bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. POPULATION GENOMICS OF STONE AGE EURASIA Morten E. Allentoft1,2*§, Martin Sikora1*§, Alba Refoyo-Martínez1§, Evan K. Irving-Pease1§, Anders Fischer3,4§, William Barrie5§, Andrés Ingason6,1§, Jesper Stenderup1, Karl-Göran Sjögren3, Alice Pearson7, Barbara Mota8,9, Bettina Schulz Paulsson3, Alma Halgren10, Ruairidh Macleod1,5,11, Marie Louise Schjellerup Jørkov12, Fabrice Demeter1,13, Maria Novosolov1, Lasse Sørensen14, Poul-Otto Nielsen14, Rasmus H.A. Henriksen1, Tharsika Vimala1, Hugh McColl1, Ashot Margaryan15,16, Melissa Ilardo17, Andrew Vaughn18, Morten Fischer Mortensen14, Anne Birgitte Nielsen19, Mikkel Ulfeldt Hede20, Peter Rasmussen14, Lasse Vinner1, Gabriel Renaud21, Aaron Stern18, Theis Zetner Trolle Jensen15, Niels Nørkjær Johannsen22, Gabriele Scorrano1, Hannes Schroeder15, Per Lysdahl23, Abigail Daisy Ramsøe1, Andrei Skorobogatov24, Andrew Joseph Schork6,25, Anders Rosengren6,1, Anthony Ruter1, Alan Outram26, Aleksey A. Timoshenko27, Alexandra Buzhilova28, Alfredo Coppa29, Alisa Zubova30, Ana Maria Silva31,59, Anders J. Hansen1, Andrey Gromov30, Andrey Logvin32, Anne Birgitte Gotfredsen1, Bjarne Henning Nielsen33, Borja González-Rabanal34, Carles Lalueza-Fox35, Catriona J. McKenzie26, Charleen Gaunitz1, Concepción Blasco36, Corina Liesau36, Cristina Martinez-Labarga37, Dmitri V. Pozdnyakov27, David Cuenca-Solana38,39, David O. Lordkipanidze40,41, Dmitri En’shin42, Domingo C. Salazar-García43,44, T. Douglas Price45, Dušan Borić29,46, Elena Kostyleva47, Elizaveta V. Veselovskaya48, Emma R. Usmanova49,50, Enrico Cappellini15, Erik Brinch Petersen51, Esben Kannegaard52, Francesca Radina53, Fulya Eylem Yediay1, Henri Duday54, Igor Gutiérrez-Zugasti38, Inna Potekhina55,56, Irina Shevnina32, Isin Altinkaya1, Jean Guilaine57, Jesper Hansen58, Joan Emili Aura Tortosa43, João Zilhão59,60, Jorge Vega61, Kristoffer Buck Pedersen62, Krzysztof Tunia63, Lei Zhao1, Liudmila N. Mylnikova27, Lars Larsson64, Laure Metz65, Levon Yeppiskoposyan66,93, Lisbeth Pedersen67, Lucia Sarti68, Ludovic Orlando69, Ludovic Slimak65, Lutz Klassen52, Malou Blank3, Manuel González-Morales38, Mara Silvestrini70, Maria Vretemark71, Marina S. Nesterova27, Marina Rykun72, Mario Federico Rolfo73, Marzena Szmyt74, Marcin Przybyła75, Mauro Calattini68, Mikhail Sablin76, Miluše Dobisíková77, Morten Meldgaard78, Morten Johansen79, Natalia Berezina28, Nick Card80, Nikolai A. Saveliev81, Olga Poshekhonova42, Olga Rickards37, Olga V. Lozovskaya82, Otto Christian Uldum79, Paola Aurino83, Pavel Kosintsev84,85, Patrice Courtaud54, Patricia Ríos36, Peder Mortensen86, Per Lotz87,88, Per Åke Persson89, Pernille Bangsgaard90, Peter de Barros Damgaard1, Peter Vang Petersen14, Pilar Prieto Martinez91, Piotr Włodarczak63, Roman V. Smolyaninov92, Rikke Maring22,52, Roberto Menduiña61, Ruben Badalyan91, Rune Iversen51, Ruslan Turin24, Sergey Vasilyiev27, Sidsel Wåhlin23, Svetlana Borutskaya28, Svetlana Skochina42, Søren Anker Sørensen87, Søren H. Andersen94, Thomas Jørgensen87, Yuri B. Serikov95, Vyacheslav I. Molodin27, Vaclav Smrcka96, Victor Merz97, Vivek Appadurai6, Vyacheslav Moiseyev30, Yvonne Magnusson98, Kurt H. Kjær1, Niels Lynnerup12, Daniel J. Lawson99, Peter H. Sudmant10,18, Simon Rasmussen100, Thorfinn Korneliussen1@, Richard Durbin7,101@, Rasmus Nielsen10,1@, Olivier Delaneau8@, Thomas Werge1,6,102@, Fernando Racimo1@, Kristian Kristiansen1,3@, Eske Willerslev1,5,101,103*@ Affiliations Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark. 2Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, Australia. 3Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden. 4Sealand Archaeology, Gl. Roesnaesvej 27, 4400 Kalundborg, Denmark. 5GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK. 6Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark. 7Department of Genetics, University of Cambridge, Cambridge, UK. 8Department of Computational Biology, University of Lausanne, Switzerland. 9Swiss Institute of Bioinformatics, University of Lausanne, Switzerland. 10Department of Integrative Biology, University of California, Berkeley, USA. 11Research department of Genetics, Evolution and Environment, University College London, London, UK. 12Laboratory of Biological Anthropology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark. 13 Muséum national d’Histoire naturelle, CNRS, Université de Paris, Musée de l’Homme, Paris, France. 14The National Museum of Denmark, Ny Vestergade 10, Copenhagen, Denmark. 15Section for Evolutionary Genomics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark. 16Centre for Evolutionary Hologenomics, University of Copenhagen, Copenhagen, Denmark. 17Anthropology Department, University of Utah, USA. 18Center for Computational Biology, University of California, Berkeley, USA. 19Department of Geology, Lund University, Lund, Sweden. 20Tårnby Gymnasium og HF, Kastrup, Denmark. 21Department of Health Technology, Section of Bioinformatics, Technical University of Denmark, Kongens Lyngby, Denmark. 22Department of Archaeology and Heritage Studies, Aarhus University, Aarhus, Denmark. 23Vendsyssel Historiske Museum, DK-9800 Hjørring, Denmark. 24Terra Ltd., Letchik Zlobin St. 20, Voronezh, 394055, Russian Federation. 25Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA. 26Department of Archaeology, University of Exeter, Exeter, UK. 27Institute of Archaeology and Ethnography, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation. 28Research Institute and Museum of Anthropology, Lomonosov Moscow State University, Mokhovaya str. 11, Moscow, Russian Federation. 29Department of Environmental Biology, Sapienza University of Rome, Rome, Italy. 30Peter the Great Museum of Anthropology and Ethnography (Kunstkamera), Russian Academy of Sciences, Saint Petersburg, Russian Federation. 31CIAS, Department of Life Science, University of 1 1 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Coimbra, Coimbra, Portugal. 32Kostanay Regional University A. Baitursynov, Kostanay, Kazakhstan. 33 Vesthimmerlands Museum, Søndergade 44, Aars, Denmark. 34Grupo EvoAdapta, Departamento de Ciencias Históricas, Universidad de Cantabria, Santander, Spain. 35Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, Spain. 36Departamento de Prehistoria y Arqueología Department, Universidad Autónoma de Madrid, Madrid, Spain. 37Department of Biology, University of Rome "Tor Vergata", Rome, Italy. 38Instituto Internacional de Investigaciones Prehistóricas de Cantabria, Universidad de Cantabria, Santander, Spain. 39Centre de Recherche en Archéologie, Archeosciences, Histoire (CReAAH), UMR-6566 CNRS, Rennes, France. 40Georgian National Museum, Tbilisi, Georgia. 41Tbilisi State University, Tbilisi, Georgia. 42IPND, Tyumen Scientific Centre, Siberian Branch of the Russian Academy of Sciences, Tyumen, Russian Federation. 43Departament de Prehistòria, Arqueologia i Història Antiga, Universitat de València, València, Spain. 44Department of Geological Sciences, University of Cape Town, Cape Town, South Africa. 45Laboratory for Archaeological Chemistry, Department of Anthropology, University of Wisconsin-Madison, Madison, USA. 46Department of Anthropology, New York University, New York, USA. 47Institute of Humanities, Ivanovo State University, Ivanovo, Russian Federation. 48Institute of Ethnology and Anthropology, Russian Academy of Sciences, Moscow, Russian Federation. 49Saryarka Archaeological Institute, Buketov Karaganda University, Karaganda, Kazakhstan. 50South Ural State University, Chelyabinsk, Russia. 51The Saxo Institute, University of Copenhagen, Copenhagen, Denmark. 52Museum Østjylland, Stemannsgade 2, Randers, Denmark. 53 Soprintendenza Archeologia Belle Arti e Paesaggio per la Città Metropolitana di Bari, Via Pier l’Eremita, 25, 70122, Bari, Italy. 54UMR 5199 PACEA, CNRS, Université de Bordeaux, 33615 Pessac, France. 55Institute of Archaeology, National Academy of Sciences of Ukraine, Kyiv, Ukraine. 56National University of Kyiv-Mohyla Academy, Kyiv, Ukraine. 57Collège de France, 75231 Paris cedex 05, France. 58Odense City Museums, Overgade 48, Odense, Denmark. 59 UNIARQ, University of Lisbon, Lisbon, Portugal. 60ICREA, University of Barcelona, Barcelona, Spain. 61ARGEA Consultores SL, C. de San Crispín, Madrid, Spain. 62Museum Sydøstdanmark, Algade 97, 4760 Vordingborg, Denmark. 63 Institute of Archaeology and Ethnology, Polish Academy of Sciences, Kraków, Poland. 64Department of Archaeology and Ancient History, Lund University, Lund, Sweden. 65CNRS UMR 5608, Toulouse Jean Jaurès University, Maison de la Recherche, 5 Allées Antonio Machado, 31058 Toulouse, Cedex 9, France. 66Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia. 67HistorieUdvikler, Gl. Roesnaesvej 27, DK-4400 Kalundborg, Denmark. 68Department of history and cultural heritage, University of Siena, Siena, Italy. 69Centre d'Anthropobiologie et de Génomique de Toulouse, CNRS UMR 5288, Université Paul Sabatier, Toulouse, France. 70Soprintendenza per i Beni Archeologici delle Marche, Via Birarelli 18, 60100, Ancona, Italy. 71Västergötlands museum, Stadsträdgården, Skara, Sweden. 72Cabinet of Anthropology, Tomsk State University, Tomsk, Russian Federation. 73Department of History, Humanities and Society, University of Rome "Tor Vergata", Rome, Italy. 74Institute for Eastern Research, Adam Mickiewicz University in Poznań, Poznań, Poland. 75Institute of Archaeology, Jagiellonian University, Ul. Gołębia 11, 31-007, Kraków, Poland. 76Zoological Institute of Russian Academy of Sciences, Universitetskaya nab. 1, 199034, St. Petersburg, Russian Federation. 77Department of Anthropology, Czech National Museum, Prague, Czech Republic. 78Department of Health and Nature, University of Greenland, Greenland. 79The Viking Ship Museum, Vindeboder 12, Roskilde, Denmark. 80Archaeology Institute, University of Highlands and Islands, Scotland, UK. 81 Scientific Research Center “Baikal region”, Irkutsk State University; 1, K. Marx st., Irkutsk, 664003, Russian Federation. 82Laboratory for Experimental Traceology, Institute for the History of Material Culture of the Russian Academy of Sciences, Dvortsovaya nab., 18, 191186, St. Petersburg, Russian Federation. 83Soprintendenza Archeologia, Belle Arti e Paesaggio per la provincia di Cosenza, Cosenza, Italy. 84Paleoecology Laboratory, Institute of Plant and Animal Ecology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russian Federation. 85 Department of History of the Institute of Humanities, Ural Federal University, Ekaterinburg, Russian Federation. 86 Centre for the Study of Early Agricultural Societies, Department of Cross-Cultural and Regional Studies, University of Copenhagen, 2300 Copenhagen, Denmark. 87Museum Nordsjælland, Frederiksgade 9, 3400 Hillerød. 88Museum Vestsjælland, Klosterstræde 18, 4300 Holbæk, Denmark. 89Museum of Cultural History, University of Oslo, P.O. Box 6762. St. Olavs Plass NO-0130 Oslo, Norway. 90ArchaeoScience, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark. 91Department of History, University of Santiago de Compostela, Spain. 92Lipetsk Regional Scientific Public Organisation "Archaeological Research", Lipetsk, Russian Federation. 92Institute of Archaeology and Ethnography, National Academy of Sciences, Yerevan, Armenia. 93Russian-Armenian University, Yerevan, Armenia. 94 Moesgaard Museum, Moesgård Allé 15, Højbjerg, Denmark. 95Nizhny Tagil State Socio-Pedagogical Institute, Nizhny Tagil, Russia. 96Institute for History of Medicine, First Faculty of Medicine, Charles University, Prague, Czech Republic. 97Centre for Archaeological Research Toraighyrov University, Pavlodar, Kazakhstan. 98Malmö Museer, Malmöhusvägen 6, Malmö, Sweden. 99Institute of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK. 100Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark. 101Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK. 102Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark. 103 MARUM, University of Bremen, Bremen, Germany. * Corresponding authors; email: morten.allentoft@curtin.edu.au, martin.sikora@sund.ku.dk, ew482@cam.ac.uk § Joint first authors 2 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. @ Joint last authors Summary The transitions from foraging to farming and later to pastoralism in Stone Age Eurasia (c. 113 thousand years before present, BP) represent some of the most dramatic lifestyle changes in human evolution. We sequenced 317 genomes of primarily Mesolithic and Neolithic individuals from across Eurasia combined with radiocarbon dates, stable isotope data, and pollen records. Genome imputation and co-analysis with previously published shotgun sequencing data resulted in >1600 complete ancient genome sequences offering fine-grained resolution into the Stone Age populations. We observe that: 1) Hunter-gatherer groups were more genetically diverse than previously known, and deeply divergent between western and eastern Eurasia. 2) We identify hitherto genetically undescribed hunter-gatherers from the Middle Don region that contributed ancestry to the later Yamnaya steppe pastoralists; 3) The genetic impact of the Neolithic transition was highly distinct, east and west of a boundary zone extending from the Black Sea to the Baltic. Large-scale shifts in genetic ancestry occurred to the west of this “Great Divide”, including an almost complete replacement of hunter-gatherers in Denmark, while no substantial ancestry shifts took place during the same period to the east. This difference is also reflected in genetic relatedness within the populations, decreasing substantially in the west but not in the east where it remained high until c. 4,000 BP; 4) The second major genetic transformation around 5,000 BP happened at a much faster pace with Steppe-related ancestry reaching most parts of Europe within 1,000years. Local Neolithic farmers admixed with incoming pastoralists in eastern, western, and southern Europe whereas Scandinavia experienced another near-complete population replacement. Similar dramatic turnover-patterns are evident in western Siberia; 5) Extensive regional differences in the ancestry components involved in these early events remain visible to this day, even within countries. Neolithic farmer ancestry is highest in southern and eastern England while Steppe-related ancestry is highest in the Celtic populations of Scotland, Wales, and Cornwall (this research has been conducted using the UK Biobank resource); 6) Shifts in diet, lifestyle and environment introduced new selection pressures involving at least 21 genomic regions. Most such variants were not universally selected across populations but were only advantageous in particular ancestral backgrounds. Contrary to previous claims, we find that selection on the FADS regions, associated with fatty acid metabolism, began before the Neolithisation of Europe. Similarly, the lactase persistence allele started increasing in frequency before the expansion of Steppe-related groups into Europe and has continued to increase up to the present. Along the genetic cline separating Mesolithic hunter-gatherers from Neolithic farmers, we find significant correlations with trait associations related to skin disorders, diet and lifestyle and mental health status, suggesting marked phenotypic differences between these groups with very different lifestyles. This work provides new insights into major transformations in recent human evolution, elucidating the complex interplay between selection and admixture that shaped patterns of genetic variation in modern populations. Introduction The transition from hunting and gathering to farming represents one of the most dramatic shifts in lifestyle and diet in human evolution with lasting effects on the modern world. For millions of years our ancestors relied on hunting and foraging for survival but c.12,000 years ago in the Fertile 3 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Crescent of the Near East, plant cultivation and animal husbandry were developed1–3. This ultimately resulted in a more sedentary lifestyle accompanied by increasing population sizes and higher social complexity. Expanding populations and the adoption of herding, carried farming practices into Europe and parts of SW Asia in the following millennia, and farming was also developed independently in other parts of the World. Today, 50% of the Earth’s habitable land is used for agriculture and very few hunter-gatherers remain4,5. Understanding the changes to the human gene pool during this shift from hunter-gathering to farming between the Mesolithic and Neolithic periods is central to understanding ourselves and the events that led to a major transformation of our planet. While the Neolithisation process has been studied extensively with ancient DNA (aDNA) technology, several key questions remain unaddressed. Population movements during the Neolithic can be traced in the gene pools across the European continent as farming was introduced from the Near East. Several regional studies have testified to varying degrees of reproductive interaction with local Mesolithic groups, ranging from genetic continuity6 to gradual population admixture7–10 to almost complete replacement11. However, our knowledge of the population structure in the Mesolithic period and how it was formed is limited, partly because of a paucity of data from