A highly polymorphic panel of 40-plex microhaplotypes for the Chinese Han population and its application in estimating the number of contributors in DNA mixtures

Application of a new composite genetic marker semen-specific methylation-microhaplotype in the analysis of semen-vaginal fluid mixtures

DNA mixtures containing semen and vaginal fluid are common biological samples in forensic analysis. However, the analysis of semen-vaginal fluid mixtures remains challenging. In this study, to solve these problems, it is proposed to combine semen-specific CpG sites and closely related microhaplotype sites to form a new composite genetic marker (semen-specific methylation-microhaplotype). Six methylation-microhaplotype loci were selected. To further improve discrimination power, five methylation-SNP loci were also included. The methylation levels and genotypes of these selected loci were obtained using massively parallel sequencing technology. Except for loci MMH04ZHA019 and MMH17ZHA059, the remaining nine loci were successfully sequenced. For the successfully sequenced loci, they performed well in identifying individuals and body fluids. An allele categorization model was developed using K-nearest neighbour algorithm, which was then used to predict allele types in semen-vaginal fluid mixtures. These loci were able to confirm the presence of semen and link semen to a true donor in semen-vaginal fluid mixtures with mixing ratios of 10:1, 9:1, 5:1, 4:1, 1:1, 1:3, 1:4, 1:8 and 1:9 (semen:vaginal fluid). This preliminary study suggests that this new composite genetic marker has great potential as a supplementary tool to commonly used genetic markers (STR, etc.) for analysing semen-vaginal fluid mixtures.

Solution to a case involving the interpretation of trace degraded DNA mixtures

DNA mixture analysis poses a significant challenge in forensic genetics, particularly when dealing with degraded and trace amount DNA samples. Multi-SNPs (MNPs) are genetic markers similar to microhaplotypes but with smaller molecular sizes (< 75 bp), making them theoretically more suitable for analyzing degraded and trace amount samples. In this case report, we investigated a cold case involving a campstool stored for over a decade, aiming to detect and locate the suspect's DNA. We employed both conventional capillary electrophoresis-based short tandem repeat (CE-STR) analysis and next-generation sequencing-based multi-SNP (NGS-MNP) analysis. The typing results and deconvolution of the mixed CE-STR profiles were inconclusive regarding the presence of the suspect's DNA in the mixed samples. However, through NGS-MNP analysis and presence probability calculations, we determined that the suspect's DNA was present in the samples from Sect. 4-1 with a probability of 1-8.41 × 10- 6 (99.999159%). This evidence contradicted the suspect's statement and aided in resolving the case. Our findings demonstrate the significant potential of MNP analysis for examining degraded and trace amount DNA mixtures in forensic investigations.

Developmental and validation of a novel small and high-efficient panel of microhaplotypes for forensic genetics by the next generation sequencing

BACKGROUND

In the domain of forensic science, the application of kinship identification and mixture deconvolution techniques are of critical importance, providing robust scientific evidence for the resolution of complex cases. Microhaplotypes, as the emerging class of genetic markers, have been widely studied in forensics due to their high polymorphisms and excellent stability.

RESULTS AND DISCUSSION

In this research, a novel and high-efficient panel integrating 33 microhaplotype loci along with a sex-determining locus was developed by the next generation sequencing technology. In addition, we also assessed its forensic utility and delved into its capacity for kinship analysis and mixture deconvolution. The average effective number of alleles (Ae) of the 33 microhaplotype loci in the Guizhou Han population was 6.06, and the Ae values of 30 loci were greater than 5. The cumulative power of discrimination and cumulative power of exclusion values of the novel panel in the Guizhou Han population were 1-5.6 × 10- 43 and 1-1.6 × 10- 15, respectively. In the simulated kinship analysis, the panel could effectively distinguish between parent-child, full-sibling, half-sibling, grandfather-grandson, aunt-nephew and unrelated individuals, but uncertainty rates clearly increased when distinguishing between first cousins and unrelated individuals. For the mixtures, the novel panel had demonstrated excellent performance in estimating the number of contributors of mixtures with 1 to 5 contributors in combination with the machine learning methods.

CONCLUSIONS

In summary, we have developed a small and high-efficient panel for forensic genetics, which could provide novel insights into forensic complex kinships testing and mixture deconvolution.

