A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota.

ABSTRACT: Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized

SUBMITTER: Sommer AJ 

PROVIDER: S-EPMC9129050 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature