Complex Trait Locus Linkage Mapping in Atherosclerosis
Bibliographic record
Abstract
E ver since the initial proposal to use polymorphic DNA markers to map genetic diseases, 1 linkage analysis (also called "positional cloning") has been used successfully to find the gene defects for hundreds of monogenic Mendelian traits. 2 Because monogenic diseases can serve as important models for understanding pathogenesis, especially if they point to novel biochemical and physiological pathways, linkage analysis has revolutionized biomedicine.A prime example of the success of linkage analysis in atherosclerosis was the discovery that ABCA1 was the causative gene for Tangier disease, 3 which has created an exciting and thriving new subfield of research.The notable success in localizing the molecular defects in monogenic disorders follows from the simple disease pathogenesis model: a single mutated disease gene is necessary and sufficient to cause the observed trait.A recent search of the Online Mendelian Inheritance in Man (OMIM) human genetic disease database roughly quantifies the extent of this success: by entering the keywords "linkage analysis" AND "autosomal," Ϸ900 individual entries were returned.And this likely underestimates the number of monogenic diseases for which the molecular genetic basis was solved by linkage analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".