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Wang Z, Belay K, Paterson J, Bewick P, Singer W, Song Q, Zhang B, Li S. Long-read sequencing reveals novel structural variation markers for key agronomic and quality traits of food-grade soybean. FRONTIERS IN PLANT SCIENCE 2025; 16:1557748. [PMID: 40265112 PMCID: PMC12011826 DOI: 10.3389/fpls.2025.1557748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2025] [Accepted: 03/17/2025] [Indexed: 04/24/2025]
Abstract
Long read sequencing has been widely used to detect structure variations that are not captured by short read sequencing in plant genomic research. In this study, we described an analysis of whole genome re-sequencing of 29 soybean varieties using nanopore long-read sequencing. The compiled germplasm reflects diverse applications of food-grade soybeans, including soy milk and tofu production, as well as consumption of natto, sprout, and edamame (vegetable soybean). We have identified 365,497 structural variations in these newly re-sequenced genomes and found that the newly identified structural variations are associated with important agronomic traits. These traits include seed weight, flowering time, plant height, oleic acid content, methionine content, and Kunitz trypsin inhibitor content, all of which significantly impact soybean production, quality, and market value. Experimental validation supports the roles of predicted candidate genes and structural variants in these biological processes. Our research provides a new source for rapid marker discovery in soybean and other crop genomes using structural variation and whole genome sequencing.
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Affiliation(s)
- Zhibo Wang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Kassaye Belay
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
- Graduate Program in Genetics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA, United States
| | - Joe Paterson
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Patrick Bewick
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - William Singer
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, United States
| | - Bo Zhang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Song Li
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
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2
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Chen H, Xu S. Population genomics advances in frontier ethnic minorities in China. SCIENCE CHINA. LIFE SCIENCES 2025; 68:961-973. [PMID: 39643831 DOI: 10.1007/s11427-024-2659-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/18/2024] [Indexed: 12/09/2024]
Abstract
China, with its large geographic span, possesses rich genetic diversity across vast frontier regions in addition to the Han Chinese majority. Importantly, demographic events and various natural and cultural environments in Chinese frontier regions have shaped the genomic diversity of ethnic minorities via local adaptations. Thus, insights into the genetic diversity and adaptive evolution of these under-represented ethnic groups are crucial for understanding evolutionary scenarios and biomedical implications in East Asian populations. Here, we focus on ethnic minorities in Chinese frontier regions and review research advances regarding genomic diversity, genetic structure, population history, genetic admixture, and local adaptation. We first provide an overview of the extensive genetic diversity across populations in different Chinese frontier regions. Next, we summarize research progress regarding genetic ancestry, demographic history, the adaptive process, and the archaic identification of multiple ethnic minorities in different Chinese frontier regions. Finally, we discuss the gaps and opportunities in genomic studies of Chinese populations and the need for a more comprehensive understanding of genomic diversity and the evolution of populations of East Asian ancestry in the post-genomic era.
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Affiliation(s)
- 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
| | - Shuhua Xu
- Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
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3
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Ping J, Liu X, Lu Y, Quan C, Fan P, Lu H, Li Q, Wang C, Zhang Z, Liu M, Chen S, Chang L, Jiang Y, Huang Q, Liu J, Wuren T, Liu H, Hao Y, Kang L, Liu G, Lu H, Wei X, Wang Y, Li Y, Guo H, Cui Y, Zhang H, Zhang Y, Zhai Y, He Y, Zheng W, Qi X, Ouzhuluobu, Ma H, Yang L, Wang X, Jin W, Cui Y, Ge R, Wu S, Wei Y, Su B, He F, Zhang H, Zhou G. A highland-adaptation variant near MCUR1 reduces its transcription and attenuates erythrogenesis in Tibetans. CELL GENOMICS 2025; 5:100782. [PMID: 40043709 PMCID: PMC11960549 DOI: 10.1016/j.xgen.2025.100782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 09/03/2024] [Accepted: 02/03/2025] [Indexed: 03/15/2025]
Abstract
To identify genomic regions subject to positive selection that might contain genes involved in high-altitude adaptation (HAA), we performed a genome-wide scan by whole-genome sequencing of Tibetan highlanders and Han lowlanders. We revealed a collection of candidate genes located in 30 genomic loci under positive selection. Among them, MCUR1 at 6p23 was a novel pronounced candidate. By single-cell RNA sequencing and comprehensive functional studies, we demonstrated that MCUR1 depletion leads to impairment of erythropoiesis under hypoxia and normoxia. Mechanistically, MCUR1 knockdown reduced mitochondrial Ca2+ uptake and then concomitantly increased cytosolic Ca2+ levels, which thereby reduced erythropoiesis via the CAMKK2-AMPK-mTOR axis. Further, we revealed rs61644582 at 6p23 as an expression quantitative trait locus for MCUR1 and a functional variant that confers an allele-specific transcriptional regulation of MCUR1. Overall, MCUR1-mediated mitochondrial Ca2+ homeostasis is highlighted as a novel regulator of erythropoiesis, deepening our understanding of the genetic mechanism of HAA.
