Comprehensive analysis of microsatellite polymorphisms in human populations
Bibliographic record
Abstract
Abstract Microsatellites (MS) are tandem repeats of short units and have been used for population genetics, individual identification, and medical genetics. However, studies of MS on a whole genome level are limited, and genotyping methods for MS have yet to be established. Here, we analyzed approximately 8.5 million MS regions using a previously developed MS caller (MIVcall method) for three large publicly available human genome sequencing data sets: the Korean Personal Genome Project (KPGP), Simons Genome Diversity Project (SGDP), and Human Genome Diversity Project (HGDP). Our analysis identified 253,114 polymorphic MS. A comparison among different populations suggests that MS in the coding region evolved by random genetic drift and natural selection. In an analysis of genetic structures, MS clearly revealed population structures as SNPs and detected clusters that were not found by SNPs in African and Oceanian populations. Based on the MS polymorphisms, we selected an effective MS set for individual identification. We also showed that our MS analysis method can be applied to ancient DNA samples. This study provides a comprehensive picture of MS polymorphisms and application to human population studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".