Scythians in Ukraine, Natufian and sub-Saharan ancestry in North Africa (ISBA 8, 21st Sep)

jena-isba8

Interesting information from ISBA 8 sesions today, as seen on Twitter (see programme in PDF, and sessions from the 19th and the 20th september).

Official abstracts are listed first (emphasis mine), then reports and images and/or link to tweets. Here is the list for quick access:

Scythian population genetics and settlement patterns

Genetic continuity in the western Eurasian Steppe broken not due to Scythian dominance, but rather at the transition to the Chernyakhov culture (Ostrogoths), by Järve et al.

The long-held archaeological view sees the Early Iron Age nomadic Scythians expanding west from their Altai region homeland across the Eurasian Steppe until they reached the Ponto-Caspian region north of the Black and Caspian Seas by around 2,900 BP1. However, the migration theory has not found support from ancient DNA evidence, and it is still unclear how much of the Scythian dominance in the Eurasian Steppe was due to movements of people and how much reflected cultural diffusion and elite dominance. We present new whole-genome results of 31 ancient Western and Eastern Scythians as well as samples pre- and postdating them that allow us to set the Scythians in a temporal context by comparing the Western Scythians to samples before and after within the Ponto-Caspian region. We detect no significant contribution of the Scythians to the Early Iron Age Ponto-Caspian gene pool, inferring instead a genetic continuity in the western Eurasian Steppe that persisted from at least 4,800–4,400 cal BP to 2,700–2,100 cal BP (based on our radiocarbon dated samples), i.e. from the Yamnaya through the Scythian period.

However, the transition from the Scythian to the Chernyakhov culture between 2,100 and 1,700 cal BP does mark a shift in the Ponto-Caspian genetic landscape, with various analyses showing that Chernyakhov culture samples share more drift and derived alleles with Bronze/Iron Age and modern Europeans, while the Scythians position outside modern European variation. Our results agree well with the Ostrogothic origins of the Chernyakhov culture and support the hypothesis that the Scythian dominance was cultural rather than achieved through population replacement.

Detail of the slide with admixture of Scythian groups in Ukraine:

scythians-admixture

Interesting to read in combination with yesterday’s re-evaluation of Scythian mobility and settlement patterns in the west (showing adaptation to the different regional cultures), The Steppe was Sown – multi-isotopic research changes our understandings of Scythian diet and mobility, by Ventresca Miller et al.

Nomadic pastoralists conventionally known as the Scythians occupied the Pontic steppe during the Iron Age, c. 700-200 BC, a period of unprecedented pan-regional interaction. Popular science accounts of the Scythians promote narratives of roving bands of nomadic warriors traversing the steppe from the Altai Mountains to the Black Sea coastline. The quantity and scale of mobility in the region is usually emphasized based on the wide distribution of material culture and the characterization of Iron Age subsistence economies in the Pontic steppe and forest-steppe as mobile pastoralism. Yet, there remains a lack of systematic, direct analysis of the mobility of individuals and their animals. Here, we present a multi-isotopic analysis of humans from Iron Age Scythian sites in Ukraine. Mobility and dietary intake were documented through strontium, carbon and oxygen isotope analyses of tooth enamel. Our results provide direct evidence for mobility among populations in the steppe and forest-steppe zones, demonstrating a range of localized mobility strategies. However, we found that very few individuals came from outside of the broader vicinity of each site, often staying within a 90 km radius. Dietary intake varied at the intrasite level and was based in agro-pastoralism.

While terrestrial protein did form a portion of the diet for some individuals, there were also high levels of a 13C-enriched food source among many individuals, which has been interpreted as millet consumption. Individuals exhibiting 87Sr/86Sr ratios that fell outside the local range were more likely to have lower rates of millet consumption than those that fell within the local range. This suggests that individuals moving to the site later in life had different economic pursuits and consumed less millet. There is also strong evidence that children and infants moved at the pan-regional scale. Contrary to the popular narrative, the majority of Scythians engaged in localized mobility as part of agricultural lifeways while pan-regional movements included family groups.

North-Africans show ancestry from the ancient Near East and sub-Saharan Africa

Pleistocene North Africans show dual genetic ancestry from the ancient Near East and sub-Saharan Africa, by van de Loosdrecht et al.

North Africa, connecting sub-Saharan Africa and Eurasia, is important for understanding human history. However, the genetic history of modern humans in this region is largely unknown before the introduction of agriculture. After the Last Glacial Maximum modern humans, associated with the Iberomaurusian culture, inhabited a wide area spanning from Morocco to Libya. The Iberomaurusian is part of the early Later Stone Age and characterized by a distinct microlithic bladelet technology, complex hunter-gathering and tooth evulsion.

Here we present genomic data from seven individuals, directly dated to ~15,000-year-ago, from Grotte des Pigeons, Taforalt in Morocco. Uni-parental marker analyses show mitochondrial haplogroup U6a for six individuals and M1b for one individual, and Y-chromosome haplogroup E-M78 (E1b1b1a1) for males. We find a strong genetic affinity of the Taforalt individuals with ancient Near Easterners, best represented by ~12,000 year old Levantine Natufians, that made the transition from complex hunter-gathering to more sedentary food production. This suggests that genetic connections between Africa and the Near East predate the introduction of agriculture in North Africa by several millennia. Notably, we do not find evidence for gene flow from Paleolithic Europeans into the ~15,000 year old North Africans as previously suggested based on archaeological similarities. Finally, the Taforalt individuals derive one third of their ancestry from sub-Saharan Africans, best approximated by a mixture of genetic components preserved in present-day West Africans (Yoruba, Mende) and Africans from Tanzania (Hadza). In contrast, modern North Africans have a much smaller sub-Saharan African component with no apparent link to Hadza. Our results provide the earliest direct evidence for genetic interactions between modern humans across Africa and Eurasia.

A detail of the cultures involved in these population movements:

north-africa-natufian-saharan

So, most likely, Natufian-related ancestry – as sub-Saharan ancestry – not related to the Afroasiatic expansion.

NOTE. This now probably outdated already by the new preprint on Dzudzuana samples, from the Caucasus.

Impact of colonization in north-eastern Siberia

Exploring the genomic impact of colonization in north-eastern Siberia by Seguin-Orlando et al.

