Ancestry Prediction Research Articles

Salivary microbiome signatures of Poles and Serbians and its potential for prediction of biogeographic ancestry

Biogeographical ancestry analysis is valuable in forensic investigations, especially in missing person cases or crimes without eyewitnesses, as it helps to infer geographic origins from genetic markers. This approach enhances forensic efforts by providing essential clues for identifying individuals with limited direct evidence. Slavic-speaking populations are poorly distinguishable based on human genome variability. However, recent studies show that even populations with close biogeographic origin could be differentiated based on salivary microbiomes. Nevertheless, the salivary microbiomes of Slavs have not been characterized yet. Therefore, this study aimed to compare the composition of the salivary microbiomes of Western and Southern Slavs’ representatives. 16S rRNA libraries from salivary microbiomes of 40 Poles (Western Slavs) and 40 Serbians (Southern Slavs) were prepared via PCR and sequenced on the MiSeq FGx platform (Illumina), giving approximately 100,000 reads per sample. Bioinformatic and statistical analyses were performed to assess the alpha and beta diversity of microbiomes and determine the differences in the abundance of bacterial genera between the groups studied. Analyses of alpha (ACE, Chao1, Shannon, and Simpson) and beta (Jaccard and Bray-Curtis dissimilarity) diversities in the salivary microbiomes clearly distinguished between Poles and Serbians. Alpha and beta diversity metrics were significantly higher in the Serbian population. Fusobacterium, Lautropia, Porphyromonas, Actinobacillus, Capnocytophaga, and Kingella were the most significantly increased genera in Serbians, whereas Veillonella, Selenomonas, Megasphaera, and Atopobium were more prevalent in Poles. In summary, our study identified significant differences in the salivary microbiomes of Poles and Serbians, with distinct microbial signatures associated with each population. These findings highlight the potential of salivary microbiome analysis as a tool for predicting biogeographic ancestry. Nevertheless, further analysis extended to other Slavic-speaking populations is necessary to clarify this issue.

FORENSIC DNA PHENOTYPING FOR CRIMINAL IDENTIFICATION

The purpose of the work – to carry out an analysis of modern achievements andpossibilities of forensic DNA phenotyping to determine the external characteristics ofa person, his biogeographical origin and age in order to facilitate criminal investigations.Conclusions. Forensic DNA Phenotyping (FDP) involves the prediction of a person’sexternal characteristics regarding appearance, biogeographic origin, and age based onDNA samples from a crime scene to support investigations and help to find unknowncriminals who cannot be identified through forensic STR profiling.DNA-based appearance prediction has moved beyond eye, hair and skin color andincluded new features such as eyebrow color, freckles, hair structure, male pattern hairloss and height. DNA-based biogeographic inference of ancestry has progressed fromcontinental ancestry to detecting subcontinental ancestry and resolving models of commonancestry in genetically mixed individuals. DNA-based age estimation is now possible notonly through blood testing, but also through somatic tissues such as saliva and bone, aswell as new markers and tools for analyzing semen. Technological advances have madeit possible to use forensic DNA technology with greatly increased multiplexing capabilityto simultaneously analyze hundreds of DNA predictors using targeted massively parallelsequencing (MPS). Forensically validated MPS-based FDP tools for DNA-based crimescene prediction are used to identify: several features of appearance; multiregional origin;several appearance features together with multiregional origin; age by different tissue types. Despite recent advances that are likely to increase the impact of FDP in criminal casesin the near future, bringing reliable prediction of appearance, ancestry, and age fromcrime scene DNA to the level of detail and accuracy, that forensic scientists may desire,requires further intensive scientific research, along with technical developments andforensic validation.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

Ancestry analysis using a self-developed 56 AIM-InDel loci and machine learning methods

