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Lei C, Liu J, Zhang R, Pan Y, Lu Y, Gao Y, Ma X, Yang Y, Guan Y, Mamatyusupu D, Xu S. Ancestral Origins and Admixture History of Kazakhs. Mol Biol Evol 2024; 41:msae144. [PMID: 38995236 PMCID: PMC11272102 DOI: 10.1093/molbev/msae144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/29/2024] [Accepted: 07/02/2024] [Indexed: 07/13/2024] Open
Abstract
Kazakh people, like many other populations that settled in Central Asia, demonstrate an array of mixed anthropological features of East Eurasian (EEA) and West Eurasian (WEA) populations, indicating a possible scenario of biological admixture between already differentiated EEA and WEA populations. However, their complex biological origin, genomic makeup, and genetic interaction with surrounding populations are not well understood. To decipher their genetic structure and population history, we conducted, to our knowledge, the first whole-genome sequencing study of Kazakhs residing in Xinjiang (KZK). We demonstrated that KZK derived their ancestries from 4 ancestral source populations: East Asian (∼39.7%), West Asian (∼28.6%), Siberian (∼23.6%), and South Asian (∼8.1%). The recognizable interactions of EEA and WEA ancestries in Kazakhs were dated back to the 15th century BCE. Kazakhs were genetically distinctive from the Uyghurs in terms of their overall genomic makeup, although the 2 populations were closely related in genetics, and both showed a substantial admixture of western and eastern peoples. Notably, we identified a considerable sex-biased admixture, with an excess of western males and eastern females contributing to the KZK gene pool. We further identified a set of genes that showed remarkable differentiation in KZK from the surrounding populations, including those associated with skin color (SLC24A5, OCA2), essential hypertension (HLA-DQB1), hypertension (MTHFR, SLC35F3), and neuron development (CNTNAP2). These results advance our understanding of the complex history of contacts between Western and Eastern Eurasians, especially those living or along the old Silk Road.
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Affiliation(s)
- Chang Lei
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, 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, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, 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, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, 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 200031, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yaqun Guan
- Department of Biochemistry and Molecular Biology, Preclinical Medicine College, Xinjiang Medical University, Urumqi 830011, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University, Urumqi 830046, 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, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
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Wang M, Huang Y, Liu K, Wang Z, Zhang M, Yuan H, Duan S, Wei L, Yao H, Sun Q, Zhong J, Tang R, Chen J, Sun Y, Li X, Su H, Yang Q, Hu L, Yun L, Yang J, Nie S, Cai Y, Yan J, Zhou K, Wang C, Zhu B, Liu C, He G. Multiple Human Population Movements and Cultural Dispersal Events Shaped the Landscape of Chinese Paternal Heritage. Mol Biol Evol 2024; 41:msae122. [PMID: 38885310 PMCID: PMC11232699 DOI: 10.1093/molbev/msae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 05/30/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
Large-scale genomic projects and ancient DNA innovations have ushered in a new paradigm for exploring human evolutionary history. However, the genetic legacy of spatiotemporally diverse ancient Eurasians within Chinese paternal lineages remains unresolved. Here, we report an integrated Y-chromosome genomic database encompassing 15,563 individuals from both modern and ancient Eurasians, including 919 newly reported individuals, to investigate the Chinese paternal genomic diversity. The high-resolution, time-stamped phylogeny reveals multiple diversification events and extensive expansions in the early and middle Neolithic. We identify four major ancient population movements, each associated with technological innovations that have shaped the Chinese paternal landscape. First, the expansion of early East Asians and millet farmers from the Yellow River Basin predominantly carrying O2/D subclades significantly influenced the formation of the Sino-Tibetan people and facilitated the permanent settlement of the Tibetan Plateau. Second, the dispersal of rice farmers from the Yangtze River Valley carrying O1 and certain O2 sublineages reshapes the genetic makeup of southern Han Chinese, as well as the Tai-Kadai, Austronesian, Hmong-Mien, and Austroasiatic people. Third, the Neolithic Siberian Q/C paternal lineages originated and proliferated among hunter-gatherers on the Mongolian Plateau and the Amur River Basin, leaving a significant imprint on the gene pools of northern China. Fourth, the J/G/R paternal lineages derived from western Eurasia, which were initially spread by Yamnaya-related steppe pastoralists, maintain their presence primarily in northwestern China. Overall, our research provides comprehensive genetic evidence elucidating the significant impact of interactions with culturally distinct ancient Eurasians on the patterns of paternal diversity in modern Chinese populations.
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Affiliation(s)
- Mengge Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- Center for Archaeological Science, Sichuan University, Chengdu 610000, China
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Yuguo Huang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
| | - Kaijun Liu
- School of International Tourism and Culture, Guizhou Normal University, Guiyang 550025, China
- MoFang Human Genome Research Institute, Tianfu Software Park, Chengdu, Sichuan 610042, China
| | - Zhiyong Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- School of Forensic Medicine, Kunming Medical University, Kunming 650500, China
| | - Menghan Zhang
- Institute of Modern Languages and Linguistics, Fudan University, Shanghai 200433, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Haibing Yuan
- Center for Archaeological Science, Sichuan University, Chengdu 610000, China
| | - Shuhan Duan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong 637100, China
| | - Lanhai Wei
- School of Ethnology and Anthropology, Institute of Humanities and Human Sciences, Inner Mongolia Normal University, Hohhot 010022, China
| | - Hongbing Yao
- Belt and Road Research Center for Forensic Molecular Anthropology Gansu University of Political Science and Law, Lanzhou 730000, China
| | - Qiuxia Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400331, China
| | - Jie Zhong
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400331, China
| | - Jing Chen
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030001, China
| | - Yuntao Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Xiangping Li
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- School of Forensic Medicine, Kunming Medical University, Kunming 650500, China
| | - Haoran Su
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- School of Laboratory Medicine and Center for Genetics and Prenatal Diagnosis, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637007, China
| | - Qingxin Yang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- School of Forensic Medicine, Kunming Medical University, Kunming 650500, China
| | - Liping Hu
- School of Forensic Medicine, Kunming Medical University, Kunming 650500, China
| | - Libing Yun
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Junbao Yang
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College and Center for Genetics and Prenatal Diagnosis, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637007, China
| | - Shengjie Nie
- School of Forensic Medicine, Kunming Medical University, Kunming 650500, China
| | - Yan Cai
- School of Laboratory Medicine and Center for Genetics and Prenatal Diagnosis, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637007, China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Jinzhong 030001, China
| | - Kun Zhou
- MoFang Human Genome Research Institute, Tianfu Software Park, Chengdu, Sichuan 610042, China
| | - Chuanchao Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, China
| | - Bofeng Zhu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Chao Liu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
- Anti-Drug Technology Center of Guangdong Province, Guangzhou 510230, China
| | - Guanglin He
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu 610000, China
- Center for Archaeological Science, Sichuan University, Chengdu 610000, China
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3
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Zhang S, Zhang R, Yuan K, Yang L, Liu C, Liu Y, Ni X, Xu S. Reconstructing complex admixture history using a hierarchical model. Brief Bioinform 2024; 25:bbad540. [PMID: 38261339 PMCID: PMC10805183 DOI: 10.1093/bib/bbad540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/04/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.
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Affiliation(s)
- Shi Zhang
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lu Yang
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuting Liu
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - Xumin Ni
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, 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, Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032 , China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 201203, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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4
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He Y, Chu Y, Guo S, Hu J, Li R, Zheng Y, Ma X, Du Z, Zhao L, Yu W, Xue J, Bian W, Yang F, Chen X, Zhang P, Wu R, Ma Y, Shao C, Chen J, Wang J, Li J, Wu J, Hu X, Long Q, Jiang M, Ye H, Song S, Li G, Wei Y, Xu Y, Ma Y, Chen Y, Wang K, Bao J, Xi W, Wang F, Ni W, Zhang M, Yu Y, Li S, Kang Y, Gao Z. T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1085-1100. [PMID: 37595788 PMCID: PMC11082261 DOI: 10.1016/j.gpb.2023.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/20/2023]
Abstract
Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploids. The quality of T2T-YAO is much better than those of all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, an accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.
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Affiliation(s)
- Yukun He
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Yanan Chu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shuming Guo
- Linfen Clinical Medicine Research Center, Linfen 041000, China; Institute of Chest and Lung Diseases, Shanxi Medical University, Taiyuan 030001, China
| | - Jiang Hu
- GrandOmics Biosciences Co., Ltd, Wuhan 430076, China
| | - Ran Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yali Zheng
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Xinqian Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Zhenglin Du
- Institute of PSI Genomics, Wenzhou 325024, China
| | - Lili Zhao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wenyi Yu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Jianbo Xue
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wenjie Bian
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Feifei Yang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Xi Chen
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Pingan Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Rihan Wu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yifan Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Changjun Shao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jing Chen
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jiwei Li
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Jing Wu
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Xiaoyi Hu
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Qiuyue Long
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Mingzheng Jiang
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Hongli Ye
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Shixu Song
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Guangyao Li
- Linfen Clinical Medicine Research Center, Linfen 041000, China
| | - Yue Wei
- Linfen Clinical Medicine Research Center, Linfen 041000, China
| | - Yu Xu
- Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Yanliang Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yanwen Chen
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Keqiang Wang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Jing Bao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wen Xi
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Fang Wang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wentao Ni
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Moqin Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yan Yu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Shengnan Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yu Kang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100490, China.
| | - Zhancheng Gao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Institute of Chest and Lung Diseases, Shanxi Medical University, Taiyuan 030001, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China.
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5
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Halili B, Yang X, Wang R, Zhu K, Hai X, Wang CC. Inferring the population history of Kyrgyz in Xinjiang, Northwest China from genome-wide array genotyping. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2023; 181:611-625. [PMID: 37310136 DOI: 10.1002/ajpa.24794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 04/29/2023] [Accepted: 05/26/2023] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Xinjiang plays a vital role in the trans-Eurasian population migration, language diffusion, and culture and technology exchange. However, the underrepresentation of Xinjiang's genomes has hindered a more comprehensive understanding of Xinjiang's genetic structure and population history. MATERIALS AND METHODS We collected and genotyped 70 southern Xinjiang's Kyrgyz (SXJK) individuals and combined the data with modern and ancient Eurasians published. We used allele-frequency methods, including PCA, ADMIXTURE, f-statistics, qpWave/qpAdm, ALDER, Treemix, and haplotype-shared methods including shared-IBD segments, fineSTRUCTURE, and GLOBETROTTER to unveil the fine-scale population structure and reconstruct admixture history. RESULTS We identified genetic substructure within the SXJK population with subgroups showing different genetic affinities to West and East Eurasians. All SXJK subgroups were suggested to have close genetic relationships with surrounding Turkic-speaking groups that is, Uyghur, Kyrgyz from north Xinjiang and Tajikistan, and Chinese Kazakh, suggesting a shared ancestry among those populations. Outgroup-f3 and symmetrical f4 statistics showed a high genetic affinity of SXJK to present-day Tungusic, Mongolic-speaking populations and Ancient Northeast Asian (ANA) related groups. Allele sharing and haplotype sharing profiles revealed the east-west admixture pattern of SXJK. The qpAdm-based admixture models showed that SXJK derived ancestry from East Eurasian (ANA and East Asian, 42.7%-83.3%) and West Eurasian (Western Steppe herders and Central Asian, 16.7%-57.3%), the recent east-west admixture event could be traced to 1000 years ago based on ALDER and GLOBETROTTER analysis. DISCUSSION The high genetic affinity of SXJK to present-day Tungusic and Mongolic-speaking populations and short-shared IBD segments indicated their shared common ancestry. SXJK harbored a close genetic affinity to ANA-related populations, indicating the Northeast Asian origin of SXJK. The West and East Eurasian admixture models observed in SXJK further provided evidence of the dynamic admixture history in Xinjiang. The east-west admixture pattern and the identified ancestral makeup of SXJK suggested a genetic continuity from some Iron Age Xinjiang populations to present-day SXJK.
