Uralic speakers formed clines of Corded Ware ancestry with WHG:ANE populations

steppe-forest-tundra-biomes-uralic

The preprint by Jeong et al. (2018) has been published: The genetic history of admixture across inner Eurasia Nature Ecol. Evol. (2019).

Interesting excerpts, referring mainly to Uralic peoples (emphasis mine):

A model-based clustering analysis using ADMIXTURE shows a similar pattern (Fig. 2b and Supplementary Fig. 3). Overall, the proportions of ancestry components associated with Eastern or Western Eurasians are well correlated with longitude in inner Eurasians (Fig. 3). Notable outliers include known historical migrants such as Kalmyks, Nogais and Dungans. The Uralic- and Yeniseian-speaking populations, as well as Russians from multiple locations, derive most of their Eastern Eurasian ancestry from a component most enriched in Nganasans, while Turkic/Mongolic speakers have this component together with another component most enriched in populations from the Russian Far East, such as Ulchi and Nivkh (Supplementary Fig. 3). Turkic/Mongolic speakers comprising the bottom-most cline have a distinct Western Eurasian ancestry profile: they have a high proportion of a component most enriched in Mesolithic Caucasus hunter-gatherers and Neolithic Iranians and frequently harbour another component enriched in present-day South Asians (Supplementary Fig. 4). Based on the PCA and ADMIXTURE results, we heuristically assigned inner Eurasians to three clines: the ‘forest-tundra’ cline includes Russians and all Uralic and Yeniseian speakers; the ‘steppe-forest’ cline includes Turkic- and Mongolic-speaking populations from the Volga and Altai–Sayan regions and Southern Siberia; and the ‘southern steppe’ cline includes the rest of the populations.

eurasian-clines-uralic-altaic
The first two PCs summarizing the genetic structure within 2,077 Eurasian individuals. The two PCs generally mirror geography. PC1 separates western and eastern Eurasian populations, with many inner Eurasians in the middle. PC2 separates eastern Eurasians along the northsouth cline and also separates Europeans from West Asians. Ancient individuals (color-filled shapes), including two Botai individuals, are projected onto PCs calculated from present-day individuals.

For the forest-tundra populations, the Nganasan + Srubnaya model is adequate only for the two Volga region populations, Udmurts and Besermyans (Fig. 5 and Supplementary Table 8).

For the other populations west of the Urals, six from the northeastern corner of Europe are modelled with additional Mesolithic Western European hunter-gatherer (WHG) contribution (8.2–11.4%; Supplementary Table 8), while the rest need both WHG and early Neolithic European farmers (LBK_EN; Supplementary Table 2). Nganasan-related ancestry substantially contributes to their gene pools and cannot be removed from the model without a significant decrease in the model fit (4.1–29.0% contribution; χ2 P ≤ 1.68 × 10−5; Supplementary Table 8).

west-urals-finno-ugrians-qpadm
Supplementary Table 8. QpAdm-based admixture modeling of the forest-tundra cline populations. For the 13 populations west of the Urals, we present a four-way admixture model, Nganasan+Srubnaya+WHG+LBK_EN, or its minimal adequate subset. Modified from the article, to include colors for cultures, and underlined best models for Corded Ware ancestry among Uralians.

NOTE. It doesn’t seem like Hungarians can be easily modelled with Nganasan ancestry, though…

For the 4 populations east of the Urals (Enets, Selkups, Kets and Mansi), for which the above models are not adequate, Nganasan + Srubnaya + AG3 provides a good fit (χ2 P ≥ 0.018; Fig. 5 and Supplementary Table 8). Using early Bronze Age populations from the Baikal Lake region (‘Baikal_EBA’; Supplementary Table 2) as a reference instead of Nganasan, the two-way model of Baikal_EBA + Srubnaya provides a reasonable fit (χ2 P ≥ 0.016; Supplementary Table 8) and the three-way model of Baikal_EBA + Srubnaya + AG3 is adequate but with negative AG3 contribution for Enets and Mansi (χ2 P ≥ 0.460; Supplementary Table 8).

east-urals-ugric-samoyedic-qpadm
Supplementary Table 8. QpAdm-based admixture modeling of the forest-tundra cline populations. For the four populations east of the Urals, we present three admixture models: Baikal_EBA+Srubnaya, Baikal_EBA+Srubnaya+AG3 and Nganasan+Srubnaya+AG3. For each model, we present qpAdm p-value, admixture coefficient estimates and associated 5 cM jackknife standard errors (estimate ± SE). Modified from the article, to include colors for cultures, and underlined best models for Corded Ware ancestry among Uralians.

