Male Pedigree Toolbox: A Versatile Software for Y-STR Data Analyses.

Y-chromosomal short tandem repeats (Y-STRs) are widely used in forensic, genealogical, and population genetics. With the recent increase in the number of rapidly mutating (RM) Y-STRs, an unprecedented level of male differentiation can be achieved, widening and improving the applications of Y-STRs in various fields, including forensics. The growing complexity of Y-STR data increases the need for automated data analyses, but dedicated software tools are scarce. To address this, we present the Male Pedigree Toolbox (MPT), a software tool for the automated analysis of Y-STR data in the context of patrilineal genealogical relationships. The MPT can estimate mutation rates and male relative differentiation rates from input Y-STR pedigree data. It can aid in determining ancestral haplotypes within a pedigree and visualize the genetic variation within pedigrees in all branches of family trees. Additionally, it can provide probabilistic classifications using machine learning, helping to establish or prove the structure of the pedigree and the level of relatedness between males, even for closely related individuals with highly similar haplotypes. The tool is flexible and easy to use and can be adjusted to any set of Y-STR markers by modifying the intuitive input file formats. We introduce the MPT software tool v1.0 and make it publicly available with the goal of encouraging and supporting forensic, genealogical, and other geneticists in utilizing the full potential of Y-STRs for both research purposes and practical applications, including criminal casework.

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.

Large-scale pedigree analysis highlights rapidly mutating Y-chromosomal short tandem repeats for differentiating patrilineal relatives and predicting their degrees of consanguinity.

Rapidly mutating Y-chromosomal short tandem repeats (RM Y-STRs) were suggested for differentiating patrilineally related men as relevant in forensic genetics, anthropological genetics, and genetic genealogy. Empirical data are available for closely related males, while differentiation rates for more distant relatives are scarce. Available RM Y-STR mutation rate estimates are typically based on father-son pair data, while pedigree-based studies for efficient analysis requiring less samples are rare. Here, we present a large-scale pedigree analysis in 9379 pairs of men separated by 1-34 meioses on 30 Y-STRs with increased mutation rates including all known RM Y-STRs (RMplex). For comparison, part of the samples were genotyped at 25 standard Y-STRs mostly with moderate mutation rates (Yfiler Plus). For 43 of the 49 Y-STRs analyzed, pedigree-based mutation rates were similar to previous father-son based estimates, while for six markers significant differences were observed. Male relative differentiation rates from the 30 RMplex Y-STRs were 43%, 84%, 96%, 99%, and 100% for relatives separated by one, four, six, nine, and twelve meioses, respectively, which largely exceeded rates obtained by 25 standard Y-STRs. Machine learning based models for predicting the degree of patrilineal consanguinity yielded accurate and reasonably precise predictions when using RM Y-STRs. Fully matching haplotypes resulted in a 95% confidence interval of 1-6 meioses with RMplex compared to 1-25 with Yfiler Plus. Our comprehensive pedigree study demonstrates the value of RM Y-STRs for differentiating male relatives of various types, in many cases achieving individual identification, thereby overcoming the largest limitation of forensic Y-chromosome analysis.

Prediction of Smoking Habits From Class-Imbalanced Saliva Microbiome Data Using Data Augmentation and Machine Learning.

Human microbiome research is moving from characterization and association studies to translational applications in medical research, clinical diagnostics, and others. One of these applications is the prediction of human traits, where machine learning (ML) methods are often employed, but face practical challenges. Class imbalance in available microbiome data is one of the major problems, which, if unaccounted for, leads to spurious prediction accuracies and limits the classifier's generalization. Here, we investigated the predictability of smoking habits from class-imbalanced saliva microbiome data by combining data augmentation techniques to account for class imbalance with ML methods for prediction. We collected publicly available saliva 16S rRNA gene sequencing data and smoking habit metadata demonstrating a serious class imbalance problem, i.e., 175 current vs. 1,070 non-current smokers. Three data augmentation techniques (synthetic minority over-sampling technique, adaptive synthetic, and tree-based associative data augmentation) were applied together with seven ML methods: logistic regression, k-nearest neighbors, support vector machine with linear and radial kernels, decision trees, random forest, and extreme gradient boosting. K-fold nested cross-validation was used with the different augmented data types and baseline non-augmented data to validate the prediction outcome. Combining data augmentation with ML generally outperformed baseline methods in our dataset. The final prediction model combined tree-based associative data augmentation and support vector machine with linear kernel, and achieved a classification performance expressed as Matthews correlation coefficient of 0.36 and AUC of 0.81. Our method successfully addresses the problem of class imbalance in microbiome data for reliable prediction of smoking habits.

Breaking the Restriction Barriers and Applying CRISPRi as a Gene Silencing Tool in Pseudoclostridium thermosuccinogenes.

