Новое исследование выявило 8000 лет генетической непрерывности в Армении

1. 

   Արմեն

   October 15, 2017 at 3:00 pm

   Ինչվերաբերվում ա այս աշխատանքին Hellenthal et. и др. (2014 г.)․ Այստեղ Հայերի մասին ասվում է որ ծաքումնաբանությանը որոշել հնարավոր չե քանի որ կամ ադմիկսնէ շատ բարդ, կամ վիբոռկան բավարար չի։ Այնպես որ գրածը այստեղ այս հոդվածի մասին չի համապատասխանում բուն Science հոդվածին։
   Երկրորդը մեռանք ասելով, չի կարելի ասել հաի գեն։ Հայերը և ցանկացած այլ ազգ աշխարհում չունեն յուրահատուկ միայն իրենց համար գեն կամ գեներ։ Գրագետ է խոսալ այս կամ այն SNP- հավանականության հանդիպման մասին, կամ SNP-ների կոմբինացիաների հանդիպման հավանականության մասին, որոնք ի դեպ, երփեկ չեն հասնում 100%։ Այսինքն չկա ոչ մի աբսալյուտ գենետիակական կրիտերի էթնիկական ծաքումը որոշելու համար։
   Երերդը, գրագետ չե խոսել Հայերի մասին, գրագետ է խոսել հայկական պոպոըլյացիայի և ուղղիղ կեմսաբանական նախնիների մասին։ Այս մեկնաբանության մեջ տեղ տեղ դա նշվում է, տեղ տեղ հեղինակը սխալ տերմինաբանություն է օգտակործում, ինչ որ մասնագետներին մտծնում է խափեության մեջ։ Չի կարելի ասել армянам 8000 лет, հարկավոր է ասել армянской популяции 8000 лет (եվ նշել ինչ մարկերների մասին է խոսքը գնում, միտոքոնդրիալ, իգրեկքրոմոսոմային թե աուտոսոմային) Ազգությունը դա մշակոիյթային կրիտերի է, իսկ այս հետազոտություններում խոսքը գնում է կենսաբանության մասին։ Օրինալ – այս հադվածի հեղինակը մոտ 100 000 տարի առաջ ունեցել ուղղակի նախնի որը բնակվել է Աֆրիկայում, բայց հեղինակը հայ է։ Այսինքն ի՞չ իրա այդ նախնին ել ա հայ։ Ոչ իհարկե։
   Ժող ջան , հեղինակ ջան, որ գրում եք գենետիկայից, լավ կանեիք մոլեկուլյար գենետիկայից և մոլեկուլյար էվոլուցիայից գոնե բազային պատկերացում կազմեիք, թեչե մասնագետը կարդում ա ձեր գիտական հոդվածների ինտեպրիտացիաները ու վայ գոռալով գլխից բռնում ա։ Ով ոնց կամենում ա ինտերպրիտացիա է անում։ Սենց չեղավ։

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   1. 

      Vigen Avetisyan

      October 15, 2017 at 5:27 pm

      Մենք հասկացանք վոր դուք շատ խ’ելոք և գիտելիքներով ոշտված անձեք: Ընդունելով այդ շատ անհասկանալի է կոնտեկստից անջատվֆած հատվածը որի շուրջ արվում են ձեր եզրակացությունները: և ինչու միայն Hellenthal et. и др. (2014 г.): Իսկ մնացածը: ՈՒզում եք ասել վոր կարդացել եք բոլորը: Ներարյալ այս և էլի սրա նման մոտ չորս անգամ ավելի աշխատությունները
      Diverse historical, archaeological, anthropological, and linguistic sources of information indicate that human populations have interacted throughout history, because of the rise and fall of empires, invasions, migrations, slavery, and trade. These interactions can result in sudden or gradual transfers of genetic material, creating admixed populations. However, the genetic legacy of these interactions remains unknown in most cases, and the historical record is incomplete. We have developed an approach that provides a detailed characterization of the mixture events in the ancestry of sampled populations on the basis of genetic data alone.

      Admixed populations should have segments of DNA from all contributing source groups (Fig. 1A), whose sizes decrease over successive generations because of recombination, and approaches have been developed to date admixture events by inferring the size of ancestry segments (1–5). Between-population frequency differences of individual alleles may provide information on ancestry sources (6, 7). On the basis of these principles, we developed an integrated approach by using genome-wide patterns of ancestry to infer jointly both fine-scale information about groups involved in admixture and its timing, allowing for the fact that migration and admixture events can occur at multiple times or involve numerous groups.