Bronze/Iron Age populations from Southern Siberia also show a similar ancestry composition with high ANE affinity (Supplementary Table 9). The additional ANE contribution beyond the Nganasan + Srubnaya model suggests a legacy from ANE-ancestry-rich clines before the Late Bronze Age.

bronze-age-iron-age-karasuk-mezhovska-tagar-qpadm
Supplementary Table 9. QpAdm-based admixture modeling of Bronze and Iron Age populations of southern Siberia. For ancieint individuals associated with Karasuk and Tagar cultures, Nganasan+Srubnaya model is insufficient. For all five groups, adding AG3 as the third ancestry or substituting Nganasan with Baikal_EBA with higher ANE affinity provides an adequate model. For each model, we present qpAdm p-value, admixture coefficient estimates and associated 5 cM jackknife standard errors (estimate ± SE). Models with p-value ≥ 0.05 are highlighted in bold face. Modified from the article, to include colors for cultures, and underlined best models for Corded Ware ancestry among Uralians.

Lara M. Cassidy comments the results of the study in A steppe in the right direction (you can read it here):

Even among the earliest available inner Eurasian genomes, east–west connectivity is evident. These, too, form a longitudinal cline, characterized by the easterly increase of a distinct ancestry, labelled Ancient North Eurasian (ANE), lowest in western European hunter-gatherers (WHG) and highest in Palaeolithic Siberians from the Baikal region. Flow-through from this ANE cline is seen in steppe populations until at least the Bronze Age, including the world’s earliest known horse herders — the Botai. However, this is eroded over time by migration from west and east, following agricultural adoption on the continental peripheries (Fig. 1b,c).

Strikingly, Jeong et al. model the modern upper steppe cline as a simple two-way mixture between western Late Bronze Age herders and Northeast Asians (Fig. 1c), with no detectable residue from the older ANE cline. They propose modern steppe peoples were established mainly through migrations post-dating the Bronze Age, a sequence for which has been recently outlined using ancient genomes. In contrast, they confirm a substantial ANE legacy in modern Siberians of the northernmost cline, a pattern mirrored in excesses of WHG ancestry west of the Urals (Fig. 1b). This marks the inhospitable biome as a reservoir for older lineages, an indication that longstanding barriers to latitudinal movement may indeed be at work, reducing the penetrance of gene flows further south along the steppe.

eurasian-clines-uralic-turkic-mongol-altaic
The genomic formation of inner Eurasians. b–d, Depiction of the three main clines of ancestry identified among Inner Eurasians. Sources of admixture for each cline are represented using proxy ancient populations, both sampled and hypothesised, based on the study’s modelling results. The major eastern and western ancestries used to model each cline are shown in bold; the peripheral admixtures that gave rise to these are also shown. Additional contributions to subsections of each cline are marked with dashed lines. b, The northernmost cline, illustrating the legacy of WHG and ANE-related populations. c,d, The upper (c) and lower (d) steppe clines are shown, both of which have substantial eastern contributions related to modern Tungusic speakers. The authors propose these populations are themselves the result of an admixture between groups related to the Nganasan, whose ancestors potentially occupied a wider range, and hunter-gatherers (HGs) from the Amur River Basin. While the upper steppe cline in c can be described as a mixture between this eastern ancestry and western steppe herders, the current model for the southern steppe cline as shown in d is not adequate and is likely confounded by interactions with diverse bordering ancestries. Credit: Ecoregions 2017, Resolve https://ecoregions2017.appspot.com/

Given the findings as reported in the paper, I think it should be much easier to describe different subclines in the “northernmost cline” than in the much more recent “Turkic/Mongolic cline”, which is nevertheless subdivided in this paper in two clines. As an example, there are at least two obvious clines with “Nganasan-related meta-populations” among Uralians, which converge in a common Steppe MLBA (i.e. Corded Ware) ancestry – one with Palaeo-Laplandic peoples, and another one with different Palaeo-Siberian populations:

siberian-clines-uralic-altaic
PCA of ancient and modern Eurasian samples. Ancient Palaeo-Laplandic, Palaeosiberian, and Altai clines drawn, with modern populations labelled. See a version with higher resolution.

