The efficacy of DNA mixture to mixture matching

Standard practice in forensic science is to compare a person of interest’s (POI) reference DNA profile with an evidence DNA profile and calculate a likelihood ratio that considers propositions including and excluding the POI as a DNA donor. A method has recently been published that provides the ability to compare two evidence profiles (of any number of contributors and of any level of resolution) comparing propositions that consider the profiles either have a common contributor, or do not have any common contributors. Using this method, forensic analysts can provide intelligence to law enforcement by linking crime scenes when no suspects may be available. The method could also be used as a quality assurance measure to identify potential sample to sample contamination. In this work we analyse a number of constructed mixtures, ranging from two to five contributors, and with known numbers of common contributors, in order to investigate the performance of using likelihood ratios for mixture to mixture comparisons. Our findings demonstrate the ability to identify common donors in DNA mixtures with the power of discrimination depending largely on the least informative mixture of the pair being considered. The ability to match mixtures to mixtures may provide intelligence information to investigators by identifying possible links between cases which otherwise may not have been considered connected.     

Investigation into the effect of mixtures comprising related people on non-donor likelihood ratios,and potential practises to mitigate providing misleading opinions

The interpretation of mixtures containing related individuals can be difficult due to allele sharing between the contributors. Challenges include the assignment of the number of contributors (NoC) to the mixture with the under assignment of NoC resulting in false exclusions of true donors. Non-donating relatives of the true contributors to mixtures of close relatives can result in likelihood ratios supporting their adventitious inclusion within the mixture. We examine the effect of non-donor likelihood ratios on mixtures of first order relatives. Mixtures of full siblings and parent-child were created by mixing the DNA from known family members in vitro, or by in silico simulation. Mixtures were interpreted using the probabilistic genotyping software STRmix™ and likelihood ratios were assigned for the true donors and non-donors who were either further relatives of the true donors or unrelated to the true donors. The two donor balanced mixtures deconvoluted straightforwardly when analysed as NoC = 2 giving approximately the experimental design 1:1 ratio. When analysed as NoC = 3 a very large number of non-donor genotypes produced LRs close to 1 including many instances of adventitious support. The in vitro three donor balanced mixtures proved difficult to assign as NoC = 3 by a blind examination of the profile. It is likely that many of these would be misassigned as NoC = 2. The analysis of the in vitro and in silico mixtures assuming NoC = 3 with no use of a conditioning profile or with the use of a conditioning profile but without informed priors on the mixture proportions (Mx priors) was ineffective. If the profile can be assigned as NoC = 3 then assignment of the Mx priors is straightforward. This analysis gave no false exclusions. Adventitious support did happen for relatives with high allele sharing. Adventitious support was not observed for any unrelated non-donors. The analysis of the three-person mixtures as NoC = 2 produced many false exclusions and fewer instances of adventitious support. The three donor unbalanced mixtures could all be assigned as NoC= 3. Analysis without Mx priors produced an alternate genotype explanation.     

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.     

Validation of a DNA mixture statistics tool incorporating allelic drop-out and drop-in

DNA mixture analysis is a current topic of discussion in the forensics literature. Of particular interest is how to approach mixtures where allelic drop-out and/or drop-in may have occurred. The Office of Chief Medical Examiner (OCME) of The City of New York has developed and validated the Forensic Statistical Tool (FST), a software tool for likelihood ratio analysis of forensic DNA samples, allowing for allelic drop-out and drop-in. FST can be used for single source samples and for mixtures of DNA from two or three contributors, with or without known contributors. Drop-out and drop-in probabilities were estimated empirically through analysis of over 2000 amplifications of more than 700 mixtures and single source samples. Drop-out rates used by FST are a function of the Identifiler^® locus, the quantity of template DNA amplified, the number of amplification cycles, the number of contributors to the sample, and the approximate mixture ratio (either unequal or approximately equal). Drop-out rates were estimated separately for heterozygous and homozygous genotypes. Drop-in rates used by FST are a function of number of amplification cycles only.FST was validated using 454 mock evidence samples generated from DNA mixtures and from items handled by one to four persons. For each sample, likelihood ratios (LRs) were computed for each true contributor and for each profile in a database of over 1200 non-contributors. A wide range of LRs for true contributors was obtained, as true contributors’ alleles may be labeled at some or all of the tested loci. However, the LRs were consistent with OCME's qualitative assessments of the results. The second set of data was used to evaluate FST LR results when the test sample in the prosecution hypothesis of the LR is not a contributor to the mixture. With this validation, we demonstrate that LRs generated using FST are consistent with, but more informative than, OCME's qualitative sample assessments and that LRs for non-contributors are appropriately assigned.     

