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Yoo L, Mendoza D, Richard AJ, Stephens JM. KAT8 beyond Acetylation: A Survey of Its Epigenetic Regulation, Genetic Variability, and Implications for Human Health. Genes (Basel) 2024; 15:639. [PMID: 38790268 PMCID: PMC11121512 DOI: 10.3390/genes15050639] [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: 04/20/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Lysine acetyltransferase 8, also known as KAT8, is an enzyme involved in epigenetic regulation, primarily recognized for its ability to modulate histone acetylation. This review presents an overview of KAT8, emphasizing its biological functions, which impact many cellular processes and range from chromatin remodeling to genetic and epigenetic regulation. In many model systems, KAT8's acetylation of histone H4 lysine 16 (H4K16) is critical for chromatin structure modification, which influences gene expression, cell proliferation, differentiation, and apoptosis. Furthermore, this review summarizes the observed genetic variability within the KAT8 gene, underscoring the implications of various single nucleotide polymorphisms (SNPs) that affect its functional efficacy and are linked to diverse phenotypic outcomes, ranging from metabolic traits to neurological disorders. Advanced insights into the structural biology of KAT8 reveal its interaction with multiprotein assemblies, such as the male-specific lethal (MSL) and non-specific lethal (NSL) complexes, which regulate a wide range of transcriptional activities and developmental functions. Additionally, this review focuses on KAT8's roles in cellular homeostasis, stem cell identity, DNA damage repair, and immune response, highlighting its potential as a therapeutic target. The implications of KAT8 in health and disease, as evidenced by recent studies, affirm its importance in cellular physiology and human pathology.
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Affiliation(s)
- Lindsey Yoo
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - David Mendoza
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Allison J. Richard
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
| | - Jacqueline M. Stephens
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
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2
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Chen GB, Liu S, Zhang L, Huang T, Tang X, Li Y, Zeng C. Building and sharing medical cohorts for research. Innovation (N Y) 2024; 5:100623. [PMID: 38665391 PMCID: PMC11043840 DOI: 10.1016/j.xinn.2024.100623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024] Open
Affiliation(s)
- Guo-Bo Chen
- Department of Genetic and Genomic Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou 310014, China
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518017, China
| | - Lei Zhang
- China National GeneBank, Shenzhen 518116, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaohua Tang
- Department of Genetic and Genomic Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou 310014, China
- Wenzhou Central Hospital, Dingli Clinical Medical School of Wenzhou Medical University, Wenzhou 325000, China
| | - Yixue Li
- Guangzhou Laboratory, Guangzhou 510320, China
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Wu Z, Li T, Jiang Z, Zheng J, Gu Y, Liu Y, Liu Y, Xie Z. Human pangenome analysis of sequences missing from the reference genome reveals their widespread evolutionary, phenotypic, and functional roles. Nucleic Acids Res 2024; 52:2212-2230. [PMID: 38364871 PMCID: PMC10954445 DOI: 10.1093/nar/gkae086] [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: 05/30/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/18/2024] Open
Abstract
Nonreference sequences (NRSs) are DNA sequences present in global populations but absent in the current human reference genome. However, the extent and functional significance of NRSs in the human genomes and populations remains unclear. Here, we de novo assembled 539 genomes from five genetically divergent human populations using long-read sequencing technology, resulting in the identification of 5.1 million NRSs. These were merged into 45284 unique NRSs, with 29.7% being novel discoveries. Among these NRSs, 38.7% were common across the five populations, and 35.6% were population specific. The use of a graph-based pangenome approach allowed for the detection of 565 transcript expression quantitative trait loci on NRSs, with 426 of these being novel findings. Moreover, 26 NRS candidates displayed evidence of adaptive selection within human populations. Genes situated in close proximity to or intersecting with these candidates may be associated with metabolism and type 2 diabetes. Genome-wide association studies revealed 14 NRSs to be significantly associated with eight phenotypes. Additionally, 154 NRSs were found to be in strong linkage disequilibrium with 258 phenotype-associated SNPs in the GWAS catalogue. Our work expands the understanding of human NRSs and provides novel insights into their functions, facilitating evolutionary and biomedical researches.
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Affiliation(s)
- Zhikun Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zehang Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jingjing Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yizhou Gu
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
- University of Wisconsin-Madison, WI, USA
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
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4
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Jia P, Dong L, Yang X, Wang B, Bush SJ, Wang T, Lin J, Wang S, Zhao X, Xu T, Che Y, Dang N, Ren L, Zhang Y, Wang X, Liang F, Wang Y, Ruan J, Xia H, Zheng Y, Shi L, Lv Y, Wang J, Ye K. Haplotype-resolved assemblies and variant benchmark of a Chinese Quartet. Genome Biol 2023; 24:277. [PMID: 38049885 PMCID: PMC10694985 DOI: 10.1186/s13059-023-03116-3] [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: 08/25/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Recent state-of-the-art sequencing technologies enable the investigation of challenging regions in the human genome and expand the scope of variant benchmarking datasets. Herein, we sequence a Chinese Quartet, comprising two monozygotic twin daughters and their biological parents, using four short and long sequencing platforms (Illumina, BGI, PacBio, and Oxford Nanopore Technology). RESULTS The long reads from the monozygotic twin daughters are phased into paternal and maternal haplotypes using the parent-child genetic map and for each haplotype. We also use long reads to generate haplotype-resolved whole-genome assemblies with completeness and continuity exceeding that of GRCh38. Using this Quartet, we comprehensively catalogue the human variant landscape, generating a dataset of 3,962,453 SNVs, 886,648 indels (< 50 bp), 9726 large deletions (≥ 50 bp), 15,600 large insertions (≥ 50 bp), 40 inversions, 31 complex structural variants, and 68 de novo mutations which are shared between the monozygotic twin daughters. Variants underrepresented in previous benchmarks owing to their complexity-including those located at long repeat regions, complex structural variants, and de novo mutations-are systematically examined in this study. CONCLUSIONS In summary, this study provides high-quality haplotype-resolved assemblies and a comprehensive set of benchmarking resources for two Chinese monozygotic twin samples which, relative to existing benchmarks, offers expanded genomic coverage and insight into complex variant categories.
