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Comparison of key diagnostics for probabilistic interpretation of STR mixture data generated with length-based and
Kyle Duke1, Daniela Cuenca1, Kevin Cheng2
1California Department of Justice Bureau of Forensic Services Jan Bashinski DNA Laboratory, 1001 W Cutting Boulevard, Richmond, CA 94804, United States.
Forensic Science International. Genetics
|June 29, 2026
Summary
This study compared two probabilistic genotyping software applications for DNA mixture deconvolution. Massively parallel sequencing (MPS) data with STRmix™ NGS showed higher accuracy than length-based data with STRmix™, demonstrating robust STR mixture interpretation.
Area of Science:
- Forensic Science
- Genetics
- Bioinformatics
Background:
- Probabilistic genotyping software (PGS) is crucial for interpreting complex DNA mixtures.
- STRmix™ v2.8 uses length-based STR data, while STRmix™ NGS v1.1.0 uses sequence-based MPS data.
- Direct comparison of these software versions is needed to assess performance differences.
Purpose of the Study:
- To directly compare the mixture deconvolution performance of STRmix™ v2.8 and STRmix™ NGS v1.1.0.
- To evaluate interpretation diagnostics including likelihood ratio, mixture proportion, and genotype weight.
- To determine if MPS data's increased discriminatory power is fully realized in software-based interpretation.
Main Methods:
- Utilized identical two-, three-, and four-person DNA mixtures.
- Generated data using both length-based (GlobalFiler™) and sequence-based (ForenSeq®) kits.
- Interpreted data sets using STRmix™ v2.8 (length-based) and STRmix™ NGS v1.1.0 (sequence-based).
Main Results:
- Sequence-based likelihood ratios were significantly higher (5 orders of magnitude) than length-based.
- Factors influencing likelihood ratio differences included assay sensitivity, isoallele detection, and population frequencies.
- Both software versions demonstrated similar deconvolution efficacy regarding template levels, mixture components, and genotype weights.
Conclusions:
- STRmix™ NGS v1.1.0 fully realizes the enhanced discriminatory power of MPS data without compromising genotyping accuracy.
- Sequence information aids in establishing user-defined assumptions for PGS systems.
- Integrating MPS technologies with probabilistic genotyping provides a robust method for improving STR mixture interpretation.
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