Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin
Bibliographic record
Abstract
Background Uromodulin, the most abundant protein excreted in normal urine, plays major roles in kidney physiology and disease. The mechanisms regulating the urinary excretion of uromodulin remain essentially unknown. Methods We conducted a meta-analysis of genome-wide association studies for raw (uUMOD) and indexed to creatinine (uUCR) urinary levels of uromodulin in 29,315 individuals of European ancestry from 13 cohorts. We tested the distribution of candidate genes in kidney segments and investigated the effects of keratin-40 (KRT40) on uromodulin processing. Results Two genome-wide significant signals were identified for uUMOD: a novel locus ( P 1.24E–08) over the KRT40 gene coding for KRT40, a type 1 keratin expressed in the kidney, and the UMOD-PDILT locus ( P 2.17E–88), with two independent sets of single nucleotide polymorphisms spread over UMOD and PDILT . Two genome-wide significant signals for uUCR were identified at the UMOD-PDILT locus and at the novel WDR72 locus previously associated with kidney function. The effect sizes for rs8067385, the index single nucleotide polymorphism in the KRT40 locus, were similar for both uUMOD and uUCR. KRT40 colocalized with uromodulin and modulating its expression in thick ascending limb (TAL) cells affected uromodulin processing and excretion. Conclusions Common variants in KRT40 , WDR72 , UMOD , and PDILT associate with the levels of uromodulin in urine. The expression of KRT40 affects uromodulin processing in TAL cells. These results, although limited by lack of replication, provide insights into the biology of uromodulin, the role of keratins in the kidney, and the influence of the UMOD-PDILT locus on kidney function.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".