A highly polymorphic panel of 40-plex microhaplotypes for the Chinese Han population and its application in estimating the number of contributors in DNA mixtures

Microhaplotypes (MHs) have great potential in multiple forensic applications and have proven to be promising markers in complex DNA mixture analysis. In this study, we developed a multiplex panel of 40 highly polymorphic MHs for the Chinese Han population, evaluated its forensic values, and explored its application in predicting the number of contributors (NOCs) in DNA mixtures. The panel consisted of 20 newly proposed loci and 20 previously reported loci with lengths spanning less than 120 bp. The average effective number of alleles (A_e) was 3.77, and the cumulative matching probability (CMP) and the cumulative power of exclusion (CPE) reached 1.2E-37 and 1–2.1E-12, respectively, in the Chinese Han population from the 1000 Genomes Project. Further validation on 150 Chinese Han individuals showed that A_e ranged from 2.62 to 4.41 with a mean value of 3.61, and CMP and CPE were 3.61E-36 and 1–1.84E-12, respectively, indicating that this panel was informative for personal identification and paternity testing in the studied population. To estimate NOC in DNA mixtures, we developed a machine learning model based on this panel. As a result, the accuracies in artificial DNA mixtures reached 95.24% for 2- to 4-person mixtures and 83.33% for 2- to 6-person mixtures. Furthermore, the NOC estimation on simulated profiles with allele dropout showed that this panel was still robust under slight dropout. In conclusion, this panel has value for forensic identification and NOC estimation of DNA mixtures.     

Forensic identification of an individual in complex DNA mixtures

The identification of a suspect in a complex DNA mixture typed with standard short tandem repeat (STR) kits has proved difficult. In the current study we present the theoretical framework of a method aimed to resolve this problem in forensic cases. The method suggests genotyping a specially designed panel of 1000–3000 single nucleotide polymorphisms (SNPs), each with a relatively low (<0.1) minor allele frequency (MAF). The rationale of this method is that any individual will carry a specific set of dozens of rare alleles and the complex DNA mixture will carry this particular set only if the one individual is represented in the DNA mixture. The efficiency of the method is evaluated by estimating the probability that a random man will not be excluded (RMNE) from the mixture. When this probability, P(RMNE), is low, one can conclude that the suspect's DNA is present in the DNA mixture. Essentially, a P(RMNE) < 10⁻⁹ is considered as proof, whereas a P(RMNE) < 10⁻⁶ is considered strong evidence. For completeness, we also analyzed the method using the likelihood ratio (LR) approach. We have analyzed the method for a variety of conditions and found that generally the method will provide highly significant results even for complex mixtures combining up to 10 individuals. The method performs well even when close relatives (one or two brothers) are present in the complex DNA mixture and when contributors or suspects come from different populations. We have also found that the method can accurately identify the number of contributors to the mixture, something that in some instances has significant forensic value on its own.     

Identification and sequencing of 59 highly polymorphic microhaplotypes for analysis of DNA mixtures

International Journal of Legal Medicine - Mixture detection remains one of the major challenges within a forensic science context. In recent years, microhaplotypes were proposed to have great...     

Evaluation of microhaplotype panels for complex kinship analysis using massively parallel sequencing

In recent years, microhaplotypes (MHs) have become a research hotspot within the field of forensic genetics. Traditional MHs contain only SNPs that are closely linked within short fragments. Herein, we broaden the concept of general MHs to include short InDels. Complex kinship identification plays an important role in disaster victim identification and criminal investigations. For distant relatives (e.g., 3rd-degree), many genetic markers are required to enhance power of kinship testing. We performed genome-wide screening for new MH markers composed of two or more variants (InDel or SNP) within 220 bp based on the Chinese Southern Han from the 1000 Genomes Project. An NGS-based 67plex MH panel (Panel B) was successfully developed, and 124 unrelated individual samples were sequenced to obtain population genetic data, including alleles and allele frequencies. Of the 67 genetic markers, 65 MHs were, as far as we know, newly discovered, and 32 MHs had effective number of allele (A_e) values greater than 5.0. The average A_e and heterozygosity of the panel were 5.34 and 0.7352, respectively. Next, 53 MHs from a previous study were collected as Panel A (average A_e of 7.43), and Panel C with 87 MHs (average A_e of 7.02) was formed by combining Panels A and B. We investigated the utility of these three panels in kinship analysis (parent-child, full siblings, 2nd-degree, 3rd-degree, 4th-degree, and 5th-degree relatives), with Panel C exhibiting better performance than the two other panels. Panel C was able to separate parent-child, full-sibling, and 2nd-degree relative duos from unrelated controls in real pedigree data, with a small false testing level (FTL) of 0.11% in simulated 2nd-degree duos. For more distant relationships, the FTL was much higher: 8.99% for 3rd-degree, 35.46% for 4th-degree, and 61.55% for 5th-degree. When a carefully chosen extra relative was known, this may enhance the testing power for distant kinship analysis. Two twins from the Q family (2–5 and 2–7) and W family (3–18 and 3–19) shared the same genotypes in all tested MHs, which led to the incorrect conclusion that an uncle-nephew duo was classified as a parent-child duo. In addition, Panel C showed great capacity for excluding close relatives (2nd-degree and 3rd-degree relatives) during paternity tests. Among 18,246 real and 10,000 simulated unrelated pairs, none were misinterpreted as a relative within 2nd-degree at a log₁₀(LR) cutoff of 4. The panels presented herein could provide supplementary power for the analysis of complex kinship.     