skeletons older than 8,000 years, compromising resolution into subsequent demographic transitions. Moreover, the spatiotemporal mapping of population dynamics east of Europe, including Siberia, Central- and North Asia during the same time period remains patchy. In these regions the ‘Neolithic’ typically refers to new forms of lithic material culture, and/or the presence of ceramics12. For instance, the Neolithic cultures of the Central Asian Steppe possessed pottery, but retained a hunter-gatherer economy alongside stone blade technology similar to the preceding Mesolithic cultures13. The archaeological record testifies to a boundary, ranging from the eastern Baltic to the Black Sea, east of which hunter-gatherer societies persist for much longer than in western Europe14. The population genomic implications of this “Great Divide” is, however, largely unknown. Southern Scandinavia represents another enigma in the Neolithisation debate15. The introduction of farming reached a 1,000-year standstill at the doorstep to Southern Scandinavia before finally progressing into Denmark around 6,000 BP. It is not known what caused this delay and whether the transition to farming in Denmark, was facilitated by the migration of people (demic diffusion), similar to the rest of Europe11,16,17 or mostly involved cultural diffusion18,19. Starting at around 5,000 BP, a new ancestry component emerged on the eastern European plains associated with Yamnaya Steppe pastoralists culture and swept across Europe mediated through expansion of the Corded Ware complex (CWC) and related cultures20,21. The genetic origin of the Yamnaya and the fine-scale dynamics of the formation and expansion of the CWC are largely unresolved questions of central importance to clarify the formation of the present day European gene pool. Rapid dietary changes and expansion into new climate zones represent shifts in environmental exposure, impacting the evolutionary forces acting on the gene pool. The Neolithisation can therefore be considered as a series of large-scale selection pressures imposed on humans from around 12,000 years ago. Moreover, close contact with livestock and higher population densities have likely enhanced exposure and transmission of infectious diseases, introducing new challenges to our survival22,23. While signatures of selection can be identified from patterns of genetic diversity in extant populations24,25, this can be challenging in species such as humans, which show very wide geographic distributions and have thus been exposed to highly diverse and changing local environments through space and time. In the complex mosaic of ancestries that constitute a modern human genome any putative signatures of selection may therefore misrepresent the timing and magnitude of the actual event unless we can use ancient DNA to chart the individual ancestry components back into the evolutionary past. 4 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. To investigate these formative processes in Eurasian prehistory, we conducted the largest ancient DNA study to date on human Stone Age skeletal material. We sequenced low-coverage genomes of 317 radiocarbon-dated (AMS) primarily Mesolithic and Neolithic individuals, covering major parts of Eurasia. We combined these with published shotgun-sequenced data to impute a dataset of >1600 diploid ancient genomes. Genomic data from 100 AMS-dated individuals from Denmark supported detailed analyses of the Stone Age population dynamics in Southern Scandinavia. When combined with genetically-predicted phenotypes, proxies for diet (δ13C/δ15N), mobility (87Sr/86Sr) and vegetation cover (pollen) we could connect this with parallel shifts in phenotype, subsidence and landscape. To test for traces of divergent selection in health and lifestyle-related genetic variants, we used the imputed ancient genomes to reconstruct polygenic risk scores for hundreds of complex traits in the ancient Eurasian populations. Additionally, we used a novel chromosome painting technique based on tree sequences, in order to model ancestry-specific allele frequency trajectories through time. This allowed us to identify many new phenotype-associated genetic variants with hitherto unknown evidence for positive selection in Eurasia throughout the Holocene. Results/Discussion Samples and data In this study we present genomic data from 317 ancient individuals (Fig 1, Extended data fig. 2, Supplement Table I). A total of 272 were radiocarbon dated within the project, while 39 dates were derived from literature and 15 were dated by archaeological context. Dates were corrected for marine and freshwater reservoir effects (Supplementary Note 8) and ranged from the Upper Palaeolithic (UP) c. 25,700 calibrated years before present (cal. BP) to the mediaeval period (c. 1200 cal. BP). However, 97% of the individuals (N=309) span 11,000 cal. BP to 3,000 cal. BP, with a heavy focus on individuals associated with various Mesolithic and Neolithic cultures. Geographically, the sampled skeletons cover a vast territory across Eurasia, from Lake Baikal to the Atlantic coast, from Scandinavia to the Middle East, and they derive from a variety of contexts, including burial mounds, caves, bogs and the seafloor (Supplementary Notes 6-7). Broadly, we can divide our research area into three large regions: 1) central, western and northern Europe, 2) eastern Europe including western Russia and Ukraine, and 3) the Urals and western Siberia. Our samples cover many of the key Mesolithic and Neolithic cultures in Western Eurasia, such as the Maglemose and Ertebølle cultures in Scandinavia, the Cardial in the Mediterranean, the Körös and Linear Pottery (LBK) in SE and Central Europe, and many archaeological cultures in Ukraine, western Russia, and the trans-Ural (e.g. Veretye, Lyalovo, Volosovo, Kitoi). Our sampling was particularly dense in Denmark from where we present a detailed and continuous sequence of 100 genomes spanning from the early Mesolithic to the Bronze Age. Dense sample sequences were also obtained from Ukraine, Western Russia, and the trans-Ural, spanning from the Early Mesolithic through the Neolithic, up to c. 5,000 BP. We extracted ancient DNA from tooth cementum or petrous bone and shotgun sequenced the 317 genomes to a depth of genomic coverage ranging from 0.01X to 7.1X (mean = 0.75X, median = 0.26X), with 81 individuals having >1X coverage. Using a new imputation method designed for low-coverage sequencing data26, we performed genotype imputation based on the 1,000 Genomes phased data as a reference panel. We also imputed >1,300 previously published shotgun-sequenced ancient genomes. This resulted in a “raw” dataset containing 8.5 million common Single Nucleotide Polymorphisms (SNPs) (>1% MAF and imputation info score > 0.5) from 1,664 imputed diploid ancient genomes. This number includes 42 high-coverage ancient genomes (Table S2.1, Supplementary Note 2) that were down-sampled to values between 0.1X and 4X for validation. 5 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. This demonstrated that 1-fold genome coverage provides remarkably high imputation accuracy (r2>0.95 at common variants with MAF above 5%) and closely matches what is obtained for modern samples (Extended Fig. 1A-D). African genomes, however, exhibit lower imputation accuracy as a result of the poor representation of this ancestry in the reference panel. For European genomes, this translates into genotyping error rates usually below 5% for the most challenging genotypes to impute (heterozygous genotypes or with two copies of the non-reference allele; Supplementary Fig. S2.1-S2.2). Imputation accuracy also depends on minor allele frequency and genomic coverage (Supplementary Fig. S2.3). We find that coverage values as low as 0.1x and 0.4X are sufficient to obtain r2 imputation accuracy of 0.8 and 0.9 at common variants (MAF>=10%), respectively. As further validation, we increased genomic coverage to 27.5X, 18.9X and 5.4X on a previously published trio (mother, father, son) from the Late Neolithic mass burial at Koszyce in Poland 27. This allowed for a validation of imputed genotypes and haplotypes using Mendel’s rules of inheritance. We obtained Mendelian error rates from 0.1% at 4X to 0.55% at 0.1X (Extended Fig. 1E). Similarly, we obtained switch error rates between 2% and 6%. Altogether, our validation analysis showed that ancient European genomes can be imputed confidently from coverages above 0.4X and highly valuable data can still be obtained with coverages as low as 0.1X when using specific QC on the imputed data, although at very low coverage a bias arise towards the major allele (see Supplementary Note 2). We filtered out samples with poor coverage or variant sites with low MAF in downstream analyses depending on the specific data quality requirements. For most analyses we use a subset of 1,492 imputed ancient genomes (213 sequenced in this study) after filtering individuals with very low coverages (<0.1X) and/or low imputation quality (average genotype probability < 0.8) and close relatives. This dataset allows us to characterise the ancient cross-continental gene pools and the demographic transitions with unprecedented resolution. We performed broad-scale characterization of this dataset using principal component analysis (PCA) and model-based clustering (ADMIXTURE), recapitulating and providing increased resolution into previously described ancestry clines in ancient Eurasian populations (Fig. 1; Extended Data Fig. 2; Supplementary Note 3d). Strikingly, inclusion of the imputed ancient genomes in the inference of the principal components reveals much higher variance among the ancient groups than previously anticipated using projection onto a PC-space inferred from modern individuals alone (Extended Data Fig. 2). This is particularly notable in a PCA of West Eurasian individuals, where genetic variation among all present-day populations is confined within a small central area of the PCA (Extended Data Fig. 2C, D). These results are consistent with much higher genetic differentiation between ancient Europeans than present-day populations reflecting lower effective population sizes and genetic isolation among ancient groups. To obtain a finer-scale characterization of genetic ancestries across space and time, we assigned imputed ancient individuals to genetic clusters by applying hierarchical community detection on a network of pairwise identity-by-descent (IBD)-sharing similarities28 (Extended Data Fig. 3; Supplementary Note 3c). The obtained clusters capture fine-scale genetic structure corresponding to shared ancestry within particular spatiotemporal ranges and/or archaeological contexts, and were used as sources and/or targets in supervised ancestry modelling (Extended Data Fig. 4; Supplementary Note 3i). We focus our subsequent analyses on three panels of putative source clusters reflecting different temporal depths: “deep”, using a set of deep ancestry source groups reflecting major ancestry poles; “postNeol”, using diverse Neolithic and earlier source groups; and “postBA”, using Late Neolithic and Bronze Age source groups (Extended Data Fig. 4). 6 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig 1. Sample overview and broad scale genetic structure. (A), (B) Geographic and temporal distribution of the 317 ancient genomes reported here. Age and geographic region of ancient individuals are indicated by plot symbol colour and shape, respectively. Random jitter was added to geographic coordinates to avoid overplotting. (C), (D) Principal component analysis of 3,316 modern and ancient individuals from Eurasia, Oceania, and the Americas (C), as well as restricted to 2,126 individuals from western Eurasia (west of Urals) (D). Principal components were defined using both modern and imputed ancient genomes passing all filters, with the remaining low-coverage ancient genomes projected. Ancient genomes sequenced in this study are indicated with black circles (imputed genomes passing all filters, n=213) or grey diamonds (pseudo-haploid projected genomes, n=104). Genomes of modern individuals are shown in grey, with population labels corresponding to their median coordinates. Deep population structure of western Eurasians Our study comprises the largest genomic dataset on European hunter-gatherers to date, including 113 imputed hunter-gatherer genomes of which 79 were sequenced in this study. Among them, we report a 0.83X genome of an Upper Palaeolithic (UP) skeleton from Kotias Klde Cave in Georgia, Caucasus (NEO283), directly dated to 26,052 - 25,323 cal BP (95%). In the PCA of all non-African individuals, it occupies a position distinct from other previously sequenced UP individuals, shifted towards west Eurasians along PC1 (Supplementary Note 3d). Using admixture graph modelling, we find that this Caucasus UP lineage derives from a mixture of predominantly West Eurasian UP hunter-gatherer ancestry (76%) with ~24% contribution from a “basal Eurasian” ghost population, first observed in West Asian Neolithic individuals29 (Extended Data Fig. 5A). Models attempting to reconstruct major post-LGM clusters such as European hunter-gatherers and Anatolian farmers without contributions from this Caucasus UP lineage provided poor admixture graph fits or were rejected in qpAdm analyses (Extended Data Fig. 5B,C). These results thus suggest a central role of 7 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. the descendants related to this Caucasus UP lineage in the formation of later West Eurasian populations, consistent with recent genetic data from the nearby Dzudzuana Cave, also in Georgia30. We performed supervised admixture modelling using a set of twelve possible source clusters representing Mesolithic hunter-gatherers from the extremes of the HG cline, as well as temporal or geographical outgroups of deep Eurasian lineages (Fig 2A). We replicate previous results of broadscale genetic structure correlated to geography in European hunter-gatherers after the LGM17, while also revealing novel insights into their fine-scale structure. Ancestry related to southern European hunter-gatherers (source: Italy_15000BP_9000 BP) predominates in western Europe. This includes Denmark, where our 28 sequenced and imputed hunter-gatherer genomes derive almost exclusively from this cluster, with remarkable homogeneity across a 5,000 year transect (Fig. 3A). In contrast, hunter-gatherer individuals from the eastern and far northern reaches of Europe show the highest proportions of Russian hunter-gatherer ancestry (source: RussiaNW_11000BP_8000BP; Fig. 2B, D), with genetic continuity until ~5,000 BP in Russia. Ancestry related to Mesolithic huntergatherer populations from Ukraine (source: Ukraine_10000BP_4000BP) is carried in highest proportions in hunter-gatherers from a geographic corridor extending from south-eastern Europe towards the Baltic and southern Scandinavia. Swedish Mesolithic individuals derive up to 60% of their ancestry from that source (Fig. 2C). Our results thus indicate northwards migrations of at least three distinct waves of hunter-gatherer ancestry into Scandinavia: a predominantly southern European source into Denmark; a source related to Ukrainian and south-eastern European huntergatherers into the Baltic and southern Sweden; and a northwest Russian source into the far north, before venturing south along the Atlantic coast of Norway31 (Fig. 2). These movements are likely to represent post glacial expansions from refugia areas shared with many plant and animal species32,33. Despite the major role of geography in shaping European hunter-gatherer structure, we also document more complex local dynamics. On the Iberian Peninsula, the earliest individuals, including a ~9,200-year-old hunter-gatherer (NEO694) from Santa Maira (eastern Spain), sequenced in this study, show predominantly southern European hunter-gatherer ancestry with a minor contribution from UP hunter-gatherer sources (Fig. 3). In contrast, later individuals from Northern Iberia are more similar to hunter-gatherers from eastern Europe, deriving ~30-40% of their ancestry from a source related to Ukrainian hunter-gatherers34,35. The earliest evidence for this gene flow is observed in a Mesolithic individual from El Mazo, Spain (NEO646) that was dated, calibrated and reservoir-corrected to c. 8,200 BP (8365-8182 cal BP, 95%) but context-dated to slightly older (8550-8330 BP, see36). The younger date coincides with some of the oldest Mesolithic geometric microliths in northern Iberia, appearing around 8,200 BP at this site36. In southern Sweden, we find higher amounts of southern European hunter-gatherer ancestry in late Mesolithic coastal individuals (NEO260 from Evensås; NEO679 from Skateholm) than in the earlier Mesolithic individuals from further inland, suggesting either geographic genetic structure in the Swedish Mesolithic population or a possible eastward expansion of hunter-gatherers from Denmark, where this ancestry prevailed (Fig. 3). An influx of southern European hunter-gathererrelated ancestry in Ukrainian individuals after the Mesolithic (Fig. 3) suggests a similar eastwards expansion in south-eastern Europe17. Interestingly, two herein reported ~7,300-year-old imputed genomes from the Middle Don River region in the Pontic-Caspian