Population genomics of the Viking world

Abstract

The Viking maritime expansion from Scandinavia (Denmark, Norway, and Sweden) marks one of the swiftest and most far-flung cultural transformations in global history. During this time (c. 750 to 1050 CE), Viking sailors reached Greenland, North America, and large parts of western Eurasia, and left a legacy that persists today. To understand the genetic structure and influence of the Viking expansion, we sequenced the genomes of 442 ancient humans from across Europe and Greenland ranging from the Bronze Age (c. 2400 BCE) to the early Modern period (c. 1600 CE), with particular emphasis on the Viking Age. We find that the period preceding the Viking Age was accompanied by foreign gene flow into Scandinavia from the south and east: spreading from Denmark and eastern Sweden to the rest of Scandinavia. Despite the close linguistic similarities of modern Scandinavian languages, we observe genetic structure within Scandinavia, suggesting that regional population differences were already present 1,000 years ago. We find evidence for a majority of Danish Viking presence in England, Swedish Viking presence in the Baltic, and Norwegian Viking presence in Ireland, Iceland, and Greenland. Additionally, we see substantial foreign European ancestry entering Scandinavia during the Viking Age. We also find that several of the members of the only archaeologically well-attested Viking expedition were close family members. By comparing Viking Scandinavian genomes with present-day Scandinavian genomes, we find that pigmentation-associated loci have undergone strong population differentiation during the last millennia. Finally, we are able to trace the allele frequency dynamics of positively selected loci with unprecedented detail, including the lactase persistence allele and various alleles associated with the immune response. We conclude that the Viking diaspora was characterized by substantial foreign engagement: distinct Viking populations influenced the genomic makeup of different regions of Europe, while Scandinavia also experienced increased contact with the rest of the continent.

A Woman with a Sword? – Weapon Grave at Suontaka Vesitorninmäki, Finland

“…Through modelling, we were able to show that even when taking into account the possibility of human DNA contamination, it seems likely that the individual had male chromosomal aneuploidy XXY (Klinefelter syndrome). This is not unprecedented, as archaeological cases of Klinefelter syndrome (XXY) have been reported earlier: there is a case from Viking Age Iceland (Ebenesersdóttir et al, 2018), one in early Neolithic Germany (Rivollat et al, 2020), and possibly one from the Orkney Islands in the Viking Age (Margaryan et al, 2020), but the find contexts of these individuals have not been reported as atypical. Therefore, to our knowledge, Suontaka would be the first case of chromosomal aneuploidy from a context which has been puzzling archaeologists as an atypical combination of mixed-gender attributes.…”

TKGWV2: an ancient DNA relatedness pipeline for ultra-low coverage whole genome shotgun data

“…However, to demonstrate the application of our pipeline to ultra low coverages, we also included some individuals from these publications that were not included in their original kinship analysis because of coverages below the defined thresholds. For the Church burial of the Faroe Islands in Margaryan et al 16 we included individuals VK239 and VK248 in the analysis, with coverages below the publication’s threshold of 0.1x (0.027× and 0.076×, respectively), and identified a new 2nd-degree relationship for VK239–VK237 (Table 1 ). In the case of Saag et al 17 , for Toomani (Muuksi) we included individuals X05, X06, and X07, who have coverages below their threshold of 0.03× (0.0290×, 0.0048×, and 0.0059×, respectively).…”

Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution?

“…However, imputation itself introduces a new source of bias, particularly if the reference panel is not representative of the ancestries found in the low-coverage samples. Nevertheless, the level of imputation bias can be empirically estimated by downsampling high-coverage aDNA libraries and testing imputed genotypes against direct observations (e.g., Margaryan et al, 2020 ). Where a suitable reference panel exists, recently developed methods for imputation from low-coverage sequencing data ( Davies et al, 2021 ; Rubinacci et al, 2021 ) show great promise for ancient DNA studies (e.g., Clemente et al, 2021 ).…”