Integrative Omics Reveal Novel Protein Targets For Chronic Obstructive Pulmonary Disease Biomarker Discovery
Bibliographic record
Abstract
Abstract Background Large genome-wide association studies (GWAS) and other genetic studies have revealed genetic loci that are associated with chronic obstructive pulmonary disease (COPD). However, the proteins responsible for COPD pathogenesis remain elusive. We used integrative-omics by combining genetics of lung function and COPD with genetics of proteome to identify proteins underlying lung function variation and COPD risk. Methods We used summary statistics from the GWAS of human plasma proteome from the INTERVAL cohort (n=3,301) and integrated these data with lung function GWAS results from the UK Biobank cohorts (n=400,102) and COPD GWAS results from the ICGC cohort (35,735 cases and 222,076 controls). We performed in parallel: a proteome-wide Bayesian colocalization, and a proteome-wide Mendelian Randomization (MR) analyses. Next, we selected proteins that colocalized with lung function and/or COPD risk and explored their causal association with lung function and/or COPD using MR analysis ( P < 0 . 05 ). Results We found 537, 607, and 250 proteins that colocalized with force expiratory volume in one second (FEV 1 ), FEV 1 /forced vital capacity (FVC), or COPD risk, respectively. Of these, 1,051 were unique proteins. The sRAGE protein demonstrated the strongest colocalization with FEV 1 /FVC and COPD risk, while QSOX2, FAM3D and F177A proteins had the strongest associations with FEV 1 . Of these, 37 proteins that colocalized with lung function and/or COPD, also had a significant causal association. These included proteins such as PDE4D, QSOX2 and RGAP1, amongst others. Conclusion Integrative-omics reveals new proteins related to lung function. These proteins may play important roles in the pathogenesis of COPD.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".