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Hao C, Hu X, Guo R, Qi Z, Jin F, Zhang X, Xie L, Liu H, Liu Y, Ni X, Li W. Targeted gene sequencing and hearing follow-up in 7501 newborns reveals an improved strategy for newborn hearing screening. Eur J Hum Genet 2024:10.1038/s41431-024-01711-x. [PMID: 39443691 DOI: 10.1038/s41431-024-01711-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Revised: 08/08/2024] [Accepted: 10/01/2024] [Indexed: 10/25/2024] Open
Abstract
Hearing loss is a common congenital condition. Concurrent newborn hearing and limited genetic screening has been implemented in China for the last decade. However, the role of gene sequencing screening has not been evaluated. In this study, we enrolled 7501 newborns (52.7% male, 47.3% female) in our Newborn Screening with Targeted Sequencing (NESTS) program, and 90 common deafness genes were sequenced for them. Hearing status assessments were conducted via telephone from February 2021 to August 2022, for children aged 3 to 48 months. Of the universal newborn hearing screening, 126 (1.7%) newborns did not pass. Targeted sequencing identified 150 genetically positive newborns (2.0%), with 25 exhibiting dual-positive results in both screening. Following diagnostic audiometry revealed 18 hearing loss newborns and half of them had abnormal results in both screening. The positive predictive value for universal newborn hearing screening alone was merely 14.3% (18/126). However, when combined with targeted sequencing, this rate increased to 36.0% (9/25). Furthermore, limited genetic screening identified 316 carriers of hot-spot variants, but none exhibited biallelic variants. All 15 hot-spot carriers who failed physical screening demonstrated normal hearing during follow-up. In this cohort study of 7501 Newborns, Combining targeted sequencing with universal newborn hearing screening demonstrated technical feasibility and clinical utility of identifying individuals with hearing loss, especially when coupled with genetic counseling and closed-loop management. It is suggested to use this integrated method as an improved strategy instead of the current limited genetic screening program in some regions of China.
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Affiliation(s)
- Chanjuan Hao
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
- Henan Key Laboratory of Inherited Metabolic Diseases, Pediatric Research Institute of Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Zhengzhou, Henan, China.
| | - Xuyun Hu
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
- Henan Key Laboratory of Inherited Metabolic Diseases, Pediatric Research Institute of Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Zhengzhou, Henan, China
| | - Ruolan Guo
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
- Henan Key Laboratory of Inherited Metabolic Diseases, Pediatric Research Institute of Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Zhengzhou, Henan, China
| | - Zhan Qi
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Feng Jin
- Shunyi Women and Children's Healthcare Hospital of Beijing Children's Hospital, Beijing, China
| | - Xiaofen Zhang
- Shunyi Women and Children's Healthcare Hospital of Beijing Children's Hospital, Beijing, China
| | - Limin Xie
- Shunyi Women and Children's Healthcare Hospital of Beijing Children's Hospital, Beijing, China
| | - Haihong Liu
- Department of Otolaryngology Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yuanhu Liu
- Shunyi Women and Children's Healthcare Hospital of Beijing Children's Hospital, Beijing, China
- Department of Otolaryngology Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xin Ni
- Department of Otolaryngology Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
| | - Wei Li
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
- Henan Key Laboratory of Inherited Metabolic Diseases, Pediatric Research Institute of Zhengzhou Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Zhengzhou, Henan, China.
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2
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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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Jiang T, Guo H, Liu Y, Li G, Cui Z, Cui X, Liu Y, Li Y, Zhang A, Cao S, Zhao T, Juan L, Kong W, Chen M, Liu D, Liu H, Zhang Y, Xu K, Wang Y, He M, Guo J, Lu M, Chen J, Zhao X, Zhao G, Dang S, Chen C, Wu X, Qin Q, Li Y, Shen H, Jin L, Liu B, Chen X, Zhao Y, Wang Y. A comprehensive genetic variant reference for the Chinese population. Sci Bull (Beijing) 2024:S2095-9273(24)00442-0. [PMID: 38945749 DOI: 10.1016/j.scib.2024.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/07/2024] [Accepted: 04/28/2024] [Indexed: 07/02/2024]
Affiliation(s)
- Tao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
| | - Hongzhe Guo
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
| | - Yadong Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
| | - Gaoyang Li
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Zhe Cui
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Xinran Cui
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Yue Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Yang Li
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Anqi Zhang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Shuqi Cao
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Tianyi Zhao
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China; School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
| | - Liran Juan
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
| | - Weize Kong
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Ming Chen
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Dianming Liu
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Hongri Liu
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Yixiao Zhang
- Department of Urology Surgery, Key Laboratory of Precision Medical Research on Major Chronic Disease, Liaoning Province, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Kelin Xu
- Fudan University Taizhou Institute of Health Sciences, Taizhou 200438, China; Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200433, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiancheng Guo
- Henan Research Center for Genomic Sequencing and Translational Medicine, Zhengzhou University, Zhengzhou 450001, China; The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China
| | - Ming Lu
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Jun Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350001, China
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Genming Zhao
- School of Public Health, Fudan University, Shanghai 200433, China
| | - Shaonong Dang
- School of Public Health, Xi'an Jiaotong University, Xi'an 710061, China
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410083, China
| | - Xiaojian Wu
- Department of Colorectal Surgery, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Sun Yat-sen University, Guangzhou 510655, China
| | - Qiyuan Qin
- Department of Colorectal Surgery, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Sun Yat-sen University, Guangzhou 510655, China
| | - Yixue Li
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Li Jin
- Fudan University Taizhou Institute of Health Sciences, Taizhou 200438, China; State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Bo Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China.
| | - Xingdong Chen
- Fudan University Taizhou Institute of Health Sciences, Taizhou 200438, China; State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai 200433, China.
| | - Yuhong Zhao
- Department of Clinical Epidemiology, Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang 110004, China.
| | - Yadong Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China; School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.
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4
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Su H, Wang M, Li X, Duan S, Sun Q, Sun Y, Wang Z, Yang Q, Huang Y, Zhong J, Chen J, Jiang X, Ma J, Yang T, Liu Y, Luo L, Liu Y, Yang J, Chen G, Liu C, Cai Y, He G. Population genetic admixture and evolutionary history in the Shandong Peninsula inferred from integrative modern and ancient genomic resources. BMC Genomics 2024; 25:611. [PMID: 38890579 PMCID: PMC11184692 DOI: 10.1186/s12864-024-10514-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Ancient northern East Asians (ANEA) from the Yellow River region, who pioneered millet cultivation, play a crucial role in understanding the origins of ethnolinguistically diverse populations in modern China and the entire landscape of deep genetic structure and variation discovery in modern East Asians. However, the direct links between ANEA and geographically proximate modern populations, as well as the biological adaptive processes involved, remain poorly understood. RESULTS Here, we generated genome-wide SNP data for 264 individuals from geographically different Han populations in Shandong. An integrated genomic resource encompassing both modern and ancient East Asians was compiled to examine fine-scale population admixture scenarios and adaptive traits. The reconstruction of demographic history and hierarchical clustering patterns revealed that individuals from the Shandong Peninsula share a close genetic affinity with ANEA, indicating long-term genetic continuity and mobility in the lower Yellow River basin since the early Neolithic period. Biological adaptive signatures, including those related to immune and metabolic pathways, were identified through analyses of haplotype homozygosity and allele frequency spectra. These signatures are linked to complex traits such as height and body mass index, which may be associated with adaptations to cold environments, dietary practices, and pathogen exposure. Additionally, allele frequency trajectories over time and a haplotype network of two highly differentiated genes, ABCC11 and SLC10A1, were delineated. These genes, which are associated with axillary odor and bilirubin metabolism, respectively, illustrate how local adaptations can influence the diversification of traits in East Asians. CONCLUSIONS Our findings provide a comprehensive genomic dataset that elucidates the fine-scale genetic history and evolutionary trajectory of natural selection signals and disease susceptibility in Han Chinese populations. This study serves as a paradigm for integrating spatiotemporally diverse ancient genomes in the era of population genomic medicine.
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Affiliation(s)
- Haoran Su
- Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, China
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- School of Laboratory Medicine, North Sichuan Medical College, Nanchong, 637007, Sichuan, China
| | - 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.
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, 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
| | - Shuhan Duan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College and Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, 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
| | - Yuntao Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, 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
| | - 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
| | - Yuguo Huang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
| | - Jie Zhong
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, 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
| | - Xiucheng Jiang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College and Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, China
| | - Jinyue Ma
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College and Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, China
| | - Ting 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
| | - Yunhui Liu
- 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
| | - Lintao Luo
- 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
| | - Yan Liu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College and Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, China
| | - Junbao Yang
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
- Institute of Basic Medicine and Forensic Medicine, North Sichuan Medical College and Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, China
| | - Gang Chen
- Hunan Key Laboratory of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410075, China
| | - Chao Liu
- Anti-Drug Technology Center of Guangdong Province, Guangzhou, 510230, China.
| | - Yan Cai
- Genetic and Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637007, Sichuan, 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.
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China.
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5
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He Y, Lei C, Wan C, Zeng S, Zhang T, Luo F, Li R, Li X, Zhao A, Xiao D, Luo Y, Shan K, Qi X, Jin X. A comprehensive whole genome database of ethnic minority populations. Sci Rep 2024; 14:13954. [PMID: 38886537 PMCID: PMC11183174 DOI: 10.1038/s41598-024-63892-1] [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: 11/28/2023] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
China, is characterized by its remarkable ethnical diversity, which necessitates whole genome variation data from multiple populations as crucial tools for advancing population genetics and precision medical research. However, there has been a scarcity of research concentrating on the whole genome of ethnic minority groups. To fill this gap, we developed the Guizhou Multi-ethnic Genome Database (GMGD). It comprises whole genome sequencing data from 476 healthy unrelated individuals spanning 11 ethnic minorities groups in Guizhou Province, Southwest China, including Bouyei, Dong, Miao, Yi, Bai, Gelo, Zhuang, Tujia, Yao, Hui, and Sui. The GMGD database comprises more than 16.33 million variants in GRCh38 and 16.20 million variants in GRCh37. Among these, approximately 11.9% (1,956,322) of the variants in GRCh38 and 18.5% (3,009,431) of the variants in GRCh37 are entirely new and do not exist in the dbSNP database. These novel variants shed light on the genetic diversity landscape across these populations, providing valuable insights with an average coverage of 5.5 ×. This makes GMGD the largest genome-wide database encompassing the most diverse ethnic groups to date. The GMGD interactive interface facilitates researchers with multi-dimensional mutation search methods and displays population frequency differences among global populations. Furthermore, GMGD is equipped with a genotype-imputation function, enabling enhanced capabilities for low-depth genomic research or targeted region capture studies. GMGD offers unique insights into the genomic variation landscape of different ethnic groups, which are freely accessible at https://db.cngb.org/pop/gmgd/ .
