DNA microarray as a tool in establishing genetic relatedness—Current status and future prospects

Effects and considerations of multiplexing ForenSeq Kintelligence libraries with a negative control

The negative template control or negative amplification control has been an essential component of the forensic DNA analysis workflow that helps monitor contamination. As such, the inclusion of a negative control in forensic DNA analysis has been a requirement for all laboratories audited under the FBI's Quality Assurance Standards. As massively parallel sequencing (MPS) becomes more conventional in forensic laboratories, considerations for the inclusion of a negative control in every sequencing run can be evaluated. Although the inclusion of a negative control in library preparation and the first sequencing run has a practical function, there is less utility for its inclusion in all subsequent sequencing runs for that library preparation. Although this is universal to all MPS assays, it is most relevant for an assay that has a low sample multiplexing capacity, such as the ForenSeq Kintelligence Kit (Qiagen/Verogen, Inc.). The ForenSeq Kintelligence Kit is an investigative genetic genealogy (IGG) sequencing-based assay that targets 10,230 forensically relevant single-nucleotide polymorphisms. The manufacturer recommends multiplexing 3 libraries per sequencing run, which includes controls. The purpose of this study was to investigate the effect of the inclusion of a negative control in every Kintelligence sequencing run. We observed that the library generated from a negative amplification control will take 7%-14% of the run output. The loss of sequencing space taken by a negative control decreased the available output for DNA-containing samples, leading in some cases to allele or locus dropout and accompanying higher numbers of sixth to seventh order unknown associations in GEDmatch PRO.

A novel approach of kinship determination based on the physical length of genetically shared regions of chromosomes

BACKGROUND

Determination of genetic relatedness between individuals plays a crucial role in resolving numerous civil cases involving familial relationships and in forensic investigation concerning missing persons. Short tandem repeats (STRs), known for their high degree of DNA polymorphism, have traditionally been the primary choice of DNA markers in genetic testing, but their application for kinships testing is limited to cases involving close kinship. SNPs have emerged as promising supplementary markers for kinship determination. Nevertheless, the challenging remains in discriminating between third-degree or more distant relatives, such as first cousins, using SNPs.

OBJECTIVE

To investigate a kinship analysis method for distant degree of familial relationships using high-density SNP data.

METHODS

A high-density SNP data from 337 individuals of Korean families using Affymetrix Axiom KORV1.0-96 Array was obtained for this study. SNPs were aligned by chromosomal positions, and identity-by-state (IBS) was determined, and then shared regions as consecutive SNPs with IBS of 1 or 2 were investigated. The physical lengths of these IBS segments were measured and summed them to create an Index, as a measure of kinship.

RESULTS

The kinship was determined by the physical length of shared chromosomal regions that are distinguished by each kinship. Using this method, the relationship was able be distinguished up to the fourth degree of kinship, and non-relatives were clearly distinguished from true relatives. We also found a potential for this approach to be used universally, regardless of microarray platforms for SNP genotyping and populations.

CONCLUSION

This method has a potential to determine the different degree of kinship between individuals and to distinguish non-relatives from true relatives, which can be of great help for practical applications in kinship determination.

Effects of DNA degradation and genotype imputation on high-density SNP microarray in pairwise kinship analysis

High-density single nucleotide polymorphisms (SNPs) can detect distant relatives even in the context of pairwise kinship analysis. Although DNA microarrays conveniently generate genome-wide SNP data, they require large quantities of high-quality DNA. Genotyping data obtained from low-quantity and low-quality samples are likely unreliable owing to the incidence of no-called or mistyped SNPs. In this study, we examined the effects of insufficient sample densities and sample degradation on the efficacy of kinship analysis. While low DNA amounts had a minor effect, DNA degradation led to a significant increase in no-call rates and error rates. Posterior probabilities of kinship determination, calculated using the index of chromosomal sharing, were markedly lower in proportion to the no-call rates and error rates. We also investigated the effect of genotype imputation to complement the no-called genome data utilizing SNPs reference panels. We found that the posterior probability of the relative-assumed person increased with genotype complementation in case of mild degradation, even with mistyped genotypes. Therefore, DNA microarray with imputation is a promising method for analyzing forensic DNA samples taken from situations where DNA quantity and quality may be compromised, such as disaster victim identification using pairwise kinship analysis.

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.

