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Mapping Post-Glacial expansions: The Peopling of Southwest Asia

  • Scientific Reports 7, Article number: 40338 (2017)
  • doi:10.1038/srep40338
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Abstract

Archaeological, palaeontological and geological evidence shows that post-glacial warming released human populations from their various climate-bound refugia. Yet specific connections between these refugia and the timing and routes of post-glacial migrations that ultimately established modern patterns of genetic variation remain elusive. Here, we use Y-chromosome markers combined with autosomal data to reconstruct population expansions from regional refugia in Southwest Asia. Populations from three regions in particular possess distinctive autosomal genetic signatures indicative of likely refugia: one, in the north, centered around the eastern coast of the Black Sea, the second, with a more Levantine focus, and the third in the southern Arabian Peninsula. Modern populations from these three regions carry the widest diversity and may indeed represent the most likely descendants of the populations responsible for the Neolithic cultures of Southwest Asia. We reveal the distinct and datable expansion routes of populations from these three refugia throughout Southwest Asia and into Europe and North Africa and discuss the possible correlations of these migrations to various cultural and climatic events evident in the archaeological record of the past 15,000 years.

Introduction

Climate shifts dramatically influenced the occupation of Southwest Asia by anatomically modern humans since their first occupation at the sites of Skhul and Qafzeh in Israel some 80 to 130 ka1. These first archaeologically identified modern humans outside of Africa appear to have left no genetic impact, with the next modern human remains in the Levant dating to around 45–50 ka1. Climate change impacts on population mobility and cultural developments have been hotly debated2. Aridity restricted larger human populations to refugia sited around the Mediterranean3, the Black Sea4, and possibly the southern Arabian Peninsula5, that were distinguished by tool cultures in the archaeological record6,7, during the Last Glacial Period (LGP) ending some 12 ka. Post-glacial warming, and later, agriculture, allowed expansions of these populations within SW Asia8. Archaeological evidence shows post-LGP population expansions shifting from mobile foraging to a more sedentary lifestyle4,9 with permanent settlements, early cultivation of wild plants10,11, stored goods, and emerging trade and exchange networks12,13. The Fertile Crescent based “Neolithic Revolution” replaced most cultures across the region, spreading Indo-European languages from an Anatolian homeland14, both westward to Europe, northward to the steppes, and eastward to the Iranian plateau and beyond.

Autosomal analyses have identified a cryptic population, “Basal Eurasians,” that injected significant genetics into the European population distinct from the Southwest Asian agricultural revolution signature15. Subsequent work16,17 has suggested that Yamnaya aDNA appears to have been a source of the genetics associated with corded ware culture, and provides further evidence for a late Neolithic through early Bronze Age wave introducing Indo-European languages.

Lineages that evolved in relatively isolated refugia populations could carry genetic evidence of that isolation and subsequent expansion. Such signals identified source populations for admixture events marking European settlement15,16,18,19. However, specific associations between Southwest Asian post-LGP expansion genetics and their original refugia, and the identification of the timing and directions of their various dispersal events, are still being determined. Most recently, analyses of complete mitochondrial genomes have identified a possible Arabian refugia in the LGP, within which several mtDNA haplogroups including R0a emerged and from which they dispersed to the Fertile Crescent, the Levant and the Horn of Africa20. Y-chromosome analysis has identified highest frequencies for J1 haplogroup to be most common in the Saudi Peninsula21 marking the Muslim expansion22 with J2 being common in the coastal Levant23, and identified early on as a possible marker of the European expansion of the post-Neolithic expansion8,24, while their origins have been identified roughly within Iran, Armenia, Georgia, and/or Eastern Turkey. However, even though J2 appeared to mark a post-Neolithic expansion, this haplogroup has been identified in ancient DNA analysis of remains archaeologically associated with the late Bronze Era25. Both Haplogroups origins have been identified roughly within Iran, Armenia, Georgia, and/or Eastern Turkey.

Here, we take the approach of identifying and dating population isolation prior to expansion, and tracing routes of dispersal throughout Southwest Asia and into Europe and Africa. We analyzed a comprehensive autosomal and Y-chromosome dataset of Eurasian and African populations identifying genetic signals of regional LGP population isolation, and contrasted expansion time estimates and dispersal routes in the region with archaeological4,9, palaeontological26, palaeobotanical27, and climate4,27 data.

Material and Methods

Samples and Genotyping

A total of 8,515 samples were analyzed (Supplementary Table S1) for their Y-chromosome genetics. All participants recruited and genotyped by our team had at least three generations of paternal ancestry in their country of birth and provided details of their geographical origin. A written informed consent was signed and obtained by each participant prior to recruitment for this study. The study protocol and the informed consent form were approved by the IRB of the Lebanese American University. The study methods were carried out in accordance with the principles of the Declaration of Helsinki. DNA was extracted from blood or buccal swabs using a standard phenol–chloroform protocol. Samples were genotyped for binary Y chromosome polymorphisms as reported previously23.

