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.     

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.     

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.     

STRmix™ collaborative exercise on DNA mixture interpretation

An intra and inter-laboratory study using the probabilistic genotyping (PG) software STRmix™ is reported. Two complex mixtures from the PROVEDIt set, analysed on an Applied Biosystems™ 3500 Series Genetic Analyzer, were selected. 174 participants responded.For Sample 1 (low template, in the order of 200 rfu for major contributors) five participants described the comparison as inconclusive with respect to the POI or excluded him. Where LRs were assigned, the point estimates ranging from 2 × 10⁴ to 8 × 10⁶. For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 10²⁸ to 2 × 10²⁹. Where LRs were calculated, the differences between participants can be attributed to (from largest to smallest impact):

• •varying number of contributors (NoC),
• •the exclusion of some loci within the interpretation,
• •differences in local CE data analysis methods leading to variation in the peaks present and their heights in the input files used,
• •and run-to-run variation due to the random sampling inherent to all MCMC-based methods.

This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.     

Testing likelihood ratios produced from complex DNA profiles

The performance of any model used to analyse DNA profile evidence should be tested using simulation, large scale validation studies based on ground-truth cases, or alignment with trends predicted by theory. We investigate a number of diagnostics to assess the performance of the model using H_d true tests. Of particular focus in this work is the proportion of comparisons to non-contributors that yield a likelihood ratio (LR) higher than or equal to the likelihood ratio of a known contributor (LR_POI), designated as p, and the average LR for H_d true tests. Theory predicts that p should always be less than or equal to 1/LR_POI and hence the observation of this in any particular case is of limited use. A better diagnostic is the average LR for H_d true which should be near to 1. We test the performance of a continuous interpretation model on nine DNA profiles of varying quality and complexity and verify the theoretical expectations.     

Identifying the most likely contributors to a Y-STR mixture using the discrete Laplace method

In some crime cases, the male part of the DNA in a stain can only be analysed using Y chromosomal markers, e.g. Y-STRs. This may be the case in e.g. rape cases, where the male components can only be detected as Y-STR profiles, because the fraction of male DNA is much smaller than that of female DNA, which can mask the male results when autosomal STRs are investigated. Sometimes, mixtures of Y-STRs are observed, e.g. in rape cases with multiple offenders. In such cases, Y-STR mixture analysis is required, e.g. by mixture deconvolution, to deduce the most likely DNA profiles from the contributors.We demonstrate how the discrete Laplace method can be used to separate a two person Y-STR mixture, where the Y-STR profiles of the true contributors are not present in the reference dataset, which is often the case for Y-STR profiles in real case work. We also briefly discuss how to calculate the weight of the evidence using the likelihood ratio principle when a suspect's Y-STR profile fits into a two person mixture. We used three datasets with between 7 and 21 Y-STR loci: Denmark (n = 181), Somalia (n = 201) and Germany (n = 3443). The Danish dataset with 21 loci was truncated to 15 and 10 loci to examine the effect of the number of loci. For each of these datasets, an out of sample simulation study was performed: A total of 550 mixtures were composed by randomly sampling two haplotypes, h₁ and h₂, from the dataset.We then used the discrete Laplace method on the remaining data (excluding h₁ and h₂) to rank the contributor pairs by the product of the contributors’ estimated haplotype frequencies. Successful separation of mixtures (defined by the observation that the true contributor pair was among the 10 most likely contributor pairs) was found in 42–52% of the cases for 21 loci, 69–75% for 15 loci and 92–99% for 10 loci or less depending on the dataset and how the discrete Laplace model was chosen. Y-STR mixtures with many loci are difficult to separate, but even haplotypes with 21 Y-STR loci can be separated.     

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to ‘explain’ an observed set of alleles. The other procedure is probabilistic using Bayes’ theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.     