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Affiliation(s)
- Yan He
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Changgui Lei
- BGI Research, Shenzhen, 518083, China
- BGI Research, Guiyang, 550000, China
- BGI Research, Wuhan, 430074, China
| | - Chanjuan Wan
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Shuang Zeng
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Guiyang, 550000, China
- BGI Research, Wuhan, 430074, China
| | - Ting Zhang
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Fei Luo
- BGI Research, Shenzhen, 518083, China
- BGI Research, Guiyang, 550000, China
- BGI Research, Wuhan, 430074, China
| | - Ruichao Li
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Xiaokun Li
- BGI Research, Shenzhen, 518083, China
- BGI Research, Guiyang, 550000, China
| | - Anshu Zhao
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Defu Xiao
- BGI Research, Shenzhen, 518083, China
- BGI Research, Guiyang, 550000, China
| | - Yunyan Luo
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
- BGI Research, Guiyang, 550000, China
| | - Keren Shan
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Xiaolan Qi
- Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education and Key Laboratory of Medical Molecular Biology of Guizhou Province, Collaborative Innovation Center for Prevention and Control of Endemic and Ethnic Regional Diseases Co-constructed by the Province and Ministry, Guizhou Medical University, Guiyang, 550004, Guizhou, China.
| | - Xin Jin
- BGI Research, Shenzhen, 518083, China.
- BGI Research, Guiyang, 550000, China.
- Shenzhen Key Laboratory of Transomics Biotechnologies, BGI Research, Shenzhen, China.
- School of Medicine, South China University of Technology, Guangzhou, China.
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6
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Sun Y, Wang M, Sun Q, Liu Y, Duan S, Wang Z, Zhou Y, Zhong J, Huang Y, Huang X, Yang Q, Li X, Su H, Cai Y, Jiang X, Chen J, Yan J, Nie S, Hu L, Yang J, Tang R, Wang CC, Liu C, Deng X, Yun L, He G. Distinguished biological adaptation architecture aggravated population differentiation of Tibeto-Burman-speaking people. J Genet Genomics 2024; 51:517-530. [PMID: 37827489 DOI: 10.1016/j.jgg.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/28/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023]
Abstract
Tibeto-Burman (TB) people have endeavored to adapt to the hypoxic, cold, and high-UV high-altitude environments in the Tibetan Plateau and complex disease exposures in lowland rainforests since the late Paleolithic period. However, the full landscape of genetic history and biological adaptation of geographically diverse TB-speaking people, as well as their interaction mechanism, remain unknown. Here, we generate a whole-genome meta-database of 500 individuals from 39 TB-speaking populations and present a comprehensive landscape of genetic diversity, admixture history, and differentiated adaptative features of geographically different TB-speaking people. We identify genetic differentiation related to geography and language among TB-speaking people, consistent with their differentiated admixture process with incoming or indigenous ancestral source populations. A robust genetic connection between the Tibetan-Yi corridor and the ancient Yellow River people supports their Northern China origin hypothesis. We finally report substructure-related differentiated biological adaptative signatures between highland Tibetans and Loloish speakers. Adaptative signatures associated with the physical pigmentation (EDAR and SLC24A5) and metabolism (ALDH9A1) are identified in Loloish people, which differed from the high-altitude adaptative genetic architecture in Tibetan. TB-related genomic resources provide new insights into the genetic basis of biological adaptation and better reference for the anthropologically informed sampling design in biomedical and genomic cohort research.
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Affiliation(s)
- Yuntao Sun
- West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, Sichuan 610041, China; Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; Center for Archaeological Science, Sichuan University, Chengdu, Sichuan 610000, China
| | - Mengge Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; Center for Archaeological Science, Sichuan University, Chengdu, Sichuan 610000, China; Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510275, China; Guangzhou Forensic Science Institute, Guangzhou, Guangdong 510055, China.
| | - Qiuxia Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400331, China
| | - Yan Liu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Clinical Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Shuhan Duan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Zhiyong Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Forensic Medicine, Kunming Medical University, Kunming, Yunnan 650500, China
| | - Yunyu Zhou
- School of Stomatology, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Jun Zhong
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Yuguo Huang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China
| | - Xinyu Huang
- West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qingxin Yang
- School of Forensic Medicine, Kunming Medical University, Kunming, Yunnan 650500, China
| | - Xiangping Li
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Forensic Medicine, Kunming Medical University, Kunming, Yunnan 650500, China
| | - Haoran Su
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Yan Cai
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China; Department of Medical Laboratory, North Sichuan Medical College, Nanchong, Sichuan 637007, China
| | - Xiucheng Jiang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Jing Chen
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, 030600, China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, 030600, China
| | - Shengjie Nie
- School of Forensic Medicine, Kunming Medical University, Kunming, Yunnan 650500, China
| | - Liping Hu
- School of Forensic Medicine, Kunming Medical University, Kunming, Yunnan 650500, China
| | - Junbao Yang
- School of Clinical Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, China
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400331, China
| | - Chuan-Chao Wang
- School of Life Sciences, Xiamen University, Xiamen, Fujian 361005, China
| | - Chao Liu
- Anti-Drug Technology Center of Guangdong Province, Guangzhou, Guangdong 510230, China
| | - Xiaohui Deng
- West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Libing Yun
- West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, Sichuan 610041, China; Center for Archaeological Science, Sichuan University, Chengdu, Sichuan 610000, China.
| | - Guanglin He
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, Sichuan 610000, China; Center for Archaeological Science, Sichuan University, Chengdu, Sichuan 610000, China.
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7
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Sun Q, Wang M, Lu T, Duan S, Liu Y, Chen J, Wang Z, Sun Y, Li X, Wang S, Lu L, Hu L, Yun L, Yang J, Yan J, Nie S, Zhu Y, Chen G, Wang CC, Liu C, He G, Tang R. Differentiated adaptative genetic architecture and language-related demographical history in South China inferred from 619 genomes from 56 populations. BMC Biol 2024; 22:55. [PMID: 38448908 PMCID: PMC10918984 DOI: 10.1186/s12915-024-01854-9] [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: 04/14/2023] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The underrepresentation of human genomic resources from Southern Chinese populations limited their health equality in the precision medicine era and complete understanding of their genetic formation, admixture, and adaptive features. Besides, linguistical and genetic evidence supported the controversial hypothesis of their origin processes. One hotspot case was from the Chinese Guangxi Pinghua Han people (GPH), whose language was significantly similar to Southern Chinese dialects but whose uniparental gene pool was phylogenetically associated with the indigenous Tai-Kadai (TK) people. Here, we analyzed genome-wide SNP data in 619 people from four language families and 56 geographically different populations, in which 261 people from 21 geographically distinct populations were first reported here. RESULTS We identified significant population stratification among ethnolinguistically diverse Guangxi populations, suggesting their differentiated genetic origin and admixture processes. GPH shared more alleles related to Zhuang than Southern Han Chinese but received more northern ancestry relative to Zhuang. Admixture models and estimates of genetic distances showed that GPH had a close genetic relationship with geographically close TK compared to Northern Han Chinese, supporting their admixture origin hypothesis. Further admixture time and demographic history reconstruction supported GPH was formed via admixture between Northern Han Chinese and Southern TK people. We identified robust signatures associated with lipid metabolisms, such as fatty acid desaturases (FADS) and medically relevant loci associated with Mendelian disorder (GJB2) and complex diseases. We also explored the shared and unique selection signatures of ethnically different but linguistically related Guangxi lineages and found some shared signals related to immune and malaria resistance. CONCLUSIONS Our genetic analysis illuminated the language-related fine-scale genetic structure and provided robust genetic evidence to support the admixture hypothesis that can explain the pattern of observed genetic diversity and formation of GPH. This work presented one comprehensive analysis focused on the population history and demographical adaptative process, which provided genetic evidence for personal health management and disease risk prediction models from Guangxi people. Further large-scale whole-genome sequencing projects would provide the entire landscape of southern Chinese genomic diversity and their contributions to human health and disease traits.
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Affiliation(s)
- Qiuxia Sun
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
| | - 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.
| | - Tao Lu
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, 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
| | - Yan Liu
- 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
| | - 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
| | - 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
| | - Yuntao Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- West China School of Basic Science & 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
| | - Shaomei Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- Department of Public Health, Chengdu Medical College, Chengdu, 610500, China
| | - Liuyi Lu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- School of Clinical Medical Sciences, North Sichuan Medical College, Nanchong, 637100, China
| | - Liping Hu
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Libing Yun
- West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Junbao Yang
- School of Clinical Medical Sciences, North Sichuan Medical College, Nanchong, 637100, China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030001, China
| | - Shengjie Nie
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Yanfeng Zhu
- Department of Public Health, Chengdu Medical College, Chengdu, 610500, China
| | - Gang Chen
- Hunan Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410075, China
| | - Chuan-Chao Wang
- State Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, 361005, Fujian, China
| | - Chao Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China
- Guangzhou Forensic Science Institute, Guangzhou, 510055, 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.
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China.
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8
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Liu S, Luo H, Zhang P, Li Y, Hao D, Zhang S, Song T, Xu T, He S. Adaptive Selection of Cis-regulatory Elements in the Han Chinese. Mol Biol Evol 2024; 41:msae034. [PMID: 38377343 PMCID: PMC10917166 DOI: 10.1093/molbev/msae034] [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: 10/02/2023] [Revised: 01/18/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Cis-regulatory elements have an important role in human adaptation to the living environment. However, the lag in population genomic cohort studies and epigenomic studies, hinders the research in the adaptive analysis of cis-regulatory elements in human populations. In this study, we collected 4,013 unrelated individuals and performed a comprehensive analysis of adaptive selection of genome-wide cis-regulatory elements in the Han Chinese. In total, 12.34% of genomic regions are under the influence of adaptive selection, where 1.00% of enhancers and 2.06% of promoters are under positive selection, and 0.06% of enhancers and 0.02% of promoters are under balancing selection. Gene ontology enrichment analysis of these cis-regulatory elements under adaptive selection reveals that many positive selections in the Han Chinese occur in pathways involved in cell-cell adhesion processes, and many balancing selections are related to immune processes. Two classes of adaptive cis-regulatory elements related to cell adhesion were in-depth analyzed, one is the adaptive enhancers derived from neanderthal introgression, leads to lower hyaluronidase level in skin, and brings better performance on UV-radiation resistance to the Han Chinese. Another one is the cis-regulatory elements regulating wound healing, and the results suggest the positive selection inhibits coagulation and promotes angiogenesis and wound healing in the Han Chinese. Finally, we found that many pathogenic alleles, such as risky alleles of type 2 diabetes or schizophrenia, remain in the population due to the hitchhiking effect of positive selections. Our findings will help deepen our understanding of the adaptive evolution of genome regulation in the Han Chinese.
