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Post by Admin on Mar 23, 2016 1:12:09 GMT
Human Y chromosomes bearing the M267*G variant (defining haplogroup J1) are distributed over a vast area comprising Europe, South-western Asia, the Arabian peninsula, North and East Africa. Eight downstream SNPs have been identified so far along the J1 genealogy,1 none of which reaches appreciable frequencies in any population. Many authors have proposed STR-based motifs to trace the genealogies of pre-historic or ethno-religious ancestries. Examples are the Dys388*13 allele associated with early neolithic agro–pastoral cultures (King RJ and Underhill P, personal communication); the Galilee and the Dys388*17/YCAIIa/b*22–22 motifs for an Arab ancestry,2, 3 the Cohanim 6-locus motif to link the descendants of a Jewish priesthood.4 However, a wide range of times since the most recent common ancestor (TMRCAs) has been proposed for J1 and its subclades (between 36 and 10 KyBP), and different conflicting scenarios have been depicted to explain their current distribution.3, 5, 6, 7, 8, 9 Figure 1 A fine-grained map of the present day distribution of J1 chromosomes is given in Figure 1. The pattern is uneven, as is typical of Y lineages with a very deep genealogy and low-size demes. Frequency peaks over 50% of the whole binary variation are present in Arabia (Yemen, Qatar), Northern Caucasus (Dagestan), Sudan and in Negev Bedouins (Supplementary Table S1). Frequency is inversely correlated to haplotype diversity (R2=0.387, P<0.001, Supplementary Table S6), with Near Easterners showing the highest diversity, Dagestanians and Arabic Sudanese the lowest. No major J1 sublineage was defined by genotyped SNPs (Supplementary Table S1) confirming the need for future research efforts in this direction. Nevertheless, in the Amhara from Ethiopia, we found the very first case of a M368(xM367) chromosome, which supports the insertion of the paragroup J1e1* in the latest Y haplogroup phylogeny.1 Figure 2 With the exception of the rare Palestinian modal haplotype,10 none of the previously described STR motifs resulted equal by descent, as they were found across ethnic groups with different cultural or geographic affiliation and in other lineages (J2, I*) than J1. Such results make their use to trace ancestries of individuals or communities (ie, Arab or Jewish) inconclusive. Calculations under the coalescent model for J1 haplotypes bearing the Cohanim motif gave time estimates that place the origin of this genealogy around 6.2 Kybp (95% CI: 4.5–8.6 Kybp), earlier than previously thought,4 and well before the origin of Judaism (David Kingdom, ∼2.0 Kybp). Mismatch and multivariate analyses (Table 1, Figure 2) both pointed to common features for the Y chromosomes of Arabic speakers from Maghrib, Sudan, Iraq and Qatar (the Arabic pool). They show low diversity values, narrow mismatch curves with mode at 5–6 mutational steps and proximity at one side of the multidimensional genetic space. Opposite features were observed in a heterogeneous group, including Europeans, Kurds, Iranians and Ethiopians (the Eurasian pool); they show high haplotype diversity, are characterized by ragged mismatch curves with modes in the 11–16 range and cluster at the centre of the MDS plot. Omanis show a mix of Eurasian pool-like and typical Arabic haplotypes as expected, considering the role of corridor played at different times by the Gulf of Oman in the dispersal of Asian and East African genes.7 Figure 3 We wondered whether clustering and similarities among mismatch curves in the Arabic pool reflect shared evolutionary history, following the hypothesis of a diffusion of J1 chromosomes mediated by the spread of Islam since 650 AD.2, 3, 9 To investigate this aspect in more detail, we compared the haplotype genealogy of the Eurasian and Arabic pools by using Median-joining networks constructed as described9 (Figure 3). The genealogy of the Arabic pool shows a star-like pattern with no geographic structuring. This feature supports a demic expansion from ancestral haplotypes currently shared by Maghrebians and Arabians and subsequent migrations. The Eurasian genealogy is deeper and suggests a longer evolution under constant size. Accordingly, we assigned priors under different size models while applying a Bayesian approach14 to estimate coalescence times for samples' genealogies (Table 1). Results for Arabic populations and associated STR motifs (Galilee, Dys388*17/YCAII*22–22) excluded the timeline of the Arab expansion (1.35 KyBP), even from their lower confidence bounds, and pointed to a mid-Holocene time frame of 5.5–7.2 KyBP (median TMRCAs). This time window is related to a pre-historic phase of regionalisation in the human occupation of Sahara and Arabia, when semi-nomadic tribes, once