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Affiliation(s)
- Jie Ping
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Xinyi Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Yiming Lu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Cheng Quan
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Pengcheng Fan
- Pharmacy Department, General Hospital of Lanzhou, Lanzhou City 730050, P.R. China; State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, P.R. China
| | - Hao Lu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Qi Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Cuiling Wang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Zheng Zhang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Mengyu Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Shunqi Chen
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Lingle Chang
- Medical College of Guizhou University, Guiyang City 550025, P.R. China
| | - Yuqing Jiang
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City 211166, P.R. China
| | - Qilin Huang
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City 211166, P.R. China
| | - Jie Liu
- Research Center for High-Altitude Medicine, Qinghai University Medical School, Xining City 810001, P.R. China; Qinghai Provincial People's Hospital, Xining City 810001, P.R. China
| | - Tana Wuren
- Research Center for High-Altitude Medicine, Qinghai University Medical School, Xining City 810001, P.R. China
| | - Huifang Liu
- Research Center for High-Altitude Medicine, Qinghai University Medical School, Xining City 810001, P.R. China
| | - Ying Hao
- College of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, P.R. China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High-Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang City 712082, P.R. China; Key Laboratory of High-Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang City 712082, P.R. China
| | - Guanjun Liu
- Henan Provincial People's Hospital, Zhengzhou City 450000, P.R. China; Affiliated Cancer Hospital of Guangxi Medical University, Nanning City 530021, P.R. China
| | - Hui Lu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Xiaojun Wei
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Yuting Wang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Yuanfeng Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China
| | - Hao Guo
- No. 945 Hospital of Joint Logistic Support Force of Chinese PLA, Ya'an City 625000, P.R. China
| | - Yongquan Cui
- No. 945 Hospital of Joint Logistic Support Force of Chinese PLA, Ya'an City 625000, P.R. China
| | - Haoxiang Zhang
- No. 954 Hospital of Joint Logistic Support Force of Chinese PLA, Shannan City 856000, P.R. China
| | - Yang Zhang
- Medical Center for Human Reproduction, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, P.R. China
| | - Yujia Zhai
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, P.R. China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming City 650223, P.R. China
| | - Wangshan Zheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming City 650223, P.R. China
| | - Xuebin Qi
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650000, China; Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa 850000, China
| | - Ouzhuluobu
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa 850000, China
| | - Huiping Ma
- Pharmacy Department, General Hospital of Lanzhou, Lanzhou City 730050, P.R. China
| | - Linpeng Yang
- Pharmacy Department, General Hospital of Lanzhou, Lanzhou City 730050, P.R. China
| | - Xin Wang
- Pharmacy Department, General Hospital of Lanzhou, Lanzhou City 730050, P.R. China
| | - Wanjun Jin
- Pharmacy Department, General Hospital of Lanzhou, Lanzhou City 730050, P.R. China
| | - Ying Cui
- Affiliated Cancer Hospital of Guangxi Medical University, Nanning City 530021, P.R. China
| | - Rili Ge
- Research Center for High-Altitude Medicine, Qinghai University Medical School, Xining City 810001, P.R. China
| | - Shizheng Wu
- Research Center for High-Altitude Medicine, Qinghai University Medical School, Xining City 810001, P.R. China; Qinghai Provincial People's Hospital, Xining City 810001, P.R. China
| | - Yuan Wei
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, P.R. China
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming City 650223, P.R. China
| | - Fuchu He
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, P.R. China
| | - Hongxing Zhang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, P.R. China.
| | - Gangqiao Zhou
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, Beijing 100850, P.R. China; Medical College of Guizhou University, Guiyang City 550025, P.R. China; Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City 211166, P.R. China.
