Genotype‐Based Association Analysis Using Discordant Pairs: A Penetrance Odds Ratio Approach
Bibliographic record
Abstract
Genotypic counts of paired relatives discordant for a complex late-onset disease are often used to test for genetic association. The power of the various statistical test options, when data on covariates are unavailable, has been the focus of recent research. Comparison of the Cochran-Armitage, Bhapkar, and McNemar tests indicates that none is superior to the others in all cases. Using an alternative approach, we found that the theoretical genotypic frequencies of the discordant pairs depend only on the penetrance odds ratios, after conditioning. These odds ratios can be estimated by maximizing a product binomial likelihood and provide insight into the mode of inheritance. We identified cases where exact maximum likelihood (ML) estimates can be explicitly obtained. This approach led us to two tests for association which depend on likelihood ratio (LR) or score statistics. We quantified the power of these tests analytically and examined their performance through simulation. We explored the utility of these tests with an example from the literature-the association between complement factor H (CFH) polymorphisms and age-related macular degeneration. The LR and Score tests serve as simple and effective ways of interpreting paired case-control data sets.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".