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A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping
BACKGROUND: The role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5402681/ https://www.ncbi.nlm.nih.gov/pubmed/28438217 http://dx.doi.org/10.1186/s40168-017-0262-x |
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author | Koh, Hyunwook Blaser, Martin J. Li, Huilin |
author_facet | Koh, Hyunwook Blaser, Martin J. Li, Huilin |
author_sort | Koh, Hyunwook |
collection | PubMed |
description | BACKGROUND: The role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. METHODS: Here, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM). RESULTS: Our extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method. CONCLUSIONS: OMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-017-0262-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5402681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54026812017-04-27 A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping Koh, Hyunwook Blaser, Martin J. Li, Huilin Microbiome Methodology BACKGROUND: The role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. METHODS: Here, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM). RESULTS: Our extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method. CONCLUSIONS: OMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-017-0262-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-04-24 /pmc/articles/PMC5402681/ /pubmed/28438217 http://dx.doi.org/10.1186/s40168-017-0262-x Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Koh, Hyunwook Blaser, Martin J. Li, Huilin A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
title | A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
title_full | A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
title_fullStr | A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
title_full_unstemmed | A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
title_short | A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
title_sort | powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5402681/ https://www.ncbi.nlm.nih.gov/pubmed/28438217 http://dx.doi.org/10.1186/s40168-017-0262-x |
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