Sequenced-based French population data from 169 unrelated individuals with Verogen's ForenSeq DNA signature prep kit

Massively Parallel Sequencing (MPS) applied to forensic genetics allows the simultaneous analysis of hundreds of genetic markers and the access to full amplicon sequences which help to increase available allele diversity. Meanwhile, sequence variation within the repeat regions represents the majority of the allele diversity, flanking regions adjacent to the repeat core provide an additional degree of variation. The forensic genetics community needs access to population data, from relevant parts of the world that contain this new sequence diversity in order to perform statistical calculations. In this study, we report sequence-based Short Tandem Repeat (STR) and identity Single Nucleotide Polymorphism (iSNPs) allele data for 169 French individuals across 58 STRs and 92 SNPs included in the Verogen ForenSeq DNA Signature Prep kit. 42 STRs out of 58 showed an increased number of alleles due to sequence variation in the repeat motif and/or the flanking regions. D9S1122 showed the largest overall gain with an increase of observed heterozygosities of almost 25 %. The combined match probability combining 27 autosomal STRs and 91 identity SNPs was 1.11E-69. Sequence-based allele frequencies included in this publication will help forensic laboratories to increase the power of discrimination for identification, kinship analysis and mixture interpretation.

Benefits and limitations of genome-wide association studies

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

Utility of ForenSeq™ DNA Signature Prep Kit in the research of pairwise 2nd-degree kinship identification

The scope of forensic kinship analysis is being extended to more distant or complex relationships. However, current methods and standards in this field do not meet the needs of casework. The next-generation sequencing (NGS) technology may hold an advantage in this field to traditional methods due to its strong power to get much more genetic information. To evaluate the effectiveness of NGS to identify the 2nd-degree kinship pairs, DNA samples of 227 individuals from 49 Hebei Han pedigrees were tested by Goldeneye™ 20A kit using capillary electrophoresis (CE) to confirm the relationships within each pedigree, and those of 111 individuals within 97 confirmed grandparent-grandchild or avuncular pairs were analyzed by ForenSeq™ DNA Signature Prep Kit using MiSeq® FGx™ DNA sequencing platform. We calculated the likelihood ratio (LR) based on ITO method and the identical by state (IBS) score of 97 kinship pairs and compared with those of 97 unrelated pairs. According to the results summarized and analyzed by Fisher discriminant analysis and leave-one-out cross-validation (LOOCV) method, ITO method showed higher accuracy than IBS method, even with less information. Therefore, we proposed a recommendation of the thresholds for pairwise 2nd-degree kinship identification for Hebei Han population based on ITO method. When using ITO method based on 94 SNPs and the length information of 27 autosomal STRs, cumulative likelihood ratio (CLR) > 1 and CLR < 0.1 are recommended as the thresholds of confirming and excluding, respectively. The accuracy applying such thresholds is greater than 95%, indicating the promising application value of NGS in this field and providing a direction for further kinship identification strategy selection. Further studies are needed to get the population genetic data of loci contained in the kit based on all sequence information including flanking regions to make full use of the NGS data to improve the accuracy of kinship analysis.

Strategies for determining kinship in wild populations using genetic data

Knowledge of kin relationships between members of wild animal populations has broad application in ecology and evolution research by allowing the investigation of dispersal dynamics, mating systems, inbreeding avoidance, kin recognition, and kin selection as well as aiding the management of endangered populations. However, the assessment of kinship among members of wild animal populations is difficult in the absence of detailed multigenerational pedigrees. Here, we first review the distinction between genetic relatedness and kinship derived from pedigrees and how this makes the identification of kin using genetic data inherently challenging. We then describe useful approaches to kinship classification, such as parentage analysis and sibship reconstruction, and explain how the combined use of marker systems with biparental and uniparental inheritance, demographic information, likelihood analyses, relatedness coefficients, and estimation of misclassification rates can yield reliable classifications of kinship in groups with complex kin structures. We outline alternative approaches for cases in which explicit knowledge of dyadic kinship is not necessary, but indirect inferences about kinship on a group- or population-wide scale suffice, such as whether more highly related dyads are in closer spatial proximity. Although analysis of highly variable microsatellite loci is still the dominant approach for studies on wild populations, we describe how the long-awaited use of large-scale single-nucleotide polymorphism and sequencing data derived from noninvasive low-quality samples may eventually lead to highly accurate assessments of varying degrees of kinship in wild populations.

