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Pandey RK, Srivastava A, Mishra RK, Singh PP, Chaubey G. Novel genetic association of the Furin gene polymorphism rs1981458 with COVID-19 severity among Indian populations. Sci Rep 2024; 14:7822. [PMID: 38570613 PMCID: PMC10991378 DOI: 10.1038/s41598-024-54607-7] [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: 12/16/2022] [Accepted: 02/14/2024] [Indexed: 04/05/2024] Open
Abstract
SARS CoV-2, the causative agent for the ongoing COVID-19 pandemic, it enters the host cell by activating the ACE2 receptor with the help of two proteasesi.e., Furin and TMPRSS2. Therefore, variations in these genes may account for differential susceptibility and severity between populations. Previous studies have shown that the role of ACE2 and TMPRSS2 gene variants in understanding COVID-19 susceptibility among Indian populations. Nevertheless, a knowledge gap exists concerning the COVID-19 susceptibility of Furin gene variants among diverse South Asian ethnic groups. Investigating the role of Furin gene variants and their global phylogeographic structure is essential to comprehensively understanding COVID-19 susceptibility in these populations. We have used 450 samples from diverse Indian states and performed linear regression to analyse the Furin gene variant's with COVID-19 Case Fatality Rate (CFR) that could be epidemiologically associated with disease severity outcomes. Associated genetic variants were further evaluated for their expression and regulatory potential through various Insilco analyses. Additionally, we examined the Furin gene using next-generation sequencing (NGS) data from 393 diverse global samples, with a particular emphasis on South Asia, to investigate its Phylogeographic structure among diverse world populations. We found a significant positive association for the SNP rs1981458 with COVID-19 CFR (p < 0.05) among diverse Indian populations at different timelines of the first and second waves. Further, QTL and other regulatory analyses showed various significant associations for positive regulatory roles of rs1981458 and Furin gene, mainly in Immune cells and virus infection process, highlighting their role in host immunity and viral assembly and processing. The Furin protein-protein interaction suggested that COVID-19 may contribute to Pulmonary arterial hypertension via a typical inflammation mechanism. The phylogeographic architecture of the Furin gene demonstrated a closer genetic affinity of South Asia with West Eurasian populations. Therefore, it is worth proposing that for the Furin gene, the COVID-19 susceptibility of South Asians will be more similar to the West Eurasian population. Our previous studies on the ACE2 and TMPRSS2 genes showed genetic affinity of South Asian with East Eurasians and West Eurasians, respectively. Therefore, with the collective information from these three important genes (ACE2, TMPRSS2 and Furin) we modelled COVID-19 susceptibilityof South Asia in between these two major ancestries with an inclination towards West Eurasia. In conclusion, this study, for the first time, concluded the role of rs1981458 in COVID-19 severity among the Indian population and outlined its regulatory potential.This study also highlights that the genetic structure for COVID-19 susceptibilityof South Asia is distinct, however, inclined to the West Eurasian population. We believe this insight may be utilised as a genetic biomarker to identify vulnerable populations, which might be directly relevant for developing policies and allocating resources more effectively during an epidemic.
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Affiliation(s)
- Rudra Kumar Pandey
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi, 221005, India.
| | - Anshika Srivastava
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi, 221005, India
| | - Rahul Kumar Mishra
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi, 221005, India
| | - Prajjval Pratap Singh
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi, 221005, India
| | - Gyaneshwer Chaubey
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi, 221005, India.
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Gao Y, Zhang X, Chen H, Lu Y, Ma S, Yang Y, Zhang M, Xu S. Reconstructing the ancestral gene pool to uncover the origins and genetic links of Hmong-Mien speakers. BMC Biol 2024; 22:59. [PMID: 38475771 PMCID: PMC10935854 DOI: 10.1186/s12915-024-01838-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Hmong-Mien (HM) speakers are linguistically related and live primarily in China, but little is known about their ancestral origins or the evolutionary mechanism shaping their genomic diversity. In particular, the lack of whole-genome sequencing data on the Yao population has prevented a full investigation of the origins and evolutionary history of HM speakers. As such, their origins are debatable. RESULTS Here, we made a deep sequencing effort of 80 Yao genomes, and our analysis together with 28 East Asian populations and 968 ancient Asian genomes suggested that there is a strong genetic basis for the formation of the HM language family. We estimated that the most recent common ancestor dates to 5800 years ago, while the genetic divergence between the HM and Tai-Kadai speakers was estimated to be 8200 years ago. We proposed that HM speakers originated from the Yangtze River Basin and spread with agricultural civilization. We identified highly differentiated variants between HM and Han Chinese, in particular, a deafness-related missense variant (rs72474224) in the GJB2 gene is in a higher frequency in HM speakers than in others. CONCLUSIONS Our results indicated complex gene flow and medically relevant variants involved in the HM speakers' evolution history.
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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, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, 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
| | - 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
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, 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
| | - Yajun Yang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Menghan Zhang
- Institute of Modern Languages and Linguistics, and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, 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.
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.
