South-East Asia samples include shared ancestry with Jōmon


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).

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.


Reconstruction of Y-DNA phylogeny helps also reconstruct Tibeto-Burman expansion


New paper (behind paywall) Reconstruction of Y-chromosome phylogeny reveals two neolithic expansions of Tibeto-Burman populations by Wang et al. Mol Genet Genomics (2018).

Interesting excerpts:

Archeological studies suggest that a subgroup of ancient populations of the Miaodigou culture (~ 6300–5500 BP) moved westward to the upper stream region of the Yellow River and created the Majiayao culture (~ 5400–4900 BP) (Liu et al. 2010), which was proposed to be the remains of direct ancestors of Tibeto-Burman populations (Sagart 2008). On the other hand, Han populations, the other major descendant group of the Yang-Shao culture (~ 7000–5500 BP), are composed of many other sub-lineages of Oα-F5 and extremely low frequencies of D-M174 (Additional files 1: Figure S1; Additional files 2: Table S1). Therefore, we propose that Oα-F5 may be one of the dominant paternal lineages in ancient populations of Yang-Shao culture and its successors.

In this study, we demonstrated that both sub-lineages of D-M174 and Oα-F5 are founding paternal lineages of modern Tibeto-Burman populations. The genetic patterns suggested that the ancestor group of modern Tibeto-Burman populations may be an admixture of two distinct ancient populations. One of them may be hunter–gatherer populations who survived on the plateau since the Paleolithic Age, represented by varied sub-lineages of sub-lineages of D-M174. The other one was comprised of farmers who migrated from the middle Yellow River basin, represented by sub-lineages of Oα-F5. In general, the genetic evidence in this study supports the conclusion that the appearance of the ancestor group of Tibeto-Burman populations was triggered by the Neolithic expansion from the upper-middle Yellow River basin and admixture with local populations on the Tibetan Plateau (Su et al. 2000).

Simplified phylogenetic tree showing sample locations. The size of the circle for each sampling location corresponds to the number of samples

Two neolithic expansion origins of Tibeto‑Burman populations

We also observed significant differences in the paternal gene pool of different subgroups of Tibeto-Burman populations. Haplogroup D-M174 contributed ~ 54% percent in a sampling of 2354 Tibetan males throughout the Tibetan Plateau (Qi et al. 2013). Previous studies have also found high frequencies of D-M174 in other populations on the Tibetan Plateau (Shi et al. 2008), including Sherpa (Lu et al. 2016) and Qiang (Wang et al. 2014). In contrast, haplogroup D-M174 is rare or absent from Tibeto-Burman populations from Northeast India and Burma (Shi et al. 2008). In populations of the Ngwi-Burmese language subgroup, the average frequencies of haplogroup D-M174 are ~ 5% (Dong et al. 2004; Peng et al. 2014). Furthermore, we found that lineage Oα1c1b-CTS5308 is mainly found in Tibeto-Burman populations from the Tibetan Plateau. In contrast, lineage Oα1c1a-Z25929 was found in Tibeto-Burman populations from Northeast India, Burma, and the Yunan and Hunan provinces of China (Additional files 1: Figure S1; Additional files 2: Table S1). In general, enrichment of lineage Oα1c1b- CTS5308 and high frequencies of D-M174 can be found in most Tibeto-Burman populations on the Tibetan Plateau and adjacent regions, whereas Tibeto-Burman populations from other regions tend to have lineage Oα1c1a-Z25929 and a little to no percentage of D-M174.

The inconsistent pattern we observed in the paternal gene pool of modern Tibeto-Burman populations suggested that there may be two distinct ancestor groups (Fig. 3). The proposed migration routes shown in Fig. 3 are somewhat different from those proposed by Su et al. (2000). According to our age estimation, most of the D1a2a-P47 samples belong to sub-lineage PH116, a young lineage that emerged ~ 2500 years ago (95% CI 1915–3188 years). On the other hand, continuous differentiation can be observed on a phylogenetic tree of lineages D1a1a1a1-PH4979 and D1a1a1a2-Z31591 since 6000 years ago. Therefore, we proposed that a group of ancient populations may have moved to the upper basin of the Yellow River and admixed intensively with local populations with high frequencies of haplogroup D-M174, including its sub-lineage D1a2a-P47 (Fig. 3). This ancestor group eventually gave birth to modern Tibeto-Burman populations on the Tibetan Plateau and adjacent regions. The other ancestor group moved toward the southwest and finally reached South East Asia (Burma and other locations) and the northeastern part of India (Fig. 3). This ancestor group may have had no or a minor admixture of D-M174 in their paternal gene pool.