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Affiliation(s)
- Peng Jia
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Lianhua Dong
- National Institute of Metrology, Beijing, 100029, China
| | - Xiaofei Yang
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Bo Wang
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Stephen J Bush
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Tingjie Wang
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jiadong Lin
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Songbo Wang
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xixi Zhao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Tun Xu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yizhuo Che
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ningxin Dang
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yujing Zhang
- National Institute of Metrology, Beijing, 100029, China
| | - Xia Wang
- National Institute of Metrology, Beijing, 100029, China
| | - Fan Liang
- GrandOmics Biosciences, Beijing, 100089, China
| | - Yang Wang
- GrandOmics Biosciences, Beijing, 100089, China
| | - Jue Ruan
- Shenzhen Branch, Guangdong Laboratory of 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, 518120, China
| | - Han Xia
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yi Lv
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
| | - Jing Wang
- National Institute of Metrology, Beijing, 100029, China.
| | - Kai Ye
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
- Faculty of Science, Leiden University, Leiden, 2311EZ, The Netherlands.
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Xiao J, Yu J. T2T-YAO, T2T-SHUN, and more. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1081-1082. [PMID: 37742994 PMCID: PMC11082254 DOI: 10.1016/j.gpb.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/18/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Yu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Zhang X. T2T-YAO Reference Genome of Han Chinese - New Step in Advancing Precision Medicine in China. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1083-1084. [PMID: 37742995 PMCID: PMC11082255 DOI: 10.1016/j.gpb.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Xue Zhang
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS&PUMC), Beijing 100005, China.
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7
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He Y, Chu Y, Guo S, Hu J, Li R, Zheng Y, Ma X, Du Z, Zhao L, Yu W, Xue J, Bian W, Yang F, Chen X, Zhang P, Wu R, Ma Y, Shao C, Chen J, Wang J, Li J, Wu J, Hu X, Long Q, Jiang M, Ye H, Song S, Li G, Wei Y, Xu Y, Ma Y, Chen Y, Wang K, Bao J, Xi W, Wang F, Ni W, Zhang M, Yu Y, Li S, Kang Y, Gao Z. T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1085-1100. [PMID: 37595788 PMCID: PMC11082261 DOI: 10.1016/j.gpb.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/20/2023]
Abstract
Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploids. The quality of T2T-YAO is much better than those of all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, an accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.
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Affiliation(s)
- Yukun He
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Yanan Chu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Shuming Guo
- Linfen Clinical Medicine Research Center, Linfen 041000, China; Institute of Chest and Lung Diseases, Shanxi Medical University, Taiyuan 030001, China
| | - Jiang Hu
- GrandOmics Biosciences Co., Ltd, Wuhan 430076, China
| | - Ran Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yali Zheng
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Xinqian Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Zhenglin Du
- Institute of PSI Genomics, Wenzhou 325024, China
| | - Lili Zhao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wenyi Yu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Jianbo Xue
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wenjie Bian
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Feifei Yang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Xi Chen
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Pingan Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Rihan Wu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yifan Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Changjun Shao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jing Chen
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jiwei Li
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Jing Wu
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Xiaoyi Hu
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Qiuyue Long
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Mingzheng Jiang
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Hongli Ye
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Shixu Song
- Department of Respiratory, Critical Care and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
| | - Guangyao Li
- Linfen Clinical Medicine Research Center, Linfen 041000, China
| | - Yue Wei
- Linfen Clinical Medicine Research Center, Linfen 041000, China
| | - Yu Xu
- Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China
| | - Yanliang Ma
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yanwen Chen
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Keqiang Wang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Jing Bao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wen Xi
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Fang Wang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Wentao Ni
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Moqin Zhang
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yan Yu
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Shengnan Li
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China
| | - Yu Kang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100490, China.
| | - Zhancheng Gao
- Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, Beijing 100044, China; Institute of Chest and Lung Diseases, Shanxi Medical University, Taiyuan 030001, China; Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing 100101, China.
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8
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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: 0] [Impact Index Per Article: 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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9
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Gao Y, Yang X, Chen H, Tan X, Yang Z, Deng L, Wang B, Kong S, Li S, Cui Y, Lei C, Wang Y, Pan Y, Ma S, Sun H, Zhao X, Shi Y, Yang Z, Wu D, Wu S, Zhao X, Shi B, Jin L, Hu Z, Lu Y, Chu J, Ye K, Xu S. A pangenome reference of 36 Chinese populations. Nature 2023:10.1038/s41586-023-06173-7. [PMID: 37316654 PMCID: PMC10322713 DOI: 10.1038/s41586-023-06173-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 05/05/2023] [Indexed: 06/16/2023]
Abstract
Human genomics is witnessing an ongoing paradigm shift from a single reference sequence to a pangenome form, but populations of Asian ancestry are underrepresented. Here we present data from the first phase of the Chinese Pangenome Consortium, including a collection of 116 high-quality and haplotype-phased de novo assemblies based on 58 core samples representing 36 minority Chinese ethnic groups. With an average 30.65× high-fidelity long-read sequence coverage, an average contiguity N50 of more than 35.63 megabases and an average total size of 3.01 gigabases, the CPC core assemblies add 189 million base pairs of euchromatic polymorphic sequences and 1,367 protein-coding gene duplications to GRCh38. We identified 15.9 million small variants and 78,072 structural variants, of which 5.9 million small variants and 34,223 structural variants were not reported in a recently released pangenome reference1. The Chinese Pangenome Consortium data demonstrate a remarkable increase in the discovery of novel and missing sequences when individuals are included from underrepresented minority ethnic groups. The missing reference sequences were enriched with archaic-derived alleles and genes that confer essential functions related to keratinization, response to ultraviolet radiation, DNA repair, immunological responses and lifespan, implying great potential for shedding new light on human evolution and recovering missing heritability in complex disease mapping.
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Affiliation(s)
- Yang Gao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xiaofei Yang
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xinjiang Tan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zhaoqing Yang
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, China
| | - Lian Deng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Baonan Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Shuang Kong
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Songyang Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Yuhang Cui
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Chang Lei
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Yimin Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Sen Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Hao Sun
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, China
| | - Xiaohan Zhao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Yingbing Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyi Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Dongdong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Shaoyuan Wu
- Jiangsu Key Laboratory of Phylogenomics & Comparative Genomics, International Joint Center of Genomics of Jiangsu Province School of Life Sciences, Jiangsu Normal University, Xuzhou, China
| | - Xingming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education Key (MOE) Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science Fudan University, Shanghai, China
| | - Binyin Shi
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China.
| | - Jiayou Chu
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, China.
| | - Kai Ye
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China.
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.
- School of Life Science and Technology, ShanghaiTech 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, 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, China.
