Imputation of Ancient Whole Genome Sus scrofa DNA Introduces Biases Toward Main Population Components in the Reference Panel
Bibliographic record
Abstract
Sequencing ancient DNA to high coverage is often limited by sample quality and cost. Imputing missing genotypes can potentially increase information content and quality of ancient data, but requires different computational approaches than modern DNA imputation. Ancient imputation beyond humans has not been investigated. In this study we report results of a systematic evaluation of imputation of three whole genome ancient Sus scrofa samples from the Early and Late Neolithic (∼7,100–4,500 BP), to test the utility of imputation. We show how issues like genetic architecture and, reference panel divergence, composition and size affect imputation accuracy. We evaluate a variety of imputation methods, including Beagle5, GLIMPSE, and Impute5 with varying filters, pipelines, and variant calling methods. We achieved genotype concordance in most cases reaching above 90%; with the highest being 98% with ∼2,000,000 variants recovered using GLIMPSE. Despite this high concordance the sources of diversity present in the genotypes called in the original high coverage genomes were not equally imputed leading to biases in downstream analyses; a trend toward genotypes most common in the reference panel is observed. This demonstrates that the current reference panel does not possess the full diversity needed for accurate imputation of ancient Sus , due to missing variations from Near Eastern and Mesolithic wild boar. Imputation of ancient Sus scrofa holds potential but should be approached with caution due to these biases, and suggests that there is no universal approach for imputation of non-human ancient species.
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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 | 0.000 |
| 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".