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The genetic prehistory of the Greater Caucasus

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Archaeogenetic studies have described the formation of Eurasian 'steppe ancestry' as a mixture of Eastern and Caucasus hunter-gatherers. However, it remains unclear when and where this ancestry arose and whether it was related to a horizon of cultural innovations in the 4th millennium BCE that subsequently facilitated the advance of pastoral societies likely linked to the dispersal of Indo-European languages. To address this, we generated genome-wide SNP data from 45 prehistoric individuals along a 3000-year temporal transect in the North Caucasus. We observe a genetic separation between the groups of the Caucasus and those of the adjacent steppe. The Caucasus groups are genetically similar to contemporaneous populations south of it, suggesting that - unlike today - the Caucasus acted as a bridge rather than an insurmountable barrier to human movement. The steppe groups from Yamnaya and subsequent pastoralist cultures show evidence for previously undetected Anatolian farmer-related ancestry from different contact zones, while Steppe Maykop individuals harbour additional Upper Palaeolithic Siberian and Native American related ancestry.
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The genetic prehistory of the Greater Caucasus 1 2 3 Chuan-Chao Wang1,2,*, Sabine Reinhold3, Alexey Kalmykov4, Antje Wissgott1, Guido 4 Brandt1, Choongwon Jeong1, Olivia Cheronet5,6,7, Matthew Ferry8,9, Eadaoin 5 Harney8,9,10, Denise Keating5,7, Swapan Mallick8,9,11, Nadin Rohland8,11, Kristin 6 Stewardson8,9, Anatoly R. Kantorovich12, Vladimir E. Maslov13, Vladimira G. 7 Petrenko13, Vladimir R. Erlikh14, Biaslan Ch. Atabiev15, Rabadan G. Magomedov16, 8 Philipp L. Kohl17, Kurt W. Alt18,19,20, Sandra L. Pichler19, Claudia Gerling19, Harald 9 Meller21, Benik Vardanyan22,23, Larisa Yeganyan23, Alexey D. Rezepkin24, Dirk 10 Mariaschk3, Natalia Berezina25, Julia Gresky26, Katharina Fuchs27, Corina Knipper28, 11 Stephan Schiffels1, Elena Balanovska29, Oleg Balanovsky29,30, Iain Mathieson31, 12 Thomas Higham32, Yakov B. Berezin25, Alexandra Buzhilova25, Viktor Trifonov33, 13 Ron Pinhasi34, Andrej B. Belinskij4, David Reich8,9,11,35, Svend Hansen3,*, Johannes 14 Krause1,35,* & Wolfgang Haak1,36,*
15 16 17 1Department of Archaeogenetics, Max-Planck Institute for the Science of Human 18 History, Kahlaische Strasse 10, D-07745 Jena, Germany 19 2Department of Anthropology and Ethnology, Xiamen University 361005 Xiamen, 20 China 21 3German Archaeological Institute, Eurasia Department, Im Dol 2-6, D-14195 Berlin, 22 Germany 23 4’Nasledie’ Cultural Heritage Unit, 355006 Stavropol, Russia 24 5Earth Institute, University College Dublin, Dublin 4, Ireland 25 6Department of Anthropology, University of Vienna, 1090 Vienna, Austria 26 7School of Archaeology, University College Dublin, Dublin 4, Ireland 27 8Department of Genetics, Harvard Medical School, Boston 02115 MA, USA 28 9Howard Hughes Medical Institute, Harvard Medical School, Boston 02115 MA, USA 29 10Department of Organismic and Evolutionary Biology, Harvard University, 30 Cambridge, MA02138, USA 31 11Broad Institute of Harvard and MIT, Cambridge 02142 MA, USA 32 12Department of Archaeology, Faculty of History, Lomonosov Moscow State 33 University, Lomonosovsky pr. 27/4, 119192, Moscow, Russia 34 13Institute of Archaeology RAS, Ul. Dm. Ulyanova 19, 117036 Moscow, Russian 35 Federation 36 14State Museum of Oriental Art, 12a Nikitskiy Boulevard, 119019 Moscow, Russian 37 Federation 38 15Ltd. Institute for Caucasus Archaeology, Ul. Katkhanova 30, 361401 Nalchik, 39 Republic Kabardino-Balkaria, Russian Federation 40 16Institute of History, Archaeology and Ethnography DNC RAS, Ul. M. Jaragskogo 41 75, 367030 Makhachkala, Republic Dagestan, Russian Federation 42 17Department of Anthropology, Wellesley College, Pendleton East 331, 106 Central 43 Street, Wellesley, MA 02481, USA 44 18Danube Private University, A-3500 Krems-Stein, Austria 45 19IPAS – Institute of Prehistory and Archaeological Science, University of Basel, CH-46 4055 Basel, Switzerland 47 20Department of Biomedical Engineering, University of Basel, CH-4123 Allschwil, 48 Switzerland 49
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21State Heritage Museum, Saxony-Anhalt, D-06114 Halle/Saale, Germany 50 22Martin-Luther-Universität Halle-Wittenberg, Germany 51 23Shirak Center for Armenological Studies of National Academy of Science RA, 52 Armenia 53 24Institute for the History of Material Culture, Russian Academy of Sciences, 54 Dvortsovaya nab.,18, 191186 Saint-Petersburg, Russia 55 25Research Institute and Museum of Anthropology of Lomonosov Moscow State 56 University, Mokhovaya 11, Moscow, Russia 57 26German Archaeological Institute, Department of Natural Sciences, Im Dol 2-6, D-58 14195 Berlin, Germany 59 27CRC 1266 "Scales of Transformation", Institut für Ur- und Frühgeschichte, 60 Christian-Albrechts-Universität, Johanna-Mestorf-Straße 2-6, 24118 Kiel, Germany 61 28Curt Engelhorn Center for Archaeometry gGmbH, 68159 Mannheim, Germany 62 29Research Centre for Medical Genetics, Moscow 115478, Russia 63 30Vavilov Institute for General Genetics, Moscow 119991, Russia 64 31 Department of Genetics, Perelman School of Medicine, University of Pennsylvania, 65 Philadelphia PA 19104, USA
66 32Oxford Radiocarbon Accelerator Unit, RLAHA, University of Oxford, OX13QY, UK 67 33Institute for the History of Material Culture, Russian Academy of Sciences, 68 Dvortsovaya nab.,18, 191186 Saint-Petersburg, Russia 69 34Department of Evolutionary Anthropology, University of Vienna, 1010 Vienna, 70 Austria 71 35Max Planck-Harvard Research Center for the Archaeoscience of the Ancient 72 Mediterranean, Cambridge, MA 02138, USA 73 36School of Biological Sciences, The University of Adelaide, Adelaide 5005, Australia 74 75 76 77 *corresponding authors: haak@shh.mpg.de, krause@shh.mpg.de,
78 wang@xmu.edu.cn, svend.hansen@dainst.de 79 80
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Abstract 81 Archaeogenetic studies have described the formation of Eurasian ‘steppe ancestry’ as 82 a mixture of Eastern and Caucasus hunter-gatherers. However, it remains unclear 83 when and where this ancestry arose and whether it was related to a horizon of cultural 84 innovations in the 4th millennium BCE that subsequently facilitated the advance of 85 pastoral societies likely linked to the dispersal of Indo-European languages. To 86 address this, we generated genome-wide SNP data from 45 prehistoric individuals 87 along a 3000-year temporal transect in the North Caucasus. We observe a genetic 88 separation between the groups of the Caucasus and those of the adjacent steppe. The 89 Caucasus groups are genetically similar to contemporaneous populations south of it, 90 suggesting that – unlike today – the Caucasus acted as a bridge rather than an 91 insurmountable barrier to human movement. The steppe groups from Yamnaya and 92 subsequent pastoralist cultures show evidence for previously undetected farmer-93 related ancestry from different contact zones, while Steppe Maykop individuals 94 harbour additional Upper Palaeolithic Siberian and Native American related ancestry. 95 96
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The 1100-kilometre long Caucasus mountain ranges extend between the Black Sea 97 and the Caspian Sea and are bound by the rivers Kuban and Terek in the north and by 98 the Kura and Araxes rivers in the south. With Mount Elbrus in Russian Kabardino-99 Balkaria rising to a height of 5642 metres and Mount Shkhara in Georgia to 5201 100 metres, the Caucasus mountain ranges form a natural barrier between the Eurasian 101 steppes and the Near East (Fig. 1). 102 103 The rich archaeological record suggests extensive periods of human occupation since 104 the Upper Palaeolithic1, 2, 3. The density of languages and cultures in the region is 105 mirrored by faunal and floral diversity, and the Caucasus has often been described as 106 a contact zone and natural refuge with copious ecological niches. However, it also 107 serves as a bio-geographic border between the steppe and regions to the south such as 108 Anatolia and Mesopotamia rather than a corridor for human4, 5 and animal movement6,
