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Ge X, Lu Y, Chen S, Gao Y, Ma L, Liu L, Liu J, Ma X, Kang L, Xu S. Genetic Origins and Adaptive Evolution of the Deng People on the Tibetan Plateau. Mol Biol Evol 2023; 40:msad205. [PMID: 37713634 PMCID: PMC10584363 DOI: 10.1093/molbev/msad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/01/2023] [Accepted: 08/30/2023] [Indexed: 09/17/2023] Open
Abstract
The Tibetan Plateau is populated by diverse ethnic groups, but most of them are underrepresented in genomics studies compared with the Tibetans (TIB). Here, to gain further insight into the genetic diversity and evolutionary history of the people living in the Tibetan Plateau, we sequenced 54 whole genomes of the Deng people with high coverage (30-60×) and analyzed the data together with that of TIB and Sherpas, as well as 968 ancient Asian genomes and available archaic and modern human data. We identified 17.74 million novel single-nucleotide variants from the newly sequenced genomes, although the Deng people showed reduced genomic diversity and a relatively small effective population size. Compared with the other Tibetan highlander groups which are highly admixed, the Deng people are dominated by a sole ancestry that could be traced to some ancient northern East Asian populations. The divergence between Deng and Tibetan people (∼4,700-7,200 years) was more recent than that between highlanders and the Han Chinese (Deng-HAN, ∼9,000-14,000 years; TIB-HAN, 7,200-10,000 years). Adaptive genetic variants (AGVs) identified in the Deng are only partially shared with those previously reported in the TIB like HLA-DQB1, whereas others like KLHL12 were not reported in TIB. In contrast, the top candidate genes harboring AGVs as previously identified in TIB, like EPAS1 and EGLN1, do not show strong positive selection signals in Deng. Interestingly, Deng also showed a different archaic introgression scenario from that observed in the TIB. Our results suggest that convergent adaptation might be prevalent on the Tibetan Plateau.
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Affiliation(s)
- Xueling Ge
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Shuanghui Chen
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yang Gao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Lifeng Ma
- Key Laboratory of High-Altitude Environment and Genes Related to Disease of Tibet Ministry of Education, Xizang Minzu University, Xianyang, Shaanxi, China
- Research Center for Tibetan Social Governance, Key Research Institute of Humanities and Social Sciences in Xizang Minzu University, State Ethnic Affairs Commission, Xizang Minzu University, Xianyang, Shaanxi, China
| | - Lijun Liu
- Key Laboratory of High-Altitude Environment and Genes Related to Disease of Tibet Ministry of Education, Xizang Minzu University, Xianyang, Shaanxi, China
- Research Center for Tibetan Social Governance, Key Research Institute of Humanities and Social Sciences in Xizang Minzu University, State Ethnic Affairs Commission, Xizang Minzu University, Xianyang, Shaanxi, China
| | - Jiaojiao Liu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Xixian Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Longli Kang
- Key Laboratory of High-Altitude Environment and Genes Related to Disease of Tibet Ministry of Education, Xizang Minzu University, Xianyang, Shaanxi, China
- Research Center for Tibetan Social Governance, Key Research Institute of Humanities and Social Sciences in Xizang Minzu University, State Ethnic Affairs Commission, Xizang Minzu University, Xianyang, Shaanxi, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
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Deng L, Zhang C, Yuan K, Gao Y, Pan Y, Ge X, He Y, Yuan Y, Lu Y, Zhang X, Chen H, Lou H, Wang X, Lu D, Liu J, Tian L, Feng Q, Khan A, Yang Y, Jin ZB, Yang J, Lu F, Qu J, Kang L, Su B, Xu S. Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders. Natl Sci Rev 2019; 6:1201-1222. [PMID: 34691999 PMCID: PMC8291452 DOI: 10.1093/nsr/nwz108] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/17/2019] [Accepted: 06/18/2019] [Indexed: 12/13/2022] Open
Abstract
Human genetic adaptation to high altitudes (>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 > T; p.Ala83Val), with the T allele derived from archaic ancestry and carried by >94% of Tibetans but absent or in low frequencies (<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.
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Affiliation(s)
- Lian Deng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chao Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yang Gao
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xueling Ge
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Yuan Yuan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxi Zhang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Hao Chen
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haiyi Lou
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoji Wang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiaojiao Liu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Lei Tian
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qidi Feng
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Asifullah Khan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Zi-Bing Jin
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
| | - Jian Yang
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
- Institute for Molecular Bioscience, Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Fan Lu
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
| | - Jia Qu
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, China National Center for International Research in Regenerative Medicine and Neurogenetics, State Key Laboratory of Ophthalmology, Optometry and Visual Science, Wenzhou 325027, China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nu-trition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
- Human Phenome Institute, Fudan University, Shanghai 201203, China
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Lu D, Lou H, Yuan K, Wang X, Wang Y, Zhang C, Lu Y, Yang X, Deng L, Zhou Y, Feng Q, Hu Y, Ding Q, Yang Y, Li S, Jin L, Guan Y, Su B, Kang L, Xu S. Ancestral Origins and Genetic History of Tibetan Highlanders. Am J Hum Genet 2016; 99:580-94. [PMID: 27569548 DOI: 10.1016/j.ajhg.2016.07.002] [Citation(s) in RCA: 129] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 07/01/2016] [Indexed: 12/30/2022] Open
Abstract
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.
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