Human genetics of mycobacterial disease
Bibliographic record
Abstract
Mycobacterial diseases are caused by members of the genus Mycobacterium, acid-fast bacteria characterized by the presence of mycolic acids within their cell walls. Claiming almost 2 million lives every year, tuberculosis (TB) is the most common mycobacterial disease and is caused by infection with M. tuberculosis and, in rare cases, by M. bovis or M. africanum. The second and third most common mycobacterial diseases are leprosy and buruli ulcer (BU), respectively. Both diseases affect the skin and can lead to permanent sequelae and deformities. Leprosy is caused by the uncultivable M. leprae while the etiological agent of BU is the environmental bacterium M. ulcerans. After exposure to these mycobacterial species, a majority of individuals will not progress to clinical disease and, among those who do, inter-individual variability in disease manifestation and outcome can be observed. Susceptibility to mycobacterial diseases carries a human genetic component and intense efforts have been applied over the past decades to decipher the exact nature of the genetic factors controlling disease susceptibility. While for BU this search was mostly conducted on the basis of candidate genes association studies, genome-wide approaches have been widely applied for TB and leprosy. In this review, we summarize some of the findings achieved by genome-wide linkage, association and transcriptome analyses in TB disease and leprosy and the recent genetic findings for BU susceptibility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".