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Li C, Qian Q, Yan C, Lu M, Li L, Li P, Fan Z, Lei W, Shang K, Wang P, Wang J, Lu T, Huang Y, Yang H, Wei H, Han J, Xiao J, Chen F. HervD Atlas: a curated knowledgebase of associations between human endogenous retroviruses and diseases. Nucleic Acids Res 2024; 52:D1315-D1326. [PMID: 37870452 PMCID: PMC10767980 DOI: 10.1093/nar/gkad904] [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: 08/14/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Human endogenous retroviruses (HERVs), as remnants of ancient exogenous retrovirus infected and integrated into germ cells, comprise ∼8% of the human genome. These HERVs have been implicated in numerous diseases, and extensive research has been conducted to uncover their specific roles. Despite these efforts, a comprehensive source of HERV-disease association still needs to be added. To address this gap, we introduce the HervD Atlas (https://ngdc.cncb.ac.cn/hervd/), an integrated knowledgebase of HERV-disease associations manually curated from all related published literature. In the current version, HervD Atlas collects 60 726 HERV-disease associations from 254 publications (out of 4692 screened literature), covering 21 790 HERVs (21 049 HERV-Terms and 741 HERV-Elements) belonging to six types, 149 diseases and 610 related/affected genes. Notably, an interactive knowledge graph that systematically integrates all the HERV-disease associations and corresponding affected genes into a comprehensive network provides a powerful tool to uncover and deduce the complex interplay between HERVs and diseases. The HervD Atlas also features a user-friendly web interface that allows efficient browsing, searching, and downloading of all association information, research metadata, and annotation information. Overall, the HervD Atlas is an essential resource for comprehensive, up-to-date knowledge on HERV-disease research, potentially facilitating the development of novel HERV-associated diagnostic and therapeutic strategies.
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Affiliation(s)
- Cuidan Li
- 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
| | - Qiheng Qian
- 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
- National Genomics Data Center, 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
| | - Chenghao Yan
- 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
| | - Mingming Lu
- 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
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Lin Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Pan Li
- 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
| | - Zhuojing Fan
- 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
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Wenyan Lei
- 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
| | - Kang Shang
- 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
| | - Peihan 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
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie 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
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianyi Lu
- 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
| | - Yuting Huang
- State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin 300071, China
| | - Hongwei Yang
- State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin 300071, China
| | - Haobin Wei
- 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
| | - Jingwan Han
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Jingfa Xiao
- 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
- National Genomics Data Center, 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
| | - Fei 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
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing100101, China
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Peng MS, Liu YH, Shen QK, Zhang XH, Dong J, Li JX, Zhao H, Zhang H, Zhang X, He Y, Shi H, Cui C, Ouzhuluobu, Wu TY, Liu SM, Gonggalanzi, Baimakangzhuo, Bai C, Duojizhuoma, Liu T, Dai SS, Murphy RW, Qi XB, Dong G, Su B, Zhang YP. Genetic and cultural adaptations underlie the establishment of dairy pastoralism in the Tibetan Plateau. BMC Biol 2023; 21:208. [PMID: 37798721 PMCID: PMC10557253 DOI: 10.1186/s12915-023-01707-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/20/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Domestication and introduction of dairy animals facilitated the permanent human occupation of the Tibetan Plateau. Yet the history of dairy pastoralism in the Tibetan Plateau remains poorly understood. Little is known how Tibetans adapted to milk and dairy products. RESULTS We integrated archeological evidence and genetic analysis to show the picture that the dairy ruminants, together with dogs, were introduced from West Eurasia into the Tibetan Plateau since ~ 3600 years ago. The genetic admixture between the exotic and indigenous dogs enriched the candidate lactase persistence (LP) allele 10974A > G of West Eurasian origin in Tibetan dogs. In vitro experiments demonstrate that - 13838G > A functions as a LP allele in Tibetans. Unlike multiple LP alleles presenting selective signatures in West Eurasians and South Asians, the de novo origin of Tibetan-specific LP allele - 13838G > A with low frequency (~ 6-7%) and absence of selection corresponds - 13910C > T in pastoralists across eastern Eurasia steppe. CONCLUSIONS Results depict a novel scenario of genetic and cultural adaptations to diet and expand current understanding of the establishment of dairy pastoralism in the Tibetan Plateau.
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Affiliation(s)
- Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, 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, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan-Hu Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, 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, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Quan-Kuan Shen
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, 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, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiao-Hua Zhang
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China
- Institute of Medical Biology, Chinese Academy of Medical Science, Peking Union Medical College, Kunming, 650118, China
| | - Jiajia Dong
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Jin-Xiu Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Hui Zhao
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China
| | - Hui Zhang
- State Key Laboratory of Primate Biomedical Research (LPBR), School of Primate Translational Medicine, Kunming University of Science and Technology (KUST), Kunming, 650000, China
| | - Xiaoming Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hong Shi
- State Key Laboratory of Primate Biomedical Research (LPBR), School of Primate Translational Medicine, Kunming University of Science and Technology (KUST), Kunming, 650000, China
| | - Chaoying Cui
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Ouzhuluobu
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Tian-Yi Wu
- National Key Laboratory of High Altitude Medicine, High Altitude Medical Research Institute, Xining, 810000, China
| | - Shi-Ming Liu
- National Key Laboratory of High Altitude Medicine, High Altitude Medical Research Institute, Xining, 810000, China
| | - Gonggalanzi
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Baimakangzhuo
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Caijuan Bai
- The First People's Hospital of Gansu Province, Lanzhou, 730000, China
| | - Duojizhuoma
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Ti Liu
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China
| | - Shan-Shan Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, 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, 650223, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Robert W Murphy
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Centre for Biodiversity and Conservation Biology, Royal Ontario Museum, Toronto, ON, M5S 2C6, Canada
| | - Xue-Bin Qi
- State Key Laboratory of Primate Biomedical Research (LPBR), School of Primate Translational Medicine, Kunming University of Science and Technology (KUST), Kunming, 650000, China.
- Tibetan Fukang Hospital, Lhasa, 850000, China.
| | - Guanghui Dong
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- Yunnan Key Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, 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, 650223, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming, 650091, China.
