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Chair SY, Chow KM, Chan CWL, Chan JYW, Law BMH, Waye MMY. Structural Variations Identified in Patients with Autism Spectrum Disorder (ASD) in the Chinese Population: A Systematic Review of Case-Control Studies. Genes (Basel) 2024; 15:1082. [PMID: 39202440 PMCID: PMC11353326 DOI: 10.3390/genes15081082] [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: 07/15/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/03/2024] Open
Abstract
Autistic spectrum disorder (ASD) is a neurodevelopmental disability characterised by the impairment of social interaction and communication ability. The alarming increase in its prevalence in children urged researchers to obtain a better understanding of the causes of this disease. Genetic factors are considered to be crucial, as ASD has a tendency to run in families. In recent years, with technological advances, the importance of structural variations (SVs) in ASD began to emerge. Most of these studies, however, focus on the Caucasian population. As a populated ethnicity, ASD shall be a significant health issue in China. This systematic review aims to summarise current case-control studies of SVs associated with ASD in the Chinese population. A list of genes identified in the nine included studies is provided. It also reveals that similar research focusing on other genetic backgrounds is demanded to manifest the disease etiology in different ethnic groups, and assist the development of accurate ethnic-oriented genetic diagnosis.
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Affiliation(s)
- Sek-Ying Chair
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (K.-M.C.); (C.W.-L.C.); (J.Y.-W.C.); (B.M.-H.L.); (M.M.-Y.W.)
- Asia-Pacific Genomic and Genetic Nursing Centre, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- The Croucher Laboratory for Human Genomics, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ka-Ming Chow
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (K.-M.C.); (C.W.-L.C.); (J.Y.-W.C.); (B.M.-H.L.); (M.M.-Y.W.)
- Asia-Pacific Genomic and Genetic Nursing Centre, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- The Croucher Laboratory for Human Genomics, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Cecilia Wai-Ling Chan
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (K.-M.C.); (C.W.-L.C.); (J.Y.-W.C.); (B.M.-H.L.); (M.M.-Y.W.)
| | - Judy Yuet-Wa Chan
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (K.-M.C.); (C.W.-L.C.); (J.Y.-W.C.); (B.M.-H.L.); (M.M.-Y.W.)
| | - Bernard Man-Hin Law
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (K.-M.C.); (C.W.-L.C.); (J.Y.-W.C.); (B.M.-H.L.); (M.M.-Y.W.)
| | - Mary Miu-Yee Waye
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; (K.-M.C.); (C.W.-L.C.); (J.Y.-W.C.); (B.M.-H.L.); (M.M.-Y.W.)
- Asia-Pacific Genomic and Genetic Nursing Centre, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- The Croucher Laboratory for Human Genomics, The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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Fan S, Kong C, Chen Y, Zheng X, Zhou R, Zhang X, Wu X, Zhang W, Ding Y, Yin Z. Copy Number Variation Analysis Revealed the Evolutionary Difference between Chinese Indigenous Pigs and Asian Wild Boars. Genes (Basel) 2023; 14:472. [PMID: 36833399 PMCID: PMC9957247 DOI: 10.3390/genes14020472] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
Copy number variation (CNV) has been widely used to study the evolution of different species. We first discovered different CNVs in 24 Anqingliubai pigs and 6 Asian wild boars using next-generation sequencing at the whole-genome level with 10× depth to understand the relationship between genetic evolution and production traits in wild boars and domestic pigs. A total of 97,489 CNVs were identified and divided into 10,429 copy number variation regions (CNVRs), occupying 32.06% of the porcine genome. Chromosome 1 had the most CNVRs, and chromosome 18 had the least. Ninety-six CNVRs were selected using VST 1% based on the signatures of all CNVRs, and sixty-five genes were identified in the selected regions. These genes were strongly correlated with traits distinguishing groups by enrichment in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways, such as growth (CD36), reproduction (CIT, RLN), detoxification (CYP3A29), and fatty acid metabolism (ELOVL6). The QTL overlapping regions were associated with meat traits, growth, and immunity, which was consistent with CNV analysis. Our findings increase the understanding of evolved genome structural variations between wild boars and domestic pigs, and provide new molecular biomarkers to guide breeding and the efficient use of available genetic resources.