Insertion/deletion (InDel) polymorphisms can be used as one of the ancestry-informative markers in ancestry analysis. In this study, a self-developed panel consisting of 56 ancestry-informative InDels was used to investigate the genetic structures and genetic relationships between Chinese Inner Mongolia Manchu group and 26 reference populations. The Inner Mongolia Manchu group was closely related in genetic background to East Asian populations, especially the Han Chinese in Beijing. Moreover, populations from northern and southern East Asia displayed obvious variations in ancestral components, suggesting the potential value of this panel in distinguishing the populations from northern and southern East Asia. Subsequently, four machine learning models were performed based on the 56 AIM-InDel loci to evaluate the performance of this panel in ancestry prediction. The random forest model presented better performance in ancestry prediction, with 91.87% and 99.73% accuracy for the five and three continental populations, respectively. The individuals of the Inner Mongolia Manchu group were assigned to the East Asian populations by the random forest model, and they exhibited closer genetic affinities with northern East Asian populations. Furthermore, the random forest model distinguished 87.18% of the Inner Mongolia Manchus from the East Asian populations, suggesting that the random forest model based on the 56 ancestry-informative InDels could be a potential tool for ancestry analysis.

MHBase: A comprehensive database of short microhaplotypes for advancing forensic genetic analysis

Microhaplotypes (MHs) were first recommended by Prof. Kidd for use in forensics because they can improve human identification, kinship analysis, mixture deconvolution, and ancestry prediction. Since their introduction, extensive research has demonstrated the advantages of MHs in forensic applications and provided useful data for different populations. Currently, two databases, ALFRED (ALlele FREquency Database) and MicroHapDB (MicroHaplotype DataBase), house the published MH information and population data. We previously constructed a single nucleotide polymorphism SNP-SNP MH database (D-SNPsDB) of MHs within 50 bp on the whole human genome for 26 populations integrating basic data such as physical genome positions, mapping of variant identifiers (rsIDs), allele frequencies, and basic variant information. Building upon the previous research, we further selected MHs containing at least two variants (SNPs and/or insertions/deletions [InDels]) within a short DNA fragment (≤ 50 bp) in 26 populations based on the 1000 Genomes Project dataset (Phase 3) to construct a more comprehensive database. Subsequently, we established a user-friendly website that allows users to search the MH database (MHBase) based on their research objectives and study population to find suitable loci and provides other functions such as querying reported loci, performing online calculations using the PHASE software, and calculating ancestral-related parameters. The loci in the database are classified as SNP-based MHs, which include only SNPs, and InDel-including MHs, which contain at least one InDel. Here, we provide a detailed overview of the MHBase and an analysis of shared loci at the global and continental levels, ancestral markers, the genetic distance within loci, and mapping with the genome annotation file. The website is an accessible and useful tool for researchers engaged in marker discovery, population studies, assay development, and panel design.

Many direct-to-consumer canine genetic tests can identify the breed of purebred dogs.

To compare pedigree documentation and genetic test results to evaluate whether user-provided photographs influence the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs. 12 registered purebred pet dogs representing 12 different breeds. Each dog owner submitted 6 buccal swabs, 1 to each of 6 DTC genetic testing companies. Experimenters registered each sample per manufacturer instructions. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. DNA analysis and breed ancestry prediction were conducted by each company. The effect of condition (ie, matching vs shuffled photograph) was evaluated for each company's breed predictions. As a positive control, a convolutional neural network was also used to predict breed based solely on the photograph. Results from 5 of the 6 tests always included the dog's registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that were more similar to the photograph than the DNA. Additionally, differences in the predictions made across all tests underscored the challenge of identifying breed ancestry, even in purebred dogs. Veterinarians are likely to encounter patients who have conducted DTC genetic testing and may be asked to explain the results of genetic tests they did not order. This systematic comparison of commercially available tests provides context for interpreting results from consumer-grade DTC genetic testing kits.