Yakutia is the coldest region in the northern hemisphere, with winter record temperatures below minus 70°C. The ability of Yakut people to adapt both culturally and biologically to extremely cold temperatures has been key to their subsistence. They are believed to descend from an ancestral population, which left its original homeland in the Lake Baykal area following the Mongol expansion between the 13th and 15th centuries AD. They originally developed a semi-nomadic lifestyle, based on horse and cattle breeding, providing transportation, primary clothing material, meat, and milk. The early colonization by Russians in the first half of the 17th century AD, and their further expansion, have massively impacted indigenous populations. It led not only to massive epidemiological outbreaks, but also to an important dietary shift increasingly relying on carbohydrate-rich resources, and a profound lifestyle transition with the gradual conversion from Shamanism to Christianity and the establishment of new marriage customs. Leveraging an exceptional archaeological collection of more than a hundred of bodies excavated by MAFSO (Mission Archéologique Française en Sibérie Orientale) over the last 15 years and naturally kept frozen by the extreme cold temperatures of Yakutia, we have started to characterize the (epi)genome of indigenous individuals who lived from the 16th to the 20th century AD. Current data include the genome sequence of approximately 50 individuals that lived prior to and after Russian contact, at a coverage from 2 to 40 fold. Combined with data from archaeology and physical anthropology, as well as microbial DNA preserved in the specimens, our unique dataset is aimed at assessing the biological consequences of the social and biological changes undergone by the Yakut people following their neolithisation by Russian colons.

Also interesting to read Balanovsky’s session, and a previous paper on the expansion of Yakuts.

Neolithic and Bronze Age Anatolia, Urals, Fennoscandia, Italy, and Hungary (ISBA 8, 20th Sep)

jena-isba8

I will post information on ISBA 8 sesions today as I see them on Twitter (see programme in PDF, and sessions from yesterday).

Official abstracts are listed first (emphasis mine), then reports and images and/or link to tweets. Here is the list for quick access:

Russian colonization in Yakutia

Exploring the genomic impact of colonization in north-eastern Siberia, by Seguin-Orlando et al.

Yakutia is the coldest region in the northern hemisphere, with winter record temperatures below minus 70°C. The ability of Yakut people to adapt both culturally and biologically to extremely cold temperatures has been key to their subsistence. They are believed to descend from an ancestral population, which left its original homeland in the Lake Baykal area following the Mongol expansion between the 13th and 15th centuries AD. They originally developed a semi-nomadic lifestyle, based on horse and cattle breeding, providing transportation, primary clothing material, meat, and milk. The early colonization by Russians in the first half of the 17th century AD, and their further expansion, have massively impacted indigenous populations. It led not only to massive epidemiological outbreaks, but also to an important dietary shift increasingly relying on carbohydrate-rich resources, and a profound lifestyle transition with the gradual conversion from Shamanism to Christianity and the establishment of new marriage customs. Leveraging an exceptional archaeological collection of more than a hundred of bodies excavated by MAFSO (Mission Archéologique Française en Sibérie Orientale) over the last 15 years and naturally kept frozen by the extreme cold temperatures of Yakutia, we have started to characterize the (epi)genome of indigenous individuals who lived from the 16th to the 20th century AD. Current data include the genome sequence of approximately 50 individuals that lived prior to and after Russian contact, at a coverage from 2 to 40 fold. Combined with data from archaeology and physical anthropology, as well as microbial DNA preserved in the specimens, our unique dataset is aimed at assessing the biological consequences of the social and biological changes undergone by the Yakut people following their neolithisation by Russian colons.

NOTE: For another interesting study on Yakutian tribes, see Relationships between clans and genetic kin explain cultural similarities over vast distances.

Ancient DNA from a Medieval trading centre in Northern Finland

Using ancient DNA to identify the ancestry of individuals from a Medieval trading centre in Northern Finland, by Simoes et al.

Analyzing genomic information from archaeological human remains has proved to be a powerful approach to understand human history. For the archaeological site of Ii Hamina, ancient DNA can be used to infer the ancestries of individuals buried there. Situated approximately 30 km from Oulu, in Northern Finland, Ii Hamina was an important trade place since Medieval times. The historical context indicates that the site could have been a melting pot for different cultures and people of diversified genetic backgrounds. Archaeological and osteological evidence from different individuals suggest a rich diversity. For example, stable isotope analyses indicate that freshwater and marine fish was the dominant protein source for this population. However, one individual proved to be an outlier, with a diet containing relatively more terrestrial meat or vegetables. The variety of artefacts that was found associated with several human remains also points to potential differences in religious beliefs or social status. In this study, we aimed to investigate if such variation could be attributed to different genetic ancestries. Ten of the individuals buried in Ii Hamina’s churchyard, dating to between the 15th and 17th century AD, were screened for presence of authentic ancient DNA. We retrieved genome-wide data for six of the individuals and performed downstream analysis. Data authenticity was confirmed by DNA damage patterns and low estimates of mitochondrial contamination. The relatively recent age of these human remains allows for a direct comparison to modern populations. A combination of population genetics methods was undertaken to characterize their genetic structure, and identify potential familiar relationships. We found a high diversity of mitochondrial lineages at the site. In spite of the putatively distant origin of some of the artifacts, most individuals shared a higher affinity to the present-day Finnish or Late Settlement Finnish populations. Interestingly, different methods consistently suggested that the individual with outlier isotopic values had a different genetic origin, being more closely related to reindeer herding Saami. Here we show how data from different sources, such as stable isotopes, can be intersected with ancient DNA in order to get a more comprehensive understanding of the human past.

A closer look at the bottom left corner of the poster (the left columns are probably the new samples):

finland-medieval-admixture

Plant resources processed in HG pottery from the Upper Volga

Multiple criteria for the detection of plant resources processed in hunter-gatherer pottery vessels from the Upper Volga, Russia, by Bondetti et al.

In Northern Eurasia, the Neolithic is marked by the adoption of pottery by hunter-gatherer communities. The degree to which this is related to wider social and lifestyle changes is subject to ongoing debate and the focus of a new research programme. The use and function of early pottery by pre-agricultural societies during the 7th-5th millennia BC is of central interest to this debate. Organic residue analysis provides important information about pottery use. This approach relies on the identification and isotopic characteristics of lipid biomarkers, absorbed into the pores of the ceramic or charred deposits adhering to pottery vessel surfaces, using a combined methodology, namely GC-MS, GC-c-IRMS and EA-IRMS. However, while animal products (e.g., marine, freshwater, ruminant, porcine) have the benefit of being lipid-rich and well-characterised at the molecular and isotopic level, the identification of plant resources still suffers from a lack of specific criteria for identification. In huntergatherer contexts this problem is exacerbated by the wide range of wild, foraged plant resources that may have been potentially exploited. Here we evaluate approaches for the characterisation of terrestrial plant food in pottery through the study of pottery assemblages from Zamostje 2 and Sakhtysh 2a, two hunter-gatherer settlements located in the Upper Volga region of Russia.

GC-MS analysis of the lipids, extracted from the ceramics and charred residues by acidified methanol, suggests that pottery use was primarily oriented towards terrestrial and aquatic animal products. However, while many of the Early Neolithic vessels contain lipids distinctive of freshwater resources, triterpenoids are also present in high abundance suggesting mixing with plant products. When considering the isotopic criteria, we suggest that plants were a major commodity processed in pottery at this time. This is supported by the microscopic identification of Viburnum (Viburnum Opulus L.) berries in the charred deposits on several vessels from Zamostje.

