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

Complex history of dog origins and translocations in the Pacific revealed by ancient mitogenomes

remote-oceania-vanuatu-lapita

Open access Complex history of dog (Canis familiaris) origins and translocations in the Pacific revealed by ancient mitogenomes, by Creig et al., Scientific Reports (2018).

Abstract:

Archaeological evidence suggests that dogs were introduced to the islands of Oceania via Island Southeast Asia around 3,300 years ago, and reached the eastern islands of Polynesia by the fourteenth century AD. This dispersal is intimately tied to human expansion, but the involvement of dogs in Pacific migrations is not well understood. Our analyses of seven new complete ancient mitogenomes and five partial mtDNA sequences from archaeological dog specimens from Mainland and Island Southeast Asia and the Pacific suggests at least three dog dispersal events into the region, in addition to the introduction of dingoes to Australia. We see an early introduction of dogs to Island Southeast Asia, which does not appear to extend into the islands of Oceania. A shared haplogroup identified between Iron Age Taiwanese dogs, terminal-Lapita and post-Lapita dogs suggests that at least one dog lineage was introduced to Near Oceania by or as the result of interactions with Austronesian language speakers associated with the Lapita Cultural Complex. We did not find any evidence that these dogs were successfully transported beyond New Guinea. Finally, we identify a widespread dog clade found across the Pacific, including the islands of Polynesia, which likely suggests a post-Lapita dog introduction from southern Island Southeast Asia.

canis-familiaris
A map of Southeast Asia and the Pacific showing the source location of the specimens and associated haplogroups (assignment to haplogroup follows Duleba and colleagues) and the median-joining network. The boundary between Near and Remote Oceania is also shown. Symbols identify the type of sequence: filled circle, ancient mitogenome; half circle, partial ancient sequence; hollow circle, modern mitogenome. Node colours represent the haplogroup, grey, A; red, A2b2, green, A2b3; yellow, A4’5; blue, B.

Conclusion:

The dispersal of dogs across the Pacific is inseparably linked to the relationships between dogs and people. Unlike movement across continental landmasses, Pacific dogs must have been transported by people across the waters that separate islands. The ancient mitogenomes sequenced from archaeological dog specimens presented here offer a novel series of individual insights into the history of dog translocation from Southeast Asia as it occurred prior to the influence of modern European dog breeds. We generated seven mitogenomes and five partial sequences from ancient MSEA, ISEA and Pacific dogs, and four modern dingoes. Despite the small sample size, our results reveal levels of complexity and discontinuity in the introduction and movement of dogs, which are mirrored in the archaeological and linguistic evidence, suggesting at least three introductions of dogs to the wider Pacific region, in addition to the earlier appearance of the dingo in Australia. Further mtDNA studies of ancient dogs and modern village populations throughout the region may contribute additional data that can be used to evaluate these hypothesised dispersals. Autosomal and Y-chromosome analyses also have the potential to generate additional information about dog dispersal, which could reveal different dispersal signatures based on sex, or phenotypic characteristics, though the environmental conditions in the region are not particularly conducive to aDNA preservation.

Our molecular genetic analyses reveal one of the earliest dogs present in ISEA around 3,000 years ago from Timor-Leste possesses a mtDNA lineage not found elsewhere in the region. We also found similarities between mtDNA of modern dingoes and NGSDs and an ancient Taiwanese sequence, which supports previous observations about possible links between Y-chromosome markers of modern dingoes and a modern Taiwanese sample. More work is required to address whether these connections reflect the genetic diversity of a shared ancestral population in mainland China, or attest to a currently unknown dispersal event linking the two populations. Archaeological evidence for the introduction of dogs to Oceania as part of the LCC is extremely limited. Nonetheless, we demonstrate that mitogenomes from dogs in terminal Lapita and post-Lapita levels of archaeological sites along the south coast of mainland New Guinea also show affinities with an Iron Age dog specimen from Taiwan, raising the possibility of at least one introduction of dogs during Austronesian expansions ultimately from the north. Finally, we have identified a major late introduction of dogs across the islands of Oceania beginning around 2,000 years ago, which appears to have originated in MSEA, not Taiwan, and culminated in the establishment of dog populations in initial colonisation-era sites throughout East Polynesia.

phylogenetic-dingo
Molecular phylogenetic analysis by maximum likelihood method, implemented in MEGA71. The evolutionary history shown inferred by using the maximum likelihood method based on the Hasegawa-Kishino-Yano model. The tree with the highest log likelihood (-25257.5243) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.0500)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 0.0010% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 45 nucleotide sequences. There were a total of 16774 positions in the final dataset.