The inclusion of certain Eurasian groups (or lack thereof) in the PCA doesn’t help to distinguish these subclines visually, and I guess the tiny “Naganasan-related” ancestral components found in some western populations (e.g. the famous ~5% among Estonians) probably don’t lend themselves easily to further subdivisions. Notice, nevertheless, the different components of the Eastern Eurasian source populations among Finno-Ugrians:

uralic-admixture-qpadm
Characterization of the Western and Eastern Eurasian source ancestries in inner Eurasian populations. [Modified from the paper, includes only Uralic populations]. a, Admixture f3 values are compared for different Eastern Eurasian (Mixe, Nganasan and Ulchi; green) and Western Eurasian references (Srubnaya and Chalcolithic Iranians (Iran_ChL); red). For each target group, darker shades mark more negative f3 values. b, Weights of donor populations in two sources characterizing the main admixture signal (date 1 and PC1) in the GLOBETROTTER analysis. We merged 167 donor populations into 12 groups (top right). Target populations were split into five groups (from top to bottom): Aleuts; the forest-tundra cline populations; the steppe-forest cline populations; the southern steppe cline populations; and ‘others’.

Also remarkable is the lack of comparison of Uralic populations with other neighbouring ones, since the described Uralic-like ancestry of Russians was already known, and is most likely due to the recent acculturation of Uralic-speaking peoples in the cradle of Russians, right before their eastward expansions.

west-eurasian-east-eurasian-ancestry
Supplementary Fig. 4. ADMIXTURE results qualitatively support PCA-based grouping of inner Eurasians into three clines. (A) Most southern steppe cline populations derive a higher proportion of their total Western Eurasian ancestry from a source related to Caucasus, Iran and South Asian populations. (B) Turkic- and Mongolic-speaking populations tend to derive their Eastern Eurasian ancestry more from the Devil’s Gate related one than from Nganasan-related one, while the opposite is true for Uralic- and Yeiseian-speakers. To estimate overall western Eurasian ancestry proportion, we sum up four components in our ADMIXTURE results (K=14), which are the dominant components in Neolithic Anatolians (“Anatolia_N”), Mesolithic western European hunter-gatherers (“WHG”), early Holocene Caucasus hunter-gatherers (“CHG”) and Mala from southern India, respectively. The “West / South Asian ancestry” is a fraction of it, calculated by summing up the last two components. To estimate overall Eastern Eurasian ancestry proportion, we sum up six components, most prevalent in Surui, Chipewyan, Itelmen, Nganasan, Atayal and early Neolithic Russian Far East individuals (“Devil’s Gate”). Eurasians into three clines. (A) Most southern steppe cline populations derive a higher proportion of their total Western Eurasian ancestry from a source related to Caucasus, Iran and South Asian populations. (B) Turkic- and Mongolic-speaking populations tend to derive their Eastern Eurasian ancestry more from the Devil’s Gate related one than from Nganasan-related one, while the opposite is true for Uralic- and Yeiseian-speakers. To estimate overall western Eurasian ancestry proportion, we sum up four components in our ADMIXTURE results (K=14), which are the dominant components in Neolithic Anatolians (“Anatolia_N”), Mesolithic western European hunter-gatherers (“WHG”), early Holocene Caucasus hunter-gatherers (“CHG”) and Mala from southern India, respectively. The “West / South Asian ancestry” is a fraction of it, calculated by summing up the last two components. To estimate overall Eastern Eurasian ancestry proportion, we sum up six components, most prevalent in Surui, Chipewyan, Itelmen, Nganasan, Atayal and early Neolithic Russian Far East individuals (“Devil’s Gate”).

A comparison of Estonians and Finns with Balts, Scandinavians, and Eastern Europeans would have been more informative for the division of the different so-called “Nganasan-like meta-populations”, and to ascertain which one of these ancestral peoples along the ancient WHG:ANE cline could actually be connected (if at all) to the Cis-Urals.