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Meng XY, Wang QL, Shi MJ, Zhang HY. Historical Pathogen-Driven Selection May Contribute to Contemporary Ethnic Difference in Bladder Cancer Susceptibility. Bladder Cancer 2023; 9:211-216. [PMID: 38993187 PMCID: PMC11181760 DOI: 10.3233/blc-230010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/06/2023] [Indexed: 07/13/2024]
Abstract
BACKGROUND The rationale for ethnic differences in bladder cancer (BCa) susceptibility is an important open question. In this study, we raised the hypothesis that the APOBEC3-rs1014971 variant associated with BCa risk and APOBEC-mutagenesis probably contribute to ethnic differences. METHODS We calculated the ethnicity-stratified 5-year age-adjusted incidence rates of BCa using the US SEER database. We performed somatic mutational-signature analyses and compared the APOBEC-related mutational contribution across BCa tumors in patients of different ethnicities. We analyzed the allele frequency distribution of APOBEC3-related rs1014971 in contemporary populations of different ethnicities and in ancient human genomes. We also analyzed the natural selection profiles and ages of the investigated SNPs. RESULTS We validated the ethnic difference in BCa risk using US SEER data, revealing Caucasians to be at >2-fold greater risk than Asians / Pacific islanders. In contemporary populations, we observed a coherent ethnic distribution in terms not only of the allele frequency of APOBEC3-related rs1014971, but also the mutational contribution of APOBEC-mediated mutagenesis in BCa tumors. Population genetics and ancient genome analyses further suggested that the diverse ethnic distribution of rs1014971 could be rooted in human evolution. CONCLUSIONS It is possible that APOBEC3-related rs1014971 is involved in the different BCa incidence across ethnic groups, and this difference is potentially derived from human evolution. Our findings suggested an evolutionary link between contemporary population-level variations in malignancy susceptibility and pathogen-driven selection in the past, not unlike previously reported cases of certain autoimmune and metabolic disorders.
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Affiliation(s)
- Xiang-Yu Meng
- Health Science Center, Hubei Minzu University, Enshi, China
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Qiao-Li Wang
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Ming-Jun Shi
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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Wang Y, Li H, Hou L, Wang S, Kang X, Yu J, Tian F, Ni W, Deng X, Liu T, You Y, Chen W. Genome-wide association study on coordination and agility in 461 Chinese Han males. Heliyon 2023; 9:e19268. [PMID: 37654465 PMCID: PMC10465941 DOI: 10.1016/j.heliyon.2023.e19268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 07/20/2023] [Accepted: 08/17/2023] [Indexed: 09/02/2023] Open
Abstract
There is growing evidence that genetic factors can influence human athletic performance. In many sports performances, excellent coordination and agility are the keys to mastery. However, few studies have been devoted to identifying genetic influences on athletic performance. Methods: We generated a derived measure of coordination and agility from the data of hexagonal jumps and T-runs and conducted genome-wide association and meta-analysis studies focused on coordination and agility. Results: The phenotypic correlation and genetic covariance analysis indicated that hexagonal jumps and T-runs were possibly influenced by the same set of genetic factors (R = 0.27, genetic covariance = 0.59). Meta-analysis identified rs117047321 genome-wide significant association (N = 143, P < 10E-5) with coordination and agility, and this association was replicated in the replication group (N = 318, P < 0.05). The CG genotype samples of this single nucleotide polymorphism (SNP) required a longer average movement time than the CC genotype samples, and the CG genotype only exists in Asia, which may belong to the East Asia-specific variation. This SNP is located on MYO5B, which is highly expressed in tissues such as the brain, heart, and muscle, suggesting that this locus might be a genetic factor related to human energy metabolism. Conclusion: Our study indicated that genetic factors can affect the athletic performance of coordination and agility. These findings may provide valuable insights for using genetic factors to evaluate sports characteristics.
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Affiliation(s)
- Yan Wang
- Clinical Biobank Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - He Li
- Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China
| | - Lei Hou
- Department of Neurology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Shan Wang
- Clinical Biobank Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Xia Kang
- Clinical Biobank Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Jihong Yu
- Clinical Biobank Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Fenfen Tian
- Clinical Biobank Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Wenfeng Ni
- Clinical Biobank Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Xiaoyu Deng
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Engineering Medicine, Beihang University, Beijing, China
| | - Tianzi Liu
- Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, Beijing, China
| | - Yanqin You
- Department of Obstetrics and Gynecology, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Wei Chen
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Engineering Medicine, Beihang University, Beijing, China
- State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, China
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Harun-Or-Roshid M, Mollah MNH. A comprehensive meta-analysis comprising 149 case-control studies to investigate the association between IL-6 gene rs1800795 polymorphism and multiple disease risk. Gene 2023; 861:147234. [PMID: 36736866 DOI: 10.1016/j.gene.2023.147234] [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: 09/14/2022] [Revised: 12/28/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Individual genome-wide association studies (GWAS) or single case-specific meta-analyses may not be sufficient evidence to take action against a specific gene function. Thus, we tried to determine a consensus association between the IL-6 gene rs1800795 polymorphism and multiple disease risks through an updated statistical meta-analysis. METHOD After systematically searching online databases, we found 149 case-control relevant datasets with a sample size of 96,153 (cases: 38,291 and controls: 57862) and conducted the meta-analysis using updated statistical models. RESULTS The analyses of this comprehensive meta-analysis revealed a significant association between IL-6 -174G/C polymorphism and overall disorder risk under all genetic models (C vs G: OR = 1.11, 95% CI = 1.08-1.13; p-value = 4.8E-17; CC vs GG: OR = 1.19, 95% CI = 1.13-1.26; p-value = 9.4E-12; CG vs GG: OR = 1.10, 95% CI = 1.06-1.14; p-value = 1.1E-07; CC + CG vs GG: OR = 1.13, 95% CI = 1.10-1.17; p-value = 1.1E-13; CC vs CG + GG: OR = 1.18, 95% CI = 1.06-1.31; p-value = 0.0019) and (OR > 1) with Asian ethnicity. The subgroup analyses based on the diseases revealed that the polymorphism was highly significantly increasing the risk of coronary artery disease (CAD) under all genetic models. Likewise, a significant association was observed with increased risk under three genetic models of inflammatory diseases (C vs G; CC vs GG; and CC vs CG + GG), and rheumatoid arthritis (C vs G; CG vs GG; and CC + CG vs GG). Conversely, the -174G/C SNP significantly decreased the risk of ischemic stroke under the two genetic models (C vs G; and CG vs GG). However, the other diseases included in this study showed no significant association with IL-6 (-174G/C) polymorphism. CONCLUSION This meta-analysis provided strong evidence for the association between IL-6 gene rs1800795 polymorphism and multiple disease risks. The IL-6 gene could be a useful prognostic biomarker for CAD, inflammatory disease, ischemic stroke, and rheumatoid arthritis.
