencing reads using k-mers |
title_full_unstemmed | Association mapping from sequencing reads using k-mers |
title_short | Association mapping from sequencing reads using k-mers |
title_sort | association mapping from sequencing reads using k-mers |
topic | Epidemiology and Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044908/ https://www.ncbi.nlm.nih.gov/pubmed/29897334 http://dx.doi.org/10.7554/eLife.32920 |
work_keys_str_mv | AT rahmanatif associationmappingfromsequencingreadsusingkmers AT hallgrimsdottiringileif associationmappingfromsequencingreadsusingkmers AT eisenmichael associationmappingfromsequencingreadsusingkmers AT pachterlior associationmappingfromsequencingreadsusingkmers |