In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 3 ( 2022-3-24), p. e0265756-
Abstract:
Numerous human conditions are associated with the microbiome, yet studies are inconsistent as to the magnitude of the associations and the bacteria involved, likely reflecting insufficiently employed sample sizes. Here, we collected diverse phenotypes and gut microbiota from 34,057 individuals from Israel and the U.S.. Analyzing these data using a much-expanded microbial genomes set, we derive an atlas of robust and numerous unreported associations between bacteria and physiological human traits, which we show to replicate in cohorts from both continents. Using machine learning models trained on microbiome data, we show prediction accuracy of human traits across two continents. Subsampling our cohort to smaller cohort sizes yielded highly variable models and thus sensitivity to the selected cohort, underscoring the utility of large cohorts and possibly explaining the source of discrepancies across studies. Finally, many of our prediction models saturate at these numbers of individuals, suggesting that similar analyses on larger cohorts may not further improve these predictions.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0265756
DOI:
10.1371/journal.pone.0265756.g001
DOI:
10.1371/journal.pone.0265756.g002
DOI:
10.1371/journal.pone.0265756.g003
DOI:
10.1371/journal.pone.0265756.g004
DOI:
10.1371/journal.pone.0265756.g005
DOI:
10.1371/journal.pone.0265756.g006
DOI:
10.1371/journal.pone.0265756.s001
DOI:
10.1371/journal.pone.0265756.s002
DOI:
10.1371/journal.pone.0265756.s003
DOI:
10.1371/journal.pone.0265756.s004
DOI:
10.1371/journal.pone.0265756.s005
DOI:
10.1371/journal.pone.0265756.s006
DOI:
10.1371/journal.pone.0265756.s007
DOI:
10.1371/journal.pone.0265756.s008
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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