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4
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Yang Q, Sun J, Wang X, Wang J, Liu Q, Ru J, Zhang X, Wang S, Hao R, Bian P, Dai X, Gong M, Zhang Z, Wang A, Bai F, Li R, Cai Y, Jiang Y. SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants. Nat Commun 2025; 16:2406. [PMID: 40069188 PMCID: PMC11897243 DOI: 10.1038/s41467-025-57756-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Accepted: 03/04/2025] [Indexed: 03/15/2025] Open
Abstract
Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here, we introduce SVLearn, a machine-learning approach for genotyping bi-allelic SVs. It exploits a dual-reference strategy to engineer a curated set of genomic, alignment, and genotyping features based on a reference genome in concert with an allele-based alternative genome. Using 38,613 human-derived SVs, we show that SVLearn significantly outperforms four state-of-the-art tools, with precision improvements of up to 15.61% for insertions and 13.75% for deletions in repetitive regions. On two additional sets of 121,435 cattle SVs and 113,042 sheep SVs, SVLearn demonstrates a strong generalizability to cross-species genotype SVs with a weighted genotype concordance score of up to 90%. Notably, SVLearn enables accurate genotyping of SVs at low sequencing coverage, which is comparable to the accuracy at 30× coverage. Our studies suggest that SVLearn can accelerate the understanding of associations between the genome-scale, high-quality genotyped SVs and diseases across multiple species.
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Affiliation(s)
- Qimeng Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Xinyu Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Jiong Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China
| | - Jinlong Ru
- Institute of Virology, Helmholtz Centre Munich - German Research Centre for Environmental Health, Neuherberg, Germany
| | - Xin Zhang
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China
| | - Sizhe Wang
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China
| | - Ran Hao
- College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China
| | - Peipei Bian
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Xuelei Dai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
- Yazhouwan National Laboratory, Sanya, Hainan, China
| | - Mian Gong
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Zhuangbiao Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Ao Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Fengting Bai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Ran Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Yudong Cai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China.
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China.
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5
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Gong J, Sun H, Wang K, Zhao Y, Huang Y, Chen Q, Qiao H, Gao Y, Zhao J, Ling Y, Cao R, Tan J, Wang Q, Ma Y, Li J, Luo J, Wang S, Wang J, Zhang G, Xu S, Qian F, Zhou F, Tang H, Li D, Sedlazeck FJ, Jin L, Guan Y, Fan S. Long-read sequencing of 945 Han individuals identifies structural variants associated with phenotypic diversity and disease susceptibility. Nat Commun 2025; 16:1494. [PMID: 39929826 PMCID: PMC11811171 DOI: 10.1038/s41467-025-56661-9] [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: 03/05/2024] [Accepted: 01/22/2025] [Indexed: 02/13/2025] Open
Abstract
Genomic structural variants (SVs) are a major source of genetic diversity in humans. Here, through long-read sequencing of 945 Han Chinese genomes, we identify 111,288 SVs, including 24.56% unreported variants, many with predicted functional importance. By integrating human population-level phenotypic and multi-omics data as well as two humanized mouse models, we demonstrate the causal roles of two SVs: one SV that emerges at the common ancestor of modern humans, Neanderthals, and Denisovans in GSDMD for bone mineral density and one modern-human-specific SV in WWP2 impacting height, weight, fat, craniofacial phenotypes and immunity. Our results suggest that the GSDMD SV could serve as a rapid and cost-effective biomarker for assessing the risk of cisplatin-induced acute kidney injury. The functional conservation from human to mouse and widespread signals of positive natural selection suggest that both SVs likely influence local adaptation, phenotypic diversity, and disease susceptibility across diverse human populations.
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Affiliation(s)
- Jiao Gong
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Huiru Sun
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Kaiyuan Wang
- Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Yanhui Zhao
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Yechao Huang
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Qinsheng Chen
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Hui Qiao
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Yang Gao
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Jialin Zhao
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Yunchao Ling
- Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ruifang Cao
- Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Jingze Tan
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Qi Wang
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Yanyun Ma
- Department of Anthropology and Human Genetics, Institute for Six-sector Economy, and MOE Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, China
| | - Jing Li
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Jingchun Luo
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
- Research Unit of dissecting the population genetics and developing new technologies for treatment and prevention of skin phenotypes and dermatological diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China
| | - Guoqing Zhang
- Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Feng Qian
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Fang Zhou
- School of Data Science and Engineering, East China Normal University, Shanghai, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Dali Li
- Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
- Research Unit of dissecting the population genetics and developing new technologies for treatment and prevention of skin phenotypes and dermatological diseases (2019RU058), Chinese Academy of Medical Sciences, Shanghai, China.
| | - Yuting Guan
- Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China.
