The Yamnaya Steppe Herders from Russia

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The Yamnaya Steppe Herders from Russia Jan 8, 2022 23:34:01 GMT

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Massive migration from the steppe is a source for Indo-European languages in Europe

Abstract
We generated genome-wide data from 69 Europeans who lived between 8,000-3,000 years ago by enriching ancient DNA libraries for a target set of almost four hundred thousand polymorphisms. Enrichment of these positions decreases the sequencing required for genome-wide ancient DNA analysis by a median of around 250-fold, allowing us to study an order of magnitude more individuals than previous studies1–⇓⇓⇓⇓⇓⇓8 and to obtain new insights about the past. We show that the populations of western and far eastern Europe followed opposite trajectories between 8,000-5,000 years ago. At the beginning of the Neolithic period in Europe, ~8,000-7,000 years ago, closely related groups of early farmers appeared in Germany, Hungary, and Spain, different from indigenous hunter-gatherers, whereas Russia was inhabited by a distinctive population of hunter-gatherers with high affinity to a ~24,000 year old Siberian6. By ~6,000-5,000 years ago, a resurgence of hunter-gatherer ancestry had occurred throughout much of Europe, but in Russia, the Yamnaya steppe herders of this time were descended not only from the preceding eastern European hunter-gatherers, but from a population of Near Eastern ancestry. Western and Eastern Europe came into contact ~4,500 years ago, as the Late Neolithic Corded Ware people from Germany traced ~3/4 of their ancestry to the Yamnaya, documenting a massive migration into the heartland of Europe from its eastern periphery. This steppe ancestry persisted in all sampled central Europeans until at least ~3,000 years ago, and is ubiquitous in present-day Europeans. These results provide support for the theory of a steppe origin9 of at least some of the Indo-European languages of Europe.

Genome-wide analysis of ancient DNA has emerged as a transformative technology for studying prehistory, providing information that is comparable in power to archaeology and linguistics. Realizing its promise, however, requires collecting genome-wide data from an adequate number of individuals to characterize population changes over time, which means not only sampling a succession of archaeological cultures2, but also multiple individuals per culture. To make analysis of large numbers of ancient DNA samples practical, we used in-solution hybridization capture10,11 to enrich next generation sequencing libraries for a target set of 394,577 single nucleotide polymorphisms (SNPs) (“390k capture”), 354,212 of which are autosomal SNPs that have also been genotyped using the Affymetrix Human Origins array in 2,345 humans from 203 populations4,12. This reduces the amount of sequencing required to obtain genome-wide data by a minimum of 45-fold and a median of 262-fold (Online Table 1). This strategy allows us to report genomic scale data on more than twice the number of ancient Eurasians as the entire preceding literature1–⇓⇓⇓⇓⇓⇓8 (Extended Data Table 1).

Extended Data Table 1:
Number of ancient Eurasian modern human samples screened in genome-wide studies to date.
Only studies that produced at least one sample at ≥0.05× coverage are listed.

We used this technology to study population transformations in Europe. We began by preparing 212 DNA libraries from 119 ancient samples in dedicated clean rooms, and testing these by light shotgun sequencing and mitochondrial genome capture (SI1, Online Table 1). We restricted to libraries with molecular signatures of authentic ancient DNA (elevated damage in the terminal nucleotide), negligible evidence of contamination based on mismatches to the mitochondrial consensus13, and, where available, a mitochondrial DNA haplogroup that matched previous results using PCR4,14,15 (SI2). For 123 libraries prepared in the presence of Uracil-DNA-glycosylase16 to reduce errors due to ancient DNA damage17, we performed 390k capture, carried out paired end sequencing, and mapped to the human genome. We restricted analysis to 95 libraries from 69 samples that had at least 0.06-fold average target coverage (average of 3.8-fold), and used majority rule to call an allele at each SNP covered at least once (Online Table 1). After combining our data (SI3) with 25 ancient samples from the literature — three Upper Paleolithic samples from Russia1,7,6, seven people of European hunter gatherer ancestry4,5,8,2, and fifteen European farmers2,8,4,3, — we had data from 94 ancient Europeans. Geographically, these came from Germany (n=41), Spain (n=10), Russia (n=14), Sweden (n=12), Hungary (n=15), Italy (n=1) and Luxembourg (n=1) (Extended Data Table 2). Following the central European chronology, these included 19 hunter-gatherers (>5,500 BCE), 28 Early Neolithic farmers (EN: ~6,000-4,000 BCE), 11 Middle Neolithic farmers (MN: ~4,000-3,000 BCE) including the Tyrolean Iceman3, 9 Late Copper/Early Bronze Age individuals (Yamnaya: ~3,300-2,700 BCE), 15 Late Neolithic individuals (LN: ~2,500-2,200 BCE), 9 Early Bronze Age individuals (~2,200-1,500 BCE), two Late Bronze Age individuals (~1,200-1,100 BCE) and one Iron Age individual (~900 BCE). Two individuals were excluded from analyses as they were related to others from the same population. The average number of SNPs covered at least once was 212,375 and the minimum was 22,869 (Fig. 1).