steppe (Golubaya Krinitsa, NEO113 & NEO212) derive ~20-30% of their ancestry from a source cluster of hunter-gatherers from the Caucasus (Caucasus_13000BP_10000BP) (Fig. 3). Additional lower coverage (nonimputed) genomes from the same site project in the same PCA space (Fig. 1D), shifted away from the European hunter-gatherer cline towards Iran and the Caucasus. Our results thus document genetic contact between populations from the Caucasus and the Steppe region as early as 7,300 years ago, providing documentation of continuous admixture prior to the advent of later nomadic 8 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Steppe cultures, in contrast to recent hypotheses, and also further to the west than previously reported17,37. Fig 2. Genetic structure of European hunter-gatherers (A) Ancestry proportions in 113 imputed ancient genomes representing European hunter-gatherer contexts (right) estimated from supervised non-negative least squares analysis using deep Eurasian source groups (left). Individuals from target groups are grouped by genetic clusters. (B)-(D) Moon charts showing spatial distribution of ancestry proportions in European hunter-gatherers deriving from three deep Eurasian source groups; Italy_15000BP_9000BP; Ukraine_10000BP_4000BP; RussiaNW_11000BP_8000BP (source origins shown with coloured symbol). Estimated ancestry proportions are indicated by both size and amount of fill of moon symbols. Major genetic transitions in Europe Previous ancient genomics studies have documented multiple episodes of large-scale population turnover in Europe within the last 10,000 years6,11,14,16,17,20,21,34,38–41. The 317 genomes reported here fill important knowledge gaps, particularly in northern and eastern Europe, allowing us to track the dynamics of these events at both continental and regional scales. Our analyses reveal profound differences in the spatiotemporal Neolithisation dynamics across Europe. Supervised admixture modelling (“deep” set) and spatiotemporal kriging42 document a broad east-west distinction along a boundary zone running from the Black Sea to the Baltic. On the western side of this “Great Divide”, the Neolithic transition is accompanied by large-scale shifts in genetic ancestry from local hunter-gatherers to Neolithic farmers with Anatolian-related ancestry (Boncuklu_10000BP; Fig. 3; Extended Data Fig. 4, 6). The arrival of Anatolian-related ancestry in different regions spans an extensive time period of over 3,000 years, from its earliest evidence in the Balkans (Lepenski Vir) at ~8,700 BP17 to c. 5,900 BP in Denmark. On the eastern side of this divide, no ancestry shifts can be observed during this period. In the East Baltic region (see also43), 9 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Ukraine and Western Russia local hunter-gatherer ancestry prevails until ~5,000 BP without noticeable input of Neolithic Anatolian-related farmer ancestry (Fig. 3; Extended Data Fig. 4, 6). This Eastern genetic continuity is in remarkable congruence with the archaeological record showing persistence of pottery-using hunter-gatherer-fisher groups in this wide region, and delayed introduction of cultivation and husbandry by several thousand years (Supplementary Note 5). From approximately 5,000 BP, an ancestry component appears on the eastern European plains in Early Bronze Age Steppe pastoralists associated with the Yamnaya culture and it rapidly spreads across Europe through the expansion of the Corded Ware complex (CWC) and related cultures20,21. We demonstrate that this “steppe” ancestry (Steppe_5000BP_4300BP) can be modelled as a mixture of ~65% ancestry related to herein reported hunter-gatherer genomes from the Middle Don River region (MiddleDon_7500BP) and ~35% ancestry related to hunter-gatherers from Caucasus (Caucasus_13000BP_10000BP) (Extended Data Fig. 4). Thus, Middle Don hunter-gatherers, who already carry ancestry related to Caucasus hunter-gatherers (Fig. 2), serve as a hitherto unknown proximal source for the majority ancestry contribution into Yamnaya genomes. The individuals in question derive from the burial ground Golubaya Krinitsa (Supplementary Note 3). Material culture and burial practices at this site are similar to the Mariupol-type graves, which are widely found in neighbouring regions of Ukraine, for instance along the Dnepr River. They belong to the group of complex pottery-using hunter-gatherers mentioned above, but the genetic composition at Golubaya Krinitsa is different from the remaining Ukrainian sites (Fig 2A, Extended Data Fig. 4). We find that the subsequent transition of the Late Neolithic and Early Bronze Age European gene pool happened at a faster pace than during the Neolithisation, reaching most parts of Europe within a ~1,000-year time period after first appearing in eastern Baltic region ~4,800 BP (Fig. 3). In line with previous reports we observe that beginning c. 4,200 BP, steppe-related ancestry was already dominant in samples from France and the Iberian peninsula, while it reached Britain only 400 years later11,38,44. Strikingly, because of the delayed Neolithisation in Southern Scandinavia these dynamics resulted in two episodes of large-scale genetic turnover in Denmark and southern Sweden within a 1,000-year period (Fig. 3). 10 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig. 3. Genetic transects of Eurasia. Regional timelines of genetic ancestry compositions within the past 15,000 years in western Eurasia (top) and the Eurasian Steppe belt east of the Urals (bottom). Ancestry proportions in 972 imputed ancient genomes from these regions (covering c. 12,000 BP to 500 BP), inferred using supervised admixture modelling with “deep” hunter-gatherer ancestry source groups. Geographic areas included in timelines are indicated with fill colour (west Eurasia) and grey shading (eastern Steppe region). Excavation locations of the ancient skeletons are indicated with black crosses. Coloured bars within the timelines represent ancestry proportions for temporally consecutive individuals, with the width corresponding to their age difference. Individuals with identical age were offset along the time axis by adding random jitter, ages. We note that the inclusion of only shotgun-sequenced samples may affect the exact timing of events in some regions from where such data are sparse. We next investigated fine-grained ancestry dynamics underlying these transitions. We replicate previous reports11,16,17,21,41,45,46 of widespread, but low-level admixture between Neolithic farmers and local hunter-gatherers resulting in a resurgence of HG ancestry in many regions of Europe during the middle and late Neolithic (Extended Data Fig. 7). Estimated hunter-gatherer ancestry proportions among early Neolithic people rarely exceed 10%, with notable exceptions observed in individuals from south-eastern Europe (Iron Gates), Sweden (Pitted Ware Culture) as well as herein reported early Neolithic genomes from Portugal (western Cardial), estimated to harbour 27% – 43% Iberian hunter-gatherer ancestry (Iberia_9000BP_7000BP). The latter result, suggesting extensive first-contact admixture, is in agreement with archaeological inferences derived from modelling the 11 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. spread of farming along west Mediterranean Europe47. Individuals associated with Neolithic farming cultures from Denmark show some of the highest overall hunter-gatherer ancestry proportions (up to ~25%), mostly derived from Western European-related hunter-gatherers (EuropeW_13500BP_8000BP) supplemented with marginal contribution from local Danish groups in some individuals (Extended Data Fig. 7D; Supplementary Note 3f). We estimated the timing of the admixture using the linkage-disequilibrium-based method DATES48 at ~6,000 BP. Both lines of evidence thus suggest that a significant part of the hunter-gatherer admixture observed in Danish Neolithic individuals occurred already before the arrival of the incoming Neolithic people in the region (Extended Data Fig. 7), and further imply Central Europe as a key region in the resurgence of HG ancestry. Interestingly, the genomes of two ~5,000-year-old Danish male individuals (NEO33, NEO898) were entirely composed of Swedish hunter-gatherer ancestry, and formed a cluster with Pitted Ware Culture (PWC) individuals from Ajvide on the Baltic island of Gotland (Sweden)49–51. Of the two individuals, NEO033 also displays an outlier Sr-signature (Fig. 4), potentially suggesting a non-local origin matching his unusual ancestry. Overall, our results demonstrate direct contact across the Kattegat and Öresund during Neolithic times (Extended Data Fig. 3, 4), in line with archaeological finds from Zealand (east Denmark) showing cultural affinities to PWC on the Swedish west coast52–55. Further, we find evidence for regional stratification in early Neolithic farmer ancestries in subsequent Neolithic groups. Specifically, southern European early farmers appear to have provided major genetic ancestry to mid- and late Neolithic groups in Western Europe, while central European early farmer ancestry is mainly observed in subsequent Neolithic groups in eastern Europe and Scandinavia (Extended Data Fig. 7D-F). These results are consistent with distinct migratory routes of expanding farmer populations as previously suggested8. For example, similarities in material culture and flint mining activities could suggest that the first farmers in South Scandinavia originated from or had close social relations with the central European Michelsberg Culture56. The second continental-wide and CWC-mediated transition from Neolithic farmer ancestry to Steppe-related ancestry was found to differ markedly between geographic regions. The contribution of local Neolithic farmer ancestry to the incoming groups was high in eastern, western and southern Europe, reaching >50% on the Iberian Peninsula (“postNeol” set; Extended Data Fig. 4, 6B, C)34. Scandinavia, however, portrays a dramatically different picture, with a near-complete replacement of the local Neolithic farmer population inferred across all sampled individuals (Extended Data Fig. 7B, C). Following the second transition, Neolithic Anatolian-related farmer ancestry remains in Scandinavia, but the source is now different. It can be modelled as deriving almost exclusively from a genetic cluster associated with the Late Neolithic Globular Amphora Culture (GAC) (Poland_5000BP_4700BP; Extended Data Fig. 4). Strikingly, after the Steppe-related ancestry was first introduced into Europe (Steppe_5000BP_4300BP), it expanded together with GAC-related ancestry across all sampled European regions (Extended Data Fig. 7I). This suggests that the spread of steppe-related ancestry throughout Europe was predominantly mediated through groups that were already admixed with GAC-related farmer groups of the eastern European plains. This finding has major implications for understanding the emergence of the CWC. A stylistic connection from GAC ceramics to CWC ceramics has long been suggested, including the use of amphora-shaped vessels and the development of cord decoration patterns57. Moreover, shortly prior to the emergence of the earliest CWC groups, eastern GAC and western Yamnaya groups exchanged cultural elements in the forest-steppe transition zone northwest of the Black Sea, where GAC ceramic amphorae and flint axes were included in Yamnaya burials, and the typical Yamnaya use of ochre was included in GAC burials58, indicating close interaction between the groups. Previous ancient genomic data from a few individuals suggested that this was limited to cultural influences and not population admixture59. However, in the light of our new genetic evidence it appears that this zone, and 12 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. possibly other similar zones of contact between GAC and Yamnaya (or other closely-related steppe/forest-steppe groups) were key in the formation of the CWC through which steppe-related ancestry and GAC-related ancestry co-dispersed far towards the west and the northcf. 60. This resulted in regionally diverse situations of interaction and admixture61,62 but a significant part of the CWC dispersal happened through corridors of cultural and demic transmission which had been established by the GAC during the preceding period63,64. Fine-scale structure and multiproxy analysis of Danish transect We present a detailed and continuous sequence of multiproxy data from Denmark, from the Early Mesolithic Maglemose, via the Kongemose and Late Mesolithic Ertebølle epochs, the Early and Middle Neolithic Funnel Beaker Culture and the Single Grave Culture, to Late Neolithic and Bronze Age individuals (Fig. 4). To integrate multiproxy data from as many skeletons as possible we made use of non-imputed data for the admixture analyses (Supplementary Note S3d) which were not restricted to the >0.1X coverage cut-off used elsewhere. This provided genetic profiles from 100 Danish individuals (Fig 4), spanning c. 7,300 years from the earliest known skeleton in Denmark (the Mesolithic “Koelbjerg Man” (NEO254, 10,648-10,282 cal. BP, 95% probability interval) and formerly known as the “Koelbjerg Woman”65), to a Bronze Age skeleton from Hove Å (NEO946) dated to 3322-2967 cal. BP (95%). Two temporal shifts in genomic admixture proportions confirm the major population genetic turnovers (Fig. 4) that was inferred from imputed data (Fig. 3). The multiproxy evidence, however, unveils the dramatic concomitant changes in all investigated phenotypic, environmental and dietary parameters (Fig. 4). 13 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 14 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig 4. Environmental, dietary, phenotypic and ancestry shifts in Denmark through time. Two dramatic population turnovers are evident from chronologically-sorted multiproxy data representing 100 Danish Stone Age and early Bronze Age skeletons sequenced in this study. The figure shows concomitant changes in several investigated parameters including (from the top) admixture proportions from non-imputed autosomal genome-wide data, Y-chromosomal and mitochondrial haplogroups, genetic phenotype predictions (based on imputed data) as well as 87Sr/86Sr and δ13C and δ15N isotope data as possible proxies for mobility and diet, respectively. Predicted height values represent differences (in cm) from the average height of the present-day Danish population, based on genotypes at 310 height-associated loci (Supplementary Note 4f). Probabilities for the indicated natural eye and hair colours are based on genotypes at 18 pigmentation-associated loci (Supplementary Note 4f) with grey denoting probability of intermediate eye colour (including grey, green and hazel). Lower panel shows changes in vegetation as predicted from pollen analyses at Lake Højby in Zealand (Supplementary Note 12). Black vertical lines mark the first presence of Anatolian farmer ancestry and Steppe-related ancestry, respectively. During the Danish Mesolithic, individuals from the Maglemose, Kongemose and Ertebølle cultures displayed a remarkable genetic homogeneity across a 5,000 year transect deriving their ancestry almost exclusively from a southern European source (source: Italy_15000BP_9000BP) that later predominates in western Europe (Fig. 2). These cultural transitions occurred in genetic continuity, apparent in both autosomal and uniparental markers, which rules out demic diffusion and supports the long-held assumption of a continuum of culture and populatione.g. 66–68. Genetic predictions indicate blue eye pigmentation with high probability in several individuals throughout the duration of the Mesolithic (Supplementary Note 4f), consistent with previous findings 11,20,45. In contrast, none of the analysed Mesolithic individuals displayed high probability of light hair pigmentation. Height predictions for Mesolithic individuals generally suggest slightly lower or perhaps less variable genetic values than in the succeeding Neolithic period. However, we caution that the relatively large genetic distance to modern individuals included in the GWAS panel make these scores poorly applicable to Mesolithic individuals (Supplementary Note 4c) and are dependent on the choice of GWAS filters used. Unfortunately, only a fraction of the 100 Danish skeletons included were suitable for stature estimation by actual measurement, why these values are not reported. Stable isotope δ13C values in collagen inform on the proportion of marine versus terrestrial protein, while δ15N values reflect the trophic level of protein sources69,70. Both the Koelbjerg Man and the second earliest human known from Denmark, (Tømmerupgårds Mose – not part of the present study; see71) showed more depleted dietary isotopic values, representing a lifestyle of inland hunterfisher-gatherers of the early Mesolithic forest. A second group consisted of coastal fisher-huntergatherers dating to the late half of the Maglemose epoch onwards (Supplementary Figs. S10.1 and S10.2). During this period global sea-level rise gradually changed the landscape of present-day Denmark from an interior part of the European continent to an archipelago, where all human groups had ample access to coastal resources within their annual territories. Increased