Additional file 2 of Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19
Bibliographic record
Abstract
Additional file 2: Table S1. Number of genes tested and Genomic inflation factor for each model and variant set. Table S2. Complete results of the genome-wide burden joint analysis, trans-pipeline meta-analysis and trans-ethnic meta-analysis on rare variants. Table S3. Best results of the genome-wide burden analysis on rare variants under a co-dominant and dominant model. Table S4. TLR7 homozygous and hemizygous variants (AF < 0.01). Table S5. Results of the genome-wide burden analysis on common and rare variants under a co-dominant model. Table S6. Results of the genome-wide burden analyses for the candidate genes identified by GWAS under co-dominant model. Table S7. Characteristics of patients and controls in the full sample and according to the inclusion in the Zhang Q. et al., Science 2020 paper. Table S8. Carriers of rare pLOF/bLOF variants in genes involved in type I IFN immunity to influenza virus. Table S9. Branchpoint variants identified by BPHunter and characteristics of the carriers. Table S10. Age and sex stratified analysis for the 15 type I IFN-related loci. Table S11. Enrichment analysis of rare variants, including missense and inframe variants, in genes involved in type I IFN immunity in the full cohort of 3269 cases and 1373 controls. Table S12. pLI and CoNeS distribution of the analyzed genes.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.868 | 0.128 |
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".