Systematic analyses of AISNPs screening and classification algorithms based on genome-wide data for forensic biogeographic ancestry inference

Identifying the biogeographic ancestral origin of biological sample left at a crime scene can provide important evidence for judicial case, as well as clue for narrowing down suspect. Ancestry informative single nucleotide polymorphism (AISNP) has become one of the most important genetic markers in recent years for screening ancestry information loci and analyzing the population genetic background and structure due to their high number and wide distributions in the human genome. In this study, based on data from 26 populations in the 1000 Genomes Project Phase 3, a Random Forest classification model was constructed with one-vs-rest classification strategy for embedded feature selection in order to obtain a panel with a small number of efficient AISNPs. The research aim was to clarify differentiations of population genetic structures among continents and subregions of East Asia. ADMIXTURE results showed that based on the 58 AISNPs selected by the machine learning algorithm, the 26 populations involved in the study could be categorized into six intercontinental ancestry components: North East Asia, South East Asia, Africa, Europe, South Asia, and America. The 24 continental-specific AISNPs and 34 East Asian-specific AISNPs were finally obtained, and used to construct the ancestry prediction model using XGBoost algorithm, resulting in the Matthews correlation coefficients of 0.94 and 0.89, and accuracies of 0.94 and 0.92, respectively. The machine learning models that we constructed using population-specific AISNPs were able to accurately predict the ancestral origins of continental and intra-East Asian populations. To summarize, screening a set of high-perform AISNPs to infer biogeographical ancestral information using embedded feature selection has potential application in creating a layered inference system that accurately differentiates from intercontinental populations to local subpopulations.

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.

Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions.

The human body has a unique way of saying when something is wrong with it. The molecules in the body fluids can be helpful in the early detection of diseases by enabling health and preventing disease progression. These biomarkers enabling better healthcare are becoming an extensive area of research interest. Biosensors that detect these biomarkers are becoming the future, especially Point Of Care (POC) biosensors that remove the need to be physically present in the hospital. Detection of complex and systemic diseases using biosensors has a long way to go. Saliva-based biosensors are gaining attention among body fluids due to their non-invasive collection and ability to detect periodontal disease and identify systemic diseases. The possibility of saliva-based diagnostic biosensors has gained much publicity, with companies sending home kits for ancestry prediction. Saliva-based testing for covid 19 has revealed effective clinical use and relevance of the economic collection. Based on universal biomarkers, the detection of systemic diseases is a booming research arena. Lots of research on saliva-based biosensors is available, but it still poses challenges and limitations as POC devices. This review paper talks about the relevance of saliva and its usefulness as a biosensor. Also, it has recommendations that need to be considered to enable it as a possible diagnostic tool.

Abstract B054: Inherited mutations of DNA damage repair genes in a racially diverse cohort of men with prostate cancer