Last Edit: Jan 9, 2022 3:27:12 GMT by Admin

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The Yamnaya Steppe Herders from Russia Jan 9, 2022 3:29:30 GMT

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Extended Data Table 2:
The 69 samples newly reported on in this study.
Samples with direct radiocarbon dates are indicated by a calibrated date “cal BCE” along with associated laboratory numbers. Dates that are estimated based on faunal elements associated with the samples are not indicated with “cal” (although they are still calibrated, absolute dates).

Figure 1
Location and SNP coverage of samples included in this study.
(a) Geographic location and time-scale (central European chronology) of the 69 newly typed ancient individuals from this study (black outline) and 25 from the literature for which shotgun sequencing data was available (no outline). (b) Number of SNPs covered at least once in the analysis dataset of 94 individuals.

We determined that 34 of the 69 newly analyzed individuals were male and used 2,258 Y chromosome SNPs targets included in the capture to obtain high resolution Y chromosome haplogroup calls (SI4). Outside Russia, and before the Late Neolithic period, only a single R1b individual was found (early Neolithic Spain) in the combined literature (n=70). By contrast, haplogroups R1a and R1b were found in 60% of Late Neolithic/Bronze Age Europeans outside Russia (n=10), and in 100% of the samples from European Russia from all periods (7,500-2,700 BCE; n=9). R1a and R1b are the most common haplogroups in many European populations today18,19, and our results suggest that they spread into Europe from the East after 3,000 BCE. Two hunter-gatherers from Russia included in our study belonged to R1a (Karelia) and R1b (Samara), the earliest documented ancient samples of either haplogroup discovered to date. These two hunter-gatherers did not belong to the derived lineages M417 within R1a and M269 within R1b that are predominant in Europeans today18,19, but all 7 Yamnaya males did belong to the M269 subclade18 of haplogroup R1b.

Principal components analysis (PCA) of all ancient individuals along with 777 present-day West Eurasians4 (Fig. 2a, SI5) replicates the positioning of present-day Europeans between the Near East and European hunter-gatherers4,20, and the clustering of early farmers from across Europe with present day Sardinians3,4,27, suggesting that farming expansions across the Mediterranean to Spain and via the Danubian route to Hungary and Germany descended from a common stock. By adding samples from later periods and additional locations, we also observe several new patterns. All samples from Russia have affinity to the ~24,000 year old MA16, the type specimen for the Ancient North Eurasians (ANE) who contributed to both Europeans4 and Native Americans4,6,8. The two hunter-gatherers from Russia (Karelia in the northwest of the country and Samara on the steppe near the Urals) form an “eastern European hunter-gatherer” (EHG) cluster at one end of a hunter-gatherer cline across Europe; people of hunter-gatherer ancestry from Luxembourg, Spain, and Hungary sit at the opposite “western European hunter-gatherer4” (WHG) end, while the hunter-gatherers from Sweden4,8 (SHG) are intermediate. Against this background of differentiated European hunter-gatherers and homogeneous early farmers, multiple events transpired in all parts of Europe included in our study. Middle Neolithic Europeans from Germany, Spain, Hungary, and Sweden from the period ~4,000-3,000 BCE are intermediate between the earlier farmers and the WHG, suggesting an increase of WHG ancestry throughout much of Europe. By contrast, in Russia, the later Yamnaya steppe herders of ~3,000 BCE plot between the EHG and the Near East / Caucasus, suggesting a decrease of EHG ancestry during the same time period. The Late Neolithic and Bronze Age samples from Germany and Hungary2 are distinct from the preceding Middle Neolithic and plot between them and the Yamnaya. This pattern is also seen in ADMIXTURE analysis (Fig. 2b, SI6), which implies that the Yamnaya have ancestry from populations related to the Caucasus and South Asia that is largely absent in 38 Early or Middle Neolithic farmers but present in all 25 Late Neolithic or Bronze Age individuals. This ancestry appears in Central Europe for the first time in our series with the Corded Ware around 2,500 BCE (SI6, Fig. 2b, Extended Data Fig. 1). The Corded Ware shared elements of material culture with steppe groups such as the Yamnaya although whether this reflects movements of people has been contentious21. Our genetic data provide direct evidence of migration and suggest that it was relatively sudden. The Corded Ware are genetically closest to the Yamnaya ~2,600 kilometers away, as inferred both from PCA and ADMIXTURE (Fig.2) and FST (0.011 ± 0.002) (Extended Data Table 3). If continuous gene flow from the east, rather than migration, had occurred, we would expect successive cultures in Europe to become increasingly differentiated from the Middle Neolithic, but instead, the Corded Ware are both the earliest and most strongly differentiated from the Middle Neolithic population.