δ13C and δ15N values imply that from the late Maglemose marine foods gradually increased in importance, to form the major supply of proteins in the final Ertebølle period71,cf. 72. Interestingly, rather stable 87Sr/86Sr isotope ratios throughout the Mesolithic indicate limited mobility, in agreement with the evidence for genetic continuity reported here and modelled in previous work73,74 Fig. 3, and/or dietary sources from homogeneous environments. The arrival of Neolithic farmer-related ancestry at c. 5,900 BP in Denmark resulted in a population replacement with very limited genetic contribution from the local hunter-gatherers. The shift is abrupt and brings changes in all the measured parameters. This is a clear case of demic diffusion, which settles a long-standing debate concerning the neolithisation process in Denmark15,56,75,76, at least at a broader population level. The continuing use of coastal kitchen middens well into the 15 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Neolithic77,78 remains, however, an enigma, although this may represent sites where local remnants of Mesolithic groups survived in partly acculturated form, or it could be middens taken over by the newcomers. Concomitant shifts in both autosomal and uniparental genetic markers show that the migration by incoming farmers was not clearly sex-biased but more likely involved nuclear family units. Diet shifted abruptly to terrestrial sources evidenced by δ13C values around -20 ‰ and δ15N values around 10 ‰ in line with archaeological evidence that domesticated crops and animals were now providing the main supply of proteins (Supplementary Note 6). Isotope values remained stable at these levels throughout the following periods, although with somewhat greater variation after c. 4,500 BP. However, five Neolithic and Early Bronze Age individuals have δ13C and δ15N values indicating intake of high trophic marine food. This is most pronouncedly seen for NEO898 (Svinninge Vejle) who was one of the two aforementioned Danish Neolithic individuals displaying typical Swedish PWC hunter-gatherer ancestry. A higher variability in 87Sr/86Sr values can be seen with the start of the Neolithic and this continues in the later periods, which suggests that the Neolithic farmers in Denmark consumed food from more diverse landscapes and/or they were more mobile than the preceding hunter-gatherers (Supplementary Note 11). The Neolithic transition also marks a considerable rise in frequency of major effect alleles associated with light hair pigmentation79, whereas polygenic score predictions for height are generally low throughout the first millennium of the Neolithic (Funnel Beaker epoch), echoing previous findings based on a smaller set of individuals45,80. We do not know how the Mesolithic Ertebølle population disappeared. Some may have been isolated in small geographical pockets of brief existence and/or adapted to a Neolithic lifestyle but without contributing much genetic ancestry to subsequent generations. The most recent individual in our Danish dataset with Mesolithic WHG ancestry is “Dragsholm Man” (NEO962), dated to 5,947-5,664 cal. BP (95%) and archaeologically assigned to the Neolithic Funnel Beaker farming culture based on his grave goods81,82. Our data confirms a typical Neolithic diet matching the cultural affinity but contrasting his WHG ancestry. Thus, Dragsholm Man represents a local person of Mesolithic ancestry who lived in the short Mesolithic-Neolithic transition period and adopted a Neolithic culture and diet. A similar case of very late Mesolithic WHG ancestry in Denmark was observed when analysing human DNA obtained from a piece of chewed birch pitch dated to 5,858– 5,661 cal. BP (95%)83. The earliest example of Anatolian Neolithic ancestry in our Danish dataset is observed in a bog skeleton of a female from Viksø Mose (NEO601) dated to 5,896-5,718 cal. BP (95%) (and hence potentially contemporaneous with Dragsholm Man) whereas the most recent Danish individual showing Anatolian ancestry without any Steppe-related ancestry is NEO943 from Stenderup Hage, dated to 4,818-4,415 cal. BP (95%). Using Bayesian modelling we estimate the duration between the first appearance of Anatolian ancestry to the first appearance of Steppe-related ancestry in Denmark to be between 876 and 1100 years (95% probability interval, Supplementary Note 9) indicating that the typical Neolithic ancestry was dominant for less than 50 generations in Denmark. From this point onwards the steppe-ancestry was introduced, signalling the rise of the late Neolithic Corded Ware derived cultures in Denmark (i.e. Single Grave Culture), followed by the later Neolithic Dagger epoch and Bronze Age cultures. While this introduced a major new component in the Danish gene pool, it was not accompanied by apparent shifts in diet. Our complex trait predictions indicate an increase in “genetic height” occurring concomitant with the introduction of Steppe-related ancestry, which is consistent with Steppe individuals (e.g., Yamnaya) being genetically taller on average45 and with previous results from other European regions80,84. These major population turnovers were accompanied by significant environmental changes, as apparent from high-resolution pollen diagrams from Lake Højby in Northwest Zealand 16 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. reconstructed using the Landscape Reconstruction Algorithm (LRA85 (Supplementary Note 8). While the LRA has previously been applied at low temporal resolution regional scale e.g. 86,87, and at local scale to Iron Age and later pollen diagrams e.g. 88,89, this is the first time this quantitative method is applied at local scale to a pollen record spanning the Mesolithic and Neolithic periods in Denmark. Comparison with existing pollen records show that the land cover changes demonstrated here reflect the general vegetation development in eastern Denmark, while the vegetation on the sandier soils of western Jutland maintains a more open character throughout the sequence (Supplementary Note 12). We find that during the Mesolithic (i.e. before c. 6,000 BP) the vegetation was dominated by primary forest trees (Tilia, Ulmus, Quercus, Fraxinus, Alnus etc.). The forest composition changed towards more secondary, early successional trees (Betula and then Corylus) in the earliest Neolithic, but only a minor change in the relationship between forest and open land is recorded. From c. 5,650 BP deforestation intensified, resulting in a very open grassland-dominated landscape. This open phase was short-lived, and secondary forest expanded from 5,500 to 5,000 BP, until another episode of forest clearance gave rise to an open landscape during the last part of the Funnel Beaker epoch. We thus conclude that the agriculture practice was characterised by repeated clearing of the forest with fire, followed by regrowth. This strategy changed with the onset of the Single Grave Culture, when the forest increased again, but this time dominated by primary forest trees, especially Tilia and Ulmus. This reflects the development of a more permanent division of the landscape into open grazing areas and forests. In contrast, in western Jutland this phase was characterised by large-scale opening of the landscape, presumably as a result of human impact aimed at creating pastureland90. Finally, we investigated the fine-scale genetic structure in southern Scandinavia after the introduction of Steppe-related ancestry using a temporal transect of 38 Late Neolithic and Early Bronze Age Danish and southern Swedish individuals. Although the overall population genomic signatures suggest genetic stability, patterns of pairwise IBD-sharing and Y-chromosome haplogroup distributions indicate at least three distinct ancestry phases during a ~1,000-year time span: i) An early stage between ~4,600 BP and 4,300 BP, where Scandinavians cluster with early CWC individuals from Eastern Europe, rich in Steppe-related ancestry and males with an R1a Ychromosomal haplotype (Extended Data Fig. 8A, B); ii) an intermediate stage until c. 3,800 BP, where they cluster with central and western Europeans dominated by males with distinct sublineages of R1b-L51 (Extended Data Fig. 8C, D; Supplementary Note 3b) and includes Danish individuals from Borreby (NEO735, 737) and Madesø (NEO752) with distinct cranial features (Supplementary Note 6); and iii) a final stage from c. 3,800 BP onwards, where a distinct cluster of Scandinavian individuals dominated by males with I1 Y-haplogroups appears (Extended Data Fig. 8E). Using individuals associated with this cluster (Scandinavia_4000BP_3000BP) as sources in supervised ancestry modelling (see “postBA”, Extended Data Fig. 4), we find that it forms the predominant source for later Iron- and Viking Age Scandinavians, as well as ancient European groups outside Scandinavia who have a documented Scandinavian or Germanic association (e.g., Anglo-Saxons, Goths; Extended Data Fig. 4). Y-chromosome haplogroup I1 is one of the dominant haplogroups in present-day Scandinavians,s, and we document its earliest occurrence in a ~4,000year-old individual from Falköping in southern Sweden (NEO220). The rapid expansion of this haplogroup and associated genome-wide ancestry in the early Nordic Bronze Age indicates a considerable reproductive advantage of individuals associated with this cluster over the preceding groups across large parts of Scandinavia. 17 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Hunter-gatherer resilience east of the Urals In contrast to the significant number of ancient hunter-gatherer genomes from western Eurasia studied to date, genomic data from hunter-gatherers east of the Urals remain sparse. These regions are characterised by an early introduction of pottery from areas further east and are inhabited by complex hunter-gatherer-fisher societies with permanent and sometimes fortified settlements (Supplementary Note 5; 91). Here, we substantially expand the knowledge on ancient Stone Age populations of this region by reporting new genomic data from 38 individuals, 28 of which date to pottery-associated huntergatherer contexts e.g. 92 between 8,300-5,000 BP (Supplementary Table II).The majority of these genomes form a previously only sparsely sampled 48,93 “Neolithic Steppe” cline spanning the Siberian Forest Steppe zones of the Irtysh, Ishim, Ob, and Yenisei River basins to the Lake Baikal region (Fig. 1C; Extended Data Fig. 2A, 3E). Supervised admixture modelling (using the “deep” set of ancestry sources) revealed contributions from three major sources in these hunter gatherers from east of Urals: early West Siberian hunter-gatherer ancestry (SteppeC_8300BP_7000BP) dominated in the western Forest Steppe; Northeast Asian hunter-gatherer ancestry (Amur_7500BP) was highest at Lake Baikal; and Paleosiberian ancestry (SiberiaNE_9800BP) was observed in a cline of decreasing proportions from northern Lake Baikal westwards across the Forest Steppe (Extended Data Fig. 4, 9). 93 We used these Neolithic hunter-gatherer clusters (“postNeol” ancestry source set, Extended Data Fig. 4) as putative source groups in more proximal admixture modelling to investigate the spatiotemporal dynamics of ancestry compositions across the Steppe and Lake Baikal after the Neolithic period. We replicate previously reported evidence for a genetic shift towards higher Forest Steppe hunter-gatherer ancestry (SteppeCE_7000BP_3600BP) in late Neolithic and early Bronze Age individuals (LNBA) at Lake Baikal 93,94. However, ancestry related to this cluster is already observed at ~7,000 BP in herein-reported Neolithic hunter-gatherer individuals both at Lake Baikal (NEO199, NEO200), and along the Angara river to the north (NEO843). Both male individuals at Lake Baikal belonged to Y-chromosome haplogroup Q1, characteristic of the later LNBA groups in the same region. (Extended Data Fig. 3, 6A). Together with an estimated date of admixture of ~6,000 BP for the LNBA groups, these results suggest gene flow between huntergatherers of Lake Baikal and the south Siberian forest steppe regions already during the early Neolithic. This is consistent with archaeological interpretations of contact. In this region, bifacially flaked tools first appeared near Baikal 95 from where the technique spread far to the west. We find its reminiscences in Late Neolithic archaeological complexes (Shiderty 3, Borly, Sharbakty 1, UstNarym, etc.) in Northern and Eastern Kazakhstan, around 6,500-6,000 BP 96,97. Our herein-reported genomes also shed light on the genetic origins of the early Bronze Age Okunevo culture in the Minusinsk Basin in Southern Siberia. In contrast to previous results, we find no evidence for Lake Baikal hunter-gatherer ancestry in the Okunevo93,94, suggesting that they instead originate from a three-way mixture of two different genetic clusters of Siberian forest steppe hunter-gatherers and Steppe-related ancestry (Extended Data Fig. 4D). We date the admixture with Steppe-related ancestry to ~4,600 BP, consistent with gene flow from peoples of the Afanasievo culture that existed near Altai and Minusinsk Basin during the early eastwards’ expansion of Yamnaya-related groups 20,94. From around 3,700 BP, individuals across the Steppe and Lake Baikal regions display markedly different ancestry profiles (Fig. 3; Extended Data Fig. 4D, 9). We document a sharp increase in non-local ancestries, with only limited ancestry contributions from local hunter-gatherers. The early stages of this transition are characterised by influx of Yamnaya-related ancestry, which decays over 18 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. time from its peak of ~70% in the earliest individuals. Similar to the dynamics in western Eurasia, Yamnaya-related ancestry is here correlated with late Neolithic GAC-related farmer ancestry (Poland_5000BP_4700BP; Extended Data Fig. 9G), recapitulating the previously documented eastward expansion of admixed Western Steppe pastoralists from the Sintashta and Andronovo complexes during the Bronze Age20,48,98. However, GAC-related ancestry is notably absent in individuals of the Okunevo culture, providing further support for two distinct eastward migrations of Western Steppe pastoralists during the early (Yamnaya) and later (Sintashta, Andronovo) Bronze Age. The later stages of the transition are characterised by increasing Central Asian (Turkmenistan_7000 BP_5000BP) and Northeast Asian-related (Amur_7500BP) ancestry components (Extended Data Fig. 9G). Together, these results show that deeply structured huntergatherer ancestry dominated the eastern Eurasian Steppe substantially longer than in western Eurasia, before successive waves of population expansions swept across the Steppe within the last 4,000 years, including a large-scale introduction of domesticated horse lineages concomitant with new equestrian equipment and spoke-wheeled chariotry 20,48,98,99. Genetic legacy of Stone Age Europeans To investigate the distribution of Stone Age and Early Bronze Age ancestry components in modern populations, we used ChromoPainter 100 to “paint” the chromosomes of individuals in the UK Biobank (https://www.ukbiobank.ac.uk) using a panel of 10 ancient donor populations (Supplementary Note 3h). Painting was done following the pipeline of Margaryan et al. 101 based on GLOBETROTTER 102, and admixture proportions were estimated using Non-Negative Least squares. Haplotypes in the modern genomes are assigned to the genetically closest ancient population as measured by meiosis events, which favours more recent matches in time. Therefore, ancestry proportions assigned to the oldest groups (e.g. WHG) should be interpreted as an excess of this ancestry, which cannot be explained by simply travelling through more recent ancient populations up to present times. First, we selected non-British individuals from the UK Biobank if their country of birth was European, African, or Asian. Because many of these individuals are admixed or British, we set up a pipeline (Supplementary Note 3g) to select individuals of a typical ancestral background for each country. This resulted in 24,511 individuals from 126 countries, who were then chromosome painted to assess the average admixture proportions for each ancestry per country. The various hunter-gatherer ancestries are not homogeneously distributed amongst modern populations (Fig. 5). WHG-related ancestry is highest in present-day individuals from the Baltic States, Belarus, Poland, and Russia; EHG-related ancestry is highest in Mongolia, Finland, Estonia and Central Asia; and CHG-related ancestry is maximised in countries east of the Caucasus, in Pakistan, India, Afghanistan and Iran, in accordance with previous results 103. The CHG-related ancestry likely reflects both Caucasus hunter-gatherer and Iranian Neolithic signals, explaining the relatively high levels in south Asia 104. Consistent with expectations 105,106, Neolithic Anatolianrelated farmer ancestry is concentrated around the Mediterranean basin, with high levels in southern Europe, the Near East, and North Africa, including the Horn of Africa, but is less frequent in Northern Europe. This is in direct contrast to the Steppe-related ancestry, which is found in high levels in northern Europe, peaking in Ireland, Iceland, Norway, and Sweden, but decreases further south. There is also evidence for its spread into southern Asia. Overall, these results refine global patterns of spatial distributions of ancient ancestries amongst modern populations. 