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Affiliation(s)
- Md Harun-Or-Roshid
- Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh.
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8
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Ning Z, Tan X, Yuan Y, Huang K, Pan Y, Tian L, Lu Y, Wang X, Qi R, Lu D, Yang Y, Guan Y, Mamatyusupu D, Xu S. Expression profiles of east-west highly differentiated genes in Uyghur genomes. Natl Sci Rev 2023; 10:nwad077. [PMID: 37138773 PMCID: PMC10150800 DOI: 10.1093/nsr/nwad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 05/05/2023] Open
Abstract
It remains unknown and debatable how European-Asian-differentiated alleles affect individual phenotypes. Here, we made the first effort to analyze the expression profiles of highly differentiated genes with eastern and western origins in 90 Uyghurs using whole-genome (30× to 60×) and transcriptome data. We screened 921 872 east-west highly differentiated genetic variants, of which ∼4.32% were expression quantitative trait loci (eQTLs), ∼0.12% were alternative splicing quantitative trait loci (sQTLs), and ∼0.12% showed allele-specific expression (ASE). The 8305 highly differentiated eQTLs of strong effects appear to have undergone natural selection, associated with immunity and metabolism. European-origin alleles tend to be more biasedly expressed; highly differentiated ASEs were enriched in diabetes-associated genes, likely affecting the diabetes susceptibility in the Uyghurs. We proposed an admixture-induced expression model to dissect the highly differentiated expression profiles. We provide new insights into the genetic basis of phenotypic differentiation between Western and Eastern populations, advancing our understanding of the impact of genetic admixture.
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Affiliation(s)
| | | | | | - Ke Huang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lei Tian
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xiaoji Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruicheng Qi
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yaqun Guan
- Department of Biochemistry and Molecular Biology, Preclinical Medicine College, Xinjiang Medical University, Urumqi 830011, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University, Urumqi 830046, China
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9
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Zhao X, Ma S, Wang B, Jiang X, Xu S. PGG.MHC: toward understanding the diversity of major histocompatibility complexes in human populations. Nucleic Acids Res 2022; 51:D1102-D1108. [PMID: 36321663 PMCID: PMC9825418 DOI: 10.1093/nar/gkac997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/15/2022] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
The human leukocyte antigen (HLA) system, or the human version of the major histocompatibility complex (MHC), is known for its extreme polymorphic nature and high heterogeneity. Taking advantage of whole-genome and whole-exome sequencing data, we developed PGG.MHC to provide a platform to explore the diversity of the MHC in Asia as well as in global populations. PGG.MHC currently archives high-resolution HLA alleles of 53 254 samples representing 190 populations spanning 66 countries. PGG.MHC provides: (i) high-quality allele frequencies for eight classical HLA loci (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1, -DPA1 and -DPB1); (ii) visualization of population prevalence of HLA alleles on global, regional, and country-wide levels; (iii) haplotype structure of 134 populations; (iv) two online analysis tools including 'HLA imputation' for inferring HLA alleles from SNP genotyping data and 'HLA association' to perform case/control studies for HLA-related phenotypes and (v) East Asian-specific reference panels for HLA imputation. Equipped with high-quality frequency data and user-friendly computer tools, we expect that the PGG.MHC database can advance the understanding and facilitate applications of MHC genomic diversity in both evolutionary and medical studies. The PGG.MHC database is freely accessible via https://pog.fudan.edu.cn/pggmhc or https://www.pggmhc.org/pggmhc.
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Affiliation(s)
| | | | | | - Xuetong Jiang
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, FudanUniversity, Shanghai 200438, China
| | | | - Shuhua Xu
- To whom correspondence should be addressed. Tel: +86 21 31246617; Fax: +86 21 31246617;
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10
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Wang Y, Ling Y, Gong J, Zhao X, Zhou H, Xie B, Lou H, Zhuang X, Jin L, Fan S, Zhang G, Xu S. PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform. Nucleic Acids Res 2022; 51:D1109-D1116. [PMID: 36243989 PMCID: PMC9825616 DOI: 10.1093/nar/gkac905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/21/2022] [Accepted: 10/04/2022] [Indexed: 01/30/2023] Open
Abstract
Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.
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Affiliation(s)
| | | | | | - Xiaohan Zhao
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | - Hanwen Zhou
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, 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, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haiyi Lou
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xinhao Zhuang
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | | | - Shaohua Fan
- Correspondence may also be addressed to Shaohua Fan.
| | - Guoqing Zhang
- Correspondence may also be addressed to Guoqing Zhang.
| | - Shuhua Xu
- To whom correspondence should be addressed. Tel: +86 21 31246617; Fax: +86 21 31246617;
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11
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Alfonsi T, Bernasconi A, Canakoglu A, Masseroli M. Genomic data integration and user-defined sample-set extraction for population variant analysis. BMC Bioinformatics 2022; 23:401. [PMID: 36175857 PMCID: PMC9520931 DOI: 10.1186/s12859-022-04927-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/13/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. RESULTS Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. CONCLUSIONS The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed.
