X-chromosomal STRs: Metapopulations and mutation rates
Bibliographic record
Abstract
The analysis of STRs located on the X chromosome has been one of the strategies used to address complex kinship cases. Its usefulness is, however, limited by the low availability of population haplotype frequency data and lack of knowledge on the probability of mutations. Due to the large amount of data required to obtain reliable estimates, it is important to investigate the possibility of grouping data from populations with similar profiles when calculating these parameters. To better understand the partition of genetic diversity among human populations for the X-STRs most used in forensics, an analysis was carried out based on data available in the literature and new data (23,949 haplotypes in total; from these 10,445 new) obtained through collaborative exercises within the Spanish and Portuguese Working Group of the International Society for Forensic Genetics. Based on the available population data, a similarity in X-STR profiles was found in European populations, and in East Asian populations, except for some isolates. A greater complexity was found for African, South American, and South and Southeast Asian populations, preventing their grouping into large metapopulations. New segregation data on 2273 father/mother/daughter trios were also obtained, aiming for a more thorough analysis of X-STR mutation rates. After combining our data with published information on father/mother/daughter trios, no mutations were detected in 13 out of 37 loci analyzed. For the remaining loci, mutation rates varied between 2.68 × 10 −4 (DXS7133) and 1.07x10 −2 (DXS10135), being 5.2 times higher in the male (4.16 ×10 −3 ) than in the female (8.01 ×10 −4 ) germline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".