Incorporating Allelic Variation for Reconstructing the Evolutionary History of Organisms from Multiple Genes: An Example from Rosa in North America
Bibliographic record
Abstract
Allelic variation within individuals holds information regarding the relationships of organisms, which is expected to be particularly important for reconstructing the evolutionary history of closely related taxa. However, little effort has been committed to incorporate such information for reconstructing the phylogeny of organisms. Haplotype trees represent a solution when one nonrecombinant marker is considered, but there is no satisfying method when multiple genes are to be combined. In this paper, we propose an algorithm that converts a distance matrix of alleles to a distance matrix among organisms. This algorithm allows the incorporation of allelic variation for reconstructing the phylogeny of organisms from one or more genes. The method is applied to reconstruct the phylogeny of the seven native diploid species of Rosa sect. Cinnamomeae in North America. The glyceralgehyde 3-phosphate dehydrogenase (GAPDH), the triose phosphate isomerase (TPI), and the malate synthase (MS) genes were sequenced for 40 individuals from these species. The three genes had little genetic variation, and most species showed incomplete lineage sorting, suggesting these species have a recent origin. Despite these difficulties, the networks (NeighborNet) of organisms reconstructed from the matrix obtained with the algorithm recovered groups that more closely match taxonomic boundaries than did the haplotype trees. The combined network of individuals shows that species west of the Rocky Mountains, Rosa gymnocarpa and R. pisocarpa, form exclusive groups and that together they are distinct from eastern species. In the east, three groups were found to be exclusive: R. nitida-R. palustris, R. foliolosa, and R. blanda-R. woodsii. These groups are congruent with the morphology and the ecology of species. The method is also useful for representing hybrid individuals when the relationships are reconstructed using a phylogenetic network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".