Our previously described samples (n = 2047)23,28,29 were further subtyped to achieve the same Haplogroup (Hg) differentiation as the new samples analyzed here23,28,29, with the additional 6 SNPs (J1e-P58, J2a-M410, J2b-M12, E1b1b1a-M78, E1b1b1b-M81 and E1b1b1c-M123). DNA samples were also typed for 11 microsatellite loci (DYS 388, 389I, 390, 391, 393, 19′, 437, 439, 389II, 392 and 438).

In addition, Y- Hg and haplotype data were also incorporated from prior studies (Supplementary Table S1), including 727 newly genotyped samples from Armenia (ARM - 402 samples), Armenians originating in SE Turkey (TUR - 126), Bahrain (BAH - 40), Iraq (IRQ - 70), the Kingdom of Saudi Arabia (KSA - 7), Cyprus (CYP - 38) and Libya (LIB - 44) (Study references included in the supplementary information.) Haplogroup markers employed by the studies were catalogued (Supplementary Table S2). The Y-STR Haplotype Reference Database (YHRD) tree and International Society of Genetic Genealogy (ISOGG 2011) tree were compared, with YHRD being used as the base nomenclature (Supplementary Table S2) for construction of most derived sets.

Autosomal data were obtained from a prior study30, which had selected 75 Lebanese samples from a pool of 1,341 by stratified random sampling. That dataset had included 994 samples from 48 populations spanning SW Asia, Europe, N. Africa, and into S. Asia. All of these samples were analyzed using Illumina 610 K or 660 K bead arrays. The results were filtered requiring 99% genotype success rate, and removal of sex-linked and mtDNA SNPs, yielding 505,859 SNPs. Further LD pruning (excluding r2 > 0.4) yielded 244,919 SNPs. These QC filtering steps were performed with PLINK31. From these, we retained 174 samples representing SW Asia populations.

Population Pooling

The initial populations were defined primarily based on modern national boundaries or sub-regions. Pooled populations were identified through preliminary BATWING (see Supplementary information) analysis as described below.

We sought to analyze the data without preconceptions of the history of southwest Asian populations, allowing the data to govern their own analysis. Our data preparations sought to ensure the STR loci and haplogroup derivations were uniform across populations and haplogroups, requiring factoring to a lowest common denominator across all haplogroup and population studies.

BATWING analyzes population splits of the form ((A,B),(C,D)) as two topologies instead of one, counting an (A,B) split prior vs. subsequent to the (C,D) split as two distinct topologies, inducing a spurious interaction between (A,B) and (C,D) split times, even though those splitting events are independent. Running BATWING with two conformations (AB, (C,D)), and ((A,B), CD), where AB represents A and B pooled, and CD represents C and D pooled removes the interaction. The two estimates of the first split provide a check for consistency. This process also establishes pooled populations. We adapted the pooled regions for the second phase of analysis when pooling of similar regions following similar expansion routes were shared by multiple haplogroups. The resulting pooled regional designations are Arabia (Saudi Arabia, Emirates, Qatar, Bahrain), Yemen (by itself), Armenia and Turkey (one region), Caucasus (Georgia), Mesopotamia (Iran, Iraq, Kuwait), Cyprus, Southern Levant (Jordan, Palestine), Northern Levant (Lebanon, Syria), Egypt, North Africa (Libya, Morocco, Tunisia, Algeria), and Ethiopia (the data from this preliminary analysis is not shown).

Since BATWING input only involved genetic data and population assignments, results might have been geographically random. Instead, the modal tree population splits easily lie on a map. The combinatorial enumeration of trees vs geography is non-trivial. However, simple limits may be estimated by considering the chances that an east-west split would have accurately classified Northern and Southern Levant (4 populations) on one side and Mesopotamia and Arabia (7 populations) by chance on the other, for example, which has a p-value = 0.003 by a Fisher exact test.

STR-based FST computations, MDS, and complete linkage agglomerative clustering were not sufficient to resolve regions in a meaningful way. AMOVA (see supplementary information) also did not show significant regional organization. Haplogroup J1′s FCT = 0, with p-value = 0.097 that a random construction would be larger. Haplogroup J2′s FCT = 0.0097, with p-value = 0.486. Haplogroup E1b1b1 shows the greatest divergences, with FCT = 0.233, with p-value = 0.00067.

The parameter set for BATWING is described in the supplementary information. Error bar 95% CIs about the median range from 1/1.75 to ½ of the median to 1.75 to 2 times the median.

Analysis

Frequency and variance maps

Maps showing haplogroup frequency distributions (Supplementary Figure S1) were derived from data from our laboratory listed in Table S1. Frequency (Supplementary Figure S2) and variance (Supplementary Figure S3) contour map construction is described in the Supplementary information.

