Improved computations for relationship inference using low-coverage sequencing data.

Pedigree inference, for example determining whether two persons are second cousins or unrelated, can be done by comparing their genotypes at a selection of genetic markers. When the data for one or more of the persons is from low-coverage next generation sequencing (lcNGS), currently available computational methods either ignore genetic linkage or do not take advantage of the probabilistic nature of lcNGS data, relying instead on first estimating the genotype. We provide a method and software (see familias.name/lcNGS) bridging the above gap. Simulations indicate how our results are considerably more accurate compared to some previously available alternatives. Our method, utilizing a version of the Lander-Green algorithm, uses a group of symmetries to speed up calculations. This group may be of further interest in other calculations involving linked loci.

Whole exome sequencing of FFPE samples-expanding the horizon of forensic molecular autopsies.

Forensic molecular autopsies have emerged as a tool for medical examiners to establish the cause of death. It is particularly useful in sudden unexplained deaths where the cause of death cannot be determined with a regular medical autopsy. We provide the first study of exome data from formalin-fixed paraffin-embedded samples (FFPE) paired with data from high-quality blood samples in forensic applications. The approach allows exploration of the potential to use FFPE samples for molecular autopsies and identify variants in extensive exome data. We leverage the high uniformity of the hybridization capture approach provided by Twist Bioscience to target the complete exome and sequence the libraries on a NextSeq 550. Our findings suggest that exome sequencing is feasible for 24 out of a total of 35 included FFPE samples. When successful, the coverage across the exome is comparatively high (> 90% covered to 20X) and uniform (fold80 below 1.5). Detailed variant comparisons for matched FFPE and blood samples show high concordance with few false variants (positive predictive value of 0.98 and a sensitivity of 0.97) with no distinct FFPE artefacts. Ultimately, we apply carefully constructed forensic gene panels in a stepwise manner to find genetic variants associated with the clinical phenotype and with relevance to the sudden unexplained death.

Curiosities of X chromosomal markers and haplotypes.

Recent progress in forensic genetics has introduced a number of closely located short tandem repeat (STR) markers on the X chromosome. Inevitably, dependencies arise that have to be accounted for. This paper will in detail explore the complex statistical interpretation of X-chromosomal STR markers, focusing on likelihood calculations. Specifically, we will investigate how the phase uncertainty of haplotypes comes into play in the statistical evaluations and what curious effects this phenomenon can have. The starting point is the different real cases where the weight of evidence has provided unexpected results that require further investigation in order to be fully understood. We will touch upon subjects such as association between alleles, recombinations, and mutations. The aim of this study is to facilitate a better understanding of the interaction between the concepts in addition to provide an understanding why good estimates of haplotype frequencies are crucial. The individual subjects have been discussed in other fields, whereas this study will focus on forensic applications where few studies have been conducted relating to the understanding of how these concepts interact.

Mixtures with relatives and linked markers.

Mixture DNA profiles commonly appear in forensic genetics, and a large number of statistical methods and software are available for such cases. However, most of the literature concerns mixtures where the contributors are assumed unrelated and the genetic markers are unlinked. In this paper, we consider mixtures of linked markers and related contributors. If no relationships are involved, linkage can be ignored. While unlinked markers can be treated independently, linkage introduces dependencies. The use of linked markers presents statistical and computational challenges, but may also lead to a considerable increase in power since the number of markers available is much larger if we do not require the markers to be unlinked. In addition, some cases that cannot be solved with an unlimited number of unlinked autosomal markers can be solved with linked markers. We focus on two special cases of linked markers: pairs of linked autosomal markers and X-chromosomal markers. A framework is presented for calculation of likelihood ratios for mixtures with general relationships and with linkage between any number of markers. Finally, we explore the effect of linkage disequilibrium, also called allelic association, on the likelihood ratio.

A general model for likelihood computations of genetic marker data accounting for linkage, linkage disequilibrium, and mutations.

