Host genetics is associated with the gut microbial community membership rather than the structure
Bibliographic record
Abstract
The issue of what factors shape the gut microbiota has been studied for years. However, questions on the contribution of host genetics to the colonizing process of the gut microbiota and to the extent that host genetics affect the gut microbiota have not yet been clearly answered. Most recently published reports have concluded that host genetics make a smaller contribution than other factors, such as diet, in determining the gut microbiota. Here we have exploited the increasing amount of fecal 16S rRNA gene sequencing data that are becoming available to conduct an analysis to assess the influence of host genetics on the diversity of the gut microbiota. By re-analyzing data obtained from over 5000 stool samples, representing individuals living on five continents and ranging in age from 3 days to 87 years, we found that the strength of the various factors affecting the membership or structure of the gut microbiota are quite different, which leads us to a hypothesis that the presence or absence of taxa is largely controlled by host genetics, whereas non-genetic factors regulate the abundance of each taxon. This hypothesis is supported by the finding that the genome similarity positively correlates with the similarity of community membership. Finally, we showed that only severe perturbations are able to alter the gut microbial community membership. In summary, our work provides new insights into understanding the complexities of the gut microbial community and how it responds to changes imposed on it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".