Estimating the number of contributors to a DNA profile

Improving DNA mixtures analysis using compound markers composed of InDels and SNPs screened from the whole genome with next-generation sequencing

Next-generation sequencing (NGS) allows for better identification of insertion and deletion polymorphisms (InDels) and their combination with adjacent single nucleotide polymorphisms (SNPs) to form compound markers. These markers can improve the polymorphism of microhaplotypes (MHs) within the same length range, and thus, boost the efficiency of DNA mixture analysis. In this study, we screened InDels and SNPs across the whole genome and selected highly polymorphic markers composed of InDels and/or SNPs within 300 bp. Further, we successfully developed and evaluated an NGS-based panel comprising 55 loci, of which 24 were composed of both SNPs and InDels. Analysis of 124 unrelated Southern Han Chinese revealed an average effective number of alleles (Ae ) of 7.52 for this panel. The cumulative power of discrimination and cumulative probability of exclusion values of the 55 loci were 1-2.37 × 10-73 and 1-1.19 × 10-28 , respectively. Additionally, this panel exhibited high allele detection rates of over 97% in each of the 21 artificial mixtures involving from two to six contributors at different mixing ratios. We used EuroForMix to calculate the likelihood ratio (LR) and evaluate the evidence strength provided by this panel, and it could assess evidence strength with LR, distinguishing real and noncontributors. In conclusion, our panel holds great potential for detecting and analyzing DNA mixtures in forensic applications, with the capability to enhance routine mixture analysis.

Using simulated microhaplotype genotyping data to evaluate the value of machine learning algorithms for inferring DNA mixture contributor numbers

Inferring the number of contributors (NoC) is a crucial step in interpreting DNA mixtures, as it directly affects the accuracy of the likelihood ratio calculation and the assessment of evidence strength. However, obtaining the correct NoC in complex DNA mixtures remains challenging due to the high degree of allele sharing and dropout. This study aimed to analyze the impact of allele sharing and dropout on NoC inference in complex DNA mixtures when using microhaplotypes (MH). The effectiveness and value of highly polymorphic MH for NoC inference in complex DNA mixtures were evaluated through comparing the performance of three NoC inference methods, including maximum allele count (MAC) method, maximum likelihood estimation (MLE) method, and random forest classification (RFC) algorithm. In this study, we selected the top 100 most polymorphic MH from the Southern Han Chinese (CHS) population, and simulated over 40 million complex DNA mixture profiles with the NoC ranging from 2 to 8. These profiles involve unrelated individuals (RM type) and related pairs of individuals, including parent-offspring pairs (PO type), full-sibling pairs (FS type), and second-degree kinship pairs (SE type). Our results indicated that how the number of detected alleles in DNA mixture profiles varied with the markers' polymorphism, kinship's involvement, NoC, and dropout settings. Across different types of DNA mixtures, the MAC and MLE methods performed best in the RM type, followed by SE, FS, and PO types, while RFC models showed the best performance in the PO type, followed by RM, SE, and FS types. The recall of all three methods for NoC inference were decreased as the NoC and dropout levels increased. Furthermore, the MLE method performed better at low NoC, whereas RFC models excelled at high NoC and/or high dropout levels, regardless of the availability of a priori information about related pairs of individuals in DNA mixtures. However, the RFC models which considered the aforementioned priori information and were trained specifically on each type of DNA mixture profiles, outperformed RFC_ALL model that did not consider such information. Finally, we provided recommendations for model building when applying machine learning algorithms to NoC inference.

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

DNA profiling involves the analysis of sequences of an individual or mixed DNA profiles to identify the persons that these profiles belong to. A critically important application of DNA profiling is in forensic science to identify criminals by finding a match between their blood samples and the DNA profile found on the crime scene. Other applications include paternity tests, disaster victim identification, missing person investigations, and mapping genetic diseases. A crucial task in DNA profiling is the determination of the number of contributors in a DNA mixture profile, which is challenging due to issues that include allele dropout, stutter, blobs, and noise in DNA profiles; these issues negatively affect the estimation accuracy and the computational complexity. Machine-learning-based methods have been applied for estimating the number of unknowns; however, there is limited work in this area and many more efforts are required to develop robust models and their training on large and diverse datasets. In this paper, we propose and develop a software tool called TAWSEEM that employs a multilayer perceptron (MLP) neural network deep learning model for estimating the number of unknown contributors in DNA mixture profiles using PROVEDIt, the largest publicly available dataset. We investigate the performance of our developed deep learning model using four performance metrics, namely accuracy, F1-score, recall, and precision. The novelty of our tool is evident in the fact that it provides the highest accuracy (97%) compared to any existing work on the most diverse dataset (in terms of the profiles, loci, multiplexes, etc.). We also provide a detailed background on the DNA profiling and literature review, and a detailed account of the deep learning tool development and the performance investigation of the deep learning method.

