09:30 to 10:15 Mikkel Andersen (Aalborg Universitet)Y Chromosomal STR Markers: Assessing Evidential Value Y chromosomal short tandem repeats (Y-STRs) are widely used in forensic genetics. The current application is mainly to detect non-matches, and subsequently release wrongly accused suspects. For matches the situation is different. For now, most analysts will just say that the haplotypes matched but they will not assess the evidential value of this match. This is understandable given the fact that a consensus of estimating the evidential value has not yet been reached. However, work on getting there is in progress. In this talk, the aim is to review some of the current methods for assessing the evidential value of a Y-STR match. This includes proposal for a new way to compare methods estimating match probabilities and a discussion of correcting for population substructure through the so-called θ (theta) method. INI 1 10:15 to 11:00 Amke Caliebe (Christian-Albrechts-Universität zu Kiel)Estimating trace-suspect match probabilities in forensics for singleton Y-STR haplotypes using coalescent theory Estimation of match probabilities for singleton haplotypes of lineage markers, i.e. for haplotypes observed only once in a reference database augmented by a suspect profile, is an important problem in forensic genetics. We compared the performance of four estimators of singleton match probabilities for Y-STRs, namely the count estimator, both with and without Brenner’s so-called kappa correction, the surveying estimator, and a previously proposed, but rarely used, coalescent-based approach implemented in the BATWING software. Extensive simulation with BATWING of the underlying population history, haplotype evolution and subsequent database sampling revealed that the coalescent-based approach and Brenner’s estimator are characterized by lower bias and lower mean squared error than the other two estimators. Moreover, in contrast to the two count estimators, both the surveying and the coalescent-based approach exhibited a good correlation between the estimated and true match probabilities. However, although its overall performance is thus better than that of any other recognized method, the coalescent-based estimator is still very computation-intense. Its application in forensic practice therefore will have to be limited to small reference databases, or to isolated cases of particular interest, until more powerful algorithms for coalescent simulation have become available. INI 1 11:00 to 11:30 Morning Coffee 11:30 to 12:15 Peter Gill (University of Oslo)Challenges of reporting complex DNA mixtures in the court-room The introduction of complex software into routine casework is not without challenge. A number of models are available to interpret complex mixtures. Some of these are commercial, whereas others are open source. For a comprehensive validation protocol see Haned et al Sci and Justice (206) 104-108). In practice, methods are divided into quantitative or qualitative models and there is no preferred method. A number of cases have been reported in the UK using different software. For example, in R v. Fazal, the prosecution and defence used different software to analyse the same case. Different likelihood ratios were obtained and both were reported to the court – a way forward to prevent confusion in court is presented. This paper also highlights the necessity of ‘equality of arms’ when software is used, illustrated by several other cases. Examples of problematic proposition formulations that may provide misleading results are described. INI 1 12:15 to 13:00 Klaas Slooten (Vrije Universiteit Amsterdam)The likelihood ratio as a random variable, with applications to DNA mixtures and kinship analysis In forensic genetics, as in other areas of forensics, the data to be statistically evaluated are the result of a chance process, and hence one may conceive that they had been different, resulting in another likelihood ratio. Thus, one thinks of the obtained likelihood ratio in the case at hand as the outcome of a random variable. In this talk we will discuss the way to formalize this intuitive notion, and show general properties that the resulting distributions of the LR must have. We illustrate and apply these general results both to the evaluation of DNA mixtures and to kinship analysis, two standard applications of forensic DNA profiles. For mixtures, we discuss how model validation can be aided by investigation of the obtained likelihood ratios. For kinship analysis we observe that for any pairwise kinship comparison, the expected likelihood ratio does not depend on the allele frequencies of the loci that are used other than through the total number of alleles. We compare the behavior of the LR as a function of the allele frequencies with that of the weight of evidence, Log(LR), and argue that the WoE is better behaved. This talk is largely based on a series of three papers in International Journal of Legal Medicine co-authored with Thore Egeland.Exclusion probabilities and likelihood ratios with applications to kinship problems, Int. J. Legal Med. 128, 2014, 415---425,Exclusion probabilities and  likelihood ratios with applications to mixtures, Int. J. Legal Med. 130, 2016, 39---57,The likelihood ratio as a random variable for linked markers in kinship analysis, Int. J. Legal Med. 130, 2016, 1445---1456 INI 1 13:00 to 13:30 Lunch @ Wolfson Court 14:00 to 15:00 Optional Discussion Forum: Discussion Room 15:00 to 15:30 Afternoon Tea 15:30 to 16:15 Michael Sigman (University of Central Florida)Assessing Evidentiary Value in Fire Debris Analysis Co-author: Mary R. Williams (National Center for Forensic Science, University of Central Florida) This presentation will examine the calculation of a likelihood ratio to assess the evidentiary value of fire debris analysis results. Models based on support vector machine (SVM), linear and quadratic discriminant analysis (LDA and QDA) and k-nearest neighbors (kNN) methods were examined for binary classification of fire debris samples as positive or negative for ignitable liquid residue (ILR). Computational mixing of data from ignitable liquid and substrate pyrolysis databases was used to generate training and cross validation samples. A second validation was performed on fire debris data from large-scale research burns, for which the ground truth (positive or negative for ILR) was assigned by an analyst with access to the gas chromatography-mass spectrometry data for the ignitable liquid used in the burn. The probabilities of class membership were calculated using an uninformative prior and a likelihood ratio was calculated from the resulting class membership probabilities . The SVM method demonstrated a high discrimination, low error rate and good calibration for the cross-validation data; however, the performance decreased significantly for the fire debris validation data, as indicated by a significant decrease in the area under the receiver operating characteristic (ROC) curve. The QDA and kNN methods showed performance trends similar to those of SVM. The LDA method gave poorer discrimination, higher error rates and slightly poorer calibration for the cross validation data; however the performance did not deteriorate for the fire debris validation data. INI 1 16:15 to 17:00 James Curran (University of Auckland)Understanding Intra-day and Inter-day Variation in LIBS Co-authors: Anjali Gupta (University of Auckland), Chris Triggs (Universtity of Auckland), Sally Coulson (ESR) LIBS (laser induced breakdown spectroscopy) is a low-cost alternative and highly portable instrument that can be used in forensic applications to determine elemental composition. It differs from more traditional instruments such as ICP-MS and $\mu$-XRF in that the output is a spectrum rather than the concentration of elements. LIBS has great appeal in forensic science but has yet to enter the mainstream. One of the reasons for this is a perceived lack of reproducibility in the measurements over successive days or weeks. In this talk I will describe a simple experiment we designed to investigate this phenomenon, and the consequences of our findings. The analysis involves both classical methodology and a simple Bayesian approach. INI 1