Abstract Introduction: DNA damage repair genes (DDRGs) play an important role in genomic stability and mutations in DDRGs contribute to clinically significant prostate cancer (PCa). However, the association of germline mutations in DDRGs with PCa progression and therapeutic stratification remain to be defined in African American (AA) men. Our objective was to genomically profile all known DDRGs and provide ancestry specific findings, focusing on PCa patients with African ancestry. Methods: Germline mutations in all DDRGs (N=276) was evaluated by whole genome sequence (WGS) analysis of archived blood DNA samples from 600 PCa patients (300 AA and 300 CA) who underwent primary treatment at Walter Reed National Military Medical Center. The WGS mean coverage exceeded 37x. Principal Component Analysis using the Peddy program was conducted, and ancestry prediction was based on a machine learning approach using 1000G as reference. Variant frequencies in CPDR PCa patients were compared to variant frequencies available from the Exome Aggregation Consortium (ExAC) controls with no PCa. Data analysis approaches included single variant association, gene-based total frequency test and clinico-pathological associations for germline mutations. All statistical tests were two-sided, and FDR adjusted. FDR < 0.05 was set as significance threshold. Results: Comprehensive analysis of 276 DDRGs revealed that germline mutation (Pathogenic/Likely Pathogenic; P/LP) carrier rate was 23.5%, which is three times higher than reported before in both AA and CA men, due to our unbiased WGS approach. We found that only 13 of the 46 DDRGs identified with P/LP mutations were common to AA and CA men, demonstrating clear disparity along ancestry lines. Importantly, RAD family genes (RAD54L, RAD54B, RAD51), which are potentially targetable, as well as PMS2 and BRCA1, were among the most frequently mutated DDRGs in AA, but not in CA men. Germline mutations in RAD51 and PMS2 were enriched in AA cohort as compared to CA men (P 0.01 and 0.03 respectively), while germline mutations in FANCA were significantly enriched in CA cohort (P < 0.01). Pathway analysis showed that the HR pathway (with 15 mutations) was associated with faster disease progression to biochemical recurrence (BCR) (P <0.05), while the NHEJ pathway (with three mutations) was found to be marginally associated with metastasis (p <0.05). Additionally, we found that germline mutations in DDRGs was associated with shorter time to BCR (P <0.05) in AA patients, but not in CA men. Conclusion: Our findings highlight distinct DDRG germline mutation profile across AA and CA prostate cancer men. This may help refine patient stratification for specific targeted therapeutic options especially for under-studied AA men. Future replication studies in an independent patient cohort are warranted to validate our findings. Citation Format: Indu Kohaar, Xijun Zhang, Gregory Chesnut, Clifton Dalgard, Matthew D. Wilkerson, Gyorgy Petrovics. Inherited mutations of DNA damage repair genes in a racially diverse cohort of men with prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B054.

Introducing the New Editorial Team

Introducing the New Editorial Team

Comprehensive genomic profiling (CGP) of clinically advanced and metastatic cutaneous adnexal carcinomas (CAs; MCADCA): A genomic landscape study.

9587 Background: Cutaneous adnexal tumors are a large group of malignant neoplasms that exhibit morphologic differentiation towards one of the four primary adnexal structures present in normal skin: hair follicles, sebaceous glands, sweat-apocrine glands, and sweat-eccrine glands. Methods: 276 cases of MCADCA underwent hybrid capture-based CGP to evaluate all classes of genomic alterations (GA). Microsatellite instability (MSI) status, tumor mutational burden (TMB), genomic loss of heterozygosity (gLOH), genomic ancestry prediction and COSMIC genomic signature were determined from sequencing results. PD-L1 was measured by IHC (TPS; Dako 22C3). Fisher exact method was used for statistical analysis with the false discovery rate corrected using Benjamini-Hochberg adjustment. Results: Sequencing was performed on the primary cutaneous tumor in 131 (47.4%) and on a local recurrence or metastatic site biopsy in 145 (52.5%) MCADCA. Male preponderance (64-81%) and age distributions mean (59-63 yrs) were similar in all groups with apocrine (APO) older than eccrine (ECC) (72 vs 62 yrs; p = .001). There were 173 (62.7%) sweat gland (SWT) derived, 55 (19.9%) were sebaceous gland (SEB) derived, 14 (5.1%) were hair follicle (HRF) derived and 34 (12.3%) were unclassified (UNK). 150/173 (86.7%) of SWT were ECC and 23 (13.3%) were APO. The SWT cases included 12 (6.9%) digital papillary ca’s (DPA), 11 (6.3%) mucinous ca’s (MAC), 19 (11.0%) poroca’s (POR), 14 (8.1%) spiradenoca’s (SPR), 10 (5.8%) syringoadenoca’s (SRNG) and 77 (44.5%) unclassified ca’s (UNC). GA/tumor were highest in SEB vs SWT (7.9 vs 4.9; p = .004) and lowest in DPA (2.1 vs 5.0 non-DPA; p = .03). There we no ancestry distribution differences. When compared with the SWT group, the SEB group had higher frequencies of RB1 GA (38.2% vs 8.1%; p < .0001) and TP53 GA (76.4% vs 43.4%; p = .0002) indicating likely neuroendocrine differentiation. The MAC group had significantly higher PTCH1 GA than the non-MAC group (36.4% vs 1.8%; p = .044). MSI-High status was highest in the SEB group vs all other groups (SEB 15.7% vs SWT 1.2%; p = .005). gLOH > 16% was most frequent in SEB when compared with SWT (19.6% vs 7.2%; p = .081). The MMR signature was more frequent in SEB than SWT (32.0% vs 2.1%; p = .005). The mean TMB was high in all MCADCA ranging from 10.4 mut/Mb in HRF to 38.8 mut/Mb in MAC with the exceptions of APO (2.7 mut/Mb; p = .001 and DPA 1.4 mut/Mb; p = .003). PD-L1 expression was not significantly different in all the MCADCA with low positive scores SWT vs SEB (37.0% vs 33.3%; NS). Conclusions: As there is limited evidence on the treatment of MCADCA, we provide a repertoire of common GA found. An increased frequency of GA is observed in SEB MCADCA. Clinical trials are needed to evaluate MCADCA-based biomarkers prognostic of ICI response. Only then can we successfully incorporate novel therapeutic strategies in the evolving landscape of treatment.