Two proposed ancestor groups and migration routes for Tibeto-Burman populations

Long‑term admixture before expansion to a high‑altitude region

It is interesting to investigate the time gap between the appearance of Neolithic cultures in the northeastern part of the Tibetan Plateau and the final phase of human expansion across the Tibetan Plateau. The Majiayao culture (~ 5400–4900 BP) is the earliest Neolithic culture in the northeastern part of the Tibetan Plateau (Liu et al. 2010). However, previous archeological study has suggested that the final phase of diffusion into the high-altitude area of the Tibetan Plateau occurred at approximately 3.6 kya (Chen et al. 2015). Our genetic evidence in this study is consistent with this scenario based on archeological evidence. Based on Y-chromosome analysis in this study, many unique lineages of Tibeto-Burman populations emerged between 6000 years ago and 2500 years ago (Additional files 3: Table S2). The most recent common age of D1a2-PH116, a sub-lineage that spread throughout the Tibetan Plateau, is only 2500 years ago.

We propose that there may be two important factors for the observed age gap. First, living in a high-altitude environment may require some crucial physical characteristics that were lacking from Neolithic immigrants from the middle Yellow River Basin. Intense genetic admixture with local people who had survived on the Tibetan Plateau since the Paleolithic Age may have actually guaranteed the expansion of humans across the Tibetan Plateau. Therefore, a long period of admixture, lasting from 5.4 to 3.6 kya, may be necessary for the appearance of a population with beneficial genetic variants that was genetically adapted to the high-altitude environment. Second, technological innovations, such as the domestication of wheat and highland barley (Chen et al. 2015), establishment of yak pastoralism (Rhode et al. 2007), and introduction of other culture elements in the Bronze Age (Ma et al. 2016), are also important factors that facilitated permanent settlements with large population sizes in the high-altitude area of the Tibetan Plateau.


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.

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.


Demographic history and genetic adaptation in the Himalayan region

Open access Demographic history and genetic adaptation in the Himalayan region inferred from genome-wide SNP genotypes of 49 populations, by Arciero et al. Mol. Biol. Evol (2018), accepted manuscript (msy094).

Abstract (emphasis mine):

We genotyped 738 individuals belonging to 49 populations from Nepal, Bhutan, North India or Tibet at over 500,000 SNPs, and analysed the genotypes in the context of available worldwide population data in order to investigate the demographic history of the region and the genetic adaptations to the harsh environment. The Himalayan populations resembled other South and East Asians, but in addition displayed their own specific ancestral component and showed strong population structure and genetic drift. We also found evidence for multiple admixture events involving Himalayan populations and South/East Asians between 200 and 2,000 years ago. In comparisons with available ancient genomes, the Himalayans, like other East and South Asian populations, showed similar genetic affinity to Eurasian hunter-gatherers (a 24,000-year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya. The high-altitude Himalayan populations all shared a specific ancestral component, suggesting that genetic adaptation to life at high altitude originated only once in this region and subsequently spread. Combining four approaches to identifying specific positively-selected loci, we confirmed that the strongest signals of high-altitude adaptation were located near the Endothelial PAS domain-containing protein 1 (EPAS1) and Egl-9 Family Hypoxia Inducible Factor 1 (EGLN1) loci, and discovered eight additional robust signals of high-altitude adaptation, five of which have strong biological functional links to such adaptation. In conclusion, the demographic history of Himalayan populations is complex, with strong local differentiation, reflecting both genetic and cultural factors; these populations also display evidence of multiple genetic adaptations to high-altitude environments.

Population samples analysed in this study. A. Map of South and East Asia, highlighting the four regions examined, and the colour assigned to each. B. Samples from the Tibetan Plateau. C.Samples from Nepal. D. Samples from Bhutan and India. The circle areas are proportional to the sample sizes. The three letter population codes in B-D are defined in supplementary table S1.

Relevant excerpts:

Genetic affinity to ancestral populations

We explored the genetic affinity between the Himalayan populations and five ancient genomes using f3-outgroup statistics. Himalayans show greater affinity to Eurasian hunter-gatherers (MA-1, a 24,000- year-old Upper Palaeolithic Siberian), and the related Bronze Age Yamnaya, than to European farmers (5,500-4,800 years ago; Fig. 5A) or to European hunter-gatherers (La Braña, 7,000 years ago; Fig. 5B), like other South and East Asian populations. We further explored the affinity of Himalayan populations by comparing them with the 45,000-year-old Upper Palaeolithic hunter-gatherer (Ust’-Ishim) and each of MA-1, La Braña, or Yamnaya. Himalayan individuals cluster together with other East Asian populations and show equal distance from Ust’-Ishim and the other ancient genomes, probably because Ust’-Ishim belongs to a much earlier period of time (supplementary fig. S15). We also explored genetic affinity between modern Himalayan populations and five ancient Himalayans (3,150 1,250 years old) from Nepal. The ancient individuals cluster together with modern Himalayan populations in a worldwide PCA (supplementary fig. S16), and the f3-outgroup statistics show modern high-altitude populations have the closest affinity with these ancient Himalayans, suggesting that these ancient individuals could represent a proxy for the first populations residing in the region (supplementary fig. S17 and supplementary table S4). Finally, we explored the genetic affinity of Himalayan samples with the archaic genomes of Denisovans and Neanderthals (Skoglund and Jakobsson 2011), and found that they show a similar sharing pattern with Denisovans and Neanderthals to the other South and East Asian populations. Individuals belonging to four Nepalese, one Cambodian, and three Chinese populations show the highest Denisovan sharing (after populations from Australia and Papua New Guinea) but these values are not significantly greater than other South and East Asian populations (supplementary figs. S18 and S19).

Genetic structure of the Himalayan region populations from analyses using unlinked SNPs. A. PCA of the Himalayan and HGDP-CEPH populations. Each dot represents a sample, coded by region as indicated. The Himalayan region samples lie between the HGDP-CEPH East Asian and South Asian samples on the right-hand side of the plot. B. PCA of the Himalayan populations alone. Each dot represents a sample, coded by country or region as indicated. Most samples lie on an arc between Bhutanese and Nepalese samples; Toto (India) are seen as extreme outlier in the bottom left corner, while Dhimal (Nepal) and Bodo (India) also form outliers.

NOTE. The variance explained in the PCA graphics seems to be too high. This happened recently also with the Damgaard et al. (2018) papers (see here the comment by Iosif Lazaridis).

Similarities and differences between high-altitude Himalayan

The most striking example is provided by the Toto from North India, an isolated tribal group with the lowest genetic diversity of the Himalayan populations examined here, indicated by the smallest long-term Ne (supplementary fig. S5), and a reported census size of 321 in 1951 (Mitra 1951), although their numbers have subsequently increased. Despite this extreme substructure, shared common ancestry among the high-altitude populations (Fig. 2C and Fig. 3) can be detected, and the Nepalese in general are distinguished from the Bhutanese and Tibetans (Fig. 2C) and they also cluster separately (Fig. 3). In a worldwide context, they share an ancestral component with South Asians (supplementary fig. S2). On the other hand, the Tibetans do not show detectable population substructure, probably due to a much more recent split in comparison with the other populations (Fig. 2C and supplementary fig. S6). The genetic similarity between the high-altitude populations, including Tibetans, Sherpa and Bhutanese, is also supported by their clustering together on the phylogenetic tree, the PCA generated from the co-ancestry matrix generated by fineSTRUCTURE (supplementary fig. S10 and S11), the lack of statistical significance for most of the D-statistics tests (Yoruba, Han; high-altitude Himalayan 1, high-altitude Himalayan 2), and the absence of correlation between the increased genetic affinity to lowland East Asians and the spatial location of the Himalayan populations (supplementary figs. S12 and S13). Together, these results suggest the presence of a single ancestral population carrying advantageous variants for high-altitude adaptation that separated from lowland East Asians, and then spread and diverged into different populations across the Himalayan region. (…)

Recent admixture events

Genetic structure of the Himalayan region populations from analyses using unlinked SNPs. C. ADMIXTURE (K values of 2 to 6, as indicated) analysis of the Himalayan samples. Note that most increases in the value of K result in single population being distinguished. Population codes in C are defined in supplementary table S1.

Himalayan populations show signatures of recent admixture events, mainly with South and East Asian populations as well as within the Himalayan region itself. Newar and Lhasa show the oldest signature of admixture, dated to between 2,000 and 1,000 years ago. Majhi and Dhimal display signatures of admixture within the last 1,000 years. Chetri and Bodo show the most recent admixture events, between 500 and 200 years ago (Fig. 4, supplementary tables S3). The comparison between the genetic tree and the linguistic association of each Himalayan population highlights the agreement between genetic and linguistic sub-divisions, in particular in the Bhutanese and Tibetan populations. Nepalese populations show more variability, with genetic sub-clusters of populations belonging to different linguistic affiliations (Fig. 3B). Modern high-altitude Himalayans show genetic affinity with ancient genomes from the same region (supplementary fig. S17), providing additional support for the idea of an ancient high-altitude population that spread across the Himalayan region and subsequently diverged into several of the present-day populations. Furthermore, Himalayan populations show a similar pattern of allele sharing with Denisovans as other South-East Asian populations (supplementary fig. S18 and S19). Overall, geographical isolation, genetic drift, admixture with neighbouring populations and linguistic subdivision played important roles in shaping the genetic variability we see in the Himalayan region today.


