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Post by Admin on Apr 8, 2022 17:02:17 GMT
FIG. 4 Modern-day peoples with affinity to WC1.Modern groups with an increasingly higher (respectively lower) inferred proportion of haplotype sharing with the Iranian Neolithic Wezmeh Cave (WC1, 7455 to 7082 cal BCE, blue triangle) compared to the Anatolian Neolithic Barcın genome (Bar8; 6212 to 6030 cal BCE, red triangle) are depicted with an increasingly stronger blue or red color, respectively. Circle sizes illustrate the relative absolute proportion of this difference between WC1 versus Bar8. The key for the modern group labels is provided in table S24. We also examined recent haplotype sharing between each modern group and ancient Neolithic genomes from Iran (WC1) and Europe (LBK, NE1), HG genomes sampled from Luxembourg (Loschbour) and the Caucasus (KK1; Kotias), a 4500-year-old genome from Ethiopia (Mota) and Ust’-Ishim, and a 45,000-year-old genome from Siberia. Modern groups from south, central, and northwestern Europe shared haplotypes predominantly with European Neolithic samples LBK and NE1, and European HGs, whereas modern Near and Middle Eastern, as well as southern Asian samples, had higher sharing with WC1 (figs. S28 and S29). Modern Pakistani, Iranian, Armenian, Tajikistani, Uzbekistani, and Yemeni samples were inferred to share >10% of haplotypes with WC1. This was true even when modern groups from neighboring geographic regions were added as potential ancestry surrogates (figs. S26 and S27 and table S23). Iranian Zoroastrians had the highest inferred sharing with WC1 out of all modern groups (table S23). Consistent with this, outgroup f3 statistics indicate that Iranian Zoroastrians are the most genetically similar to all four Neolithic Iranians, followed by other modern Iranians (Fars), Balochi (southeastern Iran, Pakistan, and Afghanistan), Brahui (Pakistan and Afghanistan), Kalash (Pakistan), and Georgians (figs. S12 to S15). Interestingly, WC1 most likely had brown eyes, relatively dark skin, and black hair, although Neolithic Iranians carried reduced pigmentation-associated alleles in several genes and derived alleles at 7 of the 12 loci showing the strongest signatures of selection in ancient Eurasians (3) (tables S29 to S33). Although there is a strong Neolithic component in these modern south Asian populations, simulation of allele sharing rejected full population continuity under plausible ancestral population sizes, indicating some population turnover in Iran since the Neolithic (7). While Early Neolithic samples from eastern and western southwest Asia differ conspicuously, comparisons to genomes from Chalcolithic Anatolia and Iron Age Iran indicate a degree of subsequent homogenization. Kumtepe6, a ~6750-year-old genome from northwestern Anatolia (16), was more similar to Neolithic Iranians than to any other non-Iranian ancient genome (figs. S17 to S20 and table S18.1). Furthermore, our male Iron Age genome (F38; 971 to 832 BCE; sequenced to 1.9×) from Tepe Hasanlu in northwestern Iran shares greatest similarity with Kumtepe6 (fig. S21) even when compared to Neolithic Iranians (table S20). We inferred additional non-Iranian or non-Anatolian ancestry in F38 from sources such as European Neolithics and even post-Neolithic Steppe populations (table S20). Consistent with this, F38 carried a N1a subclade mitochondrial DNA (mtDNA), which is common in early European and northwestern Anatolian farmers (3). In contrast, his Y chromosome belongs to subhaplogroup R1b1a2a2, also found in five Yamnaya individuals (17) and in two individuals from the Poltavka culture (3). These patterns indicate that post-Neolithic homogenization in southwestern Asia involved substantial bidirectional gene flow between the east and west of the region, as well as possible gene flow from the Steppe. Migration of people associated with the Yamnaya culture has been implicated in the spread of Indo-European languages (17, 18), and some level of Near Eastern ancestry was previously inferred in southern Russian pre-Yamnaya populations (3). However, our analyses suggest that Neolithic Iranians were unlikely to be the main source of Near Eastern ancestry in the Steppe population (table S20) and that this ancestry in pre-Yamnaya populations originated primarily in the west of southwest Asia. We also inferred shared ancestry between Steppe and Hasanlu Iron Age genomes that was distinct from EN Iranians (table S20) (7). In addition, modern Middle Easterners and South Asians appear to possess mixed ancestry from ancient Iranian and Steppe populations (tables S19 and S20). However, Steppe-related ancestry may also have been acquired indirectly from other sources (7), and it is not clear if this is sufficient to explain the spread of Indo-European languages from a hypothesized Steppe homeland to the region where Indo-Iranian languages are spoken today. Yet, the affinities of Zagros Neolithic individuals to modern populations of Pakistan, Afghanistan, Iran, and India is consistent with a spread of Indo-Iranian languages, or of Dravidian languages (which includes Brahui), from the Zagros into southern Asia, in association with farming (19). The Neolithic transition in southwest Asia involved the appearance of different domestic species, particularly crops, in different parts of the Neolithic core zone, with no single center (20). Early evidence of plant cultivation and goat management between the 10th and the 8th millennium BCE highlights the Zagros as a key region in the Neolithization process (1). Given the evidence of domestic species movement from east to west across southwest Asia (21), it is surprising that EN human genomes from the Zagros are not closely related to those from northwestern Anatolia and Europe. Instead they represent a previously undescribed Neolithic population. Our data show that the chain of Neolithic migration into Europe does not reach back to the eastern Fertile Crescent, also raising questions about whether intermediate populations in southeastern and Central Anatolia form part of this expansion. Nevertheless, it seems probable that the Zagros region was the source of an eastern expansion of the southwestern Asian domestic plant and animal economy. Our inferred persistence of ancient Zagros genetic components in modern day south Asians lends weight to a strong demic component to this expansion.
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Post by Admin on May 11, 2022 18:33:09 GMT
Population Genomics of Stone Age Eurasia
Summary The transitions from foraging to farming and later to pastoralism in Stone Age Eurasia (c. 11-3 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 1,000-years. 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
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.
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.
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Post by Admin on May 11, 2022 21:16:25 GMT
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. 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. 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. 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.
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Post by Admin on May 12, 2022 1:28:43 GMT
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.
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).
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Post by Admin on May 12, 2022 17:37:33 GMT
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. 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.
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