<scp>AF‐vapeR</scp> : A multivariate genome scan for detecting parallel evolution using allele frequency change vectors
Bibliographic record
Abstract
Abstract The repeatability of evolution at the genetic level has been demonstrated to vary along a continuum from complete parallelism to divergence. In order to better understand why this continuum exists within and among systems, hypotheses must be tested using high‐confidence candidate loci for repeatability. However, few methods have been developed to scan SNP data for signatures specifically associated with repeatability, as opposed to local adaptation. We present AF‐vapeR (Allele Frequency Vector Analysis of Parallel Evolutionary Responses), an approach designed to identify genomic regions exhibiting highly correlated allele frequency changes within haplotypes and among replicated allele frequency change vectors. The method divides the genome into windows of an equivalent number of SNPs, and within each window performs eigen decomposition over normalised allele frequency change vectors (AFVs), each derived from a replicated pair of populations/species. Properties of the resulting eigenvalue distribution can be used to compare regions of the genome for those exhibiting strong geometric parallelism, and can also be compared against a null distribution derived from randomly permuted AFVs. Furthermore, the shape of the eigenvalue distribution can reveal multiple axes of parallelism within datasets. We demonstrate the utility of this approach to detect different modes of parallel evolution using simulations, and a reduced type‐II error rate compared with intersecting F ST outliers. Lastly, we apply AF‐vapeR to four previously published datasets (stickleback, Drosophila , guppies and Galapagos finches) which comprise a range of sampling and sequencing strategies, and lineage ages. We detect known parallel regions while also identifying novel candidates. The main benefits of this approach include a reduced false‐negative rate under many conditions, an emphasis on signals associated specifically with repeatable evolution as opposed to local adaptation, and an opportunity to identify different modes of parallel evolution at the first instance.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".