109 7, 8. The extent to which the Caucasus has played an important role for human 110 population movements between south and north over the course of human history is 111 thus a critical question, and one that until now has been unanswered by 112 archaeogenetic studies. 113 114 A Neolithic lifestyle based on food production began in the Caucasus after 6000 115 calBCE9. In the following millennia the Caucasus region began to play an 116 increasingly important role in the economies of the growing urban centres in northern 117 Mesopotamia10 as a region rich in natural resources such as ores, pastures and 118 timber11. In the 4th millennium BCE the archaeological record attests to the presence 119 of the Maykop and Kura-Araxes cultural complexes, with the latter being found on 120 both flanks of the Caucasus mountain range, thus clearly demonstrating the 121 connection between north and south11. The Maykop culture was an important player 122 in the innovative horizon of the 4th millennium BCE in Western Eurasia. It is well 123 known for its rich burial mounds, especially at the eponymous Maykop site in today’s 124 Adygea, which reflect the rise of a new system of social organization12. The 4th 125 millennium BCE witnesses a concomitant rise in commodities and technologies such 126 as the wheel and wagon including associated technology, copper alloys, new 127 weaponry, and new breeds of domestic sheep13, 14. 128 129 The adjacent Pontic-Caspian and Eurasian steppe also played an important role in this 130 linked economic system, being the most likely region for the domestication of the 131 horse that revolutionised transport13. In addition, many steppe kurgans (large burial 132 mounds that are first observed in the context of the Maykop culture) have yielded the 133 remains of wheels and ox-drawn carts, highlighting a mobile economy focused on 134 cattle and sheep/goat herding15. The adoption of the horse almost certainly 135 contributed to the intensification of pastoralist practices in the Eurasian steppes, 136 allowing more efficient keeping of larger herds16, 17, 18 and facilitating the massive 137 range expansions of pastoralists associated with the Yamnaya cultural community and 138 related groups from the East European steppe19, 20. This transformation changed the 139 European gene pool during the early 3rd millennium BCE and descendants of the 140 Yamnaya eventually also transformed the ancestry of South Asia as well21. However, 141 flow of goods and ideas between the eastern European steppe zone, the Caucasus, the 142 Carpathians, and Central Europe has been documented by archaeological and ancient 143 DNA research as early as the 5th millennium BCE, long before the massive migration 144 took place22, 23, 24. Taken together, the Caucasus region played a crucial role in the 145 prehistory of Western Eurasia and this study aims to shed new light on events in the 146
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key period between the 4th and 3rd millennium BCE. 147 148 Recent ancient DNA studies have enabled the resolution of several long-standing 149 questions regarding cultural and population transformations in prehistory. One of 150 these is the Mesolithic-Neolithic transition in Europe, which saw a change from a 151 hunter-gatherer lifestyle to a sedentary, food-producing subsistence strategy. 152 Genome-wide data from pre-farming and farming communities have identified 153 distinct ancestral populations that largely reflect subsistence patterns in addition to 154 geography25. One important feature is a cline of European hunter-gatherer (HG) 155 ancestry that runs roughly from West to East (hence WHG and EHG; blue component 156 in Fig. 2A, 2C), which differs greatly from the ancestry of Early European farmers 157 that in turn is closely related to that of northwest Anatolian farmers26, 27 and more 158 remotely also to pre-farming individuals from the Levant23. The Near East and 159 Anatolia have long been seen as the regions from which European farming and animal 160 husbandry emerged. Surprisingly, these regions harboured three divergent 161 populations, with Anatolian and Levantine ancestry in the western part and a group 162 with a distinct ancestry in the eastern part first described in Upper Pleistocene 163 individuals from Georgia (Caucasus hunter-gatherers; CHG)28 and then in Mesolithic 164 and Neolithic individuals from Iran23, 29. The following two millennia, spanning from 165 the Neolithic to Chalcolithic and Early Bronze Age periods in each region, witnessed 166 migration and admixture between these ancestral groups, leading to a pattern of 167 genetic homogenization and reduced genetic distances between these Neolithic source 168 populations23. In parallel, Eneolithic individuals from the Samara region (5200-4000 169 BCE) also exhibit population mixture, specifically EHG- and CHG/Iranian ancestry, a 170 combination that forms the so-called ‘steppe-ancestry’28. This ancestry eventually 171 spread further west19, 20, where it contributed substantially to the ancestry of present-172 day Europeans, and east to the Altai region as well as to South Asia23. 173 174 To understand and characterize the genetic variation of Caucasian populations, 175 present-day groups from various geographic, cultural/ethnic and linguistic 176 backgrounds have been analyzed previously at the autosomal, Y-chromosomal and 177 mitochondrial level4, 5, 30. Yunusbayev and colleagues described the Greater Caucasus 178 region as an asymmetric semipermeable barrier based on a higher genetic affinity of 179 southern Caucasus groups to Anatolian and Near Eastern populations and a genetic 180 discontinuity between these and populations of the North Caucasus and of adjacent 181 Eurasian steppes. While autosomal and mitochondrial DNA data appear relatively 182 homogeneous across diverse ethnic and linguistic groups and the entire mountainous 183 region, the Y-chromosome diversity reveals a deeper genetic structure attesting to 184 several male founder effects, with striking correspondence to geography, language 185 groups and historical events4, 5. 186 187 In our study we aimed to investigate when and how the genetic patterns observed 188 today were formed and test whether they have been present since prehistoric times by 189 generating time-stamped human genome-wide data. We were also interested in 190 characterizing the role of the Caucasus as a conduit for gene-flow in the past and in 191 shaping the cultural and genetic makeup of the wider region (Supplementary 192 Information 1). This has important implications for understanding the means by 193 which Europe, the Eurasian steppe zone, and the earliest urban centres in the Near 194 East were connected31. We aimed to genetically characterise individuals from cultural 195 complexes such as the Maykop and Kura-Araxes and assessing the amount of gene 196
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flow in the Caucasus during times when the exploitation of resources of the steppe 197 environment intensified, since this was potentially triggered by the cultural and 198 technological innovations of the Late Chalcolithic and Early Bronze Age 6000-5000 199 years ago11. Lastly, since the spread of steppe ancestry into central Europe and the 200 eastern steppes during the early 3rd millennium BCE (5000-4500 BP) was a striking 201 migratory event in human prehistory19, 20, we also wanted to retrace the formation of 202 the steppe ancestry profile and whether this might have been influenced by 203 neighbouring farming groups to the west or from regions of early urbanization further 204 south. 205 206 Results 207 208 Genetic clustering and uniparentally-inherited markers 209 We report genome-wide data at a targeted set of 1.2 million single nucleotide 210 polymorphisms (SNPs)19, 32 for 59 Eneolithic/Chalcolithic and Bronze Age 211 individuals from the Caucasus region. After filtering out 14 individuals that were 212 first-degree relatives or showed evidence of contamination or reference bias 213 (Supplementary Information 3 and Data 1) we retained 45 individuals for downstream 214 analyses using a cut-off of 30,000 SNPs. We merged our newly generated samples 215 with previously published ancient and modern data19, 20, 23, 24, 26, 27, 29, 33, 34, 35, 36, 37, 38, 39,
216 40, 41, 42, 43 (Supplementary Data 2). We first performed principal component analysis 217 (PCA)44 and ADMIXTURE45 analysis to assess the genetic affinities of the ancient 218 individuals qualitatively (Fig. 2) and followed up quantitatively with formal f- and D-219 statistics, qpWave, qpAdm, and qpGraph44. Based on PCA and ADMIXTURE plots 220 we observe two distinct genetic clusters: one cluster falls with previously published 221 ancient individuals from the West Eurasian steppe (hence termed ‘Steppe’), and the 222 second clusters with present-day southern Caucasian populations and ancient Bronze 223 Age individuals from today’s Armenia (henceforth called ‘Caucasus’), while a few 224 individuals take on intermediate positions between the two. The stark distinction seen 225 in our temporal transect is also visible in the Y-chromosome haplogroup distribution, 226 with R1/R1b1 and Q1a2 types in the Steppe and L, J, and G2 types in the Caucasus 227 cluster (Fig. 3A, Supplementary Data 1). In contrast, the mitochondrial haplogroup 228 distribution is more diverse and almost identical in both groups (Fig. 3B, 229 Supplementary Data 1). 230 231 The two distinct clusters are already visible in the oldest individuals of our temporal 232 transect, dated to the Eneolithic period (~6300-6100 yBP/4300-4100 calBCE). Three 233 individuals from the sites of Progress 2 and Vonjuchka 1 in the North Caucasus 234 piedmont steppe (‘Eneolithic steppe’), which harbor Eastern and Caucasian hunter-235 gatherer related ancestry (EHG and CHG, respectively), are genetically very similar 236 to Eneolithic individuals from Khalynsk II and the Samara region19, 27. This extends 237 the cline of dilution of EHG ancestry via CHG/Iranian-like ancestry to sites 238 immediately north of the Caucasus foothills (Fig. 2D). 239 In contrast, the oldest individuals from the northern mountain flank itself, which are 240 three first degree-related individuals from the Unakozovskaya cave associated with 241 the Darkveti-Meshoko Eneolithic culture (analysis label ‘Eneolithic Caucasus’) show 242 mixed ancestry mostly derived from sources related to the Anatolian Neolithic 243 (orange) and CHG/Iran Neolithic (green) in the ADMIXTURE plot (Fig. 2C). While 244 similar ancestry profiles have been reported for Anatolian and Armenian Chalcolithic 245 and Bronze Age individuals20, 23, this result suggests the presence of the mixed 246