Because, after all, based on linguistics and archaeology, geneticists are not supposed to be looking for populations from the North Asian Arctic region, for “Siberian ancestry”, or for haplogroup N1c – despite previous works by their peers – , but for the Bronze Age Volga-Kama region…

Related

Aquitanians and Iberians of haplogroup R1b are exactly like Indo-Iranians and Balto-Slavs of haplogroup R1a

eba-indo-iranian-balto-slavs

The final paper on Indo-Iranian peoples, by Narasimhan and Patterson (see preprint), is soon to be published, according to the first author’s Twitter account.

One of the interesting details of the development of Bronze Age Iberian ethnolinguistic landscape was the making of Proto-Iberian and Proto-Basque communities, which we already knew were going to show R1b-P312 lineages, a haplogroup clearly associated during the Bell Beaker period with expanding North-West Indo-Europeans:

From the Bronze Age (~2200–900 BCE), we increase the available dataset from 7 to 60 individuals and show how ancestry from the Pontic-Caspian steppe (Steppe ancestry) appeared throughout Iberia in this period, albeit with less impact in the south. The earliest evidence is in 14 individuals dated to ~2500–2000 BCE who coexisted with local people without Steppe ancestry. These groups lived in close proximity and admixed to form the Bronze Age population after 2000 BCE with ~40% ancestry from incoming groups. Y-chromosome turnover was even more pronounced, as the lineages common in Copper Age Iberia (I2, G2, and H) were almost completely replaced by one lineage, R1b-M269.

iberia-admixture-y-dna
Proportion of ancestry derived from central European Beaker/Bronze Age populations in Iberians from the Middle Neolithic to the Iron Age (table S15). Colors indicate the Y-chromosome haplogroup for each male. Red lines represent period of admixture. Modified from Olalde et al. (2019).

The arrival of East Bell Beakers speaking Indo-European languages involved, nevertheless, the survival of the two non-IE communities isolated from each other – likely stemming from south-western France and south-eastern Iberia – thanks to a long-lasting process of migration and admixture. There are some common misconceptions about ancient languages in Iberia which may have caused some wrong interpretations of the data in the paper and elsewhere:

NOTE. A simple reading of Iberian prehistory would be enough to correct these. Two recent books on this subject are Villar’s Indoeuropeos, iberos, vascos y otros parientes and Vascos, celtas e indoeuropeos. Genes y lenguas.

Iberian languages were spoken at least in the Mediterranean and the south (ca. “1/3 of Iberia“) during the Bronze Age.

Nope, we only know the approximate location of Iberian culture and inscriptions from the Late Iron Age, and they occupy the south-eastern and eastern coastal areas, but before that it is unclear where they were spoken. In fact, it seems evident now that the arrival of Urnfield groups from the north marks the arrival of Celtic-speaking peoples, as we can infer from the increase in Central European admixture, while the expansion of anthropomorphic stelae from the north-west must have marked the expansion of Lusitanian.

Vasconic was spoken in both sides of the Pyrenees, as it was in the Middle Ages.

Wrong. One of the worst mistakes I am seeing in many comments since the paper was published, although admittedly the paper goes around this problem talking about “Modern Basques”. Vasconic toponyms appear south of the Pyrenees only after the Roman conquests, and tribes of the south-western Pyrenees and Cantabrian regions were likely Celtic-speaking peoples. Aquitanians (north of the western Pyrenees) are the only known ancient Vasconic-speaking population in proto-historic times, ergo the arrival of Bell Beakers in Iberia was most likely accompanied by Indo-European languages which were later replaced by Celtic expanding from Central Europe, and Iberian expanding from south-east Iberia, and only later with Latin and Vasconic.

Ligurian is non-Indo-European, and Lusitanian is Celtic-like, so Iberia must have been mostly non-Indo-European-speaking.

The fragmentary material available on Ligurian is enough to show that phonetically it is a NWIE dialect of non-Celtic, non-Italic nature, much like Lusitanian; that is, unless you follow laryngeals up to Celtic or Italic, in which case you can argue anything about this or any other IE language, as people who reconstruct laryngeals for Baltic in the common era do.