Several applications necessitate an unbiased determination of relatedness, be it in linkage or association studies or in a forensic setting. An appropriate model to compute the joint probability of some genetic data for a set of persons given some hypothesis about the pedigree structure is then required. The increasing number of markers available through high-density SNP microarray typing and NGS technologies intensifies the demand, where using a large number of markers may lead to biased results due to strong dependencies between closely located loci, both within pedigrees (linkage) and in the population (allelic association or linkage disequilibrium (LD)). We present a new general model, based on a Markov chain for inheritance patterns and another Markov chain for founder allele patterns, the latter allowing us to account for LD. We also demonstrate a specific implementation for X chromosomal markers that allows for computation of likelihoods based on hypotheses of alleged relationships and genetic marker data. The algorithm can simultaneously account for linkage, LD, and mutations. We demonstrate its feasibility using simulated examples. The algorithm is implemented in the software FamLinkX, providing a user-friendly GUI for Windows systems (FamLinkX, as well as further usage instructions, is freely available at www.famlink.se ). Our software provides the necessary means to solve cases where no previous implementation exists. In addition, the software has the possibility to perform simulations in order to further study the impact of linkage and LD on computed likelihoods for an arbitrary set of markers.

Models and implementation for relationship problems with dropout.

Allelic dropout in relationship problems may commonly appear in areas such as disaster victim identification and the identification of missing persons. If dropout is not accounted for, the results may be incorrect interpretation of profiles, loss of valuable information and biased results. In this paper, we explore different models for dropout in kinship cases and present an efficient implementation for one of the models. The implementation allows for dropout to be handled simultaneously with phenomena like silent alleles and mutations that may also cause discordances in relationship data, in addition to subpopulation correction. The implemented dropout model is freely available in the new version of the Familias software. The concepts and methods are illustrated on real and simulated data.

Single-cell transcriptome sequencing allows genetic separation, characterization and identification of individuals in multi-person biological mixtures.

Identifying individuals from biological mixtures to which they contributed is highly relevant in crime scene investigation and various biomedical research fields, but despite previous attempts, remains nearly impossible. Here we investigated the potential of using single-cell transcriptome sequencing (scRNA-seq), coupled with a dedicated bioinformatics pipeline (De-goulash), to solve this long-standing problem. We developed a novel approach and tested it with scRNA-seq data that we de-novo generated from multi-person blood mixtures, and also in-silico mixtures we assembled from public single individual scRNA-seq datasets, involving different numbers, ratios, and bio-geographic ancestries of contributors. For all 2 up to 9-person balanced and imbalanced blood mixtures with ratios up to 1:60, we achieved a clear single-cell separation according to the contributing individuals. For all separated mixture contributors, sex and bio-geographic ancestry (maternal, paternal, and bi-parental) were correctly determined. All separated contributors were correctly individually identified with court-acceptable statistical certainty using de-novo generated whole exome sequencing reference data. In this proof-of-concept study, we demonstrate the feasibility of single-cell approaches to deconvolute biological mixtures and subsequently genetically characterise, and individually identify the separated mixture contributors. With further optimisation and implementation, this approach may eventually allow moving to challenging biological mixtures, including those found at crime scenes.

Extended population genetic analysis of 12 X-STRs - Exemplified using a Norwegian population sample.

The use of X-chromosomal markers to resolve questions of relatedness has experienced a significant increase during the last years in forensic genetics. Perhaps primarily due to the emergence of commercial kits, but equally important due to an increased awareness of the utility of those markers. The X-chromosomal inheritance pattern entails that some cases, for instance paternal half-sisters, can potentially be resolved using a few X-chromosomal markers alone. For the statistical assessment in kinship cases it is of importance to have relevant population frequency data. In the present study 631 unrelated males from a Norwegian population sample are analyzed. The resulting haplotypes are compared to previously studied population samples and a deeper analysis of the linkage disequilibrium (LD) structure is conducted. We demonstrate that the power to detect LD will be low when few males, say below 300, are analyzed. We use entropy to describe the degree of LD between multiallelic loci and describe how this measure varies between different studied populations. Large population frequency databases have been recommended when using X-chromosomal markers, and we show that by combining reference databases from genetically similar populations, more precise haplotype frequency estimates can be obtained for rare haplotypes which improves the statistical assessment of the weight of evidence. In addition, we promote the use of simulations to assess the utility of STR markers in contrast to standard forensic parameters. Specifically we perform extensive simulations on cases where X-chromosomal markers are important and illustrate how the results can be used to infer the information gained from these markers.

Impact of SNP microarray analysis of compromised DNA on kinship classification success in the context of investigative genetic genealogy.

