RESUMEN

When we compare a DNA mixture profile to a single person of interest, there are often just two competing explanations considered, and the comparison of how likely these are to lead to the observed mixture is summarized by a likelihood ratio. However, in more complex cases this does not suffice, e.g., when there are multiple persons of interest. One can then compute several likelihood ratios, corresponding to several pairs of hypotheses, and subsequently decide which one(s) to report. This may lead to the computation of a rather large number of such likelihood ratios. In this article we advocate a systematic approach that starts by describing all relevant hypotheses. For each hypothesis, we then compute its likelihood (i.e., the probability to see the genetic data if the hypothesis is true). Based on the likelihoods of all considered hypotheses, one can then make a summary of the findings to report. This may be on the level of the considered hypotheses and/or with likelihood ratios per person of interest. We illustrate with several examples how this approach assists interpretation. The likelihoods summarize how the trace can help to distinguish between the considered hypotheses, in the sense that they transform the prior odds on them into posterior odds, without having to assign prior probabilities on the hypotheses for the calculation of the likelihoods themselves. On the other hand likelihood ratios (LR's) for individual PoI's cannot be obtained without these priors. In many cases these LR's will be quite insensitive to the choice of prior probabilities but in other cases they will be; we give examples of both.We argue that the table of likelihoods of the considered hypotheses is a more natural analog of the LR provided in the simple case with one PoI and two considered hypotheses, compared to the computation of a LR per PoI. We end with a discussion of the choice of prior probabilities, of the existing recommendations for this situation, and on reporting.

Asunto(s)

RESUMEN

The evidential value of a unique DNA database match has been extensively discussed. In principle the matter has been mathematically resolved, since the posterior odds on the match being with the trace donor are unambiguously defined. There are multiple ways to express these odds as a product of likelihood ratio and prior odds, and so the mathematics do not immediately tell us what to do in concrete cases, in particular which likelihood ratio to choose for reporting. With p the random match probability for the matching person, if innocent, and n the database size, both 1/p, originating from a suspect-centered framework, and 1/(np), originating from a database-centered framework, arise as likelihood ratio. Both have been defended and both have been criticized in the literature. We will clarify the situation by not introducing models and choices of prior probabilities until they are needed. This allows to derive the posterior odds in their most general form, which applies whenever we know that a single person among a list is not excluded as potential trace donor. We show that we need only three probabilities, that pertain to the observed match, to the database, and to the matching person respectively. How these required probabilities behave in a given context, then, differs from one situation to another. This is understandable since database searches may be done under various circumstances. They may be carried out with or without a suspect already in mind and, depending on the operational procedures, one may or may not be informed about the personal details of the person who gives the match. We show how to evaluate the required probabilities in all such cases. We will motivate why we believe that for some database searches, the 1/p likelihood ratio is more natural, whereas for others, 1/(np) seems the more sensible choice. This is not motivated by the mathematics: mathematically, the approaches are equivalent. It is motivated by considering which model best reflects the actual situation, taking into account what question was asked to begin with, and by the practical consideration of judging which likelihood ratio comes closer to the posterior odds based on the information available in the case. This article is intended to be both a research and a review article, and we end with an in-depth discussion of various arguments that have been brought forward in favor or against either 1/p or 1/(np).

Asunto(s)

RESUMEN

Over the last years, several papers have been published that presented likelihood ratio distributions in kinship cases. These data are useful to assess the power of the discussed technology for certain types of kinship investigation, since they tell us what range of likelihood ratios we can expect given the ground truth of the relationship between the investigated individuals. However, in some publications the fraction of (in)correctly classified pairs (when based on a likelihood ratio threshold), are presented as accuracy or error rate, with the interpretational pitfall looming that these can be seen as probabilities that are generally applicable to the investigated type of kinship, on the investigated loci and with the obtained allele frequencies. In this publication we warn against such interpretations. We point out that from the likelihood ratio, probabilistic statements about the ground truth cannot be made, and that therefore this will also not be possible from a weaker statement such as the LR exceeding some threshold value. The statement that the LR exceeds a threshold in itself does has evidential value, and we will explain how to estimate that value from the obtained empirical distributions. We also explain that the concept of error does not apply to the likelihood ratio, but only to decisions. If one takes decisions based on a LR threshold, then it is possible to define error rates, but these are predictive and conditional. They tell us how often we will make wrong classifications in each group (the related pairs and the unrelated pairs) if we apply a LR threshold. They do not tell us how likely it is, once we have made a decision, that this decision is the one that we wanted to make had we known the true relationship. In order to make such a statement we need to have more information. We illustrate the points we address with examples that we have taken from the literature.

