Identifying distant relatives using benchtop-scale sequencing.

The genetic component of forensic genetic genealogy (FGG) is an estimate of kinship, often conducted at genome scales between a great number of individuals. The promise of FGG is substantial: in concert with genealogical records and other nongenetic information, it can indirectly identify a person of interest. A downside of FGG is cost, as it is currently expensive and requires chemistries uncommon to forensic genetic laboratories (microarrays and high throughput sequencing). The more common benchtop sequencers can be coupled with a targeted PCR assay to conduct FGG, though such approaches have limited resolution for kinship. This study evaluates low-pass sequencing, an alternative strategy that is accessible to benchtop sequencers and can produce resolutions comparable to high-pass sequencing. Samples from a three-generation pedigree were augmented to include up to 7th degree relatives (using whole genome pedigree simulations) and the ability to recover the true kinship coefficient was assessed using algorithms qualitatively similar to those found in GEDmatch. We show that up to 7th degree relatives can be reliably inferred from 1 × whole genome sequencing obtainable from desktop sequencers.

Mixture detection with Demixtify.

The de facto genetic markers of forensics are short tandem repeats (STRs). There are many analytical tools designed to work with STRs, including techniques for analyzing and assessing DNA mixtures. In contrast, the nascent field of forensic genetic genealogy often relies on biallelic single nucleotide polymorphisms (SNPs). Tools designed for the forensic assessment of SNPs are somewhat lacking, especially for DNA mixtures. In this paper we introduce Demixtify, a program that detects DNA mixtures using biallelic SNPs. Demixtify is quite powerful; highly imbalanced mixtures can be detected (≤1:99, considering in silico and in vitro mixtures) when coverage is ample. Demixtify can also detect mixtures in low coverage (∼1×) samples (when the mixture is relatively balanced). Demixtify includes an empirical estimator of sequence error that is specific to the markers assayed, making it especially relevant to the forensic community. Orthogonal techniques are also developed to characterize in vitro mixtures, as well as samples thought to be single source, and the results of these approaches serve to validate the techniques presented.

Study of CTS DNA Proficiency Tests with Regard to DNA Mixture Interpretation: A NIST Scientific Foundation Review.

The National Institute of Standards and Technology has released a document entitled DNA Mixture Interpretation: A NIST Scientific Foundation Review for public comment. This has become known as the Draft NIST Foundation Review. It contains the statement: "Across these 69 data sets, there were 80 false negatives and 18 false positives reported from 110,408 possible responses (27,602 participants × two evidence items × two reference items). In the past five years, the number of participants using PGS has grown." We examine a set of proficiency test results to determine if these NIST statements could be justified. The summary reports for each relevant forensic biology test (Forensic Biology, Semen, and Mixture) in the years 2018-2021 were reviewed. Data were also provided to us by CTS upon our request. None of the false positives or negatives could be attributed to the mixture interpretation strategy and certainly not to the use of PGS.

Assessment of human nuclear and mitochondrial DNA qPCR assays for quantification accuracy utilizing NIST SRM 2372a.

In forensic DNA casework, a highly accurate real-time quantitative polymerase chain reaction (qPCR) assay is recommended per the Scientific Working Group on DNA Analysis Methods (SWGDAM) (SWGDAM Validation Guidelines for DNA Analysis Methods [1]) to determine whether a DNA sample is of sufficient quantity and robust quality to move forward with downstream short tandem repeats (STR) or sequencing analyses. Most of these assays rely on a standard curve, referred to herein and traditionally as absolute qPCR, in which an unknown is compared, relative to that curve. However, one fundamental issue with absolute qPCR is the quantifiable concentration of commercial assay standards can vary depending on (1) origin, i.e., whether from a cell line or a human subject, (2) supplier, (3) lot number, (4) shipping method, etc. In 2018, the National Institute for Standards and Technology (NIST) released a human DNA standard reference material for evaluating qPCR quantification standards, Standard Reference Material (SRM) 2372a, Romsos et al. (2018) [2] which contains three well-characterized human genomic DNA samples: Component A) a single male1 donor, Component B) a single female1 donor, and Component C) a 1:3 male2:female2 donor, each with certification data for nDNA and informational mitochondrial DNA(mtDNA)/nuclear DNA (nDNA) ratio data. The SRM 2372a was used to assess four qPCR assays: (1) Quantifiler Trio (Thermo Fisher Scientific, Waltham, MA) for nDNA quantification, (2) NovaQUANT (EMD Millipore Corporation, San Diego, CA) for nDNA and mtDNA quantification, (3) a custom duplex mtDNA assay, and (4) a custom triplex mtDNA assay. Additionally, extracts from eighteen (18) skeletal remains were tested with the latter three assays for concordance of DNA concentration and with assays (2) and (3), for the degradation state. Our assessment revealed that an accurate, efficient, and reproducible qPCR assay is dependent on (1) the quality and reliability of the DNA standard, (2) the qPCR chemistry, and (3) the specific primers, and probes (if applicable), used in an assay. Our findings indicate qPCR assays may not always quantify as expected and that performance of each lot should be verified using a well-characterized DNA standard such as the NIST SRM 2372a and adjusted if warranted.

