Analysis of complex DNA mixtures using massively parallel sequencing of SNPs with low minor allele frequencies

Application of SNPs with low minor allele frequencies in missing person identification (MPI) through kinship analysis of DNA mixtures

The need to identify a missing person (MP) through kinship analysis of DNA samples found at a crime scene has become increasingly prevalent. DNA samples from MPs can be severely degraded, contain little DNA and mixed with other contributors, which often makes it difficult to apply conventional methods in practice. This study developed a massively parallel sequencing-based panel that contains 1661 single-nucleotide polymorphisms (SNPs) with low minor allele frequencies (MAFs) (averaged at 0.0613) in the Chinese Han population, and the strategy for relationship inference from DNA mixtures comprising different numbers of contributors (NOCs) and of varying allele dropout probabilities. Based on the simulated dataset and genotyping results of 42 artificial DNA mixtures (NOC = 2-4), it was observed that the present SNP panel was sufficient for balanced mixtures when referenced to the closest relatives (parents/offspring and full siblings). When the mixture profiles suffered from dropout, incorrect assignments were markedly associated with relatedness, NOC and the dropout level. We, therefore, indicate that SNPs with low MAFs could be reliably interpreted for MP identification through the kinship analysis of complex DNA mixtures. Further studies should be extended to more possible scenarios to test the feasibility of this present approach.

Evaluation of large-scale highly polymorphic microhaplotypes in complex DNA mixtures analysis using RMNE method

DNA mixture interpretation is one of the most challenging problems in forensics. Complex DNA mixtures are more difficult to analyze when there are more than two contributors or related contributors. Microhaplotypes (MHs) are polymorphic genetic markers recently discovered and employed in DNA mixture analysis. However, the evidentiary interpretation of the MH genotyping data needs more debate. The Random Man Not Excluded (RMNE) method analyzes DNA mixtures without using allelic peak height data or the number of contributors (NoC) assumptions. This study aimed to assess how well RMNE interpreted mixed MH genotyping data. We classified the MH loci from the 1000 Genomes Project database into groups based on their Ae values. Then we performed simulations of DNA mixtures with 2-10 unrelated contributors and DNA mixtures with a pair of sibling contributors. For each simulated DNA mixture, incorrectly included ratios were estimated for three types of non-contributors: random men, parents of contributors, and siblings of contributors. Meanwhile, RMNE probability was calculated for contributors and three types of non-contributors, allowing loci mismatch. The results showed that the MH number, the MH Ae values, and the NoC affected the RMNE probability of the mixture and the incorrectly included ratio of non-contributors. When there were more MHs, MHs with higher Ae values, and a mixture with less NoC, the RMNE probability, and the incorrectly included ratio decreased. The existence of kinship in mixtures complicated the mixture interpretation. Contributors' relatives as non-contributors and related contributors in the mixture increased the demands on the genetic markers to identify the contributors correctly. When 500 highly polymorphic MHs with Ae values higher than 5 were used, the four individual types could be distinguished according to the RMNE probabilities. This study reveals the promising potential of MH as a genetic marker for mixed DNA interpretation and the broadening of RMNE as a parameter indicating the relationship of a specific individual with a DNA mixture in the DNA database search.

Evaluation of a SNP-STR haplotype panel for forensic genotype imputation

Short tandem repeat polymorphism (STR)-based individual identification is a popular and reliable method in many forensic applications. However, STRs still frequently fail to find any matched records. In such cases, if known STRs could provide more information, it would be very helpful to solve specific problems. Genotype imputation has long been used in the study of single nucleotide polymorphisms (SNPs) and has recently been introduced into forensic fields. The idea is that, through a reference haplotype panel containing SNPs and STRs, we can obtain unknown genetic information through genotype imputation based on known STR or SNP genotypes. Several recent studies have already demonstrated this exciting idea, and a 1000 Genomes SNP-STR haplotype panel has also been released. To further study the performance of genotype imputation in forensic fields, we collected STR, microhaplotype (MH) and SNP array genotypes from Chinese Han population individuals and then performed genotype imputation analysis based on the released reference panel. As a result, the average locus imputation accuracy was ∼83 % (or ∼70 %) when SNPs in the SNP array (or MH SNPs) were imputed from STRs, and was ∼30 % when highly polymorphic markers (STRs and MHs) were imputed from each other. When STRs were imputed from SNP array, the average locus imputation accuracy increased to ∼48 %. After analyzing the match scores between real STRs and the STRs imputed from SNPs, ∼80 % of studied STR records can be connected to corresponding SNP records, which may help for individual identification. Our results indicate that genotype imputation has great potential for forensic applications.

