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Post by Admin on Jun 12, 2019 18:23:26 GMT
Genomic IBD segments shared by Belarusian Lipka Tatars and Eurasian populations We assessed patterns of IBD sharing between BLT and European, Caucasus/Middle Eastern, Central Asian, Siberian/Mongolian and Chinese populations using a refined IBD algorithm21,22, and compared them with the IBD sharing between Belarusians and the same groups of populations (Fig. 3). Based on limited inference from haploid data11 we assumed Belarusians to be a good proxy for differentiating between a background, hence geography determined IBD sharing and IBD sharing due to recent migration from Siberia, Central and East Asia. Figure 3: Parameters of IBD sharing between Belarusian Lipka Tatars, Belarusians and other Eurasian populations. BLT demonstrate the highest level of IBD sharing with Central-East European host populations, followed by populations of the Volga-Uralic region (~4 and 2.6 IBD segments (5 and 4 cM) per pair, respectively) (Fig. 3). The degree of IBD sharing between both BLT and Belarusians on the one hand and populations to the south — North and South Caucasus — on the other, reduces abruptly (~1 and 0.6 segments (1.6 and 0.8 cM) per pair, respectively (Fig. 3). In contrast to this pattern, BLT display increased level of average number of IBD segments and average total length of genome shared identical-by-descent with Kazakhs from Central Asia (2 segments and 3.5 cM per pair), and most of the Siberian and Mongolian populations (~1.7 segments (2.5 cM) per pair), compared to Belarusians, who on average share 1 IBD segment (1.6 cM) per pair with individuals from those populations (Fig. 3). Thus, IBD analysis reveals pronounced admixture between BLT and their contemporary host populations on one hand, and a signal of shared genetic ancestry with populations from a region spanning Kazakhstan, South Siberia/Mongolia and northern China on the other. Likely sources of East Eurasian ancestry in the gene pool of Belarusian Lipka Tatars Among the two major genetic components revealed in the gene pools of BLT, an East Eurasian one, relating BLT to the Siberian/Mongolian region, potentially incorporates information about their nomadic ancestry. Therefore, we made an in-depth characterization of the East Eurasian component in the gene pool of BLT using three sets of data: Y-chromosome, mtDNA and genome-wide genotypes. The paternal Y-STR haplotypes of J2a(xM67), Q1a-M346, R1a-Z2125 and R1b-M478 (Supplementary Figs 4, 6 and 9, Supplementary Table 4), as well as complete mtDNA sequences of haplogroups D4j*, D4j12, D2b1 and G2a1 (Supplementary Fig. 11) from the BLT, are phylogenetically closest to those found predominantly among modern Central Asian (Kazakhs, Kyrgyz, but also Uzbeks) and Siberian/Mongolian populations (mainly Buryats, Tuvinians, Khakasses and Teleuts, but also Shors, Barga Mongols, Kalmyks, Khamnigans, Yakuts and Evenkis). Genetic links between BLT and Caucasus and, to a lesser extent, Volga-Uralic populations, are exemplified by mtDNA haplogroups D4j12 and G2a1, and Y-chromosome haplogroups J2a(xM67), G2a-U1 and J1-P58 (Supplementary Figs 11,4,2,3 and 10). The distribution of IBD segments and autosomal haplotypes demonstrate a strong affinity between BLT and populations from South Siberian/Mongolian region, and with Kazakhs (Fig. 3; Supplementary Table 16). As the BLT uniparental haplotypes are generally absent in their neighboring East Europeans, including Belarusians11,24 and as there is an excess of IBD segments between geographically distant populations of BLT and Siberians/Mongolians, we conclude that the presence of these haplotypes in the BLT gene pool is a result of a migration event(s) rather than a long-term process of genetic diffusion. Moreover, as BLT share East Eurasian-like haplotypes with various modern populations across Eurasia from the Caucasus to North-East China, it is likely that complex migration/admixture events, involving highly mobile ancestral population(s) have contributed to the formation of the BLT gene pool. Another noteworthy conclusion from our data is that whatever migration event(s) brought East Eurasian genetic components to the gene pool of modern BLT, it has involved both men and women. Belarusian Lipka Tatars as a former Turkic-speaking population Although BLT today speak Belarusian or Russian, it is documented that their ancestors spoke a Kipchak language(s) of the Turkic family but switched to Slavic sometime after their settlement in the territory of the Grand Duchy of Lithuania (Supplementary Information Text (Linguistics))5,7,36. Furthermore, it is interesting to note