diffused all over the Desert, retreated in water-rich refuges (ie, the Atlas range,15 the Sudanese plateau,16 Southern Arabia17) as a consequence of the rapid decline of monsoon rainfalls. In Eastern Sahara, it is associated with the rise of a dual productive economy, where specialised cattle pastoralism came to coexist with sedentary lifestyles, cereal farming and pottery production, clearly rooted in near East traditions. The genetic legacy of the mid-Holocene dispersal of foraging groups in the Sudanese Sahara, North Africa and Arabia would be tracked by Arabic J1-M267 chromosomes while the dispersal of agro–pastoralists with near eastern origins by other Y (E1b-M34 and E1b-M7818) or mitochondrial (U6b19) lineages. As regards chromosomes bearing alleles Dys388*13 and YCAIIa*19, which are common in populations of the Eurasian pool and in Northern Caucasians, we, in general, obtained Late Pleistocene coalescence times – around 10.1–10.9 KyBP as average median values. These time estimates, summed to the high variation and wide distribution of these subclades, are consistent with the episodes of spatial re-expansion that occurred after the Last Glacial Maximum in the northern hemisphere, the latest being triggered by the end of the Younger Dryas event (12.9–11.6 KyBP20). The different pattern observed (namely, frequency peaks in the Caucasus and Anatolia for Dys388*13, in Ethiopia and south-western Asia with the absence of haplotypes of the Arabic pool for YCAIIa*19) does not deviate from random expectations of frequency shifts under an extended Wright–Fisher model (P≫0.05). Eur J Hum Genet. 2009 Nov; 17(11): 1520–1524.
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Post by Admin on Mar 28, 2016 1:52:47 GMT
We applied a principal component analysis (PCA) to investigate the population structure of the new populations genotyped in this study from the Sudanese region (Supplementary Fig. S1a). PC1 (3.56% of the variation) follows a North-South cline and separates populations inhabiting the region between the Nile River and the Red Sea (Nubians and Arabs along the Nile, Beja and Ethiopians along the coast) from Darfurians and Nuba of South-West Sudan, and Nilotes of South Sudan. Copts are a separated group close to the North-East populations, in a more outlier position: they are the extreme of the northern genetic component. PC2 (0.7%) separates the nomadic Fulani from the other populations. Next, we combined our new populations (140 K data set) with previously studied populations of special interest for this analysis: Qatar12, Egypt13, and three sub-Saharan populations (Luhya, Yoruba and Maasai) from 1000 Genomes Project14 to have external references both in the north and south of the Sudanese region. This new data set contains 14,343 SNPs (14 K data set). Even if the number of SNPs in this second set is small, it is enough to differentiate components in the African genetic landscape15. Fig. 2 shows a PCA of this extended data set, where East African populations are distinct from both sub-Saharan and North African populations. PC1 (6.08%) separates between populations from North Africa/Middle East and sub-Saharan Africa (Fig. 2a). Copts are closer to North African and Middle East populations but remain as a separate cluster when PC2 is considered. PC2 (1.46%) along with PC1 separate the two homogeneous clusters of North-East and South-West populations: Nubians, Arabs, Beja and Ethiopians on one hand, and Nuba, Darfurians and Nilotes on the other. PC2 separates all Sudanese and Ethiopian populations from the rest. PC3 (0.56%) differentiates West-African populations (Fulani and Yoruba) from Sub-Saharan East African populations (Maasai) (Fig. 2b). Both PC analysis using data sets with different number of SNPs preserve the topology of the populations. As expected, with a low number of SNPs we observe a higher intra-population variation (Supplementary Fig. S1b). Plot shows a) PC1 and PC2 and b) PC2 and PC3 and the variation explained by them. Sudanese populations cluster in four groups according to their geographic location, with PC1 representing a north-east to south-west axis in East Africa. Populations not genotyped in this study are shown with grey filled symbols. MKK = Maasai from Kinyawa, Kenya; LWK = Luhya from Webuye, Kenya; YRI = Yoruba from Ibadan, Nigeria. Pairwise FST statistic, a measure of global population differentiation, confirmed the PCA clustering (Supplementary Table S2, Supplementary Fig. S5). Populations geographically close had low average FST values, even though population-specific characteristics were emphasized by excluding population outliers (Supplementary Fig. S4). The lowest average FST (0.003) was found both in the pair Arabs and Nubians, located at the Nile River Valley, and in the pair Beja and Ethiopians, located