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Estimating Drop-Out Probabilities in Forensic DNA Samples: A Simulation Approach To Evaluate Different Models

NCJ Number
236686
Journal
Forensic Science International: Genetics Volume: 5 Issue: 5 Dated: November 2011 Pages: 525-531
Author(s)
H. Haned; T. Egeland; D. Pontier; L. Pene; P. Gill
Date Published
November 2011
Length
7 pages
Annotation
This article proposes the use of a simulation model of the entire process associated with the analysis of STR loci, as a supplement to the purely experimental approach to support the validation of new methods.
Abstract
Allele drop-out is a well known phenomenon that is primarily caused by the stochastic effects associated with low quantity or low quality DNA samples. Recently, new interpretation models that employ the use of logistic regression have been utilized in order to estimate the probability of drop-out. The model parameters are estimated using profiles from samples of extracted DNA diluted to low template levels in order to induce drop-out. However, the authors propose that this approach is over-simplistic, because several sources of variability are not taken into account in this generalized model. For example, in real-life, small (discrete) crime-stains are analyzed where cells are (or were) intact. The integrity of the paired chromosomes of the diploid cell is preserved. In extracted DNA that is diluted to low template levels, the authors argue that the paired-chromosome integrity is lost. This directly affects the outcome of the logistic model. To date, current experimentation procedures are more akin to haploid cells and thus, different logistic models are needed for haploid and diploid cells. In order to simplify the methodology to estimate the multiple logistic regressions, the authors propose the use of a simulation model of the entire process associated with the analysis of STR loci, as a supplement to the purely experimental approach to support the validation of new methods. This is illustrate with an evaluation of some features of the logistic model proposed by Gill et al., 2009, and discuss alternative models. (Published Abstract)