Unearthing who and Y at Harewood Cemetery and inference of George Washington's Y-chromosomal haplotype.

An excavation conducted at Harewood Cemetery to identify the unmarked grave of Samuel Washington resulted in the discovery of burials presumably belonging to George Washington's paternal grandnephews and their mother, Lucy Payne. To confirm their identities this study examined Y-chromosomal, mitochondrial, and autosomal DNA from the burials and a living Washington descendant. The burial's Y-STR profile was compared to FamilyTreeDNA's database, which resulted in a one-step difference from the living descendant and an exact match to another Washington. A more complete Y-STR and Y-SNP profile from the descendant was inferred to be the Washington Y profile. Kinship comparisons performed in relation to the descendant, who is a 4th and 5th degree relative of the putative individuals, resulted in >37,000 overlapping autosomal SNPs and strong statistical support with likelihood ratios exceeding one billion. This study highlights the benefits of a multi-marker approach for kinship prediction and DNA-assisted identification of historical remains.

Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

Background: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of the data on which effect sizes are assessed and have poor generalizability to new data. Objective: The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict risk for LOAD. Methods: We construct a new state-of-the-art genetic model for risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of non-linear machine learning models rather than a single linear model. We compare the paragenic model to several PRS models from the literature trained on the same dataset. Results: The paragenic model is significantly more accurate than the PRS models under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%. It remains significantly more accurate when evaluated on an independent holdout dataset and maintains accuracy within APOE genotype strata. Conclusions: Paragenic models show potential for improving disease risk prediction for complex heritable diseases such as LOAD over PRS models.

Investigative genetic genealogy for human remains identification.

Investigative genetic genealogy (IGG) has emerged as a highly effective tool for tying a forensic DNA sample to an identity. While much of the attention paid to IGG has focused on cases where the DNA is from an unknown suspect, IGG has also been used to help close hundreds of unidentified human remains (UHR) cases. Genome-wide single-nucleotide polymorphism (SNP) genotype data can be obtained from forensic samples using microarray genotyping or whole-genome sequencing (WGS) with protocols optimized for degraded DNA. After bioinformatic processing, the SNP data can be uploaded to public GG databases that allow law enforcement usage, where it can be compared with other users' data to find distant relatives. A genetic genealogist can then build the family trees of the relatives to narrow down the identity of the source of the forensic DNA sample. To date, 367 UHR identifications using IGG have been publicly announced. The same IGG techniques developed and refined for UHR cases have significant potential for disaster victim identification, where DNA is often extremely compromised, and close family references may not be available. This paper reviews the laboratory, bioinformatic, and genealogical techniques used in IGG for UHR cases and presents three case studies that demonstrate how IGG is assisting with remains identification.

Epistatic Features and Machine Learning Improve Alzheimer's Risk Prediction Over Polygenic Risk Scores.

Background: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of data on which effect sizes are assessed, and have poor generalizability to new data. Objective: The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict lifetime risk for LOAD. Methods: We construct a new state-of-the-art genetic model for lifetime risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of machine learning models (gradient boosting machines and deep learning) instead of simple logistic regression. We compare the paragenic model to a PRS model from the literature trained on the same dataset. Results: The paragenic model is significantly more accurate than the PRS model under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%, and remains significantly more accurate when evaluated on an independent holdout dataset. Additionally, the paragenic model maintains accuracy within APOE genotypes. Conclusion: Paragenic models show potential for improving lifetime disease risk prediction for complex heritable diseases such as LOAD over PRS models.

Northeastern Asian and Jomon-related genetic structure in the Three Kingdoms period of Gimhae, Korea.

The genetic history of prehistoric and protohistoric Korean populations is not well understood because only a small number of ancient genomes are available. Here, we report the first paleogenomic data from the Korean Three Kingdoms period, a crucial point in the cultural and historic formation of Korea. These data comprise eight shotgun-sequenced genomes from ancient Korea (0.7×-6.1× coverage). They were derived from two archeological sites in Gimhae: the Yuha-ri shell mound and the Daesung-dong tumuli, the latter being the most important funerary complex of the Gaya confederacy. All individuals are from between the 4th and 5th century CE and are best modeled as an admixture between a northern China Bronze Age genetic source and a source of Jomon-related ancestry that shares similarities with the present-day genomes from Japan. The observed substructure and proportion of Jomon-related ancestry suggest the presence of two genetic groups within the population and diversity among the Gaya population. We could not correlate the genomic differences between these two groups with either social status or sex. All the ancient individuals' genomic profiles, including phenotypically relevant SNPs associated with hair and eye color, facial morphology, and myopia, imply strong genetic and phenotypic continuity with modern Koreans for the last 1,700 years.

Extended kinship analysis of historical remains using SNP capture.

DNA-assisted identification of historical remains requires the genetic analysis of highly degraded DNA, along with a comparison to DNA from known relatives. This can be achieved by targeting single nucleotide polymorphisms (SNPs) using a hybridization capture and next-generation sequencing approach suitable for degraded skeletal samples. In the present study, two SNP capture panels were designed to target ~ 25,000 (25 K) and ~ 95,000 (95 K) nuclear SNPs, respectively, to enable distant kinship estimation (up to 4th degree relatives). Low-coverage SNP data were successfully recovered from 14 skeletal elements 75 years postmortem using an Illumina MiSeq benchtop sequencer. All samples contained degraded DNA but were of varying quality with mean fragment lengths ranging from 32 bp to 170 bp across the 14 samples. SNP comparison with DNA from known family references was performed in the Parabon Fx Forensic Analysis Platform, which utilizes a likelihood approach for kinship prediction that was optimized for low-coverage sequencing data with cytosine deamination. The 25 K panel produced 15,000 SNPs on average, which allowed for accurate kinship prediction with strong statistical support in 16 of the 21 pairwise comparisons. The 95 K panel increased the average SNPs to 42,000 and resulted in an additional accurate kinship prediction with strong statistical support (17 of 21 pairwise comparisons). This study demonstrates that SNP capture combined with massively parallel sequencing on a benchtop platform can yield sufficient SNP recovery from compromised samples, enabling accurate, extended kinship predictions.

Genetic genealogy for cold case and active investigations.

Investigative genetic genealogy has rapidly emerged as a highly effective tool for using DNA to determine the identity of unknown individuals (unidentified remains or perpetrators), generating identifications in dozens of law enforcement cases, both cold and active. The amount of press coverage of these cases may have given the impression that the analysis is straightforward and the outcome guaranteed once a sample is uploaded to a database. However, the database query results serve only as clues from which in-depth genealogy and descendancy research must proceed to determine the possible identities of an unknown individual. While there certainly will be more announcements of cases solved using this new technique, there are many more cases where identification has not yet been possible due to the wide variety of complications present in these investigations. This paper lays out the fundamentals of genetic genealogy, along with the challenges that are encountered in many of these investigations, and concludes with a set of case studies that demonstrate the variety of cases encountered thus far.

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.

BACKGROUND: Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. RESULTS: We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer's disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer's disease. CONCLUSIONS: We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.