| | - Shaohua Fan
- State Key Laboratory of Genetic Engineering, Lab for Evolutionary Synthesis, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
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6
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Lu Y, Li M, Gao Z, Ma H, Chong Y, Hong J, Wu J, Wu D, Xi D, Deng W. Advances in Whole Genome Sequencing: Methods, Tools, and Applications in Population Genomics. Int J Mol Sci 2025; 26:372. [PMID: 39796227 PMCID: PMC11719799 DOI: 10.3390/ijms26010372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Revised: 12/26/2024] [Accepted: 01/02/2025] [Indexed: 01/13/2025] Open
Abstract
With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Utilizing population genomics tools to analyze resequencing data allows for the effective integration of selection signals with population history, precise estimation of effective population size, historical population trends, and structural insights, along with the identification of specific genetic loci and variations. This paper reviews current whole genome sequencing technologies, detailing primary research methods, relevant software, and their advantages and limitations within population genomics. The goal is to examine the application and progress of resequencing technologies in this field and to consider future developments, including deep learning models and machine learning algorithms, which promise to enhance analytical methodologies and drive further advancements in population genomics.
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Affiliation(s)
- Ying Lu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Mengfei Li
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Zhendong Gao
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Hongming Ma
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Yuqing Chong
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Jieyun Hong
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Jiao Wu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Dongwang Wu
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Dongmei Xi
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
| | - Weidong Deng
- Yunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (Y.L.); (M.L.); (Z.G.); (H.M.); (Y.C.); (J.H.); (J.W.); (D.W.)
- State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, Kunming 650201, China
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Jiang T, Guo H, Liu Y, Li G, Cui Z, Cui X, Liu Y, Li Y, Zhang A, Cao S, Zhao T, Juan L, Kong W, Chen M, Liu D, Liu H, Zhang Y, Xu K, Wang Y, He M, Guo J, Lu M, Chen J, Zhao X, Zhao G, Dang S, Chen C, Wu X, Qin Q, Li Y, Shen H, Jin L, Liu B, Chen X, Zhao Y, Wang Y. A comprehensive genetic variant reference for the Chinese population. Sci Bull (Beijing) 2024; 69:3820-3825. [PMID: 38945749 DOI: 10.1016/j.scib.2024.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/07/2024] [Accepted: 04/28/2024] [Indexed: 07/02/2024]
Affiliation(s)
- Tao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
| | - Hongzhe Guo
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
| | - Yadong Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China
| | - Gaoyang Li
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Zhe Cui
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Xinran Cui
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Yue Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Yang Li
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Anqi Zhang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Shuqi Cao
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Tianyi Zhao
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China; School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
| | - Liran Juan
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
| | - Weize Kong
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Ming Chen
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Dianming Liu
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Hongri Liu
- Harbin Nebula Bioinformatics Technology Company, Harbin 150001, China
| | - Yixiao Zhang
- Department of Urology Surgery, Key Laboratory of Precision Medical Research on Major Chronic Disease, Liaoning Province, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Kelin Xu
- Fudan University Taizhou Institute of Health Sciences, Taizhou 200438, China; Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200433, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiancheng Guo
- Henan Research Center for Genomic Sequencing and Translational Medicine, Zhengzhou University, Zhengzhou 450001, China; The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China
| | - Ming Lu
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Jun Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350001, China
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Genming Zhao
- School of Public Health, Fudan University, Shanghai 200433, China
| | - Shaonong Dang
- School of Public Health, Xi'an Jiaotong University, Xi'an 710061, China
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410083, China
| | - Xiaojian Wu
- Department of Colorectal Surgery, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Sun Yat-sen University, Guangzhou 510655, China
| | - Qiyuan Qin
- Department of Colorectal Surgery, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Sun Yat-sen University, Guangzhou 510655, China
| | - Yixue Li
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Li Jin
- Fudan University Taizhou Institute of Health Sciences, Taizhou 200438, China; State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Bo Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China.
| | - Xingdong Chen
- Fudan University Taizhou Institute of Health Sciences, Taizhou 200438, China; State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai 200433, China.
| | - Yuhong Zhao
- Department of Clinical Epidemiology, Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang 110004, China.
| | - Yadong Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou 450000, China; School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China.
