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In court questions are often raised related to how trace DNA was deposited, directly during the crime or innocently for instance by secondary transfer. It is therefore of interest to have knowledge of the probability of transfer or secondary transfer in different situations. Factors that could influence transfer probabilities are background DNA and the shedder status of the involved persons. In this study, we have classified participants as high or low DNA shedders. We observed DNA transfer in a simulated attack scenario, and demonstrated that shedder status has a significant influence of transfer rates. We have examined the background DNA in samples from T-shirts worn in an area with frequent human traffic and detected multiple contributors. We further demonstrated that DNA from co-workers of a T-shirt wearer can be secondarily transferred from the environment and detected in samples, and that the composition of background DNA is correlated with the shedder status of the wearer. Finally, we have illustrated the inference with the results of transfer probabilities and a fictive case with the use of a Bayesian network., (Copyright © 2017 Elsevier B.V. All rights reserved.)

As the profiling systems used in forensic analyses have become more sensitive in recent years, the risk of detecting a contamination in a DNA sample has increased proportionally. This requires more stringent work protocols and awareness to minimize the chance of contamination. Although there is high consciousness on contamination and best practice procedures in forensic labs, the same requirements are not always applied by the police. In this study we have investigated the risk of contamination from police staff. Environmental DNA was monitored by performing wipe tests (sampling of hot spots) at two large police units (scenes of crime departments). Additionally, the DNA profiles of the scenes of crime officers were compared to casework samples that their own unit had investigated in the period of 2009-2015. Furthermore, a pilot study to assess whether DNA from the outside package of an exhibit could be transferred to a DNA sample was carried out. Environmental DNA was detected in various samples from hot spots. Furthermore, 16 incidences of previously undetected police-staff contamination were found in casework that had been submitted between 2009 and 2015. In 6 cases the police officers with a matching DNA profile reported that they had not been involved with the case. We have demonstrated that DNA from the outside package can be transferred to an exhibit during examination. This experience demonstrates that when implementing the new multiplex systems, it is important to ensure that 'best practice' procedures are upgraded, and appropriate training is provided in order to ensure that police are aware of the increased contamination risks., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Today, there exists a number of tools for solving kinship cases. But what happens when information comes from a mixture? DNA mixtures are in general rarely seen in kinship cases, but in a case presented to the Norwegian Institute of Public Health, sample DNA was obtained after a rape case that resulted in an unwanted pregnancy and abortion. The only available DNA from the fetus came in form of a mixture with the mother, and it was of interest to find the father of the fetus. The mother (the victim), however, refused to give her reference data and so commonly used methods for paternity testing were no longer applicable. As this case illustrates, kinship cases involving mixtures and missing reference profiles do occur and make the use of existing methods rather inconvenient. We here present statistical methods that may handle general relationship inference based on DNA mixtures. The basic idea is that likelihood calculations for mixtures can be decomposed into a series of kinship problems. This formulation of the problem facilitates the use of kinship software. We present the freely available R package relMix which extends on the R version of Familias. Complicating factors like mutations, silent alleles, and θ-correction are then easily handled for quite general family relationships, and are included in the statistical methods we develop in this paper. The methods and their implementations are exemplified on the data from the rape case.

The statistical evidence obtained from mixed DNA profiles can be summarised in several ways in forensic casework including the likelihood ratio (LR) and the Random Man Not Excluded (RMNE) probability. The literature has seen a discussion of the advantages and disadvantages of likelihood ratios and exclusion probabilities, and part of our aim is to bring some clarification to this debate. In a previous paper, we proved that there is a general mathematical relationship between these statistics: RMNE can be expressed as a certain average of the LR, implying that the expected value of the LR, when applied to an actual contributor to the mixture, is at least equal to the inverse of the RMNE. While the mentioned paper presented applications for kinship problems, the current paper demonstrates the relevance for mixture cases, and for this purpose, we prove some new general properties. We also demonstrate how to use the distribution of the likelihood ratio for donors of a mixture, to obtain estimates for exceedance probabilities of the LR for non-donors, of which the RMNE is a special case corresponding to L R>0. In order to derive these results, we need to view the likelihood ratio as a random variable. In this paper, we describe how such a randomization can be achieved. The RMNE is usually invoked only for mixtures without dropout. In mixtures, artefacts like dropout and drop-in are commonly encountered and we address this situation too, illustrating our results with a basic but widely implemented model, a so-called binary model. The precise definitions, modelling and interpretation of the required concepts of dropout and drop-in are not entirely obvious, and we attempt to clarify them here in a general likelihood framework for a binary model.

