The origin of Tibetans remains one of the most contentious puzzles in history, anthropology, and genetics. Analyses of deeply sequenced (30×-60×) genomes of 38 Tibetan highlanders and 39 Han Chinese lowlanders, together with available data on archaic and modern humans, allow us to comprehensively characterize the ancestral makeup of Tibetans and uncover their origins. Non-modern human sequences compose ∼6% of the Tibetan gene pool and form unique haplotypes in some genomic regions, where Denisovan-like, Neanderthal-like, ancient-Siberian-like, and unknown ancestries are entangled and elevated. The shared ancestry of Tibetan-enriched sequences dates back to ∼62,000-38,000 years ago, predating the Last Glacial Maximum (LGM) and representing early colonization of the plateau. Nonetheless, most of the Tibetan gene pool is of modern human origin and diverged from that of Han Chinese ∼15,000 to ∼9,000 years ago, which can be largely attributed to post-LGM arrivals. Analysis of ∼200 contemporary populations showed that Tibetans share ancestry with populations from East Asia (∼82%), Central Asia and Siberia (∼11%), South Asia (∼6%), and western Eurasia and Oceania (∼1%). Our results support that Tibetans arose from a mixture of multiple ancestral gene pools but that their origins are much more complicated and ancient than previously suspected. We provide compelling evidence of the co-existence of Paleolithic and Neolithic ancestries in the Tibetan gene pool, indicating a genetic continuity between pre-historical highland-foragers and present-day Tibetans. In particular, highly differentiated sequences harbored in highlanders' genomes were most likely inherited from pre-LGM settlers of multiple ancestral origins (SUNDer) and maintained in high frequency by natural selection.

The Uyghur people residing in Xinjiang, a territory located in the far west of China and crossed by the Silk Road, are a key ethnic group for understanding the history of human dispersion in Eurasia. Here we assessed the genetic structure and ancestry of 951 Xinjiang's Uyghurs (XJU) representing 14 geographical subpopulations. We observed a southwest and northeast differentiation within XJU, which was likely shaped jointly by the Tianshan Mountains, which traverses from east to west as a natural barrier, and gene flow from both east and west directions. In XJU, we identified four major ancestral components that were potentially derived from two earlier admixed groups: one from the West, harboring European (25-37%) and South Asian ancestries (12-20%), and the other from the East, with Siberian (15-17%) and East Asian (29-47%) ancestries. By using a newly developed method, MultiWaver, the complex admixture history of XJU was modeled as a two-wave admixture. An ancient wave was dated back to ∼3,750 years ago (ya), which is much earlier than that estimated by previous studies, but fits within the range of dating of mummies that exhibited European features that were discovered in the Tarim basin, which is situated in southern Xinjiang (4,000-2,000 ya); a more recent wave occurred around 750 ya, which is in agreement with the estimate from a recent study using other methods. We unveiled a more complex scenario of ancestral origins and admixture history in XJU than previously reported, which further suggests Bronze Age massive migrations in Eurasia and East-West contacts across the Silk Road.

BackgroundThe genetic relationships reported by recent studies between Sherpas and Tibetans are controversial. To gain insights into the population history and the genetic basis of high-altitude adaptation of the two groups, we analyzed genome-wide data in 111 Sherpas (Tibet and Nepal) and 177 Tibetans (Tibet and Qinghai), together with available data from present-day human populations.ResultsSherpas and Tibetans show considerable genetic differences and can be distinguished as two distinct groups, even though the divergence between them (~3200–11,300 years ago) is much later than that between Han Chinese and either of the two groups (~6200–16,000 years ago). Sub-population structures exist in both Sherpas and Tibetans, corresponding to geographical or linguistic groups. Differentiation of genetic variants between Sherpas and Tibetans associated with adaptation to either high-altitude or ultraviolet radiation were identified and validated by genotyping additional Sherpa and Tibetan samples.ConclusionsOur analyses indicate that both Sherpas and Tibetans are admixed populations, but the findings do not support the previous hypothesis that Tibetans derive their ancestry from Sherpas and Han Chinese. Compared to Tibetans, Sherpas show higher levels of South Asian ancestry, while Tibetans show higher levels of East Asian and Central Asian/Siberian ancestry. We propose a new model to elucidate the differentiated demographic histories and local adaptations of Sherpas and Tibetans.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1242-y) contains supplementary material, which is available to authorized users.