19 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The availability of a large number of modern genomes (n=408,884) from self-identified “white” British individuals who share similar PCA backgrounds 107 allowed us to further examine the distribution of ancient ancestries at high resolution in Britain (Supplementary Note 3h). Although regional ancestry distributions differ by only a few percent, we find clear evidence of geographical heterogeneity across the United Kingdom as visualised by assigning individuals to their birth county and averaging ancestry proportions per county (Fig. 5, inset boxes). The proportion of Neolithic farmer ancestry is highest in southern and eastern England today and lower in Scotland, Wales, and Cornwall. Steppe-related ancestry is inversely distributed, peaking in the Outer Hebrides and Ireland, a pattern only previously described for Scotland 108. This regional pattern was already evident in the Pre-Roman Iron Age and persists to the present day even though immigrating Anglo-Saxons had relatively less Neolithic farmer ancestry than the Iron-Age population of southwest Briton (Extended Data Fig. 4). Although this Neolithic farmer/steppe-related dichotomy mirrors the modern ‘Anglo-Saxon’/‘Celtic’ ethnic divide, its origins are older, resulting from continuous migration from a continental population relatively enhanced in Neolithic farmer ancestry, starting as early as the Late Bronze Age 109. By measuring haplotypes from these ancestries in modern individuals, we are able to show that these patterns differentiate Wales and Cornwall as well as Scotland from England. We also found higher levels of WHG-related ancestry in central and Northern England. These results demonstrate clear ancestry differences within an ‘ethnic group’ (white British) traditionally considered relatively homogenous, which highlights the need to account for subtle population structure when using resources such as the UK Biobank genomes. 20 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig 5. The genetic legacy of Stone Age ancestry in modern populations. From top left clockwise: Neolithic Farmer, Yamnaya, Caucasus hunter-gatherer, Eastern hunter-gatherer, Western hunter-gatherer. Panels show average admixture proportion in modern individuals per country estimated using NNLS (large maps), average per county within the UK (top left insert), and PCA (PC2 vs PC1) of admixture proportions, with the top 10 highest countries by admixture fraction labelled and PCA loadings for that ancestry. Sociocultural insights We used patterns of pairwise IBD sharing between individuals and runs of homozygosity (ROH) within individuals (measured as the fraction of the genome within a run of homozygosity f(ROH)) to examine our data for temporal shifts in relatedness within genetic clusters. Both measures show clear trends of a reduction of within-cluster relatedness over time, in both western and eastern Eurasia (Fig. 6). This pattern is consistent with a scenario of increasing effective population sizes during this period 110. Nevertheless, we observe notable differences in temporal relatedness patterns between western and eastern Eurasia, mirroring the wider difference in population dynamics discussed above. In the west, within-group relatedness changes substantially during the Neolithic transition (~9,000 to ~6,000 BP), where clusters of Neolithic farmer-associated individuals show overall reduced IBD sharing and f(ROH) compared to clusters of HG-associated individuals (Fig. 6A,C). In the east, genetic relatedness remains high until ~4,000 BP, consistent with a much longer persistence of smaller localised hunter-gatherer groups (Fig. 6B,D). 21 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Next, we examined the data for evidence of recent parental relatedness, by identifying individuals harbouring a large fraction of their genomes (> 50cM) in long (>20cM) ROH segments 111. We only detect 39 such individuals out of a total sample of 1,540 imputed ancient genomes (Fig. 6E), in line with recent results indicating that close kin mating was not common in human prehistory 41,103,111,112 . With the exception of eight ancient American individuals from the San Nicolas Islands in California 113, no obviously discernible spatiotemporal or cultural clustering was observed among the individuals with recent parental relatedness. Interestingly, an ~1,700-year-old Sarmatian individual from Temyaysovo (tem003) 114 was found homozygous for almost the entirety of chromosome 2, but without evidence of ROHs elsewhere in the genome, suggesting an ancient case of uniparental disomy. Among several noteworthy familial relationships (see Supplementary Fig. S3c.2), we report a Mesolithic father/son burial at Ertebølle (NEO568/NEO569), as well as a Mesolithic mother/daughter burial at Dragsholm (NEO732/NEO733). Fig 6. Patterns of co-ancestry. (A)-(D) Panels show within-cluster genetic relatedness over time, measured either as the total length of genomic segments shared IBD between individuals (A, B) or the proportion of individual genomes found in a run of homozygosity f(ROH) (C,D). Results for both measures are shown separately for individuals from western (A, C) or eastern Eurasia (B, D). Small grey dots indicate estimates for individual pairs (A, B) or individuals (C, D), with larger coloured symbols indicating median values within genetic clusters. (E) Distribution of ROH lengths for 39 individuals with evidence for recent parental relatedness (>50 cM total in ROHs > 20 cM). Pathogenic structural variants in ancient vs. modern-day humans Rare, recurrent copy-number variants (CNVs) are known to cause neurodevelopmental disorders and are associated with a range of psychiatric and physical traits with variable expressivity and incomplete penetrance115,116. To understand the prevalence of pathogenic structural variants over time we examined 50 genomic regions susceptible to recurrent CNV, known to be the most prevalent drivers of human developmental pathologies117. The analysis included 1442 ancient imputed genomes passing quality control for CNV analysis (Supplementary Note 4i) and 1093 modern human genomes for comparison 118,119. We identified CNVs in ancient individuals at ten loci using a read-depth based approach and digital Comparative Genomic Hybridization 120 (Supplementary Table S4i.1; Supplementary Figs. S4i.1-S41.20). Although most of the observed CNVs (including duplications at 15q11.2 and CHRNA7, and CNVs spanning parts of the TAR locus and 22q11.2 distal) have not been unambiguously associated with disease in large studies, the identified CNVs include deletions and duplications that have been associated with developmental delay, dysmorphic features, and neuropsychiatric abnormalities such as autism (most notably at 1q21.1, 3q29, 16p12.1 and the DiGeorge/VCFS locus, but also deletions at 15q11.2 and 22 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. duplications at 16p13.11). The individual harbouring the 16p13.1 deletion, RISE586 20, a 4,000 BP woman aged 20-30 from the Únětice culture (modern day Czech Republic), had almost complete skeletal remains, which allowed us to test for the presence of various skeletal abnormalities associated with the 16p13.11 microdeletion 121. RISE586 exhibited a hypoplastic tooth, spondylolysis of the L5 vertebrae, incomplete coalescence of the S1 sacral bone, among other minor skeletal phenotypes. The skeletal phenotypes observed in this individual are relatively common (~10%) in European populations and are not specific to 16p13.1 thus do not indicate strong penetrance of this mutation in RISE586 122–125. However, these results do highlight our ability to link putatively pathogenic genotypes to phenotypes in ancient individuals. Overall, the carrier frequency in the ancient individuals is similar to that reported in the UK Biobank genomes (1.25% vs 1.6% at 15q11.2 and CHRNA7 combined, and 0.8% vs 1.1% across the remaining loci combined) 126. These results suggest that large, recurrent CNVs that can lead to several pathologies were present at similar frequencies in the ancient and modern populations included in this study. Ancestry-stratified patterns of natural selection in the last 13,000 years The Neolithic transition led to a fundamental change in lifestyle, diet and exposure to pathogens that imposed drastically new selection pressures on human populations. To detect genetic candidate targets of selection, we used a set of 1,015 imputed ancient genomes from West Eurasia that were fitted to a four-way admixture model of demographic history in this region (Supplementary Note 3i) and identified phenotype-associated variants with evidence for directional selection over the last 13,000 years, with a special focus on the Neolithic transition (Supplementary Note 4a). We adapted CLUES 127 to model time-series data (Supplementary Note 4a) and used it to infer allele frequency trajectories and selection coefficients for 33,323 quality-controlled phenotype-associated variants ascertained from the GWAS Catalogue 128. An equal number of putatively neutral, frequency-paired variants were used as a control set. To control for possible confounders, we built a causal model to distinguish direct effects of age on allele frequency from indirect effects mediated by read depth, read length, and/or error rates (Supplementary Note 4b), and developed a mapping bias test used to evaluate systematic differences between data from ancient and present-day populations (Supplementary Note 4a). Because admixture between groups with differing allele frequencies can confound interpretation of allele frequency changes through time, we also applied a novel chromosome painting technique, based on inference of a sample’s nearest neighbours in the marginal trees of a tree sequence (Supplementary Note 3i). This allowed us to accurately assign ancestral path labels to haplotypes found in both ancient and present-day individuals. By conditioning on these haplotype path labels, we could infer selection trajectories while controlling for changes in admixture proportions through time (Supplementary Note 4a). Our analysis identified no genome-wide significant (p < 5e-8) selective sweeps when using genomes from present-day individuals alone (1000 Genomes Project populations GBR, FIN and TSI), although trait-associated variants were enriched for signatures of selection compared to the control group (p < 2.2e-16, Wilcoxon signed-rank test). In contrast, when using imputed aDNA genotype probabilities, we identified 11 genome-wide significant selective sweeps in the GWAS variants, and none in the control group, consistent with selection acting on trait-associated variants (Supplementary Note 4a, Supplementary Figs. S4a.4 to S4a.14). However, when conditioned on one of our four ancestral histories—genomic regions arriving in present day genomes through Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) or Anatolian farmers (ANA)—we identified 21 genome-wide significant selection peaks (including the 11 from the pan-ancestry analysis) (Fig. 7). This suggests that admixture between ancestral populations has masked evidence of selection at many trait associated loci in modern populations. 23 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig 7. Genome-wide selection scan for trait associated variants. A) Manhattan plot of p-values from selection scan with CLUES, based on a time-series of imputed aDNA genotype probabilities. Twenty-one genome-wide significant selection peaks highlighted in grey and labelled with the most significant gene within each locus. Within each sweep, SNPs are positioned on the y-axis and coloured by their most significant marginal ancestry. Outside of the sweeps, SNPs show p-values from the pan-ancestry analysis and are coloured grey. Red dotted lines indicate genome-wide significance (p < 5e-8), while the grey dotted line shows the Bonferroni significance threshold, corrected for the number of tests (p < 1.35e-6). B) Detailed plots for three genome-wide significant sweep loci: (i) MCM6, lactase persistence; (ii) SLC45A2, skin pigmentation; and (iii) FADS2, lipid metabolism. Rows show results for the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). The first column of each loci shows zoomed Manhattan plots of the p-values for each ancestry (significant SNPs sized by their selection coefficients), and column two shows allele 24 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population). Selection on diet-associated loci We find strong changes in selection associated with lactose digestion after the introduction of farming, but prior to the expansion of the Yamnaya pastoralists into Europe around 5,000 years ago 20,21 , settling controversies regarding the timing of this selection 129–132. The strongest overall signal of selection in the pan-ancestry analysis is observed at the MCM6 / LCT locus (rs4988235; p=9.86e31; s=0.020), where the derived allele results in lactase persistence 133,134 (Supplementary Note 4a). The trajectory inferred from the pan-ancestry analysis indicates that the lactase persistence allele began increasing in frequency only c. 7,000 years ago, and has continued to increase up to present times (Fig. 7). Our ancestry-stratified analysis shows, however, that selection at the MCM6/LCT locus is much more complex than previously thought. In the pan-ancestry analysis, this sweep is led by the lactase persistence SNP rs4988235, whereas in the ancestry-stratified analysis, this signal is primarily driven by sweeps in two of the ancestral backgrounds (EHG and CHG), each of which differ in their most significant SNPs (Fig. 7). Conversely, in the WHG background, we find no evidence for selection at rs4988235, but strong selection at rs12465802 within the last c. 2,000 years. Overall, our results suggest that there were multiple, asynchronous selective sweeps in this genomic region in recent human history, and possibly targeting different loci. We also find strong selection in the FADS gene cluster — FADS1 (rs174546; p=2.65e-10; s=0.013) and FADS2 (rs174581; p=1.87e-10; s=0.013) — which are associated with fatty acid metabolism and known to respond to changes in diet from a more/less vegetarian to a more/less carnivorous diet 135–140 . In contrast to previous results 138–140, we find that much of the selection associated with a more vegetarian diet occurred in Neolithic populations before they arrived in Europe, but then continued during the Neolithic (Fig. 7). The strong signal of selection in this region in the panancestry analysis is driven primarily by a sweep occurring on the EHG, CHG and ANA haplotypic backgrounds (Fig. 7). Interestingly, we find no evidence for selection at this locus in the WHG background, and most of the allele frequency rise in the EHG background occurs after their admixture with CHG (around 8ka, 141), within whom the selected alleles were already close to present-day frequencies. This suggests that the selected alleles may already have existed at substantial frequencies in early farmer populations in the Middle East and among Caucasus Hunter gatherers (associated with the ANA and CHG and backgrounds, respectively) and were subject to continued selection as eastern groups moved northwards and westwards during the late Neolithic and Bronze Age periods. When specifically comparing selection signatures differentiating ancient hunter-gatherer and farmer populations 142, we also observe a large number of regions associated with lipid and sugar metabolism, and various metabolic disorders (Supplementary Note 4e). These include, for example, a region in chromosome 22 containing PATZ1, which regulates the expression of FADS1, and MORC2, which plays an important role in cellular lipid metabolism 143–145. Another region in chromosome 3 overlaps with GPR15, which is both related to immune tolerance and to intestinal homeostasis 146–148. Finally, in chromosome 18, we recover a selection candidate region spanning SMAD7, which is associated with inflammatory bowel diseases such as Crohn's disease 149–151. Taken together these results suggest that the transition to agriculture imposed a substantial amount of selection for humans to adapt to our new diet and that some diseases observed today in modern societies can likely be understood as a consequence of this selection. 