Deconvolution Of DNA Mixtures Research Articles

A Brief Introduction to Microhaplotypes and their Uses

Microhaplotypes are genetic markers that are short DNA sequences, typically consisting of two or more single nucleotide polymorphisms (SNPs) sufficiently close molecularly (<300 basepairs) that their alleles are inherited together from parent to child. Microhaplotypes can be considered a special class of haplotypes, “micro” because they extend for only a few dozens of basepairs, not thousands of basepairs. The advantage of microhaplotypes is that, with massively parallel sequencing of a small segment, the phase is known. Microhaps have been shown to be highly informative in demonstrating uniqueness of DNA profiles and in determining the biogeographic ancestry of an individual. Tests of biologic relationships, such as paternity tests, can be done with microhaplotypes. Deconvolution of DNA mixtures is an area in which microhaplotypes appear to be especially promising. This brief introductory review presents the scientific background for the development of microhaplotypes as a new type of genetic marker and examples of some of the recent applications.

The population genetics characteristics of a 90 locus panel of microhaplotypes

Single-nucleotide polymorphisms (SNPs) and small genomic regions with multiple SNPs (microhaplotypes, MHs) are rapidly emerging as novel forensic investigative tools to assist in individual identification, kinship analyses, ancestry inference, and deconvolution of DNA mixtures. Here, we analyzed information for 90 microhaplotype loci in 4009 individuals from 79 world populations in 6 major biogeographic regions. The study included multiplex microhaplotype sequencing (mMHseq) data analyzed for 524 individuals from 16 populations and genotype data for 3485 individuals from 63 populations curated from public repositories. Analyses of the 79 populations revealed excellent characteristics for this 90-plex MH panel for various forensic applications achieving an overall average effective number of allele values (Ae) of 4.55 (range 1.04–19.27) for individualization and mixture deconvolution. Population-specific random match probabilities ranged from a low of 10–115 to a maximum of 10–66. Mean informativeness (In) for ancestry inference was 0.355 (range 0.117–0.883). 65 novel SNPs were detected in 39 of the MHs using mMHseq. Of the 3018 different microhaplotype alleles identified, 1337 occurred at frequencies > 5% in at least one of the populations studied. The 90-plex MH panel enables effective differentiation of population groupings for major biogeographic regions as well as delineation of distinct subgroupings within regions. Open-source, web-based software is available to support validation of this technology for forensic case work analysis and to tailor MH analysis for specific geographical regions.

A Novel SNP-STR System Based on a Capillary Electrophoresis Platform.

Various compound markers encompassing two or more variants within a small region can be regarded as generalized microhaplotypes. Many of these markers have been investigated for various forensic purposes, such as individual identification, deconvolution of DNA mixtures, or forensic ancestry inference. SNP-STR is a compound biomarker composed of a single nucleotide polymorphism (SNP) and a closely linked short tandem repeat polymorphism (STR), and possess the advantages of both SNPs and STRs. In addition, in conjunction with a polymerase chain reaction (PCR) technique based on the amplification refractory mutation system (ARMS), SNP-STRs can be used for forensic unbalanced DNA mixture analysis based on capillary electrophoresis (CE), which is the most commonly used platform in worldwide forensic laboratories. Our previous research reported 11 SNP-STRs, but few of them are derived from the commonly used STR loci, for which existing STR databases can be used as a reference. For maximum compatibility with existing DNA databases, in this study, we screened 18 SNP-STR loci, of which 14 were derived from the expanded CODIS core loci set. Stable and sensitive SNP-STR multiplex PCR panels based on the CE platform were established. Assays on simulated two-person DNA mixtures showed that all allele-specific primers could detect minor DNA components in 1:500 mixtures. Population data based on 113 unrelated Chengdu Han individuals were investigated. A Bayesian framework was developed for the likelihood ratio (LR) evaluation of SNP-STR profiling results obtained from two-person mixtures. Furthermore, we report on the first use of SNP-STRs in casework to show the advantages and limitations for use in practice. Compared to 2.86 × 103 for autosomal STR kits, the combined LR reached 7.14 × 107 using the SNP-STR method in this casework example.