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Affiliation(s)
- Shuai Liu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sijia Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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9
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Huang S, Liu S, Huang M, He JR, Wang C, Wang T, Feng X, Kuang Y, Lu J, Gu Y, Xia X, Lin S, Zhou W, Fu Q, Xia H, Qiu X. The Born in Guangzhou Cohort Study enables generational genetic discoveries. Nature 2024; 626:565-573. [PMID: 38297123 DOI: 10.1038/s41586-023-06988-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 12/15/2023] [Indexed: 02/02/2024]
Abstract
Genomic research that targets large-scale, prospective birth cohorts constitutes an essential strategy for understanding the influence of genetics and environment on human health1. Nonetheless, such studies remain scarce, particularly in Asia. Here we present the phase I genome study of the Born in Guangzhou Cohort Study2 (BIGCS), which encompasses the sequencing and analysis of 4,053 Chinese individuals, primarily composed of trios or mother-infant duos residing in South China. Our analysis reveals novel genetic variants, a high-quality reference panel, and fine-scale local genetic structure within BIGCS. Notably, we identify previously unreported East Asian-specific genetic associations with maternal total bile acid, gestational weight gain and infant cord blood traits. Additionally, we observe prevalent age-specific genetic effects on lipid levels in mothers and infants. In an exploratory intergenerational Mendelian randomization analysis, we estimate the maternal putatively causal and fetal genetic effects of seven adult phenotypes on seven fetal growth-related measurements. These findings illuminate the genetic links between maternal and early-life traits in an East Asian population and lay the groundwork for future research into the intricate interplay of genetics, intrauterine exposures and early-life experiences in shaping long-term health.
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Affiliation(s)
- Shujia Huang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Mingxi Huang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Jian-Rong He
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Provincial Clinical Research Center for Child Health, Guangzhou, China
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Chengrui Wang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Tianyi Wang
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Xiaotian Feng
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Yashu Kuang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Jinhua Lu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Yuqin Gu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Xiaoyan Xia
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Department of Women's Health, Provincial Key Clinical Specialty of Woman and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Shanshan Lin
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
- Department of Women's Health, Provincial Key Clinical Specialty of Woman and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Wenhao Zhou
- Division of Neonatology and Center for Newborn Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Qiaomei Fu
- Key Laboratory of Vertebrate Evolution and Human Origins, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, Beijing, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Huimin Xia
- Provincial Clinical Research Center for Child Health, Guangzhou, China.
- Provincial Key Laboratory of Research in Structure Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
- Department of Pediatric Surgery, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
| | - Xiu Qiu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
- Provincial Clinical Research Center for Child Health, Guangzhou, China.
- Department of Women's Health, Provincial Key Clinical Specialty of Woman and Child Health, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
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10
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He G, Wang P, Chen J, Liu Y, Sun Y, Hu R, Duan S, Sun Q, Tang R, Yang J, Wang Z, Yun L, Hu L, Yan J, Nie S, Wei L, Liu C, Wang M. Differentiated genomic footprints suggest isolation and long-distance migration of Hmong-Mien populations. BMC Biol 2024; 22:18. [PMID: 38273256 PMCID: PMC10809681 DOI: 10.1186/s12915-024-01828-x] [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: 01/23/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The underrepresentation of Hmong-Mien (HM) people in Asian genomic studies has hindered our comprehensive understanding of the full landscape of their evolutionary history and complex trait architecture. South China is a multi-ethnic region and indigenously settled by ethnolinguistically diverse HM, Austroasiatic (AA), Tai-Kadai (TK), Austronesian (AN), and Sino-Tibetan (ST) people, which is regarded as East Asia's initial cradle of biodiversity. However, previous fragmented genetic studies have only presented a fraction of the landscape of genetic diversity in this region, especially the lack of haplotype-based genomic resources. The deep characterization of demographic history and natural-selection-relevant genetic architecture of HM people was necessary. RESULTS We reported one HM-specific genomic resource and comprehensively explored the fine-scale genetic structure and adaptative features inferred from the genome-wide SNP data of 440 HM individuals from 33 ethnolinguistic populations, including previously unreported She. We identified solid genetic differentiation between HM people and Han Chinese at 7.64‒15.86 years ago (kya) and split events between southern Chinese inland (Miao/Yao) and coastal (She) HM people in the middle Bronze Age period and the latter obtained more gene flow from Ancient Northern East Asians. Multiple admixture models further confirmed that extensive gene flow from surrounding ST, TK, and AN people entangled in forming the gene pool of Chinese coastal HM people. Genetic findings of isolated shared unique ancestral components based on the sharing alleles and haplotypes deconstructed that HM people from the Yungui Plateau carried the breadth of previously unknown genomic diversity. We identified a direct and recent genetic connection between Chinese inland and Southeast Asian HM people as they shared the most extended identity-by-descent fragments, supporting the long-distance migration hypothesis. Uniparental phylogenetic topology and network-based phylogenetic relationship reconstruction found ancient uniparental founding lineages in southwestern HM people. Finally, the population-specific biological adaptation study identified the shared and differentiated natural selection signatures among inland and coastal HM people associated with physical features and immune functions. The allele frequency spectrum of cancer susceptibility alleles and pharmacogenomic genes showed significant differences between HM and northern Chinese people. CONCLUSIONS Our extensive genetic evidence combined with the historical documents supported the view that ancient HM people originated from the Yungui regions associated with ancient "Three-Miao tribes" descended from the ancient Daxi-Qujialing-Shijiahe people. Then, some have recently migrated rapidly to Southeast Asia, and some have migrated eastward and mixed respectively with Southeast Asian indigenes, Liangzhu-related coastal ancient populations, and incoming southward ST people. Generally, complex population migration, admixture, and adaptation history contributed to the complicated patterns of population structure of geographically diverse HM people.
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Affiliation(s)
- Guanglin He
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China.
- Center for Archaeological Science, Sichuan University, Chengdu, 610000, China.
- Anti-Drug Technology Center of Guangdong Province, Guangzhou, 510230, China.
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China.
| | - Peixin Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Medical Information, Chongqing Medical University, Chongqing, 400331, China
| | - Jing Chen
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030001, China
| | - Yan Liu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
| | - Yuntao Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Rong Hu
- School of Sociology and Anthropology, Xiamen University, Xiamen, 361005, China
| | - Shuhan Duan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
| | - Qiuxia Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
| | - Junbao Yang
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China
| | - Zhiyong Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Libing Yun
- Institute of Forensic Medicine, West China School of Basic Science & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Liping Hu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030001, China
| | - Shengjie Nie
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Lanhai Wei
- School of Ethnology and Anthropology, Inner Mongolia Normal University, Inner Mongolia, 010028, China
| | - Chao Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China.
- Anti-Drug Technology Center of Guangdong Province, Guangzhou, 510230, China.
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Mengge Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China.
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China.
- Anti-Drug Technology Center of Guangdong Province, Guangzhou, 510230, China.
- Research Center for Genomic Medicine, North Sichuan Medical College, Nanchong, 637100, China.
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11
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Mandal AK. Recent insights into crosstalk between genetic parasites and their host genome. Brief Funct Genomics 2024; 23:15-23. [PMID: 36307128 PMCID: PMC10799329 DOI: 10.1093/bfgp/elac032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/14/2022] [Accepted: 09/21/2022] [Indexed: 01/21/2024] Open
Abstract
The bulk of higher order organismal genomes is comprised of transposable element (TE) copies, i.e. genetic parasites. The host-parasite relation is multi-faceted, varying across genomic region (genic versus intergenic), life-cycle stages, tissue-type and of course in health versus pathological state. The reach of functional genomics though, in investigating genotype-to-phenotype relations, has been limited when TEs are involved. The aim of this review is to highlight recent progress made in understanding how TE origin biochemical activity interacts with the central dogma stages of the host genome. Such interaction can also bring about modulation of the immune context and this could have important repercussions in disease state where immunity has a role to play. Thus, the review is to instigate ideas and action points around identifying evolutionary adaptations that the host genome and the genetic parasite have evolved and why they could be relevant.
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Affiliation(s)
- Amit K Mandal
- Corresponding author: A.K. Mandal, Nuffield Department of Surgical Sciences (NDS), University of Oxford, Old Road Campus Research building (ORCRB), Oxford OX3 7DQ, UK. Tel: +44 (0)1865 617123; Fax: +44 (0)1865 768876; E-mail:
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12
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Wang X, Yue M, Cheung JPY, Cheung PWH, Fan Y, Wu M, Wang X, Zhao S, Khanshour AM, Rios JJ, Chen Z, Wang X, Tu W, Chan D, Yuan Q, Qin D, Qiu G, Wu Z, Zhang TJ, Ikegawa S, Wu N, Wise CA, Hu Y, Luk KDK, Song YQ, Gao B. Impaired glycine neurotransmission causes adolescent idiopathic scoliosis. J Clin Invest 2024; 134:e168783. [PMID: 37962965 PMCID: PMC10786698 DOI: 10.1172/jci168783] [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: 01/12/2023] [Accepted: 11/08/2023] [Indexed: 11/16/2023] Open
Abstract
Adolescent idiopathic scoliosis (AIS) is the most common form of spinal deformity, affecting millions of adolescents worldwide, but it lacks a defined theory of etiopathogenesis. Because of this, treatment of AIS is limited to bracing and/or invasive surgery after onset. Preonset diagnosis or preventive treatment remains unavailable. Here, we performed a genetic analysis of a large multicenter AIS cohort and identified disease-causing and predisposing variants of SLC6A9 in multigeneration families, trios, and sporadic patients. Variants of SLC6A9, which encodes glycine transporter 1 (GLYT1), reduced glycine-uptake activity in cells, leading to increased extracellular glycine levels and aberrant glycinergic neurotransmission. Slc6a9 mutant zebrafish exhibited discoordination of spinal neural activities and pronounced lateral spinal curvature, a phenotype resembling human patients. The penetrance and severity of curvature were sensitive to the dosage of functional glyt1. Administration of a glycine receptor antagonist or a clinically used glycine neutralizer (sodium benzoate) partially rescued the phenotype. Our results indicate a neuropathic origin for "idiopathic" scoliosis, involving the dysfunction of synaptic neurotransmission and central pattern generators (CPGs), potentially a common cause of AIS. Our work further suggests avenues for early diagnosis and intervention of AIS in preadolescents.