EDIT (19 Mar 2019): It was not clear enough from this paragraph, because Ligurian-like languages in NE Iberia is just a hypothesis based on the archaeological connection of the whole southern France Bell Beaker region. My aim was to repeat the idea that Old European hydro-toponymy is older in NE Iberia (as almost anywhere in Iberia) than Iberian toponymy, so the initial hypothesis is that:

  1. a Palaeo-European language (as Villar puts it) expanded into most regions of Iberia in ancient times (he considered at some point the Mesolithic, but that is obviously wrong, as we know now); then
  2. Celts expanded at least to the Ebro River Basin; then
  3. Iberians expanded to the north and replaced these in NE Iberia; and only then
  4. after the Roman invasion, around the start of the Common Era, appear Vasconic toponyms south of the Pyrenees.

Lusitanian obviously does not qualify as Celtic, lacking the most essential traits that define Celticness…Unless you define “(Para-)Celtic” as Pre-Proto-Celtic-like, or anything of the sort to support some Atlantic continuity, in which case you can also argue that Pre-Italic or Pre-Germanic are Celtic, because you would be essentially describing North-West Indo-European

If Basques have R1b, it’s because of a culture of “matrilocality” as opposed to the “patrilocality” of Indo-Europeans

So wrong it hurts my eyes every time I read this. Not only does matrilocality in a regional group have few known effects in genetics, but there are many well-documented cases of population replacement (with either ancestry or Y-DNA haplogroups, or both) without language replacement, without a need to resort to “matrilineality” or “matrilocality” or any other cultural difference in any of these cases.

In fact, it seems quite likely now that isolated ancient peoples north of the Pyrenees will show a gradual replacement of surviving I2a lineages by neighbouring R1b, while early Iberian R1b-DF27 lineages are associated with Lusitanians, and later incoming R1b-DF27 lineages (apart from other haplogroups) are most likely associated with incoming Celts, which must have remained in north-central and central-east European groups.

NOTE. Notice how R1a is fully absent from all known early Indo-European peoples to date, whether Iberian IE, British IE, Italic, or Greek. The absence of R1a in Iberia after the arrival of Celts is even more telling of the origin of expanding Celts in Central Europe.

I haven’t had enough time to add Iberian samples to my spreadsheet, and hence neither to the ASoSaH texts nor maps/PCAs (and I don’t plan to, because it’s more efficient for me to add both, Asian and Iberian samples, at the same time), but luckily Maciamo has summed it up on Eupedia. Or, graphically depicted in the paper for the southeast:

iberia-haplogroups
Y chromosome haplogroup composition of individuals from southeast Iberia during the past 2000 years. The general Iberian Bronze and Iron Age population is included for comparison. Modified from Olalde et al. (2019).

Does this continued influx of Y-DNA haplogroups in Iberia with different cultures represent permanent changes in language? Are, therefore, modern Iberian languages derived from Lusitanian, Sorothaptic/Celtic, Greek, Phoenician, East or West Germanic, Hebrew, Berber, or Arabic languages? Obviously not. Same with Italy (see the recent preprint on modern Italians by Raveane et al. 2018), with France, with Germany, or with Greece.

If that happens in European regions with a known ancient history, why would the recent expansions and bottlenecks of R1b in modern Basques (or N1c around the Baltic, or R1a in Slavs) in the Middle Ages represent an ancestral language surviving into modern times?

Indo-Iranians

If something is clear from Narasimhan, Patterson, et al. (2018), is that we know finally the timing of the introduction and expansion of R1a-Z645 lineages among Indo-Iranians.

We could already propose since 2015 that a slow admixture happened in the steppes, based on archaeological finds, due to settlement elites dominating over common peoples, coupled with the known Uralic linguistic traits of Indo-Iranian (and known Indo-Iranian influence on Finno-Ugric) – as I did in the first version of the Indo-European demic diffusion model.

The new huge sampling of Sintashta – combined with that of Catacomb, Poltavka, Potapovka, Andronovo, and Srubna – shows quite clearly how this long-term admixture process between Uralic peoples and Indo-Iranians happened between forest-steppe CWC (mainly Abashevo) and steppe groups. The situation is not different from that of Iberia ca. 2500-2000 BC; from Narasimhan, Patterson, et al. (2018):

We combined the newly reported data from Kamennyi Ambar 5 with previously reported data from the Sintashta 5 individuals (10). We observed a main cluster of Sintashta individuals that was similar to Srubnaya, Potapovka, and Andronovo in being well modeled as a mixture of Yamnaya-related and Anatolian Neolithic (European agriculturalist-related) ancestry.

