Open AccessAn atlas of robust microbiome associations with phenotypic traits based on large-scale cohorts from two continents
Author(s) -
Daphna Rothschild,
Sigal Leviatan,
Ariel Hanemann,
Yossi Cohen,
Omer Weissbrod,
Eran Segal
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0265756
Subject(s) - microbiome , replicate , cohort , biology , human microbiome , metagenomics , evolutionary biology , phenotype , computational biology , bioinformatics , genetics , statistics , medicine , pathology , gene , mathematics
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.