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Solving complex DNA mixtures with Next Generation Sequencing

Kari Danser, MS, William Allan, MS, Mark Perlin, PhD, MD, PhD, "Solving complex DNA mixtures with Next Generation Sequencing", American Academy of Forensic Sciences Annual Scientific Conference, New Orleans, LA, 13-Feb-2026.


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Abstract

Learning Overview: After attending this presentation, attendees will understand how combining Next Generation Sequencing (NGS) with probabilistic genotyping (PG) solves complex DNA mixtures that traditional methods cannot interpret. Participants will learn how to combine NGS with PG to solve cold cases, identify unknown contributors, and advance forensic capabilities.

Impact Statement: This presentation will improve the forensic community’s ability to interpret complex DNA mixtures by applying PG interpretation to NGS data. Practitioners can use this new capability on real-world casework to better resolve challenging DNA evidence. They can combine NGS with PG to obtain more accurate identifications for stronger case outcomes.

Next Generation Sequencing (NGS) represents a transformative leap in forensic science. It generates massively parallel DNA data from degraded or low-level samples, opening new paths to solve previously unsolvable cases. When paired with advanced probabilistic genotyping, forensic experts can separate complex mixtures, extract meaningful profiles from challenging evidence, and quantify results with unparalleled precision. These innovations redefine what forensic science can achieve in casework, victim identification, and criminal investigations.

We present the Ohio v. Slater Howell case alongside STR data from an NGS validation study. In April 2014, gas station clerk Babul Saha was fatally shot during a Cleveland robbery. Investigators collected DNA from the gas pump button, a plastic bag, and a lottery register. Promega PowerPlex® Fusion amplified 22 STR loci. The bag data showed a complex seven-person mixture that the crime laboratory could not interpret using manual CPI methods.

TrueAllele® probabilistic genotyping (PG) analysis fully resolved these capillary electrophoresis (CE) mixtures [1,2,3]. The computer linked Slater Howell to the crime. But how would the analysis have looked had NGS data been used?

We retraced the case’s PG analysis steps using NGS mixture validation data in place of the original CE data. The validation study had analyzed 251 total samples of different mixture compositions, plus single source and mock casework items. The study generated NGS data with Verogen ForenSeq DNA Signature at 27 STR loci.

We assembled seven-person mock mixture data from three NGS validation samples of known composition. We combined the three read spreadsheets into one read spreadsheet to use as casework mixture data. The suspect and victim mixture weights were comparable those found in the Howell CE bag evidence. We entered this NGS data into TrueAllele.

TrueAllele computationally separated the complex NGS mixture bag data into seven genotypes, up to probability. The software then compared these evidence genotypes with known suspect and victim references. TrueAllele calculated log(LR) values between evidence and reference on both the CE and NGS data.

On the original CE case data, the reported log(LR) was 4.99 ban for the suspect and 5.35 ban for the victim. On the NGS data, TrueAllele found 7.74 suspect and 8.61 victim log(LR) scores. The NGS information increase was largely due to having 5 additional STR loci. Using TrueAllele, the mock NGS results paralleled the CE casework results.

NGS has not yet gained widespread adoption for DNA analysis in the forensic science community. A key reason has been the lack of powerful NGS data interpretation software that can extract useful information from the complex DNA mixtures seen in everyday casework. While NGS may offer better data, PG software can solve CE data.

However, combining better NGS data with PG software gives the best of both worlds. NGS improves on CE, and PG improves on CPI. Together, NGS plus PG provides a synergistic solution. As demonstrated here, coupling NGS with powerful software that unmixes DNA mixtures offers new solutions to challenging DNA evidence problems.

References

  1. Perlin, M.W., Szabady, B. Linear mixture analysis: a mathematical approach to resolving mixed DNA samples. Journal of Forensic Sciences, 46(6):1372-7, 2001.
  2. Perlin, M.W., Sinelnikov, A. An Information Gap in DNA Evidence Interpretation. PLoS ONE, 4(12):e8327, 2009.
  3. Bauer, D.W., Butt, N., Hornyak, J.M., and Perlin, M.W. Validating TrueAllele® interpretation of DNA mixtures containing up to ten unknown contributors. Journal of Forensic Sciences, 65(2):380-398, 2020.

Links


  • American Academy of Forensic Sciences 78th Annual Scientific Conference - Program
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