Multi-Indel: A Microhaplotype Marker Can Be Typed Using Capillary Electrophoresis Platforms.

Since the concept of microhaplotypes was proposed by Kidd in 2013, various microhaplotype markers have been investigated for various forensic purposes, such as individual identification, deconvolution of DNA mixtures, or forensic ancestry inference. In our opinion, various compound markers are also regarded as generalized microhaplotypes, encompassing two or more variants in a short segment of DNA (e.g., 200 bp). That is, a set of variants (referred to herein as multi-variants) within a certain length includes single nucleotide polymorphisms (SNP), insertion/deletion polymorphisms (Indels), or short tandem repeat polymorphisms (STRs). At present, multi-variant is mainly aimed at multi-SNPs. However, the haplotype genotyping of multi-variants relies on single-strand analysis, mainly using massively parallel sequencing (MPS). Here, we describe a method based on a capillary electrophoresis (CE) platform that can directly obtain haplotypes of individuals. Several microhaplotypes consisting of three or more Indels with different insertion or deletion lengths in the range of less than 200 bp were screened out, each of which had at least three haplotypes. As a result, the haplotype of an individual was reflected by the length of its polymorphism. Finally, we established a multiplex amplification system containing 18 multi-Indel markers that could identify haplotypes on each chromosome of an individual. The combined power of discrimination (CPD) and the cumulative probability of exclusion (CPE) were 0.999999999997234 and 0.9984, respectively.

Bayesian regression analysis of stutter in DNA mixtures

Probabilistic genotyping methods use a hierarchical probability model in deconvolution of DNA mixtures. The parameters of the model, including the stutter which are required to calculate the expected values of peak heights, are estimated in the validation process. Linear modeling of stutter, as a common artifact in DNA genotyping, has been reported previously. The typically right-skewed error distribution and non-negativeness of stutter to its allele peak heights ratios make generalized linear models preferable, especially Bayesian analogs, which allow even more flexibility. In this paper, we show how such models can be fitted and applied with the aid of Markov chain Monte Carlo methods.

Preliminary exploration of a novel method for the deconvolution of DNA mixtures by pyrosequencing

The deconvolution of DNA mixtures has been a thorny issue. Several kinds of genetic markers, such as STR, SNP, SNP/DIP-STR and microhaplotypes etc., as well as capillary electrophoresis, NGS and other technical platforms are used to solve this problem. In this study, we propose a novel method for the analysis of DNA mixtures. This method employed multiplex pyrosequencing to sequence several selected microhaplotypes. Employing a sparse representation-based AdvISER-MH-PYRO algorithm, the original pyrosequencing (PSQ) signals of the DNA mixtures were deciphered to determine the contributors of the mixtures. Our preliminary data show that this method can resolve the number of contributors to a DNA mixture composed of 2–4 individuals.

Evaluation of a microhaplotypes panel for forensic genetics using massive parallel sequencing technology