Improving DNA mixtures analysis using compound markers composed of InDels and SNPs screened from the whole genome with next-generation sequencing

Next-generation sequencing (NGS) allows for better identification of insertion and deletion polymorphisms (InDels) and their combination with adjacent single nucleotide polymorphisms (SNPs) to form compound markers. These markers can improve the polymorphism of microhaplotypes (MHs) within the same length range, and thus, boost the efficiency of DNA mixture analysis. In this study, we screened InDels and SNPs across the whole genome and selected highly polymorphic markers composed of InDels and/or SNPs within 300 bp. Further, we successfully developed and evaluated an NGS-based panel comprising 55 loci, of which 24 were composed of both SNPs and InDels. Analysis of 124 unrelated Southern Han Chinese revealed an average effective number of alleles (Ae ) of 7.52 for this panel. The cumulative power of discrimination and cumulative probability of exclusion values of the 55 loci were 1-2.37 × 10-73 and 1-1.19 × 10-28 , respectively. Additionally, this panel exhibited high allele detection rates of over 97% in each of the 21 artificial mixtures involving from two to six contributors at different mixing ratios. We used EuroForMix to calculate the likelihood ratio (LR) and evaluate the evidence strength provided by this panel, and it could assess evidence strength with LR, distinguishing real and noncontributors. In conclusion, our panel holds great potential for detecting and analyzing DNA mixtures in forensic applications, with the capability to enhance routine mixture analysis.

Using simulated microhaplotype genotyping data to evaluate the value of machine learning algorithms for inferring DNA mixture contributor numbers

Inferring the number of contributors (NoC) is a crucial step in interpreting DNA mixtures, as it directly affects the accuracy of the likelihood ratio calculation and the assessment of evidence strength. However, obtaining the correct NoC in complex DNA mixtures remains challenging due to the high degree of allele sharing and dropout. This study aimed to analyze the impact of allele sharing and dropout on NoC inference in complex DNA mixtures when using microhaplotypes (MH). The effectiveness and value of highly polymorphic MH for NoC inference in complex DNA mixtures were evaluated through comparing the performance of three NoC inference methods, including maximum allele count (MAC) method, maximum likelihood estimation (MLE) method, and random forest classification (RFC) algorithm. In this study, we selected the top 100 most polymorphic MH from the Southern Han Chinese (CHS) population, and simulated over 40 million complex DNA mixture profiles with the NoC ranging from 2 to 8. These profiles involve unrelated individuals (RM type) and related pairs of individuals, including parent-offspring pairs (PO type), full-sibling pairs (FS type), and second-degree kinship pairs (SE type). Our results indicated that how the number of detected alleles in DNA mixture profiles varied with the markers' polymorphism, kinship's involvement, NoC, and dropout settings. Across different types of DNA mixtures, the MAC and MLE methods performed best in the RM type, followed by SE, FS, and PO types, while RFC models showed the best performance in the PO type, followed by RM, SE, and FS types. The recall of all three methods for NoC inference were decreased as the NoC and dropout levels increased. Furthermore, the MLE method performed better at low NoC, whereas RFC models excelled at high NoC and/or high dropout levels, regardless of the availability of a priori information about related pairs of individuals in DNA mixtures. However, the RFC models which considered the aforementioned priori information and were trained specifically on each type of DNA mixture profiles, outperformed RFC_ALL model that did not consider such information. Finally, we provided recommendations for model building when applying machine learning algorithms to NoC inference.

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.

Massively parallel sequencing of 74 microhaplotypes and forensic characteristics in three Chinese Sino-Tibetan populations

Microhaplotype (MH), as an emerging type of forensic genetic marker in recent years, has the potential to support multiple forensic applications, especially for mixture deconvolution and biogeographic ancestry inference. Herein, we investigated the genotype data of 74 MHs included in a novel MH panel, the Ion AmpliSeq MH-74 Plex Microhaplotype Research Panel, in three Chinese Sino-Tibetan populations (Han, Tibetan, and Yi) using the Ion Torrent semiconductor sequencing. The sequencing performance, allele frequencies, effective number of alleles (Ae), informativeness (In), and forensic parameters were subsequently estimated and calculated. In addition, principal component analysis (PCA) and structure analysis were performed to explore the population relationships among the three populations and the ancestry component distribution. Overall, this novel MH panel is robust and reliable, and has an excellent sequencing performance. The Ae values ranged from 1.0126 to 7.0855 across all samples, and 75.68 % of MHs had Ae values >2.0000. Allele frequencies at some loci varied considerably among the three studied populations, and the mean In value was 0.0195. Moreover, the genetic affinity between Tibetans and Yis was closer than that between Tibetans and Hans. The aforementioned results suggest that the Ion AmpliSeq MH-74 Plex Microhaplotype Research Panel is highly polymorphic in three investigated populations and could be used as an effective tool for human forensics. Although these 74 MHs have demonstrated the competency in continental population stratification, a higher resolution for distinguishing intracontinental subpopulations and a more comprehensive database with sufficient reference population data still remain to be accomplished.