that several tribal names in BLT are found simultaneously in numerous contemporary Turkic- and Mongolian-speaking peoples, suggesting that the same Turkic and initially Mongolian tribes contributed to the ethnogenesis of these populations including BLT (Supplementary Table 19). Thus, both linguistic and anthroponymic evidence suggest a cultural affiliation of BLT with many Turkic-speaking populations living today across the Eurasian Steppe. Many Turkic-speaking populations, whilst genetically resembling their non-Turkic geographic neighbors, have retained genomic chunks shared with populations of South Siberia and Mongolia (SSM)2. Likewise, here we have found an excess of IBD segments shared between BLT and Siberian/Mongolian/northern Chinese populations, as well as Kazakhs from Central Asia, when compared to Belarusians (Fig. 3). We suggest, that the IBD pattern observed in BLT, currently non-Turkic speakers, reveals a “Turkic-specific” genetic signal shared to some extent by almost all modern Turkic speakers2. The proportion of the presumed East Eurasian component that is likely to incorporate this “Turkic-specific” genetic footprint in the genomes of BLT, is substantially higher (~30%) when compared to many Turkic-speaking populations in western Eurasia such as Gagauz, Turks, Iranian Azeri, Balkars, Kumyks and Turkmens, and is as high as in the Volga Tatars according to (Fig. 2B). In this context it is interesting to compare BLT with Gagauz people, who also reside in the western fringes of Eastern Europe and, similarly to BLT, are thought to originate from Medieval Turkic nomads, either from the “Russian Steppe” or migrants from Anatolia37. In contrast to BLT, however, although Gagauz switched from Islam to Orthodox Christianity in medieval times, they still speak a language close to Oghuz Turkic spoken in Turkey. Furthermore, the uniparental gene pools of Gagauz harbor no haplogroups that can be unanimously described as East Eurasian38,39; and they virtually lack an East Eurasian signal in their autosomal genomes2, confirmed in the present study (Figs 2 and 3). Hence, peoples of two mid-European Turkic enclaves must have had contrasting demographic histories; while BLT retained a strong genetic signal of their nomadic, in part East Eurasian, origin, in the case of Gagauz a language shift among a Medieval Balkan population to Turkic is a more likely scenario. Scientific Reports volume 6, Article number: 30197 (2016)
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Post by Admin on Aug 17, 2019 17:37:24 GMT
A new study published in Nature Ecology & Evolution by University of Chicago Medicine genetic researcher Luis Barreiro, Ph.D., shows that the opposite is true when comparing farmers and hunter-gatherers in southwest Uganda. Instead, the immune systems of hunter-gatherers showed more signs of positive natural selection, in particular among genes involved in the response to viruses. Fig. 1: Transcriptional differences between Batwa-HG and Bakiga-AG populations. "It's the complete opposite of what we expected based on the long-standing hypothesis that the advent of agriculture increased selective pressures imposed by pathogens in human populations," said Barreiro, the study's senior author and an associate professor in the university's section of genetic medicine. Researchers studied the blood of the Batwa, a rainforest hunter-gatherer population from southwest Uganda, and compared it to the blood of their Bantu-speaking agriculturalist neighbors, the Bakiga. Fig. 2: Differences in immune response between HG and AG populations. White blood cells from the two groups were isolated and exposed to Gardiquimod, which mimics a viral infection, and lipopolysaccharide, which simulates a bacterial infection. The authors observed increased divergence between hunter-gatherers and agriculturalists in their immune responses to viruses, compared to that for bacterial infections. A significant proportion of these differences were shown to be under genetic control and affected by recent positive natural selection. Fig. 3: Analysis of the contribution of genetics to differences in immune response between the HG-Batwa and the AG-Bakiga "These findings suggest that differences in viral exposure may have been key contributors to the divergence in immune responses between the Batwa and the Bakiga populations," said co-author George Perry, Ph.D., an associate professor of anthropology and biology at Penn State. This study, published July 29, marks the first time the immune systems of hunter-gatherers and farmers have been compared to help researchers understand how agriculture may have impacted our immune system. The team