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Background The 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. Results Sherpas 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. Conclusions Our 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.

The Middle East (ME) is an important crossroad where modern humans migrated 'out of Africa' and spread into Europe and Asia. After the initial peopling and long-term isolation leading to well-differentiated populations, the ME also had a crucial role in subsequent human migrations among Africa, Europe and Asia; thus, recent population admixture has been common in the ME. On the other hand, consanguinity, a well-known practice in the ME, often reduces genetic diversity and works in opposition to admixture. Here, we explored the degree to which admixture and consanguinity jointly affected genetic diversity in ME populations. Genome-wide single-nucleotide polymorphism data were generated in two representative ME populations (Arabian and Iranian), with comparisons made with populations worldwide. Our results revealed an overall higher genetic diversity in both ME populations relative to other non-African populations. We identified a much larger number of long runs of homozygosity in ME populations than in any other populations, which was most likely attributed to high levels of consanguineous marriages that significantly decreased both individual and population heterozygosity. Additionally, we were able to distinguish African, European and Asian ancestries in ME populations and quantify the impact of admixture and consanguinity with statistical approaches. Interestingly, genomic regions with significantly excessive ancestry from individual source populations are functionally enriched in olfactory pathways, which were suspected to be under natural selection. Our findings suggest that genetic admixture, consanguinity and natural selection have collectively shaped the genetic diversity of ME populations, which has important implications in both evolutionary studies and medical practices.

In Black Ghosts: A Journey into the Lives of Africans in China (Canon- gate Books, 2023), Noo Saro-Wiwa investigates the experiences of economic migrants from Africa in today's China. While the countries of Europe and North America and others in the Global North have established substantial roadblocks to commerce with African nations and African people, China has emerged as a land of opportunity and has become a hub of trade for Africans from diverse backgrounds. China is, however, also a place where Africans face racism, prejudice, and discrimination in a variety of ways. Through a series of encounters with different African migrants, Saro-Wiwa illuminates the human entanglements that emerge from this intersection of cultures.

Abstract While whole-genome sequencing has been applied extensively to investigate the genetic diversity of global populations, ethnic minority groups in Pakistan are generally underrepresented. In particular, little is known about the genetic origin and highland adaptation of the Pamirian Wakhi people. According to Chinese historical records, the geographical location and language usage of Wakhi may be closely related to Xinjiang Tajiks. In this study, based on high-coverage (∼30×) whole-genome sequencing of eight Wakhi and 25 Xinjiang Tajik individuals, we performed data analyses together with worldwide populations to gain insights into their genetic composition, demography, and adaptive evolution to the highland environment. The Wakhi derived more than 85% of their ancestry from West Eurasian populations (European ∼44.5%, South Asian ∼42.2%) and 10% from East Eurasian populations (Siberian ∼6.0%, East Asian ∼4.3%). Modeling the admixture history of the Wakhi indicated that the early West–East admixture occurred ∼3,875 to 2,250 years ago and that the recent admixture occurred ∼750 to 375 years ago. We identified selection signatures across EGLN3, in particular, a distinctive evolutionary signature was observed, and a certain underlying selected haplotype showed higher frequency (87.5%) in the Wakhi than in nearby Xinjiang Tajiks and other highlanders. Interestingly, we found high-frequency archaic sequences in the Wakhi genome, which overlapped with several genes related to cellular signaling transduction, including MAGI2, previously associated with high-altitude adaptation. Our analysis indicates that the Wakhi are distinct from the Xinjiang Tajiks and Tajikistan Tajiks and sheds light on the Wakhi's ancestral origin and genetic basis of high-altitude adaptation.

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.

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.

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.

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 −16 ), 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 −12 ) and TCHH (rs11803731: P  = 1.46 × 10 −3 ) 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.

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

The ancestral tracks in admixed genomes are of valuable information for population history inference. A few methods have been developed to infer admixture history based on ancestral tracks. Nonetheless, these methods suffered the same flaw that 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 is deviated 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 the 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 datasets of African Americans, Mexicans, Uyghurs, and Hazaras.