Analysis of Site Frequency Spectra from Arabidopsis with Context-Dependent Corrections for Ancestral Misinference
Bibliographic record
Abstract
Previous studies have shown that the pattern of single nucleotide polymorphism (SNP) in Arabidopsis (Arabidopsis thaliana) deviates from the distribution expected under a neutral model. Here, we test whether or not ancestral misinference could explain this deviation. We start by showing that there are significant and complex influences of context on mutation dynamics as inferred from SNP frequency, in Arabidopsis, and compare the results to observations about context dependency that have been made on a previous analysis of a maize (Zea mays) SNP dataset. The data concerning heterogeneity across sites are then used to make corrections for ancestral misinference in a context-dependent manner. Using Arabidopsis lyrata to infer the ancestral state for SNPs, we show that the resulting unfolded site frequency spectrum (SFS) in Arabidopsis is skewed toward sites with high frequency derived nucleotides. Sites are also partitioned into two general functional classes, second codon position and 4-fold degenerate sites. These two classes show different SFS; although both show an overrepresentation of high frequency derived sites, low frequency derived sites are vastly overrepresented at the second codon position, but significantly underrepresented at 4-fold degenerate sites. We find that these results are robust to corrections for ancestral misinference, even when context-dependent variation in mutation properties is taken into consideration. The data suggest that, in addition to purifying selection, complex demographic events and/or linked positive selection need to be invoked to explain the SFS, and they highlight the importance of sequence context in analyses of genome-wide variation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".