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      Fig. 1
      Ancestry painting and admixture analysis of simulated admixture.
      (A) A simulated event 30 generations ago between Brahui (80%, red) and Yoruba (20%, yellow) resulted in admixed individuals having DNA segments from each source (bottom). The true sources are then treated as unsampled. cM, centimorgan. (B) CHROMOPAINTER’s painting of the same region (yellow, Africa; green, America; red, Central-South Asia; blue, East Asia; cyan, Europe; pink, Near East; black, Oceania), showing haplotypic segments (“chunks”) shared with these groups. Our model fitting narrows the donor set largely to Central-South Asia and Africa, generating a “cleaned” painting. (C) Coancestry curves (black line) show relative probability of jointly copying two chunks from red (Balochi; FST = 0.003 with Brahui) and/or yellow (Mandenka; FST = 0.009 with Yoruba) donors, at varying genetic distances. The curves closely fit an exponential decay (green line) with a rate of 30 generations (95% CI: 27 to 33). The positive slope for the Balochi-Mandenka curve (middle) implies that these donors represent different admixing sources. (D) GLOBETROTTER’s source inference, with black diamonds indicating sampled populations with greatest similarity (FST ≤ 0.001 over minimum) to true sources, white circles other sampled populations. Red and yellow circles, with areas summing to 20% and 80%, respectively, show inferred haplotypic makeup of the two admixing sources.

      The GLOBETROTTER Method
      Our approach gains power and resolution by using alleles at multiple successive single-nucleotide polymorphisms (SNPs) (haplotypes) (8). Given a focal population within a larger data set containing many such groups, the chromosomes of individuals in this population share ancestors with those in other populations, resulting in shared “chunks” of DNA. We used CHROMOPAINTER (8) to decompose each chromosome as a series of haplotypic chunks, each inferred to be shared with an individual from one of the other groups and colored (or painted) by this group (Fig. 1B). If the focal population is admixed, the changing colors along a chromosome noisily reflect true but unknown underlying ancestry (Fig. 1B) and so can be used to learn details of the source group(s) involved. To do this, we modeled haplotypes within each unsampled source group as being found across a weighted mixture of sampled “donor” populations (9). If a source group is genetically relatively similar to a single sampled population, then this population will dominate the inferred mixture. If there is no close proxy for the admixing group in the sample, especially likely for ancient admixture events or sparsely sampled regions, several donor populations will be needed to approximate its pattern of haplotype sharing. The focal population is then automatically a haplotypic mixture of the combined donors, because it is a mixture of the source groups. Inferring the reduced set of groups within the mixture allows us to produce a “cleaned” painting (Fig. 1B) using only these groups.