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Affiliation(s)
- Bubibatima Halili
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
| | - Rui Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
| | - Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
| | - Xiangjun Hai
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, School of Sociology and Anthropology, Xiamen University, Xiamen, China
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai, China
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6
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Ning Z, Tan X, Yuan Y, Huang K, Pan Y, Tian L, Lu Y, Wang X, Qi R, Lu D, Yang Y, Guan Y, Mamatyusupu D, Xu S. Expression profiles of east-west highly differentiated genes in Uyghur genomes. Natl Sci Rev 2023; 10:nwad077. [PMID: 37138773 PMCID: PMC10150800 DOI: 10.1093/nsr/nwad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 05/05/2023] Open
Abstract
It remains unknown and debatable how European-Asian-differentiated alleles affect individual phenotypes. Here, we made the first effort to analyze the expression profiles of highly differentiated genes with eastern and western origins in 90 Uyghurs using whole-genome (30× to 60×) and transcriptome data. We screened 921 872 east-west highly differentiated genetic variants, of which ∼4.32% were expression quantitative trait loci (eQTLs), ∼0.12% were alternative splicing quantitative trait loci (sQTLs), and ∼0.12% showed allele-specific expression (ASE). The 8305 highly differentiated eQTLs of strong effects appear to have undergone natural selection, associated with immunity and metabolism. European-origin alleles tend to be more biasedly expressed; highly differentiated ASEs were enriched in diabetes-associated genes, likely affecting the diabetes susceptibility in the Uyghurs. We proposed an admixture-induced expression model to dissect the highly differentiated expression profiles. We provide new insights into the genetic basis of phenotypic differentiation between Western and Eastern populations, advancing our understanding of the impact of genetic admixture.
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Affiliation(s)
| | | | | | - Ke Huang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lei Tian
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xiaoji Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruicheng Qi
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yaqun Guan
- Department of Biochemistry and Molecular Biology, Preclinical Medicine College, Xinjiang Medical University, Urumqi 830011, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University, Urumqi 830046, China
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7
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Wu CI. The genetics of race differentiation-should it be studied? Natl Sci Rev 2023; 10:nwad068. [PMID: 37034147 PMCID: PMC10076182 DOI: 10.1093/nsr/nwad068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Affiliation(s)
- Chung-I Wu
- Chung-I Wu School of Life Sciences, Sun Yat-sen University, China Associate Editor-in-Chief for Life Sciences at NSR
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8
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Hou X, Zhang X, Li X, Huang T, Li W, Zhang H, Huang H, Wen Y. Genomic insights into the genetic structure and population history of Mongolians in Liaoning Province. Front Genet 2022; 13:947758. [PMID: 36313460 PMCID: PMC9596793 DOI: 10.3389/fgene.2022.947758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/26/2022] [Indexed: 11/29/2022] Open
Abstract
The Mongolian population exceeds six million and is the largest population among the Mongolic speakers in China. However, the genetic structure and admixture history of the Mongolians are still unclear due to the limited number of samples and lower coverage of single-nucleotide polymorphism (SNP). In this study, we genotyped genome-wide data of over 700,000 SNPs in 38 Mongolian individuals from Fuxin in Liaoning Province to explore the genetic structure and population history based on typical and advanced population genetic analysis methods [principal component analysis (PCA), admixture, FST, f3-statistics, f4-statistics, qpAdm/qpWave, qpGraph, ALDER, and TreeMix]. We found that Fuxin Mongolians had a close genetic relationship with Han people, northern Mongolians, other Mongolic speakers, and Tungusic speakers in East Asia. Also, we found that Neolithic millet farmers in the Yellow River Basin and West Liao River Basin and Neolithic hunter–gatherers in the Mongolian Plateau and Amur River Basin were the dominant ancestral sources, and there were additional gene flows related to Eurasian Steppe pastoralists and Neolithic Iranian farmers in the gene pool of Fuxin Mongolians. These results shed light on dynamic demographic history, complex population admixture, and multiple sources of genetic diversity in Fuxin Mongolians.
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9
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Zhang R, Ni X, Yuan K, Pan Y, Xu S. MultiWaverX: modeling latent sex-biased admixture history. Brief Bioinform 2022; 23:6590437. [PMID: 35598333 DOI: 10.1093/bib/bbac179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Sex-biased gene flow has been common in the demographic history of modern humans. However, the lack of sophisticated methods for delineating the detailed sex-biased admixture process prevents insights into complex admixture history and thus our understanding of the evolutionary mechanisms of genetic diversity. Here, we present a novel algorithm, MultiWaverX, for modeling complex admixture history with sex-biased gene flow. Systematic simulations showed that MultiWaverX is a powerful tool for modeling complex admixture history and inferring sex-biased gene flow. Application of MultiWaverX to empirical data of 17 typical admixed populations in America, Central Asia, and the Middle East revealed sex-biased admixture histories that were largely consistent with the historical records. Notably, fine-scale admixture process reconstruction enabled us to recognize latent sex-biased gene flow in certain populations that would likely be overlooked by much of the routine analysis with commonly used methods. An outstanding example in the real world is the Kazakh population that experienced complex admixture with sex-biased gene flow but in which the overall signature has been canceled due to biased gene flow from an opposite direction.
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Affiliation(s)
- Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xumin Ni
- School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, 100044, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.,Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, 221116, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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10
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Dai SS, Sulaiman X, Isakova J, Xu WF, Abdulloevich NT, Afanasevna ME, Ibrohimovich KB, Chen X, Yang WK, Wang MS, Shen QK, Yang XY, Yao YG, Aldashev AA, Saidov A, Chen W, Cheng LF, Peng MS, Zhang YP. The genetic echo of the Tarim mummies in modern Central Asians. Mol Biol Evol 2022; 39:6675590. [PMID: 36006373 PMCID: PMC9469894 DOI: 10.1093/molbev/msac179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The diversity of Central Asians has been shaped by multiple migrations and cultural diffusion. Although ancient DNA studies have revealed the demographic changes of the Central Asian since the Bronze Age, the contribution of the ancient populations to the modern Central Asian remains opaque. Herein, we performed high-coverage sequencing of 131 whole genomes of Indo-European-speaking Tajik and Turkic-speaking Kyrgyz populations to explore their genomic diversity and admixture history. By integrating the ancient DNA data, we revealed more details of the origins and admixture history of Central Asians. We found that the major ancestry of present-day Tajik populations can be traced back to the admixture of the Bronze Age Bactria–Margiana Archaeological Complex and Andronovo-related populations. Highland Tajik populations further received additional gene flow from the Tarim mummies, an isolated ancient North Eurasian–related population. The West Eurasian ancestry of Kyrgyz is mainly derived from Historical Era populations in Xinjiang of China. Furthermore, the recent admixture signals detected in both Tajik and Kyrgyz are ascribed to the expansions of Eastern Steppe nomadic pastoralists during the Historical Era.
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Affiliation(s)
- Shan Shan Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Xierzhatijiang Sulaiman
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Department of Pharmacology, School of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Jainagul Isakova
- Institute of Molecular Biology and Medicine, Bishkek 720040, Kyrgyzstan
| | - Wei Fang Xu
- Shenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen 518034, China
| | - Najmudinov Tojiddin Abdulloevich
- E.N. Pavlovsky Institute of Zoology and Parasitology, Academy of Sciences of Republic of Tajikistan, Dushanbe 734025, Tajikistan
| | - Manilova Elena Afanasevna
- E.N. Pavlovsky Institute of Zoology and Parasitology, Academy of Sciences of Republic of Tajikistan, Dushanbe 734025, Tajikistan
| | - Khudoidodov Behruz Ibrohimovich
- E.N. Pavlovsky Institute of Zoology and Parasitology, Academy of Sciences of Republic of Tajikistan, Dushanbe 734025, Tajikistan
| | - Xi Chen
- Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China.,State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Wei Kang Yang
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Ming Shan Wang
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Quan Kuan Shen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Xing Yan Yang
- Key Laboratory of Chemistry in Ethnic Medicinal Resource, Yunnan Minzu University, Kunming 650504, China.,School of Chemistry and Environment, Yunnan Minzu University, Kunming 650504, China
| | - Yong Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,KIZ/CUHK Joint Laboratory of Bio-resources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Almaz A Aldashev
- Institute of Molecular Biology and Medicine, Bishkek 720040, Kyrgyzstan
| | - Abdusattor Saidov
- E.N. Pavlovsky Institute of Zoology and Parasitology, Academy of Sciences of Republic of Tajikistan, Dushanbe 734025, Tajikistan
| | - Wei Chen
- College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650224, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan Agricultural University, Kunming 650224, China
| | - Lu Feng Cheng
- Department of Pharmacology, School of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Min Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,KIZ/CUHK Joint Laboratory of Bio-resources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Ya Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,KIZ/CUHK Joint Laboratory of Bio-resources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Life Sciences, Yunnan University, Kunming 650091, China
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11
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Kumar V, Wang W, Zhang J, Wang Y, Ruan Q, Yu J, Wu X, Hu X, Wu X, Guo W, Wang B, Niyazi A, Lv E, Tang Z, Cao P, Liu F, Dai Q, Yang R, Feng X, Ping W, Zhang L, Zhang M, Hou W, Liu Y, Bennett EA, Fu Q. Bronze and Iron Age population movements underlie Xinjiang population history. Science 2022; 376:62-69. [PMID: 35357918 DOI: 10.1126/science.abk1534] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The Xinjiang region in northwest China is a historically important geographical passage between East and West Eurasia. By sequencing 201 ancient genomes from 39 archaeological sites, we clarify the complex demographic history of this region. Bronze Age Xinjiang populations are characterized by four major ancestries related to Early Bronze Age cultures from the central and eastern Steppe, Central Asian, and Tarim Basin regions. Admixtures between Middle and Late Bronze Age Steppe cultures continued during the Late Bronze and Iron Ages, along with an inflow of East and Central Asian ancestry. Historical era populations show similar admixed and diverse ancestries as those of present-day Xinjiang populations. These results document the influence that East and West Eurasian populations have had over time in the different regions of Xinjiang.