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10
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Chao KH, Zimin AV, Pertea M, Salzberg SL. The first gapless, reference-quality, fully annotated genome from a Southern Han Chinese individual. G3 (BETHESDA, MD.) 2023; 13:jkac321. [PMID: 36630290 PMCID: PMC9997556 DOI: 10.1093/g3journal/jkac321] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/27/2022] [Accepted: 11/03/2022] [Indexed: 01/12/2023]
Abstract
We used long-read DNA sequencing to assemble the genome of a Southern Han Chinese male. We organized the sequence into chromosomes and filled in gaps using the recently completed T2T-CHM13 genome as a guide, yielding a gap-free genome, Han1, containing 3,099,707,698 bases. Using the T2T-CHM13 annotation as a reference, we mapped all genes onto the Han1 genome and identified additional gene copies, generating a total of 60,708 putative genes, of which 20,003 are protein-coding. A comprehensive comparison between the genes revealed that 235 protein-coding genes were substantially different between the individuals, with frameshifts or truncations affecting the protein-coding sequence. Most of these were heterozygous variants in which one gene copy was unaffected. This represents the first gene-level comparison between two finished, annotated individual human genomes.
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Affiliation(s)
- Kuan-Hao Chao
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Aleksey V Zimin
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mihaela Pertea
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Steven L Salzberg
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21211, USA
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11
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Huang T, Li J, Zhao H, Ngamphiw C, Tongsima S, Kantaputra P, Kittitharaphan W, Wang SM. Core promoter in TNBC is highly mutated with rich ethnic signature. Brief Funct Genomics 2022; 22:9-19. [PMID: 36307127 PMCID: PMC9853936 DOI: 10.1093/bfgp/elac035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/23/2022] [Accepted: 09/28/2022] [Indexed: 01/25/2023] Open
Abstract
The core promoter plays an essential role in regulating transcription initiation by controlling the interaction between transcriptional factors and sequence motifs in the core promoter. Although mutation in core promoter sequences is expected to cause abnormal gene expression leading to pathogenic consequences, limited supporting evidence showed the involvement of core promoter mutation in diseases. Our previous study showed that the core promoter is highly polymorphic in worldwide human ethnic populations in reflecting human history and adaptation. Our recent characterization of the core promoter in triple-negative breast cancer (TNBC), a subtype of breast cancer, in a Chinese TNBC cohort revealed the wide presence of core promoter mutation in TNBC. In the current study, we analyzed the core promoter in a Thai TNBC cohort. We also observed rich core promoter mutation in the Thai TNBC patients. We compared the core promoter mutations between Chinese and Thai TNBC cohorts. We observed substantial differences of core promoter mutation in TNBC between the two cohorts, as reflected by the mutation spectrum, mutation-effected gene and functional category, and altered gene expression. Our study confirmed that the core promoter in TNBC is highly mutable, and is highly ethnic-specific.
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Affiliation(s)
| | | | | | | | | | | | | | - San Ming Wang
- Corresponding author: S.M. Wang, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China. Tel.: +(853) 8822-4836; E-mail:
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12
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Haplotype-resolved Chinese male genome assembly based on high-fidelity sequencing. FUNDAMENTAL RESEARCH 2022. [DOI: 10.1016/j.fmre.2022.02.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
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13
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Lou H, Gao Y, Xie B, Wang Y, Zhang H, Shi M, Ma S, Zhang X, Liu C, Xu S. Haplotype-resolved de novo assembly of a Tujia genome suggests the necessity for high-quality population-specific genome references. Cell Syst 2022; 13:321-333.e6. [PMID: 35180379 DOI: 10.1016/j.cels.2022.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/09/2021] [Accepted: 01/27/2022] [Indexed: 12/17/2022]
Abstract
Even though the human reference genome assembly is continually being improved, it remains debatable whether a population-specific reference is necessary for every ethnic group. Here, we de novo assembled an individual genome (TJ1) from the Tujia population, an ethnic minority group most closely related to the Han Chinese. TJ1 provided a high-quality haplotype-resolved assembly of chromosome-scale with a scaffold N50 size >78 Mb. Compared with GRCh38 and other de novo assemblies, TJ1 improved short-read mapping, enhanced calling precision for structural variants, and detected rare and low-frequency variants. This revealed fine-scale differences between the closely related Han and Tujia populations, such as population-stratified variants of LCT and UBXN8, and improved screening for ancestry informative markers. We demonstrated that TJ1 could reduce false positives in clinical diagnosis and analyzed the PRSS1-PRSS2 locus as a test case. Our results suggest that population-specific assemblies are necessary for genetic and medical analysis, especially when closely related populations are studied. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Haiyi Lou
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.
| | - Yang Gao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bo Xie
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yimin Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Miao Shi
- Berry Genomics, Beijing 102200, China
| | - Sen Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxi Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China; Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou 221116, China; Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China; Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai 201203, China.
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14
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NyuWa Genome resource: A deep whole-genome sequencing-based variation profile and reference panel for the Chinese population. Cell Rep 2021; 37:110017. [PMID: 34788621 DOI: 10.1016/j.celrep.2021.110017] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 05/04/2021] [Accepted: 10/28/2021] [Indexed: 01/07/2023] Open
Abstract
The lack of haplotype reference panels and whole-genome sequencing resources specific to the Chinese population has greatly hindered genetic studies in the world's largest population. Here, we present the NyuWa genome resource, based on deep (26.2×) sequencing of 2,999 Chinese individuals, and construct a NyuWa reference panel of 5,804 haplotypes and 19.3 million variants, which is a high-quality publicly available Chinese population-specific reference panel with thousands of samples. Compared with other panels, the NyuWa reference panel reduces the Han Chinese imputation error rate by a margin ranging from 30% to 51%. Population structure and imputation simulation tests support the applicability of one integrated reference panel for northern and southern Chinese. In addition, a total of 22,504 loss-of-function variants in coding and noncoding genes are identified, including 11,493 novel variants. These results highlight the value of the NyuWa genome resource in facilitating genetic research in Chinese and Asian populations.