Identifying common donors in DNA mixtures,with applications to database searches

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.     

Distinguishing between donors and their relatives in complex DNA mixtures with binary models

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.     

SNP-microarrays can accurately identify the presence of an individual in complex forensic DNA mixtures

Common forensic and mass disaster scenarios present DNA evidence that comprises a mixture of several contributors. Identifying the presence of an individual in such mixtures has proven difficult. In the current study, we evaluate the practical usefulness of currently available “off-the-shelf” SNP microarrays for such purposes. We found that a set of 3000 SNPs specifically selected for this purpose can accurately identify the presence of an individual in complex DNA mixtures of various compositions. For example, individuals contributing as little as 5% to a complex DNA mixture can be robustly identified even if the starting DNA amount was as little as 5.0 ng and had undergone whole-genome amplification (WGA) prior to SNP analysis. The work presented in this study represents proof-of-principle that our previously proposed approach, can work with real “forensic-type” samples. Furthermore, in the absence of a low-density focused forensic SNP microarray, the use of standard, currently available high-density SNP microarrays can be similarly used and even increase statistical power due to the larger amount of available information.     

Robust detection of individual forensic profiles in DNA mixtures

For a forensic identification method to be admissible in international courts, the probability of false match must be quantified. For comparison of individuals against complex mixtures using a panel of single nucleotide polymorphisms (SNPs), the probability of a random man not excluded, P(RMNE) is one admissible standard. While the P(RMNE) of SNP alleles has been previously studied, it remains to be rigorously defined and calculated for experimentally genotyped mixtures. In this report, exact P(RMNE) values were calculated for a range of complex mixtures, verified with Monte Carlo simulations, and compared alongside experimentally determined detection probabilities.     

The number of alleles in DNA mixtures with related contributors

The maximum allele count (MAC) across loci and the total allele count (TAC) are often used to gauge the number of contributors to a DNA mixture. Computational strategies that predict the total number of alleles in a mixture arising from a certain number of contributors of a given population have been developed. Previous work considered the restricted case where all of the contributors to a mixture are unrelated. We relax this assumption and allow mixture contributors to be related according to a pedigree. We introduce an efficient computational strategy. This strategy based on first determining a probability distribution on the number of independent alleles per locus, and then conditioning on this distribution to compute a distribution of the number of distinct alleles per locus. The distribution of the number of independent alleles per locus is obtained by leveraging the Identical by Descent (IBD) pattern distribution which can be computed from the pedigree. We explain how allelic dropout and a subpopulation correction can be accounted for in the calculations.     

Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach

The number of contributors (NOC) to (complex) autosomal STR profiles cannot be determined with absolute certainty due to complicating factors such as allele sharing and allelic drop-out. The precision of NOC estimations can be improved by increasing the number of (highly polymorphic) markers, the use of massively parallel sequencing instead of capillary electrophoresis, and/or using more profile information than only the allele counts.In this study, we focussed on machine learning approaches in order to make maximum use of the profile information. To this end, a set of 590 PowerPlex® Fusion 6C profiles with one up to five contributors were generated from a total of 1174 different donors. This set varied for the template amount of DNA, mixture proportion, levels of allele sharing, allelic drop-out and degradation. The dataset contained labels with known NOC and was split into a training, test and hold-out set. The training set was used to optimize ten different algorithms with selection of profile characteristics. Per profile, over 250 characteristics, denoted ‘features’, were calculated. These features were based on allele counts, peak heights and allele frequencies. The features that were most related to the NOC were selected based on partial correlation using the training set. Next, the performance of each model (=combination of features plus algorithm) was examined using the test set. A random forest classifier with 19 features, denoted the ‘RFC19-model’ showed best performance and was selected for further validation. Results showed improved accuracy compared to the conventional maximum allele count approach and an in-house nC-tool based on the total allele count. The method is extremely fast and regarded useful for application in forensic casework.     