PCA and MDS

Principal component analysis (PCA) was applied to relative haplogroup frequencies. Multidimensional scaling (MDS), was applied to RST distances on STR loci across haplogroups from the full derived set, as well as within J2 and J*(xJ2) haplogroups. Details on these methods are in the Supplementary information.

Network

NETWORK was used to compute Reduced Median (RM) networks (Supplementary information) for haplogroups J1e, J*(xJ1e), J2a, J2b, and J2.

Y TMRCA Calculation

BATWING parameters are described in the supplementary information. Times of Most Recent Common Ancestor (TMRCA) estimated using UEP time estimation on J subhaplogroups, J1, J1e, J2, J2a1, J2a1, J2a2, J2a2a, and J2b, for whole-population data drawn from Armenians, Caucasians, Iranians, Jordanians, Lebanese, Palestinians, South East Turks, and Turks, Syrians and Africans are listed in Table 1.

Table 1: Times to most recent common ancestors for each haplogroup, measured in ka, for each region.

Y Haplogroup expansions

BATWING population splitting was applied to individual haplogroups drawn from both whole-population and haplogroup-specific studies. BATWING samples STR haplotype phylogenies and population split phylogenies, and Bayesian prior distributions to estimate effective population sizes, and mutation rates, using a Metropolis-Hastings Markov-Chain Monte-Carlo simulation. BATWING assumes a single-step mutation model, and coalescence governed by an effective population size, and it models population splitting by dividing the total parent population effective population size among the child populations. The population splitting assumption is the weakest link in application to individual haplogroups. BATWING provides a fixed then expanding effective population size model, allowing for substantial flexibility in population coalescences that the expanding haplogroups will have evolved within as they migrated. Detailed considerations are offered in the supplementary information.

Genome-wide samples and analysis

We analyzed 174 samples from 9 Southwest Asian populations (Georgians, Armenians, Turks, Lebanese, Syrians, Palestinians, Jordanians, Saudis and Yemenis) using published genome-wide marker data29,30,32. A PCA removing outliers left 155 samples (supplementary information). ADMIXTURE (supplementary information) was applied to the reduced set to identify ancestral populations and to compute ancestral FSTs. Divergence dates were estimated from FST estimates (see the supplementary information for details).

We tested whether or not the populations were genetically differentiated. Pair-wise FSTs were computed33 between all individual samples (distinct from the ancestral FSTs described above). MDS was also applied to the pairwise FST’s. We selected a number of MDS dimensions large enough so that the histograms of reconstructed distances became stable. Agglomerative clustering using Ward’s method and Neighbor-Joining (NJ) was applied to the distances reconstructed from the three leading dimensions produced by MDS (supplementary information). While agglomerative clustering does not model population dynamics or events, FSTs are related to divergence times through coalescence, mutation, and migration. This provided a visual test of geographic organization of pair-wise FST based genetic signatures. We tested whether the geographical/population associations could have occurred by chance. Mantel distance correlation tests (supplementary information) contrasted first, the pairwise distances computed from the leading PCA components, and second, FST’s, with a population similarity matrix (distance of 0 for same populations, 1 if different) according to the individuals’ population assignments.

Results

Y-chromosome haplogroups J1 and J2 and E1b1b are the major paternal lineages, accounting for 50.9% in Southwest Asia and North Africa. The Y haplogroup frequency pie-chart map (Supplementary Figure S1), and PCA (Supplementary Figure S4) both show broad geographic differentiation. Similarly, an MDS plot based on Y-STR across all haplogroups (Fig. 1a) reveals similar geographic organization, reflecting correlation between STR haplotypes and haplogroups. However, the MDS shows weak geographical organization among J2 and J*(xJ2) haplogroup Y-STR RST distances analyses compared to RST distances analyses applied to all haplogroups (Fig. 1b and c). Likewise, a reduced median NETWORK analysis of STR haplotypes within J haplogroups, namely J2d, J1e and J1xJ1e, also reveals less geographical organization (Supplementary Figure S5). Since Y-chromosomal lineages generally show strong geographical structure, the lack of such structure within J is unexpected.

Figure 1
Figure 1

MDS analyses of RST distances based on STR haplogroup variances (a) across all haplogroups, for (b) J*(xJ2), and (c) J2.