Also related, open access Elucidating biogeographical patterns in Australian native canids using genome wide SNPs, by Cairns et al., PLOS One (2018).

Abstract:

Dingoes play a strong role in Australia’s ecological framework as the apex predator but are under threat from hybridization and agricultural control programs. Government legislation lists the conservation of the dingo as an important aim, yet little is known about the biogeography of this enigmatic canine, making conservation difficult. Mitochondrial and Y chromosome DNA studies show evidence of population structure within the dingo. Here, we present the data from Illumina HD canine chip genotyping for 23 dingoes from five regional populations, and five New Guinea Singing Dogs to further explore patterns of biogeography using genome-wide data. Whole genome single nucleotide polymorphism (SNP) data supported the presence of three distinct dingo populations (or ESUs) subject to geographical subdivision: southeastern (SE), Fraser Island (FI) and northwestern (NW). These ESUs should be managed discretely. The FI dingoes are a known reservoir of pure, genetically distinct dingoes. Elevated inbreeding coefficients identified here suggest this population may be genetically compromised and in need of rescue; current lethal management strategies that do not consider genetic information should be suspended until further data can be gathered. D statistics identify evidence of historical admixture or ancestry sharing between southeastern dingoes and South East Asian village dogs. Conservation efforts on mainland Australia should focus on the SE dingo population that is under pressure from domestic dog hybridization and high levels of lethal control. Further data concerning the genetic health, demographics and prevalence of hybridization in the SE and FI dingo populations is urgently needed to develop evidence based conservation and management strategies.

dingo-australia-pca
Principal components analysis (PCA) based upon filtered whole genome SNP genotypes (58,512 sites) for 23 dingoes, 5 NGSD, 8 Borneo village dogs, 9 Vietnam village dogs, 10 Portugal village dogs and 8 Australian cattle dogs (‘Dataset B’). Colours represent population clusters: red for SE dingoes, purple for FI dingoes, blue for NW dingoes, dark green for NGSD, light green for Borneo village dogs, orange for Vietnam village dogs, yellow for Portugal village dogs and grey for Australian cattle dogs. (A) PC 1 versus PC 2. (B) PC 1 versus PC 3.

As I said in a previous post, the study of dogs may be useful to trace population migrations and to assess strong cultural contacts. Especially, as in this case, when crossbreeding among cultures is not easy…

Related:

Mitogenomes from Thailand offer insights into maternal genetic history of mainland South-East Asia

Open access New insights from Thailand into the maternal genetic history of Mainland Southeast Asia, by Kutanan et al. Eur. J. Hum. Genet. (2018) 26:898–911

Abstract (emphasis mine):

Tai-Kadai (TK) is one of the major language families in Mainland Southeast Asia (MSEA), with a concentration in the area of Thailand and Laos. Our previous study of 1234 mtDNA genome sequences supported a demic diffusion scenario in the spread of TK languages from southern China to Laos as well as northern and northeastern Thailand. Here we add an additional 560 mtDNA genomes from 22 groups, with a focus on the TK-speaking central Thai people and the Sino-Tibetan speaking Karen. We find extensive diversity, including 62 haplogroups not reported previously from this region. Demic diffusion is still a preferable scenario for central Thais, emphasizing the expansion of TK people through MSEA, although there is also some support for gene flow between central Thai and native Austroasiatic speaking Mon and Khmer. We also tested competing models concerning the genetic relationships of groups from the major MSEA languages, and found support for an ancestral relationship of TK and Austronesian-speaking groups.

south-east-asian-thailand
Map showing sample locations and haplogroup distributions. Blue stars indicate the 22 presently studied populations (Tai-Kadai, Austroasiatic, and Sino-Tibetan groups) while red and green circles represent Tai-Kadai and Austroasiatic populations from the previous study [7]. Population abbreviations are in Supplementary Table S1

Interesting excerpts:

Finally, we used simulations to test hypotheses concerning the genetic relationships of groups belonging to different language families. We found that Starosta’s model [11] provided the best fit to the mtDNA data; however, Sagart’s model [9, 10] was also highly supported. These two models both postulate a close linguistic affinity between TK and AN. Although genetic relatedness between TK and AN groups has been previously studied [7, 46, 47], to our knowledge this is the first study to use demographic simulations to select the best-fitting model. Our results support the genetic relatedness of TK and AN groups, which might reflect a postulated shared ancestry among the proto-Austronesian populations of coastal East Asia [48].