The study of Upper Volga pottery demonstrated the importance of using a multidisciplinary approach to determine the presence of plant resources in vessels. Furthermore, this informs the selection of samples, often subject to freshwater reservoir effects, for 14C dating.

Studies on hunter-gatherer pottery – appearing in eastern Europe before Middle Eastern Neolithic pottery – may be important to understand the arrival of R1a-M17 lineages to the region before ca. 7000 BC. Or not, right now it is not very clear what happened with R1b-P297 and R1a-M17, and with WHG—EHG—ANE ancestry

Bronze Age population dynamics and the rise of dairy pastoralism on the eastern Eurasian steppe

Bronze Age population dynamics and the rise of dairy pastoralism on the eastern Eurasian steppe, by Warinner et al.

Recent paleogenomic studies have shown that migrations of Western steppe herders (WSH), beginning in the Eneolithic (ca. 3300-2700 BCE), profoundly transformed the genes and cultures of Europe and Central Asia. Compared to Europe, the eastern extent of this WSH expansion is not well defined. Here we present genomic and proteomic data from 22 directly dated Bronze Age khirigsuur burials from Khövsgöl, Mongolia (ca. 1380-975 BCE). Only one individual showed evidence of WSH ancestry, despite the presence of WSH populations in the nearby Altai-Sayan region for more than a millennium. At the same time, LCMS/ MS analysis of dental calculus provides direct protein evidence of milk consumption from Western domesticated livestock in 7 of 9 individuals. Our results show that dairy pastoralism was adopted by Bronze Age Mongolians despite minimal genetic exchange with Western steppe herders.

Detail of the images:

mongol-bronze-age-pca

mongol-bronze-age-f4-ancestry

Global demographic history inferred from mitogenomes

Open access Global demographic history of human populations inferred from whole mitochondrial genomes, by Miller, Manica, and Amos, Royal Society Open Science (2018).

Relevant excerpts (emphasis mine):

Material

The Phase 3 sequence data from 20 populations, comprising five populations for each of the four main geographical regions of Europe, East Asia, South Asia and Africa, were downloaded from the 1000 Genomes Project website (www.1000genomes.org/data, [8]), including whole mitochondrial genome data for 1999 individuals. We decided not to analyse populations from the Americas due to the region’s complex history of admixture [13,14].

The European populations were as follows: Finnish sampled in Finland (FIN); European Caucasians resident in Utah, USA (CEU); British in England and Scotland (GBR); an Iberian population from Spain (IBS) and Toscani from Italy (TSI). Representing East Asia were the Han Chinese in Beijing (CHB); Southern Han Chinese (CHS); Dai Chinese from Xishuangbanna, China (CDX); Kinh population from Ho Chi Minh City, Vietnam (KHV) and Japanese from Tokyo (JPT). The South Asian populations were Punjabi Indians from Lahore, Pakistan (PJL); Gujarati Indians in Houston, USA (GIH) as well as Indian Telugu sampled in the UK (ITU); Bengali from Bangladesh (BEB) and Sri Lankan Tamil from the UK (STU). (…)

Method

We analysed our mtDNA data with the extended Bayesian skyline plot (EBSP) method, a Bayesian, non-parametric technique for inferring past population size fluctuations from genetic data. Building on the previous Bayesian skyline plot (BSP) approach, EBSP uses a piecewise-linear model and Markov chain Monte Carlo (MCMC) methods to reconstruct a populations’ demographic history [17] and is implemented in the software package BEAST v. 2.3.2 [11]. Alignments for each of the 20 populations were loaded separately into the Bayesian Evolutionary Analysis Utility tool (BEAUti v. 2.3.2) in NEXUS format.

1000-genomes-similarity-fst
Relationship between profile similarity and genetic distance, measured as Fst. Comparisons between regions, circles, are colour-coded: black ¼ AFR-EA; yellow ¼ AFR-EUR; blue ¼ AFR-SA; orange ¼ EUR-EA; green ¼ EA-SA; red ¼ EUR-SA. Comparisons within regions, squares, are coded: peach ¼ EUR; pink ¼ EA; dark blue ¼ EA; light blue ¼ AFR. Profile similarity is calculated as inferred size difference summed over 20 evenly spaced intervals (see Material and methods).

Regional demographic histories

Europe:

The five European profiles are presented in figure 2. The four southerly populations all show profiles with a stable size up to approximately 14 ka followed by a sudden, rapid increase that becomes progressively less steep towards the present. There is also a north-south trend, with confidence intervals becoming broader towards the north, particularly for the oldest time-points. The Finnish population profile appears rather different, but this is to be expected both because it is so far north and because previous studies have identified Finns as a strong genetic outlier in Europe [19–22].

europe-mtdna
Inferred demographic histories of five European populations. Dotted line is the median estimate of Ne and the thin grey lines show the boundary of the 95% CPD interval. The x-axis represents time from the present in years and all plots are on the same scale. Map shows origins of sampled populations.

South Asia:

The five profiles for South Asia are shown in figure 3. All populations reveal a period of rapid growth approximately 45–40 ka which then slows. Near the present the two southerly populations, GIH and STU both show evidence of a decline. However, this may be due to these samples being drawn from populations no longer living on the subcontinent, with the downward trend capturing a bottleneck associated with moving to Europe/America, perhaps accentuated by the tendency for immigrant populations to group by region, religion and race [23].

asia-mtdna
Inferred South Asian population demographic histories. Dotted line is the median Ne estimate and the thin grey lines show the boundary of the 95% CPD intervals. The x-axis represents time from the present in thousands of years and all plots are on the same scale. The map shows location of sampled populations.

Related

Common pitfalls in human genomics and bioinformatics: ADMIXTURE, PCA, and the ‘Yamnaya’ ancestral component

invasion-from-the-steppe-yamnaya

Good timing for the publication of two interesting papers, that a lot of people should read very carefully:

ADMIXTURE

Open access A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots, by Daniel J. Lawson, Lucy van Dorp & Daniel Falush, Nature Communications (2018).

Interesting excerpts (emphasis mine):

Experienced researchers, particularly those interested in population structure and historical inference, typically present STRUCTURE results alongside other methods that make different modelling assumptions. These include TreeMix, ADMIXTUREGRAPH, fineSTRUCTURE, GLOBETROTTER, f3 and D statistics, amongst many others. These models can be used both to probe whether assumptions of the model are likely to hold and to validate specific features of the results. Each also comes with its own pitfalls and difficulties of interpretation. It is not obvious that any single approach represents a direct replacement as a data summary tool. Here we build more directly on the results of STRUCTURE/ADMIXTURE by developing a new approach, badMIXTURE, to examine which features of the data are poorly fit by the model. Rather than intending to replace more specific or sophisticated analyses, we hope to encourage their use by making the limitations of the initial analysis clearer.