A comparison of likelihood ratios with and without assuming relatedness for DNA mixtures interpreted using a continuous model

When evaluating support for the contribution of a person of interest (POI) to a mixed DNA sample, it is generally assumed that the mixture contributors are unrelated to the POI and to each other. In practice, there may be situations where this assumption is violated, for instance if two mixture contributors are siblings. The effect on the likelihood ratio of (in)correctly assuming relatedness between mixture contributors has previously been investigated using simulation studies based on simplified models ignoring peak heights. We revisit this problem using a simulation study that applies peak height models both in the simulation and mixture interpretation part of the study. Specifically, we sample sets of mixtures comprising both related and unrelated contributors and evaluate support for the contribution of the mixture donors as well as unrelated persons with and without incorporating an assumption of relatedness. The results show, consistent with earlier studies, that including a correct assumption of relatedness increases the capacity of the probabilistic genotyping system to distinguish between mixture donors and unrelated persons. Any effect of the relatedness is found to depend strongly on the mixture ratio. We further show that the results do not change materially when a sub-population correction is applied. Finally, we suggest and discuss a likelihood ratio approach that considers relatedness between mixture contributors using a prior probability.     

A novel set of DIP-STR markers for improved analysis of challenging DNA mixtures

Currently available molecular biology tools allow forensic scientists to characterize DNA evidence found at crime scenes for a large variety of samples, including those of limited quantity and quality, and achieve high levels of individualization. Yet, standard forensic markers provide limited or no results when applied to mixed DNA samples where the contributors are present in very different proportions (unbalanced DNA mixtures). This becomes an issue mostly for the analysis of trace samples collected on the victim or from touched objects.To this end, we recently proposed an innovative type of genetic marker, named DIP-STR that relies on pairing deletion/insertion polymorphisms (DIP) with standard short tandem repeats (STR). This novel compound marker allows detection of the minor DNA contributor in a DNA mixture of any gender and cellular origin with unprecedented resolution (beyond a DNA ratio of 1:1000).To provide a novel analytical tool useful in practice to common forensic laboratories, this article describes the first set of 10 DIP-STR markers selected according to forensic technical standards. The novel DIP-STR regions are short (between 146 and 271 bp), include only highly polymorphic tri-, tetra- and pentanucleotide tandem repeats and are located on different chromosomes or chromosomal arms to provide statistically independent results. This novel set of DIP-STR can target the amplification of 0.03–0.1 ng of DNA when mixed with a 1000-fold excess of major DNA. DIP-STR relative allele frequencies are estimated based on a survey of 103 Swiss individuals. Finally, this study provides an estimate of the occurrence of informative alleles and a calculation of the corresponding random match probability of the detected minor DIP-STR genotype assessed across 10,506 pairwise conceptual mixtures.     

ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model

Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.     

Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable artificial intelligence (XAI) to help users understand why specific predictions are made. Where previous attempts at explainability for NOC estimation have relied upon using simpler, more understandable models that achieve lower accuracy, we use techniques that can be applied to any machine learning model. Our explanations incorporate SHAP values and counterfactual examples for each prediction into a single visualization. Existing methods for generating counterfactuals focus on uncorrelated features. This makes them inappropriate for the highly correlated features derived from STR data for NOC estimation, as these techniques simulate combinations of features that could not have resulted from an STR profile. For this reason, we have constructed a new counterfactual method, Realistic Counterfactuals (ReCo), which generates realistic counterfactual explanations for correlated data. We show that ReCo outperforms state-of-the-art methods on traditional metrics, as well as on a novel realism score. A user evaluation of the visualization shows positive opinions of end-users, which is ultimately the most appropriate metric in assessing explanations for real-world settings.     

