Efficient computation of the kinship coefficients
Bibliographic record
Abstract
MOTIVATION: For families, kinship coefficients are quantifications of the amount of genetic sharing between a pair of individuals. These coefficients are critical for understanding the breeding habits and genetic diversity of diploid populations. Historically, computations of the inbreeding coefficient were used to prohibit inbred marriages and prohibit breeding of some pairs of pedigree animals. Such prohibitions foster genetic diversity and help prevent recessive Mendelian disease at a population level. RESULTS: This paper gives the fastest known algorithms for computing the kinship coefficient of a set of individuals with a known pedigree, especially for large pedigrees. These algorithms outperform existing methods. In addition, the algorithms given here consider the possibility that the founders of the known pedigree may themselves be inbred and compute the appropriate inbreeding-adjusted kinship coefficients, which has not been addressed in literature. The exact kinship algorithm has running-time O(n2) for an n-individual pedigree. The recursive-cut exact kinship algorithm has running time O(s2m) where s is the number of individuals in the largest segment of the pedigree and m is the number of cuts. The approximate algorithm has running-time O(nd) for an n-individual pedigree on which to estimate the kinship coefficients of n individuals of interest from n founder kinship coefficients and d is the number of samples. AVAILABILITY AND IMPLEMENTATION: The above polynomial-time exact algorithm and the linear-time approximation algorithms are implemented as PedKin in C++ and are available under the GNU GPL v2.0 open source license. The PedKin source code is available at: http://www.intrepidnetcomputing.com/research/code/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".