spent three years establishing connections and discussing mutual research interests with the Batwa and Bakiga prior to collecting any blood samples. The Batwa have lived in settlements along the edges of the Bwindi Impenetrable Forest since 1991, after being displaced from the rainforest. As a result, the researchers limited their Batwa blood samples to individuals born before 1991 who had actually lived in the forest. Fig. 4: Evidence of selection driving population differences in immune response. Since collecting the blood samples, Barreiro, Perry and other team members have returned to Uganda multiple times to present the results of their research with these communities. The researchers cautioned that the Batwa and Bakiga populations likely diverged more than 60,000 years ago, long before the origination and spread of agriculture in Africa. Nature Ecology & Evolution volume 3, pages1253–1264 (2019) The shift from a hunter-gatherer to an agricultural mode of subsistence is believed to have been associated with profound changes in the burden and diversity of pathogens across human populations. Yet, the extent to which the advent of agriculture affected the evolution of the human immune system remains unknown. Here we present a comparative study of variation in the transcriptional responses of peripheral blood mononuclear cells to bacterial and viral stimuli between Batwa rainforest hunter-gatherers and Bakiga agriculturalists from Uganda. We observed increased divergence between hunter-gatherers and agriculturalists in the early transcriptional response to viruses compared with that for bacterial stimuli. We demonstrate that a significant fraction of these transcriptional differences are under genetic control and we show that positive natural selection has helped to shape population differences in immune regulation. Across the set of genetic variants underlying inter-population immune-response differences, however, the signatures of positive selection were disproportionately observed in the rainforest hunter-gatherers. This result is counter to expectations on the basis of the popularized notion that shifts in pathogen exposure due to the advent of agriculture imposed radically heightened selective pressures in agriculturalist populations.
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Post by Admin on Nov 1, 2019 19:21:45 GMT
Much of the Neanderthal DNA that has been retained in the modern genome influences the immune system, hair and skin, and neurological development. But to figure out which genetic regions have divergent regulatory effects in ancient and modern humans, Capra's lab didn't look at the Neanderthal DNA sequences that modern humans got. It looked at the sequences we didn't get. Fig. 1: Identifying divergent gene regulation between individuals using PrediXcan. First, Capra and his colleagues made an algorithm that could estimate how much a given DNA sequence might regulate the activity of nearby genes. They trained it on the PredictDB data repository of genetic variants and gene activity profiles from modern humans. They then generated two lists: those genes that lack an archaic regulator, and those that have one. To do so, the researchers applied their algorithm to the genome of the Altai Neanderthal, a woman who lived about 122,000 years ago in the Altai Mountains that span the intersection of Russia, China, Mongolia, and Kazakhstan. By comparing the predicted regulatory effects of the Neanderthal regions in the Neanderthal genome with the corresponding modern regions in our genome, the researchers deduced that we have 766 genes that lack archaic regulatory regions and are thus probably regulated differently in us than they were in the Altai Neanderthal. Fig. 2: Neanderthal sequences drive substantial divergent regulation compared with modern humans. Clinical relevance The genes are present on all 22 of our regular chromosomes and active in all 44 tissues analyzed. Similar (although not identical) results were achieved when the modern genomes were compared to those of two other ancient humans: a Denisovan who lived 72,000 years ago and the Vindija Neanderthal, who lived in Croatia 52,000 years ago. The set of divergently regulated genes is enriched in those that play roles in a number of important clinical issues, including heart attacks, miscarriage, and certain cancers. Usually, but not always, the Neanderthal sequence increases the risk of these events. Other divergently regulated genes rendered ancient hominins hairier than we are, with different skeletal and dental architecture. Fig. 3: Modern human variation in the regulation of GWARRs is associated with clinical