Testing likelihood ratios produced from complex DNA profiles.

The performance of any model used to analyse DNA profile evidence should be tested using simulation, large scale validation studies based on ground-truth cases, or alignment with trends predicted by theory. We investigate a number of diagnostics to assess the performance of the model using Hd true tests. Of particular focus in this work is the proportion of comparisons to non-contributors that yield a likelihood ratio (LR) higher than or equal to the likelihood ratio of a known contributor (LRPOI), designated as p, and the average LR for Hd true tests. Theory predicts that p should always be less than or equal to 1/LRPOI and hence the observation of this in any particular case is of limited use. A better diagnostic is the average LR for Hd true which should be near to 1. We test the performance of a continuous interpretation model on nine DNA profiles of varying quality and complexity and verify the theoretical expectations.

[1]  Duncan Taylor,et al.  Using continuous DNA interpretation methods to revisit likelihood ratio behaviour. , 2014, Forensic science international. Genetics.

[2]  John S Buckleton,et al.  Autosomal microsatellite allele frequencies for a nationwide dataset from the Australian Caucasian sub-population. , 2007, Forensic science international.

[3]  Steffen L. Lauritzen,et al.  Probabilistic modelling for DNA mixture analysis , 2008 .

[4]  Duncan Taylor,et al.  The interpretation of single source and mixed DNA profiles. , 2013, Forensic science international. Genetics.

[5]  Duncan Taylor,et al.  The 'factor of two' issue in mixed DNA profiles. , 2014, Journal of theoretical biology.

[6]  P Gill,et al.  A new methodological framework to interpret complex DNA profiles using likelihood ratios. , 2013, Forensic science international. Genetics.

[7]  Roger W Byard,et al.  Fatal Ischemic Enteritis with Hemorrhage—A Late Complication of Treated Wilms Tumor , 2013, Journal of forensic sciences.

[8]  M. Perlin,et al.  Validating TrueAllele® DNA Mixture Interpretation * ,† , 2011, Journal of forensic sciences.

[9]  Norah Rudin,et al.  Calculating the Weight of Evidence in Low‐Template Forensic DNA Casework , 2013, Journal of forensic sciences.

[10]  David J. Balding,et al.  Statistical Evaluation of Forensic DNA Profile Evidence , 2014 .

[11]  D. Balding,et al.  Evaluating forensic DNA profiles using peak heights, allowing for multiple donors, allelic dropout and stutters. , 2013, Forensic science international. Genetics.

[12]  P Gill,et al.  Euroforgen-NoE collaborative exercise on LRmix to demonstrate standardization of the interpretation of complex DNA profiles. , 2014, Forensic science international. Genetics.

[13]  Hinda Haned,et al.  Forensim: an open-source initiative for the evaluation of statistical methods in forensic genetics. , 2011, Forensic science international. Genetics.

[14]  D J Balding,et al.  DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands. , 1994, Forensic science international.

[15]  L. Stein,et al.  Probability and the Weighing of Evidence , 1950 .

[16]  Øyvind Bleka,et al.  Exact computation of the distribution of likelihood ratios with forensic applications. , 2014, Forensic science international. Genetics.

[17]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[18]  John Buckleton,et al.  Interpreting low template DNA profiles. , 2009, Forensic science international. Genetics.

[19]  I. W. Evett,et al.  Statistical analysis of a large file of data from STIR profiles of British Caucasians to support forensic casework , 2005, International Journal of Legal Medicine.