25 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Selection on immunity-associated variants In addition to diet-related selection, we observe selection in several loci associated with immunity/defence functions and with autoimmune disease (Supplementary Note 4a). Some of these selection events occurred earlier than previously claimed and are likely associated with the transition to agriculture and may help explain the high prevalence of autoimmune diseases today. Most notably, we detect a 33 megabase (Mb) wide selection sweep signal in chromosome 6 (chr6:19.1–50.9 Mb), spanning the human leukocyte antigen (HLA) region (Supplementary Note 4a). The selection trajectories of the variants within this locus support multiple independent sweeps, occurring at different times and with differing intensities. The strongest signal of selection at this locus in the pan-ancestry analysis is at an intergenic variant, located between HLA-A and HLA-W (rs7747253; p=8.86e-17; s=-0.018), associated with heel bone mineral density 152, the derived allele of which rapidly reduced in frequency, beginning c. 8,000 years ago (Extended Data Fig. 10). In contrast, the signal of selection at C2 (rs9267677; p= 9.82e-14; s= 0.04463), also found within this sweep, and associated with educational attainment 153, shows a gradual increase in frequency beginning c. 4,000 years ago, before rising more rapidly c. 1,000 years ago. This highlights the complex temporal dynamics of selection at the HLA locus, which not only plays a role in the regulation of the immune system, but also has association with many other non-immune-related phenotypes. The high pleiotropy in this region makes it difficult to determine which selection pressures may have driven these increases in frequencies at different periods of time. However, profound shifts in lifestyle in Eurasian populations during the Holocene, including a change in diet and closer contact with domestic animals, combined with higher mobility and increasing population sizes, are likely drivers for strong selection on loci involved in immune response. We also identified selection signals at the SLC22A4 (rs35260072; p=1.15e-10; s=0.018) locus, associated with increased itch intensity from mosquito bites 154, and find that the derived variant has been steadily rising in frequency since c. 9,000 years ago (Extended Data Fig. 11). However, in the same SLC22A4 candidate region as rs35260072, we find that the frequency of the previously reported SNP rs1050152 plateaued c. 1,500 years ago, contrary to previous reports suggesting a recent rise in frequency 45. Similarly, we detect selection at the HECTD4 (rs11066188; p=3.02e-16; s=0.020) and ATXN2 (rs653178; p=1.92e-15; s=0.019) locus, associated with celiac disease and rheumatoid arthritis 155, which has been rising in frequency for c. 9,000 years (Extended Data Fig. 12), also contrary to previous reports of a more recent rise in frequency 45. Thus, several diseaseassociated loci previously thought to be the result of recent adaptation may have been subject to selection for a longer period of time. Selection on the 17q12.13 locus We further detect signs of strong selection in a 12 Mb sweep in chromosome 17 (chr17:36.1–48.1 Mb), spanning a locus on 17q21.3 implicated in neurodegenerative and developmental disorders (Supplementary Note 4a). The locus includes an inversion and other structural polymorphisms with indications of a recent positive selection sweep in some human populations 156,157. Specifically, partial duplications of the KANSL1 gene likely occurred independently on the inverted (H2) and non-inverted (H1) haplotypes (Fig. 8B) and both are found in high frequencies (15-25%) among current European and Middle Eastern populations but are much rarer in Sub-Saharan African and East Asian populations. We used both SNP genotypes and WGS read depth information to determine inversion (H1/H2) and KANSL1 duplication (d) status in the ancient individuals studied here (see Supplementary Note 4g). 26 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The H2 haplotype is observed in two of three previously published genomes158 of Anatolian aceramic Neolithic individuals (Bon001 and Bon004) from around 10,000 BP, but data were insufficient to identify KANSL1 duplications. The oldest evidence for KANSL1 duplications is observed in an Iranian early Neolithic individual (AH1 from 9,900 BP2) followed by two Georgian Mesolithic individuals (NEO281 from 9,724 BP and KK1 6 from 9,720 BP) all of whom are heterozygous for the inversion and carry the inverted duplication. The KANSL1 duplications are also detected in two Russian Neolithic individuals: NEO560 from 7,919 BP (H1d) and NEO212 from 7,390 BP (H2d). With both H1d and H2d having spread to large parts of Europe with Anatolian Neolithic Farmer ancestry, their frequency seems unchanged in most of Europe as Steppe-related ancestry becomes dominant in large parts of the subcontinent (Extended Data Fig. 8D). The fact that both H1d and H2d are found in apparently high frequencies in both early Anatolian Farmers and the earliest Yamnaya/Steppe-related ancestry groups suggests that any selective sweep acting on the H1d and H2d variants would probably have occurred in populations ancestral to both. We note that the strongest signal of selection observed in this locus is at MAPT (rs4792897; p=4.65e-10; s=0.03 (Fig. 8A; Supplementary Note 4a), which codes for the tau protein 159 and is involved in a number of neurodegenerative disorders, including Alzheimer’s disease and Parkinson’s disease 160–164. However, the region is also enriched for evidence of reference bias in our imputed dataset—especially around the KANSL1 gene—due to complex structural polymorphisms (Supplementary Note 4i). 27 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig 8. Selection at the MAPT / 17q21.31 inversion locus. A) Results for the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population. B) Haplotypes of the 17q21.31 locus: the ancestral (non-inverted) H1 17q21.31 and the inverted H2 haplotype. Duplications of the KANSL1 gene have occurred independently on both lineages yielding H1D and H2D haplotypes. C) Frequency of the 17q21.31 inversion and duplication haplotypes across modern-day global populations (Human Genome Diversity Project 119). D) Change in the frequency of the 17q21.31 inversion haplotype through time. Selection on pigmentation-associated variants Our results identify strong selection for lighter skin pigmentation in groups moving northwards and westwards, in agreement with the hypothesis that selection is caused by reduced UV exposure and resulting vitamin D deficiency. We find that the most strongly selected alleles reached near-fixation several thousand years ago, suggesting that this was not associated with recent sexual selection as proposed 165,166 (Supplementary Note 4a). In the pan-ancestry analysis we detect strong selection at the SLC45A2 locus (rs35395; p=4.13e-23; s=0.022) locus 167,168, with the selected allele (responsible for lighter skin), increasing in frequency from c. 13,000 years ago, until plateauing c. 2,000 years ago (Fig. 7). The dominating hypothesis is that high melanin levels in the skin are important in equatorial regions owing to its protection 28 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. against UV radiation, whereas lighter skin has been selected for at higher latitudes (where UV radiation is less intense) because some UV penetration is required for cutaneous synthesis of vitamin D 169,170. Our findings confirm pigmentation alleles as major targets of selection during the Holocene 45,171,172 particularly on a small proportion of loci with large effect sizes 168. Additionally, our results provide unprecedentedly detailed information about the duration and geographic spread of these processes (Fig. 7) suggesting that an allele associated with lighter skin was selected for repeatedly, probably as a consequence of similar environmental pressures occurring at different times in different regions. In the ancestry-stratified analysis, all marginal ancestries show broad agreement at the SLC45A2 locus (Fig. 7) but differ in the timing of their frequency shifts. The ANA ancestry background shows the earliest evidence for selection, followed by EHG and WHG around c. 10,000 years ago, and CHG c. 2,000 years later. In all ancestry backgrounds except WHG, the selected haplotypes reach near fixation by c. 3,000 years ago, whilst the WHG haplotype background contains the majority of ancestral alleles still segregating in present-day Europeans. This finding suggests that selection on this allele was much weaker in ancient western hunter-gatherer groups during the Holocene compared to elsewhere. We also detect strong selection at the SLC24A5 (rs1426654; p=6.45e-09; s=0.019) which is also associated with skin pigmentation 167,173. At this locus, the selected allele increased in frequency even earlier than SLC45A2 and reached near fixation c. 3,500 years ago (Supplementary Note 4a). Selection on this locus thus seems to have occurred early on in groups that were moving northwards and westwards, and only later in the Western hunter-gatherer background after these groups encountered and admixed with the incoming populations. Selection among major axes of ancient population variation Beyond patterns of genetic change at the Mesolithic-Neolithic transition, much genetic variability observed today reflects high genetic differentiation in the hunter-gatherer groups that eventually contributed to modern European genetic diversity 142. Indeed, a substantial number of loci associated with cardiovascular disease, metabolism and lifestyle diseases trace their genetic variability prior to the Neolithic transition, to ancient differential selection in ancestry groups occupying different parts of the Eurasian continent (Supplementary Note 4d). These may represent selection episodes that preceded the admixture events described above, and led to differentiation between ancient hunter-gatherer groups in the late Pleistocene and early Holocene. One of these overlaps with the SLC24A3 gene which is a salt sensitivity gene significantly expressed in obese individuals 174,175. Another spans ROPN1 and KALRN, two genes involved in vascular disorders 176– 178 . A further region contains SLC35F3, which codes for a thiamine transport and has been associated with hypertension in a Han Chinese cohort 179,180. Finally, there is a candidate region containing several genes (CH25H, FAS) associated with obesity and lipid metabolism 181–183 and another peak with several genes (ASXL2, RAB10, HADHA, GPR113) involved in glucose homeostasis and fatty acid metabolism 184–193. These loci predominantly reflect ancient patterns of extreme differentiation between Eastern and Western Eurasian genomes, and may be candidates for selection after the separation of the Pleistocene populations that occupied different environments across the continent (roughly 45,000 years ago 103). Genetic trait reconstruction and the phenotypic legacy of ancient Europeans When comparing modern European genomes in the UK Biobank to ancient Europeans, we find strong differentiation at certain sets of trait-associated variants, and differential contribution of different ancestry groups to various traits. We reconstructed polygenic scores for phenotypes in ancient individuals, using effect size estimates obtained from GWASs performed using the 29 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. >400,000 UK Biobank genomes 107 (http://www.nealelab.is/uk-biobank) and looked for overdispersion among these scores across ancient populations, beyond what would be expected under a null model of genetic drift 194 (Supplementary Note 4c). We stress that polygenic scores and QX statistic may both be affected by population stratification, so these results should be interpreted with caution 195–198. The most significantly overdispersed scores are for variants associated with pigmentation, anthropometric differences and disorders related to diet and sugar levels, including diabetes (Fig. 9). We also find psychological trait scores with evidence for overdispersion related to mood instability and irritability, with Western Hunter-gatherers generally showing smaller genetic scores for these traits than Neolithic Farmers. Intriguingly, we find highly inconsistent predictions of height based on polygenic scores in western hunter-gatherer and Siberian groups computed using effect sizes estimated from two different - yet largely overlapping - GWAS cohorts (Supplementary Note 4c), highlighting how sensitive polygenic score predictions are to the choice of cohort, particularly when ancient populations are genetically divergent from the reference GWAS cohort 198 . Taking this into account, we do observe that the Eastern hunter-gatherer and individuals associated with the Yamnaya culture have consistently high genetic values for height, which in turn contribute to stature increases in Bronze Age Europe, relative to the earlier Neolithic populations 45,80,199 . We performed an additional analysis to examine the data for strong alignments between axes of trait-association 200 and ancestry gradients, rather than relying on particular choices for population clusters (Supplementary Note 4e). Along the population structure axis separating ancient East Asian and Siberian genomes from Steppe and Western European genomes (Fig. 1), we find significant correlations with trait-association components related to impedance, body measurements, blood measurements, eye measurement and skin disorders. Along the axis separating Mesolithic huntergatherers from Anatolian and Neolithic farmer individuals, we find significant correlations with trait-association components related to skin disorders, diet and lifestyle traits, mental health status, and spirometry-related traits (Fig. 9). Our findings show that these phenotypes were genetically different among ancient groups with very different lifestyles. However, we note that the realised value of these traits is highly dependent on environmental factors and gene-environment interactions, which we do not model in this analysis. 30 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Fig. 9 A. First two principal components of a PCA of ancient Western Eurasian samples, coloured by height polygenic scores. B. Top 6 UK Biobank trait-association components from DeGAs that have the highest correlation (P < 5e-3) with the principal component separating ancient farmers from hunter-gatherer populations (PC1 in Panel A). C. Top UK Biobank traits with highest overdispersion in polygenic scores among ancient populations, out of a total of 320 sets of trait-associated SNPs tested (QX > 79.4, P < 0.05/320). In addition to the above reconstructions of genetic traits among the ancient individuals, we also estimated the contribution from different ancestral populations (EHG, CHG, WHG, Yamnaya and Anatolian farmer) to variation in polygenic phenotypes in present-day individuals, leveraging the exceptional resolution offered by the UK Biobank genomes 107 to investigate this. We calculated ancestry-specific polygenic risk scores based on the chromosome painting of the >400,000 UKB genomes (Supplementary Note 4h); this allowed us to identify if any of the ancient ancestry components were over-represented in modern UK populations at loci significantly associated with a given trait, and also avoids exporting risk scores over space and time. Working with large numbers of imputed ancient genomes provides high statistical power to use ancient populations as “ancestral sources”. We focused on phenotypes whose polygenic scores were significantly over-dispersed in the ancient populations (Supplementary Note 4c), as well as a single high effect variant, ApoE4, known to be a significant risk factor in Alzheimer’s Disease (201,202). We emphasise that this approach makes no reference to ancient phenotypes but describes how these ancestries contributed to the modern genetic landscape. In light of the ancestry gradients within the British Isles and Eurasia (Fig. 5), these results support the hypothesis that ancestry-mediated geographic variation in disease risks and phenotypes is commonplace. It points to a way forward for disentangling how ancestry contributed to differences in risk of genetic disease – including metabolic and mental health disorders – between present-day populations. 