Pseudoclostridium thermosuccinogenes is a thermophilic bacterium capable of producing succinate from lignocellulosic-derived sugars and has the potential to be exploited as a platform organism. However, exploitation of P. thermosuccinogenes has been limited partly due to the genetic inaccessibility and lack of genome engineering tools. In this study, we established the genetic accessibility for P. thermosuccinogenes DSM 5809. By overcoming restriction barriers, transformation efficiencies of 102 CFU/µg plasmid DNA were achieved. To this end, the plasmid DNA was methylated in vivo when transformed into an engineered E. coli HST04 strain expressing three native methylation systems of the thermophile. This protocol was used to introduce a ThermodCas9-based CRISPRi tool targeting the gene encoding malic enzyme in P. thermosuccinogenes, demonstrating the principle of gene silencing. This resulted in 75% downregulation of its expression and had an impact on the strain's fermentation profile. Although the details of the functioning of the restriction modification systems require further study, in vivo methylation can already be applied to improve transformation efficiency of P. thermosuccinogenes. Making use of the ThermodCas9-based CRISPRi, this is the first example demonstrating that genetic engineering in P. thermosuccinogenes is feasible and establishing the way for metabolic engineering of this bacterium.

Male-specific age estimation based on Y-chromosomal DNA methylation.

Although DNA methylation variation of autosomal CpGs provides robust age predictive biomarkers, no male-specific age predictor exists based on Y-CpGs yet. Since sex chromosomes play an important role in aging, a Y-chromosome-based age predictor would allow studying male-specific aging effects and would also be useful in forensics. Here, we used blood-based DNA methylation microarray data of 1,057 males from six cohorts aged 15-87 and identified 75 Y-CpGs with an interquartile range of ≥0.1. Of these, 22 and six were significantly hyper- and hypomethylated with age (p(cor)<0.05, Bonferroni), respectively. Amongst several machine learning algorithms, a model based on support vector machines with radial kernel performed best in male-specific age prediction. We achieved a mean absolute deviation (MAD) between true and predicted age of 7.54 years (cor=0.81, validation) when using all 75 Y-CpGs, and a MAD of 8.46 years (cor=0.73, validation) based on the most predictive 19 Y-CpGs. The accuracies of both age predictors did not worsen with increased age, in contrast to autosomal CpG-based age predictors that are known to predict age with reduced accuracy in the elderly. Overall, we introduce the first-of-its-kind male-specific epigenetic age predictor for future applications in aging research and forensics.

Equivalent DNA methylation variation between monozygotic co-twins and unrelated individuals reveals universal epigenetic inter-individual dissimilarity.

BACKGROUND: Although the genomes of monozygotic twins are practically identical, their methylomes may evolve divergently throughout their lifetime as a consequence of factors such as the environment or aging. Particularly for young and healthy monozygotic twins, DNA methylation divergence, if any, may be restricted to stochastic processes occurring post-twinning during embryonic development and early life. However, to what extent such stochastic mechanisms can systematically provide a stable source of inter-individual epigenetic variation remains uncertain until now. RESULTS: We enriched for inter-individual stochastic variation by using an equivalence testing-based statistical approach on whole blood methylation microarray data from healthy adolescent monozygotic twins. As a result, we identified 333 CpGs displaying similarly large methylation variation between monozygotic co-twins and unrelated individuals. Although their methylation variation surpasses measurement error and is stable in a short timescale, susceptibility to aging is apparent in the long term. Additionally, 46% of these CpGs were replicated in adipose tissue. The identified sites are significantly enriched at the clustered protocadherin loci, known for stochastic methylation in developing neurons. We also confirmed an enrichment in monozygotic twin DNA methylation discordance at these loci in whole genome bisulfite sequencing data from blood and adipose tissue. CONCLUSIONS: We have isolated a component of stochastic methylation variation, distinct from genetic influence, measurement error, and epigenetic drift. Biomarkers enriched in this component may serve in the future as the basis for universal epigenetic fingerprinting, relevant for instance in the discrimination of monozygotic twin individuals in forensic applications, currently impossible with standard DNA profiling.

Microbiome-based body site of origin classification of forensically relevant blood traces.

Human blood traces are amongst the most commonly encountered biological stains collected at crime scenes. Identifying the body site of origin of a forensic blood trace can provide crucial information in many cases, such as in sexual and violent assaults. However, means for reliably and accurately identifying from which body site a forensic blood trace originated are missing, but would be highly valuable in crime scene investigations. With this study, we introduce a taxonomy-independent deep neural network approach based on massively parallel microbiome sequencing, which delivers accurate body site of origin classification of forensically-relevant blood samples, such as menstrual, nasal, fingerprick, and venous blood. A total of 50 deep neural networks were trained using a large 16S rRNA gene sequencing dataset from 773 reference samples, including 220 female urogenital tract, 190 nasal cavity, 213 skin, and 150 venous blood samples. Validation was performed with de-novo generated 16S rRNA gene massively parallel sequencing (MPS) data from 94 blood test samples of four different body sites, and achieved high classification accuracy with AUC values at 0.992 for menstrual blood (N = 23), 0.978 for nasal blood (N = 16), 0.978 for fingerprick blood (N = 30), and 0.990 for venous blood (N = 25). The obtained highly accurate classification of menstrual blood was independent of the day of the menses, as established in additional 86 menstrual blood test samples. Accurate body site of origin classification was also revealed for 45 fresh and aged mock casework blood samples from all four body sites. Our novel microbiome approach works based on the assumption that a sample is from blood, as can be obtained in forensic practise from prior presumptive blood testing, and provides accurate information on the specific body source of blood, with high potentials for future forensic applications.