Development and evaluations of the ancestry informative markers of the VISAGE Enhanced Tool for Appearance and Ancestry

The VISAGE Enhanced Tool for Appearance and Ancestry (ET) has been designed to combine markers for the prediction of bio-geographical ancestry plus a range of externally visible characteristics into a single massively parallel sequencing (MPS) assay. We describe the development of the ancestry panel markers used in ET, and the enhanced analyses they provide compared to previous MPS-based forensic ancestry assays. As well as established autosomal single nucleotide polymorphisms (SNPs) that differentiate sub-Saharan African, European, East Asian, South Asian, Native American, and Oceanian populations, ET includes autosomal SNPs able to efficiently differentiate populations from Middle East regions. The ability of the ET autosomal ancestry SNPs to distinguish Middle East populations from other continentally defined population groups is such that characteristic patterns for this region can be discerned in genetic cluster analysis using STRUCTURE. Joint cluster membership estimates showing individual co-ancestry that signals North African or East African origins were detected, or cluster patterns were seen that indicate origins from central and Eastern regions of the Middle East. In addition to an augmented panel of autosomal SNPs, ET includes panels of 85 Y-SNPs, 16 X-SNPs and 21 autosomal Microhaplotypes. The Y- and X-SNPs provide a distinct method for obtaining extra detail about co-ancestry patterns identified in males with admixed backgrounds. This study used the 1000 Genomes admixed African and admixed American sample sets to fully explore these enhancements to the analysis of individual co-ancestry. Samples from urban and rural Brazil with contrasting distributions of African, European, and Native American co-ancestry were also studied to gauge the efficiency of combining Y- and X-SNP data for this purpose. The small panel of Microhaplotypes incorporated in ET were selected because they showed the highest levels of haplotype diversity amongst the seven population groups we sought to differentiate. Microhaplotype data was not formally combined with single-site SNP genotypes to analyse ancestry. However, the haplotype sequence reads obtained with ET from these loci creates an effective system for de-convoluting two-contributor mixed DNA. We made simple mixture experiments to demonstrate that when the contributors have different ancestries and the mixture ratios are imbalanced (i.e., not 1:1 mixtures) the ET Microhaplotype panel is an informative system to infer ancestry when this differs between the contributors.

