MAPlex - A massively parallel sequencing ancestry analysis multiplex for Asia-Pacific populations
Bibliographic record
Abstract
Current forensic ancestry-informative panels are limited in their ability to differentiate populations in the Asia-Pacific region. MAPlex (Multiplex for the Asia-Pacific), a massively parallel sequencing (MPS) assay, was developed to improve differentiation of East Asian, South Asian and Near Oceanian populations found in the extensive cross-continental Asian region that shows complex patterns of admixture at its margins. This study reports the development of MAPlex; the selection of SNPs in combination with microhaplotype markers; assay design considerations for reducing the lengths of microhaplotypes while preserving their ancestry-informativeness; adoption of new population-informative multiple-allele SNPs; compilation of South Asian-informative SNPs suitable for forensic AIMs panels; and the compilation of extensive reference and test population genotypes from online whole-genome-sequence data for MAPlex markers. STRUCTURE genetic clustering software was used to gauge the ability of MAPlex to differentiate a broad set of populations from South and East Asia, the West Pacific regions of Near Oceania, as well as the other globally distributed population groups. Preliminary assessment of MAPlex indicates enhanced South Asian differentiation with increased divergence between West Eurasian, South Asian and East Asian populations, compared to previous forensic SNP panels of comparable scale. In addition, MAPlex shows efficient differentiation of Middle Eastern individuals from Europeans. MAPlex is the first forensic AIM assay to combine binary and multiple-allele SNPs with microhaplotypes, adding the potential to detect and analyze mixed source forensic DNA.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".