Substructured Population Growth in the Ashkenazi Jews Inferred with Approximate Bayesian Computation

Whole genomes from Angola and Mozambique inform about the origins and dispersals of major African migrations

As the continent of origin for our species, Africa harbours the highest levels of diversity anywhere on Earth. However, many regions of Africa remain under-sampled genetically. Here we present 350 whole genomes from Angola and Mozambique belonging to ten Bantu ethnolinguistic groups, enabling the construction of a reference variation catalogue including 2.9 million novel SNPs. We investigate the emergence of Bantu speaker population structure, admixture involving migrations across sub-Saharan Africa and model the demographic histories of Angolan and Mozambican Bantu speakers. Our results bring together concordant views from genomics, archaeology, and linguistics to paint an updated view of the complexity of the Bantu Expansion. Moreover, we generate reference panels that better represents the diversity of African populations involved in the trans-Atlantic slave trade, improving imputation accuracy in African Americans and Brazilians. We anticipate that our collection of genomes will form the foundation for future African genomic healthcare initiatives.

Genome-wide data from medieval German Jews show that the Ashkenazi founder event pre-dated the 14th century

We report genome-wide data from 33 Ashkenazi Jews (AJ), dated to the 14^th century, obtained following a salvage excavation at the medieval Jewish cemetery of Erfurt, Germany. The Erfurt individuals are genetically similar to modern AJ, but they show more variability in Eastern European-related ancestry than modern AJ. A third of the Erfurt individuals carried a mitochondrial lineage common in modern AJ and eight carried pathogenic variants known to affect AJ today. These observations, together with high levels of runs of homozygosity, suggest that the Erfurt community had already experienced the major reduction in size that affected modern AJ. The Erfurt bottleneck was more severe, implying substructure in medieval AJ. Overall, our results suggest that the AJ founder event and the acquisition of the main sources of ancestry pre-dated the 14^th century and highlight late medieval genetic heterogeneity no longer present in modern AJ.

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed.

Middle eastern genetic legacy in the paternal and maternal gene pools of Chuetas

Chuetas are a group of descendants of Majorcan Crypto-Jews (Balearic Islands, Spain) who were socially stigmatized and segregated by their Majorcan neighbours until recently; generating a community that, although after the seventeenth century no longer contained Judaic religious elements, maintained strong group cohesion, Jewishness consciousness, and endogamy. Collective memory fixed 15 surnames as a most important defining element of Chueta families. Previous studies demonstrated Chuetas were a differentiated population, with a considerable proportion of their original genetic make-up. Genetic data of Y-chromosome polymorphism and mtDNA control region showed, in Chuetas’ paternal lineages, high prevalence of haplogroups J2-M172 (33%) and J1-M267 (18%). In maternal lineages, the Chuetas hallmark is the presence of a new sub-branching of the rare haplogroup R0a2m as their modal haplogroup (21%). Genetic diversity in both Y-chromosome and mtDNA indicates the Chueta community has managed to avoid the expected heterogeneity decrease in their gene pool after centuries of isolation and inbreeding. Moreover, the composition of their uniparentally transmitted lineages demonstrates a remarkable signature of Middle Eastern ancestry—despite some degree of host admixture—confirming Chuetas have retained over the centuries a considerable degree of ancestral genetic signature along with the cultural memory of their Jewish origin.

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.