at the coast. Among North-East populations, Nubians had the highest FST values when compared with Beja and Ethiopians (average FST of 0.006 and 0.007 respectively). South-West populations showed higher population differentiation among themselves than North-East populations. When comparing North-East populations with South-West populations, all comparisons have a high FST (between 0.044 and 0.054). Copts, with a strong individual heterogeneity, are more similar to Arabs (FST = 0.019) than to any other East African population. Copts and South-West populations are the most distant populations (FST > 0.1). Fulani had on average lower FST values when compared to South-West (Nuba, Darfurians and Nilotes) than to North-East populations (Nubians, Arabs, Beja and Ethiopians). These values show a complex situation beyond the simple North African versus Sub-Saharan Africa main differentiation. To infer the ancestral populations of the East African individuals, we run ADMIXTURE from k = 2 to k = 10 in the 14 populations (the analysis for the internal nine populations is presented in Supplementary Fig. S7, S10). We analysed the results from k = 2 to k = 5 as higher numbers of ancestral components do not have a clear origin. A complex pattern of admixture is observed in East African populations (Fig. 3). At k = 2, we already detect different ancestries in the Sudanese populations. Copts show a common ancestry with North African and Middle Eastern populations (dark blue), whereas the South-West cluster (Darfurians, Nuba and Nilotes) share an ancestry component (light blue) with sub–Saharan samples. The North-East cluster (Beja, Ethiopians, Arabs and Nubians) shows both components, although the main component (~70%) is that detected in North Africa and Middle East (Fig. 3). A random subset of 18 individuals from each population was selected to avoid sample size bias. Columns represent individuals, where the size of each colour segment represents the proportion of ancestry from each cluster. Although k = 3 is the statistically supported model, here we show the results from k = 2 through k = 5 as they explain several ancestral components: North African/Middle Eastern (dark blue), Sub-Saharan (light blue), Coptic (dark green), Nilo-Saharan (light green) and Fulani (pink). MKK = Maasai from Kinyawa, Kenya; LWK = Luhya from Webuye, Kenya; YRI = Yoruba from Ibadan, Nigeria. Africa genetic landscape is shaped by geographic barriers19, but the forces clustering populations vary depending on the scale. On a regional scale, East Africa populations cluster mainly by linguistic affiliation5. However, it has been previously reported that language plays a lesser role in the genetic clustering of Sudanese populations, as geography is the main factor that groups them10. This observation is supported by our data, as shown in the PCA (Fig. 2.), where PC1 represents a north-east to south-west axis delimited by the Nile River and its main tributaries: the Blue Nile and the White Nile. Genetic and geographic distances between populations of the Sudanese region are positively correlated (Mantel test; r = 0.5105, p-value < 0.0001), with Sudanese populations clustering in four groups according to their geographic location (Supplementary Fig. S1). Nubians are the only Nilo-Saharan speaking group that does not cluster with groups of the same linguistic affiliation, but with Sudanese Afro-Asiatic speaking groups (Arabs and Beja) and Afro-Asiatic Ethiopians (Supplementary Fig. S1a). Y-chromosome and mitochondrial DNA studies reported Nubians to be more similar to Egyptians than to other Nilo-Saharan populations1,8: Nubians were influenced by Arabs as a direct result of the penetration of large numbers of Arabs into the Nile Valley over long periods of time following the arrival of Islam around 651 A.D20. Interestingly, our analyses shows a unique ancestry for Sudanese Nilo-Saharan speaking groups (Darfurians and Nuba) related to Nilotes of South Sudan, but not to other Sudanese populations or sub-Saharan populations (Fig. 3). This ancestral component is not present in places where the Bantu expansion left a strong footprint and creates a different genetic background that is not found among most African populations. Tishkoff et al.5. reported a common ancestry of Nilo-Saharan speaking populations. We also found this relationship of Nilo-Saharan Sudanese populations with other Nilo-Saharan populations from Kenya (Maasai), but not as strong, as Maasai show their own genetic component at k = 6, which is different from the Sudanese component (Supplementary Fig. S7) and do not cluster with our Nilo-Saharan speaking populations. In a previous Y-chromosome study8, most Nilo-Saharan speaking populations, except Nubians, showed little evidence of gene flow with other Sudanese populations. doi:10.1038/srep09996
