Genetic architecture of tuberculosis susceptibility: A comprehensive research synopsis, meta-analyses, and epidemiological evidence
Bibliographic record
Abstract
Background: Over the past several decades, hundreds of genetic studies have been conducted to investigate associations between variants and risk of tuberculosis. But results have been inconclusive and no comprehensive quantitative synopsis has been available. We aimed to provide a summary of the current understanding of the genetic architecture of tuberculosis susceptibility. Methods: We systematically searched PubMed, Embase and Web of Science to identify genetic association studies of tuberculosis published through October 16, 2018. We did a meta-analysis for genetic variants with at least three independent datasets. We graded levels of cumulative epidemiological evidence of significant associations with risk of tuberculosis using the guidelines proposed by the Human Genome Epidemiology Network and false-positive report probability tests. We performed functional annotations for these variants using data from the Encyclopedia of DNA Elements (ENCODE) Project and other databases. Findings: We identified 592 eligible articles comprising 250431 cases with tuberculosis and 698635 controls without this disease through screening a total of 21330 citations. Meta-analysis-analyses were done for 580 variants in 314 genes or loci. We found that 37 variants were nominally significantly associated with risk of tuberculosis. Cumulative epidemiological evidence for a significant association with risk of tuberculosis was graded strong for 12 variants in or near 12 genes. Data from the ENCODE and other databases suggested that 10 of the 12 variants with strong evidence and those correlated with them might fall within putative functional regions. These variants together explained approximately 15.82% of familial relative risk of tuberculosis. Interpretation: Our study summarizes current literature on the genetic architecture of tuberculosis susceptibility and provides useful data for designing future studies to investigate new genetic factors for risk of tuberculosis. Funding Statement: This research is supported by grants from National Natural Science Foundation of China 81472026, 81672095, 81702288, 81673255, 81874283, the Projects of the Health and family planning commission in Sichuan Province 16ZD004, China Postdoctoral Science Foundation Project 2018M643495, the Recruitment Program for Young Professionals of China, Army Medical University WX2015-013, Army Medical University First Affiliated Hospital SWH2015LC03, SWH2016ZDCX1012, SWH2016JQFY-02, and SWH2015QN11, Chongqing Special Postdoctoral Science Foundation XmT2018068.Declaration of Interests: The authors declared no conflicts of interest.Ethics Approval Statement: We conducted this study in accordance with the guidelines of the Human Genome Epidemiology Network (HuGENet) for systematic review of genetic association studies and the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Statement.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".