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Qi F, Chen X, Wang J, Niu X, Li S, Huang S, Ran X. Genome-wide characterization of structure variations in the Xiang pig for genetic resistance to African swine fever. Virulence 2024; 15:2382762. [PMID: 39092797 PMCID: PMC11299630 DOI: 10.1080/21505594.2024.2382762] [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: 10/09/2023] [Revised: 05/07/2024] [Accepted: 07/12/2024] [Indexed: 08/04/2024] Open
Abstract
African swine fever (ASF) is a rapidly fatal viral haemorrhagic fever in Chinese domestic pigs. Although very high mortality is observed in pig farms after an ASF outbreak, clinically healthy and antibody-positive pigs are found in those farms, and viral detection is rare from these pigs. The ability of pigs to resist ASF viral infection may be modulated by host genetic variations. However, the genetic basis of the resistance of domestic pigs against ASF remains unclear. We generated a comprehensive set of structural variations (SVs) in a Chinese indigenous Xiang pig with ASF-resistant (Xiang-R) and ASF-susceptible (Xiang-S) phenotypes using whole-genome resequencing method. A total of 53,589 nonredundant SVs were identified, with an average of 25,656 SVs per individual in the Xiang pig genome, including insertion, deletion, inversion and duplication variations. The Xiang-R group harboured more SVs than the Xiang-S group. The F-statistics (FST) was carried out to reveal genetic differences between two populations using the resequencing data at each SV locus. We identified 2,414 population-stratified SVs and annotated 1,152 Ensembl genes (including 986 protein-coding genes), in which 1,326 SVs might disturb the structure and expression of the Ensembl genes. Those protein-coding genes were mainly enriched in the Wnt, Hippo, and calcium signalling pathways. Other important pathways associated with the ASF viral infection were also identified, such as the endocytosis, apoptosis, focal adhesion, Fc gamma R-mediated phagocytosis, junction, NOD-like receptor, PI3K-Akt, and c-type lectin receptor signalling pathways. Finally, we identified 135 candidate adaptive genes overlapping 166 SVs that were involved in the virus entry and virus-host cell interactions. The fact that some of population-stratified SVs regions detected as selective sweep signals gave another support for the genetic variations affecting pig resistance against ASF. The research indicates that SVs play an important role in the evolutionary processes of Xiang pig adaptation to ASF infection.
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Affiliation(s)
- Fenfang Qi
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
| | - Xia Chen
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
| | - Jiafu Wang
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
| | - Xi Niu
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
| | - Sheng Li
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
| | - Shihui Huang
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
| | - Xueqin Ran
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, College of Animal Science, Guizhou University, Guiyang, Guizhou Province, China
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Chang L, Niu X, Huang S, Song D, Ran X, Wang J. Detection of structural variants linked to mutton flavor and odor in two closely related black goat breeds. BMC Genomics 2024; 25:979. [PMID: 39425017 PMCID: PMC11490145 DOI: 10.1186/s12864-024-10874-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Accepted: 10/08/2024] [Indexed: 10/21/2024] Open
Abstract
BACKGROUND Mutton quality is closely related to genetic variants and gene expression alterations during growth and development, resulting in differences in nutritional values, flavor, and odor. RESULTS We first evaluated and compared the composition of crude protein, crude fat, cholesterol, amino acid (AA), and fatty acid (FA) in the longissimus dorsi muscle of Guizhou black goats (GZB, n = 5) and Yunshang black goats (YBG, n = 6). The contents of cholesterol and FA related to odor in GZB were significantly lower than that in YBG, while the concentrations of umami amino acids and intramuscular fat were significantly higher in GZB. Furthermore, structural variants (SVs) in the genomes of GZB (n = 30) and YBG (n = 11) were explored. It was found that some regions in Chr 10/12/18 were densely involved with a large number of SVs in the genomes of GZB and YBG. By setting FST ≥ 0.25, we got 837 stratified SVs, of which 25 SVs (involved in 12 genes, e.g., CORO1A, CLIC6, PCSK2, and TMEM9) were limited in GZB. Functional enrichment analysis of 14 protein-coding genes (e.g., ENPEP, LIPC, ABCA5, and SLC6A15) revealed multiple terms and pathways related with metabolisms of AA, FA, and cholesterol. The SVs (n = 10) obtained by the whole genome resequencing were confirmed in percentages of 36.67 to 86.67% (n = 96) by PCR method. The SVa and SVd polymorphisms indicated a moderate negative correlation with HMGCS1 activity (n = 17). CONCLUSION This study is the first to comprehensively reveal potential SVs related to mutton nutritional values, flavor, and odor based on genomic compare between two black goat breeds with closely genetic relationship. The SVs generated in this study provide a data resource for deeper studies to understand the genomic characteristics and possible evolutionary outcomes with better nutritional values, flavor and extremely light odor.
