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.

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.

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.

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.

Likelihood ratio formulae for disputed parentage when the product of conception is trisomic.

We present here the derivation of paternity index formulae that covers situations of a disputed paternity trio with a trisomic product of conception. We consider six possible mechanisms for trisomy to occur: dispermy, dieggy, paternal meiosis I or II, and maternal meiosis I or II in the calculation. We also provide a biological explanation for how each of the mechanisms could give rise to a trisomy. The paper is set out in a general manner so that the tables presented can be used on any instance of trisomic offspring. This work is motivated by a case of disputed paternity where the product of conception was trisomic, i.e. the electropherogram of the product of conception possessed three alleles at each locus. The outcome was extremely strong support for the alleged father's paternity of the product of conception.

Haplotype data for 16 Y-chromosome STR loci in Aboriginal and Caucasian populations in South Australia.

UNLABELLED: Y-STR haplotype data was obtained using the AmpFlSTR(®) YFiler™ PCR Amplification Kit (Applied Biosystems, Foster City, CA) for 1079 Caucasian and 766 Australian Aboriginal individuals. Haplotype diversity was similar in both populations, however discrimination capacity was higher in Caucasians than Aborigines (0.946 compared to 0.692). Locus DYS385, which was considered as a single locus, was the most diverse marker in both populations (0.836 in Caucasians and 0.905 in Aborigines). POPULATION: The South Australian Aboriginal and Caucasian databases were compiled from casework reference profile information held on the South Australian Criminal, Reference and Evidence DNA Database (SACREDD). Ethnicity was assigned based on self-declaration.

South Australian Aboriginal sub-population data for the nine AMPFlSTR Profiler Plus short tandem repeat (STR) loci.

The weighting of DNA evidence can be estimated using various models. Each model relies on allele frequencies, which are obtained from data accumulated in a population database. This report analyses sub-population data for 325 Aboriginal Australian residents of South Australia at nine autosomal Profiler Plus short tandem repeat (STR) loci.