An Updated Analysis of the Irish Kidney Gene Project Registry
Bibliographic record
Abstract
Background: Over 700 genes have been implicated in monogenic nephropathies (MN), a significant cause of chronic kidney disease (CKD), but their prevalence is often underestimated. Diagnosing MN can personalize clinical management with better-informed choices of therapies and targeted disease surveillance and influence prognosis and genetic counseling for patients and their families. Herein, we provide an update on the diagnostic yield of various technologies (exome sequencing, targeted gene panel, and MUC-1 sequencing) and immunostaining utilized by the Irish Kidney Gene Project (IKGP). Methods: All results from the multidisciplinary genetic kidney clinic between January 2014 and March 2023 were analyzed in this prospective cohort study. All clinic visit records were analyzed for clinical and genetic factors associated with solved cases. Using the guidelines of the American College of Medical Genetics and Genomics, pathogenic or likely pathogenic variants were evaluated as disease-causing. Results: Through the IKGP, 593 families (976 individuals) had been sequenced, of which 49.4% (482/976) were female. At the last follow-up, 58.5% of patients, with an average age of 42.3 ± 16.5 years, had reached end-stage kidney disease. We were able to demonstrate a likely-pathogenic/pathogenic variant in 47.4% (281/593) of families, encompassing 52 distinct monogenic entities. Three phenotypes accounted for up to 82% of positive results in genes related to autosomal dominant polycystic kidney disease (PKD1 (number for families (n)=155), PKD2 (n=23), IFT140 (n=4)), autosomal dominant tubulointerstitial kidney disease (UMOD (n=8), MUC1 (n=10), HNF1B (n=2), and DNAJB11 (n=1)), and COL4A-related phenotypes (COL4A5 (n=20), monoallelic COL4A4 (n=1), COL4A3 (monoallelic n=5; biallelic n=3)). Disease-causing variants identified in the remaining 42 genes comprised 17.4% of the solved families. In 31.1% (n=24) of the 77 families referred with a priori diagnosis of CKD of undetermined cause were found to have a known monogenic cause. Conclusions: The use of broad genomic strategies had a high success rate, especially in the presence of family history.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".