Genome-wide association study of susceptibility to acute respiratory distress syndrome
Bibliographic record
Abstract
Abstract Introduction Acute respiratory distress syndrome (ARDS) is a severe inflammatory process of the lung, often due to sepsis, and poses significant mortality burden in intensive care units. Here we conducted the largest genome-wide association study (GWAS) of sepsis-associated ARDS to identify novel genetic risk loci that can help guide the development of new therapeutic options. Methods We performed a case-control GWAS in 716 patients with sepsis-associated ARDS and 4,399 at-risk sepsis controls from three independent studies. Results were meta-analysed across the three studies, with significance set at p <5×10 -8 . Suggestive associations were declared for variants exhibiting consistent effects, likely to replicate and nominal significance ( p <0.05) in all three studies. Prioritised loci were subjected to Bayesian fine mapping, in-silico functional assessments, and gene-based rare variant collapsing analysis using whole exome sequencing (WES) data. Two independent studies with 430 ARDS cases and 1,398 controls served as replication samples. Results We identified a variant showing genome-wide significant association with sepsis-associated ARDS risk intergenic to ANKRD31 and HMGCR , previously linked to cholesterol metabolism. Suggestive associations were found for eight other variants. The rare exonic variant analysis showed associations between HMGCR and POC5 and sepsis-associated ARDS at nominal level ( p <0.05). While no nominal significance was achieved in the two additional validation cohorts, three variants exhibited a consistent direction of effects across all 5 studies. Conclusion A common variant intergenic to ANKRD31 and HMGCR was associated with sepsis-associated ARDS risk, suggesting a link between cholesterol metabolism and ARDS risk. Validation in independent studies is needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".