Single nucleotide polymorphism (SNP) data generated with microarray technologies have been used to solve murder cases via investigative leads obtained from identifying relatives of the unknown perpetrator included in accessible genomic databases, an approach referred to as investigative genetic genealogy (IGG). However, SNP microarrays were developed for relatively high input DNA quantity and quality, while DNA typically obtainable from crime scene stains is of low DNA quantity and quality, and SNP microarray data obtained from compromised DNA are largely missing. By applying the Illumina Global Screening Array (GSA) to 264 DNA samples with systematically altered quantity and quality, we empirically tested the impact of SNP microarray analysis of compromised DNA on kinship classification success, as relevant in IGG. Reference data from manufacturer-recommended input DNA quality and quantity were used to estimate genotype accuracy in the compromised DNA samples and for simulating data of different degree relatives. Although stepwise decrease of input DNA amount from 200 ng to 6.25 pg led to decreased SNP call rates and increased genotyping errors, kinship classification success did not decrease down to 250 pg for siblings and 1st cousins, 1 ng for 2nd cousins, while at 25 pg and below kinship classification success was zero. Stepwise decrease of input DNA quality via increased DNA fragmentation resulted in the decrease of genotyping accuracy as well as kinship classification success, which went down to zero at the average DNA fragment size of 150 base pairs. Combining decreased DNA quantity and quality in mock casework and skeletal samples further highlighted possibilities and limitations. Overall, GSA analysis achieved maximal kinship classification success from 800 to 200 times lower input DNA quantities than manufacturer-recommended, although DNA quality plays a key role too, while compromised DNA produced false negative kinship classifications rather than false positive ones.

Investigative genetic genealogy: Current methods, knowledge and practice.

Investigative genetic genealogy (IGG) has emerged as a new, rapidly growing field of forensic science. We describe the process whereby dense SNP data, commonly comprising more than half a million markers, are employed to infer distant relationships. By distant we refer to degrees of relatedness exceeding that of first cousins. We review how methods of relationship matching and SNP analysis on an enlarged scale are used in a forensic setting to identify a suspect in a criminal investigation or a missing person. There is currently a strong need in forensic genetics not only to understand the underlying models to infer relatedness but also to fully explore the DNA technologies and data used in IGG. This review brings together many of the topics and examines their effectiveness and operational limits, while suggesting future directions for their forensic validation. We further investigated the methods used by the major direct-to-consumer (DTC) genetic ancestry testing companies as well as submitting a questionnaire where providers of forensic genetic genealogy summarized their operation/services. Although most of the DTC market, and genetic genealogy in general, has undisclosed, proprietary algorithms we review the current knowledge where information has been discussed and published more openly.

Technical in-depth comparison of two massive parallel DNA-sequencing methods for formalin-fixed paraffin-embedded tissue from victims of sudden cardiac death.

Sudden cardiac death (SCD) is a tragic and traumatic event. SCD is often associated with hereditary genetic disease and in such cases, sequencing of stored formalin fixed paraffin embedded (FFPE) tissue is often crucial in trying to find a causal genetic variant. This study was designed to compare two massive parallel sequencing assays for differences in sensitivity and precision regarding variants related to SCD in FFPE material. From eight cases of SCD where DNA from blood had been sequenced using HaloPlex, corresponding FFPE samples were collected six years later. DNA from FFPE samples were amplified using HaloPlex HS, sequenced on MiSeq, representing the first method, as well as amplified using modified Twist and sequenced on NextSeq, representing the second method. Molecular barcodes were included to distinguish artefacts from true variants. In both approaches, read coverage, uniformity and variant detection were compared using genomic DNA isolated from blood and corresponding FFPE tissue, respectively. In terms of coverage uniformity, Twist performed better than HaloPlex HS for FFPE samples. Despite higher overall coverage, amplicon-based HaloPlex technologies, both for blood and FFPE tissue, suffered from design and/or performance issues resulting in genes lacking complete coverage. Although Twist had considerably lower overall mean coverage, high uniformity resulted in equal or higher fraction of genes covered at ≥ 20X. By comparing variants found in the matched samples in a pre-defined cardiodiagnostic gene panel, HaloPlex HS for FFPE material resulted in high sensitivity, 98.0% (range 96.6-100%), and high precision, 99.9% (range 99.5-100%) for moderately fragmented samples, but suffered from reduced sensitivity (range 74.2-91.1%) in more severely fragmented samples due to lack of coverage. Twist had high sensitivity, 97.8% (range 96.8-98.7%) and high precision, 99.9% (range 99.3-100%) in all analyzed samples, including the severely fragmented samples.