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Affiliation(s)
- Xiaolu Wang
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
- School of Biomedical Sciences, Faculty of Medicine, Chinese University of Hong Kong, Shatin, Hong Kong, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Ming Yue
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Jason Pui Yin Cheung
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
- Department of Orthopaedics and Traumatology, University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Prudence Wing Hang Cheung
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Yanhui Fan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Meicheng Wu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Xiaojun Wang
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Sen Zhao
- Department of Orthopaedic Surgery, Department of Medical Research Center, Key Laboratory of Big Data for Spinal Deformities, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital (PUMCH) and Chinese Academy of Medical Sciences, Beijing, China
| | - Anas M. Khanshour
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children (SRC), Dallas, Texas, USA
| | - Jonathan J. Rios
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children (SRC), Dallas, Texas, USA
- Eugene McDermott Center for Human Growth and Development, Departments of Orthopaedic Surgery and Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Zheyi Chen
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Xiwei Wang
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Wenwei Tu
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Danny Chan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Qiuju Yuan
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Tai Po, Hong Kong, China
| | - Dajiang Qin
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Tai Po, Hong Kong, China
| | - Guixing Qiu
- Department of Orthopaedic Surgery, Department of Medical Research Center, Key Laboratory of Big Data for Spinal Deformities, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital (PUMCH) and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhihong Wu
- Department of Orthopaedic Surgery, Department of Medical Research Center, Key Laboratory of Big Data for Spinal Deformities, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital (PUMCH) and Chinese Academy of Medical Sciences, Beijing, China
| | - Terry Jianguo Zhang
- Department of Orthopaedic Surgery, Department of Medical Research Center, Key Laboratory of Big Data for Spinal Deformities, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital (PUMCH) and Chinese Academy of Medical Sciences, Beijing, China
| | - Shiro Ikegawa
- Laboratory of Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Nan Wu
- Department of Orthopaedic Surgery, Department of Medical Research Center, Key Laboratory of Big Data for Spinal Deformities, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Peking Union Medical College Hospital (PUMCH) and Chinese Academy of Medical Sciences, Beijing, China
| | - Carol A. Wise
- Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children (SRC), Dallas, Texas, USA
- Eugene McDermott Center for Human Growth and Development, Departments of Orthopaedic Surgery and Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yong Hu
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
- Department of Orthopaedics and Traumatology, University of Hong Kong–Shenzhen Hospital, Shenzhen, China
| | - Keith Dip Kei Luk
- Department of Orthopaedics and Traumatology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
| | - You-Qiang Song
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
- Department of Medicine, University of Hong Kong–Shenzhen Hospital, Shenzhen, China
- State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Pokfulam, Hong Kong, China
| | - Bo Gao
- School of Biomedical Sciences, Faculty of Medicine, Chinese University of Hong Kong, Shatin, Hong Kong, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong, China
- Department of Orthopaedics and Traumatology, University of Hong Kong–Shenzhen Hospital, Shenzhen, China
- Centre for Translational Stem Cell Biology, Tai Po, Hong Kong, China
- Key Laboratory of Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, Chinese University of Hong Kong, Shatin, Hong Kong, China
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13
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Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, Cen H, Chao J, Chen F, Chen H, Chen K, Chen M, Chen M, Chen M, Chen Q, Chen R, Chen S, Chen T, Chen X, Chen X, Cheng Y, Chu Y, Cui Q, Dong L, Du Z, Duan G, Fan S, Fan Z, Fang X, Fang Z, Feng Z, Fu S, Gao F, Gao G, Gao H, Gao W, Gao X, Gao X, Gao X, Gong J, Gong J, Gou Y, Gu S, Guo AY, Guo G, Guo X, Han C, Hao D, Hao L, He Q, He S, He S, Hu W, Huang K, Huang T, Huang X, Huang Y, Jia P, Jia Y, Jiang C, Jiang M, Jiang S, Jiang T, Jiang X, Jin E, Jin W, Kang H, Kang H, Kong D, Lan L, Lei W, Li CY, Li C, Li C, Li H, Li J, Li J, Li L, Li P, Li R, Li X, Li Y, Li Y, Li Z, Liao X, Lin S, Lin Y, Ling Y, Liu B, Liu CJ, Liu D, Liu GH, Liu L, Liu S, Liu W, Liu X, Liu X, Liu Y, Liu Y, Lu M, Lu T, Luo H, Luo H, Luo M, Luo S, Luo X, Ma L, Ma Y, Mai J, Meng J, Meng X, Meng Y, Meng Y, Miao W, Miao YR, Ni L, Nie Z, Niu G, Niu X, Niu Y, Pan R, Pan S, Peng D, Peng J, Qi J, Qi Y, Qian Q, Qin Y, Qu H, Ren J, Ren J, Sang Z, Shang K, Shen WK, Shen Y, Shi Y, Song S, Song T, Su T, Sun J, Sun Y, Sun Y, Sun Y, Tang B, Tang D, Tang Q, Tang Z, Tian D, Tian F, Tian W, Tian Z, Wang A, Wang G, Wang G, Wang J, Wang J, Wang P, Wang P, Wang W, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wei H, Wei Y, Wei Z, Wu D, Wu G, Wu S, Wu S, Wu W, Wu W, Wu Z, Xia Z, Xiao J, Xiao L, Xiao Y, Xie G, Xie GY, Xie J, Xie Y, Xiong J, Xiong Z, Xu D, Xu S, Xu T, Xu T, Xue Y, Xue Y, Yan C, Yang D, Yang F, Yang F, Yang H, Yang J, Yang K, Yang N, Yang QY, Yang S, Yang X, Yang X, Yang X, Yang YG, Ye W, Yu C, Yu F, Yu S, Yuan C, Yuan H, Zeng J, Zhai S, Zhang C, Zhang F, Zhang G, Zhang M, Zhang P, Zhang Q, Zhang R, Zhang S, Zhang W, Zhang W, Zhang W, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang YE, Zhang Y, Zhang Z, Zhang Z, Zhao D, Zhao F, Zhao G, Zhao M, Zhao W, Zhao W, Zhao X, Zhao Y, Zhao Y, Zhao Z, Zheng X, Zheng Y, Zhou C, Zhou H, Zhou X, Zhou X, Zhou Y, Zhou Y, Zhu J, Zhu L, Zhu R, Zhu T, Zong W, Zou D, Zuo Z. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res 2024; 52:D18-D32. [PMID: 38018256 PMCID: PMC10767964 DOI: 10.1093/nar/gkad1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023] Open
Abstract
The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
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14
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Yu C, Lan X, Tao Y, Guo Y, Sun D, Qian P, Zhou Y, Walters R, Li L, Zhu Y, Zeng J, Millwood I, Guo R, Pei P, Yang T, Du H, Yang F, Yang L, Ren F, Chen Y, Chen F, Jiang X, Ye Z, Dai L, Wei X, Xu X, Yang H, Wang J, Chen Z, Zhu H, Lv J, Jin X, Li L. A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study. Nucleic Acids Res 2023; 51:11770-11782. [PMID: 37870428 PMCID: PMC10681741 DOI: 10.1093/nar/gkad779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/02/2023] [Accepted: 09/12/2023] [Indexed: 10/24/2023] Open
Abstract
Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.
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Affiliation(s)
- Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Xianmei Lan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Ye Tao
- BGI Research, Shenzhen 518083, China
| | - Yu Guo
- National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Puyi Qian
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Yuwen Zhou
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Linxuan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Yunqing Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Jingyu Zeng
- BGI Research, Shenzhen 518083, China
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | | | - Pei Pei
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Tao Yang
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Huaidong Du
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Fan Yang
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Fangyi Ren
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Fengzhen Chen
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Xiaosen Jiang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Shenzhen 518083, China
| | - Zhiqiang Ye
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Lanlan Dai
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Xiaofeng Wei
- China National GeneBank, BGI, Shenzhen 518083, China
| | - Xun Xu
- BGI Research, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI Research, Shenzhen 518083, China
| | - Huanming Yang
- BGI Research, Shenzhen 518083, China
- Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI, Shenzhen 518083, China
- James D. Watson Institute of Genome Sciences, Hangzhou 310013, China
| | | | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom
| | | | - Jun Lv
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China
| | - Xin Jin
- BGI Research, Shenzhen 518083, China
- School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
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15
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Shi M, Tanikawa C, Munter HM, Akiyama M, Koyama S, Tomizuka K, Matsuda K, Lathrop GM, Terao C, Koido M, Kamatani Y. Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels. Brief Bioinform 2023; 25:bbad509. [PMID: 38221906 PMCID: PMC10788679 DOI: 10.1093/bib/bbad509] [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: 07/14/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024] Open
Abstract
Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.
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Affiliation(s)
- Mingyang Shi
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hans Markus Munter
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Satoshi Koyama
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Gregory Mark Lathrop
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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16
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Patel AP, Fahed AC. Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations. Curr Protoc 2023; 3:e911. [PMID: 37921506 PMCID: PMC11196001 DOI: 10.1002/cpz1.911] [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] [Indexed: 11/04/2023]
Abstract
Polygenic risk scores (PRS) estimate genetic susceptibility of an individual to disease and have the potential of providing utility in multiple clinical contexts. However, their performance, computation, and reporting in diverse populations remain challenging. Here, we present a pragmatic approach to optimize a PRS for a population of interest that leverages publicly available data and methods and consists of seven steps that are easily implemented without the requirement of expertise in complex genetics: step 1, selecting source genome-wide association studies (GWAS) and imputation; step 2, selecting methods to compute polygenic score; step 3, adjusting scores using principal components of genetic ancestry; step 4, selecting the best performing score; step 5, defining percentiles of a population distribution; step 6, validating performance of the optimized polygenic score; and step 7, implementing the optimized polygenic score in clinical practice. © 2023 Wiley Periodicals LLC.
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Affiliation(s)
- Aniruddh P Patel
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Akl C Fahed
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
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17
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Luo H, Zhang P, Zhang W, Zheng Y, Hao D, Shi Y, Niu Y, Song T, Li Y, Zhao S, Chen H, Xu T, He S. Recent positive selection signatures reveal phenotypic evolution in the Han Chinese population. Sci Bull (Beijing) 2023; 68:2391-2404. [PMID: 37661541 DOI: 10.1016/j.scib.2023.08.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/08/2023] [Accepted: 08/10/2023] [Indexed: 09/05/2023]
Abstract
Characterizing natural selection signatures and relationships with phenotype spectra is important for understanding human evolution and both biological and pathological mechanisms. Here, we identified 24 genetic loci under recent selection by analyzing rare singletons in 3946 high-depth whole-genome sequencing data of Han Chinese. The loci include immune-related gene regions (MHC cluster, IGH cluster, STING1, and PSG), alcohol metabolism-related gene regions (ADH1B, ALDH2, and ALDH3B2), and the olfactory perception gene OR4C16, in which the MHC cluster, ADH1B, and ALDH2 were also identified by TOPMed and WestLake Biobank. Among the signals, the IGH cluster is particularly interesting, in which the favored allele of variant 14_105737776_C_T (rs117518546, IgG1-G396R) promotes immune response, but also increases the risk of an autoimmune disease systemic lupus erythematosus (SLE). It is also surprising that our newly discovered ALDH3B2 evolved in the opposite direction to ALDH2 for alcohol metabolism. Besides monogenic traits, we found that multiple complex traits experienced polygenic adaptation. Particularly, multi-methods consistently revealed that lower blood pressure was favored in natural selection. Finally, we built a database named RePoS (recent positive selection, http://bigdata.ibp.ac.cn/RePoS/) to integrate and display multi-population selection signals. Our study extended our understanding of natural evolution and phenotype adaptation in Han Chinese as well as other populations.