The a posteriori probability of the number of contributors when conditioned on an assumed contributor

Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpretation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational tools that make point estimates on the Number of Contributors (NOC) to a sample have been developed, as have Bayesian methods that evaluate an A Posteriori Probability (APP) distribution on the NOC. In cases where an overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the evidence is evaluated with an inadequate set of propositions. In the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the NOC given an electropherogram, by reporting on an implementation where the user can add assumed contributors. NOCIt is a continuous system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele frequencies in a reference population. When conditioned on a known contributor, we found that the mode of the APP distribution can shift to one greater when compared with the circumstance where no known contributor is assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture. In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution and defense hypotheses, we show that this computation must not only use coincident models, but also coincident contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts.

Estimating the number of contributors to a DNA profile using decision trees

The interpretation of DNA profiles typically starts with an assessment of the number of contributors. In the last two decades, several methods have been proposed to assist with this assessment. We describe a relatively simple method using decision trees, that is fast to run and fully transparent to a forensic analyst. We use mixtures from the publicly available PROVEDIt dataset to demonstrate the performance of the method. We show that the performance of the method crucially depends on the performance of filters for stutter and other artefacts. We compare the performance of the decision tree method with other published methods for the same dataset.

An assessment of the information content of likelihood ratios derived from complex mixtures

With the increasing sensitivity of DNA typing methodologies, as well as increasing awareness by law enforcement of the perceived capabilities of DNA typing, complex mixtures consisting of DNA from two or more contributors are increasingly being encountered. However, insufficient research has been conducted to characterize the ability to distinguish a true contributor (TC) from a known non-contributor (KNC) in these complex samples, and under what specific conditions. In order to investigate this question, sets of six 15-locus Caucasian genotype profiles were simulated and used to create mixtures containing 2-5 contributors. Likelihood ratios were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile, as well as comparisons of the evidence profile to TCs and KNCs. This work was intended to illustrate the best-case scenario, in which all alleles from the TC were detected in the simulated evidence samples. Therefore the possibility of drop-out was not modeled in this study. The computer program DNAMIX was then used to compute LRs comparing the evidence profile to TCs and KNCs. This resulted in 140,000 LRs for each of the two scenarios. These complex mixture simulations show that, even when all alleles are detected (i.e. no drop-out), TCs can generate LRs less than 1 across a 15-locus profile. However, this outcome was rare, 7 of 140,000 replicates (0.005%), and associated only with mixtures comprising 5 contributors in which the numerator hypothesis includes one or more unknown contributors. For KNCs, LRs were found to be greater than 1 in a small number of replicates (75 of 140,000 replicates, or 0.05%). These replicates were limited to 4 and 5 person mixtures with 1 or more unknowns in the numerator. Only 5 of these 75 replicates (0.004%) yielded an LR greater than 1,000. Thus, overall, these results imply that the weight of evidence that can be derived from complex mixtures containing up to 5 contributors, under a scenario in which no drop-out is required to explain any of the contributors, is remarkably high. This is a useful benchmark result on top of which to layer the effects of additional factors, such as drop-out, peak height, and other variables.

Exploring the Impacts of Ordinary Laboratory Alterations During Forensic DNA Processing on Peak Height Variation, Thresholds, and Probability of Dropout

Impacts of validation design on DNA signal were explored, and the level of variation introduced by injection, capillary changes, amplification, and kit lot was surveyed by examining a set of replicate samples ranging in mass from 0.25 to 0.008 ng. The variations in peak height, heterozygous balance, dropout probabilities, and baseline noise were compared using common statistical techniques. Data indicate that amplification is the source of the majority of the variation observed in the peak heights, followed by capillary lots. The use of different amplification kit lots did not introduce variability into the peak heights, heterozygous balance, dropout, or baseline. Thus, if data from case samples run over a significant time period are not available during validation, the validation must be designed to, at a minimum, include the amplification of multiple samples of varying quantity, with known genotype, amplified and run over an extended period of time using multiple pipettes and capillaries.

Uncertainty in the number of contributors for the European Standard Set of loci

The effect of masking on the assignment of the number of contributors is assessed for the European Standard Set of loci by simulation. The risk that a two person mixture presents as single source is assessed as 2.6×10(-13), a three person mixture presents as a two person 6.7×10(-4) and a four person mixture presents as a three person 0.165.