      To assess the evidence for admixture and date events, informally we measured the scale at which the cleaned painting changes along the genome. Specifically, we produced a coancestry curve for each pair of donor populations, plotting genetic distance x against a measure of how often a pair of haplotype chunks separated by x come from each respective donor (Fig. 1C), analogously to ROLLOFF curves (4), and averaging over uncertain and typically computationally estimated haplotypic phase (9). In theory, given a single admixture event, ancestry chunks inherited from each source have an exponential size distribution, resulting in an exponential decay of these coancestry curves (9). The rate of decay in all curves will be equal to the time in generations since admixture (Fig. 1C) (4, 9, 10), allowing estimation of this date: Steeper decay corresponds to older admixture. Such a decay distinguishes true admixture from ancient spatial structure and should only occur in recipient but not donor groups involved in nonreciprocal admixture events. We test for evidence to reject (P < 0.01) a no-admixture null model, that is, no exponential decay in (normalized) coancestry curves, via bootstrapping (9). Multiple admixture times result in a mixture of exponentials (9); if admixture is detected, we test for evidence of multiple admixture times (e.g., two episodes of admixture or more continuous admixture over a longer period; empirical P < 0.05 in simulations), comparing the fit of a single exponential decay rate versus a mixture of rates.The curve heights (intercepts) provide complementary information to deconvolve the number and genetic composition of the ancestral sources before admixture (11). Fitted curves for all pairs of donor groups (Fig. 1C shows three examples) specify a pairwise intercept matrix, which, after normalization, we decompose by using a series of eigenvectors. Analogous to the standard use of eigenvector decomposition in principal components analysis (PCA) in genetics to estimate relative ancestry source contributions for different individuals (12), the eigenvectors allow us to estimate the relative contribution to different admixing sources (e.g., source 1 versus source 2) for each different donor group (9). Also as for PCA, admixture between K distinct source populations produces K – 1 significant eigenvectors (13), and we test for three or more admixing sources by testing (empirically) for evidence of two or more such eigenvectors (P < 0.05) (9). After iterative modeling to improve results, this allows us to attempt to “reverse” the admixture process (Fig. 1D) and to infer the haplotypic makeup of admixing source groups as well as admixture date(s) in our method, which we call GLOBETROTTER. Simulations To test our approach under diverse single, complex, and no-admixture scenarios, incorporating many of the complexities (such as unsampled or admixed donor groups) likely to be present in real data, we simulated admixture scenarios involving real (but hidden to our analysis) human populations (4, 9) and populations generated under a coalescent framework (14) incorporating inferred (15–18) past demographic events. Admixture was simulated between 7 and 160 generations [200 to 4400 years, assuming 28 years per human generation (19)] ago, with admixture fractions 3 to 50% and genetic differentiation (FST) between the admixing groups varying from 0.018 (similar to Europe versus Central Asia) to 0.185 (similar to West Africa versus Europe). Results are detailed online (figs. S3 to S7 and tables S1 and S5). All populations simulated without admixture, including those with long-term migration, showed no admixture evidence (P > 0.1). Power to detect admixture (P < 0.01) when present was 94%, and 95% of our 95% bootstrapped confidence intervals (CIs) contained the true admixture date, including cases with two distinct incidents of admixture or multiple groups admixing simultaneously. Inferred source accuracy was very high (9), with, for example, the mixture representation predicting a haplotype composition more correlated to the true, typically unsampled, source population than to any single sampled population >80% of the time. However, source accuracy was lower for admixing sources contributing only 5% of DNA, with around 40% of such scenarios yielding elevated (>25%) rates of falsely inferring multiple admixture times and/or admixing groups. Further testing demonstrated robustness of GLOBETROTTER, in simulations and real data, to haplotypic phase inference approach used, inclusion/exclusion of particular chromosomes, genetic map chosen to provide genetic distances, and the presence of population bottlenecks since admixture, whereas GLOBETROTTER admixture dating was improved relative to ROLLOFF (4, 9).