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Affiliation(s)
- Tommaso Alfonsi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy.
| | - Anna Bernasconi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
| | - Arif Canakoglu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy.,Dipartimento di Anestesia, Rianimazione ed Emergenza-Urgenza, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Policlinico di Milano, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Marco Masseroli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
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12
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Chen H, Lin R, Lu Y, Zhang R, Gao Y, He Y, Xu S. Tracing Bai-Yue Ancestry in Aboriginal Li People on Hainan Island. Mol Biol Evol 2022; 39:6731089. [PMID: 36173765 PMCID: PMC9585476 DOI: 10.1093/molbev/msac210] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
As the most prevalent aboriginal group on Hainan Island located between South China and the mainland of Southeast Asia, the Li people are believed to preserve some unique genetic information due to their isolated circumstances, although this has been largely uninvestigated. We performed the first whole-genome sequencing of 55 Hainan Li (HNL) individuals with high coverage (∼30-50×) to gain insight into their genetic history and potential adaptations. We identified the ancestry enriched in HNL (∼85%) is well preserved in present-day Tai-Kadai speakers residing in South China and North Vietnam, that is, Bai-Yue populations. A lack of admixture signature due to the geographical restriction exacerbated the bottleneck in the present-day HNL. The genetic divergence among Bai-Yue populations began ∼4,000-3,000 years ago when the proto-HNL underwent migration and the settling of Hainan Island. Finally, we identified signatures of positive selection in the HNL, some outstanding examples included FADS1 and FADS2 related to a diet rich in polyunsaturated fatty acids. In addition, we observed that malaria-driven selection had occurred in the HNL, with population-specific variants of malaria-related genes (e.g., CR1) present. Interestingly, HNL harbors a high prevalence of malaria leveraged gene variants related to hematopoietic function (e.g., CD3G) that may explain the high incidence of blood disorders such as B-cell lymphomas in the present-day HNL. The results have advanced our understanding of the genetic history of the Bai-Yue populations and have provided new insights into the adaptive scenarios of the Li people.
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Affiliation(s)
| | | | - 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 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | - Rui Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yang Gao
- Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
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13
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Yao K, Dai Y, Shen J, Wang Y, Yang H, Wu R, Liao Q, Wu H, Fang X, Shali S, Xu L, Hao M, Lin C, Sun Z, Liu Y, Li M, Wang Z, Gao Q, Zhang S, Li C, Gao W, Ge L, Zou Y, Sun A, Qian J, Jin L, Hong S, Zheng Y, Ge J. Exome sequencing identifies rare mutations of LDLR and QTRT1 conferring risk for early-onset coronary artery disease in Chinese. Natl Sci Rev 2022; 9:nwac102. [PMID: 36060302 PMCID: PMC9429139 DOI: 10.1093/nsr/nwac102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | | | | | | | | | - Runda Wu
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | | | - Hongyi Wu
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | | | - Shalaimaiti Shali
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Lili Xu
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Meng Hao
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, China
| | - Chenhao Lin
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, China
| | - Zhonghan Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, China
| | - Yilian Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, China
| | - Mengxin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, China
| | - Zhen Wang
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | | | - Shuning Zhang
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Chenguang Li
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Wei Gao
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Lei Ge
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Yunzeng Zou
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Aijun Sun
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Juying Qian
- Department of Cardiology, Zhongshan Hospital, Fudan University, China
- Shanghai Institute of Cardiovascular Disease, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, China
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14
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Pan Y, Zhang C, Lu Y, Ning Z, Lu D, Gao Y, Zhao X, Yang Y, Guan Y, Mamatyusupu D, Xu S. Genomic diversity and post-admixture adaptation in the Uyghurs. Natl Sci Rev 2022; 9:nwab124. [PMID: 35350227 PMCID: PMC8953455 DOI: 10.1093/nsr/nwab124] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 11/17/2022] Open
Abstract
Population admixture results in genome-wide combinations of genetic variants derived from different ancestral populations of distinct ancestry, thus providing a unique opportunity for understanding the genetic determinants of phenotypic variation in humans. Here, we used whole-genome sequencing of 92 individuals with high coverage (30–60×) to systematically investigate genomic diversity in the Uyghurs living in Xinjiang, China (XJU), an admixed population of both European-like and East-Asian-like ancestry. The XJU population shows greater genetic diversity, especially a higher proportion of rare variants, compared with their ancestral source populations, corresponding to greater phenotypic diversity of XJU. Admixture-induced functional variants in EDAR were associated with the diversity of facial morphology in XJU. Interestingly, the interaction of functional variants between SLC24A5 and OCA2 likely influences the diversity of skin pigmentation. Notably, selection has seemingly been relaxed or canceled in several genes with significantly biased ancestry, such as HERC2–OCA2. Moreover, signatures of post-admixture adaptation in XJU were identified, including genes related to metabolism (e.g. CYP2D6), digestion (e.g. COL11A1), olfactory perception (e.g. ANO2) and immunity (e.g. HLA). Our results demonstrated population admixture as a driving force, locally or globally, in shaping human genetic and phenotypic diversity as well as in adaptive evolution.