The default interpretation protocol

Most researchers are cautious but literal in their interpretation of STRUCTURE and ADMIXTURE results, as caricatured in Fig. 1, as it is difficult to interpret the results at all without making several of these assumptions. Here we use simulated and real data to illustrate how following this protocol can lead to inference of false histories, and how badMIXTURE can be used to examine model fit and avoid common pitfalls.

admixture-protocol
A protocol for interpreting admixture estimates, based on the assumption that the model underlying the inference is correct. If these assumptions are not validated, there is substantial danger of over-interpretation. The “Core protocol” describes the assumptions that are made by the admixture model itself (Protocol 1, 3, 4), and inference for estimating K (Protocol 2). The “Algorithm input” protocol describes choices that can further bias results, while the “Interpretation” protocol describes assumptions that can be made in interpreting the output that are not directly supported by model inference

Discussion

STRUCTURE and ADMIXTURE are popular because they give the user a broad-brush view of variation in genetic data, while allowing the possibility of zooming down on details about specific individuals or labelled groups. Unfortunately it is rarely the case that sampled data follows a simple history comprising a differentiation phase followed by a mixture phase, as assumed in an ADMIXTURE model and highlighted by case study 1. Naïve inferences based on this model (the Protocol of Fig. 1) can be misleading if sampling strategy or the inferred value of the number of populations K is inappropriate, or if recent bottlenecks or unobserved ancient structure appear in the data. It is therefore useful when interpreting the results obtained from real data to think of STRUCTURE and ADMIXTURE as algorithms that parsimoniously explain variation between individuals rather than as parametric models of divergence and admixture.

For example, if admixture events or genetic drift affect all members of the sample equally, then there is no variation between individuals for the model to explain. Non-African humans have a few percent Neanderthal ancestry, but this is invisible to STRUCTURE or ADMIXTURE since it does not result in differences in ancestry profiles between individuals. The same reasoning helps to explain why for most data sets—even in species such as humans where mixing is commonplace—each of the K populations is inferred by STRUCTURE/ADMIXTURE to have non-admixed representatives in the sample. If every individual in a group is in fact admixed, then (with some exceptions) the model simply shifts the allele frequencies of the inferred ancestral population to reflect the fraction of admixture that is shared by all individuals.

Several methods have been developed to estimate K, but for real data, the assumption that there is a true value is always incorrect; the question rather being whether the model is a good enough approximation to be practically useful. First, there may be close relatives in the sample which violates model assumptions. Second, there might be “isolation by distance”, meaning that there are no discrete populations at all. Third, population structure may be hierarchical, with subtle subdivisions nested within diverged groups. This kind of structure can be hard for the algorithms to detect and can lead to underestimation of K. Fourth, population structure may be fluid between historical epochs, with multiple events and structures leaving signals in the data. Many users examine the results of multiple K simultaneously but this makes interpretation more complex, especially because it makes it easier for users to find support for preconceptions about the data somewhere in the results.

In practice, the best that can be expected is that the algorithms choose the smallest number of ancestral populations that can explain the most salient variation in the data. Unless the demographic history of the sample is particularly simple, the value of K inferred according to any statistically sensible criterion is likely to be smaller than the number of distinct drift events that have practically impacted the sample. The algorithm uses variation in admixture proportions between individuals to approximately mimic the effect of more than K distinct drift events without estimating ancestral populations corresponding to each one. In other words, an admixture model is almost always “wrong” (Assumption 2 of the Core protocol, Fig. 1) and should not be interpreted without examining whether this lack of fit matters for a given question.

admixture-pitfalls
Three scenarios that give indistinguishable ADMIXTURE results. a Simplified schematic of each simulation scenario. b Inferred ADMIXTURE plots at K= 11. c CHROMOPAINTER inferred painting palettes.

Because STRUCTURE/ADMIXTURE accounts for the most salient variation, results are greatly affected by sample size in common with other methods. Specifically, groups that contain fewer samples or have undergone little population-specific drift of their own are likely to be fit as mixes of multiple drifted groups, rather than assigned to their own ancestral population. Indeed, if an ancient sample is put into a data set of modern individuals, the ancient sample is typically represented as an admixture of the modern populations (e.g., ref. 28,29), which can happen even if the individual sample is older than the split date of the modern populations and thus cannot be admixed.

This paper was already available as a preprint in bioRxiv (first published in 2016) and it is incredible that it needed to wait all this time to be published. I found it weird how reviewers focused on the “tone” of the paper. I think it is great to see files from the peer review process published, but we need to know who these reviewers were, to understand their whiny remarks… A lot of geneticists out there need to develop a thick skin, or else we are going to see more and more delays based on a perceived incorrect tone towards the field, which seems a rather subjective reason to force researchers to correct a paper.

PCA of SNP data

Open access Effective principal components analysis of SNP data, by Gauch, Qian, Piepho, Zhou, & Chen, bioRxiv (2018).

Interesting excerpts:

A potential hindrance to our advice to upgrade from PCA graphs to PCA biplots is that the SNPs are often so numerous that they would obscure the Items if both were graphed together. One way to reduce clutter, which is used in several figures in this article, is to present a biplot in two side-by-side panels, one for Items and one for SNPs. Another stratagem is to focus on a manageable subset of SNPs of particular interest and show only them in a biplot in order to avoid obscuring the Items. A later section on causal exploration by current methods mentions several procedures for identifying particularly relevant SNPs.

One of several data transformations is ordinarily applied to SNP data prior to PCA computations, such as centering by SNPs. These transformations make a huge difference in the appearance of PCA graphs or biplots. A SNPs-by-Items data matrix constitutes a two-way factorial design, so analysis of variance (ANOVA) recognizes three sources of variation: SNP main effects, Item main effects, and SNP-by-Item (S×I) interaction effects. Double-Centered PCA (DC-PCA) removes both main effects in order to focus on the remaining S×I interaction effects. The resulting PCs are called interaction principal components (IPCs), and are denoted by IPC1, IPC2, and so on. By way of preview, a later section on PCA variants argues that DC-PCA is best for SNP data. Surprisingly, our literature survey did not encounter even a single analysis identified as DC-PCA.

The axes in PCA graphs or biplots are often scaled to obtain a convenient shape, but actually the axes should have the same scale for many reasons emphasized recently by Malik and Piepho [3]. However, our literature survey found a correct ratio of 1 in only 10% of the articles, a slightly faulty ratio of the larger scale over the shorter scale within 1.1 in 12%, and a substantially faulty ratio above 2 in 16% with the worst cases being ratios of 31 and 44. Especially when the scale along one PCA axis is stretched by a factor of 2 or more relative to the other axis, the relationships among various points or clusters of points are distorted and easily misinterpreted. Also, 7% of the articles failed to show the scale on one or both PCA axes, which leaves readers with an impressionistic graph that cannot be reproduced without effort. The contemporary literature on PCA of SNP data mostly violates the prohibition against stretching axes.

pca-how-to
DC-PCA biplot for oat data. The gradient in the CA-arranged matrix in Fig 13 is shown here for both lines and SNPs by the color scheme red, pink, black, light green, dark green.