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15
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Wu Z, Jiang Z, Li T, Xie C, Zhao L, Yang J, Ouyang S, Liu Y, Li T, Xie Z. Structural variants in the Chinese population and their impact on phenotypes, diseases and population adaptation. Nat Commun 2021; 12:6501. [PMID: 34764282 PMCID: PMC8586011 DOI: 10.1038/s41467-021-26856-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 10/21/2021] [Indexed: 02/05/2023] Open
Abstract
A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing has improved the sensitivity of structural variant discovery. Here, we conduct the long-read sequencing-based structural variant analysis for 405 unrelated Chinese individuals, with 68 phenotypic and clinical measurements. We discover a landscape of 132,312 nonredundant structural variants, of which 45.2% are novel. The identified structural variants are of high-quality, with an estimated false discovery rate of 3.2%. The concatenated length of all the structural variants is approximately 13.2% of the human reference genome. We annotate 1,929 loss-of-function structural variants affecting the coding sequence of 1,681 genes. We discover rare deletions in HBA1/HBA2/HBB associated with anemia. Furthermore, we identify structural variants related to immunity which differentiate the northern and southern Chinese populations. Our study describes the landscape of structural variants in the Chinese population and their contribution to phenotypes and disease.
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Affiliation(s)
- Zhikun Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zehang Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chuanbo Xie
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
| | - Jiaqi Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Shuai Ouyang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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16
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Shi S, Qian Q, Yu S, Wang Q, Wang J, Zeng J, Du Z, Xiao J. RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks. Brief Bioinform 2021; 22:6353381. [PMID: 34402866 PMCID: PMC8575030 DOI: 10.1093/bib/bbab326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/21/2021] [Accepted: 07/25/2021] [Indexed: 12/05/2022] Open
Abstract
Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually performed well on common variants, but they may not be ideal for low-frequency and rare variants. Previous studies showed that the population similarity between study and reference panels is one of the key factors influencing the imputation accuracy. Here, we developed an imputation reference panel reconstruction method (RefRGim) using convolutional neural networks (CNNs), which can generate a study-specified reference panel for each input data based on the genetic similarity of individuals from current study and references. The CNNs were pretrained with single nucleotide polymorphism data from the 1000 Genomes Project. Our evaluations showed that genotype imputation with RefRGim can achieve higher accuracies than original reference panel, especially for low-frequency and rare variants. RefRGim will serve as an efficient reference panel reconstruction method for genotype imputation. RefRGim is freely available via GitHub: https://github.com/shishuo16/RefRGim
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Affiliation(s)
- Shuo Shi
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Qiheng Qian
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Shuhuan Yu
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Qi Wang
- Qujiang culture finance holding (Group) Co., Ltd, Xian, China
| | - Jinyue Wang
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Jingyao Zeng
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Zhenglin Du
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Jingfa Xiao
- National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
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17
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Wang M, Huang Y, Song Y, Chen J, Liu X. Study on Environmental and Lifestyle Factors for the North-South Differential of Cardiovascular Disease in China. Front Public Health 2021; 9:615152. [PMID: 34336751 PMCID: PMC8322531 DOI: 10.3389/fpubh.2021.615152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 05/17/2021] [Indexed: 11/13/2022] Open
Abstract
Human death and life span are closely related to the geographical environment and regional lifestyle. These factors considerably vary among counties and regions, leading to the geographical disparity of disease. Quantitative studies on this phenomenon are insufficient. Cerebrovascular and heart diseases are the leading causes of death. The mortality rate of cerebrovascular and heart diseases is statistically higher in northern China than in southern China; the p-value of t-test for cerebrovascular and heart diseases was 0.047 and 0.000, respectively. The population attribution fraction of 12 major risk factors for cardiovascular disease (CVD) in each province was calculated based on their exposure and relative risk. The results found that residents in northern China consume high sodium-containing food, fewer vegetables, and less sea food products, and tend to be overweight. Fine particulate matter is higher in northern China than in southern China. Cold temperatures also cause a greater number of deaths than hot temperatures. All these factors have resulted in a higher CVD mortality rate in northern China. The attributive differential for sodium, vegetable, fruit, smoking, PM2.5, omega-3, obesity, low temperature, and high temperature of heart disease between the two parts of China is 9.1, 0.7, -2.5, 0.1, 1.4, 1.3, 2.0, 4.7, and -2.1%, respectively. Furthermore, the attributive differential for the above factors of cerebrovascular disease between the two parts of China is 8.7, 0.0, -5.2, 0.1, 1.0, 0.0, 2.4, 4.7, and -2.1%. Diet high in sodium is the leading cause of the north-south differential in CVD, resulting in 0.71 less years of life expectancy in northern compared with that in southern China.
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Affiliation(s)
- Mengqi Wang
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Yi Huang
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Yanxin Song
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Jianwei Chen
- School of Geographic Sciences, Nantong University, Nantong, China
| | - Xiaoxiao Liu
- School of Geographic Sciences, Nantong University, Nantong, China
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18
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Lu TY, Chaisson MJP. Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs. Nat Commun 2021; 12:4250. [PMID: 34253730 PMCID: PMC8275641 DOI: 10.1038/s41467-021-24378-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 06/10/2021] [Indexed: 12/11/2022] Open
Abstract
Variable number tandem repeats (VNTRs) are composed of consecutive repetitive DNA with hypervariable repeat count and composition. They include protein coding sequences and associations with clinical disorders. It has been difficult to incorporate VNTR analysis in disease studies that use short-read sequencing because the traditional approach of mapping to the human reference is less effective for repetitive and divergent sequences. In this work, we solve VNTR mapping for short reads with a repeat-pangenome graph (RPGG), a data structure that encodes both the population diversity and repeat structure of VNTR loci from multiple haplotype-resolved assemblies. We develop software to build a RPGG, and use the RPGG to estimate VNTR composition with short reads. We use this to discover VNTRs with length stratified by continental population, and expression quantitative trait loci, indicating that RPGG analysis of VNTRs will be critical for future studies of diversity and disease.
