Identification of linked regions using high-density SNP genotype data in linkage analysis
Bibliographic record
Abstract
MOTIVATION: With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary. RESULTS: In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the chi(2) model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data. AVAILABILITY: http://paed.hku.hk/uploadarea/yangwl/html/index.html
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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 | 0.001 | 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".