An MPS-Based 50plex Microhaplotype Assay for Forensic DNA Analysis

Microhaplotypes (MHs) are widely accepted as powerful markers in forensic studies. They have the advantage of both short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs), with no stutter and amplification bias, short fragments and amplicons, low mutation and recombination rates, and high polymorphisms. In this study, we constructed a panel of 50 MHs that are distributed on 21 chromosomes and analyzed them using the Multiseq multiple polymerase chain reaction (multi-PCR) targeted capture sequencing protocol based on the massively parallel sequencing (MPS) platform. The sizes of markers and amplicons ranged between 11–81 bp and 123–198 bp, respectively. The sensitivity was 0.25 ng, and the calling results were consistent with Sanger sequencing and the Integrative Genomics Viewer (IGV). It showed measurable polymorphism among sequenced 137 Southwest Chinese Han individuals. No significant deviations in the Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were found at all MHs after Bonferroni correction. Furthermore, the specificity was 1:40 for simulated two-person mixtures, and the detection rates of highly degraded single samples and mixtures were 100% and 93–100%, respectively. Moreover, animal DNA testing was incomplete and low depth. Overall, our MPS-based 50-plex MH panel is a powerful forensic tool that provides a strong supplement and enhancement for some existing panels.

Development and evaluation of a novel panel containing 188 microhaplotypes for 2nd-degree kinship testing in the Hebei Han population

Distant kinship identification is one of the critical problems in forensic genetics. As a new type of genetic marker defined and discussed in the last decade, the microhaplotype (MH) has drawn much attention in such identification owing to its specific advantages to traditional short tandem repeat (STR) or single nucleotide polymorphism (SNP) markers. In this study, MH markers were screened step by step from the 1000 Genomes Project database, and a novel multiplex panel containing 188 MHs (in which 181 are reported the first time, while 1 was reported in a previous study and the other 6 have partial overlaps with known markers) was constructed for application in 2nd- and 3rd-degree kinship identification. Along with the construction, a novel MH nomenclature was proposed, in which the SNP position information they contained was taken into account to eliminate the possibility that the same locus was named differently interlaboratory. After a series of evaluations, the panel was shown to have good sequencing accuracy, high sensitivity, species specificity, and resistance to anti-PCR inhibitors or degradation. Population data of the 188 MHs were calculated based on the genetic information of 221 unrelated Hebei Han individuals, and the effective number of alleles (Ae) ranged from 2.0925 to 8.2634 (with an average of 2.9267). For the whole system, the cumulative matching probability (CMP), the cumulative power of exclusion in paternity testing of duos (CPEduo) and that of trios (CPEtrio) reached 2.8422 × 10^-137, 1-1.3109 × 10^-21, and 1-2.8975 × 10^-39, respectively, indicating that this panel was satisfactory for individual identification and paternity testing. Then, the efficiency of the 188 MHs in 2nd- and 3rd-degree kinship testing was studied based on 30 extended families consisting of 179 2nd-degree and 121 3rd-degree relatives, as well as simulations of 0.5 million pairs of those two kinships. The results showed that clear opinions would be given in 83.36% of 2nd-degree identifications with a false rate less than 10^-5, when the confirming and excluding thresholds of cumulative likelihood ratio (CLR) were set as 10⁴ and 10^-4, respectively. This panel is still not sufficient to solve the problem of 3rd-degree kinship identification alone, and approximately 300 or 870 MH loci would be needed in 2nd- or 3rd-degree kinship identification, respectively, to achieve a system efficiency not less than 0.99 with such a threshold set; such necessary numbers would be used only as a reference in further research.

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.

Applications of massively parallel sequencing in forensic genetics

Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.

State of the Art for Microhaplotypes

In recent years, the number of publications on microhaplotypes has averaged more than a dozen papers annually. Many have contributed to a significant increase in the number of highly polymorphic microhaplotype loci. This increase allows microhaplotypes to be very informative in four main areas of forensic uses of DNA: individualization, ancestry inference, kinship analysis, and mixture deconvolution. The random match Probability (RMP) can be as small as 10−100 for a large panel of microhaplotypes. It is possible to measure the heterozygosity of an MH as the effective number of alleles (Ae). Ae > 7.5 exists for African populations and >4.5 exists for Native American populations for a smaller panel of two dozen selected microhaplotypes. Using STRUCTURE, at least 10 different ancestral clusters can be defined by microhaplotypes. The Ae for a locus is also identical to the Paternity Index (PI), the measure of how informative a locus will be in parentage testing. High Ae loci can also be useful in missing persons cases. Finally, high Ae microhaplotypes allow the near certainty of seeing multiple additional alleles in a mixture of two or more individuals in a DNA sample. In summary, a panel of higher Ae microhaplotypes can outperform the standard CODIS markers.