The introduction of Short Tandem Repeat (STR) DNA was a revolution within a revolution that transformed forensic DNA profiling into a tool that could be used, for the first time, to create National DNA databases. This transformation would not have been possible without the concurrent development of fluorescent automated sequencers, combined with the ability to multiplex several loci together. Use of the polymerase chain reaction (PCR) increased the sensitivity of the method to enable the analysis of a handful of cells. The first multiplexes were simple: 'the quad', introduced by the defunct UK Forensic Science Service (FSS) in 1994, rapidly followed by a more discriminating 'six-plex' (Second Generation Multiplex) in 1995 that was used to create the world's first national DNA database. The success of the database rapidly outgrew the functionality of the original system - by the year 2000 a new multiplex of ten-loci was introduced to reduce the chance of adventitious matches. The technology was adopted world-wide, albeit with different loci. The political requirement to introduce pan-European databases encouraged standardisation - the development of European Standard Set (ESS) of markers comprising twelve-loci is the latest iteration. Although development has been impressive, the methods used to interpret evidence have lagged behind. For example, the theory to interpret complex DNA profiles (low-level mixtures), had been developed fifteen years ago, but only in the past year or so, are the concepts starting to be widely adopted. A plethora of different models (some commercial and others non-commercial) have appeared. This has led to a confusing 'debate' about the 'best' to use. The different models available are described along with their advantages and disadvantages. A section discusses the development of national DNA databases, along with details of an associated controversy to estimate the strength of evidence of matches. Current methodology is limited to searches of complete profiles - another example where the interpretation of matches has not kept pace with development of theory. STRs have also transformed the area of Disaster Victim Identification (DVI) which frequently requires kinship analysis. However, genotyping efficiency is complicated by complex, degraded DNA profiles. Finally, there is now a detailed understanding of the causes of stochastic effects that cause DNA profiles to exhibit the phenomena of drop-out and drop-in, along with artefacts such as stutters. The phenomena discussed include: heterozygote balance; stutter; degradation; the effect of decreasing quantities of DNA; the dilution effect., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

With the introduction of new multiplex PCR kits and instrumentation such as the Applied Biosystems 3500xl, there has recently been a rapid change in technology that has greatly increased sensitivity of detection so that a DNA profile can routinely be obtained from only a few cells. Research to evaluate the risks of passive transfer has not kept pace with this development; hence the risk of innocent DNA transfer at the crime-scene is currently not properly understood. The purpose of this study was to investigate the possibility of investigator-mediated transfer of DNA traces with disposable nitrile-gloves used during crime-scene examinations. We investigated the primary transfer of freshly deposited DNA from touched plastic, wood or metal substrates and secondary and tertiary transfer by a person wearing disposable nitrile-gloves and onto a third object. We show that with use of the new highly sensitive technologies available in forensic DNA analysis there is an enhanced probability to obtain a DNA-profile which has not been directly deposited on the object but is an outcome of one or more transfer events. The nitrile-gloves used by investigators during exhibit examination can act as a vector for DNA transfer from one item to another. We have shown that the amount of DNA deposited on an object affects the probability of transfer. Secondly, the type of substrate material that DNA is deposited onto has an impact on transfer rates., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

This paper deals with the statistical interpretation of DNA mixture evidence. The conventional methods used in forensic casework today use something like 16 STR-markers. Power can be increased by rather using SNP-markers. New statistical methods are then needed, and we present a regression framework. The basic idea is that the traditional forensic hypotheses, commonly denoted HD and HP, are replaced by parametric versions: a person contributes to a mixture if and only if the fraction he contributes is greater than 0. This contributed fraction is a parameter of the regression model. The regression model uses the peak heights directly and there is no need to specify or estimate the number of contributors to the mixture. Also, drop-in and drop-out pose no principal problems. Data from 25 controlled blinded experiments were used to test the model. The number of contributors varied between 2 and 5, and the fractions contributed ranged from 0.01 to 0.99. The fractions were accurately estimated by the regression analyses. There were no false positives (i.e., in no cases were non-contributors declared to contributors). Some false negatives occurred for fractions of 0.1 or lower. Simulations were performed to test the model further. The analyses show that useful estimates can be obtained from a relatively small number of SNP-markers. Reasonable results are achieved using 300 markers which is close to the 313 SNPs in the controlled experiment. Increasing the number of SNPs, the analyses demonstrate that individuals contributing as little as 1% can reliably be detected, which suggests that cases beyond the reach of conventional forensic methods today can be reported., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

