Validation of MaSTR™ software: Extensive study of fully-continuous probabilistic mixture analysis using PowerPlex®Fusion 2 – 5 contributor mixtures

Michael Adamowicz, Jennifer Clarke, Taylor Rambo, Harry Makam, Sarah Copeland, Daniel Erb, Kayla Hendricks, John McGuigan, Chris Prosser, James Todd, Teresa Snyder-Leiby

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Fully continuous probabilistic genotyping utilizes more information from the evidentiary profile; such as peak heights, stutter percentages, mixture ratio, and probability of drop-in/out, than historical approaches to provide weighted genotypes. The weighted genotypes are used to calculate a likelihood ratio that a profile is included in the mixture or not. Mixture profiles are compared to hypothetical profiles created using these parameters to calculate the probability of a given individual being a contributor to the mixture. Since direct integration over a large number of interrelated parameters is not feasible, Marcov Chain Monte Carlo (MCMC) is a widely used sampling method. This validation study of MaSTR software provides the forensic community with a report of the capabilities and reliability of this tool to assist the forensic analyst in applying their expertise to evaluate mixture profiles. The validation study was designed in accordance with SWGDAM and OSAC guidelines [1–3]. Purified, de-identified DNA from 40 donors was obtained from the Nebraska Biobank, quantified using Quantifiler® Human DNA Quantification kit, amplified with the PowerPlex®Fusion 5C system, analyzed on an ABI PRISM® 3130 Genetic Analyzer, and genotyped using GeneMarker®HID software. Results of replicates and dilutions were imported into MaSTR software to calculate the variance coefficient and stutter ratios required for the protocol data set. The protocol data set provided the software with context of expected peak height variation during the evaluation of mixtures. Two, three, four, and five-contributor mixtures with a range of contributor ratios, shared alleles, peak height variance, and dilutions were tested. The elimination database was used to evaluate MaSTR software's contamination detection capability.

Original languageEnglish (US)
Pages (from-to)641-643
Number of pages3
JournalForensic Science International: Genetics Supplement Series
Volume7
Issue number1
DOIs
StatePublished - Dec 2019

Keywords

  • Probabilistic genotyping
  • STR mixture analysis

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Genetics

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