Likelihood Ratio and Score Tests for Heterogeneity in Genetic Linkage
Bibliographic record
Abstract
One of the fundamental goals of most linkage studies is to localize a gene believed to be responsible for a trait locus by the investigation of its co-segregation with a genetic marker. Statistical techniques for linkage investigations have been introduced in the early 1930's. These were followed by the introduction of the log-odds approach (Morton 1955; Smith 1963) which provided standard methods to report and analyze linkage data collected from independent sib-ships. Because of the heterogeneity in the manner diseases affect families, use of the standard log-odds approach will result in a considerable reduction i the statistical power to detect linkage between disease trait and marker loci. This paper investigates the asymptotic properties of statistical tests that would be used to detect linkage under heterogeneity.
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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 | 0.102 | 0.476 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".