Streamlining the decision-making process for international DNA kinship matching using Worldwide allele frequencies and tailored cutoff log10LR thresholds.

The identification of human remains belonging to missing persons is one of the main challenges for forensic genetics. Although other means of identification can be applied to missing person investigations, DNA is often extremely valuable to further support or refute potential associations. When reference DNA samples cannot be collected from personal items belonging to a missing person, a direct DNA identification cannot be carried out. However, identifications can be made indirectly using DNA from the missing person's relatives. The ranking of likelihood ratio (LR) values, which measure the fit of a missing person for any given pedigree, is often the first step in selecting candidates in a DNA database. Although implementing DNA kinship matching in a national environment is feasible, many challenges need to be resolved before applying this method to an international configuration. In this study, we present an innovative and intuitive method to perform international DNA kinship matching and facilitate the comparison of DNA profiles when the ancestry is unknown or unsure and/or when different marker sets are used. This straightforward method, which is based on calculations performed with the DNA matching software BONAPARTE, Worldwide allele frequencies and tailored cutoff log10LR thresholds, allows for the classification of potential candidates according to the strength of the DNA evidence and the predicted proportion of adventitious matches. This is a powerful method for streamlining the decision-making process in missing person investigations and DVI processes, especially when there are low numbers of overlapping typed STRs. Intuitive interpretation tables and a decision tree will help strengthen international data comparison for the identification of reported missing individuals discovered outside their national borders.

Relaxing the assumption of unrelatedness in the numerator and denominator of likelihood ratios for DNA mixtures.

DNA mixtures will have multiple donors under both the prosecution and alternate propositions when assigning a likelihood ratio for forensic DNA evidence. These donors are usually assumed to be unrelated to each other. In this paper, we make a small, preliminary examination of the potential effect of relaxing this assumption. We consider the simple situation of a two-person mixture with no dropout and a two-person major/minor mixture with dropout of the minor contributor. We make no adjustment for subpopulation effects. Mixtures were simulated under two assumptions: 1. that the donors were siblings 2. or that they were unrelated. Both unresolvable and major/minor mixtures were considered. We compared the likelihood ratio assuming sibship with the likelihood ratio assuming no relatedness. The LR for hypotheses assuming no relatedness is less than the LR assuming relatedness approximately 95% of the time when relatives are present in the mixture.

Are low LRs reliable?

To answer the question "Are low likelihood ratios reliable?" requires both a definition of reliable and then a test of whether low likelihood ratios (LRs) meet that definition. We offer, from a purely statistical standpoint, that reliability can be determined by assessing whether the rate of inclusionary support for non-donors over many cases is not larger than expected from the LR value. Thus, it is not the magnitude of the LR alone that determines reliability. Turing's rule is used to inform the expected rate of non-donor inclusionary support, where the rate of non-donor inclusionary support is at most the reciprocal of the LR, i.e. Pr(LR > x|Ha) ≤1/x. There are parallel concerns about whether the value of the evidence can be communicated. We do not discuss that in depth here although it is an important consideration to be addressed with training. In this paper, we use a mixture of real and simulated data to show that the rate of non-donor inclusionary support for these data is significantly lower than the upper bound given by Turing's rule. We take this as strong evidence that low LRs are reliable.

Performance of a method for weighting a range in the number of contributors in probabilistic genotyping.

Uncertainty in the assignment of the number of contributors (NoC) can be encountered, particularly in higher-order mixtures, where alleles may be shared between contributors, may have dropped out, or may be masked by the stutter artefacts or allelic peaks of a more dominant contributor. Most probabilistic genotyping software requires the assignment of NoC prior to interpretation. NoC has been described as a nuisance parameter. Taylor et al. [1] describe a method to weigh the probability of the profile under different values of N and incorporate this into a likelihood ratio (LR). Within this paper we explore the performance of this variable number of contributors (varNoC) method programmed within the probabilistic genotyping software STRmix™. The desired combination of performance and runtime was obtained using the default STRmix™ version 2.7 MCMC settings in conjunction with a 2.5 % hyper-rectangle range, at least 10,000 naïve MC iterations and 8 MCMC chains. The varNoC LR demonstrated the typical sensitivity and specificity behaviour seen in previous studies, with a high level of reproducibility given repeat analyses. Profiles previously demonstrating ambiguity in the NoC assigned using conventional estimation methods, were able to be reliably interpreted and a varNoC LR assigned.

