Complex Genetic in Autosomal Dominant Polycystic Kidney Disease (ADPKD)
Bibliographic record
Abstract
Background: ADPKD is genetically heterogeneous and primarily due to mutations in PKD1 or PKD2. Complex inheritance with biallelic PKD1 or digenic PKD1 and PKD2 mutations (a.k.a. complex genetic) has been reported in a small number of families. Here, we report the prevalence and genotype-phenotype correlation of patients with complex genetic from the extended Toronto Genetic Epidemiology Study of PKD. Methods: All study patients underwent PKD1 and PKD2 mutation screening by targeted Next-Generation sequencing (NGS) and multiplex ligation-dependent probe amplification in mutation-negative cases. Standard algorithms for sequence alignment, base calling, and QC filtering were applied to identify rare (MAF≤1%) deleterious variants of high and moderate impact as predicted by multiple predictive algorithms. Patients with complex genetic were defined as those with two pathogenic mutations in PKD1, or in PKD1 and PKD2. Phase was determined in all patients with PKD1 biallelic mutations. Results: We found genetic complexity in 47/993 (4.7%) of families with an identifiable PKD1 or PKD2 mutations. Preliminary results from 44 of 53 patients in these families suggest that patients with a protein-truncating (PT) and a non-truncating (NT) PKD1 mutation have more severe disease (by eGFR and TKV; see figure) than patients with two PT-PKD1 mutations in-cis, two NT-PKD1 mutations in-trans, or PKD1 and PKD2 digenic mutations. Conclusions: In this large cohort study from a single geographic region, we found that patients with genetic complexity in ˜5% of families with a known PKD1 or PKD2 mutation. Delineating complex genetic by NGS has important implications for genetic counselling and may improve clinical prognostication in ADPKD. Funding: 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".