      Nevertheless, there are multiple settings that we believe are challenging for our approach. First, although the admixing sources need not be sampled—often impossible because of genetic drift, extinction, or later admixture into the sources themselves—source inference is improved when more similar extant groups are sampled, and GLOBETROTTER may miss events where we lack any extant group that can separate sources. Second, sampling of several genetically very similar groups can mask admixture events they share. Similarly, a caveat is that where genuine, recent bidirectional gene flow has occurred, admixture fractions are difficult to define and interpret. However, date estimation is predicted to still be useful, and in real data the majority of our inferred events do not appear to be bidirectional in this manner. Third, even in theory our approach finds it challenging to distinguish distinct continuous “pulses” of admixture and continuous migration over some time frame (9), because of the difficulty of separating exponential mixtures (20). If the time frame were narrow, we expect to infer a single admixture time within the range of migration dates. Where we infer two admixture dates, in particular with the same source groups, the exponential decay signal could also be consistent with more continuous migration, and so we conservatively refer to this as admixture at multiple dates. Last, we only attempt to analyze populations with signals consistent with at most three groups admixing and infer at most two admixture times, and we can provide only less precise inference of sources for the weaker or older admixture signal in these complex cases (9).
      Analysis of Worldwide Admixture
      By using GLOBETROTTER, we analyzed 1490 individuals from 95 worldwide human groups (table S10 and fig. S12) (9), composed of 17 newly genotyped groups (21), 53 from the Human Genome Diversity Panel (HGDP) (22), and 25 from other sources (23, 24), filtered to 474,491 autosomal SNPs. We phased the individuals by using IMPUTE2 (9, 25) and used fineSTRUCTURE (8) to verify homogeneity within labeled populations, to identify genetically similar and clustered groups, and to remove outlying individuals (figs. S12 to S14 and tables S10 and S11). Of the 95 populations, 80 showed evidence (P < 0.01) of admixture, although nine could not be characterized by our approach (table S12). More than half of these have evidence of multiple waves of admixture (P < 0.05), and estimated admixture times vary from <10 to >150 generations (Fig. 2). We present individual results, for each population, via an interactive map online (26). We tested consistency of our results against a previous analysis of the 53 groups within the HGDP (11), which identified 34 groups with evidence of recent admixture. We identified (P < 0.01) admixture evidence in all 34 cases (with multiple event evidence in 15 cases) and obtained 95% admixture date CIs narrower than, but consistent with, those estimated by using ROLLOFF (9, 11). For 10 of 19 HGDP groups lacking previous support for recent admixture, GLOBETROTTER also identifies no events: In the remaining populations, admixture is inferred as occurring between genetically similar sources (FST < 0.02), a challenging setting where simulations suggest our method is more powerful (9).Download high-res image Open in new tab Download Powerpoint Fig. 2 Overview of inferred admixture for 95 human populations. (A) Coancestry curve for the Maya for Spanish donor group (inferred as closest to minor admixing source), with green fitted line showing inferred exponential decay curve and a corresponding recent admixture date (with 95% CIs). (B and C) As (A), but showing the Druze and Kalash, respectively, with different indicated donors (donors indicated are proxies for minor admixing source, inferred as closest to Yoruba and Germany/Austria, respectively) and with successively older admixture. (D) On the map (locations approximate in densely sampled regions), shapes (see legend) indicate inference: no admixture, a single admixture event, or more complex admixture. Colors indicate fineSTRUCTURE clustering into 18 clades (table S11 and figs. S12 and S13). Inferred date(s) and 95% CIs are directly below the map, with two inferred admixing sources (dots and vertical bars) shown below each date (see example for simulation of Fig. 1 at left). For multiple admixture times, these two sources correspond to the more recent event; for multiple groups, they reflect the strongest admixture “direction.” Colored dots above each bar indicate clades best representing the major (top) and minor (bottom) sources. The bar is split at the inferred admixture fraction (horizontal line, fractions <5% shown as 5%). Each bar section indicates the inferred donor group haplotypic makeup, colored as the map, for one source. Shaded boxes on the inferred admixture times denote events referred to in the text, specifically (label 1) European colonization of the Americas (1492 CE to present, fuchsia); (2) Slavic (500 to 900 CE, pink) and Turkic (500 to 1100 CE, maroon) migrations; (3) Arab slave trade (650 to 1900 CE, cyan); (4) Mongol empire (1206 to 1368 CE, purple); and (5) Khmer empire (802 to 1431 CE, orange).In several instances, GLOBETROTTER clarifies or extends previous genetic analyses. For example, a previous study (27) inferred admixture in the Maya, with best source populations the Mozabites from North Africa and the Native American Surui, speculating on the basis of historical events that this might actually represent a mixture of European, West African, and Native American ancestry sources. GLOBETROTTER inferred admixture between three groups in the Maya dating to around 1670 CE (9 generations ago) (28) (Fig. 2, A and D, fuchsia box 1), with distinct sources from Europe (most genetically similar to the Spanish), West Africa (the Yoruba), and the Americas (the Pima, the nearest sampled group in the Americas). A different method, which aims to detect but not date admixture, concluded that Cambodians trace ~16% of their DNA to a group equally related to modern-day Europeans and East Asians (29). GLOBETROTTER infers a ~19% contribution from a similar source related to modern-day Central, South, and East Asians and an ~81% contribution from a source related specifically to modern-day Han and Dai, the latter a branch of the Tai people who entered the region in historical times (30) (Fig. 2D, orange box 5). Further, this event dates to 1362 CE (1194 to 1502 CE), a period spanning the end of the Indianized Khmer empire (802 to 1431 CE) (30), one of the most powerful empires in Southeast Asia, whose fall was hypothesized to relate to a Tai influx (30).A comparison with the historical record becomes progressively more difficult for older episodes. Even when events are well attested, their exact genetic impacts (if any) are rarely if ever known, motivating our approach. Nevertheless, we have identified nine groups of populations showing related events, incorporating almost all (19/20) with the strongest GLOBETROTTER admixture evidence (9). Results are presented as online maps (26). Some events appear to match well with particular historical occurrences, such as the so-called Bantu Expansion into Southern Africa (9). Events affecting a group of seven populations (Fig. 2D, purple box 4) correspond in time to the rapid expansion, initiated by Genghis Khan, of the Mongol empire (1206 to 1368 CE) (31), one of the most dramatic events in human history. These populations, including the Hazara (32, 33), the Uygur (34), and the Mongola themselves, were sampled from within the range of the Mongol empire and show an admixture event dating within the Mongol Period, with one source closely genetically related to the Mongola that progressively decreases in proportion westward, to 8% in the Turkish (Fig. 2D).Seventeen populations from the Mediterranean, the Near East, and countries bordering the Arabian Sea (Fig. 2D, blue box 3) show signals of admixture from