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Affiliation(s)
- Vikas Kumar
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China.,Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Wenjun Wang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China.,National Centre for Archaeology, Beijing 100013, China
| | - Jie Zhang
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Yongqiang Wang
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Qiurong Ruan
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Jianjun Yu
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Xiaohong Wu
- School of Archaeology and Museology, Peking University, Beijing 100871, China
| | - Xingjun Hu
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Xinhua Wu
- Institute of Archaeology, Chinese Academy of Social Science, Beijing 100710, China
| | - Wu Guo
- Institute of Archaeology, Chinese Academy of Social Science, Beijing 100710, China
| | - Bo Wang
- Xinjiang Uygur Autonomous Region Museum, Urumqi 830002, China
| | - Alipujiang Niyazi
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Enguo Lv
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Zihua Tang
- Institute of Geology and Geophysics, Chinese Academy of Science, Beijing 100020, China
| | - Peng Cao
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Feng Liu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Qingyan Dai
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Ruowei Yang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Xiaotian Feng
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Wanjing Ping
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Lizhao Zhang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Ming Zhang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Weihong Hou
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Yichen Liu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China.,Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - E Andrew Bennett
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Qiaomei Fu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China.,Shanghai Qi Zhi Institute, Shanghai 200232, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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12
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Pan Y, Zhang C, Lu Y, Ning Z, Lu D, Gao Y, Zhao X, Yang Y, Guan Y, Mamatyusupu D, Xu S. Genomic diversity and post-admixture adaptation in the Uyghurs. Natl Sci Rev 2022; 9:nwab124. [PMID: 35350227 PMCID: PMC8953455 DOI: 10.1093/nsr/nwab124] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 11/17/2022] Open
Abstract
Population admixture results in genome-wide combinations of genetic variants derived from different ancestral populations of distinct ancestry, thus providing a unique opportunity for understanding the genetic determinants of phenotypic variation in humans. Here, we used whole-genome sequencing of 92 individuals with high coverage (30–60×) to systematically investigate genomic diversity in the Uyghurs living in Xinjiang, China (XJU), an admixed population of both European-like and East-Asian-like ancestry. The XJU population shows greater genetic diversity, especially a higher proportion of rare variants, compared with their ancestral source populations, corresponding to greater phenotypic diversity of XJU. Admixture-induced functional variants in EDAR were associated with the diversity of facial morphology in XJU. Interestingly, the interaction of functional variants between SLC24A5 and OCA2 likely influences the diversity of skin pigmentation. Notably, selection has seemingly been relaxed or canceled in several genes with significantly biased ancestry, such as HERC2–OCA2. Moreover, signatures of post-admixture adaptation in XJU were identified, including genes related to metabolism (e.g. CYP2D6), digestion (e.g. COL11A1), olfactory perception (e.g. ANO2) and immunity (e.g. HLA). Our results demonstrated population admixture as a driving force, locally or globally, in shaping human genetic and phenotypic diversity as well as in adaptive evolution.
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Affiliation(s)
- Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Chao Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University , Shanghai 200438, China
| | - Zhilin Ning
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Yang Gao
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China
| | - Xiaohan Zhao
- Human Phenome Institute, Fudan University , Shanghai 201203, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University , Shanghai 200438, China
| | - Yaqun Guan
- Department of Biochemistry and Molecular Biology, Preclinical Medicine College, Xinjiang Medical University , Urumqi 830011, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University , Urumqi 830046, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University , Shanghai 200438, China
- School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China
- Human Phenome Institute, Fudan University , Shanghai 201203, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences , Kunming 650223, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University , Zhengzhou 450052, China
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13
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Peng Y, Cai X, Wang Y, Liu Z, Zhao Y. Genome‐wide analysis suggests multiple domestication events of Chinese local pigs. Anim Genet 2022; 53:293-306. [DOI: 10.1111/age.13183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 02/12/2022] [Accepted: 02/12/2022] [Indexed: 01/02/2023]
Affiliation(s)
- Yebo Peng
- State Key Laboratory of Agrobiotechnology College of Biological Sciences China Agricultural University Beijing China
| | - Xinyu Cai
- State Key Laboratory of Agrobiotechnology College of Biological Sciences China Agricultural University Beijing China
| | - Yuzhan Wang
- State Key Laboratory of Agrobiotechnology College of Biological Sciences China Agricultural University Beijing China
| | - Zexuan Liu
- State Key Laboratory of Agrobiotechnology College of Biological Sciences China Agricultural University Beijing China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology College of Biological Sciences China Agricultural University Beijing China
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14
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Ma B, Chen J, Yang X, Bai J, Ouyang S, Mo X, Chen W, Wang CC, Hai X. The Genetic Structure and East-West Population Admixture in Northwest China Inferred From Genome-Wide Array Genotyping. Front Genet 2022; 12:795570. [PMID: 34992635 PMCID: PMC8724515 DOI: 10.3389/fgene.2021.795570] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/06/2021] [Indexed: 01/02/2023] Open
Abstract
Northwest China is a contacting region for East and West Eurasia and an important center for investigating the migration and admixture history of human populations. However, the comprehensive genetic structure and admixture history of the Altaic speaking populations and Hui group in Northwest China were still not fully characterized due to insufficient sampling and the lack of genome-wide data. Thus, We genotyped genome-wide SNPs for 140 individuals from five Chinese Mongolic, Turkic speaking groups including Dongxiang, Bonan, Yugur, and Salar, as well as the Hui group. Analysis based on allele-sharing and haplotype-sharing were used to elucidate the population history of Northwest Chinese populations, including PCA, ADMIXTURE, pairwise Fst genetic distance, f-statistics, qpWave/qpAdm and ALDER, fineSTRUCTURE and GLOBETROTTER. We observed Dongxiang, Bonan, Yugur, Salar, and Hui people were admixed populations deriving ancestry from both East and West Eurasians, with the proportions of West Eurasian related contributions ranging from 9 to 15%. The genetic admixture was probably driven by male-biased migration- showing a higher frequency of West Eurasian related Y chromosomal lineages than that of mtDNA detected in Northwest China. ALDER-based admixture and haplotype-based GLOBETROTTER showed this observed West Eurasian admixture signal was introduced into East Eurasia approximately 700 ∼1,000 years ago. Generally, our findings provided supporting evidence that the flourish transcontinental communication between East and West Eurasia played a vital role in the genetic formation of northwest Chinese populations.
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Affiliation(s)
- Bin Ma
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
| | - Jinwen Chen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jingya Bai
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
| | - Siwei Ouyang
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
| | - Xiaodan Mo
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
| | - Wangsheng Chen
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China.,Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Xiangjun Hai
- Key Laboratory of Environmental Ecology and Population Health in Northwest Minority Areas, Northwest Minzu University, Lanzhou, China
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15
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Adnan A, Anwar A, Simayijiang H, Farrukh N, Hadi S, Wang CC, Xuan JF. The Heart of Silk Road "Xinjiang," Its Genetic Portray, and Forensic Parameters Inferred From Autosomal STRs. Front Genet 2021; 12:760760. [PMID: 34976009 PMCID: PMC8719170 DOI: 10.3389/fgene.2021.760760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/27/2021] [Indexed: 11/13/2022] Open
Abstract
The Xinjiang Uyghur Autonomous Region of China (XUARC) harbors almost 50 ethnic groups including the Uyghur (UGR: 45.84%), Han (HAN: 40.48%), Kazakh (KZK: 6.50%), Hui (HUI: 4.51%), Kyrgyz (KGZ: 0.86%), Mongol (MGL: 0.81%), Manchu (MCH: 0.11%), and Uzbek (UZK: 0.066%), which make it one of the most colorful regions with abundant cultural and genetic diversities. In our previous study, we established allelic frequency databases for 14 autosomal short tandem repeats (STRs) for four minority populations from XUARC (MCH, KGZ, MGL, and UZK) using the AmpFlSTR® Identifiler PCR Amplification Kit. In this study, we genotyped 2,121 samples using the GoldenEye™ 20A Kit (Beijing PeopleSpot Inc., Beijing, China) amplifying 19 autosomal STR loci for four major ethnic groups (UGR, HAN, KZK, and HUI). These groups make up 97.33% of the total XUARC population. The total number of alleles for all the 19 STRs in these populations ranged from 232 (HAN) to 224 (KZK). We did not observe any departures from the Hardy-Weinberg equilibrium (HWE) in these populations after sequential Bonferroni correction. We did find minimal departure from linkage equilibrium (LE) for a small number of pairwise combinations of loci. The match probabilities for the different populations ranged from 1 in 1.66 × 1023 (HAN) to 6.05 × 1024 (HUI), the combined power of exclusion ranged from 0.999 999 988 (HUI) to 0.999 999 993 (UGR), and the combined power of discrimination ranged from 0.999 999 999 999 999 999 999 983 (HAN) to 0.999 999 999 999 999 999 999 997 (UGR). Genetic distances, principal component analysis (PCA), STRUCTURE analysis, and the phylogenetic tree showed that genetic affinity among studied populations is consistent with linguistic, ethnic, and geographical classifications.
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Affiliation(s)
- Atif Adnan
- Department of Forensic Genetics, School of Forensic Medicine, China Medical University, Shenyang, China
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, Xiamen University, Xiamen, China
| | - Adeel Anwar
- Department of Orthopedic Surgery, The 3rd Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Halimureti Simayijiang
- Department of Forensic Medicine, School of Basic Medical Sciences, Binzhou Medical University, Yantai, China
| | - Noor Farrukh
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia
| | - Sibte Hadi
- Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, Kingdom of Saudi Arabia
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, Xiamen University, Xiamen, China
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China
| | - Jin-Feng Xuan
- Department of Forensic Genetics, School of Forensic Medicine, China Medical University, Shenyang, China
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16
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Cheng M, Ge X, Zhong C, Fu R, Ning K, Xu S. Micro-coevolution of host genetics with gut microbiome in three Chinese ethnic groups. J Genet Genomics 2021; 48:972-983. [PMID: 34562635 DOI: 10.1016/j.jgg.2021.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/23/2022]
Abstract
Understanding the micro-coevolution of the human gut microbiome with host genetics is challenging but essential in both evolutionary and medical studies. To gain insight into the interactions between host genetic variation and the gut microbiome, we analyzed both the human genome and gut microbiome collected from a cohort of 190 students in the same boarding college and representing 3 ethnic groups, Uyghur, Kazakh, and Han Chinese. We found that differences in gut microbiome were greater between genetically distinct ethnic groups than those genetically closely related ones in taxonomic composition, functional composition, enterotype stratification, and microbiome genetic differentiation. We also observed considerable correlations between host genetic variants and the abundance of a subset of gut microbial species. Notably, interactions between gut microbiome species and host genetic variants might have coordinated effects on specific human phenotypes. Bacteroides ovatus, previously reported to modulate intestinal immunity, is significantly correlated with the host genetic variant rs12899811 (meta-P = 5.55 × 10-5), which regulates the VPS33B expression in the colon, acting as a tumor suppressor of colorectal cancer. These results advance our understanding of the micro-coevolution of the human gut microbiome and their interactive effects with host genetic variation on phenotypic diversity.
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Affiliation(s)
- Mingyue Cheng
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xueling Ge
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chaofang Zhong
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ruiqing Fu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, 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; Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China; Human Phenome Institute, Fudan University, Shanghai 201203, China.