Even with such few words referring to one of the most important data in the paper about what happened in the steppes, Wang et al. (2018) help us understand what really happened with this simplistic concept of “steppe ancestry” regarding Yamna vs. Corded Ware differences:

anatolia-neolithic-steppe-eneolithic
Image modified from Wang et al. (2018). Marked are: in red, approximate limit of Anatolia_Neolithic ancestry found in Yamna populations; in blue, Corded Ware-related groups. “Modelling results for the Steppe and Caucasus 1128 cluster. Admixture proportions based on (temporally and geographically) distal and proximal models, showing additional Anatolian farmer-related ancestry in Steppe groups as well as additional gene flow from the south in some of the Steppe groups as well as the Caucasus groups (see also Supplementary Tables 10, 14 and 20).”

As with Iberia (or any prehistoric region), the details of how exactly this language change happened are not evident, but we only need a plausible explanation coupled with archaeology and linguistics. Poltavka, Potapovka, and Sintashta samples – like the few available Iberian ones ca. 2500-2000 BC – offer a good picture of the cohabitation of R1b-L23 (mainly Z2103) and R1a-Z645 (mainly Z93+): a glimpse at the likely presence of R1a-Z93 within settlements – which must have evolved as the dominant elites – in a society where the majority of the population was initially formed by nomad herders (probably most R1b-Z2103), who were usually buried outside of the main settlements.

Will the upcoming Narasimhan, Patterson et al. (2019) deal with this problem of how R1a-M417 replaced R1b-M269, and how the so-called “Steppe_MLBA” (i.e. Corded Ware) ancestry admixed with “Steppe_EMBA” (i.e. Yamnaya) ancestry in the steppes, and which one of their languages survived in the region (that is, the same the Reich Lab has done with Iberia)? Not likely. The ‘genetic wars’ in Iberia deal with haplogroup R1b-P312, and how it was neither ‘native’ nor associated with Basques and non-Indo-European peoples in general. The ‘genetic wars’ in South Asia are concerned with the steppe origin of R1a, to prove that it is not a ‘native’ haplogroup to India, and thus neither are Indo-Aryan languages. To each region a politically correct account of genetic finds, with enough care not to fully dismiss national myths, it seems.

NOTE. Funnily enough, these ‘genetic wars’ are the making of geneticists since the 1990s and 2000s, so we are still in the midst of mostly internal wars caused by what they write. Just as genetic papers of the 2020s will most likely be a reaction to what they are writing right now about “steppe ancestry” and R1a. You won’t find much change to the linguistic reconstruction in this whole period, except for the most multicolored glottochronological proposals…

The first author of the paper has engaged, as far as I could see in Twitter, in dialogue with Hindu nationalists who try to dismiss the arrival of steppe ancestry and R1a into South Asia as inconclusive (to support the potential origin of Sanskrit millennia ago in the Indus Valley Civilization). How can geneticists deal with the real problem here (the original ethnolinguistic group expanding with Corded Ware), when they have to fend off anti-steppists from Europe and Asia? How can they do it, when they themselves are part of the same societies that demand a politically correct presentation of data?

This is how the data on the most likely Indo-Iranian-speaking region should be presented in an ideal world, where – as in the Iberia paper – geneticists would look closely to the Volga-Ural region to discover what happened with Proto-Indo-Iranians from their earliest to their latest stage, instead of constantly looking for sites close to the Indus Valley to demonstrate who knows what about modern Indian culture:

indo-iranian-admixture-similar-iberians
Tentative map of the Late PIE and Indo-Iranian community in the Volga-Ural steppes since the Eneolithic. Proportion of ancestry derived from central European Corded Ware peoples. Colors indicate the Y-chromosome haplogroup for each male. Red lines represent period of admixture. Modified from Olalde et al. (2019).