sub-Saharan Africa, with most recent dates in the range 890 to 1754 CE (Fig. 2, B and D). We interpret these signals, consistent with overlapping results of previous studies (4, 20), as resulting from the Arab expansion and slave trade, which originated around the seventh century CE (35). Our event dates are highly consistent with this but also imply earlier sub-Saharan African gene flow into, for example, the Moroccans. The highest-contributing sub-Saharan donor is West African for all 12 Mediterranean populations and an East or South African Bantu-speaking group for all five Arabian Sea populations (Fig. 2D), confirming genetically different sources for these slave trades (35).A population group centered around Eastern Europe shows signals of complex admixture. FineSTRUCTURE did not fully separate groups from this region, suggesting masked shared events might be present. We therefore repainted them excluding each other as donors: We performed similar reanalyses of five additional geographic regions for the same reason (table S16 and figs. S16 to S21). The easterly Russians and Chuvash both show evidence (P < 0.05) of admixture at more than one time (Fig. 2D), at least partially predating the Mongol empire, between groups with ancestry related to Northeast Asians (e.g., the Oroqen, Mongola, and Yakut) and Europeans, respectively (table S16). Six other European populations (Fig. 2D, pink/maroon box 2) independently show evidence after the repainting for similar admixture events involving more than two groups (P < 0.02) at about the same time (Fig. 3). CIs for the admixture time(s) overlap but predate the Mongol empire, with estimates from 440 to 1080 CE (Fig. 3). In each population, one source group has at least some ancestry related to Northeast Asians, with ~2 to 4% of these groups’ total ancestry linking directly to East Asia. This signal might correspond to a small genetic legacy from invasions of peoples from the Asian steppes (e.g., the Huns, Magyar, and Bulgars) during the first millennium CE (36). The other two source groups appear much more local. One is more North European in the repainting, when we exclude other East European groups as donors, and is largely replaced by northern Slavic-speaking groups in our original analysis (Fig. 2D and table S12). The other source is more southerly (e.g., Greeks and West Asians). This local migration could explain a recent observation of an excess of identity-by-descent sharing in Eastern Europe—including in the Greeks, in whom we infer admixture involving a group represented by Poland, at the same time—that was dated to a wide range between 1000 and 2000 years ago (37). We speculate that these events may correspond to the Slavic expansion across this region at a similar time, perhaps related to displacement caused by the Eurasian steppe invaders (38).Download high-res image Open in new tab Download Powerpoint Fig. 3 Multiway admixture in Eastern Europe. Mixing percentages (pie graphs) and dates (white text) inferred by using the strongest admixture “direction” for six eastern European groups—Belarus (BE), Bulgaria (BU), Hungary (HU), Lithuania (LI), Poland (PO), Romania (RO), analyzed when disallowing copying from nearby groups—and Greece (GR), analyzed by using the full set of 94 donors. Mixing percentages indicate percentages for three geographic regions: “N. Europe” (Northwest Europe and East Europe from clades of table S11; blue), “Southern” (South Europe and West Asia; red), and “N.E. Asia” (Northeast Asia and Yakut; purple, also given above each pie), plus other (gray). All groups except Greece show evidence (P < 0.05) of multiway admixture involving sources along the approximate directions show by the arrows. Coancestry curves (black lines) for Bulgaria, fitted with an exponential decay curve (green lines), exemplify this multiway signal. Each pairing of the three donor groups, each a proxy for the admixture source from a different region (Norway, northeast Europe; Oroqen, Northeast Asia; and Greece, South Europe and West Asia), exhibits negative correlation (a dip) in ancestry weights at short genetic distances, implying at least three identifiably distinct ancestral sources mixing (approximately) simultaneously (9).Last, Central Asia shows a particularly complex inferred history after a reanalysis of 10 groups excluding each other as donors, with 9 of 10 groups showing diverse recent events (Fig. 4A). The exception is the Kalash, a genetically isolated (39) population from the Hindu Kush mountains of Pakistan (40). Distinct, ancient, and partially shared admixture signals (always dated older than 90 BCE) are seen in six groups (Fig. 4B), including the Kalash (Fig. 2C), whose strongest signal suggests a major admixture event (990 to 210 BCE) from a source related to present-day Western Eurasians, although we cannot identify the geographic origin precisely. This period overlaps that of Alexander the Great (356 to 323 BCE), whose army, local tradition holds, the Kalash are descended from (40), but these ancient events predate recorded history in the region, precluding confident interpretation.Download high-res image Open in new tab Download Powerpoint Fig. 4 Ancient and modern admixture in Central Asia. (A) Dates (white text) and minority contributing sources for recent inferred events in nine populations (circles), analyzed disallowing copying from nearby groups, show contributions from Northeast Asia (purple) in the Hazara (HA), Uygur (UY), and Uzbekistani (UZ); East Asia (maroon) in Burusho (BU); West Asia (brown) in Pathan (PA); and Africa (red) in Balochi (BA), Brahui (BR), Makrani (MA), and Sindhi (SI). Kalash (KA, gray) have no inferred recent event. (B) Inferred mixing percentages (pie graphs) and dates (white text gives upper CI bound) for additional, possibly shared, ancient events in seven groups (HA, UY, and UZ have no inferred ancient events). Pie graphs show inferred donor makeup of each group after removing the recent event contribution from (A), if any, with colors referring to donors from “East Asia” (Southeast Asia from clades of table S11; maroon), “Europe” (Northwest, East, and South Europe; fuchsia), “Central South Asia” (orange), “West Asia” (brown), and other (white). Arrows indicate “directions” of ancient admixture, with donor regions splitting into two pairs that represent different sources. Coancestry curves (black lines) for Sindhi are superimposed for two different donor pairs representing proxies for admixing groups with ancestry indicated by the solid circles, indicating highly different exponential decay rates fit as a mixture of 7 and 94 generations (green lines).Our results demonstrate that it is possible to elucidate the effect of ancient and modern migration events and to provide fine-scale details of the sources involved, the complexity of events, and the timing of mixing of groups by using genetic information alone. Where independent information exists from alternative historical or archaeological sources, our approach provides results consistent with known facts and determines the amount of genetic material exchanged. In other cases, novel mixture events we infer are plausible and often involve geographically nearby sources, supporting their validity. Admixture events within the past several thousand years affect most human populations, and this needs to be taken into account in inferences aiming to look at the more distant history of our species. Future improvements in whole-genome sequencing, greater sample sizes, and incorporation of ancient DNA, together with additional methodological extensions, are likely to allow better understanding of ancient events where little or no historical record exists, to identify many additional events, to infer sex biases, and to provide more precise event characterization than currently possible. We believe our approach will extend naturally to these settings, as well as to other species. Supplementary Materials http://www.sciencemag.org/content/343/6172/747/suppl/DC1