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17
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Zhang F, Ning C, Scott A, Fu Q, Bjørn R, Li W, Wei D, Wang W, Fan L, Abuduresule I, Hu X, Ruan Q, Niyazi A, Dong G, Cao P, Liu F, Dai Q, Feng X, Yang R, Tang Z, Ma P, Li C, Gao S, Xu Y, Wu S, Wen S, Zhu H, Zhou H, Robbeets M, Kumar V, Krause J, Warinner C, Jeong C, Cui Y. The genomic origins of the Bronze Age Tarim Basin mummies. Nature 2021; 599:256-261. [PMID: 34707286 PMCID: PMC8580821 DOI: 10.1038/s41586-021-04052-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 09/23/2021] [Indexed: 12/14/2022]
Abstract
The identity of the earliest inhabitants of Xinjiang, in the heart of Inner Asia, and the languages that they spoke have long been debated and remain contentious1. Here we present genomic data from 5 individuals dating to around 3000-2800 BC from the Dzungarian Basin and 13 individuals dating to around 2100-1700 BC from the Tarim Basin, representing the earliest yet discovered human remains from North and South Xinjiang, respectively. We find that the Early Bronze Age Dzungarian individuals exhibit a predominantly Afanasievo ancestry with an additional local contribution, and the Early-Middle Bronze Age Tarim individuals contain only a local ancestry. The Tarim individuals from the site of Xiaohe further exhibit strong evidence of milk proteins in their dental calculus, indicating a reliance on dairy pastoralism at the site since its founding. Our results do not support previous hypotheses for the origin of the Tarim mummies, who were argued to be Proto-Tocharian-speaking pastoralists descended from the Afanasievo1,2 or to have originated among the Bactria-Margiana Archaeological Complex3 or Inner Asian Mountain Corridor cultures4. Instead, although Tocharian may have been plausibly introduced to the Dzungarian Basin by Afanasievo migrants during the Early Bronze Age, we find that the earliest Tarim Basin cultures appear to have arisen from a genetically isolated local population that adopted neighbouring pastoralist and agriculturalist practices, which allowed them to settle and thrive along the shifting riverine oases of the Taklamakan Desert.
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Affiliation(s)
- Fan Zhang
- School of Life Sciences, Jilin University, Changchun, China
| | - Chao Ning
- Max Planck Institute for the Science of Human History, Jena, Germany.
| | - Ashley Scott
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Qiaomei Fu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Rasmus Bjørn
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Wenying Li
- Xinjiang Institute of Cultural Relics and Archaeology, Ürümqi, China
| | - Dong Wei
- School of Archaeology, Jilin University, Changchun, China
| | - Wenjun Wang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Linyuan Fan
- School of Life Sciences, Jilin University, Changchun, China
| | | | - Xingjun Hu
- Xinjiang Institute of Cultural Relics and Archaeology, Ürümqi, China
| | - Qiurong Ruan
- Xinjiang Institute of Cultural Relics and Archaeology, Ürümqi, China
| | - Alipujiang Niyazi
- Xinjiang Institute of Cultural Relics and Archaeology, Ürümqi, China
| | - Guanghui Dong
- MOE Key Laboratory of Western China's Environmental Systems, College of Earth & Environmental Sciences, Lanzhou University, Lanzhou, China
| | - Peng Cao
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Feng Liu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Qingyan Dai
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Xiaotian Feng
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Ruowei Yang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Zihua Tang
- Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
| | - Pengcheng Ma
- School of Life Sciences, Jilin University, Changchun, China
| | - Chunxiang Li
- School of Life Sciences, Jilin University, Changchun, China
| | - Shizhu Gao
- College of Pharmacia Sciences, Jilin University, Changchun, China
| | - Yang Xu
- School of Life Sciences, Jilin University, Changchun, China
| | - Sihao Wu
- School of Life Sciences, Jilin University, Changchun, China
| | - Shaoqing Wen
- Institute of Archaeological Science, Fudan University, Shanghai, China
| | - Hong Zhu
- School of Archaeology, Jilin University, Changchun, China
| | - Hui Zhou
- School of Life Sciences, Jilin University, Changchun, China
| | - Martine Robbeets
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Vikas Kumar
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing, China
| | - Johannes Krause
- Max Planck Institute for the Science of Human History, Jena, Germany. .,Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
| | - Christina Warinner
- Max Planck Institute for the Science of Human History, Jena, Germany. .,Department of Anthropology, Harvard University, Cambridge, MA, USA.
| | - Choongwon Jeong
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
| | - Yinqiu Cui
- School of Life Sciences, Jilin University, Changchun, China. .,Key Laboratory for Evolution of Past Life and Environment in Northeast Asia, Ministry of Education, Jilin University, Changchun, China. .,Research Center for Chinese Frontier Archaeology of Jilin University, Jilin University, Changchun, China.
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18
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Zhang R, Liu C, Yuan K, Ni X, Pan Y, Xu S. AdmixSim 2: a forward-time simulator for modeling complex population admixture. BMC Bioinformatics 2021; 22:506. [PMID: 34663213 PMCID: PMC8522168 DOI: 10.1186/s12859-021-04415-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 09/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture. RESULTS We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations. CONCLUSIONS AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.
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Affiliation(s)
- Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China. .,Human Phenome Institute, Fudan University, Shanghai, 201203, 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. .,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450052, China.
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19
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Ning C, Zheng HX, Zhang F, Wu S, Li C, Zhao Y, Xu Y, Wei D, Wu Y, Gao S, Jin L, Cui Y. Ancient Mitochondrial Genomes Reveal Extensive Genetic Influence of the Steppe Pastoralists in Western Xinjiang. Front Genet 2021; 12:740167. [PMID: 34630530 PMCID: PMC8493956 DOI: 10.3389/fgene.2021.740167] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 08/20/2021] [Indexed: 11/15/2022] Open
Abstract
The population prehistory of Xinjiang has been a hot topic among geneticists, linguists, and archaeologists. Current ancient DNA studies in Xinjiang exclusively suggest an admixture model for the populations in Xinjiang since the early Bronze Age. However, almost all of these studies focused on the northern and eastern parts of Xinjiang; the prehistoric demographic processes that occurred in western Xinjiang have been seldomly reported. By analyzing complete mitochondrial sequences from the Xiabandi (XBD) cemetery (3,500–3,300 BP), the up-to-date earliest cemetery excavated in western Xinjiang, we show that all the XBD mitochondrial sequences fall within two different West Eurasian mitochondrial DNA (mtDNA) pools, indicating that the migrants into western Xinjiang from west Eurasians were a consequence of the early expansion of the middle and late Bronze Age steppe pastoralists (Steppe_MLBA), admixed with the indigenous populations from Central Asia. Our study provides genetic links for an early existence of the Indo-Iranian language in southwestern Xinjiang and suggests that the existence of Andronovo culture in western Xinjiang involved not only the dispersal of ideas but also population movement.
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Affiliation(s)
- Chao Ning
- School of Life Sciences, Jilin University, Changchun, China.,Max Planck Institute for the Science of Human History, Jena, Germany
| | - Hong-Xiang Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, and Human Phenome Institute, Fudan University, Shanghai, China
| | - Fan Zhang
- School of Life Sciences, Jilin University, Changchun, China
| | - Sihao Wu
- School of Life Sciences, Jilin University, Changchun, China
| | - Chunxiang Li
- School of Life Sciences, Jilin University, Changchun, China
| | - Yongbin Zhao
- College of Life Science, Jilin Normal University, Siping, China
| | - Yang Xu
- School of Life Sciences, Jilin University, Changchun, China
| | - Dong Wei
- School of Archaeology, Jilin University, Changchun, China
| | - Yong Wu
- Xinjiang Cultural Relics and Archaeology Institute, Urumchi, China
| | - Shizhu Gao
- School of Pharmaceutical Sciences, Jilin University, Changchun, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, and Human Phenome Institute, Fudan University, Shanghai, China
| | - Yinqiu Cui
- School of Life Sciences, Jilin University, Changchun, China
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20
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Jin J, Cheng R, Ren Y, Shen X, Wang J, Xue Y, Zhang H, Jia X, Li T, He F, Tian H. Distinctive Gut Microbiota in Patients with Overweight and Obesity with Dyslipidemia and its Responses to Long-term Orlistat and Ezetimibe Intervention: A Randomized Controlled Open-label Trial. Front Pharmacol 2021; 12:732541. [PMID: 34512358 PMCID: PMC8426637 DOI: 10.3389/fphar.2021.732541] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/13/2021] [Indexed: 02/05/2023] Open
Abstract
This study investigated the gut microbiota and short chain fatty acids (SCFAs) characteristics of subjects with obesity from Xinjiang in northwestern China, a region with a multiethnic culture and characteristic lifestyle, and to explore the potential microbes that respond to a 12-wk medication of orlistat and ezetimibe with a randomized controlled open-label trial manner. The gut microbiota profile of patients with overweight and obesity with dyslipidemia in Xinjiang was distinctive and characterized by enrichment of Lactobacillus and the reduction of the diversity and the depletion of Actinobacteria, Bacteroides, Bifidobacterium, and Bacteroides fragilis. Prevotella-type, Gemmiger-type, and Escherichia/Shigella-type were the gut microbial patterns of the Xinjiang population. However, the fecal SCFAs levels and enterotypes were similar between healthy individuals and patients. These results indicated that the contribution of the gut microbiota to obesity was highly dependent on geography and dietary habits. Waist circumference, total triglyceride (TG), and fasting blood glucose (FBG) were significantly decreased after orlistat therapy, whereas TG, total cholesterol (TC), and low density lipoprotein cholesterol (LDL-C) were significantly decreased by ezetimibe. Overall, the gut microbiota and their SCFAs metabolites were relatively stable after treatment with the two drugs, with alteration of some low-abundant bacteria, i.e., significantly increased Proteobacteria and decreased Alloprevotella after orlistat, and increased Fusobacteria and Fusobacterium after ezetimibe therapy. These results indicated that intestinal malabsorption of dietary fat and cholesterol caused by orlistat and ezetimibe had a limited effect on the overall gut microbial community and their metabolites. Nevertheless, significant correlations between several core microbes that responded to the medications and biochemical data were found; in particular, Actinomyces and Bacteroides were positively correlated with FBG after orlistat intervention, while Clostridium XVIII and Lachnospiracea incertae sedis were negatively correlated with TC and LDL-C after ezetimibe intervention, thus indicating their roles in improving glucolipid metabolism in obesity by acting as potential microbial targets.
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Affiliation(s)
- Jin Jin
- Department of Endocrinology, West China Hospital of Sichuan University, Chengdu, China
| | - Ruyue Cheng
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yan Ren
- Department of Endocrinology, West China Hospital of Sichuan University, Chengdu, China
| | - Xi Shen
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiani Wang
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yigui Xue
- Frontier Medical Service Training Battalion of Army Military Medical University, Xinjiang, China
| | - Huimin Zhang
- People's Hospital of Akto County, Xinjiang, China
| | - Xiuhua Jia
- Health Service Center, Akto County, Xinjiang, China
| | - Tingting Li
- People's Hospital of Akto County, Xinjiang, China
| | - Fang He
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Haoming Tian
- Department of Endocrinology, West China Hospital of Sichuan University, Chengdu, China
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21
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Wu X, Ning C, Key FM, Andrades Valtueña A, Lankapalli AK, Gao S, Yang X, Zhang F, Liu L, Nie Z, Ma J, Krause J, Herbig A, Cui Y. A 3,000-year-old, basal S. enterica lineage from Bronze Age Xinjiang suggests spread along the Proto-Silk Road. PLoS Pathog 2021; 17:e1009886. [PMID: 34547027 PMCID: PMC8486138 DOI: 10.1371/journal.ppat.1009886] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 10/01/2021] [Accepted: 08/11/2021] [Indexed: 01/03/2023] Open
Abstract
Salmonella enterica (S. enterica) has infected humans for a long time, but its evolutionary history and geographic spread across Eurasia is still poorly understood. Here, we screened for pathogen DNA in 14 ancient individuals from the Bronze Age Quanergou cemetery (XBQ), Xinjiang, China. In 6 individuals we detected S. enterica. We reconstructed S. enterica genomes from those individuals, which form a previously undetected phylogenetic branch basal to Paratyphi C, Typhisuis and Choleraesuis-the so-called Para C lineage. Based on pseudogene frequency, our analysis suggests that the ancient S. enterica strains were not host adapted. One genome, however, harbors the Salmonella pathogenicity island 7 (SPI-7), which is thought to be involved in (para)typhoid disease in humans. This offers first evidence that SPI-7 was acquired prior to the emergence of human-adapted Paratyphi C around 1,000 years ago. Altogether, our results show that Salmonella enterica infected humans in Eastern Eurasia at least 3,000 years ago, and provide the first ancient DNA evidence for the spread of a pathogen along the Proto-Silk Road.
