Evidence for Pathogenicity of Atypical Splice Mutations in Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Mutation-based molecular diagnostics of autosomal dominant polycystic kidney disease (ADPKD) is complicated by locus and allelic heterogeneity, large multi-exon gene structure and duplication in PKD1, and a high level of unclassified variants. Comprehensive screening of PKD1 and PKD2 by two recent studies have shown that atypical splice mutations account for 3.5% to 5% of ADPKD. We evaluated the role of bioinformatic prediction of atypical splice mutations and determined the pathogenicity of an atypical PKD2 splice variant from a multiplex ADPKD (TOR101) family. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using PubMed, we identified 17 atypical PKD1 and PKD2 splice mutations. We found that bioinformatics analysis was often useful for evaluating the pathogenicity of these mutations, although RT-PCR is needed to provide the definitive proof. RESULTS: Sequencing of both PKD1 and PKD2 in an affected subject of TOR101 failed to identify a definite mutation, but revealed several UCVs, including an atypical PKD2 splice variant. Linkage analysis with microsatellite markers indicated that TOR101 was PKD2-linked and IVS8 + 5G-->A was shown to cosegregate only with affected subjects. RT-PCR of leukocyte mRNA from an affected subject using primers from exons 7 and 9 revealed six splice variants that resulted from activation of different combinations of donor and acceptor cryptic splice sites, all terminating with premature stop codons. CONCLUSIONS: The data provide strong evidence that IVS8 + 5G-->A is a pathogenic mutation for PKD2. This case highlights the importance of functional analysis of UCVs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".