Genome-wide analysis of 944,133 individuals provides insights into the etiology of hemorrhoidal disease
Bibliographic record
Abstract
Abstract Hemorrhoidal disease (HEM) affects a large fraction of the population but its etiology including suspected genetic predisposition is poorly understood. We conducted a GWAS meta-analysis of 218,920 HEM patients and 725,213 controls of European ancestry, demonstrating modest heritability and genetic correlation with several other diseases from the gastrointestinal, neuroaffective and cardiovascular domains. HEM polygenic risk scores validated in 180,435 individuals from independent datasets allowed the identification of those at risk and correlated with younger age of onset and recurrent surgery. We identified 102 independent HEM risk loci harboring genes whose expression is enriched in blood vessels and gastrointestinal tissues, and in pathways associated with smooth muscles, epithelial and endothelial development and morphogenesis. Network transcriptomic analyses of affected tissue from HEM patients highlighted HEM gene co-expression modules that are relevant to the development and integrity of the musculoskeletal and epidermal systems, and the organization of the extracellular matrix. We conclude HEM has a genetic component that predisposes to smooth muscle, epithelial and connective tissue dysfunction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".