Addressing uncertain assumptions in DNA evidence evaluation

Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant’s profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.     

Evaluation of large-scale highly polymorphic microhaplotypes in complex DNA mixtures analysis using RMNE method

DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2–10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors’ relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.     

Evaluation of DNA mixtures involving two pairs of relatives

This paper considers the statistical evaluation of DNA mixtures in the following situations: (1) two unknown contributors are related respectively to two typed persons, (2) two of the unknown or untyped contributors are related and the third unknown contributor is related to a typed person, or (3) there are two pairs of related unknown contributors to the DNA mixture. The corresponding formulas for evaluating the likelihood ratios on the strength of DNA evidence are derived and the kinship coefficients for the related persons are incorporated into the calculations. Two examples are analyzed for illustration.     

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.     

Exploring how the LR of a POI in a target sample is impacted by awareness of the profile of the background derived from an area adjacent to the target sample

DNA unrelated to an action of interest (background DNA) is routinely collected when sampling an area for DNA that may have originated from an action of interest. Background DNA can add to the complexity of a recovered DNA profile and could impact the discrimination power when comparing it to the reference profile of a person of interest. Recent advances in probabilistic genotyping and the development of new tools, now allow for the comparison of multiple evidentiary profiles to query for a common DNA donor. Here, we explore the additional discrimination power that can be gained by having an awareness of the background DNA present on a surface prior to the deposition of target DNA. Samples with varying number of contributors and DNA quantities were generated on cleaned plastic pipes (where ground truth was known) and items used by occupants of a single household (where ground truth was not known). The background consisted of deposits made by hands (touch) while target deposits were both touch and saliva. Samples were collected from areas consisting of only the background (A), the target and the background directly beneath it (B), and the target and additional surrounding background (B+C). Samples B and B+C yielded similar DNA amounts when the target consisted of saliva, but when the target consisted of touch, significantly more DNA was recovered from B+C. Subsequently generated DNA profiles were interpreted using STRmix™ and DBLR™. The first approach involved no conditioning while the second approach involved conditioning on the reference profiles of the known background DNA donors. The third approach involved conditioning on one common DNA donor between A and B or A and B+C. The fourth and final approach involved conditioning on two common DNA donors between A and B or A and B+C. As more information was applied to the analysis, the greater the increase in the LR for the comparison of the target sample to the POI. Conditioning on two common donors between the target and the background provided almost the same amount of information as conditioning on the references of the known background DNA donors. This resulted in an increase in the LR that was over 10 orders of magnitude for known donors in the target sample. Here we have demonstrated the value in collecting additional background samples from an area adjacent to a targeted sample, and that this has the potential to improve discrimination power.     

Transfer,persistence and recovery of DNA and mRNA vaginal mucosa markers after intimate and social contact with Bayesian network analysis for activity level reporting

In sexual assault cases, it can be challenging to identify the type of body fluids/ cell types present in a crime scene sample, especially the origin of epithelial cells. Therefore, more labs are applying mRNA body fluid analysis for saliva, skin and vaginal mucosa markers. To address activity level propositions, it is necessary to assign probabilities of transfer, persistence, prevalence and recovery of DNA and mRNA markers. In this study we analysed 158 samples (fingernail swabs, penile swabs and boxershorts) from 12 couples collected at different time points post intimate contact and after non-intimate contact in order to detect DNA from the person of interest (POI) and mRNA vaginal mucosa markers. Samples were DNA and RNA co-extracted and analysed with PowerPlex®Fusion 6C System and 19-plex mRNA primer mix respectively, using Endpoint PCR and the CE platform. Vaginal mucosa was detected up to 36 h post intimate contact, but also detected in one non-intimate contact sample. In 94% of intimate contact and 50 % of non-intimate contact samples the DNA results support the proposition that POI is the donor (LR ≥ 10,000). There was a strong association between the detection of vaginal mucosa and the average RFU value of the POI. The data were used to instantiate a comprehensive Bayesian network to evaluate the evidence at activity level, given alternate propositions conditioned upon indirect or direct transfer events. It is shown that the value of the evidence is mainly affected by the high DNA quantity (measured as mean RFU) that is recovered from the POI. The detection of vaginal mucosa had low impact upon the resultant likelihood ratio.     