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Affiliation(s)
- Lingle Chang
- College of Animal Science, Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region (Ministry of Education), Guizhou University, Guiyang, 550025, China
| | - Xi Niu
- Institute of Agro-Bioengineering/Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, Guizhou University, Guiyang, 550025, China
| | - Shihui Huang
- College of Animal Science, Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region (Ministry of Education), Guizhou University, Guiyang, 550025, China
| | - Derong Song
- Bijie Academy of Agricultural Sciences, Bijie, 551700, China
| | - Xueqin Ran
- College of Animal Science, Key Laboratory of Animal Genetics, Breeding and Reproduction in the Plateau Mountainous Region (Ministry of Education), Guizhou University, Guiyang, 550025, China.
| | - Jiafu Wang
- Institute of Agro-Bioengineering/Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Sciences, Guizhou University, Guiyang, 550025, China.
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Kang Y, Wang Z, An K, Hou Q, Zhang Z, Su J. Introgression drives adaptation to the plateau environment in a subterranean rodent. BMC Biol 2024; 22:187. [PMID: 39218870 PMCID: PMC11368017 DOI: 10.1186/s12915-024-01986-y] [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: 01/27/2024] [Accepted: 08/18/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Introgression has repeatedly been shown to play an important role in the adaptation of species to extreme environments, yet how introgression enables rodents with specialized subterranean lifestyle to acclimatize to high altitudes is still unclear. Myospalacinae is a group of subterranean rodents, among which the high-altitude plateau zokors (Eospalax baileyi) and the low-altitude Gansu zokors (E. cansus) are sympatrically distributed in the grassland ecosystems of the Qinghai-Tibet Plateau (QTP). Together, they provide a model for the study of the role of introgression in the adaptation of low-altitude subterranean rodents to high altitudes. RESULTS Applying low-coverage whole-genome resequencing and population genetics analyses, we identified evidence of adaptive introgression from plateau zokors into Gansu zokors, which likely facilitated the adaptation of the latter to the high-altitude environment of the QTP. We identified positively selected genes with functions related to energy metabolism, cardiovascular system development, calcium ion transport, and response to hypoxia which likely made critical contributions to adaptation to the plateau environment in both plateau zokors and high-altitude populations of Gansu zokors. CONCLUSIONS Introgression of genes associated with hypoxia adaptation from plateau zokors may have played a role in the adaptation of Gansu zokors to the plateau environment. Our study provides new insights into the understanding of adaptive evolution of species on the QTP and the importance of introgression in the adaptation of species to high-altitude environments.
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Affiliation(s)
- Yukun Kang
- College of Grassland Science, Key Laboratory of Grassland Ecosystem (Ministry of Education), Gansu Agricultural University, Lanzhou, 730070, China
- Gansu Agricultural University-Massey University Research Centre for Grassland Biodiversity, Gansu Agricultural University, Lanzhou, 730070, China
| | - Zhicheng Wang
- College of Grassland Science, Key Laboratory of Grassland Ecosystem (Ministry of Education), Gansu Agricultural University, Lanzhou, 730070, China
- Gansu Agricultural University-Massey University Research Centre for Grassland Biodiversity, Gansu Agricultural University, Lanzhou, 730070, China
| | - Kang An
- College of Grassland Science, Key Laboratory of Grassland Ecosystem (Ministry of Education), Gansu Agricultural University, Lanzhou, 730070, China
- Gansu Agricultural University-Massey University Research Centre for Grassland Biodiversity, Gansu Agricultural University, Lanzhou, 730070, China
| | - Qiqi Hou
- College of Grassland Science, Key Laboratory of Grassland Ecosystem (Ministry of Education), Gansu Agricultural University, Lanzhou, 730070, China
- Gansu Agricultural University-Massey University Research Centre for Grassland Biodiversity, Gansu Agricultural University, Lanzhou, 730070, China
| | - Zhiming Zhang
- College of Grassland Science, Key Laboratory of Grassland Ecosystem (Ministry of Education), Gansu Agricultural University, Lanzhou, 730070, China
- Gansu Agricultural University-Massey University Research Centre for Grassland Biodiversity, Gansu Agricultural University, Lanzhou, 730070, China
| | - Junhu Su
- College of Grassland Science, Key Laboratory of Grassland Ecosystem (Ministry of Education), Gansu Agricultural University, Lanzhou, 730070, China.
- Gansu Agricultural University-Massey University Research Centre for Grassland Biodiversity, Gansu Agricultural University, Lanzhou, 730070, China.
- Gansu Qilianshan Grassland Ecosystem Observation and Research Station, Wuwei, 733200, China.