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Post by Admin on Apr 4, 2016 1:41:54 GMT
Fig. 1. Genetic affinities of ancient Irish individuals. (A and B) Genotypes from 82 ancient samples are projected onto the first two principal components defined by a set of 354,212 SNPs from Eurasian populations in the Human Origins dataset (29) (SI Appendix, Section S9.1 and S10). Principal Component Analysis and ADMIXTURE. A projection principal component analysis (PCA) was used to investigate the genetic affinities of our four ancient Irish genomes, alongside 78 published ancient samples (SI Appendix, Table S9.1; selection criteria detailed in SI Appendix, Section S4) (5⇓⇓⇓⇓–10, 21⇓⇓⇓⇓–26) using 677 modern individuals from Western Eurasia (8) as a reference population (Fig. 1 A and B and SI Appendix, Sections S9.1 and S10). In Fig. 1A, we see a clear division between hunter–gatherer and early farmer individuals, with the Ballynahatty female plotting with five other MN samples from Germany, Spain, and Scandinavia. However, a large shift in genetic variation is seen between Ballynahatty and the three Irish Early Bronze Age samples, Rathlin1, Rathlin2, and Rathlin3, who fall in a separate central region of the graph along with Unetice and other Early Bronze Age genomes from Central and North Europe. These plots imply that ancient Irish genetic affinities segregate within European archaeological horizons rather than clustering geographically within the island. Fig. 2. Estimated distribution of ROH for ancient samples, placed in the context of values from modern populations. This plot shows total length of ROH for a series of length categories in 32 modern and 6 ancient individuals. Neolithic Origins. D and f statistics (29, 30) (Dataset S1 and SI Appendix, Section S12) support identification of Ballynahatty with other MN samples who show a majority ancestry from Near Eastern migration but with some western Mesolithic introgression. Outgroup f3 statistics indicated that Ballynahatty shared most genetic drift with other Neolithic samples with maximum scores observed for Spanish Middle, Epicardial, and Cardial Neolithic populations, and the Scandinavian individual Gok2 (SI Appendix, Section S12.1). D statistics confirmed these affinities. Interestingly, both the Spanish MN individuals and Gok2 also belong to Megalithic passage tomb cultures. Along with other MNs, Ballynahatty displays increased levels of hunter–gatherer introgression compared with earlier farming populations. D statistics revealed that, out of the three Mesolithic hunter–gatherer groupings from Haak et al. (9), eastern hunter–gatherer (EHG), Scandinavian hunter–gatherer (SHG), and western hunter–gatherer (WHG), Ballynahatty shares the most alleles with WHG and furthermore has the strongest preference for WHG over EHG and SHG out of all contemporaneous Neolithic individuals so far sampled. However, Ballynahatty forms a clade with other MN genomes to the exclusion of WHG and, symmetrically, WHG genomes form a clade to the exclusion of Ballynahatty. Of the three WHG individuals, Ballynahatty appeared to share the least amount of affinity with LaBrana (Spain) and the largest amount of affinity with Loschbour (Luxembourg). To gauge the proportion of Ballynahatty’s ancestry derived from WHG, we used the f4 ratio, f4(Mbuti, Loschbour; Ballynahatty, Dai)/f4(Mbuti, Loschbour; KO1, Dai) (29), to estimate a hunter–gatherer component of 42 ± 2% within a predominantly early farmer genome. Thus, we deduce migration has a primary role in Irish agricultural origins. Bronze Age Replacement. Prior studies (7, 9, 10) convincingly demonstrate that Central European genomes from the late Neolithic and Early Bronze Age differ from the preceding MN due to a substantial introgression originating with Steppe herders linked to cultures such as the Yamnaya. Accordingly, we used a series of tests to gauge whether the ancestries of the Rathlin Early Bronze Age genomes were subject to this influence. D statistics confirmed that Ballynahatty and other MN individuals form clades with each other to the exclusion of these Irish Bronze Age samples. Specific disruption of continuity between the Irish Neolithic and Bronze Age is clear with significant evidence of both Yamnaya and EHG introgression into Irish Bronze Age samples when placed in a clade with any MN. However, like other European Bronze Age samples, this introgression is incomplete, as they also show significant MN ancestry when placed in a clade with Yamnaya. The highest levels of MN ancestry were observed when either Ballynahatty or Gok2 (Scandinavian) was the sample under study. However, when paired with central European Bronze Age populations, the Rathlin samples show no trace of significant introgression from Ballynahatty, suggesting