Attributing Hardy‐Weinberg Disequilibrium to Population Stratification and Genetic Association in Case‐Control Studies
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.
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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 | 0.180 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".