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The Yamnaya Steppe Herders from Russia Jan 9, 2022 20:17:57 GMT

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To distinguish whether a Yamnaya or an EHG source fits the data better, we added ancient samples as outgroups (SI9). Adding any Early or Middle Neolithic farmer results in EHG-related genetic input into Late Neolithic populations being a poor fit to the data (SI9); thus, Late Neolithic populations have ancestry that cannot be explained by a mixture of EHG and Middle Neolithic. When using Yamnaya instead of EHG, however, we obtain a good fit (SI9, SI10). These results can be explained if the new genetic material that arrived in Germany was a composite of two elements: EHG and a type of Near Eastern ancestry different from that which was introduced by early farmers (also suggested by PCA and ADMIXTURE; Fig. 2, SI5, SI6). We estimate that these two elements each contributed about half the ancestry each of the Yamnaya (SI6, SI9), explaining why the population turnover inferred using Yamnaya as a source is about twice as high compared to the undiluted EHG. The estimate of Yamnaya-related ancestry in the Corded Ware is consistent when using either present populations or ancient Europeans as outgroups (SI9, SI10), and is 73.1 ± 2.2% when both sets are combined (SI10). The best proxies for ANE ancestry in Europe4 were initially Native Americans12,22, and then the Siberian MA16, but both are geographically and temporally too remote for what appears to be a recent migration into Europe4. We can now add three new pieces to the puzzle of how ANE ancestry was transmitted to Europe: first the EHG, then the Yamnaya formed by mixture between EHG and a Near Eastern related population, and then the Corded Ware who were formed by a mixture of the Yamnaya with Middle Neolithic Europeans. We caution that the sampled Yamnaya individuals from Samara might not be directly ancestral to Corded Ware individuals from Germany. It is possible that a more western Yamnaya population, or an earlier (pre-Yamnaya) steppe population may have migrated into central Europe, and future work may uncover more missing links in the chain of transmission of steppe ancestry.

By extending our model to a three way mixture of WHG, Early Neolithic and Yamnaya, we estimate that the ancestry of the Corded Ware was 79% Yamnaya-like, 4% WHG, and 17% Early Neolithic (Fig. 3). A small contribution of the first farmers is also consistent with uniparentally inherited DNA: e.g. mitochondrial DNA haplogroup N1a and Y chromosome haplogroup G2a, common in early central European farmers14,23, almost disappear during the Late Neolithic and Bronze Age, when they are largely replaced by Y haplogroups R1a and R1b (SI4) and mtDNA haplogroups I, T1, U2, U4, U5a, W, and subtypes of H14,23,24 (SI2).

The uniparental data not only confirm a link to the steppe populations but also suggest that both sexes participated in the migrations (SI2, SI4, Extended Data Table 3). The magnitude of the population turnover that occurred becomes even more evident if one considers the fact that the steppe migrants may well have mixed with eastern European agriculturalists on their way to central Europe. Thus, we cannot exclude a scenario in which the Corded Ware arriving in today’s Germany had no ancestry at all from local populations.

Our results support a view of European pre-history punctuated by two major migrations: first, the arrival of first farmers during the Early Neolithic from the Near East, and second of Yamnaya pastoralists during the Late Neolithic from the steppe (Extended Data Fig. 5). Our data further show that both migrations were followed by resurgences of the previous inhabitants: first, during the Middle Neolithic, when hunter-gatherer ancestry rose again after its Early Neolithic decline, and then between the Late Neolithic and the present, when farmer and hunter-gatherer ancestry rose after its Late Neolithic decline. This second resurgence must have started during the Late Neolithic/Bronze Age period itself, as the Bell Beaker and Unetice groups had reduced Yamnaya ancestry compared to the earlier Corded Ware, and comparable levels to that in some present-day Europeans (Fig. 3). Today, Yamnaya related ancestry is lower in southern Europe and higher in northern Europe. Further data are needed to determine whether the steppe ancestry arrived in southern Europe at the time of the Late Neolithic / Bronze Age, or is due to migrations in historical times from northern Europe25,26.

Our results provide new data relevant to debates on the origin and expansion of Indo-European languages in Europe (SI11). Although ancient DNA is silent on the question of the languages spoken by preliterate populations, it does carry evidence about processes of migration which are invoked by theories on Indo-European language dispersals. Such theories make predictions about movements of people to account for the spread of languages and material culture. The technology of ancient DNA makes it possible to reject or confirm the proposed migratory movements, as well as to identify new movements that were not previously known. The best argument for the “Anatolian hypothesis27” that Indo-European languages arrived in Europe from Anatolia ~8,500 years ago is that major language replacements are thought to require major migrations, and that after the Early Neolithic when farmers established themselves in Europe, the population base was likely to have been so large as to be impervious to subsequent turnover27,28. However, our study shows that a later major turnover did occur, and that steppe migrants replaced ~3/4 of the ancestry of central Europeans. An alternative theory is the “Steppe hypothesis”, which proposes that early Indo-European speakers were pastoralists of the grasslands north of the Black and Caspian Seas, and that their languages spread into Europe after the invention of wheeled vehicles9. Our results make a compelling case for the steppe as a source of at least some of the Indo-European languages in Europe by documenting a massive migration ~4,500 years ago associated with the Yamnaya and Corded Ware cultures, which are identified by proponents of the Steppe hypothesis as vectors for the spread of Indo-European languages into Europe.