Pairwise Kinship Analysis by the Index of Chromosome Sharing Using High-Density Single Nucleotide Polymorphisms

We developed a new approach for pairwise kinship analysis in forensic genetics based on chromosomal sharing between two individuals. Here, we defined "index of chromosome sharing" (ICS) calculated using 174,254 single nucleotide polymorphism (SNP) loci typed by SNP microarray and genetic length of the shared segments from the genotypes of two individuals. To investigate the expected ICS distributions from first- to fifth-degree relatives and unrelated pairs, we used computationally generated genotypes to consider the effect of linkage disequilibrium and recombination. The distributions were used for probabilistic evaluation of the pairwise kinship analysis, such as likelihood ratio (LR) or posterior probability, without allele frequencies and haplotype frequencies. Using our method, all actual sample pairs from volunteers showed significantly high LR values (i.e., ≥ 108); therefore, we can distinguish distant relationships (up to the fifth-degree) from unrelated pairs based on LR. Moreover, we can determine accurate degrees of kinship in up to third-degree relationships with a probability of > 80% using the criterion of posterior probability ≥ 0.90, even if the kinship of the pair is totally unpredictable. This approach greatly improves pairwise kinship analysis of distant relationships, specifically in cases involving identification of disaster victims or missing persons.

On the use of dense SNP marker data for the identification of distant relative pairs

There has been recent interest in the exploitation of readily available dense genome scan marker data for the identification of relatives. However, there are conflicting findings on how informative these data are in practical situations and, in particular, sets of thinned markers are often used with no concrete justification for the chosen spacing. We explore the potential usefulness of dense single nucleotide polymorphism (SNP) arrays for this application with a focus on inferring distant relative pairs. We distinguish between relationship estimation, as defined by a pedigree connecting the two individuals of interest, and estimation of general relatedness as would be provided by a kinship coefficient or a coefficient of relatedness. Since our primary interest is in the former case, we adopt a pedigree likelihood approach. We consider the effect of additional SNPs and data on an additional typed relative, together with choice of that relative, on relationship inference. We also consider the effect of linkage disequilibrium. When overall relatedness, rather than the specific relationship, would suffice, we propose an approximate approach that is easy to implement and appears to compete well with a popular moment-based estimator and a recent maximum likelihood approach based on chromosomal sharing. We conclude that denser marker data are more informative for distant relatives. However, linkage disequilibrium cannot be ignored and will be the main limiting factor for applications to real data.

Dynamic range extension of hybridization sensors

In hybridization based nucleic acid sensors the stringency of hybridization poses a challenge to design and experiment. For a given set of experimental parameters the affinity window of probe-target interaction is always limited and vice versa for a given probe set design, changes in experimental conditions can easily bring some measurements out of detection range. In this paper we introduce and apply a strategy to extend this dynamic range for affinity sensors, sensors which measure the amount of hybridized molecules after equilibrium is reached. The method relies on concepts of additivity of nucleic acids hybridization free energies and on equilibrium isotherms. It consists in combining the measurements from probes with different lengths, by appropriately rescaling the measured signals. We test the validity of the approach on experiments and show that by combining probes with hybridizing regions of length 21, 23 and 25 nucleotides we manage to extend the dynamic range of the intensity signals by a factor of 25. The presented concept is easy to extend, platform free and applies to any hybridization based affinity sensor.

Using object oriented bayesian networks to model linkage, linkage disequilibrium and mutations between STR markers

In a number of applications there is a need to determine the most likely pedigree for a group of persons based on genetic markers. Adequate models are needed to reach this goal. The markers used to perform the statistical calculations can be linked and there may also be linkage disequilibrium (LD) in the population. The purpose of this paper is to present a graphical Bayesian Network framework to deal with such data. Potential LD is normally ignored and it is important to verify that the resulting calculations are not biased. Even if linkage does not influence results for regular paternity cases, it may have substantial impact on likelihood ratios involving other, more extended pedigrees. Models for LD influence likelihoods for all pedigrees to some degree and an initial estimate of the impact of ignoring LD and/or linkage is desirable, going beyond mere rules of thumb based on marker distance. Furthermore, we show how one can readily include a mutation model in the Bayesian Network; extending other programs or formulas to include such models may require considerable amounts of work and will in many case not be practical. As an example, we consider the two STR markers vWa and D12S391. We estimate probabilities for population haplotypes to account for LD using a method based on data from trios, while an estimate for the degree of linkage is taken from the literature. The results show that accounting for haplotype frequencies is unnecessary in most cases for this specific pair of markers. When doing calculations on regular paternity cases, the markers can be considered statistically independent. In more complex cases of disputed relatedness, for instance cases involving siblings or so-called deficient cases, or when small differences in the LR matter, independence should not be assumed. (The networks are freely available at http://arken.umb.no/~dakl/BayesianNetworks.).

DNA analysis in Disaster Victim Identification

DNA profiling and matching is one of the primary methods to identify missing persons in a disaster, as defined by the Interpol Disaster Victim Identification Guide. The process to identify a victim by DNA includes: the collection of the best possible ante-mortem (AM) samples, the choice of post-mortem (PM) samples, DNA-analysis, matching and statistical weighting of the genetic relationship or match. Each disaster has its own scenario, and each scenario defines its own methods for identification of the deceased.