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Anatolian/Iranian/CHG related ancestry north of the Great Caucasus Range as early 247 as ~6500 years ago. 248 249 Ancient North Eurasian ancestry in ‘Steppe Maykop’ individuals 250 Four individuals from mounds in the grass steppe zone, which are archaeologically 251 associated with the ‘Steppe Maykop’ cultural complex (Supplementary Information 252 1), lack the Anatolian farmer-related component when compared to contemporaneous 253 Maykop individuals from the foothills. Instead they carry a third and fourth ancestry 254 component that is linked deeply to Upper Paleolithic Siberians (maximized in the 255 individual Afontova Gora 3 (AG3)36, 37 and Native Americans, respectively, and in 256 modern-day North Asians such as North Siberian Nganasan (Supplementary Fig. 1). 257 To illustrate this affinity with ‘ancient North Eurasians’ (ANE)26, we also ran PCA 258 with 147 Eurasian (Supplementary Fig. 2A) and 29 Native American populations 259 (Supplementary Fig. 2B). The latter represent a cline from ANE-rich steppe 260 populations such as EHG, Eneolithic individuals, AG3 and Mal’ta 1 (MA1) to 261 modern-day Native Americans at the opposite end. To formally test the excess of 262 alleles shared with ANE/Native Americans we performed f4-statistics of the form 263 f4(Mbuti, X; Steppe Maykop, Eneolithic steppe), which resulted in significantly 264 positive Z scores |Z >3| for AG3, MA1, EHG, Clovis and Kennewick for the ancient 265 populations and many present-day Native American populations (Supplementary 266 Table 1). Based on these observations we used qpWave and qpAdm methods to model 267 the number of ancestral sources contributing to the Steppe Maykop individuals and 268 their relative ancestry coefficients. Simple two-way models of Steppe Maykop as an 269 admixture of Eneolithic steppe, AG3 or Kennewick do not fit (Supplementary Table 270 2). However, we could successfully model Steppe Maykop ancestry as being derived 271 from populations related to all three sources (p-value 0.371 for rank 2): Eneolithic 272 steppe (63.5±2.9 %), AG3 (29.6±3.4%) and Kennewick (6.9±1.0%) (Fig. 4; 273 Supplementary Table 3). We note that the Kennewick related signal is most likely 274 driven by the East Eurasian part of Native American ancestry as the f4-statistics 275 (Steppe_Maykop, Fitted Steppe_Maykop; Outgroup1, Outgroup2) show that the 276 Steppe Maykop individuals share more alleles not only with Karitiana but also with 277 Han Chinese when compared with the fitted ones using Eneolithic steppe and AG3 as 278 two sources and Mbuti, Karitiana and Han as outgroups (Supplementary Table 2). 279 280 Characterising the Caucasus ancestry profile 281 The Maykop period, represented by twelve individuals from eight Maykop sites 282 (Maykop, n=2; a cultural variant ‘Novosvobodnaya’ from the site Klady, n=4; and 283 Late Maykop, n=6) in the northern foothills appear homogeneous. These individuals 284 closely resemble the preceding Caucasus Eneolithic individuals and present a 285 continuation of the local genetic profile. This ancestry persists in the following 286 centuries at least until ~3100 yBP (1100 calBCE) in the mountains, as revealed by 287 individuals from Kura-Araxes from both the northeast (Velikent, Dagestan) and the 288 South Caucasus (Kaps, Armenia), as well as Middle and Late Bronze Age individuals 289 (e.g. Kudachurt, Marchenkova Gora) from the north. Overall, this Caucasus ancestry 290 profile falls among the ‘Armenian and Iranian Chalcolithic’ individuals and is 291 indistinguishable from other Kura-Araxes individuals (‘Armenian Early Bronze Age’) 292 on the PCA plot (Fig. 2), suggesting a dual origin involving Anatolian/Levantine and 293 Iran Neolithic/CHG ancestry, with only minimal EHG/WHG contribution possibly as 294 part of the Anatolian farmer-related ancestry23. 295
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Admixture f3-statistics of the form f3(X, Y; target) with the Caucasus cluster as target 296 resulted in significantly negative Z scores |Z < -3| when CHG (or AG3 in Late 297 Maykop) were used as one and Anatolian farmers as the second potential source 298 (Supplementary Table 4). We also used qpWave to determine the number of streams 299 of ancestry and found that a minimum of two is sufficient (except for Eneolithic 300 Caucasus or Dolmen LBA, for which one source is sufficient (Supplementary Table 301 5). 302 We then tested whether each temporal/cultural group of the Caucasus cluster could be 303 modelled as a simple two-way admixture by exploring all possible pairs of sources in 304 qpWave. We found support for CHG as one source and Anatolian farmer-related 305 ancestry or a derived form such as is found in southeastern Europe as the other 306 (Supplementary Table 6). We focused on model of mixture of proximal sources (Fig. 307 4B) such as CHG and Anatolian Chalcolithic for all six groups of the Caucasus 308 cluster (Eneolithic Caucasus, Maykop and Late Makyop, Maykop-Novosvobodnaya, 309 Kura-Araxes, and Dolmen LBA), with admixture proportions on a genetic cline of 40-310 72% Anatolian Chalcolithic related and 28-60% CHG related (Supplementary Table 311 7). When we explored Romania_EN and Greece_Neolithic individuals as alternative 312 southeast European sources (30-46% and 36-49%), the CHG proportions increased to 313 54-70% and 51-64%, respectively. We hypothesize that alternative models, replacing 314 the Anatolian Chalcolithic individual with yet unsampled populations from eastern 315 Anatolia, South Caucasus or northern Mesopotamia, would probably also provide a fit 316 to the data from some of the tested Caucasus groups. The models replacing CHG with 317 Iran Neolithic-related individuals could explain the data in a two-way admixture with 318 the combination of Armenia Chalcolithic or Anatolia Chalcolithic as the other source. 319 However, models replacing CHG with EHG individuals received no support 320 (Supplementary Table 8), indicating no strong influence for admixture from the 321 adjacent steppe to the north. In an attempt to account for potentially un-modelled 322 ancestry in the Caucasus groups, we added EHG, WHG and Iran Chalcolithic as 323 additional sources in the previous two-way modelling. The resulting ancestry 324 coefficients do not deviate substantially from 0 (high standard errors) when adding 325 EHG or WHG, suggesting very limited direct ancestry from both hunter-gatherer 326 groups (Supplementary Table 9). Alternatively, when we added Iran Chalcolithic 327 individuals as a third source to the model, we observed that Kura-Araxes and 328 Maykop-Novosvobodnaya individuals had likely received additional Iran 329 Chalcolithic-related ancestry (24.9% and 37.4%, respectively; Fig. 4; Supplementary 330 Table 10). 331 332 Characterising the Steppe ancestry profile in the North Caucasus 333 Individuals from the North Caucasian steppe associated with the Yamnaya cultural 334 formation (5300-4400 BP, 3300-2400 calBCE) appear genetically almost identical to 335 previously reported Yamnaya individuals from Kalmykia20 immediately to the north, 336 the middle Volga region19, 27, Ukraine and Hungary, and to other Bronze Age 337 individuals from the Eurasian steppes who share the characteristic ‘steppe ancestry’ 338 profile as a mixture of EHG and CHG/Iranian ancestry23, 28. These individuals form a 339 tight cluster in PCA space (Figure 2) and can be shown formally to be a mixture by 340 significantly negative admixture f3-statistics of the form f3(EHG, CHG; target) 341 (Supplementary Fig. 3). This also involves individuals assigned to the North Caucasus 342 culture (4800-4500 BP, 2800-2500 calBCE) in the piedmont steppe of the central 343 North Caucasus, who share the steppe ancestry profile. Individuals from the 344 Catacomb culture in the Kuban, Caspian and piedmont steppes (4600-4200 BP, 2600-345