Tibetan high-altitude adaptation (HAA) has been studied extensively, and many candidate genes have been reported. Subsequent efforts targeting HAA functional variants, however, have not been that successful (e.g., no functional variant has been suggested for the top candidate HAA gene, EPAS1). With WinXPCNVer, a method developed in this study, we detected in microarray data a Tibetan-enriched deletion (TED) carried by 90% of Tibetans; 50% were homozygous for the deletion, whereas only 3% carried the TED and 0% carried the homozygous deletion in 2,792 worldwide samples (p < 10(-15)). We employed long PCR and Sanger sequencing technologies to determine the exact copy number and breakpoints of the TED in 70 additional Tibetan and 182 diverse samples. The TED had identical boundaries (chr2: 46,694,276-46,697,683; hg19) and was 80 kb downstream of EPAS1. Notably, the TED was in strong linkage disequilibrium (LD; r(2) = 0.8) with EPAS1 variants associated with reduced blood concentrations of hemoglobin. It was also in complete LD with the 5-SNP motif, which was suspected to be introgressed from Denisovans, but the deletion itself was absent from the Denisovan sequence. Correspondingly, we detected that footprints of positive selection for the TED occurred 12,803 (95% confidence interval = 12,075-14,725) years ago. We further whole-genome deep sequenced (>60×) seven Tibetans and verified the TED but failed to identify any other copy-number variations with comparable patterns, giving this TED top priority for further study. We speculate that the specific patterns of the TED resulted from its own functionality in HAA of Tibetans or LD with a functional variant of EPAS1.

Human genetic adaptation to high altitudes (&gt;2500 m) has been extensively studied over the last few years, but few functional adaptive genetic variants have been identified, largely owing to the lack of deep-genome sequencing data available to previous studies. Here, we build a list of putative adaptive variants, including 63 missense, 7 loss-of-function, 1,298 evolutionarily conserved variants and 509 expression quantitative traits loci. Notably, the top signal of selection is located in TMEM247, a transmembrane protein-coding gene. The Tibetan version of TMEM247 harbors one high-frequency (76.3%) missense variant, rs116983452 (c.248C &gt; T; p.Ala83Val), with the T allele derived from archaic ancestry and carried by &gt;94% of Tibetans but absent or in low frequencies (&lt;3%) in non-Tibetan populations. The rs116983452-T is strongly and positively correlated with altitude and significantly associated with reduced hemoglobin concentration (p = 5.78 × 10−5), red blood cell count (p = 5.72 × 10−7) and hematocrit (p = 2.57 × 10−6). In particular, TMEM247-rs116983452 shows greater effect size and better predicts the phenotypic outcome than any EPAS1 variants in association with adaptive traits in Tibetans. Modeling the interaction between TMEM247-rs116983452 and EPAS1 variants indicates weak but statistically significant epistatic effects. Our results support that multiple variants may jointly deliver the fitness of the Tibetans on the plateau, where a complex model is needed to elucidate the adaptive evolution mechanism.

Our goal in developing the MultiWaver software series was to be able to infer population admixture history under various complex scenarios. The earlier version of MultiWaver considered only discrete admixture models. Here, we report a newly developed version, MultiWaver 2.0, that implements a more flexible framework and is capable of inferring multiple-wave admixture histories under both discrete and continuous admixture models. MultiWaver 2.0 can automatically select an optimal admixture model based on the length distribution of ancestral tracks of chromosomes, and the program can estimate the corresponding parameters under the selected model. Specifically, for discrete admixture models, we used a likelihood ratio test (LRT) to determine the optimal discrete model and an expectation-maximization algorithm to estimate the parameters. In addition, according to the principles of the Bayesian Information Criterion (BIC), we compared the optimal discrete model with several continuous admixture models. In MultiWaver 2.0, we also applied a bootstrapping technique to provide levels of support for the chosen model and the confidence interval (CI) of the estimations of admixture time. Simulation studies validated the reliability and effectiveness of our method. Finally, the program performed well when applied to real datasets of typical admixed populations, such as African Americans, Uyghurs, and Hazaras.

Hair straightness/curliness is one of the most conspicuous features of human variation and is particularly diverse among populations. A recent genome-wide scan found common variants in the Trichohyalin (TCHH) gene that are associated with hair straightness in Europeans, but different genes might affect this phenotype in other populations. By sampling 2899 Han Chinese, we performed the first genome-wide scan of hair straightness in East Asians, and found EDAR (rs3827760) as the predominant gene (P = 4.67 × 10), accounting for 3.66 % of the total variance. The candidate gene approach did not find further significant associations, suggesting that hair straightness may be affected by a large number of genes with subtle effects. Notably, genetic variants associated with hair straightness in Europeans are generally low in frequency in Han Chinese, and vice versa. To evaluate the relative contribution of these variants, we performed a second genome-wide scan in 709 samples from the Uyghur, an admixed population with both eastern and western Eurasian ancestries. In Uyghurs, both EDAR (rs3827760: P = 1.92 × 10) and TCHH (rs11803731: P = 1.46 × 10) are associated with hair straightness, but EDAR (OR 0.415) has a greater effect than TCHH (OR 0.575). We found no significant interaction between EDAR and TCHH (P = 0.645), suggesting that these two genes affect hair straightness through different mechanisms. Furthermore, haplotype analysis indicates that TCHH is not subject to selection. While EDAR is under strong selection in East Asia, it does not appear to be subject to selection after the admixture in Uyghurs. These suggest that hair straightness is unlikely a trait under selection.

The ancestral tracks in admixed genomes are valuable for population history inference. While a few methods have been developed to infer admixture history based on ancestral tracks, these methods suffer the same flaw: only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model deviates from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation-maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real data sets of African Americans and Mexicans, and new insights were gained into the admixture history of Uyghurs and Hazaras.