Multipoint Genetic Mapping with Uniparental Disomy Data
Bibliographic record
Abstract
Uniparental disomy (UPD) refers to the presence of two copies of a chromosome from one parent and none from the other parent. In genetic studies of UPDs, many genetic markers are usually used to identify the stage of nondisjunction that leads to UPD and to uncover the associated unusual patterns of recombinations. However, genetic information in such data has not been fully utilized because of the limitations of the existing statistical methods for UPD data. In the present article, we develop a multilocus statistical approach that has the advantages of being able to simultaneously consider all genetic markers for all individuals in the same analysis and to allow general models for the crossover process to incorporate crossover interference. In particular, for a general crossover-process model that assumes only that there exists in each interval at most one crossover, we describe how to use the expectation-maximization algorithm to examine the probability distribution of the recombination events underlying meioses leading to UPD. We can also use this flexible approach to create genetic maps based on UPD data and to inspect recombination differences between meioses exhibiting UPD and normal meioses. The proposed method has been implemented in a computer program, and we illustrate the proposed approach through its application to a set of UPD15 data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".