Genetic Variation of DKK3 May Modify Renal Disease Severity in ADPKD
Bibliographic record
Abstract
Significant variation in the course of autosomal dominant polycystic kidney disease ( ADPKD) within families suggests the presence of effect modifiers. Recent studies of the variation within families harboring PKD1 mutations indicate that genetic background may account for 32 to 42% of the variance in estimated GFR (eGFR) before ESRD and 43 to 78% of the variance in age at ESRD onset, but the genetic modifiers are unknown. Here, we conducted a high-throughput single-nucleotide polymorphism (SNP) genotyping association study of 173 biological candidate genes in 794 white patients from 227 families with PKD1. We analyzed two primary outcomes: (1) eGFR and (2) time to ESRD (renal survival). For both outcomes, we used multidimensional scaling to correct for population structure and generalized estimating equations to account for the relatedness among individuals within the same family. We found suggestive associations between each of 12 SNPs and at least one of the renal outcomes. We genotyped these SNPs in a second set of 472 white patients from 229 families with PKD1 and performed a joint analysis on both cohorts. Three SNPs continued to show suggestive/significant association with eGFR at the Dickkopf 3 (DKK3) gene locus; no SNPs significantly associated with renal survival. DKK3 antagonizes Wnt/beta-catenin signaling, which may modulate renal cyst growth. Pending replication, our study suggests that genetic variation of DKK3 may modify severity of ADPKD resulting from PKD1 mutations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".