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Multivariable association discovery in population-scale meta-omics studies

PLoS computational biology, 2021-11, Vol.17 (11), p.e1009442 [Peer Reviewed Journal]

COPYRIGHT 2021 Public Library of Science ;ISSN: 1553-7358 ;ISSN: 1553-734X ;EISSN: 1553-7358 ;DOI: 10.1371/journal.pcbi.1009442 ;PMID: 34784344

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Title:
Multivariable association discovery in population-scale meta-omics studies
Author: 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
Coelho, Luis Pedro
Subjects: 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
Is Part Of: PLoS computational biology, 2021-11, Vol.17 (11), p.e1009442
Description: 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.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1553-7358
ISSN: 1553-734X
EISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1009442
PMID: 34784344
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central
Public Library of Science (PLoS)
ProQuest Central
DOAJ Directory of Open Access Journals

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Multivariable association discovery in population-scale meta-omics studies

COPYRIGHT 2021 Public Library of Science ;ISSN: 1553-7358 ;ISSN: 1553-734X ;EISSN: 1553-7358 ;DOI: 10.1371/journal.pcbi.1009442 ;PMID: 34784344

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