Accurate assessment of the weight of evidence for DNA mixtures by integrating the likelihood ratio

Several methods exist for weight of evidence calculations on DNA mixtures. Especially if dropout is a possibility, it may be difficult to estimate mixture specific parameters needed for the evaluation. For semi-continuous models, the LR for a person to have contributed to a mixture depends on the specified number of contributors and the probability of dropout for each. We show here that, for the semi-continuous model that we consider, the weight of evidence can be accurately obtained by applying the standard statistical technique of integrating the likelihood ratio against the parameter likelihoods obtained from the mixture data. This method takes into account all likelihood ratios belonging to every choice of parameters, but LR's belonging to parameters that provide a better explanation to the mixture data put in more weight into the final result. We therefore avoid having to estimate the number of contributors or their probabilities of dropout, and let the whole evaluation depend on the mixture data and the allele frequencies, which is a practical advantage as well as a gain in objectivity. Using simulated mixtures, we compare the LR obtained in this way with the best informed LR, i.e., the LR using the parameters that were used to generate the data, and show that results obtained by integration of the LR approximate closely these ideal values. We investigate both contributors and non-contributors for mixtures with various numbers of contributors. For contributors we always obtain a result close to the best informed LR whereas non-contributors are excluded more strongly if a smaller dropout probability is imposed for them. The results therefore naturally lead us to reconsider what we mean by a contributor, or by the number of contributors.     

Interpreting Y chromosome STR haplotype mixture

Mixture interpretation is a challenging problem in forensic DNA analyses. The interpretation of Y short tandem repeat (STR) haplotype mixtures, due to a lack of recombination, differs somewhat from that of the autosomal DNA markers and is more complex. We describe approaches for calculating the probability of exclusion (PE) and likelihood ratio (LR) methods to interpret Y-STR mixture evidence with population substructure incorporated. For a mixture sample, first, all possible contributor haplotypes in a reference database are listed as a candidate list. The PE is the complement of the summation of the frequencies of haplotypes in the candidate list. The LR method compares the probabilities of the evidence given alternative hypotheses. The hypotheses are possible explanations for the mixture. Population substructure may be further incorporated in likelihood calculation. The maximum number of contributors is based on the candidate list and the computing complexity is polynomial. Additionally, mixtures were simulated by combining two or three 16 Y-STR marker haplotypes derived from the US forensic Y-STR database. The average PE was related to the size of database. With a database comprised of 500 haplotypes an average PE value of at least 0.995 can be obtained for two-person mixtures. The PE decreases with an increasing number of contributors to the mixture. Using the total sample population, the average number of candidate haplotypes of two-person mixtures is 3.73 and 95% mixtures have less than or equal to 10 candidate haplotypes. More than 98.7% of two-person mixtures can only be explained by the haplotype combinations that mixtures are composed. These values are generally higher for three-person mixtures. A small proportion of three-person mixture can also be explained by only two haplotypes.     

Exploring the DNA mixture deconvolution through simulation

ABSTRACT

If an unambiguous single-source DNA profile is obtained from a crime scene, then a potential person of interest can either match or not match the crime scene profile and the likelihood ratio for the single matching genotype can be easily computed. Mixed DNA profiles on the other hand are typically ambiguous and a vast number of different likelihood ratios can be obtained depending on the genotype of a potential person of interest that is compared with the mixture later. In the absence of a person of interest it can be unclear how suitable the profile is for discriminating between donors and non-donors. We introduce a simulation method to explore the range of likelihood ratios that is expected to be obtained when a non-donor or a true donor is compared with the mixed DNA profile. Sampling is conditional on the mixture deconvolution obtained using probabilistic genotyping. These simulations help to decide whether or not a (mixed) profile is suitable for comparison to a person of interest. Moreover, the methods can be used to determine whether a profile is suitable for upload to a database and whether or not potential rework could be advised.     

