MAPlex - A massively parallel sequencing ancestry analysis multiplex for Asia-Pacific populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it