Archaic Adaptive Introgression in Modern Human Reproductive Genes
Bibliographic record
Abstract
README: Modern human genetic data with evidence of adaptive introgression from Neanderthals or Denisovans within circadian rhythm genes. The data was generated from the phased gnomAD 1KGP + HGDP callset (Koenig et al., 2024) and introgressed segments were identified by SPrime (Browning et al., 2018). RAiSD (Alachiotis & Pavlidis, 2018) was used to detect signatures of positive selection within our gene set. Genes of interest were downloaded from Greer et al. (2021). This also includes high resolution images and supplemental tables for Archaic Adaptive Introgression in Modern Human Reproductive Genes. All proper citations can be found on the preprint references listed (available here: https://www.biorxiv.org/content/10.1101/2024.11.06.622331v1). SPrime ND_Match Files Raw SPrime identified files that we used for our entire analysis. These were modified to include the archaic allele, archaic allele frequency, and average introgressed segment allele frequency. Note that these have been lifted over (Hinrichs et al., 2006) from GRCh38 (hg38) to GRCh37 (hg19) coordinates to match the genome builds of the archaic samples used in our study. As such, any manually generated variant IDs (chromosome:position:ReferenceAllele_AlternativeAllele naming convention) may no longer match the position they are currently sitting on as they were generated with hg38 coordinates. However, all of these were subsequently filtered out of our final results and any proper SNP IDs (dbSNP labels) will be accurate. RAiSD Outputs We used RAiSD (Alachiotis & Pavlidis, 2018) to detect evidence of positive selection within our dataset. Please note these coordinates are mapped to hg38.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".