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Affiliation(s)
- Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Chao Zhang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University , Shanghai 200438, China
| | - Zhilin Ning
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Dongsheng Lu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
| | - Yang Gao
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China
| | - Xiaohan Zhao
- Human Phenome Institute, Fudan University , Shanghai 201203, China
| | - Yajun Yang
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University , Shanghai 200438, China
| | - Yaqun Guan
- Department of Biochemistry and Molecular Biology, Preclinical Medicine College, Xinjiang Medical University , Urumqi 830011, China
| | - Dolikun Mamatyusupu
- College of the Life Sciences and Technology, Xinjiang University , Urumqi 830046, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Shanghai 200031, China
- State Key Laboratory of Genetic Engineering and Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University , Shanghai 200438, China
- School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China
- Human Phenome Institute, Fudan University , Shanghai 201203, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences , Kunming 650223, China
- Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University , Zhengzhou 450052, China
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15
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Pandey RK, Srivastava A, Singh PP, Chaubey G. Genetic association of TMPRSS2 rs2070788 polymorphism with COVID-19 case fatality rate among Indian populations. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2022; 98:105206. [PMID: 34995811 PMCID: PMC8730738 DOI: 10.1016/j.meegid.2022.105206] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/30/2021] [Accepted: 01/03/2022] [Indexed: 11/17/2022]
Abstract
SARS-CoV-2, the causative agent for COVID-19, an ongoing pandemic, engages the ACE2 receptor to enter the host cell through S protein priming by a serine protease, TMPRSS2. Variation in the TMPRSS2 gene may account for the disparity in disease susceptibility between populations. Therefore, in the present study, we have used next-generation sequencing (NGS) data of world populations from 393 individuals and analyzed the TMPRSS2 gene using a haplotype-based approach with a major focus on South Asia to study its phylogenetic structure and their haplotype sharing among various populations worldwide. Our analysis of phylogenetic relatedness showed a closer affinity of South Asians with the West Eurasian populations therefore, host disease susceptibility and severity particularly in the context of TMPRSS2 will be more akin to West Eurasian instead of East Eurasian. This is in contrast to our prior study on the ACE2 gene which shows South Asian haplotypes have a strong affinity towards West Eurasians. Thus ACE2 and TMPRSS2 have an antagonistic genetic relatedness among South Asians. Considering the significance of the TMPRSS2 gene in the SARS-CoV-2 pathogenicity, COVID-19 infection and intensity trends could be directly associated with increased expression therefore, we have also tested the SNPs frequencies of this gene among various Indian state populations with respect to the case fatality rate (CFR). Interestingly, we found a significant positive association between the rs2070788 SNP (G Allele) and the CFR among Indian populations. Further our cis eQTL analysis of rs2070788 shows that the GG genotype of the rs2070788 tends to have a significantly higher expression of TMPRSS2 gene in the lung compared to the AG and AA genotypes thus validating the previous observation and therefore it might play a vital part in determining differential disease vulnerability. We trust that this information will be useful in understanding the role of the TMPRSS2 variant in COVID-19 susceptibility and using it as a biomarker may help to predict populations at risk.
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Affiliation(s)
- Rudra Kumar Pandey
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi 221005, India.
| | - Anshika Srivastava
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi 221005, India
| | - Prajjval Pratap Singh
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi 221005, India
| | - Gyaneshwer Chaubey
- Cytogenetics Laboratory, Department of Zoology, Banaras Hindu University, Varanasi 221005, India.
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16
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Zhang C, Hansen MEB, Tishkoff SA. Advances in integrative African genomics. Trends Genet 2022; 38:152-168. [PMID: 34740451 PMCID: PMC8752515 DOI: 10.1016/j.tig.2021.09.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/16/2021] [Accepted: 09/28/2021] [Indexed: 12/16/2022]
Abstract
There has been a rapid increase in human genome sequencing in the past two decades, resulting in the identification of millions of previously unknown genetic variants. However, African populations are under-represented in sequencing efforts. Additional sequencing from diverse African populations and the construction of African-specific reference genomes is needed to better characterize the full spectrum of variation in humans. However, sequencing alone is insufficient to address the molecular and cellular mechanisms underlying variable phenotypes and disease risks. Determining functional consequences of genetic variation using multi-omics approaches is a fundamental post-genomic challenge. We discuss approaches to close the knowledge gaps about African genomic diversity and review advances in African integrative genomic studies and their implications for precision medicine.
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Affiliation(s)
- Chao Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew E B Hansen
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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17
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Jeon S, Blazyte A, Yoon C, Ryu H, Jeon Y, Bhak Y, Bolser D, Manica A, Shin ES, Cho YS, Kim BC, Ryoo N, Choi H, Bhak J. Regional TMPRSS2 V197M Allele Frequencies Are Correlated with COVID-19 Case Fatality Rates. Mol Cells 2021; 44:680-687. [PMID: 34588322 PMCID: PMC8490206 DOI: 10.14348/molcells.2021.2249] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 06/14/2021] [Accepted: 07/10/2021] [Indexed: 02/08/2023] Open
Abstract
Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing.
