Evidence map›Paper›PMID 41540124›Full record

ArticleNature methods2026

MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery.

William A Nickols, Thomas Kuntz, Jiaxian Shen, Sagun Maharjan, Himel Mallick, Eric A Franzosa, Kelsey N Thompson, Jacob T Nearing, Curtis Huttenhower

Abstract read
In one paragraph

Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

92 citing papers in PubMed.

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  9. High dietary BGut microbes · 2026
    Article
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  13. Environmental microbial community signatures associated withApplied and environmental microbiology · 2026
    Article
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  15. Article
  16. Article
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  18. Observational
  19. Observational
  20. Article

32 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

William A NickolsDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Thomas KuntzDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Jiaxian ShenDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0003-4929-8955
Sagun MaharjanHarvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Himel MallickDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.ORCID http://orcid.org/0000-0003-4956-2429
Eric A FranzosaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-8798-7068
Kelsey N Thompson *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-9437-9722
Jacob T Nearing *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-2261-034X
Curtis Huttenhower *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. chutten@hsph.harvard.edu.ORCID http://orcid.org/0000-0002-1110-0096

Funding

Technology CoreU19AI110820 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI WHITE, OWEN R · 2014 to 2023
$36.5M
Interdisciplinary training: Statistical Genetics/Genomics and Computational BiologyT32GM135117 · NIGMS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Curtis Huttenhower, XIHONG LIN · 2020 to 2026
$3.1M
U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) U19AI110820U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) T32GM135117
6 · The paper itself

Abstract

Microbial community analysis typically involves determining which microbial features are associated with properties such as environmental or health phenotypes. This task is impeded by data characteristics, including sparsity (technical or biological) and compositionality. Here we introduce MaAsLin 3 (microbiome multivariable associations with linear models) to simultaneously identify both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 can newly account for compositionality either experimentally (for example, quantitative PCR or spike-ins) or computationally, and it expands the range of testable biological hypotheses and covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed state-of-the-art differential abundance methods, and when applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated previously reported associations, identifying 77% with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations more accurately and specifically, especially in complex datasets.

Indexed as

MicrobiotaSoftwareHumansInflammatory Bowel DiseasesLinear ModelsMultiomicsMultivariate Analysis

Identifiers

PMID41540124
PMCPMC12982127

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.