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2200 calBCE), which succeeded the Yamnaya horizon, also show a continuation of 346 the ‘steppe ancestry’ profile. 347 348 The individuals of the Middle Bronze Age (MBA) post-Catacomb horizon (4200-349 3700 BP, 2200-1700 calBCE) such as Late North Caucasus and Lola culture represent 350 both ancestry profiles common in the North Caucasus region: individuals from the 351 mountain site Kabardinka show a typical steppe ancestry profile, whereas individuals 352 from the Late North Caucasus site Kudachurt 90 km to the west retain the ‘southern’ 353 Caucasus profile. The latter is also observed in our most recent individual from the 354 western Late Bronze Age Dolmen culture (3400-3200 BP, 1400-1200 calBCE). In 355 contrast, one individual assigned to the Lola culture resembles the ancestry profile of 356 the Steppe Maykop individuals. 357 358 Admixture into the steppe zone from the south 359 Evidence for interaction between the Caucasus and the Steppe clusters is visible in 360 our genetic data from individuals associated with the later Steppe Maykop phase 361 around 5300-5100 years ago. These ‘outlier’ individuals were buried in the same 362 mounds as those with steppe and in particular Steppe Maykop ancestry profiles but 363 share a higher proportion of Anatolian farmer-related ancestry visible in the 364 ADMIXTURE plot and are also shifted towards the Caucasus cluster in PC space 365 (Fig. 2D). This observation is confirmed by formal D-statistics (Steppe Maykop 366 outlier, Steppe Maykop; X; Mbuti), which are significantly positive when X is a 367 Neolithic or Bronze Age group from the Near East or Anatolia (Supplementary Fig. 368 4). By modelling Steppe Maykop outliers successfully as a two-way mixture of 369 Steppe Maykop and representatives of the Caucasus cluster (Supplementary Table 3), 370 we can show that these individuals received additional ‘Anatolian and Iranian 371 Neolithic ancestry’, most likely from contemporaneous sources in the south. We 372 estimated admixture time for the observed farmer-related ancestry individuals using 373 the linkage disequilibrium (LD)-based admixture inference implemented in 374 ALDER46, using Steppe Maykop outliers as the test population and Steppe Maykop as 375 well as Kura-Araxes as references. The average admixture time for Steppe Maykop 376 outliers is about 20 generations or 560 years ago, assuming a generation time of 28 377 years47 (Supplementary Information 6). 378 379 Contribution of Anatolian farmer-related ancestry to Bronze Age steppe groups 380 In principal component space Eneolithic individuals (Samara Eneolithic) form a cline 381 running from EHG to CHG (Fig. 2D), which is continued by the newly reported 382 Eneolithic steppe individuals. However, the trajectory of this cline changes in the 383 subsequent centuries. Here we observe a cline from Eneolithic_steppe towards the 384 Caucasus cluster. We can qualitatively explain this ‘tilting cline’ by developments 385 south of the Caucasus, where Iranian and Anatolian/Levantine Neolithic ancestries 386 continue to mix, resulting in a blend that is also observed in the Caucasus cluster, 387 from where it could have spread onto the steppe. The first appearance of ‘Near 388 Eastern farmer related ancestry’ in the steppe zone is evident in Steppe Maykop 389 outliers. However, PCA results also suggest that Yamnaya and later groups of the 390 West Eurasian steppe carry some farmer related ancestry as they are slightly shifted 391 towards ‘European Neolithic groups’ in PC2 (Fig. 2D) compared to Eneolithic steppe. 392 This is not the case for the preceding Eneolithic steppe individuals. The tilting cline is 393 also confirmed by admixture f3-statistics, which provide statistically negative values 394 for AG3 as one source and any Anatolian Neolithic related group as a second source 395
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(Supplementary Table 11). Detailed exploration via D-statistics in the form of 396 D(EHG, steppe group; X, Mbuti) and D(Samara_Eneolithic, steppe group; X, Mbuti) 397 show significantly negative D values for most of the steppe groups when X is a 398 member of the Caucasus cluster or one of the Levant/Anatolia farmer-related groups 399 (Supplementary Figs. 5 and 6). In addition, we used f- and D-statistics to explore the 400 shared ancestry with Anatolian Neolithic as well as the reciprocal relationship 401 between Anatolian- and Iranian farmer-related ancestry for all groups of our two main 402 clusters and relevant adjacent regions (Supplementary Fig. 4). Here, we observe an 403 increase in farmer-related ancestry (both Anatolian and Iranian) in our Steppe cluster, 404 ranging from Eneolithic steppe to later groups. In Middle/Late Bronze Age groups 405 especially to the north and east we observe a further increase of Anatolian farmer-406 related ancestry consistent with previous studies of the Poltavka, Andronovo, 407 Srubnaya and Sintashta groups23, 27 and reflecting a different process not especially 408 related to events in the Caucasus. 409 410 The exact geographic and temporal origin of this Anatolian farmer-related ancestry in 411 the North Caucasus and later in the steppe is difficult to discern from our data. Not 412 only do the Steppe groups vary in their respective affinity to each of the two, but also 413 the Caucasus groups, which represent potential sources from a geographic and 414 cultural point of view, are mixtures of them both23. We therefore used qpWave and 415 qpAdm to explore the number of ancestry sources for the Anatolian farmer-related 416 component to evaluate whether geographically proximate groups plausibly 417 contributed to the subtle shift of Eneolithic ancestry in the steppe towards those of the 418 Neolithic groups. Specifically, we tested whether any of the Eurasian steppe ancestry 419 groups can be successfully modelled as a two-way admixture between Eneolithic 420 steppe and a population X derived from Anatolian- or Iranian farmer-related ancestry, 421 respectively. Surprisingly, we found that a minimum of four streams of ancestry is 422 needed to explain all eleven steppe ancestry groups tested, including previously 423 published ones (Fig. 2; Supplementary Table 12). Importantly, our results show a 424 subtle contribution of both Anatolian farmer-related ancestry and WHG-related 425 ancestry (Fig.4; Supplementary Tables 13 and 14), which was likely contributed 426 through Middle and Late Neolithic farming groups from adjacent regions in the West. 427 A direct source of Anatolian farmer-related ancestry can be ruled out (Supplementary 428 Table 15). At present, due to the limits of our resolution, we cannot identify a single 429 best source population. However, geographically proximal and contemporaneous 430 groups such as Globular Amphora and Eneolithic groups from the Black Sea area 431 (Ukraine and Bulgaria), which represent all four distal sources (CHG, EHG, WHG, 432 and Anatolian_Neolithic) are among the best supported candidates (Fig. 4; 433 Supplementary Tables 13,14 and 15). Applying the same method to the subsequent 434 North Caucasian Steppe groups such as Catacomb, North Caucasus, and Late North 435 Caucasus confirms this pattern (Supplementary Table 17). 436 437 Using qpAdm with Globular Amphora as a proximate surrogate population (assuming 438 that a related group was the source of the Anatolian farmer-related ancestry), we 439 estimated the contribution of Anatolian farmer-related ancestry into Yamnaya and 440 other steppe groups. We find that Yamnaya individuals from the Volga region 441 (Yamnaya Samara) have 13.2±2.7% and Yamnaya individuals in Hungary 17.1±4.1% 442 Anatolian farmer-related ancestry (Fig.4; Supplementary Table 18)– statistically 443 indistinguishable proportions. Replacing Globular Amphora by Iberia Chalcolithic, 444 for instance, does not alter the results profoundly (Supplementary Table 19). This 445
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suggests that the source population was a mixture of Anatolian farmer-related 446 ancestry and a minimum of 20% WHG ancestry, a profile that is shared by many 447 Middle/Late Neolithic and Chalcolithic individuals from Europe of the 3rd millennium 448 BCE analysed thus far. 449 To account for potentially un-modelled ancestry from the Caucasus groups, we added 450 ‘Eneolithic Caucasus’ as an additional source to build a three-way model. We found 451 that Yamnaya Caucasus, Yamnaya Ukraine Ozera, North Caucasus and Late North 452 Caucasus had likely received additional ancestry (6% to 40%) from nearby Caucasus 453 groups (Supplementary Table 20). This suggests a more complex and dynamic picture 454 of steppe ancestry groups through time, including the formation of a local variant of 455 steppe ancestry in the North Caucasian steppe from the local Eneolithic, a 456 contribution of Steppe Maykop groups, and population continuity between the early 457 Yamnaya period and the Middle Bronze Age (5300-3200 BP, 3300-2200 calBCE). 458 This was interspersed by additional, albeit subtle gene-flow from the West and 459 occasional equally subtle gene flow from neighbouring groups in the Caucasus and 460 piedmont zones. 461 462 Insights from micro-transects through time 463 The availability of multiple individuals from one site (here burial mounds or kurgans) 464 allowed us to test genetic continuity on a micro-transect level. By focusing on two 465 kurgans (Marinskaya 5 and Sharakhalsun 6), for which we could successfully 466 generate genome-wide data from four and five individuals, respectively, we observe 467 that the genetic ancestry varied through time, alternating between the Steppe and 468 Caucasus ancestries (Supplementary Fig. 8). This shows that the apparent genetic 469 border between the two distinct genetic clusters was shifting over time. We also 470 detected various degrees of kinship between individuals buried in the same mound, 471 which supports the view that particular mounds reflected genealogical lineages. 472 Overall, we observe a balanced sex ratio within our sites across the individuals tested 473 (Supplementary Information 4). 474 475 A joint model of ancient populations of the Caucasus region 476 We used qpGraph to explore models that jointly explain the population splits and 477 gene flow in the Greater Caucasus region by computing f2-, f3- and f4- statistics 478 measuring allele sharing among pairs, triples, and quadruples of populations and 479 evaluating fits based on the maximum |Z|-score comparing predicted and observed 480 values of these statistics. Our fitted model recapitulates the genetic separation 481 between the Caucasus and Steppe groups with the Eneolithic steppe individuals 482 deriving more than 60% of ancestry from EHG and the remainder from a CHG-483 related basal lineage, whereas the Maykop group received about 86.4% from CHG, 484 9.6% Anatolian farming related ancestry, and 4% from EHG. The Yamnaya 485 individuals from the Caucasus derived the majority of their ancestry from Eneolithic 486 steppe individuals but also received about 16% from Globular Amphora-related 487 farmers (Fig. 5). 488 489 490 Discussion 491 492 Our data from the Greater Caucasus region cover over 3000 years of prehistory as a 493 transect through time, ranging from the Eneolithic (starting 6500 yBP, 4500 calBCE) 494 to the Late Bronze Age (ending 3200 yBP, 1200 calBCE). We observe a genetic 495