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Affiliation(s)
- Sungwon Jeon
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | - Asta Blazyte
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | - Changhan Yoon
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | - Hyojung Ryu
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | - Yeonsu Jeon
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | - Youngjune Bhak
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | | | - Andrea Manica
- Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK
| | - Eun-Seok Shin
- Division of Cardiology, Department of Internal Medicine, Ulsan Medical Center, Ulsan 44686, Korea
- Personal Genomics Institute (PGI), Genome Research Foundation (GRF), Cheongju 28160, Korea
| | | | | | - Namhee Ryoo
- Department of Laboratory Medicine, Keimyung University School of Medicine, Daegu 42601, Korea
| | - Hansol Choi
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
| | - Jong Bhak
- Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
- Department of Biomedical Engineering, College of Information and Biotechnology, UNIST, Ulsan 44919, Korea
- Geromics, Ltd., Cambridge CB1 3NF, UK
- Personal Genomics Institute (PGI), Genome Research Foundation (GRF), Cheongju 28160, Korea
- Clinomics, Inc., Ulsan 44919, Korea
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18
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Wang M, Yuan D, Zou X, Wang Z, Yeh HY, Liu J, Wei LH, Wang CC, Zhu B, Liu C, He G. Fine-Scale Genetic Structure and Natural Selection Signatures of Southwestern Hans Inferred From Patterns of Genome-Wide Allele, Haplotype, and Haplogroup Lineages. Front Genet 2021; 12:727821. [PMID: 34504517 PMCID: PMC8421688 DOI: 10.3389/fgene.2021.727821] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 07/29/2021] [Indexed: 12/15/2022] Open
Abstract
The evolutionary and admixture history of Han Chinese have been widely discussed via traditional autosomal and uniparental genetic markers [e.g., short tandem repeats, low-density single nucleotide polymorphisms). However, their fine-scale genetic landscapes (admixture scenarios and natural selection signatures) based on the high-density allele/haplotype sharing patterns have not been deeply characterized. Here, we collected and generated genome-wide data of 50 Han Chinese individuals from four populations in Guizhou Province, one of the most ethnolinguistically diverse regions, and merged it with over 3,000 publicly available modern and ancient Eurasians to describe the genetic origin and population admixture history of Guizhou Hans and their neighbors. PCA and ADMIXTURE results showed that the studied four populations were homogeneous and grouped closely to central East Asians. Genetic homogeneity within Guizhou populations was further confirmed via the observed strong genetic affinity with inland Hmong-Mien people through the observed genetic clade in Fst and outgroup f3/f4-statistics. qpGraph-based phylogenies and f4-based demographic models illuminated that Guizhou Hans were well fitted via the admixture of ancient Yellow River Millet farmers related to Lajia people and southern Yangtze River farmers related to Hanben people. Further ChromoPainter-based chromosome painting profiles and GLOBETROTTER-based admixture signatures confirmed the two best source matches for southwestern Hans, respectively, from northern Shaanxi Hans and southern indigenes with variable mixture proportions in the historical period. Further three-way admixture models revealed larger genetic contributions from coastal southern East Asians into Guizhou Hans compared with the proposed inland ancient source from mainland Southeast Asia. We also identified candidate loci (e.g., MTUS2, NOTCH4, EDAR, ADH1B, and ABCG2) with strong natural selection signatures in Guizhou Hans via iHS, nSL, and ihh, which were associated with the susceptibility of the multiple complex diseases, morphology formation, alcohol and lipid metabolism. Generally, we provided a case and ideal strategy to reconstruct the detailed demographic evolutionary history of Guizhou Hans, which provided new insights into the fine-scale genomic formation of one ethnolinguistically specific targeted population from the comprehensive perspectives of the shared unlinked alleles, linked haplotypes, and paternal and maternal lineages.
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Affiliation(s)
- Mengge Wang
- Guangzhou Forensic Science Institute, Guangzhou, China.,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Didi Yuan
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Xing Zou
- College of Basic Medicine, Chongqing University, Chongqing, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Hui-Yuan Yeh
- School of Humanities, Nanyang Technological University, Singapore, Singapore
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
| | - Lan-Hai Wei
- State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Chuan-Chao Wang
- State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
| | - Bofeng Zhu
- Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China.,Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.,Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases, College of Stomatology, Xi'an Jiaotong University, Xi'an, China
| | - Chao Liu
- Guangzhou Forensic Science Institute, Guangzhou, China.,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Guanglin He
- School of Humanities, Nanyang Technological University, Singapore, Singapore.,State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China
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19
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Liu X, Cragun D, Pang J, Adapa SR, Fonseca R, Jiang RHY. False Alarms in Consumer Genomics Add to Public Fear and Potential Health Care Burden. J Pers Med 2020; 10:jpm10040187. [PMID: 33113957 PMCID: PMC7712761 DOI: 10.3390/jpm10040187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/03/2020] [Accepted: 10/19/2020] [Indexed: 11/16/2022] Open
Abstract
We have entered an era of direct-to-consumer (DTC) genomics. Patients have relayed many success stories of DTC genomics about finding causal mutations of genetic diseases before showing any symptoms and taking precautions. However, consumers may also take unnecessary medical actions based on false alarms of “pathogenic alleles”. The severity of this problem is not well known. Using publicly available data, we compared DTC microarray genotyping data with deep-sequencing data of 5 individuals and manually checked each inconsistently reported single nucleotide variants (SNVs). We estimated that, on average, a person would have ~5 “pathogenic” alleles reported due to wrongly reported genotypes if using a 23andMe genotyping microarray. We also found that the number of wrongly classified “pathogenic” alleles per person is at least as significant as those due to wrongly reported genotypes. We show that the scale of the false alarm problem could be large enough that the medical costs will become a burden to public health.
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Affiliation(s)
- Xiaoming Liu
- College of Public Health, University of South Florida, Tampa, FL 33612, USA; (D.C.); (J.P.); (S.R.A.); (R.H.Y.J.)
- Correspondence:
| | - Deborah Cragun
- College of Public Health, University of South Florida, Tampa, FL 33612, USA; (D.C.); (J.P.); (S.R.A.); (R.H.Y.J.)
| | - Jinyong Pang
- College of Public Health, University of South Florida, Tampa, FL 33612, USA; (D.C.); (J.P.); (S.R.A.); (R.H.Y.J.)
| | - Swamy R. Adapa
- College of Public Health, University of South Florida, Tampa, FL 33612, USA; (D.C.); (J.P.); (S.R.A.); (R.H.Y.J.)
| | - Renee Fonseca
- Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA;
| | - Rays H. Y. Jiang
- College of Public Health, University of South Florida, Tampa, FL 33612, USA; (D.C.); (J.P.); (S.R.A.); (R.H.Y.J.)
