In Silico Prediction Performance for Type IV Collagen Variants
Bibliographic record
Abstract
Background: Advances in genomics technology and knowledge has led to increased sequencing for diagnosis, including in kidney disease. However, sequencing can reveal rare missense variants for which the relationship to disease is unclear. To address this need, in silico programs have been developed to assign variant categorization. Recently, pathogenic variants in COL4A3/A4/A5 have been reported to account for a significant minority of chronic kidney disease. Here we evaluate the performance of in silico programs for type IV collagen variants. Methods: Rare COL4A3/A4/A5 missense variants were identified in an FSGS cohort, unscreened controls (gnomAD) and disease databases (ClinVAR, ARUP, LOVD). Comparisons between in silico predictions, disease database classifications and functional characterization were performed. Results: In silico predictions and functional characterization classified all 9 definitely pathogenic COL4A3/A4/A5 variants in the FSGS cohort correctly. In silico predictions correctly classified the majority (93-97%) of definitely pathogenic COL4A3/A4/A5 variants in ClinVAR, ARUP and LOVD. However, a significant proportion of benign variants were predicted as pathogenic. Thirty-five percent of COL4A3/A4/A5 missense variants obtained from gnomAD were also predicted deleterious. In silico predictions tended to overestimate the effects of COL4A variants of uncertain significance (VUS) when compared to functional characterization. Conclusions: Our results demonstrate that in silico programs are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants. Limitations of our computational work include overestimation of in silico program sensitivity given that they have likely been used in the categorization of variants labelled as pathogenic in disease databases; and the lack of clinical data to correlate rare variants in gnomAD controls. Funding: Private Foundation Support, Government Support - Non-U.S.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".