Specifically, the best-fitting model suggests that after separation of the prehistoric TK from AN stocks around 5–6 kya in Southeast China, the TK spread southward throughout MSEA around 1–2 kya by a demic diffusion process, accompanied by population growth but with at most minor admixture with the autochthonous AA groups. Meanwhile, the prehistorical AN ancestors entered Taiwan and dispersed southward throughout ISEA, with these two expansions later meeting in western ISEA. The lack of mtDNA haplogroups associated with the expansion out of Taiwan in our Thai/Lao samples has two possible explanations: either the Out of Taiwan expansion did not reach MSEA (at least, in the area of present-day Thailand and Laos); or, if the prehistoric AN migrated through this area, their mtDNA lineages do not survive in modern Thai/Lao populations. Ancient DNA studies in MSEA would further clarify this issue. Moreover, although mtDNA analyses are informative in elucidating genetic perspectives in geographically and linguistically related populations, they have an obvious limitation in that they only provide insights into the maternal history of populations. Future studies of Y chromosomal and genome-wide data will provide further insights into the genetic history of Thai/Lao populations and the role of factors such as post-marital residence patterns and migration in shaping the genetic structure of the region.

Starosta’s chapter referred to in the paper is Proto-East Asian and the origin and dispersal of the languages of East and Southeast Asia and the Pacific.

Related:

Linguistic continuity despite genetic replacement in Remote Oceania

oceania-ancient-migration

Review of recent papers on East Asia, quite relevant these days: Human Genetics: Busy Subway Networks in Remote Oceania? by Anders Bergström & Chris Tyler-Smith, Current Biology (2018) 28.

Interesting excerpts (emphasis mine):

Ancient DNA is transforming our understanding of the human past by forcing geneticists to confront its real complexity [1]. Historians and archaeologists have long known that the development of human societies was complex and often haphazard, but geneticists have persistently tried to explain present-day patterns of genetic variation using simple models.

Early genetic analyses of present-day populations revealed a mix of Asian (Taiwanese) and Papuan (New Guinea or nearby) ancestries throughout Remote Oceania, with maternally-inherited mitochondrial DNA being predominantly Asian, paternally-inherited Y chromosomes mainly Papuan, and autosomes intermediate [7]. This led to the simple model mentioned above of an Austronesian-speaking population starting out from Taiwan, developing the Lapita culture in the islands near New Guinea while mixing with local Papuans, and then boldly launching out into the unknown Pacific.

The surprise came with the first studies of ancient DNA, when early Lapita people from Vanuatu and Tonga (ca. 2,500-3,000 yBP) showed completely Asian genetic ancestry, so the Papuan genetic component must have entered later.

This is what the most recent ancient DNA papers found:

remote-oceania-vanuatu

There thus seems to have been a migration of Papuan-ancestry people from the Bismarck archipelago off the coast of New Guinea, into the islands of Remote Oceania, shortly after those very islands were first settled by people from Asia. Few traces of such a migration and its cultural or technological underpinnings have been found in the archaeological record or in linguistic relationships, which is why it comes as such a surprise. The fact that these Near Oceanian people made the long journey to Vanuatu so soon after the Asian seafarers arrived in their neighbourhood, having had tens of thousands of years to do so previously, strongly suggest that the migration was somehow triggered by interactions with the new Austronesian-speaking arrivals and adoption of their sophisticated seafaring technology. The excess of Y chromosomes of Papuan origin in Remote Oceania, somewhat difficult to explain under the traditional model, might also make sense in the light of an active expansion of people from Near Oceania, as such expansions have often found to be male-biased [10]. Both studies speculate that the arrival of these Papuan-ancestry people might have contributed to the end of the Lapita period and its cultural unity.

