Genetic risk factors for the development of pulmonary disease identified by genome‐wide association
Bibliographic record
Abstract
Chronic respiratory diseases are a major cause of morbidity and mortality. Asthma and chronic obstructive pulmonary disease (COPD) combined affect over 500 million people worldwide. While environmental factors are important in disease progression, asthma and COPD have long been known to be heritable with genetic components playing an important role in the risk of developing disease. Identification of genetic variation contributing to disease progression is important for a number of reasons including identification of risk alleles, understanding underlying disease mechanisms and development of novel therapies. Genome-wide association studies (GWAS) have been successful in identifying many loci associated with lung function, COPD and asthma. In recent years, meta-analyses and improved imputation have facilitated the growth of GWAS in terms of numbers of subjects and the number of single nucleotide polymorphisms (SNP) that can be interrogated. As a consequence, there has been a significant increase in the number of signals associated with asthma, COPD and lung function. SNP that have shown association with lung function reassuringly show a significant overlap with SNP associated with COPD giving a glimpse at pathways that may be involved in COPD mechanisms including genes in, for example, developmental pathways. In asthma, association signals are often in or near genes involved in both adaptive and innate immune response pathways, epithelial cell homeostasis and airway structural changes. The challenges now are translating these genetic signals into a new understanding of lung biology, understanding how variants impact health and disease and how they may provide opportunities for therapeutic intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".