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separation between the groups in the piedmont steppe, i.e. the northern foothills of the 496 Greater Caucasus, and those groups of the bordering herb, grass and desert steppe 497 regions in the north (i.e. the ‘real’ steppe). We have summarised these broadly as 498 Caucasus and Steppe groups in correspondence with the eco-geographic vegetation 499 zones that characterise the socio-economic basis of the associated archaeological 500 cultures. 501 502 When compared to present-day human populations from the Caucasus, which show a 503 clear separation into North and South Caucasus groups along the Great Caucasus 504 mountain range (Fig. 2D), our new data highlights that the situation during the Bronze 505 Age was quite different. The fact that individuals buried in kurgans in the North 506 Caucasian piedmont and foothill zone are more closely related to ancient individuals 507 from regions further south in today’s Armenia, Georgia and Iran allows us to draw 508 two major conclusions. 509 510 First, sometime after the Bronze Age present-day North Caucasian populations must 511 have received additional gene-flow from populations north of the mountain range that 512 separates them from southern Caucasians, who largely retained the Bronze Age 513 ancestry profile. The archaeological and historic records suggest numerous incursions 514 during the subsequent Iron Age and Medieval times48, but ancient DNA from these 515 time periods is needed to test this directly. 516 517 Second, our results reveal that the Greater Caucasus Mountains were not an 518 insurmountable barrier to human movement in prehistory. Instead the foothills to the 519 north at the interface of the steppe and mountain ecozones could be seen as a transfer 520 zone of cultural innovations from the south and the adjacent Eurasian steppes to the 521 north, as attested by the archaeological record. The latter is best exemplified by the 522 two Steppe Maykop outlier individuals dating to 5100-5000 yBP/3100-3000 calBCE, 523 which carry additional Anatolian farmer-related ancestry likely derived from a 524 proximate source related to the Caucasus cluster. We could show that individuals 525 from the contemporaneous Maykop period in the piedmont region are likely 526 candidates for the source of this ancestry and might explain the regular presence of 527 ‘Maykop artefacts’ in burials that share Steppe Eneolithic traditions and are 528 genetically assigned to the Steppe group. Hence the diverse ‘Steppe Maykop’ group 529 indeed represents the mutual entanglement of Steppe and Caucasus groups and their 530 cultural affiliations in this interaction sphere. 531 532 Concerning the influences from the south, our oldest dates from the immediate 533 Maykop predecessors Darkveti-Meshoko (Eneolithic Caucasus) indicate that the 534 Caucasus genetic profile was present north of the range ~6500 BP, 4500 calBCE. 535 This is in accordance with the Neolithization of the Caucasus, which had started in the 536 flood plains of the great rivers in the South Caucasus in the 6th millennium BCE from 537 where it spread to the West and Northwest Caucasus during the 5th millennium BCE9,
538 49. It remains unclear whether the local CHG ancestry profile (represented by Late 539 Upper Palaeolithic/Mesolithic individuals from Kotias Klde and Satsurblia in today’s 540 Georgia) was also present in the North Caucasus region before the Neolithic. 541 However, if we take the Caucasus hunter-gatherer individuals from Georgia as a local 542 baseline and the oldest Eneolithic Caucasus individuals from our transect as a proxy 543 for the local Late Neolithic ancestry, we notice a substantial increase in Anatolian 544 farmer-related ancestry. This in all likelihood is linked to the process of 545
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Neolithization, which also brought this type of ancestry to Europe. As a consequence, 546 it is possible that Neolithic groups could have reached the northern flanks of the 547 Caucasus earlier50 (Supplementary Information 1) and in contact with local hunter-548 gatherers facilitated the exploration of the steppe environment for pastoralist 549 economies. Hence, additional sampling from older individuals is needed to fill this 550 temporal and spatial gap. 551 552 Our results show that at the time of the eponymous grave mound of Maykop, the 553 North Caucasus piedmont region was genetically connected to the south. Even 554 without direct ancient DNA data from northern Mesopotamia, the new genetic 555 evidence suggests an increased assimilation of Chalcolithic individuals from Iran, 556 Anatolia and Armenia and those of the Eneolithic Caucasus during 6000-4000 557 calBCE23, and thus likely also intensified cultural connections. Within this sphere of 558 interaction, it is possible that cultural influences and continuous subtle gene flow from 559 the south formed the basis of Maykop (Fig. 4; Supplementary Table 10). In fact, the 560 Maykop phenomenon was long understood as the terminus of the expansion of South 561 Mesopotamian civilisations in the 4th millennium BCE11, 12, 51. It has been further 562 suggested that along with the cultural and demographic influence the key 563 technological innovations that had revolutionised the late 4th millennium BCE in 564 western Asia had ultimately also spread to Europe52. An earlier connection in the late 565 5th millennium BCE, however, allows speculations about an alternative archaeological 566 scenario: was the cultural exchange mutual and did e.g. metal rich areas such as the 567 Caucasus contribute substantially to the development and transfer of these 568 innovations53, 54? 569 570 We also observe a degree of genetic continuity within each cluster. While this 571 continuity in each cluster spans the 3000 years covered in this study, we also detect 572 occasional gene-flow between the two clusters as well as from outside sources. 573 Moreover, our data shows that the northern flanks were consistently linked to the 574 Near East and had received multiple streams of gene flow from the south, as seen e.g. 575 during the Maykop, Kura-Araxes and late phase of the North Caucasus culture. 576 Interestingly, this renewed appearance of the southern genetic make-up in the 577 foothills corresponds to a period of climatic deterioration (known as 4.2 ky event) in 578 the steppe zone, that put a halt to the exploitation of the steppe zone for several 579 hundred years55. Further insight arises from individuals that were buried in the same 580 kurgan but in different time periods, as highlighted in the two kurgans Marinskaya 5 581 and Sharakhalsun 6. Here, we recognize that the distinction between Steppe and 582 Caucasus with reference to vegetation zones (Fig. 1) is not strict but rather reflects a 583 shifting border of genetic ancestry through time, possibly due to climatic shifts and/or 584 cultural factors linked to subsistence strategies or social exchange. It seems plausible 585 that the occurrence of Steppe ancestry in the piedmont region of the northern foothills 586 coincides with the range expansion of the Yamnaya pastoralists. However, more time-587 stamped data from this region will be needed to provide further details on the 588 dynamics of this contact zone. 589 590 An interesting observation is that steppe zone individuals directly north of the 591 Caucasus (Eneolithic Samara and Eneolithic steppe) had initially not received any 592 gene flow from Anatolian farmers. Instead, the ancestry profile in Eneolithic steppe 593 individuals shows an even mixture of EHG and CHG ancestry, which argues for an 594 effective cultural and genetic border between the contemporaneous Eneolithic 595
