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Multivariable association discovery in population-scale meta-omics studies
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Abstract
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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“Multivariable association between microbial community abundance and community type was examined with MaAsLin2 (version 1.4.0)29 taking into account potential confounding factors (age, gender, BMI, treatment with ABX and disease progression).”
From Faecalibacterium prausnitzii strain EXL01 boosts efficacy of immune checkpoint inhibitors (2024) · cites this paper as [29]
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Metadata
- Authors
- Mallick, H., Rahnavard, A., McIver, L. J., Ma, S., Zhang, Y., Nguyen, L. H., Tickle, T. L., Weingart, G., Ren, B., Schwager, E. H., Chatterjee, S., Thompson, K. N., Wilkinson, J. E., Subramanian, A., Lu, Y., Waldron, L., Paulson, J. N., Franzosa, E. A., Bravo, H. C., & Huttenhower, C.
- Affiliations
- Broad Institute (US)
- George Washington University (US)
- Harvard University (US)
- National Institutes of Health (US)
- City University of New York (US)
- Genentech (US)
- Corresponding author
- Huttenhower, Curtis
- ORCID
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-11-16
- Volume
- 17
- Issue
- 11
- Pages
- e1009442
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC8714082
- OpenAlex
- W3211779138
- Cited by
- 2,596
- References
- 71
- Citations by year
- 2026: 564 · 2025: 721 · 2024: 611 · 2023: 378 · 2022: 220 · 2021: 91
- FWCI
- 107.10
- Type
- journal article
- Language
- en
- Update notices
- new_version (2021-12-28)
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS Computational Biology (publishedVersion, public-domain)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- US National Science Foundation, Division of Environmental Biology (DEB 2028280)
- national institute of allergy and infectious diseases (U19AI110820)
- national human genome research institute (R01HG005220)
- national institute of diabetes and digestive and kidney diseases (R24DK110499, U54DK102557)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- generalized linear models, mixed models, compositional data, count data, zero-inflated data, repeated measures, false discovery rate control, statistical power, inflammatory bowel disease
- MeSH terms
- Gastrointestinal Microbiome, Computer Simulation, Humans, Inflammatory Bowel Diseases, Multivariate Analysis, Computational Biology
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