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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research
Braden T Tierney1,2,3,4, Yingxuan Tan1, Zhen Yang2,3,4
1Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
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.
Insights
Statistical models for the gut microbiome often yield inconsistent results. This study evaluated millions of strategies, finding many published microbe-disease links, especially for diabetes, lack robustness and are contradictory.
Area of Science:
- Microbiome research
- Statistical modeling
- Human health and disease
Background:
- Reliable statistical associations are crucial for understanding the human gut microbiome's role in disease.
- Previous studies have identified numerous microbe-disease associations, but their consistency is often unverified.
Purpose of the Study:
- To evaluate the robustness and consistency of microbiome-based disease indicators across multiple statistical models and cohorts.
- To develop a method for discriminating between robust and non-robust findings in microbiome association studies.
Main Methods:
- Applied millions of different association modeling strategies to analyze 6 prevalent phenotypes across 15 public cohorts (2,343 individuals).
- Assessed the consistency of taxon-disease associations, including previously reported findings.
- Quantified the influence of potential confounders (e.g., sequencing depth, BMI, glucose, cholesterol) on identified associations.
Main Results:
- Many different statistical models produced contradictory associations (positive vs. negative correlations) for the same microbe-disease pair.
- 1 in 3 previously reported microbe-disease associations showed substantial inconsistency in the direction of the association.
- >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were found to be particularly non-robust.
- Confounders significantly influenced the perceived correlation between specific microbes (e.g., Faecalibacterium prausnitzii) and health status.
Conclusions:
- A significant proportion of microbiome-disease associations, particularly for diabetes, lack robustness and consistency across different analytical approaches.
- The proposed method can help prioritize reliable findings from microbiome association studies.
- Increased confidence in prioritizing microbiome findings is achievable through rigorous evaluation of analytical consistency.
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