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


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.

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.


Ancient Di-Qiang people show early links with Han Chinese


Bernard Sécher reports on a recent article, Ancient DNA reveals genetic connections between early Di-Qiang and Han Chinese, by Li et al., BMC Evolutionary Biology (2017).


Ancient Di-Qiang people once resided in the Ganqing region of China, adjacent to the Central Plain area from where Han Chinese originated. While gene flow between the Di-Qiang and Han Chinese has been proposed, there is no evidence to support this view. Here we analyzed the human remains from an early Di-Qiang site (Mogou site dated ~4000 years old) and compared them to other ancient DNA across China, including an early Han-related site (Hengbei site dated ~3000 years old) to establish the underlying genetic relationship between the Di-Qiang and ancestors of Han Chinese.

We found Mogou mtDNA haplogroups were highly diverse, comprising 14 haplogroups: A, B, C, D (D*, D4, D5), F, G, M7, M8, M10, M13, M25, N*, N9a, and Z. In contrast, Mogou males were all Y-DNA haplogroup O3a2/P201; specifically one male was further assigned to O3a2c1a/M117 using targeted unique regions on the non-recombining region of the Y-chromosome. We compared Mogou to 7 other ancient and 38 modern Chinese groups, in a total of 1793 individuals, and found that Mogou shared close genetic distances with Taojiazhai (a more recent Di-Qiang population), Hengbei, and Northern Han. We modeled their interactions using Approximate Bayesian Computation, and support was given to a potential admixture of ~13-18% between the Mogou and Northern Han around 3300–3800 years ago.

Mogou harbors the earliest genetically identifiable Di-Qiang, ancestral to the Taojiazhai, and up to ~33% paternal and ~70% of its maternal haplogroups could be found in present-day Northern Han Chinese.

MDS plot of genetic distance Fst between 3 ancient and 38 modern Chinese groups

Interesting times now for the investigation of potential migrations associated with the expansion of Sino-Tibetan and Altaic languages


Human ancestry solves language questions? New admixture citebait


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?


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

Indo-European and Central Asian admixture in Indian population, dependent on ethnolinguistic and geodemographic divisions


Preprint paper at BioRxiv, Dissecting Population Substructure in India via Correlation Optimization of Genetics and Geodemographics, by Bose et al. (2017), a mixed group from Purdue University and IBM TJ Watson Research Center. A rather simple paper, which is nevertheless interesting in its approach to the known multiple Indian demographic divisions, and in its short reported methods and results.


India represents an intricate tapestry of population substructure shaped by geography, language, culture and social stratification operating in concert. To date, no study has attempted to model and evaluate how these evolutionary forces have interacted to shape the patterns of genetic diversity within India. Geography has been shown to closely correlate with genetic structure in other parts of the world. However, the strict endogamy imposed by the Indian caste system, and the large number of spoken languages add further levels of complexity. We merged all publicly available data from the Indian subcontinent into a data set of 835 individuals across 48,373 SNPs from 84 well-defined groups. Bringing together geography, sociolinguistics and genetics, we developed COGG (Correlation Optimization of Genetics and Geodemographics) in order to build a model that optimally explains the observed population genetic sub-structure. We find that shared language rather than geography or social structure has been the most powerful force in creating paths of gene flow within India. Further investigating the origins of Indian substructure, we create population genetic networks across Eurasia. We observe two major corridors towards mainland India; one through the Northwestern and another through the Northeastern frontier with the Uygur population acting as a bridge across the two routes. Importantly, network, ADMIXTURE analysis and f3 statistics support a far northern path connecting Europe to Siberia and gene flow from Siberia and Mongolia towards Central Asia and India.

Among the most interesting results (emphasis mine):

Our meta-analysis of the ADMIXTURE output shows that the IE and DR populations across castes shared very high ancestry, indicating the autochthonous origin of the caste system in India (Figure 2). f3 statistics show that most of the castes and tribes in India are admixed, with contributions from other castes and/or tribes, across languages affiliations (Supplementary Table 4 and Supplementary Note). The geographically isolated Tibeto-Burman tribes and the Dravidian speaking tribes appear to be the most isolated in India. Linear Discriminant Analysis on the normalized data set clearly supports genetic strati cation by castes and languages in the Indian sub-continent


