Massive Migrations? The Impact of Recent aDNA Studies on our View of Third Millennium Europe

corded-ware-map

Thanks to Joshua Jonathan, I have discovered the paper Massive Migrations? The Impact of Recent aDNA Studies on our View of Third Millennium Europe, by Martin Furholt, European Journal of Archaeology (28 SEP 2017).

Abstract:

New human aDNA studies have once again brought to the forefront the role of mobility and migration in shaping social phenomena in European prehistory, processes that recent theoretical frameworks in archaeology have downplayed as an outdated explanatory notion linked to traditional culture history. While these new genetic data have provided new insights into the population history of prehistoric Europe, they are frequently interpreted and presented in a manner that recalls aspects of traditional culture-historical archaeology that were rightly criticized through the 1970s to the 1990s. They include the idea that shared material culture indicates shared participation in the same social group, or culture, and that these cultures constitute one-dimensional, homogeneous, and clearly bounded social entities. Since the new aDNA data are used to create vivid narratives describing ‘massive migrations’, the so-called cultural groups are once again likened to human populations and in turn revitalized as external drivers for socio-cultural change. Here, I argue for a more nuanced consideration of molecular data that more explicitly incorporates anthropologically informed mobility and migration models.

I was copying and pasting whole excerpts to post them here, but I think it is best to read the full paper.

yamna-corded-ware-bell-beaker
From the paper: “Simplified map showing the extent of the most important archaeological units of classification in the third millennium cal BC in Europe discussed in this text.”

It is a great summary of potential flaws of the current reasoning in genetic papers.

It should be a must-read for any serious geneticist involved in discussions on migrations, especially regarding archaeology in Indo-European studies.

As for the answers to the paper, well, unsurprisingly quite disappointing that of Haak, neither addressing the main flaw of their proposed “Yamna -> Corded Ware migration” model, nor taking the opportunity to evaluate other potential models fitting their findings of steppe ancestry in Corded Ware peoples, not even those directly suggested to them (like the expansion of Suvorovo-Novodanilovka chiefs).

NOTE: A funny thing about the paper is that, although published at the end of September, it does not take into account certain recent developments supporting Furholt’s doubts, such as the Esperstedt’s family, the new sample of Sredni Stog (and consequently the change in interpretations of outliers in Eneolithic Ukraine populations), or even the elevated steppe ancestry found in East Bell Beaker peoples. I guess Haak’s answer to all that would still be the same thorough argument: “meh, massive Indo-European migration Yamnaya -> Corded Ware is right”…

#EDIT (30 DEC 2017): Check out the interesting article by Bruce G. Trigger, referenced by John Hawks, about the question of descriptive vs. theoretical archaeologist vs. ethnologist/anthropologist from the 1950s to the 1980s. Interesting to see how today the new playboys in Academia, geneticists, are playing the archaeologist playing the ethnologist playing the linguist in Indo-European questions, and how we are living a historical debate on essential questions for the future of all these disciplines.

Related:

Genetic landscapes showing human genetic diversity aligning with geography

world-effective-migration

New preprint at BioRxiv, Genetic landscapes reveal how human genetic diversity aligns with geography, by Peter, Petkova, and Novembre (2017).

Abstract:

Summarizing spatial patterns in human genetic diversity to understand population history has been a persistent goal for human geneticists. Here, we use a recently developed spatially explicit method to estimate “effective migration” surfaces to visualize how human genetic diversity is geographically structured (the EEMS method). The resulting surfaces are “rugged”, which indicates the relationship between genetic and geographic distance is heterogenous and distorted as a rule. Most prominently, topographic and marine features regularly align with increased genetic differentiation (e.g. the Sahara desert, Mediterranean Sea or Himalaya at large scales; the Adriatic, inter-island straits in near Oceania at smaller scales). We also see traces of historical migrations and boundaries of language families. These results provide visualizations of human genetic diversity that reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, there have regularly been factors that subtly distort the underlying relationship across space observed today. The fine-scale population structure depicted here is relevant to understanding complex processes of human population history and may provide insights for geographic patterning in rare variants and heritable disease risk.

