randPedPCA: Rapid approximation of principal components from large pedigrees
Bibliographic record
Abstract
Abstract Background Pedigrees continue to be extremely important in agriculture and conservation genetics with the pedigrees of modern breeding programmes easily comprising millions of records. The structure of such pedigrees is challenging to visualise. Being directed acyclic graphs, pedigrees can be represented as matrices. Common choices are the numerator relationship matrix, A , and the adjacency matrix T . With these matrices, the structure of pedigrees can then, in principle, be visualised via principal component analysis (PCA). However, the naive PCA of matrices for large pedigrees is challenging due to computational and memory constraints. Results We present the open-access R package randPedPCA for rapid pedigree PCA using sparse matrices and randomised linear algebra. Our rapid pedigree PCA builds on the fact that matrix-vector multiplications with the numerator relationship matrix can be carried out implicitly using the extremely sparse inverse Cholesky factor of the numerator relationship matrix. We demonstrate the utility of randPedPCA using several simulated datasets of pedigrees and SNPs. We then demonstrate the performance of randPedPCA by analysing the pedigree of the UK Labrador Retriever breeding population of almost 1.5 million individuals. Conclusions The structure of pedigrees can be efficiently and rapidly visualised using scatter plots of principal component scores. For large pedigrees, this is considerably faster than rendering plots of a pedigree graph.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".