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Affiliation(s)
- Xiyan Wu
- School of Life Sciences, Jilin University, Changchun, China
- School of History and Culture, Henan University, Kaifeng, China
| | - Chao Ning
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Felix M. Key
- Max Planck Institute for the Science of Human History, Jena, Germany
- Max Planck Institute for Infection Biology, Berlin, Germany
| | - Aida Andrades Valtueña
- Max Planck Institute for the Science of Human History, Jena, Germany
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | | | - Shizhu Gao
- College of Pharmacia Sciences, Jilin University, Changchun, China
| | - Xuan Yang
- School of Life Sciences, Jilin University, Changchun, China
| | - Fan Zhang
- School of Life Sciences, Jilin University, Changchun, China
| | - Linlin Liu
- Department of Radiation Oncology, The Second Hospital of Jilin University, Changchun, China
| | - Zhongzhi Nie
- Research Center for Chinese Frontier Archaeology, Jilin University, Changchun, China
| | - Jian Ma
- School of Cultural Heritage, Northwest University, Xi’an, China
| | - Johannes Krause
- Max Planck Institute for the Science of Human History, Jena, Germany
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Alexander Herbig
- Max Planck Institute for the Science of Human History, Jena, Germany
- Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Yinqiu Cui
- School of Life Sciences, Jilin University, Changchun, China
- Research Center for Chinese Frontier Archaeology, Jilin University, Changchun, China
- Key Laboratory for Evolution of Past Life and Environment in Northeast Asia (Jilin University), Ministry of Education, Changchun, China
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22
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Genetic insights into the paternal admixture history of Chinese Mongolians via high-resolution customized Y-SNP SNaPshot panels. Forensic Sci Int Genet 2021; 54:102565. [PMID: 34332322 DOI: 10.1016/j.fsigen.2021.102565] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/10/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022]
Abstract
The Mongolian people, one of the Mongolic-speaking populations, are native to the Mongolian Plateau in North China and southern Siberia. Many ancient DNA studies recently reported extensive population transformations during the Paleolithic to historic periods in this region, while little is known about the paternal genetic legacy of modern geographically different Mongolians. Here, we genotyped 215 Y-chromosomal single nucleotide polymorphisms (Y-SNPs) and 37 Y-chromosomal short tandem repeats (Y-STRs) among 679 Mongolian individuals from Hohhot, Hulunbuir, and Ordos in North China using the AGCU Y37 kit and our developed eight Y-SNP SNaPshot panels (including two panels first reported herein). The C-M130 Y-SNP SNaPshot panel defines 28 subhaplogroups, and the N/O/Q complementary Y-SNP SNaPshot panel defines 30 subhaplogroups of N1b-F2930, N1a1a1a1a3-B197, Q-M242, and O2a2b1a1a1a4a-CTS4658, which improved the resolution our developed Y-SNP SNaPshot panel set and could be applied for dissecting the finer-scale paternal lineages of Mongolic speakers. We found a strong association between Mongolian-prevailing haplogroups and some observed microvariants among the newly generated Y-STR haplotype data, suggesting the possibility of haplogroup prediction based on the distribution of Y-STR haplotypes. We identified three main ancestral sources of the observed Mongolian-dominant haplogroups, including the local lineage of C2*-M217 and incoming lineages from other regions of southern East Asia (O2*-M122, O1b*-P31, and N1*-CTS3750) and western Eurasia (R1*-M173). We also observed DE-M145, D1*-M174, C1*-F3393, G*-M201, I-M170, J*-M304, L-M20, O1a*-M119, and Q*-M242 at relatively low frequencies (< 5.00%), suggesting a complex admixture history between Mongolians and other incoming Eurasians from surrounding regions. Genetic clustering analyses indicated that the studied Mongolians showed close genetic affinities with other Altaic-speaking populations and Sinitic-speaking Hui people. The Y-SNP haplotype/haplogroup-based genetic legacy not only revealed that the stratification among geographically/linguistically/ethnically different Chinese populations was highly consistent with the geographical division and language classification, but also demonstrated that patrilineal genetic materials could provide fine-scale genetic structures among geographically different Mongolian people, suggesting that our developed high-resolution Y-SNP SNaPshot panels have the potential for forensic pedigree searches and biogeographical ancestry inference.
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Ma X, Yang W, Gao Y, Pan Y, Lu Y, Chen H, Lu D, Xu S. Genetic origins and sex-biased admixture of the Huis. Mol Biol Evol 2021; 38:3804-3819. [PMID: 34021754 PMCID: PMC8382924 DOI: 10.1093/molbev/msab158] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The Hui people are unique among Chinese ethnic minorities in that they speak the same language as Han Chinese (HAN) but practice Islam. However, as the second-largest minority group in China numbering well over 10 million, the Huis are under-represented in both global and regional genomic studies. Here, we present the first whole-genome sequencing effort of 234 Hui individuals (NXH) aged over 60 who have been living in Ningxia, where the Huis are mostly concentrated. NXH are genetically more similar to East Asian than to any other global populations. In particular, the genetic differentiation between NXH and HAN (FST = 0.0015) is only slightly larger than that between northern and southern HAN (FST = 0.0010), largely attributed to the western ancestry in NXH (∼10%). Highly differentiated functional variants between NXH and HAN were identified in genes associated with skin pigmentation (e.g., SLC24A5), facial morphology (e.g., EDAR), and lipid metabolism (e.g., ABCG8). The Huis are also distinct from other Muslim groups such as the Uyghurs (FST = 0.0187), especially, NXH derived much less western ancestry (∼10%) compared with the Uyghurs (∼50%). Modeling admixture history indicated that NXH experienced an episode of two-wave admixture. An ancient admixture occurred ∼1,025 years ago, reflecting the intensive west-east contacts during the late Tang Dynasty, and the Five Dynasties and Ten Kingdoms period. A recent admixture occurred ∼500 years ago, corresponding to the Ming Dynasty. Notably, we identified considerable sex-biased admixture, i.e., excess of western males and eastern females contributing to the NXH gene pool. The origins and the genomic diversity of the Hui people imply the complex history of contacts between western and eastern Eurasians.
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Affiliation(s)
- Xixian Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wenjun Yang
- Key Laboratory of Fertility Preservation and Maintenance, the General Hospital, Ningxia Medical University, Yinchuan, Ningxia 750004 China
| | - Yang Gao
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, 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, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China.,Human Phenome Institute, Fudan University, Shanghai 201203, China
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24
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Huang Y, Chen X, Liu C, Han X, Xiao C, Yi S, Huang D. Genetic analysis of 32 InDels in four ethnic minorities from Chinese Xinjiang. PLoS One 2021; 16:e0250206. [PMID: 33886624 PMCID: PMC8061914 DOI: 10.1371/journal.pone.0250206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/03/2021] [Indexed: 12/05/2022] Open
Abstract
The present study used the previously constructed 32-plex InDels panel to investigated the genetic diversity of four ethnic minorities (Hui, Mongol, Uygur and Kazakh) from Xinjiang, and analyzed the genetic relationships between the four populations and 27 reference populations. No significant deviations were observed from the Hardy-Weinberg equilibrium (HWE) at the 32 InDels for each population. The average observed heterozygosity (Hexp), average polymorphic information content (PIC), combined power of discrimination (CPD) and cumulative probability of exclusion (CPE) for the 32 InDels were all higher than the Qiagen Investigator DIPplex kit in the four populations from Xinjiang. The CPD ranged from 0.999999999999903 (Kazakh) to 0.999999999999952 (Hui) and CPE ranged from 0.9971 (Uygur) to 0.9985 (Hui), which indicated that the 32 InDels were capable for individual identification and could be a supplementary tool in paternity test for these populations. Population genetic analysis by the method of analysis of molecular variance (AMOVA), FST, phylogenetic tree, TreeMix-based topology, multi-dimensional scale analysis (MDS), principal components analysis (PCA) and STRUCTURE analysis showed that Xinjiang Hui population has a close relationship with East Asians (EAS), especially Chinese Han, and the populations of Xinjiang Mongol, Uygur and Kazakh showed mixed ancestral components related to EAS and Europeans (EUR).
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Affiliation(s)
- Yujie Huang
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xiaoying Chen
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Cong Liu
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xueli Han
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Chao Xiao
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Shaohua Yi
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Daixin Huang
- Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
- * E-mail:
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25
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Wang W, Ding M, Gardner JD, Wang Y, Miao B, Guo W, Wu X, Ruan Q, Yu J, Hu X, Wang B, Wu X, Tang Z, Niyazi A, Zhang J, Chang X, Tang Y, Ren M, Cao P, Liu F, Dai Q, Feng X, Yang R, Zhang M, Wang T, Ping W, Hou W, Li W, Ma J, Kumar V, Fu Q. Ancient Xinjiang mitogenomes reveal intense admixture with high genetic diversity. SCIENCE ADVANCES 2021; 7:7/14/eabd6690. [PMID: 33789892 PMCID: PMC8011967 DOI: 10.1126/sciadv.abd6690] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 02/11/2021] [Indexed: 06/12/2023]
Abstract
Xinjiang is a key region in northwestern China, connecting East and West Eurasian populations and cultures for thousands of years. To understand the genetic history of Xinjiang, we sequenced 237 complete ancient human mitochondrial genomes from the Bronze Age through Historical Era (41 archaeological sites). Overall, the Bronze Age Xinjiang populations show high diversity and regional genetic affinities with Steppe and northeastern Asian populations along with a deep ancient Siberian connection for the Tarim Basin Xiaohe individuals. In the Iron Age, in general, Steppe-related and northeastern Asian admixture intensified, with North and East Xinjiang populations showing more affinity with northeastern Asians and South Xinjiang populations showing more affinity with Central Asians. The genetic structure observed in the Historical Era of Xinjiang is similar to that in the Iron Age, demonstrating genetic continuity since the Iron Age with some additional genetic admixture with populations surrounding the Xinjiang region.