phenotypes. So, Neanderthals were different from us and looked different from us, not necessarily because they had different genes than we do but because our genes are regulated differently. This finding might not be groundbreaking, but it is definitely still cool. And perhaps the methodology will reveal other phenotypic differences that could not be studied with fossils. Fig. 4: Genes in introgression deserts exhibit divergent regulation between modern humans and Neanderthals. Abstract Sequencing DNA derived from archaic bones has enabled genetic comparison of Neanderthals and anatomically modern humans (AMHs), and revealed that they interbred. However, interpreting what genetic differences imply about their phenotypic differences remains challenging. Here, we introduce an approach for identifying divergent gene regulation between archaic hominins, such as Neanderthals, and AMH sequences, and find 766 genes that are likely to have been divergently regulated (DR) by Neanderthal haplotypes that do not remain in AMHs. Fig. 5: Comparison of genome-wide regulatory profiles between two Neanderthals, a Denisovan and modern humans. DR genes include many involved in phenotypes known to differ between Neanderthals and AMHs, such as the structure of the rib cage and supraorbital ridge development. They are also enriched for genes associated with spontaneous abortion, polycystic ovary syndrome, myocardial infarction and melanoma. Phenotypes associated with modern human variation in these genes’ regulation in ~23,000 biobank patients further support their involvement in immune and cardiovascular phenotypes. Comparing DR genes between two Neanderthals and a Denisovan revealed divergence in the immune system and in genes associated with skeletal and dental morphology that are consistent with the archaeological record. These results establish differences in gene regulatory architecture between AMHs and archaic hominins, and provide an avenue for exploring phenotypic differences between archaic groups from genomic information alone. Nature Ecology & Evolution volume 3, pages 1598–1606(2019)
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Post by Admin on Nov 26, 2019 18:50:21 GMT
The cultural and linguistic unity of the islands and atolls of the central Pacific was first documented in detail by Johann Reinhold Forster, a naturalist on James Cook’s second voyage of discovery to the Pacific (1772–1775). He suggested that the similarity of the languages spoken there, now known as Polynesian, reflected a comparatively shallow time-depth since their dispersal1. Forster’s seminal comparative study of Austronesian languages identified the lowland region of the Philippines in Island Southeast Asia (ISEA) as the ultimate source for the Polynesian languages and proposed a long-distance migration from there by the ancestors of today’s Polynesian speakers. This appeared to be the only explanation for the striking difference in phenotype that he observed between the peoples of the central Pacific and those of the intervening region, which is now known as Melanesia. Herein, the terms Melanesia and Micronesia are used in their geographical sense. We use the term Polynesia to include all islands and atolls whose inhabitants speak Polynesian languages, including 23 found throughout Melanesia and Micronesia, referred to as outlier Polynesia (Fig. 1a). Figure 1 Sampling locations and overview of genomic diversity. (a) Sources of population data used in the present study. The Philippine group names are abbreviated as follows: Aet (Aeta); Agt (Agta); Bat (Batak); Cas (Casiguran); Kan (Kankanaey); Taga (Tagalog); Tagb (Tagbanua); Zam (Zambales); and Phi (Philippines, incorporating all other groups from this region). Colours indicate regional affiliation of populations used for analysis of autosomal DNA: orange – mainland Southeast Asia and East Asia; dark blue – Taiwan; brown – Philippines Aeta, Agta and Batak negritos; light blue – Philippines non-negritos; red – western Indonesia; pink – eastern Indonesia; purple – northern Melanesia and New Guinea; black – Australia; green –Polynesia. The usage of populations varies with the type of analysis employed (Supplementary Table S1). Inset map shows the three populations from the Leeward Society Isles, and Tahiti, the major island in the Windward Society Isles. The red circles within Micronesia and Melanesia represent 20 of the atolls and islands referred to collectively as outlier Polynesia. The red stars denote the three additional Polynesian outlier populations (Rennell and Bellona, Tikopia), which together with Tonga, were used in analysis of ancient admixture by Skoglund, et al.25. Detailed sample