The interpretation of mixed DNA profiles from a mother, father, and child trio.

We report the interpretation of three-person mixed DNA profiles constructed from DNA from one mother, father, and child trio using the probabilistic genotyping software STRmix™. A total of 40 mixtures were examined, with varying total template and mixture proportions of the three contributors. In addition, mixtures were artificially degraded at four different rates to test the effects of degradation on the interpretation of mother, father and child trios. A total of 560 STRmix™ analyses were undertaken, examining four different interpretation strategies. Reasonable results were only achieved by conditioning on one parent as an assumed donor and applying a user-informed prior to the mixture proportion of both parents. For each of the 40 amplified mixtures, 10,000 non-donors were compared, conditioning on one parent and applying a user-informed prior to the mixture proportion of both parents. This leads to 800,000 non-donor tests.

Testing whether stutter and low-level DNA peaks are additive.

Peaks in an electropherogram could represent alleles, stutter product, or a combination of allele and stutter. Continuous probabilistic genotyping (PG) systems model the heights of peaks in an additive manner: for a shared or composite peak, PG models assume that the peak height is the sum of the allelic component and the stutter component. In this work we examine the assumption that the heights of overlapping alleles from a minor contributor and stutter peaks from a major contributor are additive. Any peak below the analytical threshold is considered unobserved; hence, in any dataset and particularly in low-template DNA profiles, some or many peaks may be unobserved or missing. Using simulation and empirical data, we show that an additive model can explain the heights of overlapping alleles from a minor contributor and stutter peaks from a major contributor as long as missing data are carefully considered. We use a naive method of imputation for the missing data which appears to perform adequately in this case. If missing data are ignored then the sum of stutter and allelic peaks is expected to be an overestimate of the average height of the composite peaks, as was observed in this study.

Interpreting a major component from a mixed DNA profile with an unknown number of minor contributors.

Modern interpretation strategies typically require an assignment of the number of contributors (N) to a DNA profile. This can prove to be a difficult task, particularly when dealing with higher order mixtures or mixtures where one or more contributors have donated low amounts of DNA. Differences in the assigned N at interpretation can lead to differences in the likelihood ration (LR). If the number of contributors cannot reasonably be assigned, then an interpretation of the profile may not be able to be progressed. In this study, we investigate mixed DNA profiles of varying complexity and interpret them altering the assigned N. We assign LRs for true- and non- contributors and compare the results given different assignments of N over a range of mixture proportions. When a component of a mixture had a proportion of at least 10%, a ratio of at least 1.5:1 to the next highest component, and a DNA amount (as determined by STRmix™) of at least 50 rfu, the LR of the component for a true contributor was not significantly affected by varying N and was therefore suitable for interpretation and the assignment of an LR. LRs produced for minor contributors were found to vary significantly as the assigned N was changed. These heuristics may be used to identify profiles suitable for interpretation.

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity.

Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.

The effect of varying the number of contributors in the prosecution and alternate propositions.

Using a simplified model, we examine the effect of varying the number of contributors in the prosecution and alternate propositions for a number of simulated examples. We compare the Slooten and Caliebe [1] solution, with several existing practices. Our own experience is that most laboratories, and ourselves, assign the number of contributors, N = n, by allele count and a manual examination of peak heights. The LRn for one or a very few values is calculated and typically one of these is presented, usually the most conservative. This gives an acceptable approximation. Reassessing the number of contributors if LR = 0 and adding one to N under both Hp and Ha to "fit" the POI may lead to a substantial overstatement of the LR. A more reasonable option is to allow optimisation of the assignment under Hp and Ha separately. We show that an additional contributor explained the single locus profile better when PHR≥0.51. This is pleasingly in line with current interpretation approaches. Collectively these trials, and the solid theoretical development, suggest that the Slooten and Caliebe approach preforms well.