These results challenge the Anatolian hypothesis by showing that not all Indo-European languages in Europe can plausibly derive from the first farmer migrations thousands of years earlier (SI11). We caution that the location of the Proto-Indo-European9,27,29,30 homeland that also gave rise to the Indo-European languages of Asia, as well as the Indo-European languages of southeastern Europe, cannot be determined from the data reported here (SI11).

Studying the mixture in the Yamnaya themselves, and understanding the genetic relationships among a broader set of ancient and present-day Indo-European speakers, may lead to new insight about the shared homeland.

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The Yamnaya Steppe Herders from Russia Jan 10, 2022 2:12:08 GMT

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Online Methods
Screening of libraries (shotgun sequencing and mitochondrial capture)
The 212 libraries screened in this study (SI1) from a total of 119 samples (SI3) were produced at Adelaide (n=151), Tübingen (n=16), and Boston (n=45) (Online Table 1).

The libraries from Adelaide and Boston had internal barcodes directly attached to both sides of the molecules from the DNA extract so that each sequence begins with the barcode10. The Adelaide libraries had 5 base pair (bp) barcodes on both sides, while the Boston libraries had 7 bp barcodes. Libraries from Tübingen had no internal barcodes, but were differentiated by the sequence of the indexing primer31.

We adapted a reported protocol for enriching for mitochondrial DNA10, with the difference that we adjusted the blocking oligonucleotides and PCR primers to fit our libraries with shorter adapters. Over the course of this project, we also lowered the hybridization temperature from 65°C to 60°C and performed stringent washes at 55°C instead of 60°C32.

We used an aliquot of approximately 500ng of each library for target enrichment of the complete mitochondrial genome in two consecutive rounds32, using a bait set for human mtDNA32. We performed enrichment in 96-well plates with one library per well, and used a liquid handler (Evolution P3, Perkin Elmer) for the capture and washing steps33. We used blocking oligonucleotides in hybridization appropriate to the adapters of the truncated libraries. After either of the two enrichment rounds, we amplified the enriched library molecules with the primer pair that keeps the adapters short (PreHyb) using Herculase Fusion II PCR Polymerase. We performed an indexing PCR of the final capture product using one or two indexing primers31. We cleaned up all PCR’s using SPRI technology34 and the liquid handler. Libraries from Tübingen were amplified with the primer pair IS5/IS631.

For libraries from Boston and Adelaide, we used a second aliquot of each library for shotgun sequencing after performing an indexing PCR31. We used unique index combinations for each library and experiment, allowing us to distinguish shotgun sequencing and mitochondrial DNA capture data, even if both experiments were in the same sequencing run. We sequenced shotgun libraries and mtDNA captured libraries from Tübingen in independent sequencing runs since the index was already attached at the library preparation stage.

We quantified the sequencing pool with the BioAnalyzer (Agilent) and/or the KAPA Library Quantification kit (KAPA biosystems) and sequenced on Illumina MiSeq, HiSeq2500 or NextSeq500 instruments for 2×75, 2×100 or 2×150 cycles along with the indexing read(s).

Enrichment for 394,577 SNP targets (“390k capture”)
The protocol for enrichment for SNP targets was similar to the mitochondrial DNA capture, with the exception that we used another bait set (390k) and about twice as much library (up to 1000ng) compared to the mtDNA capture.

The specific capture reagent used in this study is described for the first time here. To target each SNP, we used a different oligonucleotide probe design compared to ref. 1. We used four 52 base pair probes for each SNP target. One probe ends just before the SNP, and one starts just after. Two probes are centered on the SNP, and are identical except for having the alternate alleles. This probe design avoids systematic bias toward one SNP allele or another. For the template sequence for designing the San and Yoruba panels baits, we used the sequence that was submitted for these same SNPs during the design of the Affymetrix Human Origins SNP array12. For SNPs that were both in the San and Yoruba panels, we used the Yoruba template sequence in preference. For all other SNPs, we used the human genome reference sequence as a template. Online Table 2 gives the list of SNPs that we targeted, along with details of the probes used. The breakdown of SNPs into different classes is:

124,106 “Yoruba SNPs”: All SNPs in “Panel 5” of the Affymetrix Human Origins array (discovered as heterozygous in a Yoruba male: HGDP00927)12 that passed the probe design criteria specified in ref. 11.