The percentage of variation captured by each PC is often included in the axis labels of PCA graphs or biplots. In general this information is worth including, but there are two qualifications. First, these percentages need to be interpreted relative to the size of the data matrix because large datasets can capture a small percentage and yet still be effective. For example, for a large dataset with over 107,000 SNPs for over 6,000 persons, the first two components capture only 0.3693% and 0.117% of the variation, and yet the PCA graph shows clear structure (Fig 1A in [4]). Contrariwise, a PCA graph could capture a large percentage of the total variation, even 50% or more, but that would not guarantee that it will show evident structure in the data. Second, the interpretation of these percentages depends on exactly how the PCA analysis was conducted, as explained in a later section on PCA variants. Readers cannot meaningfully interpret the percentages of variation captured by PCA axes when authors fail to communicate which variant of PCA was used.

Conclusion

Five simple recommendations for effective PCA analysis of SNP data emerge from this investigation.

  1. Use the SNP coding 1 for the rare or minor allele and 0 for the common or major allele.
  2. Use DC-PCA; for any other PCA variant, examine its augmented ANOVA table.
  3. Report which SNP coding and PCA variant were selected, as required by contemporary standards in science for transparency and reproducibility, so that readers can interpret PCA results properly and reproduce PCA analyses reliably.
  4. Produce PCA biplots of both Items and SNPs, rather than merely PCA graphs of only Items, in order to display the joint structure of Items and SNPs and thereby to facilitate causal explanations. Be aware of the arch distortion when interpreting PCA graphs or biplots.
  5. Produce PCA biplots and graphs that have the same scale on every axis.

I read the referenced paper Biplots: Do Not Stretch Them!, by Malik and Piepho (2018), and even though it is not directly applicable to the most commonly available PCA graphs out there, it is a good reminder of the distorting effects of stretching. So for example quite recently in Krause-Kyora et al. (2018), where you can see Corded Ware and BBC samples from Central Europe clustering with samples from Yamna:

NOTE. This is related to a vertical distorsion (i.e. horizontal stretching), but possibly also to the addition of some distant outlier sample/s.

pca-cwc-yamna-bbc
Principal Component Analysis (PCA) of the human Karsdorf and Sorsum samples together with previously published ancient populations projected on 27 modern day West Eurasian populations (not shown) based on a set of 1.23 million SNPs (Mathieson et al., 2015). https://doi.org/10.7554/eLife.36666.006

The so-called ‘Yamnaya’ ancestry

Every time I read papers like these, I remember commenters who kept swearing that genetics was the ultimate science that would solve anthropological problems, where unscientific archaeology and linguistics could not. Well, it seems that, like radiocarbon analysis, these promising developing methods need still a lot of refinement to achieve something meaningful, and that they mean nothing without traditional linguistics and archaeology… But we already knew that.

Also, if this is happening in most peer-reviewed publications, made by professional geneticists, in journals of high impact factor, you can only wonder how many more errors and misinterpretations can be found in the obscure market of so many amateur geneticists out there. Because amateur geneticist is a commonly used misnomer for people who are not geneticists (since they don’t have the most basic education in genetics), and some of them are not even ‘amateurs’ (because they are selling the outputs of bioinformatic tools)… It’s like calling healers ‘amateur doctors’.

NOTE. While everyone involved in population genetics is interested in knowing the truth, and we all have our confirmation (and other kinds of) biases, for those who get paid to tell people what they want to hear, and who have sold lots of wrong interpretations already, the incentives of ‘being right’ – and thus getting involved in crooked and paranoid behaviour regarding different interpretations – are as strong as the money they can win or loose by promoting themselves and selling more ‘product’.

As a reminder of how badly these wrong interpretations of genetic results – and the influence of the so-called ‘amateurs’ – can reflect on research groups, yet another turn of the screw by the Copenhagen group, in the oral presentations at Languages and migrations in pre-historic Europe (7-12 Aug 2018), organized by the Copenhagen University. The common theme seems to be that Bell Beaker and thus R1b-L23 subclades do represent a direct expansion from Yamna now, as opposed to being derived from Corded Ware migrants, as they supported before.

NOTE. Yes, the “Yamna → Corded Ware → Únětice / Bell Beaker” migration model is still commonplace in the Copenhagen workgroup. Yes, in 2018. Guus Kroonen had already admitted they were wrong, and it was already changed in the graphic representation accompanying a recent interview to Willerslev. However, since there is still no official retraction by anyone, it seems that each member has to reject the previous model in their own way, and at their own pace. I don’t think we can expect anyone at this point to accept responsibility for their wrong statements.

So their lead archaeologist, Kristian Kristiansen, in The Indo-Europeanization of Europé (sic):

kristiansen-migrations
Kristiansen’s (2018) map of Indo-European migrations

I love the newly invented arrows of migration from Yamna to the north to distinguish among dialects attributed by them to CWC groups, and the intensive use of materials from Heyd’s publications in the presentation, which means they understand he was right – except for the fact that they are used to support a completely different theory, radically opposed to those defended in Heyd’s model

Now added to the Copenhagen’s unending proposals of language expansions, some pearls from the oral presentation:

  • Corded Ware north of the Carpathians of R1a lineages developed Germanic;
  • R1b borugh [?] Italo-Celtic;
  • the increase in steppe ancestry on north European Bell Beakers mean that they “were a continuation of the Yamnaya/Corded Ware expansion”;
  • Corded Ware groups [] stopped their expansion and took over the Bell Beaker package before migrating to England” [yep, it literally says that];
  • Italo-Celtic expanded to the UK and Iberia with Bell Beakers [I guess that included Lusitanian in Iberia, but not Messapian in Italy; or the opposite; or nothing like that, who knows];
  • 2nd millennium BC Bronze Age Atlantic trade systems expanded Proto-Celtic [yep, trade systems expanded the language]
  • 1st millennium BC expanded Gaulish with La Tène, including a “Gaulish version of Celtic to Ireland/UK” [hmmm, dat British Gaulish indeed].

You know, because, why the hell not? A logical, stable, consequential, no-nonsense approach to Indo-European migrations, as always.

Also, compare still more invented arrows of migrations, from Mikkel Nørtoft’s Introducing the Homeland Timeline Map, going against Kristiansen’s multiple arrows, and even against the own recent fantasy map series in showing Bell Beakers stem from Yamna instead of CWC (or not, you never truly know what arrows actually mean):

corded-ware-migrations
Nørtoft’s (2018) maps of Indo-European migrations.

I really, really loved that perennial arrow of migration from Volosovo, ca. 4000-800 BC (3000+ years, no less!), representing Uralic?, like that, without specifics – which is like saying, “somebody from the eastern forest zone, somehow, at some time, expanded something that was not Indo-European to Finland, and we couldn’t care less, except for the fact that they were certainly not R1a“.