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Affiliation(s)
- Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wanyu Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu Zheng
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yirong Shi
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiwei Niu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shilei Zhao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; China National Center for Bioinformation, Beijing 100101, China
| | - Hua Chen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; China National Center for Bioinformation, Beijing 100101, China.
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China.
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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18
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Zhao GB, Miao L, Wang M, Yuan JH, Wei LH, Feng YS, Zhao J, Kang KL, Zhang C, Ji AQ, He G, Wang L. Developmental validation of a high-resolution panel genotyping 639 Y-chromosome SNP and InDel markers and its evolutionary features in Chinese populations. BMC Genomics 2023; 24:611. [PMID: 37828453 PMCID: PMC10568895 DOI: 10.1186/s12864-023-09709-3] [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: 07/12/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
Uniparental-inherited haploid genetic marker of Y-chromosome single nucleotide polymorphisms (Y-SNP) have the power to provide a deep understanding of the human evolutionary past, forensic pedigree, and bio-geographical ancestry information. Several international cross-continental or regional Y-panels instead of Y-whole sequencing have recently been developed to promote Y-tools in forensic practice. However, panels based on next-generation sequencing (NGS) explicitly developed for Chinese populations are insufficient to represent the Chinese Y-chromosome genetic diversity and complex population structures, especially for Chinese-predominant haplogroup O. We developed and validated a 639-plex panel including 633 Y-SNPs and 6 Y-Insertion/deletions, which covered 573 Y haplogroups on the Y-DNA haplogroup tree. In this panel, subgroups from haplogroup O accounted for 64.4% of total inferable haplogroups. We reported the sequencing metrics of 354 libraries sequenced with this panel, with the average sequencing depth among 226 individuals being 3,741×. We illuminated the high level of concordance, accuracy, reproducibility, and specificity of the 639-plex panel and found that 610 loci were genotyped with as little as 0.03 ng of genomic DNA in the sensitivity test. 94.05% of the 639 loci were detectable in male-female mixed DNA samples with a mix ratio of 1:500. Nearly all of the loci were genotyped correctly when no more than 25 ng/μL tannic acid, 20 ng/μL humic acid, or 37.5 μM hematin was added to the amplification mixture. More than 80% of genotypes were obtained from degraded DNA samples with a degradation index of 11.76. Individuals from the same pedigree shared identical genotypes in 11 male pedigrees. Finally, we presented the complex evolutionary history of 183 northern Chinese Hans and six other Chinese populations, and found multiple founding lineages that contributed to the northern Han Chinese gene pool. The 639-plex panel proved an efficient tool for Chinese paternal studies and forensic applications.
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Affiliation(s)
- Guang-Bin Zhao
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - Lei Miao
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Mengge Wang
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510275, China
| | - Jia-Hui Yuan
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Lan-Hai Wei
- School of Ethnology and Anthropology, Inner Mongolia Normal University, Inner Mongolia, 010028, China
| | - Yao-Sen Feng
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - Jie Zhao
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - Ke-Lai Kang
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - Chi Zhang
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - An-Quan Ji
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China.
| | - Guanglin He
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China.
| | - Le Wang
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China.
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China.
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19
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Yang C, Zhou Y, Song Y, Wu D, Zeng Y, Nie L, Liu P, Zhang S, Chen G, Xu J, Zhou H, Zhou L, Qian X, Liu C, Tan S, Zhou C, Dai W, Xu M, Qi Y, Wang X, Guo L, Fan G, Wang A, Deng Y, Zhang Y, Jin J, He Y, Guo C, Guo G, Zhou Q, Xu X, Yang H, Wang J, Xu S, Mao Y, Jin X, Ruan J, Zhang G. The complete and fully-phased diploid genome of a male Han Chinese. Cell Res 2023; 33:745-761. [PMID: 37452091 PMCID: PMC10542383 DOI: 10.1038/s41422-023-00849-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023] Open
Abstract
Since the release of the complete human genome, the priority of human genomic study has now been shifting towards closing gaps in ethnic diversity. Here, we present a fully phased and well-annotated diploid human genome from a Han Chinese male individual (CN1), in which the assemblies of both haploids achieve the telomere-to-telomere (T2T) level. Comparison of this diploid genome with the CHM13 haploid T2T genome revealed significant variations in the centromere. Outside the centromere, we discovered 11,413 structural variations, including numerous novel ones. We also detected thousands of CN1 alleles that have accumulated high substitution rates and a few that have been under positive selection in the East Asian population. Further, we found that CN1 outperforms CHM13 as a reference genome in mapping and variant calling for the East Asian population owing to the distinct structural variants of the two references. Comparison of SNP calling for a large cohort of 8869 Chinese genomes using CN1 and CHM13 as reference respectively showed that the reference bias profoundly impacts rare SNP calling, with nearly 2 million rare SNPs miss-called with different reference genomes. Finally, applying the CN1 as a reference, we discovered 5.80 Mb and 4.21 Mb putative introgression sequences from Neanderthal and Denisovan, respectively, including many East Asian specific ones undetected using CHM13 as the reference. Our analyses reveal the advances of using CN1 as a reference for population genomic studies and paleo-genomic studies. This complete genome will serve as an alternative reference for future genomic studies on the East Asian population.
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Affiliation(s)
- Chentao Yang
- Center for Genomic Research, International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
- Center for Evolutionary & Organismal Biology, & Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Yang Zhou
- BGI-Shenzhen, Shenzhen, Guangdong, China
- BGI Research-Wuhan, BGI, Wuhan, Hubei, China
| | - Yanni Song
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Dongya Wu
- Center for Genomic Research, International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
- Center for Evolutionary & Organismal Biology, & Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Zeng
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Lei Nie
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | | | - Shilong Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Guangji Chen
- BGI-Shenzhen, Shenzhen, Guangdong, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jinjin Xu
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Hongling Zhou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Long Zhou
- Center for Evolutionary & Organismal Biology, & Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaobo Qian
- BGI-Shenzhen, Shenzhen, Guangdong, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Chenlu Liu
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China
| | | | | | - Wei Dai
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Mengyang Xu
- BGI-Shenzhen, Shenzhen, Guangdong, China
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong, China
| | - Yanwei Qi
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong, China
| | - Xiaobo Wang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China
| | - Lidong Guo
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong, China
| | - Guangyi Fan
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong, China
| | - Aijun Wang
- BGI-Qingdao, BGI-Shenzhen, Qingdao, Shandong, China
| | - Yuan Deng
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Yong Zhang
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | | | - Yunqiu He
- Center for Genomic Research, International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
- Center for Evolutionary & Organismal Biology, & Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chunxue Guo
- BGI-Shenzhen, Shenzhen, Guangdong, China
- BGI-Hangzhou, Hangzhou, Zhejiang, China
| | - Guoji Guo
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Zhou
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | | | - Jian Wang
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, China
- Jiangsu Key Laboratory of Phylogenomics & Comparative Genomics, International Joint Center of Genomics of Jiangsu Province School of Life Sciences, Jiangsu Normal University, Xuzhou, Jiangsu, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yafei Mao
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
| | - Xin Jin
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Jue Ruan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.
| | - Guojie Zhang
- Center for Genomic Research, International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
- Center for Evolutionary & Organismal Biology, & Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, China.
- Innovation Center of Yangtze River Delta, Zhejiang University, Hangzhou, Zhejiang, China.
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
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20
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Cheng S, Xu Z, Bian S, Chen X, Shi Y, Li Y, Duan Y, Liu Y, Lin J, Jiang Y, Jing J, Li Z, Wang Y, Meng X, Liu Y, Fang M, Jin X, Xu X, Wang J, Wang C, Li H, Liu S, Wang Y. The STROMICS genome study: deep whole-genome sequencing and analysis of 10K Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay. Cell Discov 2023; 9:75. [PMID: 37479695 PMCID: PMC10362040 DOI: 10.1038/s41421-023-00582-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/21/2023] [Indexed: 07/23/2023] Open
Abstract
Ischemic stroke is a leading cause of global mortality and long-term disability. However, there is a paucity of whole-genome sequencing studies on ischemic stroke, resulting in limited knowledge of the interplay between genomic and phenotypic variations among affected patients. Here, we outline the STROMICS design and present the first whole-genome analysis on ischemic stroke by deeply sequencing and analyzing 10,241 stroke patients from China. We identified 135.59 million variants, > 42% of which were novel. Notable disparities in allele frequency were observed between Chinese and other populations for 89 variants associated with stroke risk and 10 variants linked to response to stroke medications. We investigated the population structure of the participants, generating a map of genetic selection consisting of 31 adaptive signals. The adaption of the MTHFR rs1801133-G allele, which links to genetically evaluated VB9 (folate acid) in southern Chinese patients, suggests a gene-specific folate supplement strategy. Through genome-wide association analysis of 18 stroke-related traits, we discovered 10 novel genetic-phenotypic associations and extensive cross-trait pleiotropy at 6 lipid-trait loci of therapeutic relevance. Additionally, we found that the set of loss-of-function and cysteine-altering variants present in the causal gene NOTCH3 for the autosomal dominant stroke disorder CADASIL displayed a broad neuro-imaging spectrum. These findings deepen our understanding of the relationship between the population and individual genetic layout and clinical phenotype among stroke patients, and provide a foundation for future efforts to utilize human genetic knowledge to investigate mechanisms underlying ischemic stroke outcomes, discover novel therapeutic targets, and advance precision medicine.
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Affiliation(s)
- Si Cheng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Changping Laboratory, Beijing, China
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhe Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shengzhe Bian
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xi Chen
- BGI-Tianjin, BGI-Shenzhen, Tianjin, China
| | - Yanfeng Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yanran Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yang Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinxi Lin
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Tiantan Neuroimaging Center of Excellence, Beijing, China
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Xin Jin
- BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, Guangdong, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, Guangdong, China
- James D. Watson Institute of Genome Sciences, Hangzhou, Zhejiang, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hao Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China.
- BGI-Shenzhen, Shenzhen, Guangdong, China.
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
- Changping Laboratory, Beijing, China.
- Clinical Center for Precision Medicine in Stroke, Capital Medical University, Beijing, China.