information is given in Supplementary Table S1. The map was created using R v. 3.4.1 (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, www.R-project.org/), and packages ‘maps’ v. 3.2.0 (https://cran.r-project.org/package=maps) and ‘mapdata’ v. 2.2-6 (https://cran.r-project.org/package=mapdata). (b) Inset at top right shows two alternative reconstructed sub-groupings of Polynesian languages discussed in the text. The critical differences are the position of the East Polynesian languages relative to the rest of nuclear Polynesian, and their relationship to the Central Northern Outlier languages. In the sub-grouping according to Pawley31 all the Polynesian Outlier languages group within Samoic implying an early separation of Proto-East Polynesian from the rest of the Nuclear Polynesian languages. In the alternative sub-grouping proposed by Wilson32 the Central Northern Outlier languages group with the languages of East Polynesia, within a larger clade containing the other Northern Outlier languages. (c) Principal components analysis of genome-wide SNP diversity in 639 individuals populations shown in panel A; axes are scaled by the proportion of variance described by the corresponding principal component. Separating the demographic histories of Polynesia and Melanesia became difficult to sustain with developments in archaeology during the second half of the 20th century. These established that the settlement of southern Melanesia ( Cruz, Vanuatu, New Caledonia and Fiji) and western Polynesia (Tonga, Samoa, Niue and Futuna) is marked by the same archaeological horizon, known as the Lapita Cultural Complex (LCC). The LCC first appears in northern Melanesia (the Bismarck Archipelago, Bougainville, and the Solomon Islands main chain) ~3,450–3,250 BP, and quickly spread into southern Melanesia ~3,200–3,000 BP, reaching Tonga and Samoa ~2,900 BP2,3,4. At the same time, the study of comparative linguistics has shown that the Oceanic branch of the Austronesian phylum of languages, of which Polynesian is a member, is spoken throughout most of Melanesia and parts of coastal New Guinea, and appears to be a recent intrusion from ISEA5. So while there is considerable overlap between the distributions of the LCC and the Oceanic languages, there remains a phenotypic divide between southern Melanesia and western Polynesia, which is observed between Fiji and Tonga6,7. A central theme in this debate is the extent to which the development of the LCC involved local people in the Bismarck Archipelago of northern Melanesia8,9,10. An alternative is that the LCC represents the arrival of a largely pre-formed cultural package carried by speakers of proto-Oceanic languages from Taiwan, via the Philippines, in ISEA11. Hypotheses are placed on a continuum from a dendritic, radiating, phylogenetic model of cultural evolution that relies on the relative isolation of populations12, to one based on complex ongoing biological and cultural interaction between groups, leading to reticulated networks of genes and culture9. A compromise position has been promoted by the recognition of a Lapita homeland in the Bismarck Archipelago10, together with evidence that the genomes of contemporary Polynesians contain 20–30% ancestry typical of northern Melanesia and New Guinea13,14. This posits a period of limited cultural and genetic admixture involving migrants from ISEA during the early LCC phase in northern Melanesia ~3,450–3,250 BP15. Polynesian society then developed in relative isolation following the pioneering settlement of Tonga and Samoa ~2,900 BP12. Genetic evidence for this intermediate model is provided by the presence of members of Y chromosome haplogroup (hg) C2a-M208, together with its daughter lineage C2a1-P33, among Polynesian speakers16,17. This is seen as a proxy for male-mediated admixture from northern Melanesian and New Guinean sources into the gene pool of migrants from ISEA during the formative period of the LCC in the Bismarck Archipelago, prior to the settlement of southern Melanesia and western Polynesia13,18. In contrast, the near fixation in Polynesian speaking groups of the mitochondrial lineage B4a1a1 is seen as evidence of a predominantly ISEA maternal heritage13,19. Subsequent research, however, has shown that B4a1a1 is widespread throughout northern Melanesia20, including regions that show no evidence of autosomal admixture with people from ISEA21. Alternatively, therefore, hg B4a1a1 might also have been present in northern Melanesia before the emergence of the LCC22,23. Similar ambiguity now exists over the origins of paternal lineage C2a-M208, due to its presence in ISEA24 and rather low overall frequencies