WHOLE GENOME IDENTITY-BY-DESCENT DETERMINATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-throughput single nucleotide polymorphism genotyping assays conveniently produce genotype data for genome-wide genetic linkage and association studies. For pedigree datasets, the unphased genotype data is used to infer the haplotypes for individuals, according to Mendelian inheritance rules. Linkage studies can then locate putative chromosomal regions based on the haplotype allele sharing among the pedigree members and their disease status. Most existing haplotyping programs require rather strict pedigree structures and return a single inferred solution for downstream analysis. In this research, we relax the pedigree structure to contain ungenotyped founders and present a cubic time whole genome haplotyping algorithm to minimize the number of zero-recombination haplotype blocks. With or without explicitly enumerating all the haplotyping solutions, the algorithm determines all distinct haplotype allele identity-by-descent (IBD) sharings among the pedigree members, in linear time in the total number of haplotyping solutions. Our algorithm is implemented as a computer program iBDD. Extensive simulation experiments using 2 sets of 16 pedigree structures from previous studies showed that, in general, there are trillions of haplotyping solutions, but only up to a few thousand distinct haplotype allele IBD sharings. iBDD is able to return all these sharings for downstream genome-wide linkage and association studies.
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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 it