Maximal diversities, indicative of possible origins, are observed for J*(xJ2) in the Caucasus and for J2 in Armenia, decreasing south and east, although high diversities do not entirely correlate with high relative frequencies (frequency contour Supplementary Figure S2 and variance contour Supplementary Figure S3). BATWING TMRCA (Supplementary information) estimates for J*, J1, J1e, J2, and E1b1b are shown in Table 1. These results indicate slightly greater time depth information of J, J1, and J2 in Turkey and populations from the Caucasus, in agreement with their higher diversity in this area. They also show greater differentiation of the E1b1b1 haplogroups between North Africa and the rest of Asia. The great time depth of J2 and E in Iberia are consistent with a longer period of isolated evolution. Haplogroup J* is the least differentiated in time, but shows maximum values in Turkey Iran as well as North Africa, with decreasing values progressively further away from these geographical locations. Due to the phylogenetic relationship between J1 and J2 markers, the TMRCAs for these are identical. The J1 and J2 split shows deepest time in the Caucasus, Syria and Turkey at 8.9 ka and 8.4 ka, respectively.

The population split estimates (Fig. 2) show divergences largely reflecting the trend in TMRCAs with older dates closer to the Caucasus, but with differentiation times roughly 1/3 of the TMRCAs, suggesting that 2/3 of STR diversity evolution occurred prior to the earliest population differentiations that followed the Late Glacial Period expansions. Expansions therefore distributed a broad spectrum of shared STR haplotypes that had already evolved within their isolated populations. This broadly shared diversity explains the weak geographical discrimination observed in the phylogenetic networks (Supplementary Figure S5) and the lack of organization found in the MDS analysis for haplogroup specific J*(xJ2) and J2 Y STR FSTs (Fig. 1b and c).

Figure 2
Figure 2

BATWING population split time estimates for Middle Eastern populations marking expansion histories for haplogroups (a) J1, (b) J2 and (c) E1b1b1.

The J1 haplogroup shows greatest differentiation of the Caucasus at 8.9 ka, then Ethiopia at 7.9 ka, followed by Armenia and Turkey at 6.9 ka. The J2 Haplogroup shows an earliest isolation of the Caucasus at 8.4 ka. For the E1b1b1 haplogroup, Egypt, North Africa, and the rest of the populations differentiated at 11.6 ka nearly polytomously (Fig. 2).

We performed an ADMIXTURE analysis to identify and quantify ancestral components in Southwest Asians across all chromosomes using 188,974 SNPs (Fig. 3). ADMIXTURE’s cross-validation (CV) shows decreasing CV error with decreasing dimensions to K = 1, with a basin from K = 3 to K = 1. In K = 3, Saudi, Georgian, and Palestinian populations show samples marking pure derivations of the K = 3 ancestral populations. Since the cross-validation scores minimized at K = 1, its utility in identifying a best estimate for ancestral population count is weak for these populations. However, the PCA and pairwise FST results give a strong suggestion that more ancestral signals are present. For this reason, ADMIXTURE analyses were presented for higher Ks in order to offer information regarding the structure PCA presents, showing results consistent with PCA and pairwise FST. The rest show mixtures of these three, with Northern and Palestinian ancestral contributions appearing in Levantine and Fertile Crescent regions including Turkey and Armenia. The split to K = 4 primarily impacted the Palestinian ancestral group, splitting it. That 4th ancestral group is not purely represented in any of the modern populations, but is mostly represented in the Levantine through the Fertile Crescent regions. However, some of the samples clearly shifted character from mixtures to pure ancestral groups, reflecting increasing instability in the computations. PCA analysis (Fig. 4) most clearly identifies the same three evolutionary centers as extremes in the plot, strongly echoing, and largely clarifying, the information contained in the ADMIXTURE analysis. Specifically, the K = 5 ADMIXTURE identifies an ancestral population that completely dominates the Palestinian population, which corresponds to the Palestinian group on the PCA. The Saudi Arabian population shows an ancestral population marked in red for K = 5, with admixture into the Levantine populations, but nearly absent in Turkey, Armenia, and totally absent from Georgia. In the PCA, the Saudi population occupies a well-defined area, with a tail that admixes with the Levantine populations. The Georgian population is dominated by a strongly defined ancestral population, which shows admixed presence along the northern Fertile Crescent, and into the Levant. This is consistent with the genetic distances as shown in the PCA. The ADMIXTURE FST’s tended to increase with increasing K. The largest FST in the K = 3 group was 0.075, and the smallest was 0.057. This corresponds to 36.4 ka and 27.4 ka (we assume Ne = 8,060 - the harmonic average of 7,000 and 9,500 as in the supplementary information, which yields 1260 generations, or 36.4 ka for FST = 0.075, and 946 generations or 27.4 ka for FST = 0.057), all well into the LGP.

Figure 3
Figure 3

Genome-wide analysis of populations from Southwest Asia using 188,974 autosomal SNPs. (a) Map showing populations analyzed (Base map constructed from Map data: Google, Digital Globe, https://earth.google.com); (b) Population. structure inferred by ADMIXTURE. Each horizontal line represents ancestry probabilities of an individual in 2 constructed ancestral populations.