in the Bismarck Archipelago and coastal New Guinea17. An important advance in this debate is the recovery of ancient genomic DNA from LCC contexts on Vanuatu (~2,900 BP) (n = 3) and post-Lapita Tonga (~2,500 BP) (n = 1), since the results indicate people with close to 100% ancestry related to an ISEA heritage25. These data show that some settlers of the LCC period appear to have transited northern Melanesia and New Guinea from ISEA without receiving any significant amounts of genetic admixture. A second major finding is that the 20–30% ancestry originating from northern Melanesia and New Guinea, detected in contemporary genomes from the eastern fringe of southern Melanesia and western Polynesia, appears to have arrived during the 2nd millennium BP (1,900–1,200 BP). This result is consistent with post-LCC movements of people into southern Melanesia and western Polynesia, in a process of polygenesis, being responsible for the differences in phenotype observed between the two regions6. The potential significance of this proposed post-LCC migration for the phylogenetic approach to cultural evolution cannot be overstated. This is because the model is based on an Ancestral Polynesian Society (APS) developing in a western Polynesian homeland during the mid 3rd millennium BP, followed by a rapid settlement of eastern Polynesia ~2,200 BP12. The settlement of eastern Polynesia, however, has witnessed significant reductions in the earliest secure radiometric dates in recent years. These currently stand at ~950 BP and come from Rai’atea in the Leeward Society Isles26,27, thereby excluding the original calibration for the model and subsequent revisions to it28. The archaeology for the phylogenetic model can also be challenged because the evidence post 2,500 BP suggests isolation of Tonga and Samoa, rather than the interaction invoked for the development of Proto-Polynesian language29. By ~950 BP, society in western Polynesia was differentiated, both culturally and linguistically, indicating that, if this late chronology is accurate, the source population for eastern Polynesia was likely a regional group rather than the hypothetical APS29,30. A central component of the original phylogenetic model is the long-standing sub-grouping of the Polynesian languages. The initial divergence of Nuclear Polynesian from the Tongic languages is followed by a second-order split, between Proto-East Polynesian (Rapa Nui, Marquesan and Tahitic) and the rest of the Nuclear Polynesian languages (Samoic and all the Polynesian outlier languages)31 (Fig. 1b, left-hand tree). This sub-grouping recognizes the separation of Tongic and Samoic but is difficult to reconcile with a settlement of eastern Polynesia commencing ~950 BP, since it necessitates the second-order split, involving Proto-East Polynesian, to occur up to ~1,200 years earlier. An alternative linguistic sub-grouping that places the East Polynesian languages together with those of the central northern outliers (east coast of the northern Solomon Islands) provides a potential solution for the apparent discordance between archaeology and language32,33 (Fig. 1b, right-hand tree). This also challenges the orthodoxy within Polynesian studies that eastern Polynesia was settled directly from Samoa11,12,28. For Kirch and Green28, Samoa is ancient Hawa’iki, the cradle of Polynesian culture. In contrast, for Wilson32 Hawa’iki represents the ancient name for the Leeward Society Isles, which are referred to as the cultural and spiritual hub of eastern Polynesia in oral histories of the region, from where other islands and atolls were settled34. The Leeward Society Isles, therefore, are of central importance to understanding the reasons for these conflicting signals from archaeology and language. If the ancestors of the Leeward Society Islanders experienced the same episode of ancient admixture as people in western Polynesia and outlier Polynesia during the mid 2nd millennium BP25, this would support the late settlement chronology. In this study, we report the first genomic data from Bora Bora, Rai’atea and Taha’a, three of the Leeward Society Isles. We use the analysis of genotype and haplotype data to ascertain whether the signals of admixture present in these eastern Polynesian populations are similar to those from western and outlier Polynesia and identify potential donors to the ancestors of the Leeward Society Islanders. Further insights into the demographic history of eastern Polynesia is provided by the first deep re-sequencing of Polynesian Y chromosomes, complemented by high-resolution genotyping of key paternal and maternal lineages from the Leeward Society Isles and New Zealand.