Exploring likelihood ratios assigned for siblings of the true mixture contributor as an alternate contributor.

Relatives tend to have more DNA in common than unrelated people. The closer the biological relationship, the higher the chance of alleles being identical by descent between the individuals. Therefore, when considering a mixed DNA profile, close relatives of the true contributor may not always be excluded as a possible contributor to a mixture due to allele sharing. In these situations, it might be more appropriate under the alternate proposition to consider that the DNA could have originated from a relative of the person of interest rather than an unrelated individual. The probabilistic genotyping software STRmix™ automatically provides LRs considering close biological relatives as alternate sources of the DNA. In this paper, we investigate the support for siblings of the true contributor to a mixture (who are not present in the mixture themselves). We interpret the mixtures and assign LRs using STRmix™ and investigate whether the resulting LRs could be used to indicate whether the true contributor could be a sibling of the POI. Most siblings will have one or more alleles that are not observed in the mixture profile. Support for siblings to have contributed can only occur when allelic dropout is a possibility at the loci where the siblings have alleles that are not observed in the profile. In these data, that was only observed in components with assigned template of 588 rfu or less.

A new implementation of a semi-continuous method for DNA mixture interpretation.

A new calculation module within the PopStats module of the CODIS software package, based on the underlying mathematics presented in the MixKin software package, has been developed for assigning the Likelihood Ratio (LR) of DNA mixture profiles. This module uses a semi-continuous model that allows for population structure and allelic drop-out and drop-in but does not require allelic peak heights or other laboratory-specific parameters. This new implementation (named SC Mixture), like MixKin, does not specify or estimate a probability of drop-out. Instead, each contributor to a mixture has an independent drop-out rate, and the probability of the mixture profile for a specified proposition concerning the contributors is integrated over the range of possible drop-out rates. The allelic drop-in rate and the population structure parameter, theta, used by the software are specified by the user. The user can examine up to five contributors to a mixture, however, conditioning on assumed contributors and limiting the number of unknowns in both numerator and denominator hypotheses greatly improves performance. We report results from an extensive validation study performed for ten mixtures with each of one (single source), two, three, four, or five contributors, with four combinations of drop-in rate and a population structure parameter. Each mixture was run as a complete profile or with the random removal of alleles to simulate drop-out. All 1620 combinations were evaluated with PopStats, MixKin, and LRmix and considerable consistency was found among the results with all three packages.

Mitochondrial DNA control region variation in Lebanon, Jordan, and Bahrain.

This study investigated the mitochondrial DNA (mtDNA) control region variation in Middle Eastern populations (610 individuals from Lebanon, Jordan and the Kingdom of Bahrain) for which population data are scarce. FST comparison among populations revealed that there are significant differences in mtDNA distributions between Bahrain and the two other populations, while Lebanon and Jordan showed no significant differences. This was also reflected by the distribution of the observed lineages that differed prominently between Bahrain and the other two investigated populations. Jordan and Lebanon fit the hitherto known genetic results of the Levant population. Data are available via EMPOP (https://empop.online) and GenBank.

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): 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.

Probabilistic genotyping software: An overview.

