A Composite-Conditional-Likelihood Approach for Gene Mapping Based on Linkage Disequilibrium in Windows of Marker Loci
Bibliographic record
Abstract
A composite-conditional-likelihood (CCL) approach is proposed to map the position of a trait-influencing mutation (TIM) using the ancestral recombination graph (ARG) and importance sampling to reconstruct the genealogy of DNA sequences with respect to windows of marker loci and predict the linkage disequilibrium pattern observed in a sample of cases and controls. The method is designed to fine-map the location of a disease mutation, not as an association study. The CCL function proposed for the position of the TIM is a weighted product of conditional likelihood functions for windows of a given number of marker loci that encompass the TIM locus, given the sample configuration at the marker loci in those windows. A rare recessive allele is assumed for the TIM and single nucleotide polymorphisms (SNPs) are considered as markers. The method is applied to a range of simulated data sets. Not only do the CCL profiles converge more rapidly with smaller window sizes as the number of simulated histories of the sampled sequences increases, but the maximum-likelihood estimates for the position of the TIM remain as satisfactory, while requiring significantly less computing time. The simulations also suggest that non-random samples, more precisely, a non-proportional number of controls versus the number of cases, has little effect on the estimation procedure as well as sample size and marker density beyond some threshold values. Moreover, when compared with some other recent methods under the same assumptions, the CCL approach proves to be competitive.
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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".