      Materials and Methods

      Supplementary Text

      Figs. S1 to S21

      Tables S1 to S16

      Appendix

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      Acknowledgments: We are grateful for the John Fell Fund–University of Oxford, the NIH, the Wellcome Trust (S.M., grant 098387/Z/12/Z), the Biotechnology and Biological Sciences Research Council, the Royal Society/Wellcome Trust (G.H., grant 098386/Z/12/Z), and the Istituto Italiano di Antropologia for funding. J.F.W. is a director, stockholder, and employee of ScotlandsDNA (and formerly of EthnoAncestry). We thank S. Karachanak, D. Toncheva, P. Anagnostou, F. Cali, F. Brisighelli, V. Romano, G. LeFranc, C. Buresi, J. Ben Chibani, A. Haj-Khelil, S. Denden, R. Ploski, T. Hervig, T. Moen, P. Krajewski, and R. Herrera for providing samples for our genotyping and the blood donors and the staff of the Unità Operativa Complessa di Medicina Trasfusionale, Azienda Ospedaliera Umberto I, Siracusa (Italy). Data analyzed in this study may be downloaded via http://admixturemap.paintmychromosomes.com/. Raw genotype data are available at the Gene Expression Omnibus database online (www.ncbi.nlm.nih.gov/geo/), series accession number GSE53626.

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