Multivariable association discovery in population-scale meta-omics studies

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...

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Vydané v:PLoS computational biology Ročník 17; číslo 11; s. e1009442
Hlavní autori: Mallick, Himel, Rahnavard, Ali, McIver, Lauren J., Ma, Siyuan, Zhang, Yancong, Nguyen, Long H., Tickle, Timothy L., Weingart, George, Ren, Boyu, Schwager, Emma H., Chatterjee, Suvo, Thompson, Kelsey N., Wilkinson, Jeremy E., Subramanian, Ayshwarya, Lu, Yiren, Waldron, Levi, Paulson, Joseph N., Franzosa, Eric A., Bravo, Hector Corrada, Huttenhower, Curtis
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 16.11.2021
Public Library of Science (PLoS)
Predmet:
Analysis
Biology and Life Sciences
Computational Biology
Computer and Information Sciences
Computer Simulation
Engineering and Technology
Epidemiology
Gastrointestinal Microbiome
Humans
Inflammatory Bowel Diseases - genetics
Inflammatory Bowel Diseases - metabolism
Inflammatory Bowel Diseases - pathology
Medicine and Health Sciences
Metabolomics
Methods
Multivariate Analysis
Observational studies
Physical Sciences
Research and Analysis Methods
United States
ISSN:1553-7358, 1553-734X, 1553-7358
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Shrnutí: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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I have read the journal’s policy and the authors of this manuscript have the following competing interests: CH is on the Scientific Advisory Board for Seres Therapeutics and Empress Therapeutics. The remaining authors have declared that no competing interests exist. Author Yiren Lu was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge.
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ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009442