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Affiliation(s)
- Wenjun Wang
- College of Life Sciences, Northwest University, Xi'an 710069, China
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Manyu Ding
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jacob D Gardner
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongqiang Wang
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Bo Miao
- College of Life Sciences, Northwest University, Xi'an 710069, China
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Wu Guo
- Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100710, China
| | - Xinhua Wu
- Institute of Archaeology, Chinese Academy of Social Sciences, Beijing 100710, China
| | - Qiurong Ruan
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Jianjun Yu
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Xingjun Hu
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Bo Wang
- Xinjiang Uygur Autonomous Region Museum, Urumqi 830002, China
| | - Xiaohong Wu
- School of Archaeology and Museology, Peking University, Beijing 100871, China
| | - Zihua Tang
- Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
| | - Alipujiang Niyazi
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Jie Zhang
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Xien Chang
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Yunpeng Tang
- School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Meng Ren
- School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Peng Cao
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
| | - Feng Liu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
| | - Qingyan Dai
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
| | - Xiaotian Feng
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
| | - Ruowei Yang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
| | - Ming Zhang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianyi Wang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
- School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Wanjing Ping
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Weihong Hou
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Wenying Li
- Institute of Cultural Relics and Archaeology in Xinjiang, Urumqi 830011, China
| | - Jian Ma
- School of Cultural Heritage, Northwest University, Xi'an 710069, China
| | - Vikas Kumar
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China.
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
| | - Qiaomei Fu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing 100044, China.
- Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Beijing 100044, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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26
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Massively parallel sequencing of 165 ancestry-informative SNPs and forensic biogeographical ancestry inference in three southern Chinese Sinitic/Tai-Kadai populations. Forensic Sci Int Genet 2021; 52:102475. [PMID: 33561661 DOI: 10.1016/j.fsigen.2021.102475] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 12/06/2020] [Accepted: 01/20/2021] [Indexed: 01/01/2023]
Abstract
Ancestry informative markers (AIMs), which are distributed throughout the human genome, harbor significant allele frequency differences among diverse ethnic groups. The use of sets of AIMs to reconstruct population history and genetic relationships is attracting interest in the forensic community, because biogeographic ancestry information for a casework sample can potentially be predicted and used to guide the investigative process. However, subpopulation ancestry inference within East Asia remains in its infancy due to a lack of population reference data collection and incomplete validation work on newly developed or commercial AIM sets. In the present study, 316 Chinese persons, including 85 Sinitic-speaking Haikou Han, 120 Qiongzhong Hlai and 111 Daozhen Gelao individuals belonging to Tai-Kadai-speaking populations, were analyzed using the Precision ID Ancestry Panel (165 AISNPs). Combined with our previous 165-AISNP data (375 individuals from 6 populations), the 1000 Genomes Project and forensic literature, comprehensive population genetic comparisons and ancestry inference were further performed via ADMIXTURE, TreeMix, PCA, f-statistics and N-J tree. Although several nonpolymorphic loci were identified in the three southern Chinese populations, the forensic parameters of this ancestry inference panel were better than those for the 23 STR-based Huaxia Platinum System, which is suitable for use as a robust tool in forensic individual identification and parentage testing. The results based on the ancestry assignment and admixture proportion evaluation revealed that this panel could be used successfully to assign individuals at a continental scale but also possessed obvious limitations in discriminatory power in intercontinental individuals, especially for European-Asian admixed Uyghurs or in populations lacking reference databases. Population genetic analyses further revealed five continental population clusters and three East Asian-focused population subgroups, which is consistent with linguistic affiliations. Ancestry composition and multiple phylogenetic analysis further demonstrated that the geographically isolated Qiongzhong Hlai harbored a close phylogenetic relationship with Austronesian speakers and possessed a homogenous Tai-Kadai-dominant ancestry, which could be used as the ancestral source proxy in population history reconstruction of Tai-Kadai-speaking populations and as one of the representatives for forensic database establishment. In summary, more population-specific AIM sets focused on East Asian subpopulations, comprehensive algorithms and high-coverage population reference data should be developed and validated in the next step.
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Abstract
East Asia constitutes one-fifth of the global population and exhibits substantial genetic diversity. However, genetic investigations on populations in this region have been largely under-represented compared with European populations. Nonetheless, the last decade has seen considerable efforts and progress in genome-wide genotyping and whole-genome sequencing of the East-Asian ethnic groups. Here, we review the recent studies in terms of ancestral origin, population relationship, genetic differentiation, and admixture of major East- Asian groups, such as the Chinese, Korean, and Japanese populations. We mainly focus on insights from the whole-genome sequence data and also include the recent progress based on mitochondrial DNA (mtDNA) and Y chromosome data. We further discuss the evolutionary forces driving genetic diversity in East-Asian populations, and provide our perspectives for future directions on population genetics studies, particularly on underrepresented indigenous groups in East Asia.
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Affiliation(s)
- Ziqing Pan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
- School of Life Science and Technology, ShanghaiTech Universit, 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, School of Life Sciences, Fudan University, Shanghai, 200438, China.
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Yang X, Yuan K, Ni X, Zhou Y, Guo W, Xu S. AdmixSim: A Forward-Time Simulator for Various Complex Scenarios of Population Admixture. Front Genet 2020; 11:601439. [PMID: 33343638 PMCID: PMC7744625 DOI: 10.3389/fgene.2020.601439] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/29/2020] [Indexed: 11/15/2022] Open
Abstract
Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here. Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data. Conclusions:AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.
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Affiliation(s)
- Xiong Yang
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China
| | - Ying Zhou
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wei Guo
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai, China
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29
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Liu Y, Zhang H, He G, Ren Z, Zhang H, Wang Q, Ji J, Yang M, Guo J, Yang X, Sun J, Ba J, Peng D, Hu R, Wei LH, Wang CC, Huang J. Forensic Features and Population Genetic Structure of Dong, Yi, Han, and Chuanqing Human Populations in Southwest China Inferred From Insertion/Deletion Markers. Front Genet 2020; 11:360. [PMID: 32425974 PMCID: PMC7205039 DOI: 10.3389/fgene.2020.00360] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 03/24/2020] [Indexed: 12/20/2022] Open
Abstract
Guizhou province in southwest China has abundant genetic and cultural diversities, but the forensic features and genetic structure of Guizhou populations remain poorly understood due to the sparse sampling of present-day populations. Here, we present 30 insertion/deletion polymorphisms (InDels) data of 591 human individuals collected from four populations, Dong, Yi, Han, and Chuanqing residing in Guizhou. We calculated the forensic parameters of 30 InDel loci and found that this panel meets the efficiency of forensic personal identification based on the high combined power of discrimination, but it could only be used as a complementary tool in the parentage testing because of the lower combined probability of exclusion values. The studied populations are genetically closer related to geographically adjacent or linguistically related populations in southern China, such as the Tai-Kadai and Hmong-Mien speaking groups. The unrecognized ethnic Chuanqing people show an additional genetic affinity with Han Chinese, highlighting the role of possible military immigrations in their origin.
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Affiliation(s)
- Yubo Liu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Han Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Guanglin He
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China.,Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jianxin Guo
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jin Sun
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jinxing Ba
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Dan Peng
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Rong Hu
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, and School of Life Sciences, Xiamen University, Xiamen, China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
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30
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Genetic structure and forensic characteristics of the Kyrgyz population from Kizilsu Kirghiz autonomous prefecture based on autosomal DIPs. Int J Legal Med 2020; 136:539-541. [PMID: 32219528 DOI: 10.1007/s00414-020-02277-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 03/12/2020] [Indexed: 10/24/2022]
Abstract
Living in the heart of Eurasia, the Kyrgyz ethnic minority have a complex human evolutionary and migration history. However, the genetic architecture of the Kyrgyz population has not been fully explored. We studied 526 Kyrgyz samples from Kizilsu Kirghiz Autonomous Prefecture in Xinjiang using the Investigator® DIPplex kit. All loci followed Hardy-Weinberg equilibrium (HWE). The combined power of discrimination (CPD) and combined power of paternity exclusion (CPE) was 0.9999999999988 and 0.9936, respectively. Compared with 90 reference populations, five InDels (HLD99, HLD81, HLD64, HLD118, and HLD111) have the potential to distinguish the Kyrgyz/Uyghur/Kazak population from other East Asian populations. Our results suggested a close genetic relationship between the Kyrgyz population and the Uyghur/Kazak populations, followed by South Asian populations. This was in accordance with the inland migration hypothesis or modern human migration influenced by warfare. Overall, this system can be used as a powerful tool in forensic individual identification and as a complementary tool in paternity cases and biogeographic ancestry analyses.
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Ren Z, Guo J, He G, Zhang H, Zou X, Zhang H, Wang Q, Ji J, Yang M, Zhang J, Zhang Z, Nabijiang Y, Huang J, Wang CC. Forensic genetic polymorphisms and population structure of the Guizhou Bouyei people based on 19 X-STR loci. Ann Hum Biol 2019; 46:574-580. [PMID: 31795774 DOI: 10.1080/03014460.2019.1697362] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Background: Guizhou province is located in southwest China with abundant genetic, linguistic and cultural diversity. The Bouyei is one of the 18 officially recognised minority groups in Guizhou, accounting for about 97% of the total Bouyei population in China. However, the genetic history and forensic characterisation of the Bouyei people is largely unknown due to a lack of genetic data.Aim: We aim to investigate genetic polymorphisms and forensic characterisation of the Guizhou Bouyei population, as well as the relationships between the Bouyei and other East Asian populations.Subjects and methods: We genotyped 19 X-STRs in 188 males and 165 females of Guizhou Bouyei using the AGCU X19 STR Kit. We estimated allele frequencies, forensic parameters and genetic distances between the Bouyei and other East Asian populations. We presented the genetic distances in a phylogenetic tree, an MDS plot and a PCA plot.Results: In Guizhou Bouyei individuals, we observed 216 alleles with corresponding frequencies ranging from 0.0019 to 0.6757. All of the six combined powers of PDm, PDf, MEC Krüger, MEC Kishida, MEC Desmarais and MEC Desmarais in allele diversity and haplotype diversity are larger than 0.99999995. We found genetic affinities among the Bouyei people and their geographical neighbouring populations in Guizhou, such as the Sui, Miao and Han.Conclusions: The highly polymorphic and informative forensic parameters of the 19 X-STRs in Bouyei people show the powerful potential of those markers in forensic identification and parentage tests. The genetic relationships of the Bouyei with other East Asian populations correspond well with geographic affiliations as well as linguistic classifications.