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populations in the North Caucasus, notably Steppe and Caucasus. Due to the temporal 596 limitations of our dataset, we currently cannot determine whether this ancestry is 597 stemming from an existing natural genetic gradient running from EHG far to the north 598 to CHG/Iran in the south or whether this is the result of farmers with Iranian farmer/ 599 CHG-related ancestry reaching the steppe zone independent of and prior to a stream 600 of Anatolian farmer-like ancestry, where they mixed with local hunter-gatherers that 601 carried only EHG ancestry. 602 603 Another important observation is that all later individuals in the steppe region, starting 604 with Yamnaya, deviate from the EHG-CHG admixture cline towards European 605 populations in the West. This documents that these individuals had received 606 Anatolian farmer-related ancestry, as documented by quantitative tests and recently 607 also shown for two Yamnaya individuals from Ukraine (Ozera) and one from 608 Bulgaria24. For the North Caucasus region, this genetic contribution could have 609 occurred through immediate contact with groups in the Caucasus or further south. An 610 alternative source, explaining the increase in WHG-related ancestry, would be contact 611 with contemporaneous Chalcolithic/EBA farming groups at the western periphery of 612 the Yamnaya culture distribution area, such as Globular Amphora and Tripolye 613 (Cucuteni–Trypillia) individuals from Ukraine, which also have been shown to carry 614 Anatolian Neolithic farmer-derived ancestry24. 615 616 Archaeological arguments would be consonant with both scenarios. Contact between 617 early Yamnaya and late Maykop groups at the end of the 4th millennium BCE is 618 suggested by impulses seen in early Yamnaya complexes. A western sphere of 619 interaction is evident from striking resemblances of imagery inside burial chambers of 620 Central Europe and the Caucasus56 (Supplementary Fig. 9), and particular similarities 621 also exist in geometric decoration patterns in stone cist graves in the Northern Pontic 622 steppe57, on stone stelae in the Caucasus58, and on pottery of the Eastern Globular 623 Amphora Culture, which links the eastern fringe of the Carpathians and the Baltic 624 Sea56. This implies an overlap of symbols with a communication and interaction 625 network that formed during the late 4th millennium BCE and operated across the 626 Black Sea area involving the Caucasus59, 60, and later also involved early Globular 627 Amphora groups in the Carpathians and east/central Europe61. The role of early 628 Yamnaya groups within this network is still unclear57. However, this interaction zone 629 pre-dates any direct influence of Yamnaya groups in Europe or the succeeding 630 formation of the Corded Ware62, 63 and its persistence opens the possibility of subtle 631 bidirectional gene-flow, several centuries before the massive range expansions of 632 pastoralist groups that reached Central Europe in the mid-3rd millennium BCE19, 35. 633 634 We were surprised to discover that Steppe Maykop individuals from the eastern desert 635 steppes harboured a distinctive ancestry component that relates them to Upper 636 Palaeolithic Siberian individuals (AG3, MA1) and Native Americans. This is 637 exemplified by the more commonly East Asian features such as the derived EDAR 638 allele, which has also been observed in EHG from Karelia and Scandinavian hunter-639 gatherers (SHG). The additional affinity to East Asians suggests that this ancestry 640 does not derive directly from Ancestral North Eurasians but from a yet-to-be-641 identified ancestral population in north-central Eurasia with a wide distribution 642 between the Caucasus, the Ural Mountains and the Pacific coast21, of which we have 643 discovered the so far southwestern-most and also youngest (e.g. the Lola culture 644 individual) genetic representative.
645
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646 The insight that the Caucasus mountains served not only as a corridor for the spread 647 of CHG/Neolithic Iranian ancestry but also for later gene-flow from the south also has 648 a bearing on the postulated homelands of Proto-Indo-European (PIE) languages and 649 documented gene-flows that could have carried a consecutive spread of both across 650 West Eurasia17, 64. Perceiving the Caucasus as an occasional bridge rather than a strict 651 border during the Eneolithic and Bronze Age opens up the possibility of a homeland 652 of PIE south of the Caucasus, which itself provides a parsimonious explanation for an 653 early branching off of Anatolian languages. Geographically this would also work for 654 Armenian and Greek, for which genetic data also supports an eastern influence from 655 Anatolia or the southern Caucasus. A potential offshoot of the Indo-Iranian branch to 656 the east is possible, but the latest ancient DNA results from South Asia also lend 657 weight to an LMBA spread via the steppe belt21. The spread of some or all of the 658 proto-Indo-European branches would have been possible via the North Caucasus and 659 Pontic region and from there, along with pastoralist expansions, to the heart of 660 Europe. This scenario finds support from the well attested and now widely 661 documented ‘steppe ancestry’ in European populations, the postulate of increasingly 662 patrilinear societies in the wake of these expansions (exemplified by R1a/R1b), as 663 attested in the latest study on the Bell Beaker phenomenon35. 664 665 666 Materials and Methods 667 668 Sample collection 669 Samples from archaeological human remains were collected and exported under a 670 collaborative research agreement between the Max-Planck Institute for the Science of 671 Human History, the German Archaeological Institute and the Lomonosov Moscow 672 State University and Anuchin Research Institute and Museum of Anthropology 673 (permission no. 114-18/204-03). 674 675 Ancient DNA analysis 676 We extracted DNA and prepared next-generation sequencing libraries from 107 677 samples in two dedicated ancient DNA laboratories at Jena and Boston. Samples 678 passing initial QC were further processed at the Max Planck Institute for the Science 679 of Human History, Jena, Germany following the established protocols for DNA 680 extraction and library preparation65, 66. Fourteen of these samples were processed at 681 Harvard Medical School, Boston, USA following a published protocol by replacing 682 the extender-MinElute-column assembly with the columns from the Roche High Pure 683 Viral Nucleic Acid Large Volume Kit to extract DNA from about 75mg of sample 684 powder from each sample. All libraries were subjected to partial (“half”) Uracil-685 DNA-glycosylase (UDG) treatment before blunt end repair. We performed in-solution 686 enrichment (1240K capture)27 for a targeted set of 1,237,207 SNPs that comprises two 687 previously reported sets of 394,577 SNPs (390k capture) and 842,630 SNPs, and then 688 sequenced on an in-house Illumina HiSeq 4000 or NextSeq 500 platform for 76bp 689 either single or paired-end. 690 691 The sequence data was demultiplexed, adaptor clipped with leehom67 and then further 692 processed using EAGER68, which included mapping with BWA (v0.6.1)69 against 693 human genome reference GRCh37/hg19, and removing duplicate reads with the same 694 orientation and start and end positions. To avoid an excess of remaining C-to-T and 695
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G-to-A transitions at the ends of the reads, three bases of the ends of each read were 696 clipped for each sample using trimBam 697 (https://genome.sph.umich.edu/wiki/BamUtil:_trimBam). We generated “pseudo-698 haploid” calls by selecting a single read randomly for each individual at each of the 699 targeted SNP positions using the in-house genotype caller pileupCaller 700 (https://github.com/stschiff/sequenceTools/tree/master/src-pileupCaller). 701 702 Quality control 703 We report, but have not analyzed, data from individuals that had less than 30,000 704 SNPs hit on the 1240K set. We removed individuals with evidence of contamination 705 based on heterozygosity in the mtDNA genome data, a high rate of heterozygosity on 706 the X chromosome despite being male estimated with ANGSD70, or an atypical ratio 707 of the reads mapped to X versus Y chromosomes. 708 709 Merging new and published ancient and modern population data 710 We merged our newly generated ancient samples with ancient populations from the 711 publicly available datasets13, 19, 20, 24, 27, 28, 33, 35, 37 (Supplementary Data 2), as well as 712 genotyping data from worldwide modern populations using Human Origins arrays 713 published in the same publications. We also included newly genotyped populations 714 from the Caucasus and Asia, described in detail in Jeong et al.71. 715 716 Principal Component Analysis 717 We carried out principal component analysis on Human Origins Dataset using the 718 smartpca program of EIGENSOFT44, using default parameters and the lsqproject: 719 YES, numoutlieriter: 0, and shrinkmode:YES options to project ancient individuals 720 onto the first two components. 721 722 ADMIXTURE analysis 723 We carried out ADMIXTURE (v1.23)45 analysis after pruning for linkage 724 disequilibrium in PLINK72 with parameters --indep-pairwise 200 25 0.4, which 725 retained 301,801 SNPs for the Human Origins Dataset. We ran ADMIXTURE with 726 default 5-fold cross-validation (--cv=5), varying the number of ancestral populations 727 between K=2 and K=22 in 100 bootstraps with different random seeds. 728 729 f-statistics 730 We computed D-statistics and f4-statistics using qpDstat program of ADMIXTOOLS44 731 with default parameters. We computed the admixture f3-statistics using the qp3Pop 732 program of ADMIXTOOLS with the flag inbreed: YES. ADMIXTOOLS computes 733 standard errors using the default block jackknife. 734 735 Testing for streams of ancestry and inference of mixture proportions 736 We used qpWave and qpAdm19 as implemented in ADMIXTOOLS to test whether a set 737 of test populations is consistent with being related via N streams of ancestry from a 738 set of outgroup populations and estimate mixture proportions for a Test population as 739 a combination of N ‘reference’ populations by exploiting (but not explicitly modeling) 740 shared genetic drift with a set of outgroup populations. Mbuti.DG, Ust_Ishim.DG, 741 Kostenki14, MA1, Han.DG, Papuan.DG, Onge.DG, Villabruna, Vestonice16, 742 ElMiron, Ethiopia_4500BP.SG, Karitiana.DG, Natufian, Iran_Ganj_Dareh_Neolithic. 743 The “DG” samples are extracted from high coverage genomes sequenced as part of 744 the Simons Genome Diversity Project33. For some analyses, we used an extended set 745
