Next Generation Sequencing to Determine Etiology of Renal Disease: A Canadian Prospective Cohort Study
Bibliographic record
Abstract
Background: Recent data suggest that monogenic (single gene) diseases are underestimated in chronic kidney disease (CKD), particularly in adults with CKD of unknown etiology (CKDu). Retrospective research-based studies show that up to 70% pediatric and 30% adult-onset CKD is monogenic. Prospective studies are needed to help guide nephrologists on the integrating next generation sequencing into routine clinical settings. Methods: This study is an ongoing prospective, cohort study to evaluate the diagnostic yield of next generation sequencing testing in a Canadian clinic. The secondary objective is to determine the outcomes following establishment of a genetic diagnosis, to help guide physicians and policymakers on implementation of next generation sequencing diagnostic into routine clinical care. A targeted phenotype driven gene panel was performed if the subtype of CKD was evident at time of presentation. Exome sequencing was utilized for patients with non-diagnostic panels or in whom the subtype of CKD was unknown (i.e. CKDu). Results: To date, 148 families (209 individuals) have been recruited. We report on 72 families with CKD in whom next generation sequencing testing results are currently available. The median age of onset of CKD was 44 years (IQR 35-52). In 55% the subtype of CKD was unknown. A genetic diagnosis was confirmed in 42% of families. Exome sequencing yielded a genetic diagnosis in a further 10% who had a negative phenotype driven gene panel or had CKDu. Conclusions: This is the first study to prospectively characterize monogenic causation of CKD in a Canadian cohort. Genetic sequencing demonstrates a high prevalence of monogenic disease in CKD. Both gene-panel and exome sequencing identified pathogenic mutations associated with renal disease. Genetic sequencing informed prognosis and resolved diagnostic confusion in all positive cases, while in some cases help guide management or facilitate decision making in biologically related living donors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".