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 numerous human traits, which we show to replicate in cohorts from both continents. Using machine learning models trained on microbiome data, we predict human traits with high accuracy across 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.

中文翻译：

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基于来自两大洲的大规模队列研究的具有表型特征的强大微生物组关联图集

许多人的状况与微生物组有关，但是关于协会和所涉及细菌的数量的研究并不一致，这很可能反映了样本数量不足。在这里，我们收集了来自以色列和美国的34,057个人的不同表型和肠道菌群。使用广泛扩展的微生物基因组集分析这些数据，我们得出了细菌与许多人类特征之间强大且众多未报告的关联的地图集，我们证明了它们在这两个大陆的队列中都可以复制。使用通过微生物组数据训练的机器学习模型，我们可以预测各大洲的人类特征。将我们的同类群组细分为较小的同类群组可产生高度可变的模型，从而对所选同类群组具有敏感性，强调了大型队列的效用，并可能解释了研究之间差异的根源。最后，我们的许多预测模型都满足了这些人数的要求，这表明对较大人群的类似分析可能不会进一步改善这些预测。