Precision dna mixture interpretation with single-cell profiling

Jianye Ge, Jonathan L. King, Amy Smuts, Bruce Budowle

Research output: Contribution to journalArticlepeer-review

Abstract

Wet-lab based studies have exploited emerging single-cell technologies to address the challenges of interpreting forensic mixture evidence. However, little effort has been dedicated to developing a systematic approach to interpreting the single-cell profiles derived from the mixtures. This study is the first attempt to develop a comprehensive interpretation workflow in which single-cell profiles from mixtures are interpreted individually and holistically. In this approach, the genotypes from each cell are assessed, the number of contributors (NOC) of the single-cell profiles is estimated, followed by developing a consensus profile of each contributor, and finally the consensus profile(s) can be used for a DNA database search or comparing with known profiles to determine their potential sources. The potential of this single-cell interpretation workflow was assessed by simulation with various mixture scenarios and empirical allele drop-out and drop-in rates, the accuracies of estimating the NOC, the accuracies of recovering the true alleles by consensus, and the capabilities of deconvolving mixtures with related contributors. The results support that the single-cell based mixture interpretation can provide a precision that cannot beachieved with current standard CE-STR analyses. A new paradigm for mixture interpretation is available to enhance the interpretation of forensic genetic casework.

Original languageEnglish
Article number1649
JournalGenes
Volume12
Issue number11
DOIs
StatePublished - Nov 2021

Keywords

  • Clustering algorithm
  • Consensus profile
  • DNA forensics
  • DNA mixture
  • Mixture interpretation
  • Number of contributors
  • Single-cell

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