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Affiliation(s)
- Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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19
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Zhang L, Qin Z, Huang T, Tam B, Ruan Y, Guo M, Wu X, Li J, Zhao B, Chian JS, Wang X, Wang L, Wang SM. Prevalence and spectrum of DNA mismatch repair gene variation in the general Chinese population. J Med Genet 2021; 59:652-661. [PMID: 34172528 PMCID: PMC9252855 DOI: 10.1136/jmedgenet-2021-107886] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/06/2021] [Indexed: 01/18/2023]
Abstract
Background Identifying genetic disease-susceptible individuals through population screening is considered as a promising approach for disease prevention. DNA mismatch repair (MMR) genes including MLH1, MSH2, MSH6 and PMS2 play essential roles in maintaining microsatellite stability through DNA mismatch repair, and pathogenic variation in MMR genes causes microsatellite instability and is the genetic predisposition for cancer as represented by the Lynch syndrome. While the prevalence and spectrum of MMR variation has been extensively studied in cancer, it remains largely elusive in the general population. Lack of the knowledge prevents effective prevention for MMR variation–caused cancer. In the current study, we addressed the issue by using the Chinese population as a model. Methods We performed extensive data mining to collect MMR variant data from 18 844 ethnic Chinese individuals and comprehensive analyses for the collected MMR variants to determine its prevalence, spectrum and features of the MMR data in the Chinese population. Results We identified 17 687 distinct MMR variants. We observed substantial differences of MMR variation between the general Chinese population and Chinese patients with cancer, identified highly Chinese-specific MMR variation through comparing MMR data between Chinese and non-Chinese populations, predicted the enrichment of deleterious variants in the unclassified Chinese-specific MMR variants, determined MMR pathogenic prevalence of 0.18% in the general Chinese population and determined that MMR variation in the general Chinese population is evolutionarily neutral. Conclusion Our study provides a comprehensive view of MMR variation in the general Chinese population, a resource for biological study of human MMR variation, and a reference for MMR-related cancer applications.
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Affiliation(s)
- Li Zhang
- University of Macau, Taipa, Macau, China
| | - Zixin Qin
- University of Macau, Taipa, Macau, China
| | - Teng Huang
- University of Macau, Taipa, Macau, China
| | | | | | - Maoni Guo
- University of Macau, Taipa, Macau, China
| | | | - Jiaheng Li
- University of Macau, Taipa, Macau, China
| | - Bojin Zhao
- University of Macau, Taipa, Macau, China
| | | | | | - Lei Wang
- University of Macau, Taipa, Macau, China
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20
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Biotech in China 2021, at the beginning of the 14th five-year period ("145"). Appl Microbiol Biotechnol 2021; 105:3971-3985. [PMID: 33937929 PMCID: PMC8088835 DOI: 10.1007/s00253-021-11317-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 04/13/2021] [Accepted: 04/21/2021] [Indexed: 02/08/2023]
Abstract
Abstract As China assumes a more and more dominant role in global science, this mini-review attempts to provide a bird’s eye view on how the bio-digital revolution impacts China’s biosciences and bioindustry. Triggered by top-down political programs and the buildup of an impressive infrastructure in science, information technology, and education, China’s biomedical and MedTech industries prosper. Plant and animal breeding programs transform agriculture and food supply as much as the Internet of things, and synthetic biology offers new opportunities for the manufacturing of specialty chemicals within the Chinese version of a “bioeconomy.” It is already becoming apparent that the new five-year period “145” (2021–2025) will further emphasize emission control, bioenvironmental protection, and more supply of biomass-derived energy. This review identifies key drivers in China’s government, industry, and academia behind these developments and details many access points for deeper studies. Key points Biotechnology in China Biomedical technology New five-year period
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21
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Guo S, Liu J, Li W, Yang Y, Lv L, Xiao X, Li M, Guan F, Luo XJ. Genome wide association study identifies four loci for early onset schizophrenia. Transl Psychiatry 2021; 11:248. [PMID: 33907183 PMCID: PMC8079394 DOI: 10.1038/s41398-021-01360-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/26/2021] [Accepted: 04/09/2021] [Indexed: 12/14/2022] Open
Abstract
Early onset schizophrenia (EOS, defined as first onset of schizophrenia before age 18) is a rare form of schizophrenia (SCZ). Though genome-wide association studies (GWASs) have identified multiple risk variants for SCZ, most of the cases included in these GWASs were not stratified according to their first age at onset. To date, the genetic architecture of EOS remains largely unknown. To identify the risk variants and to uncover the genetic basis of EOS, we conducted a two-stage GWAS of EOS in populations of Han Chinese ancestry in this study. We first performed a GWAS using 1,256 EOS cases and 2,661 healthy controls (referred as discovery stage). The genetic variants with a P < 1.0 × 10-04 in discovery stage were replicated in an independent sample (903 EOS cases and 3,900 controls). We identified four genome-wide significant risk loci for EOS in the combined samples (2,159 EOS cases and 6,561 controls), including 1p36.22 (rs1801133, Pmeta = 4.03 × 10-15), 1p31.1 (rs1281571, Pmeta = 4.14 × 10-08), 3p21.31 (rs7626288, Pmeta = 1.57 × 10-09), and 9q33.3 (rs592927, Pmeta = 4.01 × 10-11). Polygenic risk scoring (PRS) analysis revealed substantial genetic overlap between EOS and SCZ. These discoveries shed light on the genetic basis of EOS. Further functional characterization of the identified risk variants and genes will help provide potential targets for therapeutics and diagnostics.
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Affiliation(s)
- Suqin Guo
- grid.412990.70000 0004 1808 322XHenan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002 China ,grid.412990.70000 0004 1808 322XHenan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan 453002 China
| | - Jiewei Liu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, 650223, China.
| | - Wenqiang Li
- grid.412990.70000 0004 1808 322XHenan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002 China ,grid.412990.70000 0004 1808 322XHenan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan 453002 China
| | - Yongfeng Yang
- grid.412990.70000 0004 1808 322XHenan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002 China ,grid.412990.70000 0004 1808 322XHenan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan 453002 China
| | - Luxian Lv
- grid.412990.70000 0004 1808 322XHenan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan 453002 China ,grid.412990.70000 0004 1808 322XHenan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan 453002 China
| | - Xiao Xiao
- grid.9227.e0000000119573309Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223 China
| | - Ming Li
- grid.9227.e0000000119573309Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223 China
| | - Fanglin Guan
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China.
| | - Xiong-Jian Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, 650223, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China. .,KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, 650223, China.
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22
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Qi GA, Zheng YT, Lin F, Huang X, Duan LW, You Y, Liu H, Wang Y, Xu HM, Chen GB. EigenGWAS: An online visualizing and interactive application for detecting genomic signatures of natural selection. Mol Ecol Resour 2021; 21:1732-1744. [PMID: 33665976 DOI: 10.1111/1755-0998.13370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 01/17/2021] [Accepted: 02/25/2021] [Indexed: 11/30/2022]
Abstract
Detecting genetic regions under selection in structured populations is of great importance in ecology, evolutionary biology and breeding programmes. We recently proposed EigenGWAS, an unsupervised genomic scanning approach that is similar to F ST but does not require grouping information of the population, for detection of genomic regions under selection. The original EigenGWAS is designed for the random mating population, and here we extend its use to inbred populations. We also show in theory and simulation that eigenvalues, the previous corrector for genetic drift in EigenGWAS, are overcorrected for genetic drift, and the genomic inflation factor is a better option for this adjustment. Applying the updated algorithm, we introduce the new EigenGWAS online platform with highly efficient core implementation. Our online computational tool accepts plink data in a standard binary format that can be easily converted from the original sequencing data, provides the users with graphical results via the R-Shiny user-friendly interface. We applied the proposed method and tool to various data sets, and biologically interpretable results as well as caveats that may lead to an unsatisfactory outcome are given. The EigenGWAS online platform is available at www.eigengwas.com, and can be localized and scaled up via R (recommended) or docker.