The very first settlers of Vanuatu would have spoken Austronesian languages, and the Papuan-ancestry people who arrived shortly after would very likely have spoken Papuan languages. Yet today, all languages of Vanuatu are Austronesian. The arrivals from Near Oceania thus seem to have largely replaced the first settlers but adopted their languages. Posth and colleagues [5] argue that the languages of Vanuatu actually contain some elements of Papuan origin, and that the ancient DNA results are compatible with a more gradual process of cultural interaction and genetic mixing, rather than sudden replacement. Nonetheless, linguistic continuity in the face of this almost complete genetic replacement is extremely unusual in human history, perhaps even unprecedented as Posth and colleagues [5] suggest.

We are seeing now from the Anatolian expansion and in the formation of the Indo-Iranian community that such processes were actually not as unusual as some had previously thought…

Related:

Population size potentially affecting rates of language change

language-change-size

Open access Population Size and the Rate of Language Evolution: A Test Across Indo-European, Austronesian, and Bantu Languages, by Greenhill et al. Front. Psychol (2018) 9:576.

Summary (emphasis mine):

What role does speaker population size play in shaping rates of language evolution? There has been little consensus on the expected relationship between rates and patterns of language change and speaker population size, with some predicting faster rates of change in smaller populations, and others expecting greater change in larger populations. The growth of comparative databases has allowed population size effects to be investigated across a wide range of language groups, with mixed results. One recent study of a group of Polynesian languages revealed greater rates of word gain in larger populations and greater rates of word loss in smaller populations. However, that test was restricted to 20 closely related languages from small Oceanic islands. Here, we test if this pattern is a general feature of language evolution across a larger and more diverse sample of languages from both continental and island populations. We analyzed comparative language data for 153 pairs of closely-related sister languages from three of the world’s largest language families: Austronesian, Indo-European, and Niger-Congo. We find some evidence that rates of word loss are significantly greater in smaller languages for the Indo-European comparisons, but we find no significant patterns in the other two language families. These results suggest either that the influence of population size on rates and patterns of language evolution is not universal, or that it is sufficiently weak that it may be overwhelmed by other influences in some cases. Further investigation, for a greater number of language comparisons and a wider range of language features, may determine which of these explanations holds true.

Interesting excerpts:

Our analysis suggests that, as for Polynesian languages, smaller Indo-European languages have greater rates of word loss from basic vocabulary. This result is consistent with the claim that smaller populations are at greater risk of loss of language elements, and other aspects of culture, due to effects of incomplete sampling of variants over generations. However, we note that the relatively small sample size for this dataset complicates the interpretation of this result. Least squares regression after Welch & Waxman test has the same false positive rate but has much less power than Poisson regression when sample size is small (~ten or fewer pairs, Hua et al., 2015). This makes it difficult to interpret the inconsistent results of these two analyses, as they may be due to their difference in the statistical power. Hence, the negative relationship between rates of loss and population size for Indo-European languages would benefit from additional investigation. We do not find evidence for a negative relationship between population size and word loss rates in the Austronesian and Bantu groups. This finding suggests that either these datasets contain too few language variants to have sufficient power to detect rate differences, or that the increased loss rate in small populations is not a universal phenomenon, or that it is a relatively weak force in some language groups and thus may be overwhelmed by other social, linguistic or demographic factors.

Regarding potential drawbacks of the study:

[M]easuring speech community size is notoriously difficult. How exactly does one delimit a speech community (Crystal, 2008) and what degree of proficiency in a language is sufficient to be part of the community (Bloomfield, 1933)? This task is made harder as there are few national censuses that collect detailed speaker statistics. Further, speaker population size can change rapidly with many modern world languages (especially the Indo-European languages) experiencing rapid growth over the last few hundred years (Crystal, 2008), while others have experienced catastrophic declines (Bowern, 2010). For the same reasons, the difficulty of obtaining accurate population estimates is also a problem in biology. Furthermore, the relevant parameter for genetic change—the effective population size—is difficult to estimate directly, even when accurate census information is available (Wang et al., 2016). Likewise, there may be an important role played by population and network density—tight-knit networks may inhibit change, while loosely integrated speech communities (regardless of their size), may facilitate change (Granovetter, 1973; Milroy and Milroy, 1992). One way forward here is perhaps to simulate rates of change over a range of population sizes and network topologies (c.f. Reali et al., 2018).

As conclusions:

Firstly, we provide some evidence that rates of language change can be affected by demographic factors. Even if the effect is not universal, the finding of significant associations between population size and patterns of linguistic change in some languages urges caution for any analysis of language evolution that makes an assumption of uniform rates of change. These results also potentially provide a window on processes of language change in these lineages, providing further impetus to investigate the effect of number of speakers on patterns of language transmission and loss. A more detailed study of language change for a larger number of comparisons might clarify the relationship between population size and word loss rates, particularly within the Indo-European language family.