- Center of excellence for Omics Research (CORe), Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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21
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Wang J, Wu J, Sun Q, Wu Q, Li Y, Duan S, Yang L, Wu W, Wang Z, Liu Y, Tang R, Yang J, Wang C, Liu C, Xu J, Wang M, He G. Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations. BMC Genomics 2023; 24:317. [PMID: 37308851 DOI: 10.1186/s12864-023-09412-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/27/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Yungui Plateau in Southwest China is characterized by multi-language and multi-ethnic communities and is one of the regions with the wealthiest ethnolinguistic, cultural and genetic diversity in East Asia. There are numerous Tai-Kadai (TK)-speaking populations, but their detailed evolutionary history and biological adaptations are still unclear. RESULTS Here, we genotyped genome-wide SNP data of 77 unrelated TK-speaking Zhuang and Dong individuals from the Yungui Plateau and explored their detailed admixture history and adaptive features using clustering patterns, allele frequency differentiation and sharing haplotype patterns. TK-speaking Zhuang and Dong people in Guizhou are closely related to geographically close TK and Hmong-Mien (HM)-speaking populations. Besides, we identified that Guizhou TK-speaking people have a close genetic relationship with Austronesian (AN)-speaking Atayal and Paiwan people, which is supported by the common origin of the ancient Baiyue tribe. We additionally found subtle genetic differences among the newly studied TK people and previously reported Dais via the fine-scale genetic substructure analysis based on the shared haplotype chunks. Finally, we identified specific selection candidate signatures associated with several essential human immune systems and neurological disorders, which could provide evolutionary evidence for the allele frequency distribution pattern of genetic risk loci. CONCLUSIONS Our comprehensive genetic characterization of TK people suggested the strong genetic affinity within TK groups and extensive gene flow with geographically close HM and Han people. We also provided genetic evidence that supported the common origin hypothesis of TK and AN people. The best-fitted admixture models further suggested that ancestral sources from northern millet farmers and southern inland and coastal people contributed to the formation of the gene pool of the Zhuang and Dong people.
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Affiliation(s)
- Jiawen Wang
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China.
| | - Jun Wu
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, 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
| | - Qian Wu
- Qiannan Prefecture People's Hospital, Buyi and Miao Autonomous Prefecture of QianNan, Buyi and Miao Autonomous Prefecture of QianNan, 558000, China
| | - Youjing Li
- Congjiang People's Hospital, Congjiang, 557499, 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, 637000, China
| | - Lin Yang
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China
| | - Wenxin Wu
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Yan Liu
- 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, 637000, China
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
| | - Junbao Yang
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
| | - Chuanchao Wang
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, Xiamen University, Xiamen, 361000, China
| | - Chao Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Jianwei Xu
- Department of Pharmacology, School of Basic Medicine, Guizhou Medical University, Guiyang, 550004, China.
| | - Mengge Wang
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, 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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22
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Shi Y, Niu Y, Zhang P, Luo H, Liu S, Zhang S, Wang J, Li Y, Liu X, Song T, Xu T, He S. Characterization of genome-wide STR variation in 6487 human genomes. Nat Commun 2023; 14:2092. [PMID: 37045857 PMCID: PMC10097659 DOI: 10.1038/s41467-023-37690-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 03/27/2023] [Indexed: 04/14/2023] Open
Abstract
Short tandem repeats (STRs) are abundant and highly mutagenic in the human genome. Many STR loci have been associated with a range of human genetic disorders. However, most population-scale studies on STR variation in humans have focused on European ancestry cohorts or are limited by sequencing depth. Here, we depicted a comprehensive map of 366,013 polymorphic STRs (pSTRs) constructed from 6487 deeply sequenced genomes, comprising 3983 Chinese samples (~31.5x, NyuWa) and 2504 samples from the 1000 Genomes Project (~33.3x, 1KGP). We found that STR mutations were affected by motif length, chromosome context and epigenetic features. We identified 3273 and 1117 pSTRs whose repeat numbers were associated with gene expression and 3'UTR alternative polyadenylation, respectively. We also implemented population analysis, investigated population differentiated signatures, and genotyped 60 known disease-causing STRs. Overall, this study further extends the scale of STR variation in humans and propels our understanding of the semantics of STRs.
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Affiliation(s)
- Yirong Shi
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yiwei Niu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Peng Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huaxia Luo
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shuai Liu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Sijia Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jiajia Wang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yanyan Li
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinyue Liu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tingrui Song
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.
| | - Shunmin He
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
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23
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He G, Wang M, Miao L, Chen J, Zhao J, Sun Q, Duan S, Wang Z, Xu X, Sun Y, Liu Y, Liu J, Wang Z, Wei L, Liu C, Ye J, Wang L. Multiple founding paternal lineages inferred from the newly-developed 639-plex Y-SNP panel suggested the complex admixture and migration history of Chinese people. Hum Genomics 2023; 17:29. [PMID: 36973821 PMCID: PMC10045532 DOI: 10.1186/s40246-023-00476-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Non-recombining regions of the Y-chromosome recorded the evolutionary traces of male human populations and are inherited haplotype-dependently and male-specifically. Recent whole Y-chromosome sequencing studies have identified previously unrecognized population divergence, expansion and admixture processes, which promotes a better understanding and application of the observed patterns of Y-chromosome genetic diversity. RESULTS Here, we developed one highest-resolution Y-chromosome single nucleotide polymorphism (Y-SNP) panel targeted for uniparental genealogy reconstruction and paternal biogeographical ancestry inference, which included 639 phylogenetically informative SNPs. We genotyped these loci in 1033 Chinese male individuals from 33 ethnolinguistically diverse populations and identified 256 terminal Y-chromosomal lineages with frequency ranging from 0.0010 (singleton) to 0.0687. We identified six dominant common founding lineages associated with different ethnolinguistic backgrounds, which included O2a2b1a1a1a1a1a1a1-M6539, O2a1b1a1a1a1a1a1-F17, O2a2b1a1a1a1a1b1a1b-MF15397, O2a2b2a1b1-A16609, O1b1a1a1a1b2a1a1-F2517, and O2a2b1a1a1a1a1a1-F155. The AMOVA and nucleotide diversity estimates revealed considerable differences and high genetic diversity among ethnolinguistically different populations. We constructed one representative phylogenetic tree among 33 studied populations based on the haplogroup frequency spectrum and sequence variations. Clustering patterns in principal component analysis and multidimensional scaling results showed a genetic differentiation between Tai-Kadai-speaking Li, Mongolic-speaking Mongolian, and other Sinitic-speaking Han Chinese populations. Phylogenetic topology inferred from the BEAST and Network relationships reconstructed from the popART further showed the founding lineages from culturally/linguistically diverse populations, such as C2a/C2b was dominant in Mongolian people and O1a/O1b was dominant in island Li people. We also identified many lineages shared by more than two ethnolinguistically different populations with a high proportion, suggesting their extensive admixture and migration history. CONCLUSIONS Our findings indicated that our developed high-resolution Y-SNP panel included major dominant Y-lineages of Chinese populations from different ethnic groups and geographical regions, which can be used as the primary and powerful tool for forensic practice. We should emphasize the necessity and importance of whole sequencing of more ethnolinguistically different populations, which can help identify more unrecognized population-specific variations for the promotion of Y-chromosome-based forensic applications.
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Affiliation(s)
- Guanglin He
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China.
| | - Mengge Wang
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Lei Miao
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - Jing Chen
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030001, China
| | - Jie Zhao
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China
| | - Qiuxia Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
| | - Shuhan Duan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
| | - Zhiyong Wang
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Forensic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Xiaofei Xu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
| | - Yuntao Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Yan Liu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610041, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Lanhai Wei
- School of Ethnology and Anthropology, Inner Mongolia Normal University, Hohhot, 010028, Inner Mongolia, China
| | - Chao Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Jian Ye
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China.
| | - Le Wang
- National Engineering Laboratory for Forensic Science, Key Laboratory of Forensic Genetics of Ministry of Public Security, Institute of Forensic Science, Ministry of Public Security, Beijing, 100038, China.
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24
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Yang G, Mishra M, Perera MA. Multi-Omics Studies in Historically Excluded Populations: The Road to Equity. Clin Pharmacol Ther 2023; 113:541-556. [PMID: 36495075 PMCID: PMC10323857 DOI: 10.1002/cpt.2818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Minoli A. Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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25
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Solomon BD, Adam MP, Fong CT, Girisha KM, Hall JG, Hurst AC, Krawitz PM, Moosa S, Phadke SR, Tekendo-Ngongang C, Wenger TL. Perspectives on the future of dysmorphology. Am J Med Genet A 2023; 191:659-671. [PMID: 36484420 PMCID: PMC9928773 DOI: 10.1002/ajmg.a.63060] [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: 06/06/2022] [Revised: 08/30/2022] [Accepted: 11/12/2022] [Indexed: 12/13/2022]
Abstract
The field of clinical genetics and genomics continues to evolve. In the past few decades, milestones like the initial sequencing of the human genome, dramatic changes in sequencing technologies, and the introduction of artificial intelligence, have upended the field and offered fascinating new insights. Though difficult to predict the precise paths the field will follow, rapid change may continue to be inevitable. Within genetics, the practice of dysmorphology, as defined by pioneering geneticist David W. Smith in the 1960s as "the study of, or general subject of abnormal development of tissue form" has also been affected by technological advances as well as more general trends in biomedicine. To address possibilities, potential, and perils regarding the future of dysmorphology, a group of clinical geneticists, representing different career stages, areas of focus, and geographic regions, have contributed to this piece by providing insights about how the practice of dysmorphology will develop over the next several decades.