world-migration-effective
Regional patterns of genetic diversity. a: scale bar for relative effective migration rate. Posterior effective migration surfaces for b: Western Eurasia (WEA) e: Central/Eastern Eurasia (CEA) g: Africa (AFR) h Southern African hunter-gatherers (SAHG) k: and Southeast Asian (SEA) analysis panels. ‘X’ marks locations of samples noted as displaced or recently admixed, ‘H’ denotes Hunter-Gatherer populations (both ‘X’ and ‘H’ samples are omitted from the EEMS model fit); in panel g, red circles indicate Nilo-Saharan speakers and in panel h, ‘B’ denotes Bantu-speaking populations. Approximate location of troughs are shown with dashed lines (see Extended Data Figure 4). PCA plots: c: WEA d:Europeans in WEA f: CEA i: SAHG j: AFR l: SEA. Individuals are displayed as grey dots. Large dots reflect median PC position for a sample; with colors reflecting geography matched to the corresponding EEMS figure. In the EEMS plots, approximate sample locations are annotated. For exact locations, see annotated Extended Data Figure 4 and Table S1. Features discussed in the main text and supplement are labeled. FST values per panelemphasize the low absolute levels of differentiation.”

Among ‘effective migration surfaces‘ (or potential past migration routes), the Pontic-Caspian steppe and its most direct connection with the Carpathian basin, the Danubian plains, appear maybe paradoxically as a constant ‘trough’ (below average migration rate) in all maps.

After all, we could have agreed that this region should be a priori thought as the route of many migrations from the steppe and Asia into Central Europe (and thus of ‘effective migration’) in prehistoric, proto-historic and historic times, such as Suvorovo-Novodanilovka (Pre-Anatolian), Yamna (Late Indo-European), probably Srubna, Scythian-Cimmerian, Sarmatian, Huns, Goths, Avars, Slavs, Mongols

It most likely (at least partially) represents a rather recent historical barrier to admixture, involving successive Byzantine, South Slavic, and Ottoman spheres of influence positioned against Balto-Slavic societies of Eastern Europe.

europe-migration-routes
Location of troughs in West Eurasia (below average migration rate in more than 95% of MCMC iterations) are given in brown. Sample locations and EEMS grid are displayed for the West Eurasian analysis panel. FST values are provided per panel to emphasize the low absolute levels of differentiation.

Featured image, from the article: “Large-scale patterns of population structure. a: EEMS posterior mean effective migration surface for Afro-Eurasia (AEA) panel. ‘X’ marks locations of samples excluded as displaced or recently admixed. ‘H marks locations of excluded hunter-gatherer populations. Regions and features discussed in the main text are labeled. Approximate locations of troughs are annotated with dashed lines (see Extended Data Figure 4). b: PCA plot of AEA panel: Individuals are displayed as grey dots, colored dots reflect median of sample locations; with colors reflecting geography and matching with the EEMS plot. Locations displayed in the EEMS plot reflect the position of populations after alignment to grid vertices used in the model (see methods).”

Images and text available under a CC-BY-NC-ND 4.0 International License.

Discovered via Razib Khan’s blog.

Related:

Asian ancestry of the Roma people in Europe

New article, Tau haplotypes support the Asian ancestry of the Roma population settled in the Basque Country, by Alfonso-Sánchez et al., Nature (2017).

Abstract:

We examined tau haplotype frequencies in two different ethnical groups from the Basque Country (BC): Roma people and residents of European ancestry (general population). In addition, we analyzed the spatial distribution of tau haplotypes in Eurasian populations to explore the genetic affinities of the Romani groups living in Europe in a broader scope. The 17q21.31 genomic region was characterized through the genotyping of two diagnostic single nucleotide polymorphisms, SNPs (rs10514879 and rs199451), which allow the identification of H1 and H2 haplotypes. A significant heterozygous deficit was detected in the Romani for rs10514879. The H2 haplotype frequency proved to be more than twice in the BC general population (0.283) than in the Roma people (0.127). In contrast, H2 frequency proved to be very similar between Basque and Hungarian Romani, and similar to the H2 frequencies found in northwestern India and Pakistan as well. Several statistical analyses unveiled genetic structuring for the MAPT diversity, mirrored in a significant association between geography and genetic distances, with an upward trend of H2 haplotype frequencies from Asia to Europe. Yet, Roma samples did not fit into this general spatial patterning because of their discrepancy between geographical position and H2 frequency. Despite the long spatial coexistence in the Basque region between the residents of European ancestry and the Roma, the latter have preserved their Asian genetic ancestry. Bearing in mind the lack of geographical barriers between both ethnical groups, these findings support the notion that sociocultural mores might promote assortative matings in human populations.