Disparity in the detection of chromosome 15 centromere in patients of African ancestry with a plasma cell neoplasm

PurposeFluorescence in situ hybridization (FISH) is the current gold standard assay that provides information related to risk stratification and therapeutic selection for individuals with plasma cell neoplasms. The differential hybridization of FISH probe sets in association with individuals’ genetic ancestry has not been previously reported. MethodsThis retrospective study included 1224 bone marrow samples from individuals who had an abnormal plasma cell proliferative disorder FISH result and a concurrent conventional G-banded chromosome study. DNA from bone marrow samples obtained from the G-banded chromosome study was genotyped, and a biogeographical ancestry prediction was carried out. ResultsUsing a cohort of individuals with a plasma cell neoplasm, we identified reduced hybridization of a chromosome 15 centromere FISH probe (D15Z4). Metaphase FISH studies of cells with 2 copies of chromosome 15 demonstrated a failure of the D15Z4 FISH probe to hybridize to one chromosome 15 centromere, revealing a false-positive monosomy 15 FISH result in some individuals. Surprisingly, individuals with a monosomy 15 FISH result had a median African ancestry of 77.2% (95% CI 74.1%-80.3%), compared with a median African ancestry of 2.2% (95% CI 2.0%-2.5%) in the non–monosomy 15 cohort (P value = 9.4 × 10−10). Thus, individuals with African ancestry had an 8.02-fold (95% CI 3.73-17.25) increased probability of having a false-positive monosomy 15 result (P value = 9.92 × 10−8). ConclusionThis study emphasizes a concern regarding the reliability of diagnostic genomic tools and their application in interpreting genetic testing results in diverse patient populations. We discuss alternative methodologies to better represent different ancestry groups in clinical diagnostic testing.

A custom hybridisation enrichment forensic intelligence panel to infer biogeographic ancestry, hair and eye colour, and Y chromosome lineage

Massively parallel sequencing can provide genetic data for hundreds to thousands of loci in a single assay for various types of forensic testing. However, available commercial kits require an initial PCR amplification of short-to-medium sized targets which limits their application for highly degraded DNA. Development and optimisation of large PCR multiplexes also prevents creation of custom panels that target different suites of markers for identity, biogeographic ancestry, phenotype, and lineage markers (Y-chromosome and mtDNA). Hybridisation enrichment, an alternative approach for target enrichment prior to sequencing, uses biotinylated probes to bind to target DNA and has proven successful on degraded and ancient DNA. We developed a customisable hybridisation capture method, that uses individually mixed baits to allow tailored and targeted enrichment to specific forensic questions of interest. To allow collection of forensic intelligence data, we assembled and tested a custom panel of hybridisation baits to infer biogeographic ancestry, hair and eye colour, and paternal lineage (and sex) on modern male and female samples with a range of self-declared ancestries and hair/eye colour combinations. The panel correctly estimated biogeographic ancestry in 9/12 samples (75%) but detected European admixture in three individuals from regions with admixed demographic history. Hair and eye colour were predicted correctly in 83% and 92% of samples respectively, where intermediate eye colour and blond hair were problematic to predict. Analysis of Y-chromosome SNPs correctly assigned sex and paternal haplogroups, the latter complementing and supporting biogeographic ancestry predictions. Overall, we demonstrate the utility of this hybridisation enrichment approach to forensic intelligence testing using a combined suite of biogeographic ancestry, phenotype, and Y-chromosome SNPs for comprehensive biological profiling.

Evaluation of the (hu)MANid program for sex and ancestry estimation in a diverse, contemporary CT scan-based sample.

Human remains from forensic and bioarcheological contexts are often fragmentary, requiring methods for estimating a forensic profile that are based upon limited skeletal features. In 2017, Berg and Keryhercz created an online application, (hu)MANid, that provides sex and ancestry estimation from mandibular morphoscopic traits and linear measurements. In this study, we examine the utility of the (hu)MANid application in a diverse, urban US adult sample (aged 20-45; n=143) derived from computed tomography (CT) scans. We secondarily conduct a preliminary analysis of the program's utility in a sample of adolescents (aged 15-17; n=40). Six morphoscopic, and eleven morphometric traits were recorded as directed by the literature associated with the (hu)MANid program. Percent correct classification and posterior predictive values were calculated for the sex and ancestry estimations output by the program; chi-squared tests were employed to compare self-reported and predicted ancestry. In the adult sample, sex was accurately predicted for 75.52% of the sample. Ancestry prediction, however, was less favorable ranging from 19.3% to 50% correct. For the adolescent sample, correct sex estimation (45%) did not surpass what could occur by chance alone, though ancestry prediction fared better than in the larger adult sample (percent correct prediction overall average: 47.5%, range 35.71%-71.43%). The (hu)MANid application shows utility for use with CT scan-derived adult samples for sex estimation, but caution is warranted for ancestry estimation and use with samples that may not have reached full adult maturity.