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Affiliation(s)
- Benjamin D. Solomon
- Medical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Margaret P. Adam
- Department of Pediatrics, University of Washington, Seattle, Washington, United States of America
| | - Chin-To Fong
- Department of Genetics, University of Rochester, Rochester, New York, United States of America
| | - Katta M. Girisha
- Department of Medical Genetics, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Judith G. Hall
- University of British Columbia and Children’s and Women’s Health Centre of British Columbia, Canada
- Department of Pediatrics and Medical Genetics, British Columbia Children’s Hospital, Vancouver, British Columbia, Canada
| | - Anna C.E. Hurst
- Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Peter M. Krawitz
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Shahida Moosa
- Division of Molecular Biology and Human Genetics, Stellenbosch University
- Medical Genetics, Tygerberg Hospital, Tygerberg, South Africa
| | - Shubha R. Phadke
- Department of Medical Genetics, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India
| | - Cedrik Tekendo-Ngongang
- Medical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Tara L. Wenger
- Division of Genetic Medicine, University of Washington, Seattle, Washington, United States of America
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26
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Guanglin H, Lan-Hai W, Mengge W. Editorial: Forensic investigative genetic genealogy and fine-scale structure of human populations. Front Genet 2023; 13:1067865. [PMID: 36685813 PMCID: PMC9849385 DOI: 10.3389/fgene.2022.1067865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/22/2022] [Indexed: 01/06/2023] Open
Affiliation(s)
- He Guanglin
- 1Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, China,*Correspondence: He Guanglin, ; Wang Mengge,
| | - Wei Lan-Hai
- 2School of Ethnology and Anthropology, Inner Mongolia Normal University, Hohhot, China
| | - Wang Mengge
- 3Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China,*Correspondence: He Guanglin, ; Wang Mengge,
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27
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Fan S, Zhao T, Sun L. The global prevalence and ethnic heterogeneity of iron-refractory iron deficiency anaemia. Orphanet J Rare Dis 2023; 18:2. [PMID: 36604716 PMCID: PMC9814447 DOI: 10.1186/s13023-022-02612-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Iron-refractory iron deficiency anaemia (IRIDA) is an autosomal recessive iron deficiency anaemia caused by mutations in the TMPRSS6 gene. Iron deficiency anaemia is common, whereas IRIDA is rare. The prevalence of IRIDA is unclear. This study aimed to estimate the carrier frequency and genetic prevalence of IRIDA using Genome Aggregation Database (gnomAD) data. METHODS The pathogenicity of TMPRSS6 variants was interpreted according to the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) standards and guidelines. The minor allele frequency (MAF) of TMPRSS6 gene disease-causing variants in 141,456 unique individuals was examined to estimate the global prevalence of IRIDA in seven ethnicities: African/African American (afr), American Admixed/Latino (amr), Ashkenazi Jewish (asj), East Asian (eas), Finnish (fin), Non-Finnish European (nfe) and South Asian (sas). The global and population-specific carrier frequencies and genetic prevalence of IRIDA were calculated using the Hardy-Weinberg equation. RESULTS In total, 86 pathogenic/likely pathogenic variants (PV/LPV) were identified according to ACMG/AMP guideline. The global carrier frequency and genetic prevalence of IRIDA were 2.02 per thousand and 1.02 per million, respectively. CONCLUSIONS The prevalence of IRIDA is greater than previous estimates.
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Affiliation(s)
- Shanghua Fan
- grid.412632.00000 0004 1758 2270Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, 430060 China
| | - Ting Zhao
- grid.414011.10000 0004 1808 090XDepartment of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, 450003 China
| | - Liu Sun
- Department of Information Technology, School of Mathematics and Information Technology, Yuxi Normal University, Yuxi, 653100, China.
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28
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Wang C, Dai J, Qin N, Fan J, Ma H, Chen C, An M, Zhang J, Yan C, Gu Y, Xie Y, He Y, Jiang Y, Zhu M, Song C, Jiang T, Liu J, Zhou J, Wang N, Hua T, Liang S, Wang L, Xu J, Yin R, Chen L, Xu L, Jin G, Lin D, Hu Z, Shen H. Analyses of rare predisposing variants of lung cancer in 6,004 whole genomes in Chinese. Cancer Cell 2022; 40:1223-1239.e6. [PMID: 36113475 DOI: 10.1016/j.ccell.2022.08.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/08/2022] [Accepted: 08/15/2022] [Indexed: 12/24/2022]
Abstract
We present the largest whole-genome sequencing (WGS) study of non-small cell lung cancer (NSCLC) to date among 6,004 individuals of Chinese ancestry, coupled with 23,049 individuals genotyped by SNP array. We construct a high-quality haplotype reference panel for imputation and identify 20 common and low-frequency loci (minor allele frequency [MAF] ≥ 0.5%), including five loci that have never been reported before. For rare loss-of-function (LoF) variants (MAF < 0.5%), we identify BRCA2 and 18 other cancer predisposition genes that affect 5.29% of individuals with NSCLC, and 98.91% (181 of 183) of LoF variants have not been linked previously to NSCLC risk. Promoter variants of BRCA2 also have a substantial effect on NSCLC risk, and their prevalence is comparable with BRCA2 LoF variants. The associations are validated in an independent case-control study including 4,410 individuals and a prospective cohort study including 23,826 individuals. Our findings not only provide a high-quality reference panel for future array-based association studies but depict the whole picture of rare pathogenic variants for NSCLC.
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Affiliation(s)
- Cheng Wang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Juncheng Dai
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Na Qin
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Jingyi Fan
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Hongxia Ma
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), Gusu School, Nanjing Medical University, Suzhou 215002, Jiangsu, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Congcong Chen
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Mingxing An
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Jing Zhang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Caiwang Yan
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yayun Gu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yuan Xie
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yuanlin He
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yue Jiang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Meng Zhu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Ci Song
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Tao Jiang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Jia Liu
- Department of Health Promotion & Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi 214145, Jiangsu, China
| | - Jun Zhou
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Nanxi Wang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Tingting Hua
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Shuang Liang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Lu Wang
- Department of Health Promotion & Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi 214145, Jiangsu, China
| | - Jing Xu
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Rong Yin
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Department of Thoracic Surgery Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210029, Jiangsu, China
| | - Liang Chen
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Lin Xu
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Department of Thoracic Surgery Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210029, Jiangsu, China
| | - Guangfu Jin
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center and Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zhibin Hu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), Gusu School, Nanjing Medical University, Suzhou 215002, Jiangsu, China.
| | - Hongbing Shen
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), Gusu School, Nanjing Medical University, Suzhou 215002, Jiangsu, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China.
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Li Z, Jiang X, Fang M, Bai Y, Liu S, Huang S, Jin X. CMDB: the comprehensive population genome variation database of China. Nucleic Acids Res 2022; 51:D890-D895. [PMID: 35871305 PMCID: PMC9825573 DOI: 10.1093/nar/gkac638] [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: 06/20/2022] [Accepted: 07/22/2022] [Indexed: 01/30/2023] Open
Abstract
A high-quality genome variation database derived from a large-scale population is one of the most important infrastructures for genomics, clinical and translational medicine research. Here, we developed the Chinese Millionome Database (CMDB), a database that contains 9.04 million single nucleotide variants (SNV) with allele frequency information derived from low-coverage (0.06×-0.1×) whole-genome sequencing (WGS) data of 141 431 unrelated healthy Chinese individuals. These individuals were recruited from 31 out of the 34 administrative divisions in China, covering Han and 36 other ethnic minorities. CMDB, housing the WGS data of a multi-ethnic Chinese population featuring wide geographical distribution, has become the most representative and comprehensive Chinese population genome database to date. Researchers can quickly search for variant, gene or genomic regions to obtain the variant information, including mutation basic information, allele frequency, genic annotation and overview of frequencies in global populations. Furthermore, the CMDB also provides information on the association of the variants with a range of phenotypes, including height, BMI, maternal age and twin pregnancy. Based on these data, researchers can conduct meta-analysis of related phenotypes. CMDB is freely available at https://db.cngb.org/cmdb/.
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Affiliation(s)
| | | | | | - Yong Bai
- BGI-Shenzhen, Shenzhen518083, Guangdong, China
| | - Siyang Liu
- BGI-Shenzhen, Shenzhen518083, Guangdong, China
| | - Shujia Huang
- Correspondence may also be addressed to Shujia Huang.
| | - Xin Jin
- To whom correspondence should be addressed.
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30
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Van Der Merwe N, Ramesar R, De Vries J. Whole Exome Sequencing in South Africa: Stakeholder Views on Return of Individual Research Results and Incidental Findings. Front Genet 2022; 13:864822. [PMID: 35754817 PMCID: PMC9216214 DOI: 10.3389/fgene.2022.864822] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/30/2022] [Indexed: 11/17/2022] Open
Abstract
The use of whole exome sequencing (WES) in medical research is increasing in South Africa (SA), raising important questions about whether and which individual genetic research results, particularly incidental findings, should be returned to patients. Whilst some commentaries and opinions related to the topic have been published in SA, there is no qualitative data on the views of professional stakeholders on this topic. Seventeen participants including clinicians, genomics researchers, and genetic counsellors (GCs) were recruited from the Western Cape in SA. Semi-structured interviews were conducted, and the transcripts analysed using the framework approach for data analysis. Current roadblocks for the clinical adoption of WES in SA include a lack of standardised guidelines; complexities relating to variant interpretation due to lack of functional studies and underrepresentation of people of African ancestry in the reference genome, population and variant databases; lack of resources and skilled personnel for variant confirmation and follow-up. Suggestions to overcome these barriers include obtaining funding and buy-in from the private and public sectors and medical insurance companies; the generation of a locally relevant reference genome; training of health professionals in the field of genomics and bioinformatics; and multidisciplinary collaboration. Participants emphasised the importance of upscaling the accessibility to and training of GCs, as well as upskilling of clinicians and genetic nurses for return of genetic data in collaboration with GCs and medical geneticists. Future research could focus on exploring the development of stakeholder partnerships for increased access to trained specialists as well as community engagement and education, alongside the development of guidelines for result disclosure.
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Affiliation(s)
- Nicole Van Der Merwe
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Institute for Infectious Diseases and Molecular Medicine, Department of Pathology, Faculty of Medicine and Health Sciences, University of Cape Town, Cape Town, South Africa.,Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Raj Ramesar
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Institute for Infectious Diseases and Molecular Medicine, Department of Pathology, Faculty of Medicine and Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jantina De Vries
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.,Neuroscience Institute, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa
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31
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Chen L, Zhou Z, Zhang Y, Xu H, Wang S. EASplex: A panel of 308 AISNPs for East Asian ancestry inference using next generation sequencing. Forensic Sci Int Genet 2022; 60:102739. [PMID: 35709629 DOI: 10.1016/j.fsigen.2022.102739] [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: 01/07/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 11/26/2022]
Abstract
Ancestry inference is useful in many scientific fields, such as forensic genetics, medical genetics, and molecular archaeology. Various ancestry inferring methods have been released for major continental populations. However, few reports refer to sub-populations within the East Asian population using hundreds of ancestry informative SNPs (AISNPs). In this study, we developed a 308-AISNP panel (EASplex NGS DNA panel) using multiplex PCR and next generation sequencing (NGS). This panel included 56 SNPs relevant for the continent-level ancestry inference and 252 Japanese-, Korean-, and/or Han Chinese-specific AISNPs to address the ancestry inference of global populations and regional populations among Japanese (JPT), Korean minority (CHK), and Han Chinese (CHH). A total of 87 CHK and 59 CHH samples were used to check the performance of the EASplex NGS DNA panel. By analyzing 146 profiles of samples with JPT and CHH data from Beijing and South China in 1000 genomes project, the following results were obtained: (1) the 146 tested samples were correctly assigned to the East Asian group; (2) the paired population assignment rate was 99.73% for JPT and CHH, 95% for JPT and CHK, and 90.11% for CHK and CHH; and (3) the whole population assignment was 92.14% for the JPT, CHK, and CHH data. Overall, the EASplex NGS DNA panel displayed informativeness for continental ancestry inference and regional ancestry inference among JPT, CHH, and CHK and has the potential for use in forensic and genetic population studies.