On the use of dense sets of SNP markers and their potential in relationship inference.

With the advent of high density SNP arrays and the progress of next generation sequencing, demands for new methods to handle the subsequent data analysis have exploded. Forensic laboratories are generally hesitant to implement new methods in casework unless they are thoroughly tested and validated. This is particularly true when a third party contractor is involved in the analysis. In this paper we explore data from dense sets of SNP markers and study how different errors could potentially affect the results. Particularly, we study the effects of genotyping errors, linkage disequilibrium as well as the use of inappropriate population frequencies. We demonstrate that ignoring these concepts may lead to false conclusions for some different relationship cases and outline solutions to mitigate these problems.

Forensic genealogy-A comparison of methods to infer distant relationships based on dense SNP data.

The concept forensic genealogy was discussed already in 2005 but has recently emerged in relation to the use of public genealogy databases to find relatives of the donor of a crime stain. In this study we explored the results and evaluation of searches conducted in such databases. In particular, we focused on the statistical classification that entails from the search and study the variation observed for different relationship classes. The forensic guidelines advocate the use of the likelihood ratio (LR) as a mean to measure the weight of evidence, which requires exact formulation of competing hypotheses. We contrast the LR approach with alternative approaches relying on identical by state (IBS) measures to estimate the total length of shared genomic segments as well as identical by descent (IBD) coefficients for a pair of individuals. We used freely accessible data from the 1000 Genome project to perform extensive simulations, generating data for a number of distinct relationships. Specifically we studied some overarching relationship classes and the performance of the above-mentioned evaluative approaches to classify a known pair of relatives into each class. The results indicate that the traditional LR approach as a single source of classification is as good as, and in some cases even better than, the alternative approaches. In particular the true classification rate is higher for some distant relationship. However, the LR approach is both computer-intensive and sensitive to population frequencies as well as genetic maps (positions of the markers). We further showed that when combining different classification approaches, a lower false classification rate was achieved while still maintaining a high true classification rate.

Evaluating the statistical power of DNA-based identification, exemplified by 'The missing grandchildren of Argentina'.

Methods and implementations of DNA-based identification are well established in several forensic contexts. However, assessing the statistical power of these methods has been largely overlooked, except in the simplest cases. In this paper we outline general methods for such power evaluation, and apply them to a large set of family reunification cases, where the objective is to decide whether a person of interest (POI) is identical to the missing person (MP) in a family, based on the DNA profile of the POI and available family members. As such, this application closely resembles database searching and disaster victim identification (DVI). If parents or children of the MP are available, they will typically provide sufficient statistical evidence to settle the case. However, if one must resort to more distant relatives, it is not a priori obvious that a reliable conclusion is likely to be reached. In these cases power evaluation can be highly valuable, for instance in the recruitment of additional family members. To assess the power in an identification case, we advocate the combined use of two statistics: the Probability of Exclusion, and the Probability of Exceedance. The former is the probability that the genotypes of a random, unrelated person are incompatible with the available family data. If this is close to 1, it is likely that a conclusion will be achieved regarding general relatedness, but not necessarily the specific relationship. To evaluate the ability to recognize a true match, we use simulations to estimate exceedance probabilities, i.e. the probability that the likelihood ratio will exceed a given threshold, assuming that the POI is indeed the MP. All simulations are done conditionally on available family data. Such conditional simulations have a long history in medical linkage analysis, but to our knowledge this is the first systematic forensic genetics application. Also, for forensic markers mutations cannot be ignored and therefore current models and implementations must be extended. All the tools are freely available in Familias (http://www.familias.no) empowered by the R library paramlink. The above approach is applied to a large and important data set: 'The missing grandchildren of Argentina'. We evaluate the power of 196 families from the DNA reference databank (Banco Nacional de Datos Genéticos, http://www.bndg.gob.ar. As a result we show that 58 of the families have poor statistical power and require additional genetic data to enable a positive identification.

DNA Commission of the International Society for Forensic Genetics (ISFG): Guidelines on the use of X-STRs in kinship analysis.

