Does Simultaneous Consideration of Multiple Regions Improve Disease Gene Localization?
Bibliographic record
Abstract
Improvement in localization of disease susceptibility genes by simultaneous consideration of multiple interacting loci was assessed using the Genetic Analysis Workshop 12 simulated data. Evidence of linkage at primary loci was used to weight families for analyses at secondary loci. To identify regions linked to disease susceptibility genes, parametric and allele-sharing genome scans were performed in the extended pedigrees and nuclear families, respectively. The position of the peak allele-sharing lod was used as the estimate of a disease gene location. In weighted analyses, the positions where the greatest lod increases occurred were taken as alternative estimates of the gene locations. Variability of the location estimates of disease genes given by the unweighted and weighted analyses was compared. Similar analyses were carried out using true disease loci to determine weights. Weighted analyses did not in general improve the localization of disease genes in this data set, even with a large sample of 1,928 nuclear families, due to the features of the underlying additive liability threshold 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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".