Genome- and Exome-Wide Identification of Common-to-Rare Variants Associated with Middle Ear Cholesteatoma
Bibliographic record
Abstract
HYPOTHESIS: To investigate the genetic susceptibility of middle ear cholesteatoma (MEC) and construct an MEC risk prediction model by integrating genetic risk with clinical factors. BACKGROUND: MEC represents a relatively rare disorder that is associated with high morbidity, whereas its genetic etiology remains poorly understood. METHODS: Using genetic data from the UK Biobank (UKB), we performed both genome-wide association study (GWAS) and exome-wide association study (ExWAS) involving 702 MEC patients and 491,503 controls. Gene-based and gene set-based association studies were then performed to identify risk genes and gene sets of MEC, respectively. In addition, logistic regression models were applied to identify clinically significant MEC-associated diseases, of which the genetic and causal relationships with MEC were further characterized using linkage disequilibrium score regression, genetic analysis incorporating pleiotropy and annotation, and Mendelian randomization. Moreover, logistic regression models were employed to construct MEC risk prediction models by integrating genetic risk with clinical factors. RESULTS: Our study identified 159 common variants across 8 genomic loci and 39 rare variants spanning 17 genomic regions that were significantly associated with MEC, with PLD1 being prioritized as the top-ranked MEC candidate target gene. Additionally, 10 different types of diseases showed significant associations with MEC, but no inconclusive genetic or causal relationship was established between them. Moreover, we successfully constructed a high-performance MEC risk prediction model with an area under the curve of 0.704, showing the potential for clinical application. CONCLUSIONS: These findings advance our understanding of the genetic susceptibility of MEC and provide insights into its risk prediction, thus contributing to improved MEC prevention and management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".