146,135 “San SNPs”: All SNPs in “Panel 4” of the Affymetrix Human Origins array (discovered as heterozygous in a San male: HGDP01029)12 that passed probe design criteria11. The full Affymetrix Human Origins array Panel 4 contains several tens of thousands of additional SNPs overlapping those from Panel 5, but we did not wish to redundantly capture Panel 5 SNPs.

98,166 “Compatibility SNPs”: SNPs that overlap between the Affymetrix Human Origins the Affymetrix 6.0, and the Illumina 610 Quad arrays, which are not already included in the “Yoruba SNPs” or “San SNPs” lists12 and that also passed the probe design design criteria11.

26,170 “Miscellaneous SNPs”: SNPs that did not overlap the Human Origins array. The subset analyzed in this study were 2,258 Y chromosome SNPs (http://isogg.org/tree/ISOGG_YDNA_SNP_Index.html) that we used for Y haplogroup determination.

Processing of sequencing data
We restricted analysis to read pairs that passed quality control according to the Illumina software (“PF reads”).

We assigned read pairs to libraries by searching for matches to the expected index and barcode sequences (if present, as for the Adelaide and Boston libraries). We allowed no more than 1 mismatch per index or barcode, and zero mismatches if there was ambiguity in sequence assignment or if barcodes of 5 bp length were used (Adelaide libraries).

We used Seqprep (https://github.com/jstjohn/SeqPrep) to search for overlapping sequence between the forward and reverse read, and restricted to molecules where we could identify a minimum of 15 bp of overlap. We collapsed the two reads into a single sequence, using the consensus nucleotide if both reads agreed, and the read with higher base quality in the case of disagreement. For each merged nucleotide, we assigned the base quality to be the higher of the two reads. We further used Seqprep to search for the expected adapter sequences at either ends of the merged sequence, and to produce a trimmed sequence for alignment.

We mapped all sequences using BWA-0.6.135. For mitochondrial analysis we mapped to the mitochondrial genome RSRS36. For whole genome analysis we mapped to the human reference genome hg19. We restricted all analyses to sequences that had a mapping quality of MAPQ≥37.

We sorted all mapped sequences by position, and used a custom script to search for mapped sequences that had the same orientation and start and stop positions. We stripped all but one of these sequences (keeping the best quality one) as duplicates.

Mitochondrial sequence analysis and assessment of ancient DNA authenticity
For each library for which we had average coverage of the mitochondrial genome of at least 10-fold after removal of duplicated molecules, we built a mitochondrial consensus sequence, assigning haplogroups for each library as described in SI2.

We used contamMix-1.0.9 to search for evidence of contamination in the mitochondrial DNA13. This software estimates the fraction of mitochondrial DNA sequences that match the consensus more closely than a comparison set of 311 worldwide mitochondrial genomes. This is done by taking the consensus sequence of reads aligning to the RSRS mitochondrial genome, and requiring a minimum coverage of 5 after filtering bases where the quality was <30. Raw reads are then realigned to this consensus. In addition, the consensus is multiply aligned with the other 311 mitochondrial genomes using kalign (2.0.4)37 to build the necessary inputs for contamMix, trimming the first and last 5 bases of every read to mitigate against the confounding factor of ancient damage. This software had difficulty running on datasets with higher coverage, and for these datasets, we down-sampled to 50,000 reads, which we found produced adequate contamination estimation.

For all sequences mapping to the mitochondrial DNA that had a cytosine at the terminal nucleotide, we measured the proportion of sequences with a thymine at that position. For population genetic analysis, we only used partially UDG-treated libraries with a minimum of 3% C→T substitutions as recommended by ref. 33. In cases where we used a fully UDG-treated library for 390k analysis, we examined mitochondrial capture data from a non-UDG-treated library made from the same extract, and verified that the non-UDG library had a minimum of 10% C→T at the first nucleotide as recommended by ref. 38. Metrics for the mitochondrial DNA analysis on each library are given in Online Table 1.

390k capture, sequence analysis and quality control
For 390k analysis, we restricted to reads that not only mapped to the human reference genome hg19 but that also overlapped the 354,212 autosomal SNPs genotyped on the Human Origins array4. We trimmed the last two nucleotides from each sequence because we found that these are highly enriched in ancient DNA damage even for UDG-treated libraries. We further restricted analyses to sites with base quality≥30.

We made no attempt to determine a diploid genotype at each SNP in each sample. Instead, we used a single allele – randomly drawn from the two alleles in the individual – to represent the individual at that site20,39. Specifically, we made an allele call at each target SNP using majority rule over all sequences overlapping the SNP. When each of the possible alleles was supported by an equal number of sequences, we picked an allele at random. We set the allele to “no call” for SNPs at which there was no read coverage.