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Affiliation(s)
- Guo-An Qi
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yuan-Ting Zheng
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Feng Lin
- Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Xin Huang
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Li-Wen Duan
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yue You
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Hailan Liu
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Ying Wang
- Phase I Clinical Research Center, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Hai-Ming Xu
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Guo-Bo Chen
- Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China.,Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, People's Hospital of Hangzhou Medical College, Hangzhou, China
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23
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Burrell JA, Stephens JM. KAT8, lysine acetyltransferase 8, is required for adipocyte differentiation in vitro. Biochim Biophys Acta Mol Basis Dis 2021; 1867:166103. [PMID: 33617987 DOI: 10.1016/j.bbadis.2021.166103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 02/07/2021] [Accepted: 02/16/2021] [Indexed: 10/22/2022]
Abstract
KAT8 is a lysine acetyltransferase (KAT) that plays a role in a variety of cellular functions ranging from DNA damage repair to apoptosis. The role of KAT8 in adipocyte development and function has not been studied. Notably, a large genome-wide association study identified KAT8 as part of a novel locus that significantly contributed to body mass index and other metabolic phenotypes. Hence, we examined the expression and regulation of KAT8 during adipocyte development. KAT8 mRNA and protein levels were examined over a time course of adipocyte development, and KAT8 was found to be present in both the cytosol and nucleus of 3T3-L1 adipocytes. Although KAT8 expression was not highly regulated by adipogenesis, its expression was required for the adipogenesis of 3T3-L1 cells. Loss of KAT8 expression in preadipocytes inhibited their ability to differentiate as judged by both lipid accumulation and adipocyte marker gene expression. However, if KAT8 was knocked down after clonal expansion, its absence did not inhibit adipocyte differentiation. Also, loss of KAT8 in adipocytes did not impact lipid accumulation or the expression of adiponectin or other fat markers. Although our data demonstrate that KAT8 is required for adipocyte differentiation, further studies are necessary to determine the functions and regulation of KAT8 in adipose tissue.
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Affiliation(s)
- Jasmine A Burrell
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, United States of America
| | - Jacqueline M Stephens
- Pennington Biomedical Research Center, Baton Rouge, LA 70808, United States of America; Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, United States of America.
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24
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Qin W, Du Z, Xiao J, Duan H, Shu Q, Li H. Evaluation of clinical impact of pharmacogenomics knowledge involved in CPIC guidelines on Chinese pediatric patients. Pharmacogenomics 2021; 21:209-219. [PMID: 31967514 DOI: 10.2217/pgs-2019-0153] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Aim: To evaluate the clinical benefits of implementing pharmacogenomics testing for Chinese pediatric patients. Materials & methods : Based on the drug-gene interactions involved in the Clinical Pharmacogenetics Implementation Consortium guidelines, whole-genome sequencing data from the Chinese Academy of Sciences Precision Medicine Initiative project and the medication data of pediatric patients from a children's hospital, the prevalence of the Chinese population with actionable pharmacogenomic variants was calculated, the prescribing pattern for pediatric patients was analyzed. Results: 37.0% of the drugs involved in the Clinical Pharmacogenetics Implementation Consortium guidelines were used by Chinese pediatric patients, 8.91% inpatients and 0.89% outpatients received at least one pharmacogenomics medication, 1.24% (4803) inpatients and 0.16% (2940) outpatients were estimated to be at high risk of pharmacogenomic-related adverse therapeutic outcomes. Conclusion: Implementing pharmacogenomics testing can improve therapeutic outcomes for many Chinese pediatric patients.
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Affiliation(s)
- Weifeng Qin
- The Children's Hospital, Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou 310052, PR China.,College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, PR China
| | - Zhenglin Du
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, PR China
| | - Jingfa Xiao
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, PR China
| | - Huilong Duan
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, PR China
| | - Qiang Shu
- The Children's Hospital, Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou 310052, PR China
| | - Haomin Li
- The Children's Hospital, Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou 310052, PR China
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25
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Germline variants of DNA repair genes in early onset mantle cell lymphoma. Oncogene 2020; 40:551-563. [PMID: 33191405 DOI: 10.1038/s41388-020-01542-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 10/19/2020] [Accepted: 10/28/2020] [Indexed: 11/08/2022]
Abstract
Although somatic mutations of DNA repair genes are frequent in mantle cell lymphoma (MCL), our understanding of their germline defects is limited. In a Chinese family with maternal Lynch syndrome and paternal B cell non-Hodgkin lymphoma, one sibling developed both Lynch syndrome and MCL. Lynch syndrome is caused by heterozygous mutations in mismatch repair (MMR) genes. To understand the genetic predispositions in the family, we performed exome sequencing and analyses of affected individuals and their tumor samples. A novel germline indel, MLH1 Gly101fsX1, was identified as the cause of Lynch syndrome, and unstable microsatellite loci and mutational signatures as evidence of defective MMR were revealed in the MCL sample. Furthermore, we included additional 15 MCL patients with early onset, and found by exome sequencing that 11 patients carried heterozygous germline variants of 20 DNA repair genes, including MSH2 in MMR. In the MCL with MSH2 Arg359fsX16, unstable microsatellite loci and defective MMR signatures were also found. In addition, five patients also had heterozygous germline variants of genes involved in B cell functions. Thus, our study found germline variants of genes in single-strand break repair, double-strand break repair, and Fanconi anemia pathway in early onset MCL; and for the first time we identified germline defects of MMR in two MCLs.