Machine Learning overview for biogeographical ancestry prediction - a PLS-DA approach

Biogeographical ancestry (BGA) of a trace or person/skeleton refers to the component of ethnicity, which is composed of biological and cultural elements and is biologically determined. Nowadays, many people are interested in researching their genealogy, and the ability to distinguish biogeographic information about populations and subgroups using DNA analysis plays an essential role in various fields, such as forensics. For example, it is advantageous for investigative and intelligence purposes to infer the biogeographic origin of perpetrators or victims of unsolved cases when reference profiles of perpetrators or database matches are not available for comparison purposes. Current approaches to biogeographic ancestry estimation using SNPs data are generally based on PCA and STRUCTURE software. The present study provides an alternative method that incorporates multivariate data analysis and Machine Learning strategies to assess the BGA discriminatory power of unknown samples using various commercial panels. Using datasets from the 1000 Genomes Project, Simons Genome Diversity Project, and Human Genome Diversity Project, which include African, American, Asian, European, and Oceanic individuals, powerful multivariate techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and XGBoost were used and their discriminatory power was compared.

Using DNA to develop a profile of phenotypic characteristics of historic period human remains

Massively parallel sequencing (MPS) is a powerful tool to uncover relationships and features of individuals from human skeletal remains. In this study, we employed the Verogen ForenSeq Signature Prep Kit STR and SNP panel MPS on a MiSeq FGx instrument to develop a phenotypic profile and ancestry predictions of historic period human remains from two locations in Maryland and Virginia, USA. Externally visible characteristics (EVCs) and human identity markers applied to familial relationships can be used to augment the bioarcheological findings. Of fifteen historic period human remains we tested in this preliminary study, tooth, temporal bone, and femur and tibia long bone DNA led to biogeographical ancestry predictions. Phenotype predictions of eye color, hair color and skin tone were developed from DNA extracted from a tooth, temporal bone, fibula, and femur using the Verogen Universal Analysis Software and Erasmus HIrisPlex-S.

Investigation of 74 microhaplotypes for kinship testing in US populations

Microhaplotypes are markers that consist of haplotype blocks of SNPs, which can be analyzed by massively parallel sequencing technologies. These allow determining the haplotype phase at every locus by clonal sequencing each DNA strand. MHs are polymorphic loci with same size alleles, no stutter, and lower mutation rate than STRs. They can provide the same power of discrimination of STR-kits, thus useful for mixture deconvolution, but more accurate ancestry prediction than STRs. In this study we investigated the potential of a recently developed 74plex-MH panel for kinship testing using the Familias software.Samples from families of four major US population groups were collected and genotyped using the 74plex-MH panel. MH allele frequency data from 347 individuals were imported into Familias software along with STR allele frequency data of 29 loci (NIST dataset) from 1036 individuals. Different family scenarios were tested and these included unrelated vs parent-child, unrelated vs full siblings, unrelated vs half siblings, unrelated vs cousin pairs. The prediction of the kinship relation for the four populations of interest was reported as Log10 of the likelihood ratio (LR).Overall, the panel of 74MHs and 29STRs showed similar performance in predicting the correct kinship scenarios tested. Correct prediction was reported for parent-child, full siblings, and half sibling scenarios, but not for the cousin pairs scenario. The panel of 74 MHs showed larger Log10LR values than the 29 STR-assay, thus demonstrating the effectiveness of this biomarker as a tool for kinship testing in addition to mixture deconvolution and ancestry prediction.