We restricted population genetic analysis to libraries with a minimum of 0.06-fold average coverage on the 390k SNP targets, and for which there was an unambiguous sex determination based on the ratio of X to Y chromosome reads (SI4) (Online Table 1). For individuals for whom there were multiple libraries per sample, we performed a series of quality control analysis. First, we used the ADMIXTURE software40,41 in supervised mode, using Kharia, Onge, Karitiana, Han, French, Mbuti, Ulchi and Eskimo as reference populations. We visually inspected the inferred ancestry components in each individual, and removed individuals with evidence of heterogeneity in inferred ancestry components across libraries. For all possible pairs of libraries for each sample, we also computed statistics of the form D(Library1, Library2; Probe, Mbuti), where Probe is any of a panel of the same set of eight reference populations), to determine whether there was significant evidence of the Probe population being more closely related to one library from an ancient individual than another library from that same individual. None of the individuals that we used had strong evidence of ancestry heterogeneity across libraries. For samples passing quality control for which there were multiple libraries per sample, we merged the sequences into a single BAM.

We called alleles on each merged BAM using the same procedure as for the individual libraries. We used ADMIXTURE41 as well as PCA as implemented in EIGENSOFT42 (using the lsqproject: YES option to project the ancient samples) to visualize the genetic relationships of each set of samples with the same culture label with respect to 777 diverse present-day West Eurasians4. We visually identified outlier individuals, and renamed them for analysis either as outliers or by the name of the site at which they were sampled (Extended Data Table 1). We also identified two pairs of related individuals based on the proportion of sites covered in pairs of ancient samples from the same population that had identical allele calls using PLINK43. From each pair of related individuals, we kept the one with the most SNPs.

Population genetic analyses
We determined genetic sex using the ratio of X and Y chromosome alignments44 (SI4), and mitochondrial haplogroup for all samples (SI2), and Y chromosome haplogroup for the male samples (SI4). We studied population structure (SI5, SI6). We used f-statistics to carry out formal tests of population relationships (SI6) and built explicit models of population history consistent with the data (SI7). We estimated mixture proportions in a way that was robust to uncertainty about the exact population history that applied (SI8). We estimated the minimum number of streams of migration into Europe needed to explain the data (SI9, SI10). The estimated mixture proportions shown in Fig. 3 were obtained using the lsqlin function of Matlab and the optimization method described in SI9 with 15 world outgroups.

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The Yamnaya Steppe Herders from Russia Jan 10, 2022 20:23:29 GMT

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Supplementary Information 3 Description of ancient samples and archaeological context
Overview
The archaeology of prehistoric Europe has revealed numerous material cultures that differed from each other over space and time. Some cultures left behind artifacts similar to those that preceded them. Other cultures left behind artifacts that mark a clear break from preceding local traditions.

The largest single group of samples analyzed in this study was from the transect through time study from the Early Neolithic to the Early Bronze Age reported in Brandt et al. 20131. We selected samples from that study based on two criteria: We predominantly chose samples based on overall DNA quality, which we assessed based on performance in our previous study (largely PCR success rates, replication of results, and completeness of SNP typing). From the subset of samples that performed well, we chose ones that could be assigned most confidently to an archaeological culture via accompanying grave goods/context, orientation of the burials, available radiocarbon dates, and seemingly typical mtDNA haplotype. In addition, we added to this study some individuals for which the cultural attribution was less clear, in that there were no characteristic grave goods or context. These individuals were included to test the ability of the 390k SNP capture strategy to assign a potentially unknown or unprovenanced sample to a candidate population in combination with absolute dating. For the latter set of samples, we obtained 19 new radiocarbon dates at the Curt-Engelholm-Centre for Archaeometry, Mannheim, Germany.

In what follows we provide archaeological context information for the 69 samples for which genome-wide data is newly presented in this study (a total of 119 individuals were screened as detailed in Online Table 1, but here we discuss only the subset that produced genome-wide data). We present the samples in chronological order, largely following the central European terminology, i.e. Mesolithic (>6000-5000 BCE), Early Neolithic (6000-4000 BCE), Middle Neolithic (4000-2800 BCE), Late Neolithic (2800-2200 BCE), and Early Bronze Age (2200-1600 BCE; Figure 1a). We note that there are different traditional terminology for samples from the Russian steppe and regions of southern Europe.

We use “BCE” (Before Common Era) to refer to a calibrated date that is estimated based on archaeological context (e.g. radiocarbon dates from associated faunal remains), and “calBCE” to refer to a calibrated radiocarbon date range (1σ) directly from human skeletal remains, followed by the respective dating laboratory number.

Hunter-gatherer samples
We generated data on two hunter-gatherer samples from western Russia (the easternmost part of Europe) to bridge the geographical gap between the West European site Loschbour (WHG)2 and the Siberian site Mal’ta in Siberia, Russia3, from which hunter-gatherer genetic data are available from the literature.

