Biology subjects

Renaud, G.

Publications and source records attributed to Renaud, G..

22 records · Page 2Linked to original sources

bioRxiv2019

Population genomics of the Viking world

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), the Vikings reached most of western Eurasia, Greenland, and North America, and left a cultural legacy that persists till 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 BC) 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.

genetics↗

bioRxiv2019

A likelihood method for estimating present-day human contamination in ancient DNA samples using low-depth haploid chromosome data

1MotivationThe presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested while few are aimed at low-depth data, a common feature in aDNA datasets.\n\nResultsWe develop a new X-chromosome-based maximum likelihood method for estimating present-day human contamination in low-depth sequencing data. We implement our method for general use, assess its performance under conditions typical of ancient human DNA research, and compare it to previous nuclear data-based methods through extensive simulations. For low-depth data, we show that existing methods can produce unusable estimates or substantially underestimate contamination. In contrast, our method provides accurate estimates for a depth of coverage as low as 0.5x on the X-chromosome when contamination is below 25%. Moreover, our method still yields meaningful estimates in very challenging situations, i.e., when the contaminant and the target come from closely related populations or with increased error rates. With a running time below five minutes, our method is applicable to large scale aDNA genomic studies.\n\nAvailability and implementationThe method is implemented in C++ and R and is freely available in https://github.com/sapfo/contaminationX.\n\nContactmorenomayar@gmail.com, annasapfo.malaspinas@unil.ch.

genomics↗

bioRxiv2018

The population history of northeastern Siberia since the Pleistocene

Far northeastern Siberia has been occupied by humans for more than 40 thousand years. Yet, owing to a scarcity of early archaeological sites and human remains, its population history and relationship to ancient and modern populations across Eurasia and the Americas are poorly understood. Here, we analyze 34 ancient genome sequences, including two from fragmented milk teeth found at the ~31.6 thousand-year-old (kya) Yana RHS site, the earliest and northernmost Pleistocene human remains found. These genomes reveal complex patterns of past population admixture and replacement events throughout northeastern Siberia, with evidence for at least three large-scale human migrations into the region. The first inhabitants, a previously unknown population of \"Ancient North Siberians\" (ANS), represented by Yana RHS, diverged ~38 kya from Western Eurasians, soon after the latter split from East Asians. Between 20 and 11 kya, the ANS population was largely replaced by peoples with ancestry related to present-day East Asians, giving rise to ancestral Native Americans and \"Ancient Paleosiberians\" (AP), represented by a 9.8 kya skeleton from Kolyma River. AP are closely related to the Siberian ancestors of Native Americans, and ancestral to contemporary communities such as Koryaks and Itelmen. Paleoclimatic modelling shows evidence for a refuge during the last glacial maximum (LGM) in southeastern Beringia, suggesting Beringia as a possible location for the admixture forming both ancestral Native Americans and AP. Between 11 and 4 kya, AP were in turn largely replaced by another group of peoples with ancestry from East Asia, the \"Neosiberians\" from which many contemporary Siberians derive. We detect gene flow events in both directions across the Bering Strait during this time, influencing the genetic composition of Inuit, as well as Na Dene-speaking Northern Native Americans, whose Siberian-related ancestry components is closely related to AP. Our analyses reveal that the population history of northeastern Siberia was highly dynamic throughout the Late Pleistocene and Holocene. The pattern observed in northeastern Siberia, with earlier, once widespread populations being replaced by distinct peoples, seems to have taken place across northern Eurasia, as far west as Scandinavia.

genomics↗

bioRxiv2017

glactools: a command-line toolset for the management of genotype likelihoods and allele counts

MotivationResearch projects involving population genomics routinely need to store genotyping information, population allele frequencies, combine files from different samples, query the data and export it to various formats. This is often done using bespoke in-house scripts which cannot be easily adapted to new projects and seldom constitute reproducible workflows.\n\nResultsWe introduce glactools, a set of command-line utilities which can import data from genotypes or population-wide allele frequencies into an intermediate representation, compute various operations on it and export the data to several file formats used by population genetics software. This intermediate format can take 2 forms, one to store per-individual genotype likelihoods and a second for allele counts from one or more individuals. glactools allows users to perform operations such as intersecting datasets, merging individuals into populations, creating subsets, perform queries (e.g. return sites where a given population does not share an allele with a second one) and compute summary statistics to answer biologically relevant questions.\n\nAvailabilityglactools is freely available for use under the GPL. It requires a C++ compiler and the htslib library. (https://grenaud.github.io/glactools/).\n\nContactgabriel.reno@gmail.com\n\nSupplementary informationSupplementary methods and results are available at Bioinformatics online.

bioinformatics↗