This and Kristiansen’s arrows are the most comical invented migration routes of 2018; and that is saying something, given the dozens of similar maps that people publish in forums and blogs each week.

NOTE. You can read a more reasonable account of how haplogroup R1b-L51 and how R1-Z645 subclades expanded, and which dialects most likely expanded with them.

We don’t know where these scholars of the Danish workgroup stand at this moment, or if they ever had (or intended to have) a common position – beyond their persistent ideas of Yamnaya™ ancestral component = Indo-European and R1a must be Indo-European – , because each new publication changes some essential aspects without expressly stating so, and makes thus everything still messier.

It’s hard to accept that this is a series of presentations made by professional linguists, archaeologists, and geneticists, as stated by the official website, and still harder to imagine that they collaborate within the same professional workgroup, which includes experienced geneticists and academics.

I propose the following video to close future presentations introducing innovative ideas like those above, to help the audience find the appropriate mood:

Related

Mitogenomes show continuity of Neolithic populations in Southern India

New paper (behind paywall) Neolithic phylogenetic continuity inferred from complete mitochondrial DNA sequences in a tribal population of Southern India, by Sylvester et al. Genetica (2018).

This paper used a complete mtDNA genome study of 113 unrelated individuals from the Melakudiya tribal population, a Dravidian speaking tribe from the Kodagu district of Karnataka, Southern India.

Some interesting excerpts (emphasis mine):

Autosomal genetic evidence indicates that most of the ethnolinguistic groups in India have descended from a mixture of two divergent ancestral populations: Ancestral North Indians (ANI) related to People of West Eurasia, the Caucasus, Central Asia and the Middle East, and Ancestral South Indians (ASI) distantly related to indigenous Andaman Islanders (Reich et al. 2009). It is presumed that proto-Dravidian language, most likely originated in Elam province of South Western Iran, and later spread eastwards with the movement of people to the Indus Valley and later the subcontinent India (McAlpin et al. 1975; Cavalli-Sforza et al. 1988; Renfrew 1996; Derenko et al. 2013). West Eurasian haplogroups are found across India and harbor many deep-branching lineages of Indian mtDNA pool, and most of the mtDNA lineages of Western Eurasian ancestry must have a recent entry date less than 10 Kya (Kivisild et al. 1999a). The frequency of these lineages is specifically found among the higher caste groups of India (Bamshad et al. 1998, 2001; Basu et al. 2003) and many caste groups are direct descendants of Indo-Aryan immigrants (Cordaux et al. 2004). These waves of various invasions and subsequent migrations resulted in major demographic expansions in the region, which added new languages and cultures to the already colonized populations of India. Although previous genetic studies of the maternal gene pools of Indians had revealed a genetic connection between Iranian populations and the Arabian Peninsula, likely the result of both ancient and recent gene flow (Metspalu et al. 2004; Terreros et al. 2011).

mtdna-dravidian-south

Haplogroup HV14

mtDNA haplogroup HV14 has prominence in North/Western Europe, West Eurasia, Iran, and South Caucasus to Central Asia (Malyarchuk et al. 2008; Schonberg et al. 2011; Derenko et al. 2013; De Fanti et al. 2015). Although Palanichamy identified haplogroup HV14a1 in three Indian samples (Palanichamy et al. 2015), it is restricted to limited unknown distribution. In the present study, by the addition of considerable sequences from the Melakudiya population, a unique novel subclade designated as HV14a1b was found with a high frequency (43%) allowed us to reveal the earliest diverging sequences in the HV14 tree prior to the emergence of HV14a1b in Melakudiya. (…) The coalescence age for haplogroup HV14 in this study is dated ~ 16.1 ± 4.2 kya and the founder age of haplogroup HV14 in Melakudiya tribe, which is represented by a novel clade HV14a1b is ~ 8.5 ± 5.6 kya

hv14-mtdna-haplogroup
Maximum Parsimonious tree of complete mitogenomes constructed using 38 sequences from Melakudiya tribe and 11 previously published sequences belonging to haplogroup HV14 [Supplementary file Table S2] Suffixes @ indicate back mutation, a plus sign (+) an insertion. Control region mutations are underlined, and synonymous transitions are shown in normal font and non-synonymous mutations are shown in bold font. Coalescence ages (Kya) for complete coding region are shown in normal font and synonymous transitions are shown in Italics

Haplogroup U7a3a1a2

The coalescence age of haplogroup U7a3a1a2 dates to ~ 13.3 ± 4.0 kya. (…)

Although, haplogroup U7 has its origin from the Near East and is widespread from Europe to India, the phylogeny of Melakudiya tribe with subclade U7a3a1a2 clusters with populations of India (caste and tribe) and neighboring populations (Irwin et al. 2010; Ranaweera et al. 2014; Sahakyan et al. 2017), hint about the in-situ origin of the subclade in India from Indo-Aryan immigrants.

I am not a native English speaker, but this paper looks like it needs a revision by one.

Also – without comparison with ancient DNA – it is not enough to show coalescence age to prove an origin of haplogroup expansion in the Neolithic instead of later bottlenecks. However, since we are talking about mtDNA, it is likely that their analysis is mostly right.

Finally, one thing is to prove that the origin of the Indus Valley Civilization lies (in part) in peoples from the Iranian plateau, and to show with ASI ancestry that they are probably the origin of Proto-Dravidian expansion, and another completely different thing is to prove an Elamo-Dravidian connection.

Since that group is not really accepted in linguistics, it is like talking about proving – through that Iran Neolithic ancestry – a Sumero-Dravidian, or a Hurro-Dravidian connection…

Related

South-East Asia samples include shared ancestry with Jōmon

pca-south-east-asia-jomon

New paper (behind paywall) The prehistoric peopling of Southeast Asia, by McColl et al. (Science 2018) 361(6397):88-92 from a recent bioRxiv preprint.

Interesting is this apparently newly reported information including a female sample from the Ikawazu Jōmon of Japan ca. 570 BC (emphasis mine):

The two oldest samples — Hòabìnhians from Pha Faen, Laos [La368; 7950 with 7795 calendar years before the present (cal B.P.)] and Gua Cha, Malaysia (Ma911; 4415 to 4160 cal B.P.)—henceforth labeled “group 1,” cluster most closely with present-day Önge from the Andaman Islands and away from other East Asian and Southeast-Asian populations (Fig. 2), a pattern that differentiates them from all other ancient samples. We used ADMIXTURE (14) and fastNGSadmix (15) to model ancient genomes as mixtures of latent ancestry components (11). Group 1 individuals differ from the other Southeast Asian ancient samples in containing components shared with the supposed descendants of the Hòabìnhians: the Önge and the Jehai (Peninsular Malaysia), along with groups from India and Papua New Guinea.