Figure 4: PCA showing Genome-wide analysis of populations from Southwest Asia using 188,974 autosomal SNPs.
Figure 4

Eigenvalues were 3.76 × 105 for PC1 (not plotted, reported for scale), 2.16 × 105 for PC2, and 1.62 × 105 for PC3.

The Ward and NJ cluster results for pairwise FSTs (Supplementary Figure S6) show the northern populations tending to isolate toward one end of the cluster, and southern populations isolating toward the other end, with Levantine and others in the middle, showing substantial differentiation between these branches. Mantel tests comparing geography to FSTfor pairwise samples showed p-values < 10−6 given 106 iterations.

The unrooted NJ trees show strikingly varying branch lengths, and remarkably differentiated substructure within the major putative northern and southern refugia, as well as some suggestion of a Levantine refugium. For those populations expanding from the north, the trees reveal a similar east/west split as identified for the J1/J2 expansions. We note the pairwise FST date estimates are much younger, at 6.6 ka (assuming initial divergence around 11 ka) than the ancestral differentiation dates suggested by the ADMIXTURE derived FST ancestral date estimates of 37.4 ka and 27.4 ka.

Discussion

Our Y chromosome and autosomal analysis identified genetic signatures of three likely centers of isolated evolution followed by population expansions. These modern centers correspond to archaeologically known LGP refugia4,9,27 in Southwest Asia4,5,34. The first, identified by Obsidian sourcing35 as an expansion center in Georgia/eastern Turkey12,13. Clear evidence of trade, and tool cultures mark the second refuge in the northern Levant7. The third in the southern Arabian Peninsula5 identified recently by Gandini et al.20. Archaeologically, the time period marking post glacial population expansions through Southwest Asia is associated with the early and middle Pre-Pottery Neolithic B, the increased reliance on domesticated plants and animals, increased evidence of trade and exchange along the earlier established obsidian trade routes, and coastal to inland trade of marine resources36 (Fig. 5).

Figure 5
Figure 5

(A) Reconstructed air temperatures from the GISP 2 Ice core in Greenland (blue solid line); Isotope values from Soreq cave, Israel (orange dotted line). (B) Chronology of cultural entities in the Levant. (C) Archaeological evidence of human activities in the Upper Paleolithic; human occupation signs 29–15.2 ka, wood charcoal, nuts 15.9–11.2 ka, ▲centers of obsidian trade 16–14 ka. (D) Evidence of human activities in the Neolithic; plant domestication 12.5–9.6 ka, human occupation signs 11.9–5 ka, animal domestication, charred plants, agriculture and charcoal 10.2–7.3 ka. (Base map constructed from Map data: Google, Digital Globe, https://earth.google.com).

BATWING characterized three features in the genetic record during the expansion of J1 and J2. The earliest dates, with the oldest differentiations for J2 at 8.4 ka, and J1 at 8.9 ka, show an early divergence between the Caucasus from the rest of the populations. BATWING does not model admixture; population split estimates dates to the time admixture ceases. In practice, very minor admixture can cause split times to be underestimated. The branches, to the east down through Mesopotamia and to the west down through the Levant, maintained mutual isolation. Those earliest dispersions show post-last-glacial-period dates but could have been earlier than BATWING estimated if admixture persisted. Later, a significant number of branches occurred within a very narrow window of time, nearly polytomously, followed by relatively stability, marking the emergence of stronger regional isolation suggesting sedentism. That window marks a period near the end of the Holocene Climatic Optimum (HCO - 9 ka–5 ka). Generally, the expansions show movement from northern regions until the HCO, with subsequent isolation and differentiation in the arable regions (Fertile Crescent, Levant, etc.) during aridification.

Subsequent expansions of J1 show the Mesopotamian branch extending to Arabia and Yemen at about 4 ka. The signal of the original North African J1 expansion is overwhelmed in Egypt by more recent Levantine J1s (Fig. 2a). J2 shows similar but deeper isolation during the HCO, with a similar expansion date into North Africa (5.9 ka) as J1 (4.9 ka, Fig. 2b). Yet, its isolation in Egypt is far older, around 8.1 ka. Further, J2′s Mesopotamian population differentiated earlier from the Levantine populations (7.3 ka), with much more recent admixture among J2′s in Turkey (5.1 ka).

Y-M35 (E1b1b1) dominates North Africa much more than Southwest Asia, with a Neolithic split at 11.6 ka between eastern and western North Africa. BATWING will interpret unique haplotypes among rare E1b1b1 samples as older isolation. Most of the populations show apparent isolation dating to the HCO with some associations showing as recent links between central Asians and the Southern Levant (2.9 ka), and the Caucasus with Mesopotamia (4.6 ka Fig. 2).