NIST interlaboratory studies involving DNA mixtures (MIX13): A modern analysis.

MIX13 was an interlaboratory exercise directed by NIST in 2013. The goal of the exercise was to evaluate the general state of interpretation methods in use at the time across the forensic community within the US and Canada and to measure the consistency in mixture interpretation. The findings were that there was a large variation in analysts' interpretations between and within laboratories. Within this work, we sought to evaluate the same mock mixture cases analyzed in MIX13 but with a more current view of the state-of-the-science. Each of the five cases were analyzed using the Identifiler™ multiplex and interpreted with the combined probability of inclusion, CPI, and four different modern probabilistic genotyping systems. Cases 1-4 can be interpreted without difficulty by any of the four PG systems examined. Cases 1 and 4 could also be interpreted successfully with the CPI by assuming two donors. Cases 2 and 3 cannot be interpreted successfully with the CPI because of potential of allele dropout. Case 3 demonstrated the need to consider relevant background information before interpretation of the profile. This case does not show that there is some barrier to interpretation caused by relatedness beyond the increased allelic overlap that can occur. Had this profile been of better template it might have been interpreted using the CPI despite the (potential) relatedness of contributors. Case 5 suffers from over-engineering. It is unclear whether reference 5C, a non-donor, can be excluded by manual methods. Inclusion of reference 5C should be termed an adventitious match not a false inclusion. Beyond this statement this case does not contribute to the interlaboratory study of analyst/laboratory interpretation method performance, instead, it explores the limits of DNA analysis. Taken collectively the analysis of these five cases demonstrates the benefits of changing from CPI to a PG system.

A response to "Likelihood ratio as weight of evidence: A closer look" by Lund and Iyer.

Recently, Lund and Iyer (L&I) raised an argument regarding the use of likelihood ratios in court. In our view, their argument is based on a lack of understanding of the paradigm. L&I argue that the decision maker should not accept the expert's likelihood ratio without further consideration. This is agreed by all parties. In normal practice, there is often considerable and proper exploration in court of the basis for any probabilistic statement. We conclude that L&I argue against a practice that does not exist and which no one advocates. Further we conclude that the most informative summary of evidential weight is the likelihood ratio. We state that this is the summary that should be presented to a court in every scientific assessment of evidential weight with supporting information about how it was constructed and on what it was based.

Internal validation of STRmix™ - A multi laboratory response to PCAST.

We report a large compilation of the internal validations of the probabilistic genotyping software STRmix™. Thirty one laboratories contributed data resulting in 2825 mixtures comprising three to six donors and a wide range of multiplex, equipment, mixture proportions and templates. Previously reported trends in the LR were confirmed including less discriminatory LRs occurring both for donors and non-donors at low template (for the donor in question) and at high contributor number. We were unable to isolate an effect of allelic sharing. Any apparent effect appears to be largely confounded with increased contributor number.

Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles.

