Inferring gene flow between populations using statistical methods
Bibliographic record
Abstract
Gene flow is the transfer of genetic material from one population to another.<br/>It is very common and important in the description of the genetic history of<br/>a population. Gene flow is hidden in the genome as migrated segments of<br/>different genetic material. Inferring the gene flow based on sequenced genomes<br/>is challenging. I present my work on estimating gene flow using three different<br/>statistical models.<br/>With two sequences from different populations, the model Isolation Mi-<br/>gration CoalHMM can determine the amount of gene flow between the pop-<br/>ulations after their initial split. It takes all possible migrated segments into<br/>account using the powerful HMM algorithms. In collaboration I have build<br/>new CoalHMM’s incorporating several pairs of sequences to infer direction and<br/>variation in the gene flow. I show that estimating direction and variation jointly<br/>is too hard. I show that estimating direction is possible with some uncertainty.<br/>I apply the methods to a dataset of extinct and extant elephants and show that<br/>there is extensive gene flow.<br/>Instead of considering all possible segments with an HMM, I examine the<br/>potential of only considering the most likely segments with particle filtering.<br/>I present challenges and advantageous choices when implementing a particle<br/>filter for this problem.<br/>The second statistical model infers gene flow from a covariance matrix be-<br/>tween several populations. Some relations between entries in the covariance<br/>matrix can only be explained by gene flow. In collaboration I have developed a<br/>method that fits the best phylogeny with gene flow events for an observed co-<br/>variance matrix. The method uses MCMC. A phylogeny with gene flow events<br/>is called an admixture graph and the method is called AdmixtureBayes. I show<br/>that AdmixtureBayes has a smaller error than the most popular admixture<br/>graph estimators on simulated data. AdmixtureBayes produces a posterior<br/>sample of admixture graphs and I demonstrate the possibilities with such a<br/>sample on a real dataset of Native American genomes.<br/>The last statistical model infers very recent gene flow by classifying hybrid<br/>individuals. The genome of a hybrid individual has big segments of alleles<br/>originating from different populations. Using the allele frequencies from those<br/>populations, it is possible to infer the segments. In collaboration, I implemented<br/>ImmediateAncestry which infers the segments and the most likely hybrid type<br/>with an HMM. I show that the classifier has good accuracy on simulated data.<br/>The classifier is not robust for a real dataset of chimpanzees, so I discuss reasons<br/>and remedies.
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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) | 0.004 | 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".