Prevalence Estimates of Polycystic Kidney and Liver Disease by Population Sequencing
Bibliographic record
Abstract
Background Estimating the prevalence of autosomal dominant polycystic kidney disease (ADPKD) is challenging because of age-dependent penetrance and incomplete clinical ascertainment. Early studies estimated the lifetime risk of ADPKD to be about one per 1000 in the general population, whereas recent epidemiologic studies report a point prevalence of three to five cases per 10,000 in the general population. Methods To measure the frequency of high-confidence mutations presumed to be causative in ADPKD and autosomal dominant polycystic liver disease (ADPLD) and estimate lifetime ADPKD prevalence, we used two large, population sequencing databases, gnomAD (15,496 whole-genome sequences; 123,136 exome sequences) and BRAVO (62,784 whole-genome sequences). We used stringent criteria for defining rare variants in genes involved in ADPKD ( PKD1 , PKD2 ), ADPLD ( PRKCSH , SEC63 , GANAB , ALG8 , SEC61B , LRP5 ), and potential cystic disease modifiers; evaluated variants for quality and annotation; compared variants with data from an ADPKD mutation database; and used bioinformatic tools to predict pathogenicity. Results Identification of high-confidence pathogenic mutations in whole-genome sequencing provided a lower boundary for lifetime ADPKD prevalence of 9.3 cases per 10,000 sequenced. Estimates from whole-genome and exome data were similar. Truncating mutations in ADPLD genes and genes of potential relevance as cyst modifiers were found in 20.2 cases and 103.9 cases per 10,000 sequenced, respectively. Conclusions Population whole-genome sequencing suggests a higher than expected prevalence of ADPKD-associated mutations. Loss-of-function mutations in ADPLD genes are also more common than expected, suggesting the possibility of unrecognized cases and incomplete penetrance. Substantial rare variation exists in genes with potential for phenotype modification in ADPKD.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.002 | 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".