Secondly, we have shown that the significant patterns of language change identified in a previous study are not a universal phenomenon. Unlike the study of Polynesian languages, we did not find any significant relationships between word gain rate and population size, and the association between loss rates and population size was not evident for all language families analyzed. The lack of universal relationships suggests that it may be difficult to draw general conclusions about the influence of demographic factors on patterns and rates of language change. Many other factors have been proposed to influence rates of language change (Greenhill, 2014) including population density, social structure (Nettle, 1999; Labov, 2007; Ke et al., 2008; Trudgill, 2011), degree of contact, and connectedness with other languages (Matras, 2009; Bowern, 2010), degree of language diffusion within a speech community (Wichmann et al., 2008), degree of bilingualism or multilingualism (Lupyan and Dale, 2010; Bentz and Winter, 2013), language group diversity (Atkinson et al., 2008) and environmental factors such as habitat heterogeneity and latitude (Bowern, 2010; Blust, 2013; Amano et al., 2014). These factors might mediate or overwhelm the effect of speaker population size.

We find no evidence to support the hypothesis that uptake of new words should be faster in small populations, which is based on the assumption that new words can diffuse more efficiently through a smaller speaker population than a larger one (Nettle, 1999). Nor do we find support for the suggestion that large, widespread languages have a tendency to lose linguistic features a greater rate (Lupyan and Dale, 2010). However, this latter hypothesis is predominantly expected to explain loss of complex linguistic morphology (such as case systems), which may be harder for non-native speakers to learn, rather than basic vocabulary studied here which may be comparatively easier for second language learners to acquire (but see Kempe and Brooks, 2018). Further, our results cannot be interpreted as confirmation of previous studies that suggest there is no effect of population size on rates (Wichmann and Holman, 2009). The detection of significant patterns in rates of lexical change with population size variation in the Polynesian and Indo-European languages, but the failure to identify similar patterns in the Bantu and Austronesian data, suggests that patterns of rates may need to be investigated on a case-by-case basis.

Related:

Ancient genomes document multiple waves of migration in south-east Asian prehistory

southeast-asia-reich

Open access preprint at bioRxiv Ancient genomes document multiple waves of migration in Southeast Asian prehistory, by Lipson, Cheronet, Mallick, et al. (2018).

Abstract (emphasis mine):

Southeast Asia is home to rich human genetic and linguistic diversity, but the details of past population movements in the region are not well known. Here, we report genome-wide ancient DNA data from thirteen Southeast Asian individuals spanning from the Neolithic period through the Iron Age (4100-1700 years ago). Early agriculturalists from Man Bac in Vietnam possessed a mixture of East Asian (southern Chinese farmer) and deeply diverged eastern Eurasian (hunter-gatherer) ancestry characteristic of Austroasiatic speakers, with similar ancestry as far south as Indonesia providing evidence for an expansive initial spread of Austroasiatic languages. In a striking parallel with Europe, later sites from across the region show closer connections to present-day majority groups, reflecting a second major influx of migrants by the time of the Bronze Age.

south-east-asian-admixture-graph
Schematics of admixture graph results. (A) Wider phylogenetic context. (B) Details of the Austroasiatic clade. Branch lengths are not to scale, and the order of the two events on the Nicobarese lineage in (B) is not well determined (Supplementary Text).

Featured image, from the article: “Overview of samples. (A) Locations and dates of ancient individuals. Overlapping positions are shifted slightly for visibility. (B) PCA with East and Southeast Asians. We projected the ancient samples onto axes computed using the present-day populations (with the exception of Mlabri, who were projected instead due to their large population-speci c drift). Present-day colors indicate language family affiliation: green, Austroasiatic; blue, Austronesian; orange, Hmong-Mien; black, Sino-Tibetan; magenta, Tai-Kadai.”

See also:

Genomics reveals four prehistoric migration waves into South-East Asia

Open access preprint article at bioRxiv Ancient Genomics Reveals Four Prehistoric Migration Waves into Southeast Asia, by McColl, Racimo, Vinner, et al. (2018).

