Carrying out common DNA donor analysis using DBLR™ on two or five-cell mini-mixture subsamples for improved discrimination power in complex DNA mixtures

Kaitlin Huffman, Maarten Kruijver, Jack Ballantyne, Duncan Taylor

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Probabilistic genotyping systems are able to analyse complex mixed DNA profiles and show good power to discriminate contributors from non-contributors. However, the abilities of the statistical analyses are still unavoidably bound by the quality of information being analysed. If a profile has a high number of contributors, or a contributor that is present in trace amounts, then the amount of information about those individuals in the DNA profile is limited. Recent work has shown the ability to gain better resolution of the genotypes of contributors to complex profiles using cell subsampling. This is the process of taking many sets of a limited number of cells and individually profiling each set. These ‘mini-mixtures’ can provide greater information about the genotypes of underlying contributors. In our work we take the resulting profiles from multiple subsamplings of complex DNA profiles in equal amounts and show how testing for, and then assuming, a common DNA donor can further improve the ability to resolve the genotypes of contributors. Using direct cell sub-sampling and statistical analysis software DBLR™, we were able to recover single source profiles of uploadable quality from five out of the six contributors of an equally proportioned mixture. Through the analysis of mixtures in this work we provide a template for carrying out common donor analysis for maximum effect.

Original languageEnglish
Article number102908
Number of pages13
JournalForensic Science International: Genetics
Volume66
Early online date22 Jun 2023
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Common donor analysis
  • Complex DNA mixture deconvolution
  • DBLR™
  • Mini-mixtures
  • Mixture-to-mixture analysis
  • Probabilistic genotyping
  • Single/few cell analysis

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