Forensic Y-SNP analysis beyond SNaPshot: High-resolution Y-chromosomal haplogrouping from low quality and quantity DNA using Ion AmpliSeq and targeted massively parallel sequencing.

Y-chromosomal haplogroups assigned from male-specific Y-chromosomal single nucleotide polymorphisms (Y-SNPs) allow paternal lineage identification and paternal bio-geographic ancestry inference, both being relevant in forensic genetics. However, most previously developed forensic Y-SNP tools did not provide Y haplogroup resolution on the high level needed in forensic applications, because the limited multiplex capacity of the DNA technologies used only allowed the inclusion of a relatively small number of Y-SNPs. In a proof-of-principle study, we recently demonstrated that high-resolution Y haplogrouping is feasible via two AmpliSeq PCR analyses and simultaneous massively parallel sequencing (MPS) of 530 Y-SNPs allowing the inference of 432 Y-haplogroups. With the current study, we present a largely improved Y-SNP MPS lab tool that we specifically designed for the analysis of low quality and quantity DNA often confronted with in forensic DNA analysis. Improvements include i) Y-SNP marker selection based on the "minimal reference phylogeny for the human Y chromosome" (PhyloTree Y), ii) strong increase of the number of targeted Y-SNPs allowing many more Y haplogroups to be inferred, iii) focus on short amplicon length enabling successful analysis of degraded DNA, and iv) combination of all amplicons in a single AmpliSeq PCR and simultaneous sequencing allowing single DNA aliquot use. This new MPS tool simultaneously analyses 859 Y-SNPs and allows inferring 640 Y haplogroups. Preliminary forensic developmental validation testing revealed that this tool performs highly accurate, is sensitive and robust. We also provide a revised software tool for analysing the sequencing data produced by the new MPS lab tool including final Y haplogroup assignment. We envision the tools introduced here for high-resolution Y-chromosomal haplogrouping to determine a man's paternal lineage and/or paternal bio-geographic ancestry to become widely used in forensic Y-chromosome DNA analysis and other applications were Y haplogroup information from low quality / quantity DNA samples is required.

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it provides crucial information for crime reconstruction. However, the overlap in human cell type composition between these three epithelial materials provides challenges for their differentiation and identification when using previously proposed human cell biomarkers, while their microbiota composition largely differs. By using validated 16S rRNA gene massively parallel sequencing data from the Human Microbiome Project of 1636 skin, oral and vaginal samples, 50 taxonomy-independent deep learning networks were trained to classify these three tissues. Validation testing was performed in de-novo generated high-throughput 16S rRNA gene sequencing data using the Ion Torrent™ Personal Genome Machine from 110 test samples: 56 hand skin, 31 saliva and 23 vaginal secretion specimens. Body-site classification accuracy of these test samples was very high as indicated by AUC values of 0.99 for skin, 0.99 for oral, and 1 for vaginal secretion. Misclassifications were limited to 3 (5%) skin samples. Additional forensic validation testing was performed in mock casework samples by de-novo high-throughput sequencing of 19 freshly-prepared samples and 22 samples aged for 1 up to 7.6 years. All of the 19 fresh and 20 (91%) of the 22 aged mock casework samples were correctly tissue-type classified. Moreover, comparing the microbiome results with outcomes from previous human mRNA-based tissue identification testing in the same 16 aged mock casework samples reveals that our microbiome approach performs better in 12 (75%), similarly in 2 (12.5%), and less good in 2 (12.5%) of the samples. Our results demonstrate that this new microbiome approach allows for accurate tissue-type classification of three human epithelial materials of skin, oral and vaginal origin, which is highly relevant for future forensic investigations.

Yleaf: Software for Human Y-Chromosomal Haplogroup Inference from Next-Generation Sequencing Data.

Next-generation sequencing (NGS) technologies offer immense possibilities given the large genomic data they simultaneously deliver. The human Y-chromosome serves as good example how NGS benefits various applications in evolution, anthropology, genealogy, and forensics. Prior to NGS, the Y-chromosome phylogenetic tree consisted of a few hundred branches, based on NGS data, it now contains many thousands. The complexity of both, Y tree and NGS data provide challenges for haplogroup assignment. For effective analysis and interpretation of Y-chromosome NGS data, we present Yleaf, a publically available, automated, user-friendly software for high-resolution Y-chromosome haplogroup inference independently of library and sequencing methods.