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Affiliation(s)
- Lu Chen
- Beijing Institute of Microbiology and Epidemiology, 27 Taiping Road, Beijing 100850, PR China
| | - Zhe Zhou
- Beijing Institute of Microbiology and Epidemiology, 27 Taiping Road, Beijing 100850, PR China.
| | - Yongji Zhang
- Department of Pathology and Forensic Medicine, College of Medicine, Yanbian University, No. 977 Park Road, Jilin 133002, PR China
| | - Hao Xu
- Beijing Institute of Microbiology and Epidemiology, 27 Taiping Road, Beijing 100850, PR China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, 27 Taiping Road, Beijing 100850, PR China.
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32
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Desai S, Mishra R, Ahmad S, Hait S, Joshi A, Dutt A. TMC-SNPdb 2.0: an ethnic-specific database of Indian germline variants. Database (Oxford) 2022; 2022:6583650. [PMID: 35551364 PMCID: PMC9216475 DOI: 10.1093/database/baac029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/05/2022] [Accepted: 04/12/2022] [Indexed: 02/05/2023]
Abstract
Cancer is a somatic disease. The lack of Indian-specific reference germline variation resources limits the ability to identify true cancer-associated somatic variants among Indian cancer patients. We integrate two recent studies, the GenomeAsia 100K and the Genomics for Public Health in India (IndiGen) program, describing genome sequence variations across 598 and 1029 healthy individuals of Indian origin, respectively, along with the unique variants generated from our in-house 173 normal germline samples derived from cancer patients to generate the Tata Memorial Centre-SNP database (TMC-SNPdb) 2.0. To show its utility, GATK/Mutect2-based somatic variant calling was performed on 224 in-house tumor samples to demonstrate a reduction in false-positive somatic variants. In addition to the ethnic-specific variants from GenomeAsia 100K and IndiGenomes databases, 305 132 unique variants generated from 173 in-house normal germline samples derived from cancer patients of Indian origin constitute the Indian specific, TMC-SNPdb 2.0. Of 305 132 unique variants, 11.13% were found in the coding region with missense variants (31.3%) as the most predominant category. Among the non-coding variations, intronic variants (49%) were the highest contributors. The non-synonymous to synonymous SNP ratio was observed to be 1.9, consistent with the previous version of TMC-SNPdb and literature. Using TMC SNPdb 2.0, we analyzed a whole-exome sequence from 224 in-house tumor samples (180 paired and 44 orphans). We show an average depletion of 3.44% variants per paired tumor and significantly higher depletion (P-value < 0.001) for orphan tumors (4.21%), demonstrating the utility of the rare, unique variants found in the ethnic-specific variant datasets in reducing the false-positive somatic mutations. TMC-SNPdb 2.0 is the most exhaustive open-source reference database of germline variants occurring across 1800 Indian individuals to analyze cancer genomes and other genetic disorders. The database and toolkit package is available for download at the following: Database URL http://www.actrec.gov.in/pi-webpages/AmitDutt/TMCSNPdb2/TMCSNPdb2.html.
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Affiliation(s)
| | | | - Suhail Ahmad
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai, Maharashtra 410210, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 400094, India
| | - Supriya Hait
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai, Maharashtra 410210, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 400094, India
| | - Asim Joshi
- Integrated Cancer Genomics Laboratory, Advanced Centre for Treatment, Research, and Education in Cancer, Kharghar, Navi Mumbai, Maharashtra 410210, India
- Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai, Maharashtra 400094, India
| | - Amit Dutt
- *Corresponding author: Tel: +91-22-27405056/30435056; Fax: +91-22-27405085;
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Niu Y, Teng X, Zhou H, Shi Y, Li Y, Tang Y, Zhang P, Luo H, Kang Q, Xu T, He S. Characterizing mobile element insertions in 5675 genomes. Nucleic Acids Res 2022; 50:2493-2508. [PMID: 35212372 PMCID: PMC8934628 DOI: 10.1093/nar/gkac128] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 02/07/2022] [Accepted: 02/11/2022] [Indexed: 12/30/2022] Open
Abstract
Mobile element insertions (MEIs) are a major class of structural variants (SVs) and have been linked to many human genetic disorders, including hemophilia, neurofibromatosis, and various cancers. However, human MEI resources from large-scale genome sequencing are still lacking compared to those for SNPs and SVs. Here, we report a comprehensive map of 36 699 non-reference MEIs constructed from 5675 genomes, comprising 2998 Chinese samples (∼26.2×, NyuWa) and 2677 samples from the 1000 Genomes Project (∼7.4×, 1KGP). We discovered that LINE-1 insertions were highly enriched in centromere regions, implying the role of chromosome context in retroelement insertion. After functional annotation, we estimated that MEIs are responsible for about 9.3% of all protein-truncating events per genome. Finally, we built a companion database named HMEID for public use. This resource represents the latest and largest genomewide study on MEIs and will have broad utility for exploration of human MEI findings.
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Affiliation(s)
- Yiwei Niu
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xueyi Teng
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Honghong Zhou
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yirong Shi
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanyan Li
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiheng Tang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Huaxia Luo
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Quan Kang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Xu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shunmin He
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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34
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Li YW, Guo Z, Wang LL, Zhou L, Lyu XD, Song YP. [Analysis of clinical significance and prognostic impact of TET2 single nucleotide polymorphism I1762V in patients with acute myeloid leukemia]. ZHONGHUA XUE YE XUE ZA ZHI = ZHONGHUA XUEYEXUE ZAZHI 2022; 43:241-246. [PMID: 35405783 PMCID: PMC9072067 DOI: 10.3760/cma.j.issn.0253-2727.2022.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Indexed: 11/08/2022]
Abstract
Objective: This study aimed to investigate the clinical and prognostic significance of TET2 single nucleotide polymorphism I1762V in patients with acute myeloid leukemia (AML) . Methods: The high-throughput sequencing method was used to sequence 58 hematological tumor-related genes in bone marrow samples from 413 patients with AML. TET2 I1762V and other somatic mutations were annotated and compared with patients' clinical information and prognosis. Results: I1762V was found in 154 patients with AML, which was significantly different from the general population in NyuWa Chinese Population Variant Database (χ(2)=72.4, P<0.001) . I1762V was not related to sex, age, and karyotype of patients with AML (P>0.05) . Patients with I1762V had a significantly higher proportion of NPM1 and KIT gene mutations than others (P<0.001) . NPM1 and KIT mutations were mutually exclusive. The survival analysis results revealed that the overall survival (OS) and progression-free survival (PFS) of patients with AML with I1762V were significantly greater than those of wild-type patients (HR=0.57, P=0.030; HR=0.55, P=0.020) , whereas the OS and PFS in patients with AML with DNMT3A mutation (with or without I1762V mutation) were lower than those of wild-type patients (HR=1.79, P=0.030; HR=1.74, P=0.040) . Conclusion: TET2 SNP I1762V has been linked to AML. I1762V is a prognostic factor of patients with AML, which can be used to guide the treatment and evaluate the prognosis of AML.
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Affiliation(s)
- Y W Li
- Central Lab, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Z Guo
- Central Lab, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - L L Wang
- Central Lab, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - L Zhou
- Central Lab, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - X D Lyu
- Central Lab, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Y P Song
- Department of Hematology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan 450008, China
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35
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Ou M, Leung HM, Leung AS, Luk HM, Yan B, Liu CM, Tong TF, Mok MS, Ko WY, Law WC, Lam TW, Lo IM, Luo R. HKG: an open genetic variant database of 205 Hong Kong cantonese exomes. NAR Genom Bioinform 2022; 4:lqac005. [PMID: 35156024 PMCID: PMC8826781 DOI: 10.1093/nargab/lqac005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 12/04/2021] [Accepted: 01/06/2022] [Indexed: 11/23/2022] Open
Abstract
HKG is the first fully accessible variant database for Hong Kong Cantonese, constructed from 205 novel whole-exome sequencing data. There has long been a research gap in the understanding of the genetic architecture of southern Chinese subgroups, including Hong Kong Cantonese. HKG detected 196 325 high-quality variants with 5.93% being novel, and 25 472 variants were found to be unique in HKG compared to three Chinese populations sampled from 1000 Genomes (CHN). PCA illustrates the uniqueness of HKG in CHN, and the admixture study estimated the ancestral composition of HKG and CHN, with a gradient change from north to south, consistent with their geological distribution. ClinVar, CIViC and PharmGKB annotated 599 clinically significant variants and 360 putative loss-of-function variants, substantiating our understanding of population characteristics for future medical development. Among the novel variants, 96.57% were singleton and 6.85% were of high impact. With a good representation of Hong Kong Cantonese, we demonstrated better variant imputation using reference with the addition of HKG data, thus successfully filling the data gap in southern Chinese to facilitate the regional and global development of population genetics.
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Affiliation(s)
- Min Ou
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | | | | | - Ho-Ming Luk
- Clinical Genetic Service, Department of Health, Hong Kong
| | - Bin Yan
- Department of Computer Science, The University of Hong Kong, Hong Kong
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong
| | - Chi-Man Liu
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | | | | | | | | | - Tak-Wah Lam
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | | | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong
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36
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Li Y, Zhou H, Chen X, Zheng Y, Kang Q, Hao D, Zhang L, Song T, Luo H, Hao Y, Chen R, Zhang P, He S. SmProt: A Reliable Repository with Comprehensive Annotation of Small Proteins Identified from Ribosome Profiling. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:602-610. [PMID: 34536568 PMCID: PMC9039559 DOI: 10.1016/j.gpb.2021.09.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 12/30/2022]
Abstract
Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames (sORFs), which were usually missed in previous genome annotation. The significance of small proteins has been revealed in current years, along with the discovery of their diverse functions. However, systematic annotation of small proteins is still insufficient. SmProt was specially developed to provide valuable information on small proteins for scientific community. Here we present the update of SmProt, which emphasizes reliability of translated sORFs, genetic variants in translated sORFs, disease-specific sORF translation events or sequences, and remarkably increased data volume. More components such as non-ATG translation initiation, function, and new sources are also included. SmProt incorporated 638,958 unique small proteins curated from 3,165,229 primary records, which were computationally predicted from 419 ribosome profiling (Ribo-seq) datasets or collected from literature and other sources from 370 cell lines or tissues in 8 species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli). In addition, small protein families identified from human microbiomes were also collected. All datasets in SmProt are free to access, and available for browse, search, and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.
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Affiliation(s)
- Yanyan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Honghong Zhou
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaomin Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Zheng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Quan Kang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lili Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Huaxia Luo
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yajing Hao
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Geneway Decoding Bio-Tech Co. Ltd, Foshan 528316, China.
| | - Peng Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Shunmin He
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
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