The interpretation of mixed profiles from DNA evidentiary material is one of the more challenging duties of the forensic scientist. Traditionally, analysts have used a "binary" approach to interpretation where inferred genotypes are either included or excluded from the mixture using a stochastic threshold and other biological parameters such as heterozygote balance, mixture ratio, and stutter ratios. As the sensitivity of STR multiplexes and capillary electrophoresis instrumentation improved over the past 25 years, coupled with the change in the type of evidence being submitted for analysis (from high quality and quantity (often single-source) stains to low quality and quantity (often mixed) "touch" samples), the complexity of DNA profile interpretation has equally increased. This review provides a historical perspective on the movement from binary methods of interpretation to probabilistic methods of interpretation. We describe the two approaches to probabilistic genotyping (semi-continuous and fully continuous) and address issues such as validation and court acceptance. Areas of future needs for probabilistic software are discussed.

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.

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.

NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): Variation observed and lessons learned.

Interlaboratory studies are a type of collaborative exercise in which many laboratories are presented with the same set of data to interpret, and the results they produce are examined to get a "big picture" view of the effectiveness and accuracy of analytical protocols used across participating laboratories. In 2005 and again in 2013, the Applied Genetics Group of the National Institute of Standards and Technology (NIST) conducted interlaboratory studies involving DNA mixture interpretation. In the 2005 NIST MIX05 study, 69 laboratories interpreted data in the form of electropherograms of two-person DNA mixtures representing four different mock sexual assault cases with different contributor ratios. In the 2013 NIST MIX13 study,108 laboratories interpreted electropherogram data for five different case scenarios involving two, three, or four contributors, with some of the contributors potentially related. This paper describes the design of these studies, the variations observed among laboratory results, and lessons learned.

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.

The peopling of South America and the trans-Andean gene flow of the first settlers.

Genetic and archaeological data indicate that the initial Paleoindian settlers of South America followed two entry routes separated by the Andes and the Amazon rainforest. The interactions between these paths and their impact on the peopling of South America remain unclear. Analysis of genetic variation in the Peruvian Andes and regions located south of the Amazon River might provide clues on this issue. We analyzed mitochondrial DNA variation at different Andean locations and >360,000 autosomal SNPs from 28 Native American ethnic groups to evaluate different trans-Andean demographic scenarios. Our data reveal that the Peruvian Altiplano was an important enclave for early Paleoindian expansions and point to a genetic continuity in the Andes until recent times, which was only marginally affected by gene flow from the Amazonian lowlands. Genomic variation shows a good fit with the archaeological evidence, indicating that the genetic interactions between the descendants of the settlers that followed the Pacific and Atlantic routes were extremely limited.

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.

Genetic mapping of 15 human X chromosomal forensic short tandem repeat (STR) loci by means of multi-core parallelization.

Typing of X chromosomal short tandem repeat (X STR) markers has become a standard element of human forensic genetic analysis. Joint consideration of many X STR markers at a time increases their discriminatory power but, owing to physical linkage, requires inter-marker recombination rates to be accurately known. We estimated the recombination rates between 15 well established X STR markers using genotype data from 158 families (1041 individuals) and following a previously proposed likelihood-based approach that allows for single-step mutations. To meet the computational requirements of this family-based type of analysis, we modified a previous implementation so as to allow multi-core parallelization on a high-performance computing system. While we obtained recombination rate estimates larger than zero for all but one pair of adjacent markers within the four previously proposed linkage groups, none of the three X STR pairs defining the junctions of these groups yielded a recombination rate estimate of 0.50. Corroborating previous studies, our results therefore argue against a simple model of independent X chromosomal linkage groups. Moreover, the refined recombination fraction estimates obtained in our study will facilitate the appropriate joint consideration of all 15 investigated markers in forensic analysis.

Expected net gain data of low-template DNA analyses.

Low-template DNA analyses are affected by stochastic effects which can produce a configuration of peaks in the electropherogram (EPG) that is different from the genotype of the DNA׳s donor. A probabilistic and decision-theoretic model can quantify the expected net gain (ENG) of performing a DNA analysis by the difference between the expected value of information (EVOI) and the cost of performing the analysis. This article presents data on the ENG of performing DNA analyses of low-template DNA for a single amplification, two replicate amplifications, and for a second replicate amplification given the result of a first analysis. The data were obtained using amplification kits AmpFlSTR Identifiler Plus and Promega׳s PowerPlex 16 HS, an ABI 3130xl genetic sequencer, and Applied Biosystem׳s GeneMapper ID-X software. These data are supplementary to an original research article investigating whether a forensic DNA analyst should perform a single DNA analysis or two replicate analyses from a decision-theoretic point of view, entitled "Low-template DNA: a single DNA analysis or two replicates?" (Gittelson et al., 2016) [1].