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Post by Admin on Nov 27, 2019 17:49:41 GMT
Results Data Here we present a new genomic dataset sampled from the Leeward Society Islands, eastern Polynesia. We report high-resolution autosomal genotyping data from 30 individuals, complemented by genotyping and/or re-sequencing of uniparental loci (mtDNA and Y) from 81 individuals, including seven Y chromosomes re-sequenced by a target-capture method. In addition, we present new uniparental data from 49 Maori individuals sampled in New Zealand (Supplementary Tables S1–S3). The dataset is analyzed together with publicly available data from Island Southeast Asia, Melanesia and Polynesia (Supplementary Tables S1, S4 and S5). For detailed information about samples used in the present study, please refer to the Materials and Methods section. Autosomal analysis The first two PCs of the principal components analysis (PCA, Fig. 1c) account for 38% of the variation in the studied dataset. The close overlap between eastern Polynesians and Samoans on the PC1 axis suggests similar amounts of genetic ancestry shared with New Guinea and northern Melanesia. The model-based analysis of autosomal SNPs using ADMIXTURE35 shows that, at K = 4, 70–80% of the Leeward Society Islander genomes can be characterized by the component typical of ISEA/East Asia (Fig. 2a); the remaining 20–30% of their genetic ancestry is best represented by Papuan speakers from New Guinea (light purple). From K = 5, Polynesians take their own ancestry (green), which, like their deflection on the PCA plot, is most likely due to genetic drift or, alternatively, cryptic relatedness or extreme inbreeding in studied populations. However, the latter is unlikely due to the lack of close relatives (up to third-degree, inclusive) in four Polynesian groups, and normal range of inbreeding coefficients when comparing to other human populations (F IS , Supplementary Table S6). Figure 2 The lowest cross-validation (CV) score of ADMIXTURE is observed at K = 11, but no additional ancestries appear in Polynesians after K = 10, which has the second lowest CV score (Fig. 2a, Supplementary Figs S1 and S2). At K = 10, a dark blue component appears that is almost fixed in the Kankanaey of northwestern Luzon. The distinctive and uniform profiles of additional ISEA, Melanesian, and East Asian ancestries in two (Tonga and Samoa) out of four, otherwise very closely related, Polynesian groups hint that these may be the result of an old admixture process, rather than genetic drift, extreme bottlenecks or algorithmic artifacts. In contrast, the noticeably uneven distribution of the East Asian (yellow) and western European (grey) ancestry components within the profiles of the Leeward Society individuals (Fig. 2b) is consistent with recent historical admixture events (see haplotype-based admixture analysis below). The outgroup f336 allele-sharing plot shows the length of a phylogenetic branch shared between two study populations and African Yoruba. For the Leeward Society Isles (Supplementary Fig. S3, Supplementary Table S7), the f3 allele-sharing results are consistent with a most recent evolutionary history shared with Samoa, Tahiti, and Tonga. It also suggests that the Kankanaey of the Philippines and Taiwanese aborigines are the next closest populations to all four Polynesian groups. These results remain robust to the different SNP subsets or population clustering schemes used in the present study (Supplementary Figs S3, S4, Supplementary Table S7). In contrast, the f3 admixture plots (Supplementary Fig. S5, Supplementary Table S7), which detect the presence of admixture in a study population from two reference groups, display different results for western and eastern Polynesia. These differences could be explained by a reduced effective population size for eastern Polynesians, caused by bottlenecks during the initial settlement process, or because Tonga and Samoa have experienced additional admixture since they last shared a common ancestral gene pool with Tahiti and the Leeward Society Isles.
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