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of outgroup populations, including some of the following additional ancient 746 populations to constrain standard errors: WHG, EHG, and Levant Neolithic. 747 748 Dating of gene-flow events 749 We estimated the time depth of selected admixture events using the linkage 750 disequilibrium (LD)-based admixture inference implemented in ALDER46. 751 752 Admixture graph modelling 753 Admixture graph modelling was carried out with the qpGraph software as 754 implemented in ADMIXTOOLS44 using Mbuti.DG as an outgroup. 755 756 Sex determination and Y chromosomal and mtDNA haplogroup assignment 757 We determined the sex of the newly reported samples in this study by counting the 758 number of reads overlapping with the targets of 1240k capture reagent37. We 759 extracted the reads of high base and mapping quality (samtools depth -q30 -Q37) 760 using samtools v1.3.173. We calculated the ratios of the numbers of reads mapped on 761 X chromosome or Y chromosome compared with that mapped on autosomes (X-rate 762 and Y-rate, respectively). Samples with an X-rate < 0.42 and a Y-rate > 0.26 were 763 assigned as males and those with an X-rate > 0.68 and a Y-rate < 0.02 were assigned 764 as females. 765 We used EAGER and samtools v1.3.1 to extract reads from the 1240k SNP and 766 mitocapture data mapped to the rCRS. We used Geneious R8.1.974 to locally realign, 767 visually inspect the pileups for contamination, and to call consensus sequences, which 768 were used for haplotyping in HaploGrep 275. In addition, we used the software 769 contamMix 1.0.10, which employs a Bayesian approach to estimate contamination in 770 the mitochondrial genome76. 771 We called Y chromosomal haplogroups for males using the captured SNPs on Y 772 chromosome by restricting to sequences with mapping quality 30 and bases with 773 base quality 30. We determined Y chromosomal haplogroups by identifying the 774 most derived allele upstream and the most ancestral allele downstream in the 775 phylogenetic tree in the ISOGG version 11.89 (accessed March 31, 2016) 776 (http://www.isogg.org/tree). 777 778 Kinship analysis 779 We used outgroup-f3 statistics and the methods lcMLkin77 and READ78 to determine 780 genetic kinship between individuals. 781 782 Phenotypic SNP calls 783 We determined the allele information of 5 SNPs (rs4988235, rs16891982, rs1426654, 784 rs3827760, rs12913832) thought to be affected by selection in our ancient samples 785 using the captured SNPs by restricting to sequences with mapping quality 30 and 786 bases with base quality 30 (Supplementary Information 7). 787 788 Abbreviations 789 We use the following abbreviated labels throughout the manuscript: E, Early; M, 790 Middle; L, Late; N, Neolithic; BA, Bronze Age; WHG, EHG, CHG, Western, 791 Eastern, Caucasus hunter-gatherers, respectively; Mal’ta 1, MA1; Afontova Gora 3, 792 AG3. 793 794 Data availability 795
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Data is deposited in the European Nucleotide Archive under the accession numbers 796 XXX–XXX (will be made available during revision). 797 798 Acknowledgments 799 We thank Stephen Clayton and all members of the MPI-SHH Archaeogenetics 800 Department for support, Michelle O’Reilly and Hans Sell for graphics support, and 801 Iosif Lazaridis and Nick Patterson for critical discussions. We thank Susanne 802 Lindauer, Ronny Friedrich, Robin van Gyseghem and Ute Blach for radiocarbon 803 dating support. This work was funded by the Max Planck Society and the German 804 Archaeological Institute (DAI). C.C.W. was funded by Nanqiang Outstanding Young 805 Talents Program of Xiamen University (X2123302) and the Fundamental Research 806 Funds for the Central Universities. 807 808 Author contributions 809 SH, JK, CCW, SR and WH conceived the idea for the study design. AW, GB, OC, 810 MF, EH, DK, SM, NR, KS and WH performed and supervised wet and dry lab work. 811 SH, AK, ARK, VEM, VGP, VRE, BCA, RGM, PLK, KWA, SLP, CG, HM, BV, LY, 812 ADR, DM, NYB, JG, KF, CK, YBB, APB, VT, RP, SH and ABB assembled skeletal 813 material, contextual information and provided site descriptions. CCW, SR and WH 814 analysed data. CJ, IM, SS, EB, OB provided additional data and methods. WH, CCW, 815 SR, SH, VT, RP, TH, DR and JK wrote the manuscript with input from all authors. 816 817 818
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Figures and Figure legends 1099 1100
1101 Fig. 1. Map of sites and archaeological cultures mentioned in this study. 1102 Temporal and geographic distribution of archaeological cultures are shown for two 1103 windows in time that are critical for our data. The zoomed map shows the location of 1104 sites in the Caucasus. The size of the circle reflects number of individuals that 1105 produced genome-wide data. The dashed line illustrates a hypothetical geographic 1106 border between genetically distinct Steppe and Caucasus clusters. (BB=Bell Beaker; 1107 CW=Corded Ware; TRB=Trichterbecher/Funnel Beaker; SOM=Seine-Oise-Marne 1108 complex) 1109 1110
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1111 1112
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Fig. 2. ADMIXTURE and PCA results, and chronological order of ancient 1113 Caucasus individuals. (a) ADMIXTURE results (k=12) of the newly genotyped 1114 individuals (filled symbols with black outlines) sorted by genetic clusters (Steppe and 1115 Caucasus) and in chronological order (coloured bars indicate the relative 1116 archaeological dates, (b) white circles the mean calibrated radiocarbon date and the 1117 errors bars the 2-sigma range. (c) ADMIXTURE results of relevant prehistoric 1118 individuals mentioned in the text (filled symbols) and (d) shows these projected onto 1119 a PCA of 84 modern-day West Eurasian populations (open symbols). 1120 1121
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1122 Fig. 3. Comparison of Y-chromosome (A) and mitochondrial (B) haplogroup 1123 distribution in the Steppe and Caucasus cluster. 1124 1125
0%
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Ste ppe (n=15) Cau cas us ( n=12 )
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G2b
G2a
J2
J1
J
L
Q1a2
R1b1
R1
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Steppe (n=27) Caucasus (n=21)
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T1
HV
H
X
W
I
N1a
K
U7
U5
U4
U2
U1
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1126 1127 Fig. 4. Modelling results for the Steppe and Caucasus cluster. Admixture 1128 proportions based on (temporally and geographically) distal and proximal models, 1129 showing additional Anatolian farmer-related ancestry in Steppe groups as well as 1130 additional gene flow from the south in some of the Steppe groups as well as the 1131 Caucasus groups (see also Supplementary Tables 10, 14 and 20). 1132 1133
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1134 Fig. 5. Admixture Graph modelling of the population history of the Caucasus 1135 region. We started with a skeleton tree without admixture including Mbuti, 1136 Loschbour and MA1. We grafted onto this EHG, CHG, Globular_Amphora, 1137 Eneolithic_steppe, Maykop, and Yamnaya_Caucasus, adding them consecutively to 1138 all possible edges in the tree and retaining only graph solutions that provided no 1139 differences of |Z|>3 between fitted and estimated statistics. The worst match is 1140 |Z|=2.824 for this graph. We note that the maximum discrepancy is f4(MA1, Maykop; 1141 EHG, Eneolithic_steppe) = -3.369 if we do not add the 4% EHG ancestry to Maykop. 1142 Drifts along edges are multiplied by 1000 and dashed lines represent admixture. 1143
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... further analysis. In contrast, almost all Solkota cave samples (SKK1, [3][4][5][6][7][8][9][10][11][12] showed preservation of aDNA, most with multiple genera identifications (Fig. 5). In the case of SKK 2, screening prior to sequencing showed no discernible presence of DNA and this sample was therefore excluded from further analysis. ...