31 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Taken together, these analyses help to settle the famous discussion of selection in Europe relating to height 45,80,203. The finding that steppe individuals have consistently high genetic values for height (Supplementary Note 4c), is mirrored by the UK Biobank results, which find that the ‘Steppe’ ancestral components (Yamnaya/EHG) contributed to increased height in present-day populations (Supplementary Note 4h). This shows that the height differences in Europe between north and south may not be due to selection, as claimed in many previous studies, but may be a consequence of differential ancestry. Likewise, European hunter gatherers are genetically predicted to have dark skin pigmentation and dark brown hair 11,20,21,79,83,168,204,205, and indeed we see that the WHG, EHG and CHG components contributed to these phenotypes in present-day individuals whereas the Yamnaya and Anatolian farmer ancestry contributed to light brown/blonde hair pigmentation (Supplementary Note 4h). Interestingly, loci associated with overdispersed mood-related polygenic phenotypes recorded among the UK Biobank individuals (like increased anxiety, guilty feelings, and irritability) showed an overrepresentation of the Anatolian farmer ancestry component; and the WHG component showed a strikingly high contribution to traits related to diabetes. We also found that the ApoE4 effect allele is preferentially found on a WHG/EHG haplotypic background, suggesting it likely was brought to western Europe by early hunter-gatherers (Supplementary Note 4h). This is in line with the present-day European distribution of this allele, which is highest in north-eastern Europe, where the proportion of these ancestries are higher than in other regions of the continent 206. Conclusion Our study has provided fundamental new insights into one of the most transformative periods of human biological and cultural evolution. We have demonstrated that a clear east-west division known from Stone Age material culture, extending from the Black Sea to the Baltic and persisting across several millennia, was genetically deeply rooted in populations with different ancestries. We showed that the genetic impact of the Neolithic transition was highly distinct, east and west of this boundary. We have identified a hitherto unknown source of ancestry in hunter-gatherers from the Middle Don region contributing ancestry to the Yamnaya pastoralists, and we have documented how the later spread of steppe-related ancestry into Europe was very rapid and mediated through admixture with people from the Globular Amphora Culture. Additionally, we have observed two near-complete population replacements in Denmark within just 1,000 years, concomitantly with major changes in material culture, which rules out cultural diffusion as a main driver and settles generation-long archaeological debates. Our analyses revealed that the ability to detect signatures of natural selection in modern human genomes is drastically limited by conflicting selection pressures in different ancestral populations masking the signals. Developing methods to trace selection in individual ancestry components allowed us to effectively double the number of significant selection peaks, which helped clarify the trajectories of a number of traits related to diet and lifestyle. Our results emphasise how the interplay between major ancient selection and admixture events occurring across Europe and Asia in the Stone and Bronze Ages have profoundly shaped patterns of genetic variation in modern human populations. 32 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Figures Extended Data Fig.1. Imputation accuracy of aDNA. Panel A shows imputation accuracy across the 42 highcoverage ancient genomes when downsampled to lower depth of coverage values. Panels B-D show imputation accuracy for 1X depth of coverage across 21 high-coverage ancient European genomes. In panels A-D, imputation accuracy is shown as the squared Pearson correlation between imputed and true genotype dosages as a function of minor allele frequency of the target variant sites. Panel E shows imputation accuracy as Mendelian error rates for a resequenced Neolithic trio 27 downsampled to a range of low coverage values. 33 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 34 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 2. Genetic structure of the 317 herein-reported ancient genomes. (A)-(D) Principal component analysis of 3,316 modern and ancient individuals from Eurasia, Oceania and the Americas (A, B), as well as restricted to 2,126 individuals from western Eurasia (west of Urals) (C, D). Shown are analyses with principal components inferred either using both modern and imputed ancient genomes passing all filters, and projecting low coverage ancient genomes (A, C); or only modern genomes and projecting all ancient genomes (B, D). Ancient genomes sequenced in this study are indicated either with black circles (imputed genomes) or grey diamonds (projected genomes). (E) Modelbased clustering results using ADMIXTURE for 284 newly reported genomes (excluding close relatives and individuals flagged for possible contamination ). Results shown are based on ADMIXTURE runs from K=2 to K=15 on 1,584 ancient individuals. Low-coverage individuals represented by pseudo-haploid genotypes are indicated with alpha transparency. 35 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 36 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 3. Genetic clustering of ancient individuals. Genetic clusters inferred from pairwise identity-bydescent (IBD) sharing of 1,401 ancient Eurasian individuals, indicated using colored symbols throughout (A) Network graph of pairwise IBD sharing between 596 ancient Eurasians predating 3,000 BP, highlighting within- and betweencluster relationships. Each node represents an individual, and the width of edges connecting nodes indicates the fraction of the genome shared IBD between the respective pair of individuals. Network edges were restricted to the 10 highest sharing connections for each individual, and the layout was computed using the force-directed Fruchterman-Reingold algorithm. (B) Temporal distribution of clustered individuals, grouped by broad ancestry cluster. (C), (D) Geographical distribution of clustered individuals, shown for individuals predating 3,000 BP (C) and after 3,000 BP (D). (E)-(H) Fine-scale population structure among genetic clusters. Modern individuals are shown in gray, with population labels corresponding to their median coordinates. (E), (F) PCA of 3,119 Eurasian (E) or 2,126 west Eurasian (F) individuals. (G), (H) t-distributed stochastic neighbour embedding (t-SNE) using the first 12 principal components of the all Eurasian panel (E). Shown are embeddings with two different exaggeration factors ρ, emphasising local (G, ρ=1) or global (H, ρ=30) structure. 37 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 38 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 4. Admixture modelling. Supervised admixture modelling using non-negative least squares on IBD sharing profiles. Panels (A)-(D) show estimated ancestry proportions of four global Eurasian clusters, corresponding to (A) European hunter-gatherers before 4,000 BP; (B) Individuals from Europe and Western Asia from around 10,000 BP until historical times, including Neolithic farmers, Caucasus hunter-gatherers and recent individuals with genetic affinity to the Levant; (C) European individuals after 5,000 BP, as well as pastoralist groups from the Eurasian Steppe; (D) Central, East and North Asian individuals with east Eurasian genetic affinities. Column pairs show results of modelling target individuals (left columns) using three panels of increasingly distal source groups (right columns): “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Note that some clusters of individuals can be either sources or targets across distinct panels. 39 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 5. Deep Eurasian population structure. (A) Admixture graph fit relating deep Eurasian lineages predating the Last Glacial Maximum (LGM) to later West Eurasian ancestry clusters (worst |Z| = 3.65). (B) Rotating outgroup qpAdm analysis showing fit results for modelling post-LGM target groups as mixtures of all possible combinations involving one to five source groups. Colours of the individual matrix cells indicate the fit for a particular model, either rejected at p<0.01 (grey), 0.01≤ p<0.05 (light blue) or p ≥ 0.05 (dark blue). Cells with crosses indicate infeasible models involving negative admixture proportions. (C) Estimated ancestry proportions from qpAdm for post-LGM target groups inferred from the model fitting with least number of source groups. Extended Data Fig 6. Spatiotemporal kriging of four major ancestry clusters over the last 12,000 years of human history. LVN = ancestry maximized in Anatolian farmer populations. WHG = ancestry maximized in western European hunter- 40 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. gatherers. EHG = ancestry maximized in eastern European hunter-gatherers. IRN = ancestry maximized in Iranian Neolithic individuals and Caucasus hunter-gatherers. 41 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 7. Genetic transitions in Europe. (A)-(C) Ancestry proportions contributed from preceding local groups to later individuals during the two major western Eurasian genetic transitions. (A) contribution to individuals 42 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. with farmer ancestry from preceding local hunter-gatherer groups; (B,C) contribution to individuals with Steppe-related ancestry from preceding local farmer groups. Coloured areas in all maps indicate the geographic extent of individuals included in respective regions. (D) Composition of hunter-gatherer ancestry proportions from different source groups in individuals with farmer ancestry, shown as heatmap (top) and barplots (bottom). Grey bars represent contributions from local hunter-gatherers (E)-(G) Moon charts showing spatial distribution of estimated ancestry proportions of mid- to late Neolithic farmer individuals from three clusters of early Neolithic European farmers (locations indicated with coloured symbols). Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. (H, I) Estimated time of admixture between (H) local hunter-gatherer groups and farmers and (I) eastern European farmers with GAC-related ancestry and Steppe pastoralist groups. Black diamonds and error bars represent point estimate and standard errors of admixture time, coloured bars show temporal range of included target individuals. The time to admixture was adjusted backwards by the average age of individuals for each region. (J) Correlation between estimated proportions of Steppe-related and GAC farmer-related ancestries, across west Eurasian target individuals. Symbol shape and colour indicate the genetic cluster of respective individuals. 43 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 8. Fine-scale structure in Late Neolithic Scandinavians. (A)-(E) Geographic locations and PCA based on pairwise IBD sharing (middle) of 148 European individuals predating 3,000 BP. Geographic locations are shown for 65 individuals belonging to the five genetic clusters observed in 38 ancient Scandinavians (temporal sequence shown in timeline in centre of plot). Individual assignments and frequency distribution of major Y chromosome haplogroups are indicated in maps and timeline. Plot symbols with black circles indicate the 38 Scandinavian individuals in the PCA panels. Ancestry proportions for the 38 Scandinavian individuals estimated using proximal source groups from outside Scandinavia (“postNeolScand” source set) are shown on the right of the respective cluster results. 44 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 9. Genetic transformations across the Eurasian Steppe. (A)-(C) Principal component analysis of modern and ancient individuals from Eurasia, Oceania and the Americas, highlighting estimated ancestry proportions from “deep” Siberian ancestry sources (individuals highlighted with dashed line). Present-day individuals are shown in gray, with population labels corresponding to their median coordinates. (D)-(E) Moon charts showing spatial distribution of estimated ancestry proportions of Siberian hunter-gatherers before 5,000 BP from “deep” Siberian ancestry sources (names and locations indicated with coloured symbols). Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. (G) Timelines of ancestry proportions from “postNeol” sources in Central and North Asian ancient individuals after 5,000 BP. Symbol shape and colour indicate the genetic cluster of each individual. 45 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 10. Selection at the HLA locus. Results for the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p-values for each ancestry (significant SNPs sized by their selection coefficients), and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population). Extended Data Fig. 11. Selective sweep at the SLC22A4 locus. Results for the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p-values for each ancestry (significant SNPs sized by their selection coefficients), and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population). 46 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Extended Data Fig. 12. Selective sweep at the HECTD4 locus. Results for the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p-values for each ancestry (significant SNPs sized by their selection coefficients), and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population). Data availability All collapsed and paired-end sequence data for novel samples sequenced in this study will be made publicly available on the European Nucleotide Archive, together with trimmed sequence alignment map files, aligned using human build GRCh37. Previously published ancient genomic data used in this study is detailed in Supplementary Table VII, and are all already publicly available. Bioarchaeological data (including Accelerator Mass Spectrometry results) are included in the online supplementary materials of this submission. Code availability The modified version of CLUES used in this study is available from https://github.com/standardaaron/clues. 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A Quarter Century of APOE and Alzheimer’s Disease: Progress to Date and the Path Forward. Neuron 101, 820–838 (2019). Acknowledgements This publication, the culmination of a research effort lasting over a decade, is dedicated to the memory of Pia Bennike, who was part of a small core team that initiated the project. Sadly Pia passed away in 2017 but this study, and many others, testifies to her tremendous efforts and knowledge on Danish prehistoric skeletal material. We are deeply indebted to former and present staff members at the National Museum, the Anthropological Laboratory, the regional museums and citizen scientists of Denmark, who for many generations carefully collected, recorded and curated the prehistoric skeletal remains that form a key component of this study. We are equally thankful to curators of the many other institutions across major parts of Eurasia, who to the benefit of following generations curated human skeletal remains and gave us access and permission to sample this precious material. We also thank all the former and current staff at the Lundbeck Foundation GeoGenetics Centre and the GeoGenetics Sequencing Core, and to colleagues across the many institutions detailed below. We are particularly grateful to Line Olsen as project manager for the Lundbeck Foundation GeoGenetics Centre project, and to Pernille Selmer Olsen for assisting with sample processing. We thank UK Biobank Ltd. for access to the UK Biobank genomic resource. We are thankful to Illumina Inc. for collaboration and to L. Speidel for assistance in running Relate. EW thanks St. John’s College, Cambridge, for providing a stimulating environment of discussion and learning. The Lundbeck Foundation GeoGenetics Centre is supported by the the Lundbeck Foundation (R302-20182155, R155-2013-16338), the Novo Nordisk Foundation (NNF18SA0035006), the Wellcome Trust (UNS69906), Carlsberg Foundation (CF18-0024), the Danish National Research Foundation (44113220) and the University of Copenhagen (KU2016 programme). This research has been conducted using the UK Biobank Resource and the iPSYCH Initiative, funded by the Lundbeck Foundation (R102-A9118 and R1552014-1724). This work was further supported by the Swedish Foundation for Humanities and Social Sciences grant (Riksbankens Jubileumsfond M16-0455:1) to KK. M.E.A. was supported by Marie Skłodowska-Curie Actions of the EU (grant no. 300554), The Villum Foundation (grant no. 10120) and Independent Research Fund Denmark (grant no. 7027-00147B). W.B. is supported by the Hanne and Torkel Weis-Fogh Fund (Department of Zoology, University of Cambridge); AP is funded by Wellcome grant WT214300, B.S.d.M and O.D. by the Swiss National Science Foundation (SFNS PP00P3_176977) and European Research Council (ERC 679330); M.N. by the Human Frontier Science Program Postdoctoral Fellowship (LT000143/2019-L4); R. Macleod by an SSHRC doctoral studentship grant (G101449: ‘Individual Life Histories in Long-Term Cultural Change’); G.R. by a Novo Nordisk Foundation Fellowship (gNNF20OC0062491); N.N.J. by Aarhus University Research Foundation; H.S. by a Carlsberg Foundation Fellowship (CF19-0601); G.S. by Marie Skłodowska-Curie Individual Fellowship ‘PALAEO-ENEO’ (grant 61 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. agreement number 751349); A.J. Schork by a Lundbeckfonden Fellowship (R335-2019-2318) and the National Institute on Aging (NIH award numbers U19AG023122, U24AG051129, and UH2AG064706); A.V.L. and I.V.S. by the Science Committee, Ministry of Education and Science of the Republic of Kazakhstan (AP08856317); B.G.R. and MGM by the Spanish Ministry of Science and Innovation (Project HAR2016-75605-R); C.M.-L. and O.R. by the Italian Ministry for the Universities (grants ‘2010-11 prot.2010EL8TXP_001 Biological and cultural heritage of the central-southern Italian population trough 30 thousand Years’ and ‘2008 prot. 2008B4J2HS_001 Origin and diffusion of farming in central-southern Italy: a molecular approach’); D.C.