The population split estimates (Fig. 2) show divergences largely reflecting the trend in TMRCAs with older dates closer to the Caucasus, but with differentiation times that are roughly 1/3 of the TMRCAs, suggesting that 2/3 of STR diversity evolution occurred prior to the earliest population differentiations that followed the Late Glacial Period expansions. Expansions, therefore, distributed a broad spectrum of STR haplotypes that had already evolved within their refugia. This diversity explains the weak geographical discrimination observed in the phylogenetic networks (Figure S5) and the lack of organization found in the MDS analysis for haplogroup specific J*(xJ2) and J2 Y STR FSTs (Fig. 1b and c).

Our autosomal data analyses, PCA and ADMIXTURE, and MDS and Neighbor-Joining cluster analysis applied to individual FSTs, suggest expansions from three centers dominated the peopling of Southwest Asia: an expansion from an ancestral northern population consistent with the Y-chromosome derived Georgian refugia, a southern expansion from a refugium in the Arabian Peninsula, and a Levantine center. Though the leading two PCA components of a prior analysis including European data showed a linear extension from Turkey to the Saudi peninsula, it did not identify a Levantine ancestral population15. More surprising, our Y-chromosome BATWING analysis did not even reveal the existence of ancestral expansion from Arabian Peninsula or the Levantine region. A previous study also noted that Saudi Y genetics derived from elsewhere21. Today, the J1 haplogroup dominates the Arabian Peninsula region (Supplementary Figure S2), though its greatest diversity (Supplementary Figure S3), marking its origin, is seen in the north. Meanwhile, the J2 diversity contour maps suggests centers from the Levant and Tarsus mountains. The strong coastal vs. inland distributions of these two haplogroups23,37 belies the proximity of J1 and J2 origins.

The Levantine autosomal expansion appears, to parallel the Natufian expansion (10 ka according to pairwise-FST numbers)7 and the BATWING dates marking regional differentiations are similar to the pairwise FST derived dates. The larger scale ADMIXTURE results however, correspond more closely with the earlier isolations observed in Y STR estimates of African population J and E (e.g. E-M81 is estimated at 14.2 kya reported at YTree https://www.yfull.com/tree/E-M81/) haplogroups. This contrast suggests ongoing mobility and admixture within Southwest Asia subsequent to the expansion into Africa, since BATWING identifies splitting times since the end of admixture, or else the BATWING dates within Southwest Asia refer to a more recent expansion that completely subsumed an earlier expansion that reached into Africa. This mobility and admixture was greatly reduced following post HCO agricultural settlement.

The Y-chromosomal and autosomal ancestral reconstructions from modern populations in Southwest Asia and Europe presented here seem to agree with other evidence of ancient expansions. However, this contradicts more recent aDNA autosomal analysis described below.

A recent study15 incorporating aDNA data, along with modern population structure, inferred that most Europeans derive from three primary population groups. One of these, Early European Farmers, was putatively derived from West Eurasian Hunter Gatherers and “Basal Eurasians” which enjoyed isolated evolution distinct from all other out-of-Africa groups prior to genetic expansion into Europe. The Basal Eurasians appear to be the main candidate group associated with the earliest transition from West Eurasian hunter-gathering to agriculture15. Two subsequent studies seeking to identify the origin of these groups identified Yamnaya as a likely genetic origin in addition to contributing to Indo-European languages16,17. They differed on possible origins of Yamnaya, with one team, Haak et al., suggesting association possibly through the Caucasus16 and the other, Allentoft et al. denying it17. Interestingly, the Haak et al. study’s Y chromosome Yamnaya haplogroups were R1b1a or R1b1a2. Another recent study38 supports this result, though claims affinity with an “Armenian-like Near Eastern source.” Our study has identified the Caucasus refugium as the likely source for the J1 and J2 haplogroups that now dominate Southwest Asia, and previously appeared to mark the Neolithic Revolution’s expansion into Europe39. Yet, haplogroups J1 and J2 are distinctly lacking in the earlier Yamnaya samples.

The geographic separation of modern J haplogroups in Southwest Asia, and the prevalence of J2 but not J1 in the expansion into Europe is striking given their close origins39,40.

Population expansions from the identified refugia in this study, as demonstrated by archaeological tool culture impacts, served as sources for the demic diffusion of the Neolithic Revolution2,11 and the peopling of the Fertile Crescent. We further identify directions and timings of expansions and subsequent isolations which correlate well with both archaeological and climate data2,4,7,9,10,11,27,34,36,41. While aDNA studies have shown that genetic haplogroups in some populations elsewhere have been almost completely replaced over time19, the archaeological association of the expansions, and the reflection of those ancient expansions in the genetics of modern populations in the same region is notable. Additional aDNA studies in Southwest Asia will allow these population expansions to be tested further.