The individual we refer to as ‘Karelia’ in this study is

UZ0074/I0061 (MAE RAS collection number 5773-74, grave number 142) from the ~5500 BCE Mesolithic site Yuzhnyy Oleni Ostrov (island in Lake Onega) in Karelia, Western Russia, for which a complete mitochondrial genome was published recently4. Mitochondrial HVS I data from eight other individuals from the same site have also been described5.

The individual we refer to as ‘Samara hunter-gatherer’

I0124/SVP44 (5640-5555 calBCE, Beta-392490) is an adult male from grave 1 in a Neolithic-Eneolithic settlement producing artifacts from the Elshanka, Samara, and Repin cultures. The specific site is Lebyazhinka IV, on the Sok River, Samara oblast, Russia. (‘Neolithic’ here refers to the presence of ceramics, not to domesticated animals or plants.) The radiocarbon date of this individual, based on a femur, is centuries before the appearance of domesticated animals in the middle Volga region. Lebyazhinka IV and the neighboring Lebyazhinka V site were occupied seasonally by multiple cultures between 7000-3500 BCE; a few graves were found in the settled areas6.

Early Neolithic
The Early Neolithic in Europe in this study is represented by new samples reported from sites in Hungary, Germany and Spain.

The central European Neolithic (6200–3950 BCE) is first manifested in the Starčevo culture, the Transdanubian Linienbandkeramik (LBKT), and the central European distribution of the Linienbandkeramik (LBK); these were the first people in the region to exploit agriculture and animal husbandry7. The Linienbandkeramik appears earliest in the archaeological record of western Hungary (Transdanubia), where it incorporated novel technologies and cultural elements from the preceding, southeastern Starčevo culture, which in turn also shows similarities in material culture to early farming groups further southeast, including Anatolia, the Levant and the Near East. During the Neolithic transition, the LBK expanded relatively rapidly along the major waterways and fertile Loess plains towards central Europe, extending as far as the Paris Basin in the West and Ukraine in the East8,9.

Starčevo in Hungary: Alsónyék-Bátaszék, Mérnöki telep
The Alsónyék-Bátaszék, Mérnöki telep site was excavated by the Institute of Archaeology, Hungarian Academy of Sciences, between 2006 and 2009 (excavators: A. Osztás, I. Zalai-Gaál). This site is a part of the site complex “Alsónyék”, which forms the largest agglomeration of Neolithic settlements and cemeteries in Hungary. Alsónyék is situated along the Tolna county part of the M6 motorway track in south Hungary. At Mérnöki telep, traces of the Early Neolithic Starčevo culture and other Neolithic features, including LBK and the Late Neolithic Lengyel culture, came to light across a total of 80 hectares. Out of the 1568 archaeological features, more than 400 could be associated with the Starčevo culture including 26 burials. The majority of features were pits, in various shapes and sizes; however, ditches and various oven types were also found. The skeletal remains were all found inside ovens or in pits, and were all in flexed positions. Only one burial contained grave goods10. All 26 Starčevo skeletons were investigated as part of the German Research Foundation (DFG) funded bioarchaeological (ancient DNA and stable isotopes) project entitled “The population history of the Carpathian Basin in the Neolithic period and its influence on the colonization of central Europe”. Skeletons in uncertain stratigraphic positions, and buried without any grave goods, were radiocarbon dated at the CEZA laboratory in Mannheim, Germany. Of these, individual

BAM25a/I0174 (feature 1532, 5710-5550 calBCE, MAMS 11939) was included in this study.

Transdanubian LBK in Hungary: Szemely-Hegyes
The south-Hungarian site Szemely-Hegyes is located in Baranya county along the M6 motorway. The site was excavated between 2006 and 2007 by a commercial archaeology contractor (Ásatárs Ltd; excavators T. Paluch and K. Somogyi). Approximately 1400 archaeological features were documented across 4 hectares, including finds from the LBK, Sopot culture, Copper Age Balaton-Lasinja, and Furchenstich groups11 (T. Paluch, K. Somogyi, J. Jakucs personal communication), including ten Neolithic graves. An LBK settlement with 20 houses, several pits, ditches and ovens was also uncovered. Six out of the ten Neolithic graves could be assigned to either the Transdanubian LBK or to the Sopot period, and the skeletons were radiocarbon dated at Beta Analytic in Miami, USA. Sample

SZEH4b/I0176 (feature 1001, 5210-4940 calBCE, Beta - 310038) associated with the Transdanubian LBK was analyzed here.

LBK in Germany: Halberstadt-Sonntagsfeld
The most intensively studied region in this study is Mittelelbe-Saale (MES) in Saxony-Anhalt, Germany. MES is situated within a tight network of waterways along ancient established trade routes. The region is climatically and geologically well characterized and benefits from its location in the rain-shadow of the nearby Harz Mountains, its deposits of ores and salts, and its fertile black and para-brown soils12–⇓14. The earliest MES culture sampled in this study is the LBK.