that earlier Irish populations may not have been a source of their partial MN ancestry. These analyses, taken with the PCA and ADMIXTURE results, indicate that the Irish Bronze Age is composed of a mixture of European MN and introgressing Steppe ancestry (9, 10). To estimate the proportion of Yamnaya to MN ancestry in each Irish Bronze Age sample, we took three approaches. First, from ADMIXTURE analysis (Fig. 1), we examined the green Caucasus ancestry component. We presume an ultimate source of this as the Yamnaya where it features at a proportion of 40% of their total ancestry. In our three Irish Bronze Age samples, it is present at levels between 6–13%, which, when scaled up to include the remaining 60% of Yamnaya ancestry, imply a total of 14–33% Yamnaya ancestry and therefore 67–86% MN in the Irish Bronze Age. Second, for each Bronze Age Irish individual, we calculated the proportion of MN ancestry by using the ratio f4(Mbuti, Ballynahatty; X, Dai)/f4(Mbuti, Ballynahatty; Gok2, Dai), which gave estimates between 72 ± 4% to 74 ± 5%, implying again a substantial Yamnaya remainder. Third, we followed the methods described in Haak et al. (9), which use a collection of outgroup populations, to estimate the mixture proportions of three different sources, Linearbandkeramik (Early Neolithic; 35 ± 6%), Loschbour (WHG; 26 ± 12%), and Yamnaya (39 ± 8%), in the total Irish Bronze Age group. These three approaches give an overlapping estimate of ∼32% Yamnaya ancestry. Fig. 3. Comparison of Irish and Central European ancient genomes for haplotype-based affinity to modern populations. Interpolated heatmaps comparing relative haplotype donations by two Irish (Ballynahatty, Rathlin1) and two Hungarian (NE1, BR2) ancient genomes. Haplotype-Based Resolution of Continuity. Haplotype-based approaches are more powerful than those using unlinked genetic loci in identifying fine genetic structure, such as that displayed among Europeans, and are relatively robust to bias from marker ascertainment (35, 36). We ran ChromoPainter in fineSTRUCTURE (Version 2) (35) to decompose each ancient genome into a series of haplotypic chunks, and identified which modern individuals from a diverse set of Eurasian populations (37) shared the same, or most similar, haplotype at each given chunk. We then considered the pattern of chunk donation between each ancient genome and modern populations. Unsurprisingly, the pattern of haplotypic affinity of Ballynahatty among modern European populations is strongly correlated to that of the earlier Neolithic samples (SI Appendix, Fig. S14.2; r > 0.74, P < 10−7), with southern Mediterranean samples in each analysis showing highest levels of chunk copying. However, some differences are discernable; the Hungarian and Stuttgart Neolithic genomes tend toward higher values in eastern Mediterranean (Sicilian, Italian, and Greek samples; Fig. 3 and SI Appendix, Fig. S14.1), and the Irish Neolithic has highest values in the west (Sardinian and Spanish). A further difference lies in the comparison of each to the affinities shown by the Luxembourg WHG, Loschbour, which shows no correlation in its modern affinities with the earlier continental Neolithics but does show a significant relationship (P = 5.4 × 10−4) with those of Ballynahatty, undoubtedly because of greater WHG admixture in her ancestry. A reconstruction of the Ballynahatty Neolithic skull by Elizabeth Black. Her genes tell us she had black hair and brown eyes. Phenotypic Analysis. Ireland is unusual in displaying world maximum frequencies of a number of important genetic variants, particularly those involved in lactase persistence and two recessive diseases: cystic fibrosis and hemochromatosis (38⇓⇓–41, 42, 43). Interestingly, both the high coverage Neolithic and Bronze Age individuals were heterozygous for the hemochromatosis alleles H63D and C282Y, respectively. Modern Irish allele frequencies are 15% and 11% for these variants, with the latter, more penetrant variant responsible for a world maximum of this disease in Ireland (44, 45). Additionally and in accordance with other data suggesting a late spread of the lactase persistence phenotype now prevalent in western Europe (7, 10), the Neolithic Ballynahatty was homozygous for the nonpersistent genotype and Rathlin1 was heterozygous and thus tolerant of drinking raw milk into adulthood. We were able to deduce that Neolithic Ballynahatty had a dark hair shade (99.5% probability), most likely black (86.1% probability), and brown eyes (97.3% probability) (46). Bronze Age Rathlin1 probably had a light hair shade (61.4%) and brown eyes (64.3%). However, each Rathlin genome possessed indication of at least one copy of a haplotype associated with blue eye color in the HERC2/OCA2 region. Lara M. Cassidy, 368–373, doi: 10.1073/pnas.1518445113