Massive parallel DNA sequencing (MPS) makes it possible to explore a new type of genetic marker, known as microhaplotypes or microhaps. These loci were recently introduced in the landscape of forensic genetic and appear to be useful for identification purposes, reconstruction of family relationships, ancestry prediction and DNA mixtures deconvolution. Microhaplotypes loci, based on 89 loci in ALFRED, were selected and their genetic variations in 100 Italian individuals were evaluated by using MPS, in order to make inference about utility of a set of microhaps in forensic genetics. After MPS, the panel was reduced to 87 microhaps, comprised of 266 different SNPs and spread across 22 human autosomes. Genotype and haplotype frequencies were estimated, as well as the effective number of alleles at each locus (Ae), which relates to the usefulness of the locus in resolution of relationships and deconvolution of DNA mixtures. Overall, the Ae values for the 87 microhaps range from 1.010 to 8.344, with about 80% showing values greater than 2.0. Noteworthy, 32 microhaps display Ae values greater than 3.0 and 18 loci Ae above 4.0.To explore the suitability of microhaplotypes in mixture deconvolution, the probability of detecting a mixture, as a function of Ae, was inferred for different groups of loci. Considering the fourteen loci with Ae between 3.0 and 3.999 the probability of detecting a mixture was at least 0.99973, while considering the ten loci with Ae between 4.0 and 4.999 the probability was at least 0.99998. Moreover, when considering just the six loci with Ae between 5.0 and 5.999 the probability of detecting a mixture was at least 0.99984, while when considering just the two loci with Ae above 6 the probability was 0.97228. Combining these 32 MH loci, the theoretical probability of detecting a mixture was 0.999999999999973. These results make the subset of 32 loci with Ae above three informative for mixture resolution.The individual matching probabilities (PI) of the 87 microhaps ranged from 0.032 to 0.9802. Considering the 32 microhap loci with Ae greater than 3.0, the cumulative PI value was 1.6 × 10−33, while considering the 18 microhap loci with Ae above 4.0, the cumulative PI value was 2.34 × 10-21. Overall the results of this study confirmed the utility of microhaps in forensic genetics.

Enhanced DNA mixture deconvolution of sexual offense samples using the DEPArray™ system

The interpretation of DNA mixtures remains a significant challenge in the analysis of forensic evidence. The ability to selectively identify, collect, and analyze single cells or groups of cells has wide implications in the analysis of forensic samples and the subsequent deconvolution of DNA mixtures, particularly in the processing and interpretation of sexual offense evidence where the deconvolution of heterogeneous sources is essential. Single cell separation technology can be used to address this mixture separation challenge, specifically using the DEPArray™ system from Menarini Silicon Biosystems. We propose that the DEPArray™ will enable enhancements to the standard workflow for forensic biology/DNA analytical laboratories. We have demonstrated that the DEPArray™ workflow will lead to fewer mixture samples, enable purification of sperm and epithelial cell fractions without the need for differential extraction, improve the amplification success rate of samples and improve the interpretation of low template DNA samples. Sperm profiles were identified in 27 of 32 DEPArray™ processed samples, with 26 of 27 (96.2%) yielding single source profiles. In contrast, single source profiles were obtained from 9 of 28 (32.1%) differentially extracted samples. The use of the DEPArray™ also eliminates the need for additional confirmatory tests for the presence of human sperm and permits direct identification of the type and number of cells being analyzed eliminating the need for qPCR-based DNA quantification.

PACE: Probabilistic Assessment for Contributor Estimation— A machine learning-based assessment of the number of contributors in DNA mixtures

The deconvolution of DNA mixtures remains one of the most critical challenges in the field of forensic DNA analysis. In addition, of all the data features required to perform such deconvolution, the number of contributors in the sample is widely considered the most important, and, if incorrectly chosen, the most likely to negatively influence the mixture interpretation of a DNA profile. Unfortunately, most current approaches to mixture deconvolution require the assumption that the number of contributors is known by the analyst, an assumption that can prove to be especially faulty when faced with increasingly complex mixtures of 3 or more contributors. In this study, we propose a probabilistic approach for estimating the number of contributors in a DNA mixture that leverages the strengths of machine learning. To assess this approach, we compare classification performances of six machine learning algorithms and evaluate the model from the top-performing algorithm against the current state of the art in the field of contributor number classification. Overall results show over 98% accuracy in identifying the number of contributors in a DNA mixture of up to 4 contributors. Comparative results showed 3-person mixtures had a classification accuracy improvement of over 6% compared to the current best-in-field methodology, and that 4-person mixtures had a classification accuracy improvement of over 20%. The Probabilistic Assessment for Contributor Estimation (PACE) also accomplishes classification of mixtures of up to 4 contributors in less than 1s using a standard laptop or desktop computer. Considering the high classification accuracy rates, as well as the significant time commitment required by the current state of the art model versus seconds required by a machine learning-derived model, the approach described herein provides a promising means of estimating the number of contributors and, subsequently, will lead to improved DNA mixture interpretation.