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Affiliation(s)
- Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jianxin Guo
- Department of History, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Guanglin He
- Department of History, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Han Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Xing Zou
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Jing Zhang
- Department of History, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Ziqian Zhang
- Department of History, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yilizhati Nabijiang
- Department of History, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Chuan-Chao Wang
- Department of History, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
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Chen P, Zou X, Wang M, Gao B, Su Y, He G. Forensic features and genetic structure of the Hotan Uyghur inferred from 27 forensic markers. Ann Hum Biol 2019; 46:589-600. [PMID: 31762339 DOI: 10.1080/03014460.2019.1687751] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Background: The Uyghur is the fifth largest group in China and also the largest ethnic minority in Xinjiang.Aim: To explore the genetic variations of 27 forensic genetic markers included in the newly developed SureID® PanGlobal Human DNA Identification System and analyse the genetic relationship between Xinjiang Uyghur and their neighbours.Subjects and methods: We genotyped 27 markers in 2,189 unrelated Uyghur individuals from the Hotan Prefecture in Southwest Xinjiang. Comprehensive population genetic studies among Chinese populations and worldwide populations were conducted via various statistics.Results: The combined power of discrimination (CPD) and the combined power of exclusion (CPE) of the new-generation autosomal STR amplification system in the Hotan Uyghur are 9,9999-E01 and 9,9999-E01, respectively. Population genetic studies indicate that the Hotan Uyghur show a close genetic relationship with geographically different Uyghurs and Kazakhs, while significant genetic differentiation exists between the Hotan Uyghur and some ethnicities from other non-Turkic-speaking populations. The results of population comparisons among the 52 worldwide populations demonstrate that geographically approached intercontinental populations have close genetic relationships.Conclusions: 24 autosomal STRs are highly polymorphic and informative in the Uyghur and this system is suitable for forensic personal identification and paternity testing. Our findings not only reveal that Chinese Uyghur is a homogenous population based on forensic genetic markers, but also indicate that population genetic affinity is closely related to the adjacent populations with common ethnic origin.
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Affiliation(s)
- Pengyu Chen
- Center of Forensic Expertise, Affiliated Hospital of Zunyi Medical University, Zunyi, China.,School of Forensic Medicine, Zunyi Medical University, Zunyi, China
| | - Xing Zou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China
| | - Mengge Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China
| | - Bo Gao
- Yili Public Security Bureau of Xinjiang, Institute of forensic science, Kuitun, China
| | - Yongdong Su
- Forensic Identification Center, Public Security Bureau of Tibet Autonomous Region, Lhasa, China
| | - Guanglin He
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China
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Lan Q, Xie T, Jin X, Fang Y, Mei S, Yang G, Zhu B. MtDNA polymorphism analyses in the Chinese Mongolian group: Efficiency evaluation and further matrilineal genetic structure exploration. Mol Genet Genomic Med 2019; 7:e00934. [PMID: 31478599 PMCID: PMC6785450 DOI: 10.1002/mgg3.934] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 05/26/2019] [Accepted: 07/07/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Profiling of mitochondrial DNA is surely to provide valuable investigative clues for forensic cases involving highly degraded specimens or complex maternal lineage kinship determination. But traditionally used hypervariable region sequencing of mitochondrial DNA is less frequently suggested by the forensic community for insufficient informativeness. Genome-wide sequencing of mitochondrial DNA can provide considerable amount of variant information but can be high cost at the same time. METHODS Efficiency of the 60 mitochondrial DNA polymorphic sites dispersing across the control region and coding region of mitochondrial DNA genome was evaluated with 106 Mongolians recruited from the Xinjiang Uyghur Autonomous Region, China, and allele-specific PCR technique was employed for mitochondrial DNA typing. RESULTS Altogether 58 haplotypes were observed and the haplotypic diversity, discrimination power and random match probability were calculated to be 0.981, 0.972, and 0.028, respectively. Mitochondrial DNA haplogroup affiliation exhibited an exceeding percentage (12.26%) of west Eurasian lineage (H haplogroup) in the studied Mongolian group, which needed to be further verified with more samples. Furthermore, the genetic relationships between the Xinjiang Mongolian group and the comparison populations were also investigated and the genetic affinity was discovered between the Xinjiang Mongolian group and the Xinjiang Kazak group in this study. CONCLUSION It was indicated that the panel was potentially enough to be used as a supplementary tool for forensic applications. And the matrilineal genetic structure analyses based on mitochondrial DNA variants in the Xinjiang Mongolian group could be helpful for subsequent anthropological studies.
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Affiliation(s)
- Qiong Lan
- Department of Forensic Genetics, School of Forensic MedicineSouthern Medical UniversityGuangzhouChina
| | - Tong Xie
- Department of Forensic Genetics, School of Forensic MedicineSouthern Medical UniversityGuangzhouChina
| | - Xiaoye Jin
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of StomatologyXi'an Jiaotong UniversityXi'anChina
- Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of StomatologyXi'an Jiaotong UniversityXi'anChina
| | - Yating Fang
- Department of Forensic Genetics, School of Forensic MedicineSouthern Medical UniversityGuangzhouChina
| | - Shuyan Mei
- Department of Forensic Genetics, School of Forensic MedicineSouthern Medical UniversityGuangzhouChina
| | - Guang Yang
- Department of Laboratory Medicine and PathologyMayo ClinicRochesterMNUSA
| | - Bofeng Zhu
- Department of Forensic Genetics, School of Forensic MedicineSouthern Medical UniversityGuangzhouChina
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of StomatologyXi'an Jiaotong UniversityXi'anChina
- Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of StomatologyXi'an Jiaotong UniversityXi'anChina
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A comprehensive exploration of the genetic legacy and forensic features of Afghanistan and Pakistan Mongolian-descent Hazara. Forensic Sci Int Genet 2019; 42:e1-e12. [DOI: 10.1016/j.fsigen.2019.06.018] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/23/2019] [Accepted: 06/23/2019] [Indexed: 01/09/2023]
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35
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Ancient Genomes Reveal Yamnaya-Related Ancestry and a Potential Source of Indo-European Speakers in Iron Age Tianshan. Curr Biol 2019; 29:2526-2532.e4. [DOI: 10.1016/j.cub.2019.06.044] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 04/08/2019] [Accepted: 06/13/2019] [Indexed: 11/21/2022]
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36
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Zhang C, Gao Y, Liu J, Xue Z, Lu Y, Deng L, Tian L, Feng Q, Xu S. PGG.Population: a database for understanding the genomic diversity and genetic ancestry of human populations. Nucleic Acids Res 2019; 46:D984-D993. [PMID: 29112749 PMCID: PMC5753384 DOI: 10.1093/nar/gkx1032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/17/2017] [Indexed: 12/16/2022] Open
Abstract
There are a growing number of studies focusing on delineating genetic variations that are associated with complex human traits and diseases due to recent advances in next-generation sequencing technologies. However, identifying and prioritizing disease-associated causal variants relies on understanding the distribution of genetic variations within and among populations. The PGG.Population database documents 7122 genomes representing 356 global populations from 107 countries and provides essential information for researchers to understand human genomic diversity and genetic ancestry. These data and information can facilitate the design of research studies and the interpretation of results of both evolutionary and medical studies involving human populations. The database is carefully maintained and constantly updated when new data are available. We included miscellaneous functions and a user-friendly graphical interface for visualization of genomic diversity, population relationships (genetic affinity), ancestral makeup, footprints of natural selection, and population history etc. Moreover, PGG.Population provides a useful feature for users to analyze data and visualize results in a dynamic style via online illustration. The long-term ambition of the PGG.Population, together with the joint efforts from other researchers who contribute their data to our database, is to create a comprehensive depository of geographic and ethnic variation of human genome, as well as a platform bringing influence on future practitioners of medicine and clinical investigators. PGG.Population is available at https://www.pggpopulation.org.
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Affiliation(s)
- Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Gao
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jiaojiao Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zhe Xue
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Lian Deng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qidi Feng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
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Population genetics, diversity and forensic characteristics of Tai–Kadai-speaking Bouyei revealed by insertion/deletions markers. Mol Genet Genomics 2019; 294:1343-1357. [DOI: 10.1007/s00438-019-01584-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Accepted: 05/30/2019] [Indexed: 12/13/2022]
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38
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Common variants in SATB2 are associated with schizophrenia in Uygur Chinese population. Psychiatr Genet 2019; 29:120-126. [PMID: 31162297 DOI: 10.1097/ypg.0000000000000229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Schizophrenia is one of the most severe mental disorders and its etiology is supposed to be an interaction between genes and environmental factors. Previous genome-wide association studies of schizophrenia have reported multiple susceptibility loci including rs6704641 in the SATB2 gene. Recently, this locus was further confirmed as a genome-wide significant locus for association with schizophrenia by trans-ancestry meta-analysis of Han Chinese and Caucasian samples. However, there is no report of genetic analysis in Uygur Chinese population, which is considered to have a combined genetic background between eastern Asia and Caucasian. This study is aimed to explore whether SATB2 gene is significantly associated with schizophrenia in Uygur Chinese population, thus providing additional evidence for elucidating the role of SATB2 gene in schizophrenia. PARTICIPANTS AND METHODS In this study, we performed a case-control analysis focusing on seven tag single nucleotide polymorphisms located in SATB2 gene among 985 patients with schizophrenia and 1218 healthy controls recruited from the Xinjiang Province of China. RESULTS We found that rs6704641 was significantly associated with schizophrenia in both allelic and genotypic distributions (Pallele = 0.008, Pgenotype = 0.028 after correction). In addition, rs16831466 is significantly associated with schizophrenia in allelic distributions (corrected Pallele = 0.041). Besides, several haplotypes of single nucleotide polymorphism are significantly associated with schizophrenia too. CONCLUSION Our results suggest that SATB2 is also a susceptibility gene for schizophrenia in Uygur Chinese population, and subsequent functional experiments are necessary to reveal its role in the pathogenesis.
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Chen P, Wang B, Gao B, He G. Forensic features and genetic structure of 23 autosomal STRs in Artux Turkic-speaking population residing in southwestern Xinjiang Uyghur Autonomous Region. Int J Legal Med 2019; 133:1393-1395. [DOI: 10.1007/s00414-019-02072-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 04/18/2019] [Indexed: 02/05/2023]
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40
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He G, Wang Z, Zou X, Wang M, Liu J, Wang S, Ye Z, Chen P, Hou Y. Tai-Kadai-speaking Gelao population: Forensic features, genetic diversity and population structure. Forensic Sci Int Genet 2019; 40:e231-e239. [PMID: 30910535 DOI: 10.1016/j.fsigen.2019.03.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 03/14/2019] [Accepted: 03/16/2019] [Indexed: 12/12/2022]
Abstract
Genetic analyses of geographically and ethno-linguistically different populations are essential for understanding population stratification and genomic structure in medical Genome-Wide Association Studies (GWAS) and genetic variation and diversity related to forensic and population genetics studies. Here, we genotyped 30 autosomal insertion/deletion (Indel) markers from 502 Tai-Kadai-speaking Gelao individuals residing in the rugged topographical area in Southeastern China. In addition, two comprehensive population genetic comparisons of 15,327 individuals from 95 worldwide populations and of 6122 individuals from Asia and adjoining populations were conducted based on allele frequency data and raw genotype data, respectively. All studied markers were found to be in Hardy-Weinberg equilibrium. The combined power of discrimination in the Gelao minority group was 0.999999999975, and the combined probability of exclusion was 0.9879. Our results from the forensic statistical parameters indicated that this Indel panel can be independently used as a powerful tool in forensic individual identification but can only be used as a complementary tool in paternity cases involving East Asians. We also found significant allele frequency differences between the Gelao and other continental populations with respect to the markers grouped in clusters ∼Ⅳ, suggesting that these can be used as forensic ancestry informative Indel markers to distinguish the Gelao from other continental populations. Genetic ancestry analyses demonstrated that Tai-Kadai-speaking Gelao share a dominant ancestry component with Hmong-Mien-speaking Miao. Our population genetic results from multidimensional scaling plots, principal component analysis, neighboring-joining tree construction and hierarchical clustering also suggested that the Zunyi Gelao are genetically closer to their linguistically or geographically close populations, such as the Han Chinese, Guizhou Bouyei and the Hubei Tujia, than to Turkic and Tibeto-Burman speakers.