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Affiliation(s)
- Shuhao Fan
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Chengcheng Kong
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230036, China
| | - Yige Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Xianrui Zheng
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Ren Zhou
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Xiaodong Zhang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Xudong Wu
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Wei Zhang
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Yueyun Ding
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
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Deng L, Pan Y, Wang Y, Chen H, Yuan K, Chen S, Lu D, Lu Y, Mokhtar SS, Rahman TA, Hoh BP, Xu S. Genetic Connections and Convergent Evolution of Tropical Indigenous Peoples in Asia. Mol Biol Evol 2022; 39:msab361. [PMID: 34940850 PMCID: PMC8826522 DOI: 10.1093/molbev/msab361] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Tropical indigenous peoples in Asia (TIA) attract much attention for their unique appearance, whereas their genetic history and adaptive evolution remain mysteries. We conducted a comprehensive study to characterize the genetic distinction and connection of broad geographical TIAs. Despite the diverse genetic makeup and large interarea genetic differentiation between the TIA groups, we identified a basal Asian ancestry (bASN) specifically shared by these populations. The bASN ancestry was relatively enriched in ancient Asian human genomes dated as early as ∼50,000 years before the present and diminished in more recent history. Notably, the bASN ancestry is unlikely to be derived from archaic hominins. Instead, we suggest it may be better modeled as a survived lineage of the initial peopling of Asia. Shared adaptations inherited from the ancient Asian ancestry were detected among the TIA groups (e.g., LIMS1 for hair morphology, and COL24A1 for bone formation), and they are enriched in neurological functions either at an identical locus (e.g., NKAIN3), or different loci in an identical gene (e.g., TENM4). The bASN ancestry could also have formed the substrate of the genetic architecture of the dark pigmentation observed in the TIA peoples. We hypothesize that phenotypic convergence of the dark pigmentation in TIAs could have resulted from parallel (e.g., DDB1/DAK) or genetic convergence driven by admixture (e.g., MTHFD1 and RAD18), new mutations (e.g., STK11), or notably purifying selection (e.g., MC1R). Our results provide new insights into the initial peopling of Asia and an advanced understanding of the phenotypic convergence of the TIA peoples.
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Affiliation(s)
- Lian Deng
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - 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
| | - Yinan Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 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
| | - Kai Yuan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Sihan Chen
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Siti Shuhada Mokhtar
- Institute of Medical Molecular Biotechnology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia
| | - Thuhairah Abdul Rahman
- Clinical Pathology Diagnostic Centre Research Laboratory, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia
| | - Boon-Peng Hoh
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Faculty of Medicine and Health Sciences, UCSI University, Cheras, Kuala Lumpur, Malaysia
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
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Liu Y, Lv Y, Zarrei M, Dong R, Yang X, Higginbotham EJ, Li Y, Zhao D, Song F, Yang Y, Zhang H, Wang Y, Scherer SW, Gai Z. Chromosomal microarray analysis of 410 Han Chinese patients with autism spectrum disorder or unexplained intellectual disability and developmental delay. NPJ Genom Med 2022; 7:1. [PMID: 35022430 PMCID: PMC8755789 DOI: 10.1038/s41525-021-00271-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 11/09/2021] [Indexed: 12/20/2022] Open
Abstract
Copy number variants (CNVs) are recognized as a crucial genetic cause of neurodevelopmental disorders (NDDs). Chromosomal microarray analysis (CMA), the first-tier diagnostic test for individuals with NDDs, has been utilized to detect CNVs in clinical practice, but most reports are still from populations of European ancestry. To contribute more worldwide clinical genomics data, we investigated the genetic etiology of 410 Han Chinese patients with NDDs (151 with autism and 259 with unexplained intellectual disability (ID) and developmental delay (DD)) using CMA (Affymetrix) after G-banding karyotyping. Among all the NDD patients, 109 (26.6%) carried clinically relevant CNVs or uniparental disomies (UPDs), and 8 (2.0%) had aneuploidies (6 with trisomy 21 syndrome, 1 with 47,XXY, 1 with 47,XYY). In total, we found 129 clinically relevant CNVs and UPDs, including 32 CNVs in 30 ASD patients, and 92 CNVs and 5 UPDs in 79 ID/DD cases. When excluding the eight patients with aneuploidies, the diagnostic yield of pathogenic and likely pathogenic CNVs and UPDs was 20.9% for all NDDs (84/402), 3.3% in ASD (5/151), and 31.5% in ID/DD (79/251). When aneuploidies were included, the diagnostic yield increased to 22.4% for all NDDs (92/410), and 33.6% for ID/DD (87/259). We identified a de novo CNV in 14.9% (60/402) of subjects with NDDs. Interestingly, a higher diagnostic yield was observed in females (31.3%, 40/128) compared to males (16.1%, 44/274) for all NDDs (P = 4.8 × 10-4), suggesting that a female protective mechanism exists for deleterious CNVs and UPDs.
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Affiliation(s)
- Yi Liu
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Yuqiang Lv
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Mehdi Zarrei
- The Centre for Applied Genomics and Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Rui Dong
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Xiaomeng Yang
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Edward J Higginbotham
- The Centre for Applied Genomics and Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Yue Li
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Dongmei Zhao
- Pediatric Health Care Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Fengling Song
- Pediatric Health Care Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Yali Yang
- Rehabilitation Center, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Haiyan Zhang
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Ying Wang
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China
| | - Stephen W Scherer
- The Centre for Applied Genomics and Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada. .,McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A1, Canada.
| | - Zhongtao Gai
- Pediatric Research Institute, Qilu Children's Hospital of Shandong University, Ji'nan, 250022, China.
