Tests for the presence of two linked disease susceptibility genes
Why this work is in the frame
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Bibliographic record
Abstract
For diseases with complex genetic etiology, more than one susceptibility gene may exist in a single chromosomal region. Under explicit assumptions about the number of disease genes in a region, generalized estimating equations (GEE) can be used to estimate the putative disease gene location(s) and expected identical-by-descent allele sharing in affected sib pairs at these gene(s). Extending the work of Liang et al. developed a method for simultaneous localization of two susceptibility genes in one region using marker identical-by-descent (IBD) sharing in affected sib pairs. Here we propose methods to evaluate the evidence for two versus one disease loci in a region in a quasi-likelihood/GEE framework. We describe tests based on approximate quasi-likelihood ratio and generalized score test statistics. Because of difficulties in determining the asymptotic null distributions of these statistics and the small sample sizes that can be available in genetic studies, we recommend that significance be evaluated empirically. Application of the described methods to data from a genome scan for type 1 diabetes yielded some evidence for two linked disease genes on chromosome 6, approximately 20 cM apart (p value for an approximate quasi-likelihood ratio test=0.049). In simulation studies, we found that both tests performed quite well for a range of scenarios. Power to detect the presence of two linked disease genes increased with the number of affected sib pairs, greater IBD sharing at the two loci, and larger distance between the two loci.
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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 | 0.002 | 0.007 |
| 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