The interpretation of DNA evidence can entail analysis of challenging STR typing results. Genotypes inferred from low quality or quantity specimens, or mixed DNA samples originating from multiple contributors, can result in weak or inconclusive match probabilities when a binary interpretation method and necessary thresholds (such as a stochastic threshold) are employed. Probabilistic genotyping approaches, such as fully continuous methods that incorporate empirically determined biological parameter models, enable usage of more of the profile information and reduce subjectivity in interpretation. As a result, software-based probabilistic analyses tend to produce more consistent and more informative results regarding potential contributors to DNA evidence. Studies to assess and internally validate the probabilistic genotyping software STRmix™ for casework usage at the Federal Bureau of Investigation Laboratory were conducted using lab-specific parameters and more than 300 single-source and mixed contributor profiles. Simulated forensic specimens, including constructed mixtures that included DNA from two to five donors across a broad range of template amounts and contributor proportions, were used to examine the sensitivity and specificity of the system via more than 60,000 tests comparing hundreds of known contributors and non-contributors to the specimens. Conditioned analyses, concurrent interpretation of amplification replicates, and application of an incorrect contributor number were also performed to further investigate software performance and probe the limitations of the system. In addition, the results from manual and probabilistic interpretation of both prepared and evidentiary mixtures were compared. The findings support that STRmix™ is sufficiently robust for implementation in forensic laboratories, offering numerous advantages over historical methods of DNA profile analysis and greater statistical power for the estimation of evidentiary weight, and can be used reliably in human identification testing. With few exceptions, likelihood ratio results reflected intuitively correct estimates of the weight of the genotype possibilities and known contributor genotypes. This comprehensive evaluation provides a model in accordance with SWGDAM recommendations for internal validation of a probabilistic genotyping system for DNA evidence interpretation.

Population data on the expanded CODIS core STR loci for eleven populations of significance for forensic DNA analyses in the United States.

Allele distributions for twenty-three autosomal short tandem repeat (STR) loci - D1S1656, D2S441, D2S1338, D3S1358, D5S818, D7S820, D8S1179, D10S1248, D12S391, D13S317, D16S539, D18S51, D19S433, D21S11, D22S1045, CSF1PO, FGA, Penta D, Penta E, SE33, TH01, TPOX and vWA - were determined in Caucasians, Southwestern Hispanics, Southeastern Hispanics, African Americans, Bahamians, Jamaicans, Trinidadians, Chamorros, Filipinos, Apaches, and Navajos. The data are included in the FBI PopStats software for calculating statistical estimates of DNA typing results and cover the expanded CODIS Core STR Loci required of U.S. laboratories that participate in the National DNA Index System (NDIS).

Evaluation of forensic DNA mixture evidence: protocol for evaluation, interpretation, and statistical calculations using the combined probability of inclusion.

BACKGROUND: The evaluation and interpretation of forensic DNA mixture evidence faces greater interpretational challenges due to increasingly complex mixture evidence. Such challenges include: casework involving low quantity or degraded evidence leading to allele and locus dropout; allele sharing of contributors leading to allele stacking; and differentiation of PCR stutter artifacts from true alleles. There is variation in statistical approaches used to evaluate the strength of the evidence when inclusion of a specific known individual(s) is determined, and the approaches used must be supportable. There are concerns that methods utilized for interpretation of complex forensic DNA mixtures may not be implemented properly in some casework. Similar questions are being raised in a number of U.S. jurisdictions, leading to some confusion about mixture interpretation for current and previous casework. RESULTS: Key elements necessary for the interpretation and statistical evaluation of forensic DNA mixtures are described. Given the most common method for statistical evaluation of DNA mixtures in many parts of the world, including the USA, is the Combined Probability of Inclusion/Exclusion (CPI/CPE). Exposition and elucidation of this method and a protocol for use is the focus of this article. Formulae and other supporting materials are provided. CONCLUSIONS: Guidance and details of a DNA mixture interpretation protocol is provided for application of the CPI/CPE method in the analysis of more complex forensic DNA mixtures. This description, in turn, should help reduce the variability of interpretation with application of this methodology and thereby improve the quality of DNA mixture interpretation throughout the forensic community.

Uncertainty in the number of contributors in the proposed new CODIS set.

The probability that multiple contributors are detected within a forensic DNA profile improves as more highly polymorphic loci are analysed. The assignment of the correct number of contributors to a profile is important when interpreting the DNA profiles. In this work we investigate the probability of a mixed DNA profile appearing as having originated from a fewer number of contributors for the African American, Asian, Caucasian and Hispanic US populations. We investigate a range of locus configurations from the proposed new CODIS set. These theoretical calculations are based on allele frequencies only and ignore peak heights. We show that the probability of a higher order mixture (five or six contributors) appearing as having originated from one less individual is high. This probability decreases as the number of loci tested increases.