Now try and tell Hindu nationalists that Sanskrit expanded from an Early Bronze Age steppe community of R1b-rich nomadic herders that spoke Pre-Indo-Iranian, which was dominated and eventually (genetically) mostly replaced by elite Uralic-speaking R1a peoples from the Russian forest, hence the known phonetic (and some morphological) traits that remained. Good luck with the Europhobic shitstorm ahead..

Balto-Slavic

Iberian cultures, already with a majority of R1b lineages, show a clear northward expansion over previously Urnfield-like groups of north-east Iberia and Mediterranean France (which we now know probably represent the migration of Celts from central Europe). Similarly, Eastern Balts already under a majority of R1a lineages expanded likely into the Baltic region at the same time as the outlier from Turlojiškė (ca. 1075 BC), which represents the first obvious contacts of central-east Europe with the Baltic.

Iberia shows a more recent influx of central and eastern Mediterranean peoples, one of which eventually succeeded in imposing their language in Western Europe: Romans were possibly associated mainly with R1b-U152, apart from many other lineages. Proto-Slavs probably expanded later than Celts, too, connected to the disintegration of the Lusatian culture, and they were at some point associated with R1a-M458 and R1a-Z280(xZ92) lineages, apart from others already found in Early Slavs.

pca-balto-slavs-tollense-valley
PCA of central-eastern European groups which may have formed the Balto-Slavic-speaking community derived from Bell Beaker, evident from the position ‘westwards’ of CWC in the PCA, and surrounding cultures. Left: Early Bronze Age. Right: Tollense Valley samples.

This parallel between Iberia and eastern Europe is no coincidence: as Europe entered the Bronze Age, chiefdom-based systems became common, and thus the connection of ancestry or haplogroups with ethnolinguistic groups became weaker.

What happened earlier (and who may represent the Pre-Balto-Slavic community) will be clearer when we have enough eastern European samples, but basically we will be able to depict this admixture of NWIE-speaking BBC-derived peoples with Uralic-speaking CWC-derived groups (since Uralic is known to have strongly influenced Balto-Slavic), similar to the admixture found in Indo-Iranians, more or less like this:

iberian-admixture-balto-slavic
Tentative map of the North-West Indo-European and Balto-Slavic community in central-eastern Europe since the East Bell Beaker expansion. Proportion of ancestry derived from Corded Ware peoples. Colors indicate the Y-chromosome haplogroup for each male. Red lines represent period of admixture. Modified from Olalde et al. (2019).

The Early Scythian period marked a still stronger chiefdom-based system which promoted the creation of alliances and federation-like groups, with an earlier representation of the system expanding from north-eastern Europe around the Baltic Sea, precisely during the spread of Akozino warrior-traders (in turn related to the Scythian influence in the forest-steppes), who are the most likely ancestors of most N1c-V29 lineages among modern Germanic, Balto-Slavic, and Volga-Finnic peoples.

Modern haplogroup+language = ancient ones?

It is not difficult to realize, then, that the complex modern genetic picture in Eastern Europe and around the Urals, and also in South Asia (like that of the Aegean or Anatolia) is similar to the Iron Age / medieval Iberian one, and that following modern R1a as an Indo-European marker just because some modern Indo-European-speaking groups showed it was always a flawed methodology; as flawed as following R1b for ancient Vasconic groups, or N1c for ancient Uralic groups.

Why people would argue that haplogroups mean continuity (e.g. R1b with Basques, N1c with Finns, R1a with Slavs, etc.) may be understood, if one lives still in the 2000s. Just like why one would argue that Corded Ware is Indo-European, because of Gimbutas’ huge influence since the 1960s with her myth of “Kurgan peoples”. Not many denied these haplogroup associations, because there was no reason to do it, and those who did usually aligned with a defense of descriptive archaeology.