DNA mixture evidence pertains to cases where several individuals may have contributed to a biological stain. Statistical methods and software for such problems are available and a large number of cases can be handled adequately. However, one class of mixture problems remains untreated in full generality in the literature, namely when the contributors may be related. Disregarding a plausible close relative of the perpetrator as an alternative contributor (identical twin is the most extreme case) may lead to overestimating the evidence against a suspect. Existing methods only accommodate pairwise relationships such as the case where the suspect and the victim are siblings, for example. In this paper we consider relationships in full generality, conveniently represented by pedigrees. In particular, these pedigrees may involve inbreeding, for instance when the parents of an individual of interest are first cousins. Furthermore our framework handles situations where the opposing parties in a court case (prosecution and defence) propose different family relationships. Consequently, our approach combines classical mixture and kinship problems. The basic idea of this paper is to formulate the problem in a way that allows for the exploitation of currently available methods and software designed originally for linkage applications. We have developed a freely available R package, euroMix based on another package, paramlink, and we illustrate the ideas and methods on real and simulated data., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

DNA evidence in criminal cases may be challenging to interpret if several individuals have contributed to a DNA-mixture. The genetic markers conventionally used for forensic applications may be insufficient to resolve cases where there is a small fraction of DNA (say less than 10%) from some contributors or where there are several (say more than 4) contributors. Recently methods have been proposed that claim to substantially improve on existing approaches. The basic idea is to use high-density single nucleotide polymorphism (SNP) genotyping arrays including as many as 500,000 markers or more and explicitly exploit raw allele intensity measures. It is claimed that trace fractions of less than 0.1% can be reliably detected in mixtures with a large number of contributors. Specific forensic issues pertaining to the amount and quality of DNA are not discussed in the paper and will not be addressed here. Rather our paper critically examines the statistical methods and the validity of the conclusions drawn in Homer et al. (2008). We provide a mathematical argument showing that the suggested statistical approach will give misleading results for important cases. For instance, for a two person mixture an individual contributing less than 33% is expected to be declared a non-contributor. The quoted threshold 33% applies when all relative allele frequencies are 0.5. Simulations confirmed the mathematical findings and also provide results for more complex cases. We specified several scenarios for the number of contributors, the mixing proportions and allele frequencies and simulated as many as 500,000 SNPs. A controlled, blinded experiment was performed using the Illumina GoldenGate(®) 360 SNP test panel. Twenty-five mixtures were created from 2 to 5 contributors with proportions ranging from 0.01 to 0.99. The findings were consistent with the mathematical result and the simulations. We conclude that it is not possible to reliably infer the presence of minor contributors to mixtures following the approach suggested in Homer et al. (2008). The basic problem is that the method fails to account for mixing proportions., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

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., (Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.)

Motivation: Family relationships can be estimated from DNA marker data. Applications arise in a large number of areas including evolution and conservation research, genealogical research in human, plant and animal populations, forensic problems and genetic mapping via linkage and association analyses. Traditionally, likelihood-based approaches to relationship estimation have used unlinked genetic markers. Due to the fact that some relationships cannot be distinguished from data at unlinked markers, and given the limited number of such markers available, there are considerable constraints on the type of identification problem that can be satisfactorily addressed with such approaches. The aim of this article is to explore the potential of linked autosomal single nucleotide polymorphism markers in this context. Throughout, we will view the problem of relationship estimation as one of pedigree identification rather than identity-by-descent, and thus focus on applications where determination of the exact relationship is important., Results: We show that the increase in information obtained by exploiting large sets of linked markers substantially increases the number of problems that can be solved. Results are presented based on simulations as well as on real data., Availability: The R library FEST is freely available from http://folk.uio.no/thoree/FEST.