Forensic genetic laboratories perform an increasing amount of genetic analyses of the X chromosome, in particular to solve complex cases of kinship analysis. For some biological relationships X-chromosomal markers can be more informative than autosomal markers, and there are a large number of markers, methods and databases that have been described for forensic use. Due to their particular mode of inheritance, and their physical location on a single chromosome, some specific considerations are required when estimating the weight of evidence for X-chromosomal marker DNA data. The DNA Commission of the International Society for Forensic Genetics (ISFG) hereby presents guidelines and recommendations for the use of X-chromosomal markers in kinship analysis with a special focus on the biostatistical evaluation. Linkage and linkage disequilibrium (association of alleles) are of special importance for such evaluations and these concepts and the implications for likelihood calculations are described in more detail. Furthermore it is important to use appropriate computer software that accounts for linkage and linkage disequilibrium among loci, as well as for mutations. Even though some software exist, there is still a need for further improvement of dedicated software.

The successful use of familial searching in six Hungarian high profile cases by applying a new module in Familias 3.

Commonly DNA profiles obtained from biological samples found at crime scenes or of unknown bodies are searched against a large national or local database of reference and crime scene DNA profiles. The aim is to find a direct match, i.e. an identical profile or in the case of a mixture, at least an overlap of all alleles. Familial searching is, instead, the concept of searching for relatives of a stain donor in the database, i.e. a quest for relatives of the possible perpetrator or an unidentified body. The current paper provides the tool to perform effective searches for relatives of a target DNA profile in a large database. In addition, the algorithm accounts for mixtures which is common in crime stains. Specifically we focus on six high profile Hungarian cases where familial searching has succeeded in providing investigative leads. An implementation of the methods is included in the latest version of the software Familias, freely available at www.familias.no.

FamLinkX - implementation of a general model for likelihood computations for X-chromosomal marker data.

The use of genetic markers located on the X chromosome has seen a significant increase in the last years and their utility has been well studied. This paper describes the software FamLinkX, freely available at http://www.famlink.se, implementing a new algorithm for likelihood computations accounting for linkage, linkage disequilibrium and mutations. It is obvious that such software is sought for among forensic users as more and more X-chromosomal markers become available. We provide some simulated examples demonstrating the utility of the implementation as well as its application in forensic casework. Though algebraic derivations are generally unfeasible, the paper outlines some theoretical considerations and provides a discussion on the validation of the software. The focus of this paper is to compare the software to existing methods in a forensic setting, perform a validation study as well as to provide an idea of the discriminatory power for X-chromosomal markers.

Familias 3 - Extensions and new functionality.

In relationship testing the aim is to determine the most probable pedigree structure given genetic marker data for a set of persons. Disaster Victim Identification (DVI) based on DNA data from presumed relatives of the missing persons can be considered to be a collection of relationship problems. Forensic calculations in investigative mode address questions like "How many markers and reference persons are needed?" Such questions can be answered by simulations. Mutations, deviations from Hardy-Weinberg Equilibrium (or more generally, accounting for population substructure) and silent alleles cannot be ignored when evaluating forensic evidence in case work. With the advent of new markers, so called microvariants have become more common. Previous mutation models are no longer appropriate and a new model is proposed. This paper describes methods designed to deal with DVI problems and a new simulation model to study distribution of likelihoods. There are softwares available, addressing similar problems. However, for some problems including DVI, we are not aware of freely available validated software. The Familias software has long been widely used by forensic laboratories worldwide to compute likelihoods in relationship scenarios, though previous versions have lacked desired functionality, such as the above mentioned. The extensions as well as some other novel features have been implemented in the new version, freely available at www.familias.no. The implementation and validation are briefly mentioned leaving complete details to Supplementary sections.

FamLink--a user friendly software for linkage calculations in family genetics.

The present number of STR loci adopted in relationship testing is chiefly limited to unlinked markers, in most cases residing on different chromosomes. In order to solve more complex cases of relatedness, e.g. deficient paternities and disputed sibships, the number of core loci can be extended. The inclusion of multiple loci on the same chromosome will, however, increase the risk of possible linkage between markers. We present a new software, FamLink, freely available from http://www.FamLink.se, that can perform statistical calculations based on pedigree structures and account for linkage between pairs of markers. In addition, FamLink can simulate genotype data in order to study the effect of accounting for linkage or not. We demonstrate the importance of taking linkage properly into account using examples and real cases.