Heterogeneous Disease Modeling for Hardy‐Weinberg Disequilibrium in Case‐Control Studies: Application to Renal Stones and Calcium‐Sensing Receptor Polymorphisms
Bibliographic record
Abstract
Renal stone formation due to hypercalciuria is a relatively common disorder with clear evidence for genetic predisposition, but cryptic phenotypic heterogeneity has hampered identification of candidate genes. The R990G single-nucleotide polymorphism (SNP) of the calcium sensing receptor (CASR) gene has been associated with hypercalciuria in stone formers and shows the appropriate functional phenotype in cell culture. In our preliminary association analysis of a case-control cohort, however, we observed significant Hardy-Weinberg disequilibrium (HWD) for the cases (n= 223), but not controls (n= 676) at the R990G locus, pointing us toward the general disease model incorporating HWD. Because there is an adjacent CASR SNP, A986S, which is in negative linkage disequilibrium with R990G, we extended the general disease model to enable testing of a two-site hypothesis. In our data set, there is no lack of fit (P= .345) for the single-locus model for the R990G genotype, and likelihood ratio testing favors a recessive effect with an eight-fold increase in risk (P < .001) for GG homozygotes, relative to wild-type, based on a population prevalence of 2%. Addition of the A986S genotype provides no additional information either by itself or when included in our two-site model.
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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 | 0.206 | 0.321 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".