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Wang L, Xing Y, Yu X, Ming J, Liu X, Li X, Fu J, Zhou J, Gao B, Hu D, Pan C, Ji L, Ji Q. Greater macrovascular and microvascular morbidity from type 2 diabetes in northern compared with southern China: A cross-sectional study. J Diabetes Investig 2020; 11:1285-1294. [PMID: 32227466 PMCID: PMC7477533 DOI: 10.1111/jdi.13262] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 03/15/2020] [Accepted: 03/23/2020] [Indexed: 01/19/2023] Open
Abstract
AIMS/INTRODUCTION There are substantial differences in genes, diet, culture and environment between the northern and southern Chinese populations, which might influence treatment strategy and screening policy. We studied the differences in type 2 diabetes and diabetic complications between northern and southern China. MATERIALS AND METHODS We carried out a cross-sectional survey using data from the China Cardiometabolic Registries on blood pressure, blood lipids and blood glucose in 25,398 Chinese type 2 diabetes patients. Macrovascular, microvascular and other complications were collected by self-report or medical records, and then divided into the northern and southern groups by the boundary of the Yangtze River. RESULTS Northern patients were younger, and had heavier weight, greater body mass index and waist circumference, higher blood pressure, higher total cholesterol, higher low-density lipoprotein cholesterol, and higher hemoglobin A1C. The prevalence of cardiovascular, cerebrovascular and macrovascular complications were 1.76-fold, 1.24-fold and 1.47-fold more in northern than that in southern Chinese patients. In addition, the prevalence of diabetic nephropathy, retinopathy, neuropathy and microvascular complications in northern Chinese patients also increased. When stratified by age, the difference in both cardiovascular disease and ischemic stroke morbidity became significant, even in the 35-44 years age group. CONCLUSIONS More macrovascular and microvascular complications were found in northern compared with southern patients, and the largest difference also appeared in the younger age groups <55 years, which might be meaningful to a screening and treatment strategy according to geographic differences.
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Affiliation(s)
- Li Wang
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Ying Xing
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Xinwen Yu
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Jie Ming
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Xiangyang Liu
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Xiaomiao Li
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Jianfang Fu
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Jie Zhou
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Bin Gao
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
| | - Dayi Hu
- Department of CardiologyPeking University People’s HospitalBeijingChina
| | - Changyu Pan
- Department of EndocrinologyBeijing 301 Military General HospitalBeijingChina
| | - Linong Ji
- Department of Endocrinology and MetabolismPeking University People’s HospitalBeijingChina
| | - Qiuhe Ji
- Department of Endocrinology and MetabolismXijing HospitalXi’an, ShaanxiChina
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Gao Y, Zhang C, Yuan L, Ling Y, Wang X, Liu C, Pan Y, Zhang X, Ma X, Wang Y, Lu Y, Yuan K, Ye W, Qian J, Chang H, Cao R, Yang X, Ma L, Ju Y, Dai L, Tang Y, Zhang G, Xu S. PGG.Han: the Han Chinese genome database and analysis platform. Nucleic Acids Res 2020; 48:D971-D976. [PMID: 31584086 PMCID: PMC6943055 DOI: 10.1093/nar/gkz829] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 09/11/2019] [Accepted: 09/27/2019] [Indexed: 02/06/2023] Open
Abstract
As the largest ethnic group in the world, the Han Chinese population is nonetheless underrepresented in global efforts to catalogue the genomic variability of natural populations. Here, we developed the PGG.Han, a population genome database to serve as the central repository for the genomic data of the Han Chinese Genome Initiative (Phase I). In its current version, the PGG.Han archives whole-genome sequences or high-density genome-wide single-nucleotide variants (SNVs) of 114 783 Han Chinese individuals (a.k.a. the Han100K), representing geographical sub-populations covering 33 of the 34 administrative divisions of China, as well as Singapore. The PGG.Han provides: (i) an interactive interface for visualization of the fine-scale genetic structure of the Han Chinese population; (ii) genome-wide allele frequencies of hierarchical sub-populations; (iii) ancestry inference for individual samples and controlling population stratification based on nested ancestry informative markers (AIMs) panels; (iv) population-structure-aware shared control data for genotype-phenotype association studies (e.g. GWASs) and (v) a Han-Chinese-specific reference panel for genotype imputation. Computational tools are implemented into the PGG.Han, and an online user-friendly interface is provided for data analysis and results visualization. The PGG.Han database is freely accessible via http://www.pgghan.org or https://www.hanchinesegenomes.org.
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Affiliation(s)
- Yang Gao
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Chao Zhang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Liyun Yuan
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - YunChao Ling
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoji Wang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxi Zhang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xixian Ma
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuchen Wang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
| | - Kai Yuan
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wei Ye
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiaqiang Qian
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huidan Chang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruifang Cao
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiao Yang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ling Ma
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuanhu Ju
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Long Dai
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuanyuan Tang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Guoqing Zhang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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