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Ivancevic A, Simpson DM, Joyner OM, Bagby SM, Nguyen LL, Bitler BG, Pitts TM, Chuong EB. Endogenous retroviruses mediate transcriptional rewiring in response to oncogenic signaling in colorectal cancer. SCIENCE ADVANCES 2024; 10:eado1218. [PMID: 39018396 PMCID: PMC466953 DOI: 10.1126/sciadv.ado1218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/13/2024] [Indexed: 07/19/2024]
Abstract
Cancer cells exhibit rewired transcriptional regulatory networks that promote tumor growth and survival. However, the mechanisms underlying the formation of these pathological networks remain poorly understood. Through a pan-cancer epigenomic analysis, we found that primate-specific endogenous retroviruses (ERVs) are a rich source of enhancers displaying cancer-specific activity. In colorectal cancer and other epithelial tumors, oncogenic MAPK/AP1 signaling drives the activation of enhancers derived from the primate-specific ERV family LTR10. Functional studies in colorectal cancer cells revealed that LTR10 elements regulate tumor-specific expression of multiple genes associated with tumorigenesis, such as ATG12 and XRCC4. Within the human population, individual LTR10 elements exhibit germline and somatic structural variation resulting from a highly mutable internal tandem repeat region, which affects AP1 binding activity. Our findings reveal that ERV-derived enhancers contribute to transcriptional dysregulation in response to oncogenic signaling and shape the evolution of cancer-specific regulatory networks.
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Affiliation(s)
- Atma Ivancevic
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - David M. Simpson
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Olivia M. Joyner
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Stacey M. Bagby
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lily L. Nguyen
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
- Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ben G. Bitler
- Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Todd M. Pitts
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Edward B. Chuong
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
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Ji Y, Zhao J, Gong J, Sedlazeck FJ, Fan S. Unveiling novel genetic variants in 370 challenging medically relevant genes using the long read sequencing data of 41 samples from 19 global populations. Mol Genet Genomics 2024; 299:65. [PMID: 38972030 PMCID: PMC11955097 DOI: 10.1007/s00438-024-02158-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 06/16/2024] [Indexed: 07/08/2024]
Abstract
BACKGROUND A large number of challenging medically relevant genes (CMRGs) are situated in complex or highly repetitive regions of the human genome, hindering comprehensive characterization of genetic variants using next-generation sequencing technologies. In this study, we employed long-read sequencing technology, extensively utilized in studying complex genomic regions, to characterize genetic alterations, including short variants (single nucleotide variants and short insertions and deletions) and copy number variations, in 370 CMRGs across 41 individuals from 19 global populations. RESULTS Our analysis revealed high levels of genetic variants in CMRGs, with 68.73% exhibiting copy number variations and 65.20% containing short variants that may disrupt protein function across individuals. Such variants can influence pharmacogenomics, genetic disease susceptibility, and other clinical outcomes. We observed significant differences in CMRG variation across populations, with individuals of African ancestry harboring the highest number of copy number variants and short variants compared to samples from other continents. Notably, 15.79% to 33.96% of short variants were exclusively detectable through long-read sequencing. While the T2T-CHM13 reference genome significantly improved the assembly of CMRG regions, thereby facilitating variant detection in these regions, some regions still lacked resolution. CONCLUSION Our results provide an important reference for future clinical and pharmacogenetic studies, highlighting the need for a comprehensive representation of global genetic diversity in the reference genome and improved variant calling techniques to fully resolve medically relevant genes.
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Affiliation(s)
- Yanfeng Ji
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Science, Fudan University, Shanghai, 200438, China
| | - Junfan Zhao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Science, Fudan University, Shanghai, 200438, China
| | - Jiao Gong
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Science, Fudan University, Shanghai, 200438, China
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
| | - Shaohua Fan
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, School of Life Science, Fudan University, Shanghai, 200438, China.