Two different quantities have been suggested for quantification of evidence in cases where a suspect is found by a search through a database of DNA profiles. The likelihood ratio, typically motivated from a Bayesian setting, is preferred by most experts in the field. The so-called np rule has been suggested through frequentist arguments and has been suggested by the American National Research Council and Stockmarr (1999, Biometrics55, 671-677). The two quantities differ substantially and have given rise to the DNA database search controversy. Although several authors have criticized the different approaches, a full explanation of why these differences appear is still lacking. In this article we show that a P-value in a frequentist hypothesis setting is approximately equal to the result of the np rule. We argue, however, that a more reasonable procedure in this case is to use conditional testing, in which case a P-value directly related to posterior probabilities and the likelihood ratio is obtained. This way of viewing the problem bridges the gap between the Bayesian and frequentist approaches. At the same time it indicates that the np rule should not be used to quantify evidence.

The broad topic of this paper is the evaluation of DNA evidence in criminal cases. More specifically, we deal with mixture evidence which refers to cases where there are, or could be, several contributors to a biological stain based on, e.g., blood or semen. The present paper addresses DNA mixtures based on single nucleotide polymorphism (SNP) markers, i.e., diallelic markers. Based on STR analysis, it is in most cases easy to identify the presence of a mixture since three or four bands will show up with a high probability for at least one locus. Obviously, this will not be the case for diallelic markers and interpreting mixtures will be a great challenge. We address this problem by first approaching the more general problem of estimating the number of contributors to a stain. In addition we discuss how the markers should be selected and how many are required.

Y-chromosome DNA profiles are promising tools in population genetics and forensic science. Here we present DNA profiles of 300 unrelated Y-chromosomes of Norwegian origin. The profile is composed of eight short tandem repeats (STRs) and one single nucleotide polymorphism (SNP). In more than 2/3 of the haplotypes the modular structure in the 5' end of the minisatellite locus DYF155S1 was revealed by minisatellite variant repeat PCR (MVR-PCR) These haplotypes were also typed for deletions of fragment 50f2C (DYF155S2). Allele distribution and paternity exclusion parameters are given for each marker. The degree of haplotype diversity and its implication for statistics are evaluated. In the 300 samples 177 different haplotypes were encountered, of which 137 were observed once only. Analysis showed that the main source of variation is within the population. The Fst values were less than 0.015 in general. Haplotype grouping by the SNP demonstrated two haplogroups (Tat/T and Tat/C). Haplogroup Tat/C--found in 5.7% of the present material - is the same haplogroup as encountered in 60% of Finnish males [Am. J. Hum. Genet. 62 (1998) 1171]. Mutation analysis in 150 father/son pairs (a total of 1200 meiotic events) revealed an average mutation frequency of 0.0042 (95% CI 0.0014-0.0097).

The paper extends on the traditional methodology used to quantify DNA evidence in paternity or identification cases. By extending we imply that there are more than two alternatives to choose between. In a standard paternity case the two competing explanations H(1): "John Doe is the father of the child and H(2): "A random man is the father of the child, are typically considered. A paternity index of 100000 implies that the data is 100000 more likely assuming hypothesis H(1) rather than H(2). If H(2) is replaced by "A brother of John Doe is the father", the LR may change dramatically. The main topic of this paper is to determine the most probable pedigree given a certain set of data including DNA profiles. In the previous example this corresponds to determining the most likely relation between John Doe and the child. Based on DNA obtained from victims of a fire, bodies found in an ancient grave or from individuals seeking to confirm their anticipated family relations, we would like to determine the most probable pedigree. The approach we present provides the possibility to combine non-DNA evidence, say age of individuals, and DNA profiles. The program familias, obtainable as shareware from http://www.nr.no/familias, delivers the probabilities for the various family constellations. More precisely, the information (if any) prior to DNA is combined with the DNA-profiles in a Bayesian manner to deliver the posterior probabilities. We exemplify using the well published Romanov data where the accepted solution emerges among 4536 possibilities considered. Various other applications based on forensic case work are discussed. In addition we have simulated data to resemble an incest case. Since the true family relation is known in this case, we may evaluate the method.

