Identification of genes associated with testicular germ cell tumor susceptibility through a transcriptome-wide association study
Bibliographic record
Abstract
Transcriptome-wide association studies (TWASs) have the potential to identify susceptibility genes associated with testicular germ cell tumors (TGCTs). We conducted a comprehensive TGCT TWAS by integrating genome-wide association study (GWAS) summary data with predicted expression models from normal testis, TGCT tissues, and a cross-tissue panel that encompasses shared regulatory features across 22 normal tissues, including the testis. Gene associations were evaluated while accounting for variant-level effects from GWASs, followed by fine-mapping analyses in regions exhibiting multiple TWAS signals, and finally supplemented by colocalization analysis. Expression and protein patterns of identified TWAS genes were further examined in relevant tissues. Our analysis tested 19,805 gene-disease links, revealing 165 TGCT-associated genes with a false discovery rate of less than 0.01. We prioritized 46 candidate genes by considering GWAS-inflated signals, correlations between neighboring genes, and evidence of colocalization. Among these, 23 genes overlap with 22 GWAS loci, with 7 being associations not previously implicated in TGCT risk. Additionally, 23 genes located within 21 loci are at least 1 Mb away from published GWAS index variants. The 46 prioritized genes display expression levels consistent with expected expression levels in human gonadal cell types and precursor tumor cells and significant enrichment in TGCTs. Additionally, immunohistochemistry revealed protein-level accumulation of two candidate genes, ARID3B and GINM1, in both precursor and tumor cells. These findings enhance our understanding of the genetic predisposition to TGCTs and underscore the importance of further functional investigations into these candidate genes.
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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) | 0.000 | 0.001 |
| 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) | 0.001 | 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".