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Post by Admin on Apr 21, 2016 22:50:51 GMT
Fig. 1 Eastern Beringia during the LGM and retreat of the ice sheets. The geographic isolation of the Americas delayed human settlement until the end of the Pleistocene [20 to 10 thousand years ago (ka)]; however, despite this relatively recent date, the specific time, place, and route of entry remain uncertain. It is likely that the first peoples moved from Asia across the Bering Land Bridge (1, 2), the landmass between Eurasia and America exposed by lowered sea levels during the Last Glacial Maximum (LGM). However, at this time, much of northern North America was covered by the Cordilleran and Laurentide ice sheets, which blocked access from eastern Beringia (Bering Land Bridge and Alaska/Yukon) southward to the rest of the Americas (Fig. 1A). Shortly after the Cordilleran ice sheet began to retreat ~17 ka (3), a potential Pacific coastal route became available ~15 ka (Fig. 1B) (3, 4), whereas an alternative route through an inland ice-free corridor along the eastern side of the Rocky Mountains opened around ~11.5 to 11 ka (4–6). The timing and route used in the migration event are important in understanding the size, number, and speed of the first migratory wave(s). Timing and route are also pivotal in resolving contentious issues such as the nature of peoples before Clovis—the first widespread archaeologically recognized culture in North America (13.2 to 12.8 ka) (1). Genetic studies of Native American populations are complicated by the demographic collapse and presumed major loss of genetic diversity following European colonization at the end of the 15th century (7). However, geographically widespread signals of low diversity and shared ancestry (8–13)—particularly striking in maternally inherited mitochondrial and paternally inherited Y-chromosome sequence data—suggest that small founding groups possibly initially entered the Americas in a single migration event that gave rise to most of the ancestry of Native Americans today (9, 12, 14). In contrast, the distribution of some of the rare founding mitochondrial haplogroups (D4h3a along the Pacific coast of North and South America, and X2a in northwestern North America) suggests that distinct migrations along the coastal route and the ice-free corridor occurred within less than 2000 years (15). Recent studies have identified a weak Australasian genomic signature in several Native American groups from the Amazon, compatible with two founding migrations (16), although the Australasian gene flow may have occurred after the initial peopling (17). Irrespective of the number of migration waves, the founding population appears to have rapidly grown and expanded southward (8, 14, 18), with low levels of gene flow between areas following initial dispersion (12, 14). Fig. 2 Comparison of Bayesian estimates of the TMRCA of the Native American founder haplogroups and of the divergence from Siberian lineages. The 92 pre-Columbian mitogenomes were sequenced to an average coverage depth of 112× (5.6× to 854.2×; table S2). Sequences were assigned to 84 distinct haplotypes, which fell within the expected overall mitochondrial diversity of Native South Americans (13), that is, haplogroups A2, B2, C1b, C1c, C1d, and D1 (figs. S2 to S5). The Native South American haplogroup D4h3a was not observed in our ancient data set, although we sampled the South American southern cone (Arroyo Seco 2, Argentina) where this lineage is common today (15). None of the 84 haplotypes identified from ancient samples are represented in the existing genealogy of global human mitochondrial diversity [that is, PhyloTree mt; (24)] (figs. S2 to S5) or in the literature (fig. S6). Although modern Native American genetic diversity is not well characterized, this result clearly illustrates the importance of sampling pre-Columbian specimens to fully measure the past genetic diversity and to reconstruct the process of the peopling of the Americas. The estimated times to most recent common ancestor (TMRCA) for haplogroups A2, B2, C1, D1, and D4h3a were highly synchronous (Fig. 3 and fig. S8), confirming previous interpretations that all five haplogroups were part of one initial population (25). The TMRCA fell within the range of previous molecular date estimates, although the narrower 95% credible intervals considerably increased the precision (Fig. 2A). Older dates for the initial diversification within each haplogroup have been previously calculated using the human-chimpanzee calibration (25, 26), whereas much younger dates resulted from calibrations using non–Native American mitochondrial lineages associated with biogeographic events (20). This clearly illustrates the impact of time-dependent rate estimates and the critical