Additional file 1 of Biological basis of extensive pleiotropy between blood traits and cancer risk
Bibliographic record
Abstract
Additional file 1: Table S1. Blood traits, cancer types and GWAS data sources. Table S2. Plasma samples of women carriers of pathogenic variants in BRCA1/2, affected or unaffected by breast cancer after blood test (< 12 months) and used for circulating sRNA-seq. Table S3. Plasma samples of sporadic women affected or unaffected by breast cancer after blood test (< 12 months) and used for sRNA-seq. Table S4. Multivariate Cox regression analysis of cancer diagnosis in UKBB (all cancers; >12 months from basal blood test). Table S5. Multivariate Cox regression analysis of cancer diagnosis in UKBB (all cancers; within 12 months from basal blood test). Table S6. Multivariate Cox regression analysis of cancer diagnosis in women of the UKBB (all cancers; >12 months from basal blood test). Table S7. Multivariate Cox regression analysis of cancer diagnosis in men of the UKBB (all cancers; >12 months from basal blood test). Table S8. Patient and incident cases included in the analyses. Table S9. Multivariate Cox regression analysis of breast cancer diagnosis in UKBB (>12 months from basal blood test). Table S10. Multivariate Cox regression analysis of colon cancer diagnosis in UKBB (>12 months from basal blood test). Table S11. Multivariate Cox regression analysis of lung cancer diagnosis in UKBB (>12 months from basal blood test). Table S12. Multivariate Cox regression analysis of prostate cancer diagnosis in UKBB (>12 months from basal blood test). Table S13. Heritability and genetic correlations between blood cell traits and cancer risk. Table S14. Genomic inflation (lambda factor) analysis for the comparisons between cancer risk and blood trait GWAS results. Table S15. Pleiotropy leading SNPs linking blood traits and cancer risk. Table S16. Pan-cancer pleiotropic SNPs (Rashkin et al., 2020) identified in the blood-cancer pleiotropy study (conjFDR < 0.05). Table S17. Pleiotropic gene candidates previously associated with leukocyte telomere length (Codd et al., 2021). Table S18. Genomic hotspots (1, 3, or 5 Mb) with significant enrichment in pleiotropic variants and linked to > 2 cancer traits. Table S19. Regulatory marks enriched in the blood-cancer pleiotropic variants (DNAse I hypersensitivity (sheffield_dnase), transcription factor binding sites (encode_tfbs), and epigenetic marks (oadmap_epigenomics) data). Table S20. Master regulators of hematopoiesis. Table S21. Pleiotropic gene candidates identified in the hematopoiesis-related gene modules (Velten et al., 2017). Table S22. Pleiotropic variants linked to RNY-containing loci. Table S23. GWAS-catalog cancer risk associations linked to RNY-containing loci (chromosomes 1-22). Table S24. Regulatory marks enriched in the 5' and 3' TSS regions of the pleiotropic RNY relative to non-pleiotropic RNY loci. Table S25. SLE risk variants (GWAS) correlated with blood-cancer pleiotropic variants in RNY-containing loci.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.841 | 0.117 |
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".