Importance sampling allows Hd true tests of highly discriminating DNA profiles

H_d true testing is a way of assessing the performance of a model, or DNA profile interpretation system. These tests involve simulating DNA profiles of non-donors to a DNA mixture and calculating a likelihood ratio (LR) with one proposition postulating their contribution and the alternative postulating their non-contribution. Following Turing it is possible to predict that “The average LR for the H_d true tests should be one” [1]. This suggests a way of validating softwares. During discussions on the ISFG software validation guidelines [2] it was argued by some that this prediction had not been sufficiently examined experimentally to serve as a criterion for validation. More recently a high profile report [3] has emphasised large scale empirical examination.A limitation with H_d true tests, when non-donor profiles are generated at random (or in accordance with expectation from allele frequencies), is that the number of tests required depends on the discrimination power of the evidence profile. If the H_d true tests are to fully explore the genotype space that yields non-zero LRs then the number of simulations required could be in the 10 s of orders of magnitude (well outside practical computing limits). We describe here the use of importance sampling, which allows the simulation of rare events to occur more commonly than they would at random, and then adjusting for this bias at the end of the simulation in order to recover all diagnostic values of interest. Importance sampling, whilst having been employed by others for H_d true tests, is largely unknown in forensic genetics. We take time in this paper to explain how importance sampling works, the advantages of using it and its application to H_d true tests. We conclude by showing that employing an importance sampling scheme brings H_d true testing ability to all profiles, regardless of discrimination power.     

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.     

Assessment of mock cases involving complex low template DNA mixtures: A descriptive study

Complex DNA mixtures with low template (LT) components provide the most challenging cases to interpret and report. In this study, we designed such mixtures and we describe how reporting officers (ROs) at the Netherlands Forensic Institute (NFI) assess these when embedded in a mock case setting. DNA mixtures containing LT DNA from two to four contributors, sporadic contamination (mimicked by adding 6 pg of DNA, which represents once cell equivalent) and/or DNA of relatives (brothers), were amplified four-fold using the AmpFlSTR^® NGM? PCR Amplification Kit. Consensus profiles were then generated which included the alleles detected in at least half of the replicates. Four mock cases were created by including reference profiles of a hypothetical victim and suspect. The mock cases were assessed by eight ROs following the stepwise interpretation approach currently in use at the NFI. With this approach, the results of the comparisons between the DNA profiles of the evidentiary trace and the reference profiles are classified into four categories of evidential value [1]. The interpretations by the ROs were compared to the likelihood ratios (LRs) obtained from a probabilistic model that allows a calculation of LRs to assist the interpretation of LT DNA evidence and both were compared to the true composition of the designed mixtures.     

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.     

NOCIt: A computational method to infer the number of contributors to DNA samples analyzed by STR genotyping

Repetitive sequences in the human genome called short tandem repeats (STRs) are used in human identification for forensic purposes. Interpretation of DNA profiles generated using STRs is often problematic because of uncertainty in the number of contributors to the sample. Existing methods to identify the number of contributors work on the number of peaks observed and/or allele frequencies. We have developed a computational method called NOCIt that calculates the a posteriori probability (APP) on the number of contributors. NOCIt works on single source calibration data consisting of known genotypes to compute the APP for an unknown sample. The method takes into account signal peak heights, population allele frequencies, allele dropout and stutter—a commonly occurring PCR artifact. We tested the performance of NOCIt using 278 experimental and 40 simulated DNA mixtures consisting of one to five contributors with total DNA mass from 0.016 to 0.25 ng. NOCIt correctly identified the number of contributors in 83% of the experimental samples and in 85% of the simulated mixtures, while the accuracy of the best pre-existing method to determine the number of contributors was 72% for the experimental samples and 73% for the simulated mixtures. Moreover, NOCIt calculated the APP for the true number of contributors to be at least 1% in 95% of the experimental samples and in all the simulated mixtures.     

