The potential impacts of human genetics on virus emergence
Bibliographic record
Abstract
Human monogenic traits can confer resistance to viral infection in exposed individuals or predisposition to severe disease in infected individuals. Enhanced susceptibility can be driven directly by mutations in genes essential for control of the virus or indirectly via the production of autoantibodies against components of host defense. While the impact of viruses on individuals carrying these genotypes permitted their identification and has been amply studied, little is known about the impact of these human genotypes on the natural history of viruses, including not only persisting but also emerging viruses. We envisage several scenarios, including the possibility that genetically susceptible individuals serve as patient zeros, superspreaders, or mutation incubators, or that genetically resistant individuals even permit the selection of new viral mutants. Viruses are continually shared between individuals and even host species, where they can benefit from adaption to new environments. Current human viruses, as well as novel viruses from animal reservoirs, will continue to threaten the human population. Improvements in the scale of human genomic sequencing and analysis will permit testing hypotheses about the impact of human genetics on the origin and trajectory of viral infections, including future pandemics, which may ultimately help to prevent or curtail impending outbreaks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".