Additional Information

How to cite this article: Platt, D. E. et al. Mapping Post-Glacial expansions: The Peopling of Southwest Asia. Sci. Rep. 7, 40338; doi: 10.1038/srep40338 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    & Human Evolution Out of Africa: The Role of Refugia and Climate Change. Science 335, 1317–1321, doi: 10.1126/science.1215627 (2012).

  2. 2.

    Reassessing the Emergence of Village Life in the Near East. Journal of Archaeological Research 13, 231–290 (2005).

  3. 3.

    et al. Mitochondrial DNA signals of late glacial recolonization of Europe from near eastern refugia. American journal of human genetics 90, 915–924, doi: 10.1016/j.ajhg.2012.04.003 (2012).

  4. 4.

    & Modelling late Quaternary changes in plant distribution, vegetation and climate using pollen data from Georgia, Caucasus. Journal of Biogeography 36, 529–545 (2009).

  5. 5.

    New Light on Human Prehistory in the Arabo-Persian Gulf Oasis. Current Anthropology 51, 849–883 (2010).

  6. 6.

    , , & Direct Luminescence Chronology of the Epipaleolithic Kebaran Site of Nahal Hadera V, Israel. Geoarchaeology: An International Journal 18, 461–475 (2003).

  7. 7.

    The Natufian culture in the Levant, threshold to the origins of agriculture. Evolutionary Anthropology 6, 159–177, doi: 10.1002/(sici)1520-6505(1998)6:5<159::aid-evan4>3.0.co;2-7 (1998).

  8. 8.

    et al. Origin, diffusion, and differentiation of Y-chromosome haplogroups E and J: inferences on the Neolithization of Europe and later migratory events in the Mediterranean area. American journal of human genetics 74, 1023–1034, doi: 10.1086/386295 (2004).

  9. 9.

    Evidence of Late glacial and Holocene climatic change and human impact in eastern Anatolia: high-resolution pollen, charcoal, isotopic and geochemical records from the laminated sediments of Lake Van, Turkey. The Holocene 13, 665–675 (2003).

  10. 10.

    & Neolithization Processes in the Levant: The Outer Envelope. Current Anthropology 52, S195–S208 (2011).

  11. 11.

    The Origins of Agriculture in the Near East. Current Anthropology 52, S221–S235 (2011).

  12. 12.

    , & Obsidian and Early Cultural Contact in the Near East. Proceedings of the Prehistoric Society 32, 30–72 (1966).

  13. 13.

    , , & Networks and Neolithisation: Sourcing obsidian from Körtik Tepe (SE Anatolia). Journal of Archaeological Science 40, 556–569 (2013).

  14. 14.

    et al. Mapping the Origins and Expansion of the Indo-European Language Family. Science 337, 957–960 (2012).

  15. 15.

    et al. Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513, 409–413, doi: 10.1038/nature13673 (2014).

  16. 16.

    et al. Massive migration from the steppe was a source for Indo-European languages in Europe. Nature 522, 207–211, doi: 10.1038/nature14317 (2015).

  17. 17.

    et al. Population genomics of Bronze Age Eurasia. Nature 522, 167–172, doi: 10.1038/nature14507 (2015).

  18. 18.

    et al. Whole mitochondrial DNA sequencing in Alpine populations and the genetic history of the Neolithic Tyrolean Iceman. Sci Rep 6, 18932, doi: 10.1038/srep18932 (2016).

  19. 19.

    et al. Upper Palaeolithic genomes reveal deep roots of modern Eurasians. Nat Commun 6, 8912, doi: 10.1038/ncomms9912 (2015).

  20. 20.

    et al. Mapping human dispersals into the Horn of Africa from Arabian Ice Age refugia using mitogenomes. Scientific Reports 6, 25472, doi: 10.1038/srep25472 (2016).

  21. 21.

    et al. Saudi Arabian Y-Chromosome diversity and its relationship with nearby regions. BMC Genet 10, 59, doi: 10.1186/1471-2156-10-59 (2009).

  22. 22.

    et al. Y-chromosomal diversity in Lebanon is structured by recent historical events. American journal of human genetics 82, 873–882, doi: S0002-9297(08)00206-1 (2008).

  23. 23.

    et al. Geographical structure of the Y-chromosomal genetic landscape of the Levant: a coastal-inland contrast. Ann Hum Genet 73, 568–581, doi: 10.1111/j.1469-1809.2009.00538.x (2009).

  24. 24.

    et al. Y chromosomal haplogroup J as a signature of the post-neolithic colonization of Europe. Hum Genet 115, 357–371, doi: 10.1007/s00439-004-1168-9 (2004).

  25. 25.

    et al. Genome flux and stasis in a five millennium transect of European prehistory. Nat Commun 5, 5257, doi: 10.1038/ncomms6257 (2014).

  26. 26.

    The Middle Paleolithic of the East Mediterranean Levant. Journal of World Prehistory 17, 313–394 (2003).