Asunto(s)

RESUMEN

Recently, a debate has arisen around the number of contributors postulated in hypotheses for the purpose of weight of evidence calculations on DNA mixture profiles. Specifically the issue at stake is whether or not one should have the same number of contributors under both hypotheses for which a likelihood ratio is calculated. In this paper we aim to clarify this issue. We take the general approach of considering the number of contributors as a nuisance parameter. Two central assumptions then determine the form of the overall likelihood ratio: whether the prior distributions of the nuisance parameter are equivalent given both hypotheses and whether they depend on the hypotheses. Examples are given for both scenarios where we have either independence or strong dependence between the prior distributions of the number of contributors and the hypotheses. Moreover, examples for different kinship scenarios are presented. In conclusion, the overall likelihood ratio does not only depend on likelihood ratios for fixed values of the nuisance parameter but may also vary considerably with different prior distributions.

Asunto(s)

RESUMEN

We present the results of a comparison of likelihood ratios obtained from DNA mixture data, obtained either by a peak height model (EuroForMix) and by a discrete model, using probabilities of dropout and integrating over them (MixKin). We use the mixture data that have been published in [1] and were made publicly available. We show that, for mixtures for which replicate analyses were available in this set, there is almost no difference in weight of evidence, suggesting that the additional information in the peak heights is limited. On the other hand, for mixtures for which only a single replicate is available, the weight of evidence for true donors can be substantially higher with the peak height model, especially so for major donors and also for minor donors provided they are the only minor contributor to the mixture. False positive rates were very low with both methods, but for related non-contributors the risk of a false positive is much higher with the discrete method unless kinship is taken into account in the likelihood ratio calculation.

Asunto(s)

RESUMEN

Several methods exist to compute the likelihood ratio LR(M, g) evaluating the possible contribution of a person of interest with genotype g to a mixed trace M. In this paper we generalize this LR to a likelihood ratio LR(M1, M2) involving two possibly mixed traces M1 and M2, where the question is whether there is a donor in common to both traces. In case one of the traces is in fact a single genotype, then this likelihood ratio reduces to the usual LR(M, g). We explain how our method conceptually is a logical consequence of the fact that LR calculations of the form LR(M, g) can be equivalently regarded as a probabilistic deconvolution of the mixture. Based on simulated data, and using a semi-continuous mixture evaluation model, we derive ROC curves of our method applied to various types of mixtures. From these data we conclude that searches for a common donor are often feasible in the sense that a very small false positive rate can be combined with a high probability to detect a common donor if there is one. We also show how database searches comparing all traces to each other can be carried out efficiently, as illustrated by the application of the method to the mixed traces in the Dutch DNA database.

Asunto(s)

RESUMEN

Familial searching, the act of searching a database for a relative of an unknown individual whose DNA profile has been obtained, is usually restricted to cases where the DNA profile of that person has been unambiguously determined. Therefore, it is normally applied only with a good quality single source profile as starting point. In this article we investigate the performance of the method if applied to mixtures with and without allelic dropout, when likelihood ratios are computed with a semi-continuous (binary) model. We show that mixtures with dropout do not necessarily perform worse than mixtures without, especially if some separation between the donors is possible due to their different dropout probabilities. The familial searching true and false positive rates of mixed profiles on 15 loci are in some cases better than those of single source profiles on 10 loci. Thus, the information loss due to the fact that the person of interest's DNA has been mixed with that of other, and is affected by dropout, can be less than the loss of information corresponding to having 5 fewer loci available for a single source trace. Profiles typed on 10 autosomal loci are often involved in familial searching casework since many databases, including the Dutch one, in part consist of such profiles. Therefore, from this point of view, there seems to be no objection to extend familial searching to mixed or degraded profiles.

