Distinguishing Between Histories of Speciation and Introgression Using Genomic Data
Bibliographic record
Abstract
Introgression creates complex, non-bifurcating relationships among species. At individual loci and across the genome, both introgression and incomplete lineage sorting interact to produce a wide range of different gene tree topologies. These processes can obscure the history of speciation among lineages, and, as a result, identifying the history of speciation vs. introgression remains a challenge. Here, we use theory and simulation to investigate how introgression can mislead multiple approaches to species tree inference. We find that arbitrarily low amounts of introgression may potentially mislead both gene tree and parsimony approaches to species tree inference if the level of incomplete lineage sorting is sufficiently high. We also show that an alternative approach based on minimum gene tree node heights is inconsistent and depends on the rate of introgression across the genome. To distinguish between speciation and introgression, we apply supervised machine learning models to a set of features that can easily be obtained from phylogenomic datasets. We find that multiple of these models are highly accurate in classifying the species history in simulated datasets. We also show that, if the histories of speciation and introgression can be identified, PhyloNet will return highly accurate estimates of the contribution of each history to the data (i.e. edge weights). Overall, our results highlight the promise of supervised machine learning as a potentially powerful complement to phylogenetic methods in the analysis of introgression from genomic data.
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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 | 0.000 | 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".