Towards predicting the geographical origin of ancient samples with metagenomic data.

Reconstructing the history-such as the place of birth and death-of an individual sample is a fundamental goal in ancient DNA (aDNA) studies. However, knowing the place of death can be particularly challenging when samples come from museum collections with incomplete or erroneous archives. While analyses of human DNA and isotope data can inform us about the ancestry of an individual and provide clues about where the person lived, they cannot specifically trace the place of death. Moreover, while ancient human DNA can be retrieved, a large fraction of the sequenced molecules in ancient DNA studies derive from exogenous DNA. This DNA-which is usually discarded in aDNA analyses-is constituted mostly by microbial DNA from soil-dwelling microorganisms that have colonized the buried remains post-mortem. In this study, we hypothesize that remains of individuals buried in the same or close geographic areas, exposed to similar microbial communities, could harbor more similar metagenomes. We propose to use metagenomic data from ancient samples' shotgun sequencing to locate the place of death of a given individual which can also help to solve cases of sample mislabeling. We used a k-mer-based approach to compute similarity scores between metagenomic samples from different locations and propose a method based on dimensionality reduction and logistic regression to assign a geographical origin to target samples. We apply our method to several public datasets and observe that individual samples from closer geographic locations tend to show higher similarities in their metagenomes compared to those of different origin, allowing good geographical predictions of test samples. Moreover, we observe that the genus Streptomyces commonly infiltrates ancient remains and represents a valuable biomarker to trace the samples' geographic origin. Our results provide a proof of concept and show how metagenomic data can also be used to shed light on the place of origin of ancient samples.

Imputation of ancient human genomes.

Due to postmortem DNA degradation and microbial colonization, most ancient genomes have low depth of coverage, hindering genotype calling. Genotype imputation can improve genotyping accuracy for low-coverage genomes. However, it is unknown how accurate ancient DNA imputation is and whether imputation introduces bias to downstream analyses. Here we re-sequence an ancient trio (mother, father, son) and downsample and impute a total of 43 ancient genomes, including 42 high-coverage (above 10x) genomes. We assess imputation accuracy across ancestries, time, depth of coverage, and sequencing technology. We find that ancient and modern DNA imputation accuracies are comparable. When downsampled at 1x, 36 of the 42 genomes are imputed with low error rates (below 5%) while African genomes have higher error rates. We validate imputation and phasing results using the ancient trio data and an orthogonal approach based on Mendel's rules of inheritance. We further compare the downstream analysis results between imputed and high-coverage genomes, notably principal component analysis, genetic clustering, and runs of homozygosity, observing similar results starting from 0.5x coverage, except for the African genomes. These results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method that can improve ancient DNA studies.

Structural variant calling: the long and the short of it.

Recent research into structural variants (SVs) has established their importance to medicine and molecular biology, elucidating their role in various diseases, regulation of gene expression, ethnic diversity, and large-scale chromosome evolution-giving rise to the differences within populations and among species. Nevertheless, characterizing SVs and determining the optimal approach for a given experimental design remains a computational and scientific challenge. Multiple approaches have emerged to target various SV classes, zygosities, and size ranges. Here, we review these approaches with respect to their ability to infer SVs across the full spectrum of large, complex variations and present computational methods for each approach.