Forensic characterization of 124 SNPs in the central Indian population using precision ID Identity Panel through next-generation sequencing

With the advent of next-generation sequencing technology, SNP markers are being explored as a useful alternative to conventional capillary electrophoresis-based STR typing. Low mutation rate and short-sized amplicons are added advantages of SNP markers over the STRs. However, to achieve a sufficient level of discrimination among individuals, a higher number of SNPs need to be characterized simultaneously. Hence, the NGS technique is highly useful to analyze a sufficiently higher number of SNPs simultaneously. Though the technique is in its nascent stage, an attempt has been made to assess its usability in the central Indian population by analyzing 124 SNPs (90 autosomal and 34 Y-chromosome) in 95 individuals. Various quality parameters such as locus balance, locus strand balance, heterozygosity balance, and noise level showed a good quality sequence obtained from the Ion GeneStudio S5 instrument. Obtained frequency of SNP alleles ranged from 0.001 to 0.377 in autosomal SNPs. rs9951171 was found to be the most informative SNP in the studied population with the highest PD and lowest MP value. The cumulative MP of 90 SNPs was found to be 4.76698 × 10^-37. Analysis of 34 Y-chromosome SNPs reveals 11 unique haplogroups in 54 male samples with R1a1 as the most frequent haplogroup found in 22.22% of samples. Interpopulation comparison by FST analysis, PCA plot, and STRUCTURE analysis showed genetic stratification of the studied population suggesting the utility of SNP markers present in the Precision ID Identity Panel for forensic demands of the Indian population.

A novel computational strategy to predict the value of the evidence in the SNP-based forensic mixtures

This study introduces a methodology for inferring the weight of the evidence (WoE) in the single nucleotide polymorphism (SNP)-typed DNA mixtures of forensic interest. First, we redefined some algebraic formulae to approach the semi-continuous calculation of likelihoods and likelihood ratios (LRs). To address the allelic dropouts, a peak height ratio index (“h,” an index of heterozygous state plausibility) was incorporated into semi-continuous formulae to act as a proxy for the “split-drop” model of calculation. Second, the original ratio at which a person of interest (POI) has entered into the mixture was inferred by evaluating the DNA amounts conferred by unique genotypes to any possible permutation of any locus of the typing protocol (unique genotypes are genotypes that appear just once in the relevant permutation). We compared this expected ratio (MR_ex) to all the mixing ratios emerging at all other permutations of the mixture (MR_obs) using several (1 - χ²) tests to evaluate the probability of each permutation to exist in the mixture according to quantitative criteria. At the level of each permutation state, we multiplied the (1 - χ²) value to the genotype frequencies and the h index. All the products of all the permutation states were finally summed to give a likelihood value that accounts for three independent properties of the mixtures. Owing to the (1 - χ²) index and the h index, this approach qualifies as a fully continuous methodology of LR calculation. We compared the MRs and LRs emerging from our methodology to those generated by the EuroForMix software ver. 3.0.3. When the true contributors were tested as POIs, our procedure generated highly discriminant LRs that, unlike EuroForMix, never overcame the corresponding single-source LRs. When false contributors were tested as POIs, we obtained a much lower LR value than that from EuroForMix. These two findings indicate that our computational method is more reliable and realistic than EuroForMix.

The development of forensic DNA analysis: New debates on the issue of fundamental human rights

Before the advent of forensic DNA profiling, forensic techniques such as fingerprint examination and blood type comparison were used in the identification of suspects. DNA profiling has since become the gold standard of forensic science, and forensic DNA analysis techniques continue to evolve. Recent developments such as familial searching and phenotyping have raised ethical questions and concerns reflecting those expressed in the late 1980s when forensic DNA analysis was first introduced. At that time, attempts to use DNA evidence in criminal trials were met with challenges to its evidential value and admissibility. A common concern was whether the probative value of the evidence would outweigh its potentially prejudicial effect. This gave rise to a complex three-way debate, which revolved around first, the admissibility of the scientific principles in criminal courts; second, the scientific process involved in analysing DNA samples; and third, the impact that forensic DNA analysis may have on fundamental human rights. Ultimately, debates about the scientific process and the admissibility of such evidence in criminal trials overshadowed the debate about potential infringements of fundamental human rights. This resulted in a lack of critical discussion around the erosion of civil liberties through the use of scientific technologies. This paper revisits the early debates on the development of forensic DNA analysis. It draws parallels with current developments and analyses the potential for current and future human rights infringements, highlighting that the libertarian model offers a necessary counterbalance to the other arguments, due to its concern for maintaining fundamental rights.