The evaluation of mixed stains from different ethnic origins: general result and common cases

In some situations, it can be inferred from the crime circumstances that the mixed stain donors are of different ethnic groups. The evaluation of DNA mixtures with contributors coming from more than one ethnic group is considered under the assumption of independence of alleles within and between ethnic groups. A general formula is derived for the assessment of the weight of evidence in mixed stain problems. This formula is equivalent to that of Fukshansky and Bär, but we give a different derivation. For the convenience of practitioners, the explicit expressions of the likelihood ratios for 14 common cases are presented. The effect of different ethnic groups to the assessment of the evidence is shown in the well-known Simpson case.     

Analysis of nondegraded and degraded DNA mixtures of close relatives using massively parallel sequencing

Identification of the minor contributor in DNA mixture of close relatives remains a dilemma in forensic genetics. Massively parallel sequencing (MPS) can analyze multiple short tandem repeats (STRs) and single nucleotide polymorphism (SNPs) concurrently and detect non-overlapping alleles of the minor contributors in DNA mixtures. A commercial kit for MPS of 59 identity informative STRs (iiSTRs) and 94 autosomal identity-informative SNPs (iiSNPs) was used to analyzed 34 nondegraded and 33 highly degraded two-person artificial DNA mixtures of close relatives with various minor to major ratios (1:9, 1:19, 1:29, 1:39, 1:79, 1:99). EuroForMix software was used to determine the minor contributors in the mixtures based on the likelihood ratios calculated from the MPS data, and relMix software was used to perform kinship analysis of the contributors. The STRs and SNPs of the 34 nondegraded and 33 degraded DNA mixtures were genotyped using MPS. Using EuroForMix based on the genotypes of autosomal iiSTRs and autosomal iiSNPs, 82.4% (28/34) and 54.5% (18/33) of minor donors could be accurately assigned for the nondegraded and degraded DNA mixtures, respectively. The relMix software correctly inferred the relationship between contributors in 97.1% (33/34) of nondegraded mixtures and in 97.0% (32/33) of degraded mixtures. In conclusion, combined EuroForMix and MPS data of STRs and SNPs can assist in the assignment of minor donors in nondegraded DNA mixtures of close relatives, and relMix can be used to infer relationship among contributors.     

Searching mixed DNA profiles directly against profile databases

DNA databases have revolutionised forensic science. They are a powerful investigative tool as they have the potential to identify persons of interest in criminal investigations. Routinely, a DNA profile generated from a crime sample could only be searched for in a database of individuals if the stain was from single contributor (single source) or if a contributor could unambiguously be determined from a mixed DNA profile. This meant that a significant number of samples were unsuitable for database searching. The advent of continuous methods for the interpretation of DNA profiles offers an advanced way to draw inferential power from the considerable investment made in DNA databases. Using these methods, each profile on the database may be considered a possible contributor to a mixture and a likelihood ratio (LR) can be formed. Those profiles which produce a sufficiently large LR can serve as an investigative lead.In this paper empirical studies are described to determine what constitutes a large LR. We investigate the effect on a database search of complex mixed DNA profiles with contributors in equal proportions with dropout as a consideration, and also the effect of an incorrect assignment of the number of contributors to a profile. In addition, we give, as a demonstration of the method, the results using two crime samples that were previously unsuitable for database comparison. We show that effective management of the selection of samples for searching and the interpretation of the output can be highly informative.     

Complex DNA mixture analysis in a forensic context: Evaluating the probative value using a likelihood ratio model

The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors.The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a ‘gold standard’ LR, to the LR that would be obtained in casework. The ‘gold standard’ LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The ‘casework LR’ was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim.We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework.     

