A Neanderthal OAS1 isoform Protects Against COVID-19 Susceptibility and Severity: Results from Mendelian Randomization and Case-Control Studies
Bibliographic record
Abstract
Abstract Proteins detectable in peripheral blood may influence COVID-19 susceptibility or severity. However, understanding which circulating proteins are etiologically involved is difficult because their levels may be influenced by COVID-19 itself and are also subject to confounding factors. To identify circulating proteins influencing COVID-19 susceptibility and severity we undertook a large-scale two-sample Mendelian randomization (MR) study, since this study design can rapidly scan hundreds of circulating proteins and reduces bias due to reverse causation and confounding. We identified genetic determinants of 931 circulating proteins in 28,461 SARS-CoV-2 uninfected individuals, retaining only single nucleotide polymorphism near the gene encoding the circulating protein. We found that a standard deviation increase in OAS1 levels was associated with reduced COVID-19 death or ventilation (N = 4,336 cases / 623,902 controls; OR = 0.54, P = 7×10 −8 ), COVID-19 hospitalization (N = 6,406 / 902,088; OR = 0.61, P = 8×10 −8 ) and COVID-19 susceptibility (N = 14,134 / 1,284,876; OR = 0.78, P = 8×10 −6 ). Results were consistent in multiple sensitivity analyses. We then measured OAS1 levels in 504 patients with repeated plasma samples (N=1039) with different COVID-19 outcomes and found that increased OAS1 levels in a non-infectious state were associated with protection against very severe COVID-19, hospitalization and susceptibility. Further analyses suggested that a Neanderthal isoform of OAS1 affords this protection. Thus, evidence from MR and a case-control study supported a protective role for OAS1 in COVID-19 outcomes. Available medicines, such as phosphodiesterase-12 inhibitors, increase OAS1 and could be explored for their effect on COVID-19 susceptibility and severity.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".