We also find a distinctive relationship between the group 1 samples and the Ikawazu Jōmon of Japan (IK002). Outgroup f3 statistics (11, 16) show that group 1 shares the most genetic drift with all ancient mainland samples and Jōmon (fig. S12 and table S4). All other ancient genomes share more drift with present-day East Asian and Southeast Asian populations than with Jōmon (figs. S13 to S19 and tables S4 to S11). This is apparent in the fastNGSadmix analysis when assuming six ancestral components (K = 6) (fig. S11), where the Jōmon sample contains East Asian components and components found in group 1. To detect populations with genetic affinities to Jōmon, relative to present-day Japanese, we computed D statistics of the form D(Japanese, Jōmon; X, Mbuti), setting X to be different presentday and ancient Southeast Asian individuals (table S22). The strongest signal is seen when X=Ma911 and La368 (group 1 individuals), showing a marginally nonsignificant affinity to Jōmon (11). This signal is not observed with X = Papuans or Önge, suggesting that the Jōmon and Hòabìnhians may share group 1 ancestry (11).

jomon-japanese-migrations
Model for plausible migration routes into SEA. This schematic is based on ancestry patterns observed in the ancient genomes. Because we do not have ancient samples to accurately resolve how the ancestors of Jōmon and Japanese populations entered the Japanese archipelago, these migrations are represented by dashed arrows. A mainland component in Indonesia is depicted by the dashed red-green line. Gr, group; Kra, Kradai.

(…) Finally, the Jōmon individual is best-modeled as a mix between a population related to group 1/Önge and a population related to East Asians (Amis), whereas present-day Japanese can be modeled as a mixture of Jōmon and an additional East Asian component (Fig. 3 and fig. S29)

Interesting in relation to the oral communication of the SMBE O-03-OS02 Whole genome analysis of the Jomon remain reveals deep lineage of East Eurasian populations by Gakuuhari et al.:

Post late-Paleolithic hunter-gatherers lived throughout the Japanese archipelago, Jomonese, are thought to be a key to understanding the peopling history in East Asia. Here, we report a whole genome sequence (x1.85) of 2,500-year old female excavated from the Ikawazu shell-mound, unearthed typical remains of Jomon culture. The whole genome data places the Jomon as a lineage basal to contemporary and ancient populations of the eastern part of Eurasian continent, and supports the closest relationship with the modern Hokkaido Ainu. The results of ADMIXTURE show the Jomon ancestry is prevalent in present-day Nivkh, Ulchi, and people in the main-island Japan. By including the Jomon genome into phylogenetic trees, ancient lineages of the Kusunda and the Sherpa/Tibetan, early splitting from the rest of East Asian populations, is emerged. Thus, the Jomon genome gives a new insight in East Asian expansion. The Ikawazu shell-mound site locates on 34,38,43 north latitude, and 137,8, 52 east longitude in the central main-island of the Japanese archipelago, corresponding to a warm and humid monsoon region, which has been thought to be almost impossible to maintain sufficient ancient DNA for genome analysis. Our achievement opens up new possibilities for such geographical regions.

Related

Expansion of domesticated goat echoes expansion of early farmers

goat-neolithic

New paper (behind paywall) Ancient goat genomes reveal mosaic domestication in the Fertile Crescent, by Daly et al. Science (2018) 361(6397):85-88.

Interesting excerpts (emphasis mine):

Thus, our data favor a process of Near Eastern animal domestication that is dispersed in space and time, rather than radiating from a central core (3, 11). This resonates with archaeozoological evidence for disparate early management strategies from early Anatolian, Iranian, and Levantine Neolithic sites (12, 13). Interestingly, our finding of divergent goat genomes within the Neolithic echoes genetic investigation of early farmers. Northwestern Anatolian and Iranian human Neolithic genomes are also divergent (14–16), which suggests the sharing of techniques rather than large-scale migrations of populations across Southwest Asia in the period of early domestication. Several crop plants also show evidence of parallel domestication processes in the region (17).

PCA affinity (Fig. 2), supported by qpGraph and outgroup f3 analyses, suggests that modern European goats derive from a source close to the western Neolithic; Far Eastern goats derive from early eastern Neolithic domesticates; and African goats have a contribution from the Levant, but in this case with considerable admixture from the other sources (figs. S11, S16, and S17 and tables S26 and 27). The latter may be in part a result of admixture that is discernible in the same analyses extended to ancient genomes within the Fertile Crescent after the Neolithic (figs. S18 and S19 and tables S20, S27, and S31) when the spread of metallurgy and other developments likely resulted in an expansion of inter-regional trade networks and livestock movement.

goat-middle-east
Maximumlikelihood phylogeny and geographical distributions of ancient mtDNA haplogroups. (A) A phylogeny placing ancient whole mtDNA sequences in the context of known haplogroups. Symbols denoting individuals are colored by clade membership; shape indicates archaeological period (see key). Unlabeled nodes are modern bezoar and outgroup sequence (Nubian ibex) added for reference.We define haplogroup T as the sister branch to the West Caucasian tur (9). (B and C) Geographical distributions of haplogroups show early highly structured diversity in the Neolithic period (B) followed by collapse of structure in succeeding periods (C).We delineate the tiled maps at 7250 to 6950 BP, a period >bracketing both our earliest Chalcolithic sequence (24, Mianroud) and latest Neolithic (6, Aşağı Pınar). Numbered archaeological sites also include Direkli Cave (8), Abu Ghosh (9), ‘Ain Ghazal (10), and Hovk-1 Cave (11) (table S1) (9).

Our results imply a domestication process carried out by humans in dispersed, divergent, but communicating communities across the Fertile Crescent who selected animals in early millennia, including for pigmentation, the most visible of domestic traits.

Related

BMAC: long term interaction between agricultural communities and steppe pastoralists in Central Asia

indo-european-indo-iranian-migrations

Interesting new paper Mixing metaphors: sedentary-mobile interactions and local-global connections in prehistoric Turkmenistan, by Rouse & Cerasetti, Antiquity (2018) 92:674-689.

Relevant excerpts (emphasis mine):

The Murghab alluvial fan in southern Turkmenistan witnessed some of the earliest encounters between sedentary farmers and mobile pastoralists from different cultural spheres. During the late third and early second millennia BC, the Murghab was home to the Oxus civilisation and formed a central node in regional exchange networks (Possehl 2005; Kohl 2007). The Oxus civilisation (or the Bactria-Margiana Archaeological Complex) relied on intensive agriculture to support a hierarchical society and specialised craft production of metal and precious stone objects for prestige display and long-distance exchange (Sarianidi 1981; Hiebert 1994). By c. 1800 BC (the local Late Bronze Age), the internal coherence of the Oxus civilisation began to break down, along with the inter-regional exchange networks; the settlement structure of the Murghab shifted from a tiered system of urban centres, villages and hamlets, to a more dispersed pattern of smaller-scale agricultural settlements (Salvatori 2008). Contemporaneous evidence for small campsites (with a distinct ceramic tradition) suggests an influx of mobile pastoralists from the Central Eurasian Steppe and foothills (Cerasetti 1998; Masson 2002; Cattani et al. 2008). This striking combination of the sites and material cultures of both late Oxus farmers and ‘steppe’ pastoralists spans more than 500 years of Murghab prehistory (Salvatori 2008; Rouse & Cerasetti 2017).

