Abstract
Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency-or robustness-of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon-disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe-disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders-sequencing depth, glucose levels, cholesterol, and body mass index, for example-influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.
Highlights
Meta-analysis combined with modeling vibration of effects recovers and prioritizes associations
Many (214, 37.8%) of these findings were directly from papers present in the data used in this study
We benchmarked the data transformation and modeling strategies underlying these associations (S1 Text, S1 Fig). We refer to this model, which contains the phenotypic variable of interest as the sole covariate, as the baseline model (S1 Table). Three of these diseases (T2D, type 1 diabetes (T1D), and colorectal cancer (CRC)) had data spread across multiple cohorts
Summary
With its role in host health, the microbiome field is rapidly accelerating toward the clinic in the form of new diagnostics and therapeutics. An instrumental first step toward this lofty goal, is vetting individual microbial features (e.g., species abundance) for their association with disease. Measuring the robustness of reported microbiome-disease associations literature review information) can be found at https://github.com/chiragjp/ubiome_robustness. All the relevant microbiome datasets can be downloaded from the R package associated with curatedMetagenomicData [Pasolli et al Accessible, curated metagenomic data through ExperimentHub. Nat Methods. ]. All analyzed data from this study can be located at https://figshare.com/projects/ Microbiome_robustness/127607
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