roma-tau-asian-genetic
“Regression line and 95% confidence intervals (dashed lines) in a regression analysis of tau H2 haplotype frequencies on the rotated geographical coordinates (H2 freq = 0.4256 − coord × 0.000083) of 35 European and Asian populations (coefficient of determination, r2 = 0.515). Populations examined in this study are highlighted with a frame. Solid circles are European populations, solid squares are Middle Eastern populations, and solid triangles represent South Asian populations. Romani populations are designated by stars. Population labels: BCRoma (Basque Country Roma), BC-resid (Basque Country general population), BC-Spain (Iberian Basques), BC-French (French Basques), UK (British), IT-Sardn (Sardinia, Italy), ITBergm (Bergamo, Italy), ITBresc (Brescia, Italy), IT-Tuscn (Tuscany, Italy), HU-Roma (Hungarian Roma), Palestn(Palestinians), and Samartn (Samaritans)”

I just realized I forgot to include the migration of Indo-Aryan Roma people in the map of medieval migrations… I shall correct that in future versions.

Migration_des_Roms
Map showing the migrations of Romani people through Europe and Asia minor. From Wikipedia.

Featured image: Map of Romani dialects. From Wikipedia, by ArnoldPlaton.

Migration vs. Acculturation models for Aegean Neolithic in Genetics — still depending strongly on Archaeology

aegean-neolithic-anatolia

Recent paper in Proceedings of the Royal Society B: Archaeogenomic analysis of the first steps of Neolithization in Anatolia and the Aegean, by Kılınç et al. (2017).

Abstract:

The Neolithic transition in west Eurasia occurred in two main steps: the gradual development of sedentism and plant cultivation in the Near East and the subsequent spread of Neolithic cultures into the Aegean and across Europe after 7000 cal BCE. Here, we use published ancient genomes to investigate gene flow events in west Eurasia during the Neolithic transition. We confirm that the Early Neolithic central Anatolians in the ninth millennium BCE were probably descendants of local hunter–gatherers, rather than immigrants from the Levant or Iran. We further study the emergence of post-7000 cal BCE north Aegean Neolithic communities. Although Aegean farmers have frequently been assumed to be colonists originating from either central Anatolia or from the Levant, our findings raise alternative possibilities: north Aegean Neolithic populations may have been the product of multiple westward migrations, including south Anatolian emigrants, or they may have been descendants of local Aegean Mesolithic groups who adopted farming. These scenarios are consistent with the diversity of material cultures among Aegean Neolithic communities and the inheritance of local forager know-how. The demographic and cultural dynamics behind the earliest spread of Neolithic culture in the Aegean could therefore be distinct from the subsequent Neolithization of mainland Europe.

The analysis of the paper highlights two points regarding the process of Neolithisation in the Aegean, which is essential to ascertain the impact of later Indo-European migrations of Proto-Anatolian and Proto-Greek and other Palaeo-Balkan speakers(texts partially taken verbatim from the paper):