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26
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Kothapalli KSD, Park HG, Brenna JT. Polyunsaturated fatty acid biosynthesis pathway and genetics. implications for interindividual variability in prothrombotic, inflammatory conditions such as COVID-19 ✰,✰✰,★,★★. Prostaglandins Leukot Essent Fatty Acids 2020; 162:102183. [PMID: 33038834 PMCID: PMC7527828 DOI: 10.1016/j.plefa.2020.102183] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/29/2020] [Accepted: 09/29/2020] [Indexed: 12/15/2022]
Abstract
COVID-19 symptoms vary from silence to rapid death, the latter mediated by both a cytokine storm and a thrombotic storm. SARS-CoV (2003) induces Cox-2, catalyzing the synthesis, from highly unsaturated fatty acids (HUFA), of eicosanoids and docosanoids that mediate both inflammation and thrombosis. HUFA balance between arachidonic acid (AA) and other HUFA is a likely determinant of net signaling to induce a healthy or runaway physiological response. AA levels are determined by a non-protein coding regulatory polymorphisms that mostly affect the expression of FADS1, located in the FADS gene cluster on chromosome 11. Major and minor haplotypes in Europeans, and a specific functional insertion-deletion (Indel), rs66698963, consistently show major differences in circulating AA (>50%) and in the balance between AA and other HUFA (47-84%) in free living humans; the indel is evolutionarily selective, probably based on diet. The pattern of fatty acid responses is fully consistent with specific genetic modulation of desaturation at the FADS1-mediated 20:3→20:4 step. Well established principles of net tissue HUFA levels indicate that the high linoleic acid and low alpha-linoleic acid in populations drive the net balance of HUFA for any individual. We predict that fast desaturators (insertion allele at rs66698963; major haplotype in Europeans) are predisposed to higher risk and pathological responses to SARS-CoV-2 could be reduced with high dose omega-3 HUFA.
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Affiliation(s)
- Kumar S D Kothapalli
- Dell Pediatric Research Institute, Depts of Pediatrics, of Chemistry, and of Nutrition, University of Texas at Austin, 1400 Barbara Jordan Blvd, Austin, TX, United States.
| | - Hui Gyu Park
- Dell Pediatric Research Institute, Depts of Pediatrics, of Chemistry, and of Nutrition, University of Texas at Austin, 1400 Barbara Jordan Blvd, Austin, TX, United States.
| | - J Thomas Brenna
- Dell Pediatric Research Institute, Depts of Pediatrics, of Chemistry, and of Nutrition, University of Texas at Austin, 1400 Barbara Jordan Blvd, Austin, TX, United States; Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States.
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27
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Cai R, Dong Y, Fang M, Guo C, Ma X. De novo genome assembly of a Han Chinese male and genome-wide detection of structural variants using Oxford Nanopore sequencing. Mol Genet Genomics 2020; 295:871-876. [PMID: 32274588 DOI: 10.1007/s00438-020-01672-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 03/30/2020] [Indexed: 01/01/2023]
Abstract
Advances in third-generation sequencing technologies provide an opportunity to investigate the complex organizational structure of the genome and unravel the genetic mechanisms of disease and physiological traits. Here we report the sequencing and de novo assembly of a healthy male northern Han Chinese genome and detection of structural variants using only nanopore sequencing data. We performed de novo assembly after filtering the raw data. Then, we aligned the assembled contigs to the human reference genome, and visualized chromosomes plot, which illustrated the contiguity of the nanopore assembly. Additionally, genomic structural variants were detected using a structure variation detection tool with long-read sequencing data. Median coverage depth was 30-fold and the read N50 was 27,136 bp. 96.51% of reads had at least one alignment to the human reference genome. The final assembled genome was 2.85 GB in size, with an N50 contig size of 5.4 MB. We identified 20,085 structural variants. Third-generation sequencing technologies have many advantages in de novo whole-genome assembly and detection of structural variants. Our results provide reference data for disease research, and can be used as a novel population-specific dataset of structural variants to support the efficient development of personalized precision medicine.
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Affiliation(s)
- Ruikun Cai
- National Research Institute for Family Planning, Beijing, 100081, China.,National Human Genetic Resources Center, Beijing, 102206, China
| | - Yichao Dong
- National Research Institute for Family Planning, Beijing, 100081, China.,Graduate School of Peking Union Medical College, Beijing, 100730, China
| | - Mingxia Fang
- National Research Institute for Family Planning, Beijing, 100081, China.,Graduate School of Peking Union Medical College, Beijing, 100730, China
| | - Changlong Guo
- National Research Institute for Family Planning, Beijing, 100081, China. .,National Human Genetic Resources Center, Beijing, 102206, China.
| | - Xu Ma
- National Research Institute for Family Planning, Beijing, 100081, China. .,National Human Genetic Resources Center, Beijing, 102206, China. .,Graduate School of Peking Union Medical College, Beijing, 100730, China.
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28
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Lu H, Liang Y, Guan B, Shi Y, Gong Y, Li J, Kong W, Liu J, Fang D, Liu L, He Q, Shakeel M, Li X, Zhou L, Ci W. Aristolochic acid mutational signature defines the low-risk subtype in upper tract urothelial carcinoma. Theranostics 2020; 10:4323-4333. [PMID: 32292497 PMCID: PMC7150494 DOI: 10.7150/thno.43251] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 02/25/2020] [Indexed: 01/10/2023] Open
Abstract
Rationale: Dietary exposure to aristolochic acids and similar compounds (collectively, AA) is a significant risk factor for nephropathy and subsequent upper tract urothelial carcinoma (UTUC). East Asian populations, who have a high prevalence of UTUC, have an unusual genome-wide AA-induced mutational pattern (COSMIC signature 22). Integrating mutational signature analysis with clinicopathological information may demonstrate great potential for risk ranking this UTUC subtype. Methods: We performed whole-genome sequencing (WGS) on 90 UTUC Chinese patients to extract mutational signatures. Genome sequencing data for urinary cell-free DNA from 26 UTUC patients were utilized to noninvasively identify the mutational signatures. Genome sequencing for primary tumors on 8 out of 26 patients was also performed. Metastasis-free survival (MFS) and cancer-specific survival (CSS) were measured using Kaplan-Meier methods. Results: Data analysis showed that a substantial proportion of patients harbored the AA mutational signature and were associated with AA-containing herbal drug intake, female gender, poor renal function, and multifocality. Field cancerization was found to partially contribute to multifocality. Nevertheless, AA Sig subtype UTUC patients exhibited favorable outcomes of CSS and MFS compared to the No-AA Sig subtype. Additionally, AA Sig subtype patients showed a higher tumor mutation burden, higher numbers of predicted neoantigens, and infiltrating lymphocytes, suggesting the potential for immunotherapy. We also confirmed the AA signature in AA-treated human renal tubular HK-2 cells. Notably, the AA subtype could be ascertained using a clinically applicable sequencing strategy (low coverage) in both primary tumors and urinary cell-free DNA as a basis for therapy selection. Conclusion: The AA mutational signature as a screening tool defines low-risk UTUC with therapeutic relevance. The AA mutational signature, as a molecular prognostic marker using either ureteroscopy and/or urinary cell-free DNA, is especially useful for diagnostic uncertainty when kidney-sparing treatment and/or immune checkpoint inhibitor therapy were considered.