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20
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Triose Kinase Controls the Lipogenic Potential of Fructose and Dietary Tolerance. Cell Metab 2020; 32:605-618.e7. [PMID: 32818435 DOI: 10.1016/j.cmet.2020.07.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 04/16/2020] [Accepted: 07/28/2020] [Indexed: 01/01/2023]
Abstract
The surge in fructose consumption is a major factor behind the rapid rise of nonalcoholic fatty liver disease in modern society. Through flux and genetic analyses, we demonstrate that fructose is catabolized at a much higher rate than glucose, and triose kinase (TK) couples fructolysis with lipogenesis metabolically and transcriptionally. In the absence of TK, fructose oxidation is accelerated through the activation of aldehyde dehydrogenase (ALDH) and serine biosynthesis, accompanied by increased oxidative stress and fructose aversion. TK is also required by the endogenous fructolysis pathway to drive lipogenesis and hepatic triglyceride accumulation under high-fat diet and leptin-deficient conditions. Intriguingly, a nonsynonymous TK allele (rs2260655_A) segregated during human migration out of Africa behaves as TK null for its inability to rescue fructose toxicity and increase hepatic triglyceride accumulation. Therefore, we posit TK as a metabolic switch controlling the lipogenic potential of fructose and its dietary tolerance.
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21
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Srivastava A, Pandey RK, Singh PP, Kumar P, Rasalkar AA, Tamang R, van Driem G, Shrivastava P, Chaubey G. Most frequent South Asian haplotypes of ACE2 share identity by descent with East Eurasian populations. PLoS One 2020; 15:e0238255. [PMID: 32936832 PMCID: PMC7494073 DOI: 10.1371/journal.pone.0238255] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 08/12/2020] [Indexed: 12/31/2022] Open
Abstract
It was shown that the human Angiotensin-converting enzyme 2 (ACE2) is the receptor of recent coronavirus SARS-CoV-2, and variation in this gene may affect the susceptibility of a population. Therefore, we have analysed the sequence data of ACE2 among 393 samples worldwide, focusing on South Asia. Genetically, South Asians are more related to West Eurasian populations rather than to East Eurasians. In the present analyses of ACE2, we observed that the majority of South Asian haplotypes are closer to East Eurasians rather than to West Eurasians. The phylogenetic analysis suggested that the South Asian haplotypes shared with East Eurasians involved two unique event polymorphisms (rs4646120 and rs2285666). In contrast with the European/American populations, both of the SNPs have largely similar frequencies for East Eurasians and South Asians, Therefore, it is likely that among the South Asians, host susceptibility to the novel coronavirus SARS-CoV-2 will be more similar to that of East Eurasians rather than to that of Europeans.
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Affiliation(s)
- Anshika Srivastava
- Department of Zoology, Cytogenetics Laboratory, Banaras Hindu University, Varanasi, India
| | - Rudra Kumar Pandey
- Department of Zoology, Cytogenetics Laboratory, Banaras Hindu University, Varanasi, India
| | - Prajjval Pratap Singh
- Department of Zoology, Cytogenetics Laboratory, Banaras Hindu University, Varanasi, India
| | - Pramod Kumar
- National Centre for Disease Control, Delhi, India
| | | | - Rakesh Tamang
- Department of Zoology, University of Calcutta, Kolkata, India
| | - George van Driem
- Institut für Sprachwissenschaft, Universität Bern, Bern, Switzerland
| | - Pankaj Shrivastava
- Department of Home (Police), DNA Fingerprinting Unit, State Forensic Science Laboratory, Government of MP, Sagar, India
| | - Gyaneshwer Chaubey
- Department of Zoology, Cytogenetics Laboratory, Banaras Hindu University, Varanasi, India
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22
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Caron NR, Chongo M, Hudson M, Arbour L, Wasserman WW, Robertson S, Correard S, Wilcox P. Indigenous Genomic Databases: Pragmatic Considerations and Cultural Contexts. Front Public Health 2020; 8:111. [PMID: 32391301 PMCID: PMC7193324 DOI: 10.3389/fpubh.2020.00111] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 03/19/2020] [Indexed: 12/01/2022] Open
Abstract
The potential to grow genomic knowledge and harness the subsequent clinical benefits has escalated the building of background variant databases (BVDs) for genetic diagnosis across the globe. Alongside the upsurge of this precision medicine, potential benefits have been highlighted for both rare genetic conditions and other diagnoses. However, with the ever-present “genomic divide,” Indigenous peoples globally have valid concerns as they endure comparatively greater health disparities but stand to benefit the least from these novel scientific discoveries and progress in healthcare. The paucity of Indigenous healthcare providers and researchers in these fields contributes to this genomic divide both in access to, and availability of culturally safe, relevant and respectful healthcare using this genetic knowledge. The vital quest to provide equitable clinical research, and provision and use of genomic services and technologies provides a strong rationale for building BVDs for Indigenous peoples. Such tools would ground their representation and participation in accompanying genomic health research and benefit acquisition. We describe two, independent but highly similar initiatives–the “Silent Genomes” in Canada and the “Aotearoa Variome” in New Zealand–as exemplars that have had to address the aforementioned issues and work to create Indigenous BVDs with these populations. Taking into account the baseline inequities in genomic medicine for Indigenous populations and the ongoing challenges of implementing genomic research with Indigenous communities, we provide a rationale for multiple changes required that will assure communities represented in BVDs, as well as Indigenous researchers, that their participation will maximize benefits and minimize risk.
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Affiliation(s)
- Nadine Rena Caron
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.,Genome Sciences Center, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Meck Chongo
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.,Northern Medical Program, University of Northern British Columbia Canada, Prince George, BC, Canada
| | - Maui Hudson
- Faculty of Māori and Indigenous Studies, University of Waikato, Hamilton, New Zealand
| | - Laura Arbour
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Wyeth W Wasserman
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Stephen Robertson
- Department of Mathematics & Statistics, University of Otago, Dunedin, New Zealand
| | - Solenne Correard
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Phillip Wilcox
- Department of Mathematics & Statistics, University of Otago, Dunedin, New Zealand
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23
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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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