  • The observation that the two central Anatolian populations cluster together to the exclusion of Neolithic populations of south Levant or of Iran restates the conclusion that farming in central Anatolia in the PPN was established by local groups instead of immigrants, which is consistent with the described cultural continuity between central Anatolian Epipalaeolithic and Aceramic communities. This reiterates the earlier conclusion that the early Neolithisation in the primary zone was largely a process of cultural interaction instead of gene flow.
aegean-neolithic-pca
Principal component analysis (PCA) with modern and ancient genomes. The eigenvectors were calculated using 50 modern west Eurasian populations, onto which genome data from ancient individuals were projected. The gray circles highlight the four ancient gene pools of west Eurasia. Modern-day individuals are shown as gray points. In the Near East, Pre-Neolithic (Epipaleolithic/Mesolithic) and Neolithic individuals genetically cluster by geography rather than by cultural context. For instance, Neolithic individuals of Anatolia cluster to the exclusion of individuals from the Levant or Iran). In Europe, genetic clustering reflects cultural context but not geography: European early Neolithic individuals are genetically distinct from European pre-Neolithic individuals but tightly cluster with Anatolians. PPN: Pre-Pottery/Aceramic Neolithic, PN: Pottery Neolithic, Tepecik: Tepecik-Çiftlik (electronic supplementary material, table S1 lists the number of SNPs per ancient individual).
  • The realisation that there are still two possibilities regarding the question of whether Aegean Neolithisation (post-7000 cal BC) involved similar acculturation processes, or was driven by migration similar to Neolithisation in mainland Europe — a long-standing debate in Archaeology:
    1. Migration from Anatolia to the Aegean: the Aegean Neolithisation must have involved replacement of a local, WHG-related Mesolithic population by incoming easterners. Central Anatolia or south Anatolia / north Levant (of which there is no data) are potential origins of the components observed. Notably, the north Aegeans – Revenia (ca. 6438-6264 BC) and Barcın (ca. 6500-6200 BC) – show higher diversity than the central Anatolians, and the population size of Aegeans was larger than that of central Anatolians. The lack of WHG in later samples indicates that they must have been fully replaced by the eastern migrant farmers.
    2. Adoption of Neolithic elements by local foragers: Alternatively, the Aegean coast Mesolithic populations may have been part of the Anatolian-related gene pool that occupied the Aegean seaboard during the Early Holocene, in an “out-of-the-Aegean hypothesis. Following the LGM, Aegean emigrants would have dispersed into central Anatolia and established populations that eventually gave rise to the local Epipalaeolithic and later Neolithic communities, in line with the earliest direct evidence for human presence in central Anatolia ca 14 000 cal BCE
  • On the archaeological evidence (excerpt):

    Instead of a single-sourced colonization process, the Aegean Neolithization may thus have flourished upon already existing coastal and interior interaction networks connecting Aegean foragers with the Levantine and central Anatolian PPN populations, and involved multiple cultural interaction events from its early steps onward [16,20,64,74]. This wide diversity of cultural sources and the potential role of local populations in Neolithic development may set apart Aegean Neolithization from that in mainland Europe. While Mesolithic Aegean genetic data are awaited to fully resolve this issue, researchers should be aware of the possibility that the initial emergence of the Neolithic elements in the Aegean, at least in the north Aegean, involved cultural and demographic dynamics different than those in European Neolithization.

    Featured image, from the article: “Summary of the data analyzed in this study. (a) Map of west Eurasia showing the geographical locations and (b) timeline showing the time period (years BCE) of ancient individuals investigated in the study. Blue circles: individuals from pre-Neolithic context; red triangles: individuals from Neolithic contexts”.

    Related:

Before steppe ancestry: Europe’s genetic diversity shaped mainly by local processes, with varied sources and proportions of hunter-gatherer ancestry

neolithic-mesolithic-europe

The definitive publication of a BioRxiv preprint article, in Nature: Parallel palaeogenomic transects reveal complex genetic history of early European farmers, by Lipson et al. (2017).

The dataset with all new samples is available at the Reich Lab’s website. You can try my drafts on how to do your own PCA and ADMIXTURE analysis with some of their new datasets.

Abstract:

Ancient DNA studies have established that Neolithic European populations were descended from Anatolian migrants who received a limited amount of admixture from resident hunter-gatherers. Many open questions remain, however, about the spatial and temporal dynamics of population interactions and admixture during the Neolithic period. Here we investigate the population dynamics of Neolithization across Europe using a high-resolution genome-wide ancient DNA dataset with a total of 180 samples, of which 130 are newly reported here, from the Neolithic and Chalcolithic periods of Hungary (6000–2900 BC, n = 100), Germany (5500–3000 BC, n = 42) and Spain (5500–2200 BC, n = 38). We find that genetic diversity was shaped predominantly by local processes, with varied sources and proportions of hunter-gatherer ancestry among the three regions and through time. Admixture between groups with different ancestry profiles was pervasive and resulted in observable population transformation across almost all cultural transitions. Our results shed new light on the ways in which gene flow reshaped European populations throughout the Neolithic period and demonstrate the potential of time-series-based sampling and modelling approaches to elucidate multiple dimensions of historical population interactions.