-S. and I.G.Z. by the Spanish Ministry of Science and Innovation (Project HAR2017-86262-P). D.C.S.G. acknowledges funding from the Generalitat Valenciana (CIDEGENT/2019/061) and the Spanish Government (EUR2020-112213); D.B. was supported by the NOMIS Foundation and Marie Skłodowska-Curie Global Fellowship 'CUSP' (grant no. 846856); E.R.U. by the Science Committee, Ministry of Education and Science of the Republic of Kazakhstan (АР09261083: "Transcultural Communications in the Late Bronze Age (Western Siberia - Kazakhstan - Central Asia)"); E.C. by Villum Fonden (17649); J.E.A.T. by the Spanish Ministry of Economy and Competitiveness, (HAR2013‐46861‐R) and Generalitat Valenciana (Aico/ 2018/125 and Aico 2020/97). L.Y. acknowledges funding by the Science Committee of the Armenian Ministry of Education and Science (Project 21AG1F025), L.V. by ERC Consolidator Grant ‘PEGASUS’ (agreement no. 681605); M. Sablin by the Russian Ministry of Science and Higher Education (075-15-2021-1069); N.C. by Historic Environment Scotland; S.V. by the Russian Ministry of Science and Higher Education (075-15-2020-910); V.M. by the Science Committee, Ministry of Education and Science of the Republic of Kazakhstan (AR08856925). V.A. is supported by a Lundbeckfonden Fellowship (R335-2019-2318); P.H.S. by the National Institute of General Medical Sciences (R35GM142916); S.R. by the Novo Nordisk Foundation (NNF14CC0001); R.D. by the Wellcome Trust (WT214300); R.N. by the National Institute of General Medical Sciences (NIH grant R01GM138634); F. Racimo by a Villum Fonden Young Investigator Grant (no. 00025300). T.W. and V.A. are supported by the Lundbeck Foundation iPSYCH initiative (R248-2017-2003). Author Information These authors contributed equally: Morten E. Allentoft, Martin Sikora, Alba Refoyo-Martínez, Evan K. Irving-Pease, Anders Fischer, William Barrie & Andrés Ingason These authors equally supervised research: Thorfinn Korneliussen, Richard Durbin, Rasmus Nielsen, Olivier Delaneau, Thomas Werge, Fernando Racimo, Kristian Kristiansen & Eske Willerslev Affiliations Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark Morten E. Allentoft, Martin Sikora, Alba Refoyo-Martínez, Evan K. Irving-Pease, Andrés Ingason, Jesper Stenderup, Fabrice Demeter, Maria Novosolov, Ruairidh Macleod, Rasmus A. Henriksen, Tharsika Vimala, Hugh McColl, Lasse Vinner, Gabriele Scorrano, Abigail Ramsøe, Anders Rosengren, Anthony Ruter, Anne Birgitte Gotfredsen, Charleen Gaunitz, Fulya Eylem Yediay, Isin Altinkaya, Lei Zhao, Peter de Barros Damgaard, Kurt H. Kjær, Thorfinn Korneliussen, Rasmus Nielsen, Thomas Werge, Fernando Racimo, Kristian Kristiansen & Eske Willerslev Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, Australia 62 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Morten E. Allentoft GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK William Barrie, Alice Pearson, Ruairidh Macleod & Eske Willerslev Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden Anders Fischer, Karl-Göran Sjögren, Bettina Schulz Paulsson, Malou Blank & Kristian Kristiansen Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark Andrés Ingason, Vivek Appadurai, The National Museum of Denmark, Ny Vestergade 10, Copenhagen, Denmark Lasse Sørensen, Poul Otto Nielsen, Morten Fischer Mortensen, Peter Rasmussen, Peter Vang Petersen, Sealand Archaeology, Gl. Roesnaesvej 27, 4400 Kalundborg, Denmark Anders Fischer Department of Genetics, University of Cambridge, Cambridge, UK Alice Pearson & Richard Durbin Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK Richard Durbin & Eske Willerslev Department of Computational Biology, University of Lausanne, Switzerland Bárbara Sousa da Mota & Olivier Delaneau Swiss Institute of Bioinformatics, University of Lausanne, Switzerland Bárbara Sousa da Mota Department of Integrative Biology, University of California, Berkeley, USA Alma S. Halgren, Peter H. Sudmant & Rasmus Nielsen Center for Computational Biology, University of California, Berkeley, USA Andrew Vaughn, Aaron J. Stern, Peter H. Sudmant & Rasmus Nielsen Laboratory of Biological Anthropology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark Marie Louise Schjellerup Jørkov & Niels Lynnerup Muséum National d'Histoire Naturelle, Paris, France Fabrice Demeter Research department of Genetics, Evolution and Environment, University College London, London, UK Ruairidh Macleod 63 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Section for Evolutionary Genomics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark Ashot Margaryan, Theis Zetner Trolle Jensen, Hannes Schroeder, Enrico Cappellini Centre for Evolutionary Hologenomics, University of Copenhagen, Copenhagen, Denmark Ashot Margaryan Anthropology Department, University of Utah, USA Melissa Illardo Department of Geology, Lund University, Lund, Sweden Anne Birgitte Nielsen Department of Archaeology and Ancient History, Lund University, Lund, Sweden Lars Larsson Tårnby Gymnasium og HF, Kastrup, Denmark Mikkel Ulfeldt Hede Department of Health Technology, Section of Bioinformatics, Technical University of Denmark, Kongens Lyngby, Denmark Gabriel Renaud Department of Archaeology and Heritage Studies, Aarhus University, Aarhus, Denmark Niels N. Johannsen, Rikke Maring Institute of Biological Psychiatry, Mental Health Centre Sct Hans, Copenhagen University Hospital, Denmark Andrew J. Schork, Anders Rosengren & Thomas Werge Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark Thomas Werge Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA Andrew J. Schork Terra Ltd., Letchik Zlobin St. 20, Voronezh, 394055, Russian Federation Andrey Skorobogatov, Ruslan Turin Department of Archaeology, University of Exeter, Exeter, UK Alan K. Outram & Catriona J. McKenzie Institute of Archaeology and Ethnography, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation Aleksey A. Timoshchenko, Dmitri V. Pozdnyakov, Liudmila N. Mylnikova, Marina S. Nesterova & Vyacheslav I. Molodin 64 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Institute of Ethnology and Anthropology, Russian Academy of Sciences, Moscow, Russian Federation Elizaveta V. Veselovskaya, Sergey Vasilyev Centre for Egyptological studies, Russian Academy of Sciences, Moscow, Russia Sergey Vasilyev Research Institute and Museum of Anthropology, Lomonosov Moscow State University, Mokhovaya str. 11, Moscow, Russian Federation Alexandra Buzhilova, Natalia Berezina, Svetlana Borutskaya Department of Environmental Biology, Sapienza University of Rome, Rome, Italy Alfredo Coppa, Dušan Borić Peter the Great Museum of Anthropology and Ethnography (Kunstkamera), Russian Academy of Sciences, Saint Petersburg, Russian Federation Alisa Zubova & Vyacheslav Moiseyev CIAS, Department of Life Science, University of Coimbra, Coimbra, Portugal Ana Maria Silva UNIARQ, University of Lisbon, Lisbon, Portugal Ana Maria Silva & João Zilhão Kostanay Regional University A. Baitursynov, Kostanay, Kazakhstan Andrey V. Logvin, Irina V. Shevnina Vesthimmerlands Museum, Søndergade 44, Aars, Denmark Bjarne Henning Nielsen Museum Nordsjælland, Frederiksgade 9, 3400 Hillerød Per Lotz, Søren Anker Sørensen, Thomas Jørgensen Museum Vestsjælland, Klosterstræde 18, 4300 Holbæk, Denmark Per Lotz Vendsyssel Historiske Museum, DK-9800 Hjørring, Denmark Per Lysdahl, Sidsel Wåhlin Moesgaard Museum, Moesgård Allé 15, Højbjerg, Denmark Søren H. Andersen Grupo EvoAdapta, Departamento de Ciencias Históricas, Universidad de Cantabria, Santander, Spain Borja González-Rabanal Museu de Ciències Naturals de Barcelona, Barcelona, Spain Carles Lalueza-Fox 65 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, Spain Carles Lalueza-Fox ICREA, University of Barcelona, Barcelona, Spain João Zilhão Departamento de Prehistoria y Arqueología Department, Universidad Autónoma de Madrid, Madrid, Spain Concepción Blasco, Corina Liesau, Patricia Ríos Department of Biology, University of Rome "Tor Vergata", Rome, Italy Cristina Martinez-Labarga, Olga Rickards Department of History, Humanities and Society, University of Rome "Tor Vergata", Rome, Italy Mario Federico Rolfo Instituto Internacional de Investigaciones Prehistóricas de Cantabria, Universidad de Cantabria, Santander, Spain David Cuenca-Solana, Igor Gutiérrez Zugasti & Manuel González-Morales Centre de Recherche en Archéologie, Archeosciences, Histoire (CReAAH), UMR-6566 CNRS, Rennes, France David Cuenca-Solana Georgian National Museum, Tbilisi, Georgia David O. Lordkipanidze Tbilisi State University, Tbilisi, Georgia David O. Lordkipanidze IPND, Tyumen Scientific Centre, Siberian Branch of the Russian Academy of Sciences, Tyumen, Russian Federation Dmitri Enshin, Olga Poshekhonova, Svetlana N. Skochina Departament de Prehistòria, Arqueologia i Història Antiga, Universitat de València, València, Spain Domingo C. Salazar-García, J.Emili Aura Tortosa Department of Geological Sciences, University of Cape Town, Cape Town, South Africa Domingo C. Salazar-García Department of Anthropology, New York University, New York, USA. Dušan Borić Institute of Humanities, Ivanovo State University, Ivanovo, Russian Federation Elena Kostyleva Saryarka Archaeological Institute, Buketov Karaganda University, Karaganda, Kazakhstan 66 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Emma R. Usmanova South Ural State University, Chelyabinsk, Russia Emma R. Usmanova The Saxo Institute, University of Copenhagen, Copenhagen, Denmark Erik Brinch Petersen, Rune Iversen Museum Østjylland, Stemannsgade 2, Randers, Denmark Esben Kannegaard, Lutz Klassen Soprintendenza Archeologia Belle Arti e Paesaggio per la Città Metropolitana di Bari, Via Pier l’Eremita, 25, 70122, Bari, Italy Francesca Radina Soprintendenza per i Beni Archeologici delle Marche, Via Birarelli 18, 60100, Ancona, Italy Mara Silvestrini Soprintendenza Archeologia, Belle Arti e Paesaggio per la provincia di Cosenza, Cosenza, Italy Paola Aurino UMR 5199 PACEA, CNRS, Université de Bordeaux, 33615 Pessac, France Henri Duday, Patrice Courtaud Institute of Archaeology, National Academy of Sciences of Ukraine, Kyiv, Ukraine Inna Potekhina National University of Kyiv-Mohyla Academy, Kyiv, Ukraine Inna Potekhina Collège de France, 75231 Paris cedex 05, France Jean Guilaine Odense City Museums, Overgade 48, Odense, Denmark Jesper Hansen Museum Sydøstdanmark, Algade 97, 4760 Vordingborg, Denmark Kristoffer Buck Pedersen Institute of Archaeology and Ethnology, Polish Academy of Sciences, Kraków, Poland Krzysztof Tunia, Piotr Włodarczak CNRS UMR 5608, Toulouse Jean Jaurès University, Maison de la Recherche, 5 Allées Antonio Machado, 31058 Toulouse, Cedex 9, France Laure Metz & Ludovic Slimak ARGEA Consultores SL, C. de San Crispín, Madrid, Spain 67 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Jorge Vega, Roberto Menduiña Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia Levon Yepiskoposyan Russian-Armenian University, Yerevan, Armenia Levon Yepiskoposyan Institute of Archaeology and Ethnography, National Academy of Sciences, Yerevan, Armenia Ruben Badalyan HistorieUdvikler, Gl. Roesnaesvej 27, DK-4400 Kalundborg, Denmark Lisbeth Pedersen Department of history and cultural heritage, University of Siena, Siena, Italy Lucia Sarti, Mauro Calattini Centre d'Anthropobiologie et de Génomique de Toulouse, CNRS UMR 5288, Université Paul Sabatier, Toulouse, France Ludovic Orlando Västergötlands museum, Stadsträdgården, Skara, Sweden Maria Vretemark Cabinet of Anthropology, Tomsk State University, Tomsk, Russian Federation Marina P. Rykun Institute for Eastern Research, Adam Mickiewicz University in Poznań, Poznań, Poland Marzena H. Szmyt Institute of Archaeology, Jagiellonian University, Ul. Gołębia 11, 31-007, Kraków, Poland Marcin Przybyła Zoological Institute of Russian Academy of Sciences, Universitetskaya nab. 1, 199034, St. Petersburg, Russian Federation Mikhail Sablin Department of Anthropology, Czech National Museum, Prague, Czech Republic Miluše Dobisíková Department of Health and Nature, University of Greenland, Greenland Morten Meldgaard The Viking Ship Museum, Vindeboder 12, Roskilde, Denmark Morten Johansen, Otto Christian Uldum Archaeology Institute, University of Highlands and Islands, Scotland, UK 68 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Nick Card Laboratory for Experimental Traceology, Institute for the History of Material Culture of the Russian Academy of Sciences, Dvortsovaya nab., 18, 191186, St. Petersburg, Russian Federation Olga V. Lozovskaya Scientific Research Center “Baikal region”, Irkutsk State University; 1, K. Marx st., Irkutsk, 664003, Russian Federation Nikolai A. Saveliev Paleoecology Laboratory, Institute of Plant and Animal Ecology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russian Federation Pavel Kosintsev Department of History of the Institute of Humanities, Ural Federal University, Ekaterinburg, Russian Federation Pavel Kosintsev Centre for the Study of Early Agricultural Societies, Department of Cross-Cultural and Regional Studies, University of Copenhagen, 2300 Copenhagen, Denmark Peder Mortensen Museum of Cultural History, University of Oslo, P.O. Box 6762. St. Olavs Plass NO-0130 Oslo, Norway Per Persson ArchaeoScience, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark Pernille Bangsgaard Department of History, University of Santiago de Compostela, Spain Pilar Prieto Martinez Lipetsk Regional Scientific Public Organisation "Archaeological Research", Lipetsk, Russian Federation Roman V. Smolyaninov Laboratory for Archaeological Chemistry, Department of Anthropology, University of WisconsinMadison, Madison, USA T. Douglas Price Nizhny Tagil State Socio-Pedagogical Institute, Nizhny Tagil, Russia Yuri B. Serikov Institute for History of Medicine, First Faculty of Medicine, Charles University, Prague, Czech Republic Vaclav Smrcka 69 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Centre for Archaeological Research Toraighyrov University, Pavlodar, Kazakhstan Victor Merz Malmö Museer, Malmöhusvägen 6, Malmö, Sweden Yvonne Magnusson Institute of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK Daniel J. Lawson Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark Simon Rasmussen MARUM, University of Bremen, Bremen, Germany Eske Willerslev Contributions M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., and A.I. contributed equally to this work. M.E.A., M.S., T.S.K., R.D., R.N., O.D., T.W., F. Racimo, K.K. and E.W. led the study. M.E.A., M.S., A.F., C.L.-F., R.N., T.W., K.K. and E.W. conceptualised the study. M.E.A., M.S., H.S., L.O., T.S.K., R.D., R.N., O.D., T.W., F. Racimo, K.K. and E.W. supervised the research. M.E.A., L.O., R.D., R.N., T.W., K.K. and E.W. acquired funding for research. A.F., J.S., K.G.S., M.L.S.J., M.U.H., A.A.T., A.C., A.Z., A.M.S., A.J.H, A.G., A.V.L., B.H.N., B.G.R, C.B., C.L., C.M-L., D.V., D.C.-S., D.L., D.N., D.C.S.-G., D.B., E.K., E.V.V., E.R.U., E. Kannegaard, F. Radina, H.D., I.G.Z., I.P., I.V.S., J.G., J.H., J.E.A.T., J.Z., J.V., K.B.P., K.T., L.N., L.L., L.M., L.Y., L.P., L. Sarti, L. Slimak, L.K., M.G.M., M. Silvestrini, M.V., M.S.N., M.P.R., M.H.S., M.P., M.C., M. Sablin, N.C., O.P., O.R., O.V.L., P.A., P.K., P.C., P. Ríos, P. Lotz, P. Lysdahl, P.P., P.B., P.d.B.D., P.V.P., P.P.M., P.W., R.V.S., R. Maring, R. Menduiña, R.B., R.T., S.V., S.W., S.B., S.N.S., S.A.S., S.H.A., T.D.P., T.J., Y.B.S., V.I.M., V.S., V.M, Y.M. and N.L. were involved in sample collection. M.E.A., M.S., A.R.-M., E.K.I.-P., W.B., A.I., J.S., A.P., B.S.d.M., M.I., L.V., A.J. Stern, C.G., F.E.Y, D.J.L., T.S.K., R.D., R.N., O.D., F. Racimo, K.K. and E.W. were involved in developing and applying methodology. J.S., C.G. and L.V. led the DNA laboratory work research component. K.G.S. led bioarchaeological data curation. M.E.A., M.S., A.R.-M., E.K.I.-P., W.B., A.I., A.P., B.S.d.M., B.S.P., A.S.H., R.A.H., T.V., H.M., A.M., A.V., A.B.N., P. Rasmussen, G.R., A. Ramsøe, A.S., A.J. Schork, A. Rosengren, C.J.M., I.A., L.Z., R.Maring, V.S., V.A., P.H.S, S.R., T.S.K., O.D. and F. Racimo undertook formal analyses of data. M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., A.I., K.G.S., D.J.L., P.H.S., T.S.K., and F. Racimo drafted the main text (M.E.A. and M.S. led this). M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., A.I., K.G.S., A.P., B.S.d.M., B.S.P, A.S.H., R. Macleod, R.A.H., T.V., M.F.M., A.B.N., M.U.H., P. Rasmussen, A.J. Stern, N.N.J., H.S., G.S., A. Ramsøe, A.S., A. Rosengren, A.K.O., A.B., A.C., A.G., A.V.L., A.B.G., C.J.M., D.C.S.-G., E. Kostyleva, E.R.U., E. Kannegaard, I.G.Z., I.P., I.V.S., J.G., J.H., J.E.A.T., L.Z, L.Y., L.P., L.K., M.B., M.G.M., M.V., M.P.R., M.J., N.B., O.V.L., O.C.U., P.K., P. Lysdahl, P.B., P.W., R.V.S., R. Maring, R.B., R.I., S.V., S.W., S.B., S.H.A., T.J., V.S., D.J.L., P.H.S., S.R., T.S.K., O.D. and F. Racimo drafted supplementary notes and materials. M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., A.I., G.G.S., A.S.H., M.L.S.J., F.D., R. Macleod, L. Sørensen, P.O.N., R.A.H., T.V., H.M., A.M., N.N.J., H.S., A. Ramsøe, A.S., A.J. Schork, A. Ruter, A.K.O., B.H.N., B.G.R., D.C.-S., D.C.S.-G., I.G.Z., I.P., J.G., J.E.A.T., L.Z., L.O., L.K., M.G.M., P.d.B.D., R.I., S.A.S., D.J.L., P.H.S., T.S.K., R.D., R.N., O.D., T.W., F. Racimo, K.K. and E.W. were involved in reviewing drafts and editing (M.E.A., A.F., K.G.S., F.D., R. Macleod, H.M. and T.V. led this). All co-authors read, commented on, and agreed upon the submitted manuscript. 70 bioRxiv preprint doi: https://doi.org/10.1101/2022.05.04.490594; this version posted May 6, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Corresponding authors Correspondence to Morten E. Allentoft (morten.allentoft@curtin.edu.au), Martin Sikora (martin.sikora@sund.ku.dk), Eske Willerslev (ew482@cam.ac.uk). Ethics declarations Competing interests The authors declare no competing interests. 71