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The Ji'erzankale Necropolis (吉尔赞喀勒墓地) is located on the Pamir Plateau in the Xinjiang Uyghur Autonomous Region of China. Composed of numerous stone circle graves and directly radiocarbon dated to the Iron Age (c. 2400‐2600 years cal BP), this site is unique in that numerous lines of archaeological evidence suggest that those interred here were followers of the Zoroastrian religion. Here we present carbon (δ13C) and nitrogen (δ15N) stable isotope ratio measurements of seeds (Nitraria pamirica, n=7), animals (n=14) and humans (n=24) to reconstruct ancient diet and lifeways at the Ji’erzankale Necropolis. The results of the Nitraria pamirica reflect the natural C3 vegetation and arid environment of this region. The δ13C (mean ± SD = –18.6 ± 0.8‰) and δ15N (mean ± SD = +8.1 ± 1.6‰) results of the animals (13 sheep and 1 hare) display a mostly C3 terrestrial diet with variable levels of protein consumption. Adult humans (n=19) have δ13C (mean ± SD = –17.9 ± 0.2‰) and δ15N (mean ± SD = +13.1 ± 0.3‰) results that tightly cluster above the sheep by ~+5‰. This is evidence that the diet of this population was relatively homogenous and mainly based on sheep and/or their secondary products and did not have a large input of C4 crops such as foxtail (Setaria italica) or common millet (Panicum miliaceum).
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This paper charts how ancient DNA has revolutionised the Indo-European question. Early findings pointed to the Pontic-Caspian Steppe as an immediate source for “at least some” branches of Indo-European “in Europe”. But for the rest of the family, too, ancient DNA coverage has fast filled out through time and space, across the Balkans, Mycenaean Greece, Anatolia, the Fertile Crescent and now northernmost South Asia. The results dovetail fully with neither the farming nor the pastoralist hypothesis, but do fit with some components of both scenarios. This is explored here also with regard to how food production itself arose, changed and spread in multiple different directions and phases.
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The indigenous populations of inner Eurasia—a huge geographic region covering the central Eurasian steppe and the northern Eurasian taiga and tundra—harbour tremendous diversity in their genes, cultures and languages. In this study, we report novel genome-wide data for 763 individuals from Armenia, Georgia, Kazakhstan, Moldova, Mongolia, Russia, Tajikistan, Ukraine and Uzbekistan. We furthermore report additional damage-reduced genome-wide data of two previously published individuals from the Eneolithic Botai culture in Kazakhstan (~5,400 bp). We find that present-day inner Eurasian populations are structured into three distinct admixture clines stretching between various western and eastern Eurasian ancestries, mirroring geography. The Botai and more recent ancient genomes from Siberia show a decrease in contributions from so-called ‘ancient North Eurasian’ ancestry over time, which is detectable only in the northern-most ‘forest-tundra’ cline. The intermediate ‘steppe-forest’ cline descends from the Late Bronze Age steppe ancestries, while the ‘southern steppe’ cline further to the south shows a strong West/South Asian influence. Ancient genomes suggest a northward spread of the southern steppe cline in Central Asia during the first millennium bc. Finally, the genetic structure of Caucasus populations highlights a role of the Caucasus Mountains as a barrier to gene flow and suggests a post-Neolithic gene flow into North Caucasus populations from the steppe.
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Archaeogenomic research has proven to be a valuable tool to trace migrations of historic and prehistoric individuals and groups, whereas relationships within a group or burial site have not been investigated to a large extent. Knowing the genetic kinship of historic and prehistoric individuals would give important insights into social structures of ancient and historic cultures. Most archaeogenetic research concerning kinship has been restricted to uniparental markers, while studies using genome-wide information were mainly focused on comparisons between populations. Applications which infer the degree of relationship based on modern-day DNA information typically require diploid genotype data. Low concentration of endogenous DNA, fragmentation and other post-mortem damage to ancient DNA (aDNA) makes the application of such tools unfeasible for most archaeological samples. To infer family relationships for degraded samples, we developed the software READ (Relationship Estimation from Ancient DNA). We show that our heuristic approach can successfully infer up to second degree relationships with as little as 0.1x shotgun coverage per genome for pairs of individuals. We uncover previously unknown relationships among prehistoric individuals by applying READ to published aDNA data from several human remains excavated from different cultural contexts. In particular, we find a group of five closely related males from the same Corded Ware culture site in modern-day Germany, suggesting patrilocality, which highlights the possibility to uncover social structures of ancient populations by applying READ to genome-wide aDNA data. READ is publicly available from https://bitbucket.org/tguenther/read.
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Innovation is the social act of appropriating new practices or techniques into an existing life. During the late 4th millennium BC, the populations of the North Caucasus and the neighbouring steppe adopted animal traction and vehicles into their lifescapes. The representation of this innovation, however, suggests different intellectual discourses in the appropriation process. The Maikop communities selected the powerful driving force – cattle teams – for their burial representations, whereas the steppe communities chose to highlight the means of transportation – wagons. While the one emphasised a new form of extended labour and neglected the objects of traction, the other highlighted the new means of transportation and mobility, disregarding the ‘engines’ in the process. This article discusses the different ways the same innovation can be appropriated in different communities and draws upon bioarchaeological studies to question the practical relevance of these innovations for the everyday life of the adopting societies.
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http://rdcu.be/ydIL Link to the full text!! Ancient DNA studies have established that Neolithic European populations were descended from Anatolian migrants1, 2, 3, 4, 5, 6, 7, 8 who received a limited amount of admixture from resident hunter-gatherers3, 4, 5, 9. Many open questions remain, however, about the spatial and temporal dynamics of population interactions and admixture during the Neolithic period. Here we investigate the population dynamics of Neolithization across Europe using a high-resolution genome-wide ancient DNA dataset with a total of 180 samples, of which 130 are newly reported here, from the Neolithic and Chalcolithic periods of Hungary (6000–2900 BC, n = 100), Germany (5500–3000 BC, n = 42) and Spain (5500–2200 BC, n = 38). We find that genetic diversity was shaped predominantly by local processes, with varied sources and proportions of hunter-gatherer ancestry among the three regions and through time. Admixture between groups with different ancestry profiles was pervasive and resulted in observable population transformation across almost all cultural transitions. Our results shed new light on the ways in which gene flow reshaped European populations throughout the Neolithic period and demonstrate the potential of time-series-based sampling and modelling approaches to elucidate multiple dimensions of historical population interactions.
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Archaeozoological data from 401 sites in south-western Asia and south-eastern Europe dating to the time span 7000–1500 BC were analysed to address the question of when and where the economic shift occurred from a milk- and meat-oriented sheep husbandry to one with a focus on wool production. This article offers some preliminary results from meta-analyses of an associated database. Among the parameters studied fluctuations in the demographic composition of herds as well as osteometric data indicating changes in animal size and body shape have yielded some indirect evidence for incipient and/or increasing importance of wool exploitation in sheep. In south-western Asia this development started around 4000 BC, while in south-eastern Europe it began a thousand years later.