Attributing Hardy‐Weinberg Disequilibrium to Population Stratification and Genetic Association in Case‐Control Studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Loci exhibiting Hardy-Weinberg disequilibrium (HWD) are often excluded from association studies, because HWD may indicate genotyping error, population stratification or selection bias. For case-control studies, HWD can result from a genetic effect at the locus. We extend the modelling to accommodate both stratification and genetic effects. Theoretical genotype frequencies and HWD coefficients are derived under a general genetic model for a population with two strata. Maximum likelihood is used to estimate model parameters and a test for lack of fit identifies the models most consistent with the data. Simulations were used to assess the method. The technique was applied to a group of ethnically and clinically heterogeneous kidney stone formers and controls, both exhibiting HWD for the R990G SNP of the CASR gene. Results indicate the best fitting model incorporates both stratification and genetic association. The ability of our method to apportion HWD to stratification and genetic effects may well be a significant advance in dealing with heterogeneity in case-control genetic association studies.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it