Asunto(s)

RESUMEN

While likelihood ratio calculations were until the recent past limited to the evaluation of mixtures in which all alleles of all donors are present in the DNA mixture profile, more recent methods are able to deal with allelic dropout and drop-in. This opens up the possibility to obtain likelihood ratios for mixtures where this was not previously possible, but it also means that a full match between the alleged contributor and the crime stain is no longer necessary. We investigate in this article what the consequences are for relatives of the actual donors, because they typically share more alleles with the true donor than an unrelated individual. We do this with a semi-continuous binary approach, where the likelihood ratios are based on the observed alleles and the dropout probabilities for each donor, but not on the peak heights themselves. These models are widespread in the forensic community. Since in many cases a simple model is used where a uniform dropout probability is assumed for all (or for all unknown) contributors, we explore the extent to which this alters the false positive probabilities for relatives of donors, compared to what would have been obtained with the correct probabilities of dropout for each donor.

Asunto(s)

RESUMEN

In recent years, the use of DNA data for personal identification has become a crucial feature for forensic applications such as disaster victim identification (DVI). Computational methods to cope with these kinds of problems must be designed to handle large scale events with a high number of victims, obtaining likelihood ratios and posterior odds with respect to different identification hypotheses. Trying to minimize identification error rates (i.e., false negatives and false positives), a number of computational methods, based either on the choice between alternative mutation models or on the adoption of a different strategy, are proposed and evaluated. Using simulation of DNA profiles, our goal is to suggest which is the most appropriate way to address likelihood ratio computation in DVI cases, especially to be able to efficiently deal with complicating issues such as mutations or null alleles, considering that data about these latter are limited and fragmentary.

Asunto(s)

RESUMEN

It is well known in forensic genetics that mutations on STR loci need not lead to genetic inconsistencies between the genotypes of parents and children (cf. [3,6,1]). In this article we look at a generalization of this phenomenon of "hidden" mutations: we investigate the probability with which a mutation of d repeat units appears to be one of k

Asunto(s)

RESUMEN

The interpretation of DNA mixtures has proven to be a complex problem in forensic genetics. In particular, low template DNA samples, where alleles can be missing (allele drop-out), or where alleles unrelated to the crime-sample are amplified (allele drop-in), cannot be analysed with classical approaches such as random man not excluded or random match probability. Drop-out, drop-in, stutters and other PCR-related stochastic effects, create uncertainty about the composition of the crime-sample, making it difficult to attach a weight of evidence when (a) reference sample(s) is (are) compared to the crime-sample. In this paper, we use a probabilistic model to calculate likelihood ratios when there is uncertainty about the composition of the crime-sample. This model is essentially exploratory in the sense that it allows the exploration of LRs when two key-parameters, drop-out and drop-in are varied within their plausible ranges of variation. We build on the work of Curran et al., and improve their probabilistic model to allow more flexibility in the way the model parameters are applied. Two new main modifications are brought to their model: (i) different drop-out probabilities can be applied to different contributors, and (ii) different parameters can be used under the prosecution and the defence hypotheses. We illustrate how the LRs can be explored when the drop-out and drop-in parameters are varied, and suggest the use of Monte Carlo simulations to derive plausible ranges for the probability of drop-out. Although the model is suited for both high and low template samples, we illustrate the advantages of the exploratory approach through two DNA mixtures (involving two and at least three individuals) with low template components.

Asunto(s)

RESUMEN

A defendant whose DNA profile matches that of a crime stain may argue that he has several, say n, brothers and that one of them may have been the origin of the crime stain. If the probability for any of the brothers considered separately to match the crime stain profile is p, we show that the probability that at least one of the n brothers match is strictly smaller than np. This latter quantity therefore is an easy to compute and conservative value to report.

Asunto(s)

RESUMEN

Disaster victim identification (DVI) can be aided by DNA-evidence, by comparing the DNA-profiles of unidentified individuals with those of surviving relatives. The DNA-evidence is used optimally when such a comparison is done by calculating the appropriate likelihood ratios. Though conceptually simple, the calculations can be quite involved, especially with large pedigrees, precise mutation models etc. In this article we describe a series of test cases designed to check if software designed to calculate such likelihood ratios computes them correctly. The cases include both simple and more complicated pedigrees, among which inbred ones. We show how to calculate the likelihood ratio numerically and algebraically, including a general mutation model and possibility of allelic dropout. In Appendix A we show how to derive such algebraic expressions mathematically. We have set up these cases to validate new software, called Bonaparte, which performs pedigree likelihood ratio calculations in a DVI context. Bonaparte has been developed by SNN Nijmegen (The Netherlands) for the Netherlands Forensic Institute (NFI). It is available free of charge for non-commercial purposes (see www.dnadvi.nl for details). Commercial licenses can also be obtained. The software uses Bayesian networks and the junction tree algorithm to perform its calculations.