There were some interesting finds on a regional level, with some late survival of hunter-gatherer ancestry (and Y-DNA haplogroups) in certain specific sites, but nothing especially surprising. This survival of HG ancestry and lineages in Iberia and other regions may be used to revive (yet again) the controversy over the origin of non-Indo-European languages of Europe attested in historical times, such as the only (non-Uralic) one surviving to this day, the Basque language.

This study kept confirming the absence of Y-DNA R1b-M269 subclades in Central Europe before the arrival of Yamna migrants, though, which offers strong reasons to reject the Indo-European from the west hypothesis.

Here are first the PCA of samples included in this paper, and then the PCA of ancient Eurasians (Mathieson et al. 2017) and modern populations (Lazaridis et al. 2014) for comparison of similar clusters:

mesolithic-neolithic-PCA
First two principal components from the PCA. We computed the principal components (PCs) for a set of 782 present-day western Eurasian individuals genotyped on the Affymetrix Human Origins array (background grey points) and then projected ancient individuals onto these axes. A close-up omitting the present-day Bedouin population is shown. From Lipton et al. (2017(
pca-south-east-europe
PCA of South-East European and other European samples from Mathieson et al. (2017)
pca-ancient-modern-europe
Ancient and modern samples on Lazaridis et al. (2014)

Related:

Holocene rise in mobility in at least three stages: Strong link between technological change and human mobility in Western Eurasia

estimating-mobility

New interesting article at PNAS: Estimating mobility using sparse data: Application to human genetic variation, by Loog et al (2017).

Download links and supplemental information.

Significance

Migratory activity is a critical factor in shaping processes of biological and cultural change through time. We introduce a method to estimate changes in underlying migratory activity that can be applied to genetic, morphological, or cultural data and is well-suited to samples that are sparsely distributed in space and through time. By applying this method to ancient genome data, we infer a number of changes in human mobility in Western Eurasia, including higher mobility in pre- than post-Last Glacial Maximum hunter–gatherers, and oscillations in Holocene mobility with peaks centering on the Neolithic transition and the beginnings of the Bronze Age and the Late Iron Age.

Abstract

Mobility is one of the most important processes shaping spatiotemporal patterns of variation in genetic, morphological, and cultural traits. However, current approaches for inferring past migration episodes in the fields of archaeology and population genetics lack either temporal resolution or formal quantification of the underlying mobility, are poorly suited to spatially and temporally sparsely sampled data, and permit only limited systematic comparison between different time periods or geographic regions. Here we present an estimator of past mobility that addresses these issues by explicitly linking trait differentiation in space and time. We demonstrate the efficacy of this estimator using spatiotemporally explicit simulations and apply it to a large set of ancient genomic data from Western Eurasia. We identify a sequence of changes in human mobility from the Late Pleistocene to the Iron Age. We find that mobility among European Holocene farmers was significantly higher than among European hunter–gatherers both pre- and postdating the Last Glacial Maximum. We also infer that this Holocene rise in mobility occurred in at least three distinct stages: the first centering on the well-known population expansion at the beginning of the Neolithic, and the second and third centering on the beginning of the Bronze Age and the late Iron Age, respectively. These findings suggest a strong link between technological change and human mobility in Holocene Western Eurasia and demonstrate the utility of this framework for exploring changes in mobility through space and time.

Featured image, from the article: Estimation of mobility through time from empirical data. (A) Relative mobility rate estimates in Western Eurasia over the last 14,000 y, using a 4,000-y sliding window (121 windows). The solid black line represents the mean α value from 10,000 date resampled iterations; the colored area represents the 95% confidence intervals of the jackknife distribution.