Hyperpolymorphic short tandem repetitive DNA sequences, STRs or microsatellites, have become widely used in human identification, particularly in criminal cases and in mass disasters. In such cases the substrates for the analyses may be decomposed biological material, a fact that has to be taken into account when choosing the appropriate casework methods. In this paper we report the evaluation of five different DNA extraction methods, namely the phenol-chloroform, the silica based, the InstaGene Matrix (BioTest), the glass fiber filter, and the Chelex based methods. The substrates for the analyses are decomposed human liver tissue specimens from forensic autopsy cases. Extracted DNA was quantified and DNA profiled by a set of seven STRs. We have compared laboratory time consumption and costs of the five methods, showing that the Chelex method is the more rapid and less expensive of the methods, the phenol-chlorophorm and silica extractions being the most time consuming and resource demanding ones. A full profile was obtained by the silica method in nine out of ten cases and this method failed to give a reliable type in four out of 70 STR analyses. The phenol-chlorophorm and the glass fiber filter methods failed in 16 analyses, the InstaGene Matrix (BioTest) in 25 and the Chelex extracts in 56 of the 70 STR analyses. By multiple logistic regression we show that the difference between the silica procedure and the other methods are statistically significant. In our hands, the silica gel extraction procedure is an obvious choice when the biological material available is decomposed human tissue--even if this procedure is one of the more laborious ones.

Different SCN1A mutations are known to cause a variety of phenotypes, such as generalized epilepsy with febrile seizures plus (GEFS+), Dravet syndrome and familial hemiplegic migraine (FHM). In Dravet syndrome, most mutations are de novo and familial cases are rare. In this study, Dravet syndrome is observed in two maternal half sisters. They have healthy fathers and their common mother has never experienced seizures, but has a lifelong history of migraine. Direct sequencing of DNA extracted from blood revealed a heterozygous SCN1A nonsense mutation c.3985C>T in the sisters, but not in the mother. The mutation induces a premature stop codon and probably leads to a non-functional protein. Further examination of the mother's DNA showed that she has a mosaicism of the mutation. This report of parental SCN1A nonsense mutation mosaicism in familial Dravet syndrome suggests that mosaicism might be more common than previously suspected and emphasizes the importance of taking mosaicism into account in genetic counselling of Dravet syndrome and SCN1A mutations. Furthermore, whether the migraine of the mother could be influenced by her SCN1A mutation mosaicism is not known, but increased awareness of migraine in future studies of SCN1A related epilepsies could clarify this intriguing link between migraine and epilepsy. [ABSTRACT FROM AUTHOR]

The objective of this paper is to show how various sources of information can be modelled and integrated to address relationship identification problems. Applications come from areas as diverse as evolution and conservation research, genealogical research in human, plant and animal populations, and forensic problems including paternity cases, identification following disasters, family reunions and immigration issues. We propose assigning a prior probability distribution to the sample space of pedigrees, calculating the likelihood based on DNA data using available software and posterior probabilities using Bayes' Theorem. Our emphasis here is on the modelling of this prior information in a formal and consistent manner. We introduce the distinction between local and global prior information, whereby local information usually applies to particular components of the pedigree and global prior information refers to more general features. When it is difficult to decide on a prior distribution, robustness to various choices should be studied. When suitable prior information is not available, a flat prior can be used which will then correspond to a strict likelihood approach. In practice, prior information is often considered for these problems, but in a generally ad hoc manner. This paper offers a consistent alternative. We emphasise that many practical problems can be addressed using freely available software. [ABSTRACT FROM AUTHOR]

In a number of practical cases it is important to determine the likely geographical origin of an individual or a biological sample. A dead body, old bones or a sample of semen may be available. Information on where the sample might come from can assist investigation or research. The first part of this paper is independent of specific data structure. We formulate the problem as a classification problem. Bayes' theorem allows different sources of information or data to be reconciled conveniently. The main part of the paper involves high dimensional data for which simple, standard methods are not likely to work properly. Mitochondrial DNA (mtDNA) data is a typical example of such data. We propose a procedure involving essentially two steps. First, principal component analysis is used to reduce the dimension of the data. Next, quadratic discriminant analysis performs the actual classification. A cross validation procedure is implemented to select the optimal number of principal components. The importance of using separate data sets for model fitting and testing is emphasized. This method distinguishes well between individuals with a self reported European (Icelandic or German) origin and SE Africans. In this case the error rate is 2.0%. [ABSTRACT FROM AUTHOR]