Susceptibility genes for lung diseases in the major histocompatibility complex revealed by lung expression quantitative trait loci analysis
Bibliographic record
Abstract
The major histocompatibility complex (MHC) has been linked with hundreds of diseases [1]. The MHC is one of the most complex regions of the human genome, because of the high gene density, extended linkage disequilibrium (LD) and sequence diversity [2]. Recent genome-wide association studies (GWAS) have identified polymorphisms located in the MHC that are associated with lung diseases and related traits: asthma, cystic fibrosis, idiopathic interstitial pneumonia, lung cancer and lung function. However, due to the limitations of GWAS and tissue-specific characteristics of gene expression [3], the causal genes and genetic mechanisms mediating the heritable risk within this locus remain to be found. New susceptibility genes for asthma, idiopathic interstitial pneumonia, lung cancer and lung function in the MHC The authors would like to thank Christine Racine and Sabrina Biardel at the Institut universitaire de cardiologie et de pneumologie de Québec (Québec, QC, Canada) site of the Respiratory Health Network Biobank of the FRQS for their valuable assistance. At the University of British Columbia (Vancouver, BC, Canada) the authors thank the biobank coordinator Mark Elliott. At the University of Groningen, University Medical Center Groningen (Groningen, the Netherlands) Marnix Jonker is thanked for his support in selecting, handling and sending of lung tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".