  27. 27.

    , , & Vegetation and climate changes in the South Eastern Mediterranean during the Last Glacial-Interglacial cycle (86 ka): new marine pollen record. Quaternary Science Reviews 30, 3960–3972, doi: 10.1016/j.quascirev.2011.10.016 (2011).

  28. 28.

    et al. Influences of history, geography, and religion on genetic structure: the Maronites in Lebanon. Eur J Hum Genet 19, 334–340, doi: 10.1038/ejhg.2010.177 (2011).

  29. 29.

    et al. Identifying genetic traces of historical expansions: Phoenician footprints in the Mediterranean. The American Journal of Human Genetics 83, 633–642 (2008).

  30. 30.

    et al. Genome-wide diversity in the levant reveals recent structuring by culture. PLoS Genet 9, e1003316, doi: 10.1371/journal.pgen.1003316 PGENETICS-D-12-02218 (2013).

  31. 31.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics 81, 559–575, doi: S0002-9297(07)61352-4 (2007).

  32. 32.

    et al. The genome-wide structure of the Jewish people. Nature 466, 238–242, doi: 10.1038/nature09103 nature09103 (2010).

  33. 33.

    , & Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583–589 (1992).

  34. 34.

    Climatic Fluctuations and Early Farming in West and East Asia. Current Anthropology 52, S175–S193 (2011).

  35. 35.

    & The characterization of obsidian and its application to the Mediterranean region. Proceedings of the Prehistoric Society 30, 111–133 (1964).

  36. 36.

    et al. Climate forcing due to the 8200 cal yr BP event observed at Early Neolithic sites in the eastern Mediterranean. Quaternary Research 66, 401–420 (2006).

  37. 37.

    et al. The emergence of Y-chromosome haplogroup J1e among Arabic-speaking populations. European journal of human genetics: EJHG 18, 348–353, doi: 10.1038/ejhg.2009.166 (2010).

  38. 38.

    et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature, doi: 10.1038/nature16152 (2015).

  39. 39.

    et al. The genetic legacy of Paleolithic Homo sapiens sapiens in extant Europeans: a Y chromosome perspective. Science 290, 1155–1159 (2000).

  40. 40.

    et al. Y-chromosomal diversity in Europe is clinal and influenced primarily by geography, rather than by language. American journal of human genetics 67, 1526–1543, doi: 10.1086/316890 (2000).

  41. 41.

    et al. Y-chromosomal evidence of the cultural diffusion of agriculture in Southeast Europe. European journal of human genetics: EJHG 17, 820–830, doi: 10.1038/ejhg.2008.249 (2009).

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Acknowledgements

Support for this research was provided by the National Geographic Society, IBM and the Waitt Family Foundation under The Genographic Project. CTS is supported by The Wellcome Trust (098051). We thank the sample donors for taking part in this study. We thank Prof. Davide Pettener for providing us with valuable input. We thank the Genographic consortium for their guidance and for making the data available for scientists.

Author information

Affiliations

  1. Computational Biology Center, IBM TJ Watson Research Centre, Yorktown Hgts, NY, USA

    • Daniel E. Platt
  2. The Lebanese American University, Chouran, Beirut, Lebanon

    • Marc Haber
    • , Bouchra Douaihy
    • , Georges Khazen
    • , Angélique Salloum
    • , Francis Mouzaya
    •  & Pierre A. Zalloua
  3. The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    • Marc Haber
    •  & Chris Tyler-Smith
  4. Département Sciences de la Vie et de la Terre, Université Saint-Joseph, Mkalles, Lebanon

    • Magda Bou Dagher-Kharrat
  5. University of Portsmouth, School of Biological Sciences, Portsmouth PO1 2DY, UK

    • Maziar Ashrafian Bonab
  6. Department of Evolutionary Experimental Biology, Via Selmi, 340126 Bologna, Italy

    • Donata Luiselli
  7. McDonald Institute for Archaeological Research, University of Cambridge, CB2 3ER, UK

    • Colin Renfrew
  8. Department of Anatomy and Allan Wilson Centre for Molecular Ecology and Evolution, University of Otago, Dunedin, New Zealand

    • Elizabeth Matisoo-Smith
  9. Harvard School of Public Health, Boston, MA, USA

    • Pierre A. Zalloua

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Contributions

D.E.P., P.A.Z., E.M.S. designed the study and wrote the manuscript. M.H. genotyped the samples and wrote part of the material and methods and results section, M.B.K. prepared Figure 5 and wrote part of the introduction and discussion, B.D., G.K., A.S., and F.M. conducted part of the analysis, M.A.B. and D.L. provided samples and additional analysis, C.T.S. and C.R. contributed to the design of the project and provided essential input to the write up.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Pierre A. Zalloua.

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