Aligning gene trees with family trees
Bibliographic record
Abstract
Abstract Relatedness between individuals can be measured at the genealogical level (by describing shared ancestors in a pedigree) or at a genetic level (by describing shared haplotypes across the genome). The shared haplotype structure can be conveniently summarized as a sequence of trees along the genome, where each tree describes the last shared genetic ancestors between individuals at a locus. While many tools exist to infer tree sequences from genetic data, and many large pedigree datasets are available, few tools exist to identify the relationship between the two – finding which genetic ancestor corresponds to which pedigree ancestor, and conversely. In this paper, we propose an algorithm to solve this problem by providing, for each genetic tree, the list of all consistent ancestry paths within a genealogical tree. We also provide variants of the algorithm with moderate robustness to both tree sequence and pedigree errors. We demonstrate the scalability of our approach on the BALSAC genealogical dataset, which includes millions of individuals in Quebec, Canada. We find that 20 carriers usually provide enough information to reliably identify a common ancestor 15 generations ago or find the parent of origin of alleles among probands, but the number of possible ancestry paths within the pedigree can remain large. We apply the method to reconstruct the inheritance of a causal allele for type 1 myotonic dystrophy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".