Asunto(s)

RESUMEN

The aim of this study was to assess the effect of several insect growth regulators (IGRs) on the nontarget soil arthropod Folsomia candida (Collembola). The survival and reproduction rates of F. candida were evaluated after 28 days of exposure to six IGRs (methoprene, fenoxycarb, precocene II, tebufenozide, hexaflumuron and teflubenzuron) and to one herbicide (diuron) in artificial soil. The differences in the sensitivity of F. candida to these different substances are high. The chitin synthesis inhibitors teflubenzuron and hexaflumuron were the most toxic compounds with an EC50 of 0.05 mg/kg (dry weight) for teflubenzuron and an EC50 of 0.6mg/kg for hexaflumuron. Teflubenzuron is toxic for F. candida at concentrations that are probably close to environmental levels of this insecticide. Inhibition of reproduction is strongly related to adult survival for the juvenile hormone agonist methoprene and for the antijuvenile hormone precocene II, with an EC50 of 173 mg/kg and a LC50 of 178mg/kg for methoprene and an EC50 of 15 mg/kg and a LC50 of 26 mg/kg for precocene II. Fenoxycarb, another juvenile hormone analog, showed a dose-response curve for mortality different from that of methoprene; at concentrations such as 3052 mg/kg no effect on adult survival was observed. However, the EC50 value of 113mg/kg is of the same order of magnitude as that obtained for methoprene. A test with compressed soil contaminated with fenoxycarb was conducted to observe parameters such as numbers of eggs laid and juveniles hatched. No differences were observed between these two endpoints for fenoxycarb. An EC50 of 109 mg/kg was obtained for the ecdysone agonist tebufenozide. The herbicide diuron showed a relatively high toxicity for F. candida with an EC50 of 20 mg/kg. Our results show that some of the tested IGRs can have effects on Collembola at environmentally relevant concentrations (toxicity/exposure ratios < 5 for teflubenzuron, hexaflumuron, and diuron).

Asunto(s)

RESUMEN

The antifouling herbicide Irgarol 1051 has been detected in recent years in numerous estuaries, marinas, harbors and coastal areas, and in some harbors on Lake Geneva, but so far only a few studies have investigated the ecotoxicological effects of this compound on microalgae. The purpose of this study was to assess the ecotoxicological impact of Irgarol 1051 on the algal communities of Lake Geneva, and to compare its phytotoxicity to that of the common triazine herbicide, atrazine. We investigated the response of phytoplanktonic and periphytonic algal communities and single-species isolates collected from the lake, to the PS II inhibitor Irgarol 1051 (growth, proxy of photosynthetic activity and community structure). A short-term bioassay was developed based on in vivo fluorescence, together with nanocosm experiments with natural algal communities, and single-species tests on algal strains isolated from the lake. The toxicity of Irgarol 1051 towards periphyton and phytoplankton was shown to be higher than that of atrazine. Indications of the tolerance induced by this triazine in the algal communities of Lake Geneva, suggests that even at the levels of contamination reported in some parts of the lake, Irgarol 1051 is already exerting selection pressure. Information about sensitivities, selection and tolerance from laboratory experiments are used to explain the observations in natural microalgal communities from the lake.

Asunto(s)

RESUMEN

In industrial effluents, the presence of an infinite number of possible mixtures of substances and the high variability of chemical conditions ask for an evaluation of biodegradability by a global and simple method. Biological oxygen demand after five days (BOD5) using synthetic wastewater was studied by two different ways: dilution and manometric methods. It can therefore be established that BOD5 obtained by adding manufactured inocula to the synthetic medium (effluent containing known and easily biodegradable substances) is close to the values obtained with inocula taken from the treated effluent of an urban and a rural purification plant. It was found that BOD5 measurement of effluents presenting factors affecting biodegradation, similar of those found in industrial effluents, is very questionable. The BOD is in this case influenced by the synergic and antagonist interactions between numerous and variable parameters like as pH, nature and concentration of inoculum, concentration of nutriments, amount and nature of assimilable substances, presence of toxicants, and presence of nitrification inhibitors, which are typical of real industrial wastewaters.

Asunto(s)

Asunto(s)