A mixture detection method based on separate amplification using primer specific alleles of INDELs-a study based on two person's DNA mixture

Samples containing unbalanced DNA mixtures from individuals often occur in forensic DNA examination and clinical detection. Because of the PCR amplification bias, the minor contributor DNA is often masked by the major contributor DNA when using traditional STR or SNP typing techniques. Here we propose a method based in allele-specific Insertion/Deletion (INDEL) genotyping to detect DNA mixtures in forensic samples. Fourteen INDELs were surveyed in the Chinese Han population of Shanxi Province. The INDELs were amplified using two separate primer-specific reactions by real-time PCR. The difference Ct value of the 2 reactions (D-value) were used for determination of the single source DNA. INDELs types and further confirmed by electrophoresis separation. The minor allele frequency (MAF) was above 0.2 in 10 INDELs. The detection limit was 0.3125 ng–1.25 ng template DNA for real-time PCR in all 14 INDEL markers. For single source 10 ng DNA, the average D-value was 0.31 ± 0.14 for LS type, 6.96 ± 1.05 for LL type and 7.20 ± 1.09 for SS type. For the series of simulated DNA mixture, the Ct value varied between the ranges of single source DNA, depending on their INDEL typing and mixture ratios. This method can detect the specific allele of the minor DNA contributor as little as 1:50 in rs397782455 and rs397696936; 1:100 in rs397832665, rs397822382 and rs397897230; the detection limit of the minor DNA contributor was as little as 1:500–1:1000 in the rest INDEL markers, a much higher sensitivity compared with traditional STR typing. The D-value variation depended on the alternation of dilution ratio and INDEL types. When the dilution was 1:1000, the maximum and minimum D-values were 8.84 ± 0.11 in rs397897230 and 4.27 ± 0.19 in rs397897239 for LL and SS type mixture, the maximum and minimum D-values were 9.32 ± 0.54 in rs397897230 and 4.38 ± 0.26 in rs 397897239 for LL(SS) and LS type mixture, separately. Any D-value between 0.86 and 5.11 in the 14 INDELs indicated the presence of mixture. The separate amplification strategy based on real-time PCR provides a promising and convenient method for detection of unbalanced DNA mixture for Chinese Han population.     

Discrimination of haplotype in mitochondrial DNA mixtures using LNA-mediated PCR clamping

Locked nucleic acid (LNA) has been widely used for various genetic analyses, and has many benefits, in terms of the specificity or sensitivity of amplification, because LNA-containing primers/probes form more stable duplexes with template DNA than probes lacking LNA. Here, we developed a new method for discriminating HV1 haplotypes from mitochondrial DNA (mtDNA) mixtures by applying PCR clamping using LNA. PCR clamping is based on the selective inhibition of amplification using LNA-containing probes, which can discriminate single-nucleotide differences. Before designing probes, we selected 171 sequences with single-nucleotide variations from the HV1 region, and evaluated the specificity of LNA-containing probes for them by predicting Tm values. The differences of Tm between mismatched and exactly matched probe–template duplexes depended markedly on the type of LNA nucleotides for discriminating single-nucleotide differences, and the cytosine LNA nucleotide at the site of variations in the probes was most effective to discriminate these differences. For mixture analysis, each probe targeted one or two variations (16209C, 16217C, 16257A/16261T, 16297C/16298C, 16304C, 16362C, or 16362T) that are particularly common in the Japanese population, and seven designed probes completely inhibited the amplification of exactly matched templates. We prepared mixed samples by mixing DNA from two individuals at a ratio of 1:9, 1:4, 1:1, 4:1, or 9:1, and then performed Sanger sequencing analysis after PCR clamping with each probe. Our method distinguished each haplotype at lower ratios from two-person mixtures, and enabled sensitive detection at 12 pg of total DNA including 600 copies of mtDNA. Moreover, we analyzed three-person mixtures with representative sequences, and detected the minor haplotype of one individual present at a rate of 10% by adding two selected probes. The ability to discriminate haplotypes in mixed samples by using LNA-mediated PCR clamping indicates the potential value of mtDNA analysis in criminal investigations.     

A simple,rapid and simultaneous analysis of complex volatile hydrocarbon mixtures in blood using gas chromatography/mass spectrometry with a wide-bore capillary column

Summary A screening method for detecting volatile hydrocarbons in blood has been developed using gas chromatography/mass spectrometry with a wide-bore capillary column and a headspace method. Toluene-d₈ and indan were used as the internal standards for quantitative analysis. Hydrocarbons with retention indices from 600 to 1200 were simultaneously and quantitatively detected in relatively low concentrations (0.01 g/m1) in reconstructed ion chromatography. This method could prove useful in forensic cases in which urgent examination of complex hydrocarbon mixtures, e.g. petroleum components, is required. Offprint requests to: K. Hara