Association of Genetically Predicted Skipping of COL4A4 Exon 27 with Hematuria and Albuminuria
Bibliographic record
Abstract
Key Points Using transcriptome-wide association studies, we identified an association between splicing out of exon 27 of COL4A4 and hematuria. We confirmed the presence of COL4A4 exon 27 splicing in an independent cohort. Functional assays revealed that the COL4A4 transcript with exon 27 spliced out affects collagen IV trimer assembly and secretion. Background Hematuria is an established sign of glomerular disease and can be associated with kidney failure, but there has been limited scientific study of this trait. Methods Here, we combined genetic data from the UK Biobank with predicted gene expression and splicing from Genotype Tissue Expression kidney cortex samples ( n =65) in a transcriptome-wide association study to identify additional potential biological mechanisms influencing hematuria. Results The transcriptome-wide association study using kidney cortex identified significant associations for five genes in expression and three significant splicing events. Notably, we identified an association between the skipping of COL4A4 exon 27, which is genetically predicted by intronic rs11898094 (minor allele frequency 13%), and hematuria. Association between this variant was also found with urinary albumin excretion. We found independent evidence supporting the same variant predicting this skipping event in glomeruli-derived mRNA transcriptomics data ( n =245) from the Nephrotic Syndrome Study Network. The functional significance of loss of exon 27 was demonstrated using the split NanoLuc-based α 3 α 4 α 5(IV) heterotrimer assay, in which type IV collagen heterotrimer formation was quantified by luminescence. The causal splicing variant for this skipping event is yet to be identified. Conclusions In summary, by integrating multiple data types, we identified a potential splicing event associated with hematuria and albuminuria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".