The LBK Halberstadt-Sonntagsfeld site was discovered during construction of a new development and was excavated between 1999 and 2002, uncovering 1324 archaeological features across an area of 9947m2. The majority of the finds could be attributed to the LBK, with contours and remnants of seven long houses, several pits and 42 graves15. The remaining finds could be attributed to the Middle Neolithic Bernburg culture as well as the Unetice and Urnfield cultures of the Bronze Age. The site is a classical example of LBK settlement burials, where the majority of graves were grouped around five long houses, and one group of six graves located inside the central yard area of the settlement. Skeletal material from the site is well preserved and led to mitochondrial DNA results for 39 individuals1. We included seven individuals clearly associated with an LBK house:

HAL5/I0046 (grave 2, feature 241.1, 5206-5004 calBCE, MAMS 21479)

HAL25/I0048 (grave 28, feature 861, 5206-5052 calBCE, MAMS 21482)

HAL14/I0056 (grave 15, feature 430, 5206-5052 calBCE, MAMS 21480)

HAL34/I0057 (grave 38, feature 992, 5207-5067 calBCE, MAMS 21483)

HAL4/I0100 (grave 1, feature 139, 5032-4946 calBCE, KIA40341)

HAL2/I0659 (grave 35, feature 999, 5079-4997 calBCE, KIA40350 and 5066-4979 calBCE, KIA30408; Fig. S3.1)

HAL24/I0821 (grave 27, feature 867, 5034-4942 calBCE, KIA40348)

Radiocarbon dates obtained from these samples confirmed the attribution to the older and intermediate phase of the LBK period.

Figure S3.1.
Skeleton HAL2 (I0659 in this study) from grave 35 at the Halberstadt-Sonntagsfeld site
(Photo: J. Lipták, LDA Sachsen-Anhalt, Germany).

LBK in Germany: Oberwiederstedt-Unterwiederstedt
Several sites around Wiederstedt, Mansfeld-Südharz, and Saxony-Anhalt were excavated in the late 20th century. Promontory areas above the Wipper River in the Harz region in Hettstedt were repeatedly used during LBK times, as reflected in traces of settlements. In addition, excavations in 2000 revealed a mass grave of 12 individuals of which individual

UWS4/I0054 (grave 6, feature 1 14, 5209-5070 calBCE, MAMS 21485) was included in this study16.

LBK in Germany: Karsdorf
The site of Karsdorf is located in the valley of Unstrut, Burgenlandkreis, Saxony-Anhalt, Germany. The slope on which Karsdorf is located is characterized by alluvial loess. The place itself was settled intensively since the earliest phase of the LBK in the region. The settlement area is at least 50 acres in size and nearly 30 houses have been excavated. So-called ‘settlement burials’ were regularly found in pits in the center of the settlement area, of which individual

KAR6/I0795 (feature 170, 5207-5070 calBCE, MAMS 22823) was sampled for this study.

LBK in Germany: Stuttgart-Mühlhausen
The site Stuttgart-Mühlhausen “Viesenhäuser Hof” was excavated in several campaigns between 1931 and 1993. It reflects a long period of habitation starting from the Early Neolithic to the Iron Age. The Early Neolithic is represented by a large number of well-preserved burials from the LBK, separated into two areas including burials from the early phases of the LBK (area-2; 5500-5300 BCE) and the middle and later phases (area-1). Three individuals from area-2 are newly reported in this study (a fourth LBK sample from the Viesenhäuser Hof site was previously sequenced to high coverage and is co-analyzed with the newly sequenced LBK in this study2). Based on morphology, individual

LBK1976/I0022 (grave II-106, area-2, 5500-5300 BCE) is a female who died at an estimated age of 50-65 years. The skeleton derives from a grave that was excavated among 90 others from area-2 of the cemetery. The woman was buried in the characteristic way of the LBK, lying in flexed position on the right side, but lacks any grave goods. The burial was oriented from SW-NE with the skull facing NW. Most of the body parts were represented and in anatomical order.

The second well-preserved skeleton

LBK1992/I0025 (grave II-113, area-2, 5500-5300 BCE) is a male who died at an estimated age of 50-70 years. The skeleton was found prone in the grave with the position of the left leg indicated a flexed position. The burial was oriented from SE-NW with the skull facing south. The grave only contained a single pottery sherd found in the filling of the grave.

The third skeleton is a female

LBK2155/I0026 (grave II-125, area-2, 5500-5300 BCE) who died at 35-50 years. This individual was found prone with flexed legs imitating a seated position. The grave was oriented from W-E with the skull facing north. The only associated finding was a pottery fragment from the filling.