influence of the calibration framework (27). Our use of a large number of temporally and phylogenetically distributed tip dates provides an accurate calibration of the molecular rates relevant to Native American early history (28), allowing a uniquely precise timeline for the peopling of the Americas. Fig. 3 Dated Bayesian mitogenomic tree and reconstruction of past effective female population size. The most recent genetic divergence observed between the ancestors of Siberians and Native Americans (24.9 ka; section S5 and Fig. 2B) is the last point at which we can detect apparent gene flow (that is, a shared lineage) between the Siberian population and the ancestral Native American population. We can assume that the real population divergence occurred after this point. In addition, if we accept that the estimated TMRCA of each of the five Native American haplogroups provides an independent estimate of the timing of the same small population’s isolation, we can use the 95% credible intervals to constrain the lower bound (section S5). The resulting estimate that the two populations became fully isolated between 24.9 and 18.4 ka is in accordance with calculations from modern complete genomes which indicate that Siberians and Native Americans split no later than ~23 ka (17). Gene flow to and from east Siberia certainly appears to have ceased by the height of the LGM (18.4 ka; Fig. 2B). Our data cannot determine whether the separation between Siberian and Native American ancestral populations occurred in Siberia or Beringia. However, the start of isolation (24.9 to 18.4 ka) closely coincides with the LGM. We hypothesize that cold arid conditions drove populations on the western (that is, Siberian) margins of the Bering Land Bridge to migrate to southern refugia (Fig. 1A), as suggested by the absence of megafauna kill sites younger than the far north Yana Rhinoceros Horn site 32 ka (1). In contrast, any populations east of the Kamchatka and Chukotka Peninsulas would not have been able to retreat farther south than the Aleutian ice belt and would thus remain isolated in eastern Beringia (Fig. 1A). We cannot accurately estimate the size of this founding population, but the effective female population that subsequently entered the Americas appears to be ~2000, which accords well with previous studies (9, 10, 25). Although this number cannot be directly translated into census population size, it suggests that the human population isolated in eastern Beringia was relatively small, probably not exceeding a few tens of thousands of people (section S6). The presence of large numbers of megafauna in eastern Beringia during the late Pleistocene, including the LGM, indicates an ice-free region dominated by shrub tundra (29), which would have been more than capable of sustaining such a population size (section S6). Thus, our observations are consistent with the idea that the founding Native American population used the exposed Bering Land Bridge and adjacent regions in Alaska/Yukon as a refugium during the height of the LGM, before climatic change and the retreat of the ice sheets allowed access to the remainder of the Americas. Unfortunately, the large temporal and geographic gaps in the archaeological record between the Yana Rhinoceros Horn site (~32 ka, western Beringia) and the Swan Point site (~14 ka, eastern Beringia) provide little additional information about this process (Fig. 1A) (1) or how the ancestral Native Americans were isolated from their Asian counterparts. Fig. 4 Effects of population structure and European colonization on South American mitochondrial diversity. We found that demographic models with one panmictic population (model A in Fig. 4 and fig. S10) or with a simple geographic separation of populations harboring the ancient and modern lineages (that is, a localized small-scale separation across the continent; model D) were poor fits for the data (table S6). The addition of a post-European bottleneck with survival of both ancient and modern lineages (models B and E) did not improve the goodness of fit. More complex models combined geographic separation and early and ongoing loss of ancient lineages—accelerated (model F) or not (model G) by the European landfall—to account for demographic processes such as serial founder effects and drift, or population turnover and elimination during the expansion of Central Andean empires. Such models were also poor fits for the data (table S6). Only a model that combined both the geographic separation of populations harboring modern and ancient haplotypes and the subsequent rapid extinction of the ancient lineages following European colonization fit our empirical observations (model C, minimum probability of 0.995; table S6). As a further test, when the demographic model C was removed from