Resolving Sample Traces in Complex Mixtures with Microarray Analyses

This chapter reviews the microarray technology and deal with the majority of aspects regarding microarrays. It focuses on today’s knowledge of separation techniques and methodologies of complex signal, i.e. samples. Overall, the chapter reviews the current knowledge on the topic of microarrays and presents the analyses and techniques used, which facilitate such approaches. It starts with the theoretical framework on microarray technology; second, the chapter gives a brief review on statistical methods used for microarray analyses, and finally, it contains a detailed review of the methods used for discriminating traces of nucleic acids within a complex mixture of samples.

Estimating the number of contributors to two-, three-, and four-person mixtures containing DNA in high template and low template amounts

AIM

To develop guidelines to estimate the number of contributors to two-, three-, and four-person mixtures containing either high template DNA (HT-DNA) or low template DNA (LT-DNA) amounts.

METHODS

Seven hundred and twenty-eight purposeful two-, three-, and four-person mixtures composed of 85 individuals of various ethnicities with template amounts ranging from 10 to 500 pg were examined. The number of alleles labeled at each locus and the number of labeled different and repeating alleles at each locus as well over all loci for 2 HT-DNA or 3 LT-DNA replicates were determined. Guidelines based on these data were then evaluated with 117 mixtures generated from items handled by known individuals.

RESULTS

The number of different alleles over all loci and replicates was used to initially categorize mixtures. Ranges were established based on the averages plus and minus 2 standard deviations, and to encompass all observations, the maximum and the minimum values. To differentiate samples that could be classified in more than one grouping, the number of loci with 4 or more repeating or different alleles, which were specific to three- and four-person mixtures, were verified. Misclassified samples showed an extraordinary amount of allele sharing or stutter.

CONCLUSIONS

These guidelines proved to be useful tools to distinguish low template and high template two-, three-, and four-person mixtures. Due to the inherent higher probability of allele sharing, four-person mixtures were more challenging. Because of allelic drop-out, this was also the case for samples with very low amounts of template DNA or extreme mixture ratios.

Inferring the number of contributors to mixed DNA profiles

Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.

Consensus and pool profiles to assist in the analysis and interpretation of complex low template DNA mixtures

Forensic analysis of low template (LT) DNA mixtures is particularly complicated when (1) LT components concur with high template components, (2) more than three contributors are present, or (3) contributors are related. In this study, we generated a set of such complex LT mixtures and examined two methods to assist in DNA profile analysis and interpretation: the “n/2” consensus method (Benschop et al. 2011) and the pool profile approach. N/2 consensus profiles include alleles that are reproducibly amplified in at least half of the replications. Pool profiles are generated by injecting a blend of independently amplified PCR products on a capillary electrophoresis instrument. Both approaches resulted in a similar increase in the percentage of detected alleles compared to individual profiles, and both rarely included drop-in alleles in case mixtures of pristine DNAs were used. Interestingly, the consensus and the pool profiles often showed differences for the actual alleles detected for the LT component(s). We estimated the number of contributors using different methods. Better approximations were obtained with data in the consensus and pool profiles compared to the data of the individual profiles. Consensus profiles contain allele calls only, while pool profiles consist of both allele calls and peak height information, which can be of use in (statistical) profile analysis. All advantages and limitations of the various types of profiles were assessed, and based on the results we infer that both consensus and pool profiles (or a combination thereof) are helpful in the interpretation of complex LT DNA mixtures.

Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays

We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.

Sex-specific fluorescent labelling of cells for laser microdissection and DNA profiling

Sex-specific isolation of cells from mixtures would greatly facilitate forensic casework. Thus, male and female cell mixtures were marked with a fluorescent X/Y-probe CEP X SpectrumOrange/Y SpectrumGreen DNA probe kit for fluorescence in situ hybridization, and single cells were isolated via laser microdissection (LMD). DNA profiling of LMD isolated, hybridized cells showed usable short tandem repeat profiles for at least 20 cells, which are comparable with results from other studies. To simulate casework samples, the method was also optimized for air-dried samples.

Digoxigenin labelling and laser capture microdissection of male cells

Laser capture microdissection (LMD) is a relatively new technique for the isolation of single cells. The application in forensic investigations has become more and more widespread, especially to select spermatozoa out of mixtures with vaginal cells. In particular in cases with low numbers of sperm it could be profitable to isolate all male cells (e.g. sperm and male epithelial cells) instead of focussing on the sperm only. Therefore, the specific labelling and detection of the male cells in a male/female cell mixture is necessary. In order to label all cells carrying a Y-chromosome we used a digoxigenin labelled chromosome Y hybridisation probe (Q Biogen). The stained cells were isolated with the SL microCut LMD system from Molecular Machines & Industries AG (MMI). At least ten diploid male cells were required to obtain a partial STR profile, with 20 cells, a full profile could be obtained.