Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations
Bibliographic record
Abstract
Causal genes of chronic obstructive pulmonary disease (COPD) remain elusive. The current study aims at integrating genome-wide association studies (GWAS) and lung expression quantitative trait loci (eQTL) data to map COPD candidate causal genes and gain biological insights into the recently discovered COPD susceptibility loci. Two complementary genomic datasets on COPD were studied. First, the lung eQTL dataset which included whole-genome gene expression and genotyping data from 1038 individuals. Second, the largest COPD GWAS to date from the International COPD Genetics Consortium (ICGC) with 13 710 cases and 38 062 controls. Methods that integrated GWAS with eQTL signals including transcriptome-wide association study (TWAS), colocalization and Mendelian randomization-based (SMR) approaches were used to map causality genes, i.e. genes with the strongest evidence of being the functional effector at specific loci. These methods were applied at the genome-wide level and at COPD risk loci derived from the GWAS literature. Replication was performed using lung data from GTEx. We collated 129 non-overlapping risk loci for COPD from the GWAS literature. At the genome-wide scale, 12 new COPD candidate genes/loci were revealed and six replicated in GTEx including CAMK2A, DMPK, MYO15A, TNFRSF10A, BTN3A2 and TRBV30. In addition, we mapped candidate causal genes for 60 out of the 129 GWAS-nominated loci and 23 of them were replicated in GTEx. Mapping candidate causal genes in lung tissue represents an important contribution to the genetics of COPD, enriches our biological interpretation of GWAS findings, and brings us closer to clinical translation of genetic associations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".