Abstract (emphasis mine):

Two distinct population models have been put forward to explain present-day human diversity in Southeast Asia. The first model proposes long-term continuity (Regional Continuity model) while the other suggests two waves of dispersal (Two Layer model). Here, we use whole-genome capture in combination with shotgun sequencing to generate 25 ancient human genome sequences from mainland and island Southeast Asia, and directly test the two competing hypotheses. We find that early genomes from Hoabinhian hunter-gatherer contexts in Laos and Malaysia have genetic affinities with the Onge hunter-gatherers from the Andaman Islands, while Southeast Asian Neolithic farmers have a distinct East Asian genomic ancestry related to present-day Austroasiatic-speaking populations. We also identify two further migratory events, consistent with the expansion of speakers of Austronesian languages into Island Southeast Asia ca. 4 kya, and the expansion by East Asians into northern Vietnam ca. 2 kya. These findings support the Two Layer model for the early peopling of Southeast Asia and highlight the complexities of dispersal patterns from East Asia.

A model for plausible migration routes into Southeast Asia, based on the ancestry patterns observed in the ancient genomes.

Related:

Population turnover in Remote Oceania shortly after initial settlement

oceania-ancient-migration

Open Access article Population Turnover in Remote Oceania Shortly after Initial Settlement, by Lipson, Skoglund, Spriggs, et al. (2018), based on the recent preprint at bioRxiv.

Summary:

Ancient DNA from Vanuatu and Tonga dating to about 2,900–2,600 years ago (before present, BP) has revealed that the “First Remote Oceanians” associated with the Lapita archaeological culture were directly descended from the population that, beginning around 5000 BP, spread Austronesian languages from Taiwan to the Philippines, western Melanesia, and eventually Remote Oceania. Thus, ancestors of the First Remote Oceanians must have passed by the Papuan-ancestry populations they encountered in New Guinea, the Bismarck Archipelago, and the Solomon Islands with minimal admixture [ 1 ]. However, all present-day populations in Near and Remote Oceania harbor >25% Papuan ancestry, implying that additional eastward migration must have occurred. We generated genome-wide data for 14 ancient individuals from Efate and Epi Islands in Vanuatu from 2900–150 BP, as well as 185 present-day individuals from 18 islands. We find that people of almost entirely Papuan ancestry arrived in Vanuatu by around 2300 BP, most likely reflecting migrations a few hundred years earlier at the end of the Lapita period, when there is also evidence of changes in skeletal morphology and cessation of long-distance trade between Near and Remote Oceania [ 2, 3 ]. Papuan ancestry was subsequently diluted through admixture but remains at least 80%–90% in most islands. Through a fine-grained analysis of ancestry profiles, we show that the Papuan ancestry in Vanuatu derives from the Bismarck Archipelago rather than the geographically closer Solomon Islands. However, the Papuan ancestry in Polynesia—the most remote Pacific islands—derives from different sources, documenting a third stream of migration from Near to Remote Oceania

oceania-vanuatu
The population of Vanuatu in the Pacific was largely replaced2,900–2,300 years ago
This second wave of migrants came from New Britain, east of New Guinea
A third wave spread different ancestry to the far-flung islands of Polynesia

See also:

Population turnover in remote Oceania shortly after initial settlement

papuan-oceanian

Interesting preprint at BioRxiv by the team of the Reich lab, Population Turnover in Remote Oceania Shortly After Initial Settlement, by Mark Lipson, Pontus Skoglund, Matthew Spriggs, et al. (2018).

Abstract (emphasis mine):

Ancient DNA analysis of three individuals dated to ~3000 years before present (BP) from Vanuatu and one ~2600 BP individual from Tonga has revealed that the first inhabitants of Remote Oceania (“First Remote Oceanians”) were almost entirely of East Asian ancestry, and thus their ancestors passed New Guinea, the Bismarck Archipelago, and the Solomon Islands with minimal admixture with the Papuan groups they encountered. However, all present-day populations in Near and Remote Oceania harbor 25-100% Papuan ancestry, implying that there must have been at least one later stream of migration eastward from Near Oceani>. We generated genome-wide data for 14 ancient individuals from Efate and Epi Islands in Vanuatu ranging from 3,000-150 BP, along with 185 present-day Vanuatu individuals from 18 islands. We show that people of almost entirely Papuan ancestry had arrived in Vanuatu by 2400 BP, an event that coincided with the end of the Lapita cultural period, changes in skeletal morphology, and the cessation of long-distance trade between Near and Remote Oceania. First Remote Oceanian ancestry subsequently increased via admixture but remains at 10-20% in most islands. Through a fine-grained comparison of ancestry profiles in Vanuatu and Polynesia with diverse groups in Near Oceania, we find that Papuan ancestry in Vanuatu is consistent with deriving from the Bismarck Archipelago instead of the geographically closer Solomon Islands. Papuan ancestry in Polynesia also shows connections to the ancestry profiles present in the Bismarck Archipelago but is more similar to Tolai from New Britain and Tutuba from Vanuatu than to the ancient Vanuatu individuals and the great majority of present-day Vanuatu populations. This suggests a third eastward stream of migration from Near to Remote Oceania bringing a different type of Papuan ancestry.