the analysis, the principal components remained mostly unchanged (fig. S11). This observation supports the robustness of the PCMLR model in correctly discriminating between the different demographic alternatives (38). However, none of the remaining demographic models (model A, B, D, E, F, or G) strongly fit all empirical observations (table S8), showing that model C is the only strong fit for the empirical data. As a result, our ancient mitochondrial data suggest that European colonization was followed by local mass mortality and extinction of lineages associated with major population centers of the pre-Columbian past. Our results contrast with previous observations that Native American genetic diversity has been temporally and geographically stable for at least the past 2000 years (33). However, the apparent contradiction between our study and earlier work is likely attributable to a significant improvement of sequence resolution. We also caution that the demographic models tested in this study are likely too simple to encompass the particulars of the colonization of the Americas—despite exploring scenarios with early emergence of geographic structure, as well as ongoing haplotype loss resulting from either serial founder effects and drift, or population displacement and elimination during the expansion of major Central Andean imperial states. Finally, more complete mitochondrial sequence data from early and modern-day populations will be needed to further refine the demographic models. In particular, it will be critical to ensure a strong spatial overlap between archaeological sites and present-day population locations (for example, Peru). DOI: 10.1126/sciadv.1501385
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Post by Admin on May 21, 2016 22:58:53 GMT
The largest ever study of global genetic variation in the Y chromosome – only held by men – has revealed when populations exploded, beginning 55,000 years ago. This complex diagram shows a type of family tree, where branch lengths are proportional to the estimated times between splits, with the most ancient division occurring around 190,000 years ago. Coloured triangles represent the major clades (shown above) Half of all Western European men are descendants of a Bronze Age 'king' who lived 4,000 years ago. That's according to the largest ever study of global genetic variation in the Y chromosome. Researchers believe the monarch was one of the earliest people to rule Europe in the Stone Age. His identity remains a mystery, but scientists believe he fathered a group of nobles who then spread across Europe. They brought with them advances in technology such as metal work and wheeled transport, according to a report in the Telegraph. Dr Chris Tyler-Smith, from the Wellcome Trust Sanger Institute, told the paper: 'Genetics can't tell us why it happened but we know that a tiny number of elite males were controlling reproduction and dominating the population. 'Half of the European population is descended from just one man. 'We can only speculate as to what happened. The best explanation is that they may have resulted from advances in technology that could be controlled by small groups of men.' The results revealed when populations exploded at several points in history, beginning 55,000 years ago. Such 'bursts of extreme expansion' could be due to intrepid communities moving to new regions with a surplus of resources and the sharing of technologies helped the groups to thrive. The study analysed sequence differences between the Y chromosomes of more than 1200 men from 26 populations around the world using data generated by the 1000 Genomes Project. The Y chromosome is only passed from father to son and so is wholly linked to male characteristics and behaviours. Mutations reveal which are related to each other and how far apart they are genetically so that researchers can build a family tree. We report the sequences of 1,244 human Y chromosomes randomly ascertained from 26 worldwide populations by the 1000 Genomes Project. We discovered more than 65,000 variants, including single-nucleotide variants, multiple-nucleotide variants, insertions and deletions, short tandem repeats, and copy number variants. Of these, copy number variants contribute the greatest predicted functional impact. We constructed a calibrated phylogenetic tree on the basis of binary single-nucleotide variants and projected the more complex variants onto it, estimating the number of mutations for each class. Our phylogeny shows bursts of extreme expansion in male numbers that have occurred independently among each of the five continental superpopulations examined, at times of known migrations and technological innovations. Nature Genetics (2016) doi:10.1038/ng.3559
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