Our meta-analysis of the ADMIXTURE plot in Figure 4A quantifies the ADMIXTURE results (darker colors indicate higher pairwise shared ancestry). Indian populations show a greater proportion of shared ancestry with the so-called Indian Northwestern Frontier populations, namely the tribal populations spanning Afghanistan and Pakistan. Central Asian populations share higher degrees of ancestry with IE and DR Froward castes. Uygurs share high degrees of ancestry with Indian populations.


f3 statistics (all negative Z-scores are shown) indicate Chinese and Siberian ancestry contributing to the Tibeto-Burman tribal speakers. On the other hand, the Mongols and the Europeans have contributed significant amounts of ancestry to the Indo-European and Tibeto-Burman forward castes. F3 statistics also show that the Central Asians are an admixed population with signs of admixture from Caucasus and other parts of Europe.

Among the results for proportions of shared ancestry between Indians and Eurasians (FIG. 4), there is an obvious influence of European admixture (Caucasus, and Southern, Central, and Northern EU), potentially from the Yamna-Corded Ware expansion, in IE_ForwardCaste, which is lessened in IE_BackwardCaste and also in IE_Tribal, while DR_ForwardCaste shows again more admixture than IE_Tribal, but diminishing with lower castes and quite low in DR_Tribal.

Ancestry from Central Asia is strong with a similar pattern, which hints at the influence of Sintashta, Andronovo, and BMAC influence in the expansion of the Steppe component, even more than a later Turkic component.

On the other hand, the influence from Turkey is difficult to assess, given the complex genetic history of Anatolia, but the map contained in Fig. 6 doesn’t feel right, not only from a genetic viewpoint, but also from linguistic and archaeological points of view. This is the typical map created with admixture analyses that is wrong because of not taking into account anthropological theories.

Quite interesting is then the influence of admixture in these different ethnolinguistic groups, Indo-European and Dravidic, which points to an initially greater expansion of Indo-European speakers, and later resurge of Dravidian languages.

Featured image contains simplified origin and data of samples studied, from the article.


Two more studies on the genetic history of East Asia: Han Chinese and Thailand


A comprehensive map of genetic variation in the world’s largest ethnic group – Han Chinese, by Charleston et al. (2017).

It is believed – based on uniparental markers from modern and ancient DNA samples and array-based genome-wide data – that Han Chinese originated in the Central Plain region of China during prehistoric times, expanding with agriculture and technology northward and southward, to become the largest Chinese ethnic group.


As are most non-European populations around the globe, the Han Chinese are relatively understudied in population and medical genetics studies. From low-coverage whole-genome sequencing of 11,670 Han Chinese women we present a catalog of 25,057,223 variants, including 548,401 novel variants that are seen at least 10 times in our dataset. Individuals from our study come from 19 out of 22 provinces across China, allowing us to study population structure, genetic ancestry, and local adaptation in Han Chinese. We identify previously unrecognized population structure along the East-West axis of China and report unique signals of admixture across geographical space, such as European influences among the Northwestern provinces of China. Finally, we identified a number of highly differentiated loci, indicative of local adaptation in the Han Chinese. In particular, we detected extreme differentiation among the Han Chinese at MTHFR, ADH7, and FADS loci, suggesting that these loci may not be specifically selected in Tibetan and Inuit populations as previously suggested. On the other hand, we find that Neandertal ancestry does not vary significantly across the provinces, consistent with admixture prior to the dispersal of modern Han Chinese. Furthermore, contrary to a previous report, Neandertal ancestry does not explain a significant amount of heritability in depression. Our findings provide the largest genetic data set so far made available for Han Chinese and provide insights into the history and population structure of the world’s largest ethnic group.

Using Shanghai individuals as representatives, shared drift between Chinese and ancient humans are computed by calculating the outgroup f3 statistics of the form f3(Mbuty;X, Y), with ancient individuals separated into approximately Palaeolithic, Mesolithic, Neolithic , and Chalcolithic-Medieval times. it is found that modern Chinese individuals show greater shared drift with pre-Neolithic hunter-gatherers rather than Neolithic farmers (Featured image from the article).

EDIT (17/7/2017): Davidski at Eurogenes shares an interesting view on this kind of results:

These sorts of estimates always look way off. And I doubt that it’s largely the result of the Silk Road, which linked China to the Near East and Mediterranean rather than to Northern Europe. More likely it reflects gene flow from the Pontic-Caspian steppe in Eastern Europe during the Bronze and Iron ages, via the Afanasievo, Andronovo, and other closely related steppe peoples

New insights from Thailand into the maternal genetic history of Mainland Southeast Asia, by Kutanan et al. (2017)


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 1,234 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 sequences 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 extension and expansion of TK people through MSEA, although there is also some support for an admixture model. 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.