Title

Dna Mixture Genotyping By Probabilistic Computer Interpretation Of Binomially-Sampled Laser Captured Cell Populations: Combining Quantitative Data For Greater Identification Information

Keywords

Binomial sampling; DNA mixtures; Joint likelihood; LCM; Probabilistic genotype; TrueAllele® system

Abstract

Two person DNA admixtures are frequently encountered in criminal cases and their interpretation can be challenging, particularly if the amount of DNA contributed by both individuals is approximately equal. Due to an inevitable degree of uncertainty in the constituent genotypes, reduced statistical weight is given to the mixture evidence compared to that expected from the constituent single source contributors. The ultimate goal of mixture analysis, then, is to precisely discern the constituent genotypes and here we posit a novel strategy to accomplish this. We hypothesised that LCM-mediated isolation of multiple groups of cells ('binomial sampling') from the admixture would create separate cell sub-populations with differing constituent weight ratios. Furthermore we predicted that interpreting the resulting DNA profiling data by the quantitative computer-based TrueAllele® interpretation system would result in an efficient recovery of the constituent genotypes due to newfound abilities to compute a maximum LR from sub-samples with skewed weight ratios, and to jointly interpret all possible pairings of sub-samples using a joint likelihood function. As a proof of concept, 10 separate cell samplings of size 20 recovered by LCM from each of two 1:1 buccal cell mixtures were DNA-STR profiled using a specifically developed LCN methodology, with the data analyzed by the TrueAllele® Casework system. In accordance with the binomial sampling hypothesis, the sub-samples exhibited weight ratios that were well dispersed from the 50% center value (50 ± 35% at the 95% level). The maximum log(LR) information for a genotype inferred from a single 20 cell sample was 18.5 ban, with an average log(LR) information of 11.7 ban. Co-inferring genotypes using a joint likelihood function with two sub-samples essentially recovered the full genotype information. We demonstrate that a similar gain in genotype information can be obtained with standard (28-cycle) PCR conditions using the same joint interpretation methods. Finally, we discuss the implications of this work for routine forensic practice. © 2012 Forensic Science Society.

Publication Date

6-1-2013

Publication Title

Science and Justice

Volume

53

Issue

2

Number of Pages

103-114

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.scijus.2012.04.004

Socpus ID

84876698338 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84876698338

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