Developmental validation of PACE™: Automated artifact identification and contributor estimation for use with GlobalFiler™ and PowerPlex® fusion 6c generated data

DNA mixture interpretation remains one of the major challenges in forensic DNA analysis. DNA mixture samples are inherently complex due to several factors including the variations in the quantity of DNA, the presence of non-allelic artifactual peaks and the presence of multiple contributors with variable levels of allele sharing. The Probabilistic Assessment for Contributor Estimation (PACE) is a fully continuous probabilistic machine learning-based method to predict the number of contributors (n) in a sample, and was previously developed for use with the Identifiler amplification kit. This system required manual preprocessing of data and was limited, exclusively, to samples amplified using said kit. This study introduces PACE™ v1.3.7 for use with both the GlobalFiler and PowerPlex Fusion 6c amplification kits. An automated artifact identification and management system has been added to accompany the rapid estimation of the number of donors in a given mixture. The artifact management module, when evaluated using previously unseen data, identified true allelic peaks and removed artifacts such as elevated baseline noise, stutter, and pull-up with accuracy over 93.5%. The systems yield the correct n classifications in over 90% of the samples, and demonstrate consistent accuracies as the number of donors and the overall mixture complexity increase. Misclassified samples generally exhibited high levels of allele sharing among donors, low DNA template amounts and high incidence of allelic dropout. This system offers a means for both artifact management and n estimation as well as a quantitative and reproducible method of assessing the quality of a profile.     

Open source software EuroForMix can be used to analyse complex SNP mixtures

A series of two- and three-person mixtures of varying dilutions were prepared and analysed with Life Technologies’ HID-Ion AmpliSeq™ Identity Panel v2.2 using the Ion PGM™ massively parallel sequencing (MPS) system. From this panel we used 134 autosomal SNPs. Using the reference samples of three donors, we evaluated the strength of evidence with likelihood ratio (LR) calculations using the open-source quantitative EuroForMix program and compared the results with a previous study using a qualitative software (LRmix). SNP analysis is a special case of STRs, restricted to a maximum of two alleles per locus. We showed that simple two-person mixtures can be readily analysed with both LRmix and Euroformix, but the performance of three- or more person mixtures is generally inefficient with LRmix. Taking account of the “peak height” information, by substituting ‘sequence read’ coverage values from the MPS data for each SNP allele, greatly improves the discrimination between true and non-contributors. The higher the mixture proportion (Mx) of the person of interest is, the higher the LR. Simulation experiments (up to six contributors) showed that the strength of the evidence is dependent upon Mx, but relatively insensitive to the number of contributors. If a higher number of loci were multiplexed, the analysis of mixtures would be much improved, because the extra information would enable lower Mx values to be evaluated. In summary, incorporating the 'sequence read' (coverage) into the quantitative model shows a significant benefit over the qualitative approach. Calculations are quite fast (six seconds for three contributors).     

Estimating number of contributors in massively parallel sequencing data of STR loci

In recent years a number of computer-based algorithms have been developed for the deconvolution of complex DNA mixtures in forensic science. These procedures utilize likelihood ratios that quantify the evidence for a hypothesis for the presence of a person of interest in a DNA profile compared to an alternative hypothesis. Proper operation of these software systems requires an assumption regarding the total number of contributors present in the mixture. Unfortunately, estimates based on counting the number of alleles at a locus can be inaccurate due to the sharing and masking of alleles at individual loci. The effects of allele masking become increasingly severe as the number of contributors increases, rendering estimates about high-order mixtures uncertain. The accuracy of these estimates can be improved by increasing the number of STR markers in panels, and by using highly polymorphic markers. Increasing the number of STR markers from 13 to 20 (expanded CODIS panel) improves the accuracy of allele count-based estimation methods for low-order mixtures, but accuracy for high-order mixtures (> 3 contributors) remains poor due to allele masking. An alternative technique, massively parallel sequencing, holds great potential to improve the accuracy of the estimate of number of contributors due to its ability to detect sequence polymorphisms within alleles. This process results in an expansion of the number of alleles when compared to that obtained using capillary electrophoresis. Here, we show that the detection of these additional sequence-defined alleles in 22-marker panels improves number of contributor estimates in conceptual mixtures of 4 and 5 contributors.