However, it is a growing paradox that some people interested in genetics today would now, after the Iberian paper, need to:

  • accept that ancient Iberians and probably Aquitanians (each from different regions, and probably from different “Basque-Iberian dialects” in the Chalcolithic, if both were actually related) show eventually expansions with R1b-L23, the haplogroup most obviously associated with expanding Indo-Europeans;
  • acknowledge that modern Iberians have many different lineages derived from prehistoric or historic peoples (Celts, Phoenicians, Greeks, Romans, Jews, Goths, Berbers, Arabs), which have undergone different bottlenecks, the last ones during the Reconquista, but none of their languages have survived;
  • realize that a similar picture is to be found everywhere in central and western Europe since the first proto-historic records, with language replacement in spite of genetic continuity, such as the British Isles (and R1b-L21 continuity) after the arrival of Celts, Romans, Anglo-Saxons, Vikings, or Normans;
  • but, at the same time, continue blindly asserting that haplogroup R1a + “steppe ancestry” represent some kind of supernatural combination which must show continuity with their modern Indo-Iranian or Balto-Slavic language from time immemorial.
sintashta-y-dna
Replacement of R1b-L23 lineages during the Early Bronze Age in eastern Europe and in the Eurasian steppes: emergence of R1a in previous Yamnaya and Bell Beaker territories. Modified from EBA Y-DNA map.

Behave, pretty please

The ‘conservative’ message espoused by some geneticists and amateur genealogists here is basically as follows:

  • Let’s not rush to new theories that contradict the 2000s, lest some people get offended by granddaddy not being these pure whatever wherever as they believed, and let’s wait some 5, 10, or 20 years, as long as necessary – to see if some corner of the Yamna culture shows R1a, or some region in north-eastern Europe shows N1c, or some Atlantic Chalcolithic sample shows R1b – to challenge our preferred theories, if we actually need to challenge anything at all, because it hurts too much.
  • Just don’t let many of these genetic genealogists or academics of our time be unhappy, pretty please with sugar on top, and let them slowly adapt to reality with more and more pet theories to fit everything together (past theories + present data), so maybe when all of them are gone, within 50 or 70 years, society can smoothly begin to move on and propose something closer to reality, but always as politically correct as possible for the next generations.
  • For starters, let’s discuss now (yet again) that Bell Beakers may not have been Indo-European at all, despite showing (unlike Corded Ware) clearly Yamna male lineages and ancestry, because then Corded Ware and R1a could not have been Indo-European and that’s terrible, so maybe Bell Beakers are too brachycephalic to speak Indo-European or something, or they were stopped by the Fearsome Tisza River, or they are not pure Dutch Single Grave in The South hence not Indo-European, or whatever, and that’s why Iron Age Iberians or Etruscans show non-Indo-European languages. That’s not disrespectful to the history of certain peoples, of course not, but talking about the evident R1a-Uralic connection is, because this is The South, not The North, and respect works differently there.
  • Just don’t talk about how Slavs and Balts enter history more than 1,500 years later than Indo-European peoples in Western and Southern Europe, including Iberia, and assume a heroic continuity of Balts and Slavs as pure R1a ‘steppe-like’ peoples dominating over thousands of kms. in the Baltic, Fennoscandia, eastern Europe, and northern Asia for 5,000 years, with multiple Balto-Slavs-over-Balto-Slavs migrations, because these absolute units of Indo-European peoples were a trip and a half. They are the Asterix and Obelix of white Indo-European prehistory.
  • Perhaps in the meantime we can also invent some new glottochronological dialectal scheme that fits the expansion of Sredni Stog/Corded Ware with (Germano-?)Indo-Slavonic separated earlier than any other Late PIE dialect; and Finno-Volgaic later than any other Uralic dialect, in the Middle Ages, with N1c.
balto-slavic-pca
Genetic structure of the Balto-Slavic populations within a European context according to the three genetic systems, from Kushniarevich et al. (2015). Pure Balto-Slavs from…hmm…yeah this…ancient…region…or people…cluster…Whatever, very very steppe-like peoples, the True Indo-Europeans™, so close to Yamna…almost as close as Finno-Ugrians.

To sum up: Iberia, Italy, France, the British Isles, central Europe, the Balkans, the Aegean, or Anatolia, all these territories can have a complex history of periodic admixture and language replacement everywhere, but some peoples appearing later than all others in the historical record (viz. Basques or Slavs) apparently cannot, because that would be shameful for their national or ethnic myths, and these should be respected.

Ignorance of the own past as a blank canvas to be filled in with stupid ethnolinguistic continuity, turned into something valuable that should not be challenged. Ethnonationalist-like reasoning proper of the 19th century. How can our times be called ‘modern’ when this kind of magical thinking is still prevalent, even among supposedly well-educated people?

Related

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