Joint Bayesian analysis of forensic mixtures

Evaluation of series of PCR experiments referring to the same evidence is not infrequent in a forensic casework. This situation is met when ‘series of results in mixture’ (EPGs produced by reiterating PCR experiments over the same DNA mixture extract) have to be interpreted or when ‘potentially related traces’ (mixtures that can have contributors in common) require a combined interpretation.In these cases, there can be uncertainty on the genotype assignment, since: (a) more than one genotype combination fall under the same peak profile; (b) PCR preferential amplification alters pre-PCR allelic proportions; (c) other, more unpredictable technical problems (dropouts/dropins, etc.) take place.The uncertainty in the genotype assignment is in most cases addressed by empirical methods (selection of just one particular profile; extraction of consensual or composite profiles) that disregard part of the evidence. Genotype assignment should conversely take advantage from a joint Bayesian analysis (JBA) of all STRs peak areas generated at each experiment. This is the typical case of Bayesian analysis in which adoption of object-oriented Bayesian networks (OOBNs) could be highly helpful. Starting from experimentally designed mixtures, we created typical examples of ‘series of results in mixture’ of ‘potentially related traces’. JBA was some administered to the whole peak area evidence, by specifically tailored OOBNs models, which enabled genotype assignment reflecting all the available evidence. Examples of a residual ambiguity in the genotype assignment came to light at assumed genotypes with partially overlapping alleles (for example: AB + AC → ABC). In the ‘series of results in mixture’, this uncertainty was in part refractory to the joint evaluation. Ambiguity was conversely dissipated at the ‘potentially related’ trace example, where the ABC allelic scheme at the first trace was interpreted together with other unambiguous combinations (ABCD; AB) at the related trace. We emphasize the need to carry out extensive, blind sensitivity tests specifically addressing the residual ambiguity that arises from overlapping results mixed at various quantitative ratios.     

Investigating a common approach to DNA profile interpretation using probabilistic software

Recently there has been a drive for standardisation of DNA profile interpretation within and between different forensic laboratories. The continuous interpretation software STRmix⢢ has been adopted for use by laboratories in Australia and New Zealand for profile interpretation. Within this paper we examine the concordance in profile interpretation of three crime samples by twenty different analysts across twelve different international laboratories using STRmix⢢. The three profiles selected for this study exhibited a range of template and complexity. The use of probabilistic software has compelled a level of concordance between different analysts however there remain differences within profile interpretation, particularly with the objective assignment of the number of contributors to profiles.     

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.     

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.     

A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles

The investigation of the performance of models to interpret complex DNA profiles is best undertaken using real DNA profiles. Here we used a data set to reflect the variety typically encountered in real casework. The “crime-stains” were constructed from known individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted from blood, 2–3 person mixtures, degradation/no-degradation, differences in allele sharing, dropout/no dropout, etc. Two siblings were also included in the test-set in order to challenge the systems. Two kinds of analyses were performed, namely tests on whether a person of interest is a contributor based on weight-of-evidence (likelihood ratio) calculations, and deconvolution test to estimate the profile of unknown constituent parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool. When we classified persons of interests into being true contributors or non-contributors, we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels compared to LRmix. In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor. Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool.     

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.     

Expanding beyond the current core STR loci: An exploration of 73 STR markers with increased diversity for enhanced DNA mixture deconvolution

Current approaches to mixture deconvolution of complex biological samples at times do not have the capability to resolve component contributors in DNA evidence. Additional short tandem repeat (STR) loci were sought that may improve the forensic genetic analysis of mixtures. This study presents exploratory data of a multiplex comprised of 73 highly polymorphic STRs (referred herein as the 73Plex) that were selected because of their high diversity due to sequence variation. These STRs (or a subset of them) may be considered as candidates that may augment current core markers capabilities for DNA mixture deconvolution. Population genetics analyses were performed for each locus using DNA samples from 451 individuals comprising three U.S. populations. Sequence-based heterozygosities ranged from 72% to 98%, where only two loci (D10A97 and D6A7) fell below 80%. Mixture deconvolution capabilities for two-person mixtures were assessed based on complete allele resolution per locus (i.e., four alleles observed) of pairwise mixtures using in silico methods. A subset of 20 highly informative loci (referred herein as the 20Plex) from the 73Plex was compared to the 20 CODIS core loci on all population samples with full DNA profiles for both panels (i.e., no locus dropout; n = 443). Based on proportion of loci displaying four alleles, the 20Plex outperformed the CODIS core loci with increases of 82.6% and 89.3% using length-based and sequence-based alleles, respectively. A combination of 17 STR from the 20Plex and 3 CODIS loci gave the highest capacity for resolving allelic components per locus. These data illustrate the increased value of utilizing sequenced-based alleles of additional STR loci. Furthermore, there are a number of candidate STR loci that could notably augment the current core STR loci and enhance mixture interpretation capabilities.