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Affiliation(s)
- Guanglin He
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Xing Zou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Mengge Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Shouyu Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Ziwei Ye
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China
| | - Pengyu Chen
- Center of Forensic Expertise, Affiliated Hospital of Zunyi Medical University, Zunyi 563099, Guizhou, China; School of Forensic Medicine, Zunyi Medical University, Zunyi 563099, Guizhou, China.
| | - Yiping Hou
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, China.
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Bhaskaran SP, Chandratre K, Gupta H, Zhang L, Wang X, Cui J, Kim YC, Sinha S, Jiang L, Lu B, Wu X, Qin Z, Huang T, Wang SM. Germline variation in BRCA1/2 is highly ethnic-specific: Evidence from over 30,000 Chinese hereditary breast and ovarian cancer patients. Int J Cancer 2019; 145:962-973. [PMID: 30702160 PMCID: PMC6617753 DOI: 10.1002/ijc.32176] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 01/14/2019] [Accepted: 01/25/2019] [Indexed: 01/04/2023]
Abstract
BRCA1 and BRCA2 play essential roles in maintaining the genome stability. Pathogenic germline mutations in these two genes disrupt their function, lead to genome instability and increase the risk of developing breast and ovarian cancers. BRCA mutations have been extensively screened in Caucasian populations, and the resulting information are used globally as the standard reference in clinical diagnosis, treatment and prevention of BRCA-related cancers. Recent studies suggest that BRCA mutations can be ethnic-specific, raising the question whether a Caucasian-based BRCA mutation information can be used as a universal standard worldwide, or whether an ethnicity-based BRCA mutation information system need to be developed for the corresponding ethnic populations. In this study, we used Chinese population as a model to test ethnicity-specific BRCA mutations considering that China has one of the latest numbers of breast cancer patients therefore BRCA mutation carriers. Through comprehensive data mining, standardization and annotation, we collected 1,088 distinct BRCA variants derived from over 30,000 Chinese individuals, one of the largest BRCA data set from a non-Caucasian population covering nearly all known BRCA variants in the Chinese population (https://dbBRCA-Chinese.fhs.umac.mo). Using this data, we performed multi-layered analyses to determine the similarities and differences of BRCA variation between Chinese and non-Chinese ethnic populations. The results show the substantial differences of BRCA data between Chinese and non-Chinese ethnicities. Our study indicates that the current Caucasian population-based BRCA data is not adequate to represent the BRCA status in non-Caucasian populations. Therefore, ethnic-based BRCA standards need to be established to serve for the non-Caucasian populations.
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Affiliation(s)
- Shanmuga Priya Bhaskaran
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Khyati Chandratre
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Hemant Gupta
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Li Zhang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Xiaoyu Wang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Jian Cui
- Eppley Cancer Institute, University of Nebraska Medical Center, Omaha, NE
| | - Yeong C Kim
- Eppley Cancer Institute, University of Nebraska Medical Center, Omaha, NE
| | - Siddharth Sinha
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Luhan Jiang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Boya Lu
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Xiaobing Wu
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Zixin Qin
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - Teng Huang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
| | - San Ming Wang
- Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
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Ni X, Yuan K, Liu C, Feng Q, Tian L, Ma Z, Xu S. MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures. Eur J Hum Genet 2019; 27:133-139. [PMID: 30206356 PMCID: PMC6303267 DOI: 10.1038/s41431-018-0259-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 07/12/2018] [Accepted: 08/09/2018] [Indexed: 11/08/2022] Open
Abstract
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.
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Affiliation(s)
- Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Kai Yuan
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chang Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qidi Feng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhiming Ma
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.
- University of Chinese Academy of Sciences, Beijing, 100049, 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.
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Sanchez-Mazas A, Nunes JM. Does NGS typing highlight our understanding of HLA population diversity? Hum Immunol 2019; 80:62-66. [DOI: 10.1016/j.humimm.2018.10.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 10/08/2018] [Accepted: 10/09/2018] [Indexed: 01/08/2023]
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Feng Q, Lu D, Xu S. AncestryPainter: A Graphic Program for Displaying Ancestry Composition of Populations and Individuals. GENOMICS PROTEOMICS & BIOINFORMATICS 2018; 16:382-385. [PMID: 30472416 PMCID: PMC6364040 DOI: 10.1016/j.gpb.2018.05.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Revised: 04/13/2018] [Accepted: 05/09/2018] [Indexed: 01/20/2023]
Abstract
Ancestry composition of populations and individuals has been extensively investigated in recent years due to advances in the genotyping and sequencing technologies. As the number of populations and individuals used for ancestry inference increases remarkably, say more than 100 populations or 1000 individuals, it is usually challenging to present the ancestry composition in a traditional way using a rectangular graph. To address this issue, we developed a program, AncestryPainter, which can illustrate the ancestry composition of populations and individuals with a rounded and nice-looking graph to save space. Individuals are depicted as length-fixed bars partitioned into colored segments representing different ancestries, and the population of interest can be highlighted as a pie chart in the center of the circle plot. In addition, AncestryPainter can also be applied to display personal ancestry in a way similar to that for displaying population ancestry. AncestryPainter is publicly available at http://www.picb.ac.cn/PGG/resource.php.
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Affiliation(s)
- Qidi Feng
- CAS Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongsheng Lu
- CAS Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- CAS Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, 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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Genetic structure and polymorphisms of Gelao ethnicity residing in southwest china revealed by X-chromosomal genetic markers. Sci Rep 2018; 8:14585. [PMID: 30275508 PMCID: PMC6167355 DOI: 10.1038/s41598-018-32945-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 09/19/2018] [Indexed: 01/10/2023] Open
Abstract
X-chromosome short tandem repeat markers (X-STRs), due to their special inheritance models, physical location on a single chromosome and the absence of recombination in male meiosis, play an important role in forensic and population genetics. While a series of genetic analyses focusing on the genetic diversity and forensic characteristics of X-STRs are well studied for ethnically/linguistically diverse and demographically large Chinese populations, genetic evidence from Gelao ethnicity is still sparse. Here, we genotyped the first batch of 19 X-STRs in 513 Chinese Gelao individuals (265 females and 248 males), and reported genetic polymorphisms, forensic characteristics based on the single locus and seven linkage groups. DXS10135 with the highest PIC (0.9106) and LG1 (DXS10148-DXS10135-DXS8378) with the largest HD (0.9970) are polymorphic and informative. The CPDs in Gelao males and females are respectively larger than 0.999999999997095 and 0.99999999999999999999918, and the combined MECs are larger than 0.999999975715109. Subsequently, we investigated the population relationships among 14 Chinese populations based on 19 X-STRs and among 23 populations based on 11 overlapped X-STRs. Our results revealed genetic differentiations among Tibeto-Burman, Altaic and other Chinese homogenous populations, and demonstrated that Guizhou Gelao has the genetically closer relationships with Han Chinese and geographically close Guizhou Miao.
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Pośpiech E, Chen Y, Kukla-Bartoszek M, Breslin K, Aliferi A, Andersen JD, Ballard D, Chaitanya L, Freire-Aradas A, van der Gaag KJ, Girón-Santamaría L, Gross TE, Gysi M, Huber G, Mosquera-Miguel A, Muralidharan C, Skowron M, Carracedo Á, Haas C, Morling N, Parson W, Phillips C, Schneider PM, Sijen T, Syndercombe-Court D, Vennemann M, Wu S, Xu S, Jin L, Wang S, Zhu G, Martin NG, Medland SE, Branicki W, Walsh S, Liu F, Kayser M. Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA. Forensic Sci Int Genet 2018; 37:241-251. [PMID: 30268682 DOI: 10.1016/j.fsigen.2018.08.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 07/18/2018] [Accepted: 08/27/2018] [Indexed: 10/28/2022]
Abstract
Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.
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Affiliation(s)
- Ewelina Pośpiech
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Gronostajowa st. 9, 30-387, Kraków, Poland; Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa st. 7A, 30-387, Kraków, Poland
| | - Yan Chen
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beichen West Road 1-104, Chaoyang, Beijing, 100101, PR China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China
| | - Magdalena Kukla-Bartoszek
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa st. 7, 30-387, Kraków, Poland
| | - Krystal Breslin
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), IN, USA
| | - Anastasia Aliferi
- King's Forensics, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, United Kingdom
| | - Jeppe D Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, DK-2100, Copenhagen, Denmark
| | - David Ballard
- King's Forensics, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, United Kingdom
| | - Lakshmi Chaitanya
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Ana Freire-Aradas
- Institute of Legal Medicine, Medical Faculty, University of Cologne, Melatengürtel 60/62, D-50823, Cologne, Germany; Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Kristiaan J van der Gaag
- Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA, The Hague, The Netherlands
| | - Lorena Girón-Santamaría
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Theresa E Gross
- Institute of Legal Medicine, Medical Faculty, University of Cologne, Melatengürtel 60/62, D-50823, Cologne, Germany
| | - Mario Gysi
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Gabriela Huber
- Institute of Legal Medicine, Medical University of Innsbruck, Müllerstrasse 44, 6020, Innsbruck, Austria
| | - Ana Mosquera-Miguel
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Charanya Muralidharan
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), IN, USA
| | - Małgorzata Skowron
- Department of Dermatology, Collegium Medicum of the Jagiellonian University, Skawińska st. 8, 31-066, Kraków, Poland
| | - Ángel Carracedo
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain; Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, KSA, Saudi Arabia
| | - Cordula Haas
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, DK-2100, Copenhagen, Denmark
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Müllerstrasse 44, 6020, Innsbruck, Austria; Forensic Science Program, The Pennsylvania State University, 13 Thomas Building, University Park, PA, 16802, USA
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Peter M Schneider
- Institute of Legal Medicine, Medical Faculty, University of Cologne, Melatengürtel 60/62, D-50823, Cologne, Germany
| | - Titia Sijen
- Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA, The Hague, The Netherlands
| | - Denise Syndercombe-Court
- King's Forensics, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, United Kingdom
| | - Marielle Vennemann
- Institute of Legal Medicine, University of Münster, Röntgenstr. 23, 48149, Münster, Germany
| | - Sijie Wu
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China
| | - Shuhua Xu
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, 2005 Song Hu Road Shanghai, 200438, PR China; School of Life Science and Technology, Shanghai-Tech University, 393 Middle Huaxia Road, Pudong, Shanghai, 201210, PR China
| | - Li Jin
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, 2005 Song Hu Road Shanghai, 200438, PR China
| | - Sijia Wang
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, 2005 Song Hu Road Shanghai, 200438, PR China
| | - Ghu Zhu
- Queensland Institute of Medical Research, Royal Brisbane Hospital, QLD 4029, Brisbane, Australia
| | - Nick G Martin
- Queensland Institute of Medical Research, Royal Brisbane Hospital, QLD 4029, Brisbane, Australia
| | - Sarah E Medland
- Queensland Institute of Medical Research, Royal Brisbane Hospital, QLD 4029, Brisbane, Australia
| | - Wojciech Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa st. 7A, 30-387, Kraków, Poland
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), IN, USA
| | - Fan Liu
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beichen West Road 1-104, Chaoyang, Beijing, 100101, PR China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands.
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