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29
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Zeng J, Yuan N, Zhu J, Pan M, Zhang H, Wang Q, Shi S, Du Z, Xiao J. RETRACTED: CGVD: a genomic variation database for Chinese populations. Nucleic Acids Res 2020; 48:D1174-D1180. [PMID: 31665422 PMCID: PMC7145633 DOI: 10.1093/nar/gkz952] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/21/2022] Open
Abstract
Precision medicine calls upon deeper coverage of population-based sequencing and thorough gene-content and phenotype-based analysis, which lead to a population-associated genomic variation map or database. The Chinese Genomic Variation Database (CGVD; https://bigd.big.ac.cn/cgvd/) is such a database that has combined 48.30 million (M) SNVs and 5.77 M small indels, identified from 991 Chinese individuals of the Chinese Academy of Sciences Precision Medicine Initiative Project (CASPMI) and 301 Chinese individuals of the 1000 Genomes Project (1KGP). The CASPMI project includes whole-genome sequencing data (WGS, 25–30×) from ∼1000 healthy individuals of the CASPMI cohort. To facilitate the usage of such variations for pharmacogenomics studies, star-allele frequencies of the drug-related genes in the CASPMI and 1KGP populations are calculated and provided in CGVD. As one of the important database resources in BIG Data Center, CGVD will continue to collect more genomic variations and to curate structural and functional annotations to support population-based healthcare projects and studies in China and worldwide.
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Affiliation(s)
- Jingyao Zeng
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Na Yuan
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Junwei Zhu
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Mengyu Pan
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100101, China
| | - Hao Zhang
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Wang
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100101, China
| | - Shuo Shi
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100101, China
| | - Zhenglin Du
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jingfa Xiao
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100101, China
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Zhang Z, Zhao W, Xiao J, Bao Y, He S, Zhang G, Li Y, Zhao G, Chen R, Gao Y, Zhang C, Yuan L, Zhang G, Xu S, Zhang C, Gao Y, Ning Z, Lu Y, Xu S, Zeng J, Yuan N, Zhu J, Pan M, Zhang H, Wang Q, Shi S, Jiang M, Lu M, Qian Q, Gao Q, Shang Y, Wang J, Du Z, Xiao J, Tian D, Wang P, Tang B, Li C, Teng X, Liu X, Zou D, Song S, Xiong Z, Li M, Yang F, Ma Y, Sang J, Li Z, Li R, Wang Z, Zhu Q, Zhu J, Li X, Zhang S, Tian D, Kang H, Li C, Dong L, Ying C, Duan G, Song S, Li M, Zhao W, Zhi X, Ling Y, Cao R, Jiang Z, Zhou H, Lv D, Liu W, Klenk HP, Zhao G, Zhang G, Zhang Y, Zhang Z, Zhang H, Xiao J, Chen T, Zhang S, Chen X, Zhu J, Wang Z, Kang H, Dong L, Wang Y, Ma Y, Wu S, Li Z, Gong Z, Chen M, Li C, Tian D, Teng X, Wang P, Tang B, Liu X, Zou D, Song S, Fang S, Zhang L, Guo J, Niu Y, Wu Y, Li H, Zhao L, Li X, Teng X, Sun X, Sun L, Chen R, Zhao Y, Wang J, Zhang P, Li Y, Zheng Y, Chen R, He S, Teng X, Chen X, Xue H, Teng Y, Zhang P, Kang Q, Hao Y, Zhao Y, Chen R, He S, Cao J, Liu L, Li Z, Li Q, Zou D, Du Q, Abbasi AA, Shireen H, Pervaiz N, Batool F, Raza RZ, Ma L, Niu G, Zhang Y, Zou D, Zhu T, Sang J, Li M, Hao L, Zou D, Wang G, Li M, Li R, Li M, Li R, Bao Y, Yan J, Sang J, Zou D, Li C, Wang Z, Zhang Y, Zhu T, Song S, Wang X, Hao L, Li Z, Zhang Y, Zou D, Zhao Y, Wang H, Zhang Y, Xia X, Guo H, Zhang Z, Zou D, Ma L, Dong L, Tang B, Zhu J, Zhou Q, Wang Z, Kang H, Chen X, Lan L, Bao Y, Zhao W, Zou D, Zhu J, Tang B, Bao Y, Lan L, Zhang X, Ma Y, Xue Y, Sun Y, Zhai S, Yu L, Sun M, Chen H, Zhang Z, Zhao W, Xiao J, Bao Y, Hao L, Hu H, Guo AY, Lin S, Xue Y, Wang C, Xue Y, Ning W, Xue Y, Zhang X, Xiao Y, Li X, Tu Y, Xue Y, Wu W, Ji P, Zhao F, Luo H, Gao F, Guo Y, Xue Y, Yuan H, Zhang YE, Zhang Q, Guo AY, Zhou J, Xue Y, Huang Z, Cui Q, Miao YR, Guo AY, Ruan C, Xue Y, Yuan C, Chen M, Jin JP, Tian F, Gao G, Shi Y, Xue Y, Yao L, Xue Y, Cui Q, Li X, Li CY, Tang Q, Guo AY, Peng D, Xue Y. Database Resources of the National Genomics Data Center in 2020. Nucleic Acids Res 2020; 48:D24-D33. [PMID: 31702008 PMCID: PMC7145560 DOI: 10.1093/nar/gkz913] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 09/30/2019] [Accepted: 10/02/2019] [Indexed: 11/23/2022] Open
Abstract
The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
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Mapping Genome Variants Sheds Light on Genetic and Phenotypic Differentiation in Chinese. GENOMICS PROTEOMICS & BIOINFORMATICS 2019; 17:226-228. [PMID: 31513928 PMCID: PMC6818364 DOI: 10.1016/j.gpb.2019.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 08/28/2019] [Indexed: 12/01/2022]
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