The mixed farmer-pastoralist archaeological record of the Murghab has influenced competing interpretations of Later Bronze Age socio-political and economic relationships. Some scholars argue that the ‘collapse’ of the Oxus civilisation was at least partly due to the hostile incursions of nomads (Marushchenko 1956; Kuz’mina&Lyapin 1984; Vinogradova & Kuz’mina 1996). Others suggest that pastoralists took advantage of the Murghab’s crumbling power structure by moving into the area, but occupying only marginal, agriculturally unsuitable zones (P’yankova 1993), or merging with the late Oxus farming populations (Masson 2002). These models broadly follow ‘trade or raid’ paradigms of farmer-pastoralist interaction, whereby the perceived shortages of pastoralist communities force them to rely on agriculturalists for subsistence, material and cultural inputs (Kroeber 1947; Ferdinand 2003; Potts 2014). Such models may explain certain cases of Near Eastern pastoral economic specialisation, or historical contact scenarios between Eurasian steppe and agricultural communities on China’s northern frontier (Lattimore 1979; Barfield 2001; Alizadeh 2009; Khazanov 2009). Near Eastern and Eurasian interaction paradigms, however, fit increasingly poorly with the archaeological evidence for early farmer-pastoralist encounters in southern Central Asia.

We present data from four Murghab pastoralist campsites dating to the third to second millennia BC, restricting our discussion to the materials and practices employed by Oxus-period pastoralists to navigate shifting social, political and economic networks. Our aim is to highlight how variable strategies broadly identified under the rubric of ‘agropastoralism’ can be teased apart to recognise mechanisms of social boundary-making. Individually, these four sites present chronologically and locally distinct snapshots of farmer-pastoralist interactions across different realms of exchange (e.g. subsistence, technology and ideology); they provide examples of how pastoralists and farmers mutually participated in each other’s material and social norms. Together, these sites reveal how varied farmer-pastoralist engagement with technology and material culture did not lead inevitably to the assimilation of the two groups; rather, they worked consciously within existing systems of cultural practice to maintain distinct ‘farmer’ and ‘pastoralist’ identities, potentially over a 900-year period.

oxus-bmac-pastoralism
Region of Central Asia as discussed in this article. Areas traditionally identified with farming-dependent Oxus communities and non-Oxus mobile pastoralists are shown, acknowledging that in both areas mixed agropastoral practices have occurred in the past and present.

Conclusions

(…)First, the results indicate a cultural model of ‘being’ a pastoralist that was maintained actively over hundreds of years, in part by its material difference from that of local farmers. Second, the variability of materials, technologies and practices shared at these campsites suggests that no hegemonic power controlled trade relationships or regulated economic dependency between Oxus farmers and non-Oxus mobile pastoralists in the Murghab. Indeed, current data indicate that pastoralist occupation in the Murghab intensified during the waning of Oxus political centralisation, suggesting that the loosening of state-level structures provided the opportunity for intercultural interactions, rather than interactions being promoted or facilitated from the top. Finally, in the removal of broad-brush narratives that polarise ‘the steppe’ and ‘the sown’, and the integration of evidence suggesting that mobile pastoralists influenced the crop systems of farmers in southern Central Asia (Spengler et al. 2014b), these four sites allow us to recognise the means by which farmers and pastoralists re-shaped cultural institutions while reinforcing the meaningfulness of the associated social categories. Current work in the Murghab complements detailed studies of pastoralists in other Eurasian contexts (e.g. Frachetti 2008; Rogers 2012; Honeychurch 2015) in beginning to unravel simplistic notions of broad cross-cultural exchanges in Eurasian prehistory and the political entities traditionally seen as directing them.

The whole article is very interesting, and the four sites studied and their relevance for the said interactions are described in detail, and in chronological order. If you have the opportunity, read it.

I found it interesting that the article mentions the traditional scholarly opposition of agriculturalists vs. pastoralists (‘civilised/barbarian’, ‘state/tribe’ and ‘centre/periphery’) as an idea of Eurasian origin, and having deep ‘Western’ roots. Reading what many OIT (or anti-AIT, as they like to call themselves) supporters write, it seems to me as though they have entirely accepted and in fact are eager to promote this ‘Western’ narrative from the mid-20th century…

Steppe MLBA

This is what Narasimhan et al. (2018) had to say about the BMAC – Steppe pastoralists interaction:

We document a southward spread of genetic ancestry from the Eurasian Steppe, correlating with the archaeologically known expansion of pastoralist sites from the Steppe to Turan in the Middle Bronze Age (2300-1500 BCE). These Steppe communities mixed genetically with peoples of the Bactria Margiana Archaeological Complex (BMAC) whom they encountered in Turan (primarily descendants of earlier agriculturalists of Iran), but there is no evidence that the main BMAC population contributed genetically to later South Asians. Instead, Steppe communities integrated farther south throughout the 2nd millennium BCE, and we show that they mixed with a more southern population that we document at multiple sites as outlier individuals exhibiting a distinctive mixture of ancestry related to Iranian agriculturalists and South Asian hunter-gathers.

yamna-steppe-emba-mlba-cloud
Narasimhan et al. (2018): “Modeling results.(A) Admixture events originating from 7 “Distal” populations leading 538 to the formation of the modern Indian cloud shown geographically. Clines or 2-way mixtures of 539 ancestry are shown in rectangles, and clouds (3-way mixtures) are shown in ellipses.

(…) The absence in the BMAC cluster of the Steppe_EMBA ancestry that is ubiquitous in South Asia today—along with qpAdm analyses that rule out BMAC as a substantial source of ancestry in South Asia (Fig. 3A)—suggests that while the BMAC was affected by the same demographic forces that later impacted South Asia (the southward movement of Middle to Late Bronze Age Steppe pastoralists described in the next section), it was also bypassed by members of these groups who hardly mixed with BMAC people and instead mixed with peoples further south. In fact, the data suggest that instead of the main BMAC population having a demographic impact on South Asia, there was a larger effect of gene flow in the reverse direction, as the main BMAC genetic cluster is slightly different from the preceding Turan populations in harboring ~5% of their ancestry from the AASI.

(…)between 2100-1700 BCE, we observe BMAC outliers from three sites with Steppe_EMBA ancestry in the admixed form typically carried by the later Middle to Late Bronze Age Steppe groups (Steppe_MLBA). This documents a southward movement of Steppe ancestry through this region that only began to have a major impact around the turn of the 2nd millennium BCE.

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