Estimating drop-out probabilities in forensic DNA samples: a simulation approach to evaluate different models

H. Haned, T. Egeland, D. Pontier, L. Pene, P. Gill

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


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 utilised 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, we propose that this approach is over-simplistic, because several sources of variability are not taken into account in this generalised model. For example, in real-life, small (discrete) crime-stains are analysed 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, we 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, we 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. We illustrate with an evaluation of some features of the logistic model proposed by Gill et al., 2009 [12] and discuss alternative models.
Original languageEnglish
Pages (from-to)525-531
Number of pages7
JournalForensic Science International: Genetics
Issue number5
Publication statusPublished - Nov 2011


  • STR
  • mixtures
  • logistic regression
  • maximum likelihood
  • simulations
  • drop out
  • profiles
  • allelic dropout
  • PCR


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