qpgraph-oceanian-papuan
Admixture graph model with inferred parameters, alternative visualization. Branch lengths are given in units of f2 genetic drift distance times 1000, and admixture proportions are indicated along corresponding dotted lines. Red, Solomon Islands majority source; blue, Bismarck Archipelago majority source; purple, New Guinea-related source; green, First Remote Oceanian; brown, mixed ancestry. The order of admixture events specified is arbitrary.

See also:

Human ancestry solves language questions? New admixture citebait

human_ancestry

A paper at Scientific Reports, Human ancestry correlates with language and reveals that race is not an objective genomic classifier, by Baker, Rotimi, and Shriner (2017).

Abstract (emphasis mine):

Genetic and archaeological studies have established a sub-Saharan African origin for anatomically modern humans with subsequent migrations out of Africa. Using the largest multi-locus data set known to date, we investigated genetic differentiation of early modern humans, human admixture and migration events, and relationships among ancestries and language groups. We compiled publicly available genome-wide genotype data on 5,966 individuals from 282 global samples, representing 30 primary language families. The best evidence supports 21 ancestries that delineate genetic structure of present-day human populations. Independent of self-identified ethno-linguistic labels, the vast majority (97.3%) of individuals have mixed ancestry, with evidence of multiple ancestries in 96.8% of samples and on all continents. The data indicate that continents, ethno-linguistic groups, races, ethnicities, and individuals all show substantial ancestral heterogeneity. We estimated correlation coefficients ranging from 0.522 to 0.962 between ancestries and language families or branches. Ancestry data support the grouping of Kwadi-Khoe, Kx’a, and Tuu languages, support the exclusion of Omotic languages from the Afroasiatic language family, and do not support the proposed Dené-Yeniseian language family as a genetically valid grouping. Ancestry data yield insight into a deeper past than linguistic data can, while linguistic data provide clarity to ancestry data.

Regarding European ancestry:

Southern European ancestry correlates with both Italic and Basque speakers (r = 0.764, p = 6.34 × 10−49). Northern European ancestry correlates with Germanic and Balto-Slavic branches of the Indo-European language family as well as Finno-Ugric and Mordvinic languages of the Uralic family (r = 0.672, p = 4.67 × 10−34). Italic, Germanic, and Balto-Slavic are all branches of the Indo-European language family, while the correlation with languages of the Uralic family is consistent with an ancient migration event from Northern Asia into Northern Europe. Kalash ancestry is widely spread but is the majority ancestry only in the Kalash people (Table S3). The Kalasha language is classified within the Indo-Iranian branch of the Indo-European language family.

Sure, admixture analysis came to save the day. Yet again. Now it’s not just Archaeology related to language anymore, it’s Linguistics; all modern languages and their classification, no less. Because why the hell not? Why would anyone study languages, history, archaeology, etc. when you can run certain algorithms on free datasets of modern populations to explain everything?

What I am criticising here, as always, is not the study per se, its methods (PCA, the use of Admixture or any other tools), or its results, which might be quite interesting – even regarding the origin or position of certain languages (or more precisely their speakers) within their linguistic groups; it’s the many broad, unsupported, striking conclusions (read the article if you want to see more wishful thinking).

This is obviously simplistic citebait – that benefits only journals and authors, and it is therefore tacitly encouraged -, but not knowledge, because it is not supported by any linguistic or archaeological data or expertise.

Is anyone with a minimum knowledge of languages, or general anthropology, actually reviewing these articles?

Related:

Featured image: Ancestry analysis of the global data set, from the article.