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13
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Li C, Chen B, Langda S, Pu P, Zhu X, Zhou S, Kalds P, Zhang K, Bhati M, Leonard A, Huang S, Li R, Cuoji A, Wang X, Zhu H, Wu Y, Cuomu R, Gui B, Li M, Wang Y, Li Y, Fang W, Jia T, Pu T, Pan X, Cai Y, He C, Wang L, Jiang Y, Han JL, Chen Y, Zhou P, Pausch H, Wang X. Multi-omic Analyses Shed Light on The Genetic Control of High-altitude Adaptation in Sheep. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae030. [PMID: 39142817 PMCID: PMC12016566 DOI: 10.1093/gpbjnl/qzae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/09/2024] [Accepted: 02/29/2024] [Indexed: 08/16/2024]
Abstract
Sheep were domesticated in the Fertile Crescent and then spread globally, where they have been encountering various environmental conditions. The Tibetan sheep has adapted to high altitudes on the Qinghai-Tibet Plateau over the past 3000 years. To explore genomic variants associated with high-altitude adaptation in Tibetan sheep, we analyzed Illumina short-reads of 994 whole genomes representing ∼ 60 sheep breeds/populations at varied altitudes, PacBio High fidelity (HiFi) reads of 13 breeds, and 96 transcriptomes from 12 sheep organs. Association testing between the inhabited altitudes and 34,298,967 variants was conducted to investigate the genetic architecture of altitude adaptation. Highly accurate HiFi reads were used to complement the current ovine reference assembly at the most significantly associated β-globin locus and to validate the presence of two haplotypes A and B among 13 sheep breeds. The haplotype A carried two homologous gene clusters: (1) HBE1, HBE2, HBB-like, and HBBC, and (2) HBE1-like, HBE2-like, HBB-like, and HBB; while the haplotype B lacked the first cluster. The high-altitude sheep showed highly frequent or nearly fixed haplotype A, while the low-altitude sheep dominated by haplotype B. We further demonstrated that sheep with haplotype A had an increased hemoglobin-O2 affinity compared with those carrying haplotype B. Another highly associated genomic region contained the EGLN1 gene which showed varied expression between high-altitude and low-altitude sheep. Our results provide evidence that the rapid adaptive evolution of advantageous alleles play an important role in facilitating the environmental adaptation of Tibetan sheep.
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Affiliation(s)
- Chao Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
- Animal Genomics, ETH Zürich, Zürich 8092, Switzerland
| | - Bingchun Chen
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Suo Langda
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Peng Pu
- School of Biological and Pharmaceutical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Xiaojia Zhu
- College of Life Sciences, Shaanxi Normal University, Xi’an 710119, China
| | - Shiwei Zhou
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Peter Kalds
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ke Zhang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Meenu Bhati
- Animal Genomics, ETH Zürich, Zürich 8092, Switzerland
| | | | - Shuhong Huang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ran Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Awang Cuoji
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Xiran Wang
- College of Life Sciences, Shaanxi Normal University, Xi’an 710119, China
| | - Haolin Zhu
- College of Life Sciences, Shaanxi Normal University, Xi’an 710119, China
| | - Yujiang Wu
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Renqin Cuomu
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Ba Gui
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Ming Li
- Zoology and Evolutionary Biology, Department of Biology, University of Konstanz, 78457 Konstanz, Germany
| | - Yutao Wang
- College of Life and Geographic Sciences, Kashi University, Kashi 844000, China
| | - Yan Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Wenwen Fang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ting Jia
- Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China
| | - Tianchun Pu
- Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China
| | - Xiangyu Pan
- Department of Medical Research, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yudong Cai
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Chong He
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs/Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Liming Wang
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
| | - Yu Jiang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Livestock Genetics Program, International Livestock Research Institute, Nairobi 00100, Kenya
| | - Yulin Chen
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ping Zhou
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
| | - Hubert Pausch
- Animal Genomics, ETH Zürich, Zürich 8092, Switzerland
| | - Xiaolong Wang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
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14
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Modenini G, Abondio P, Sazzini M, Boattini A. Polymorphic transposable elements provide new insights on high-altitude adaptation in the Tibetan Plateau. Genomics 2024; 116:110854. [PMID: 38701989 DOI: 10.1016/j.ygeno.2024.110854] [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: 01/16/2024] [Revised: 03/23/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024]
Abstract
Several studies demonstrated that populations living in the Tibetan plateau are genetically and physiologically adapted to high-altitude conditions, showing genomic signatures ascribable to the action of natural selection. However, so far most of them relied solely on inferences drawn from the analysis of coding variants and point mutations. To fill this gap, we focused on the possible role of polymorphic transposable elements in influencing the adaptation of Tibetan and Sherpa highlanders. To do so, we compared high-altitude and middle/low-lander individuals of East Asian ancestry by performing in silico analyses and differentiation tests on 118 modern and ancient samples. We detected several transposable elements associated with high altitude, which map genes involved in cardiovascular, hematological, chem-dependent and respiratory conditions, suggesting that metabolic and signaling pathways taking part in these functions are disproportionately impacted by the effect of environmental stressors in high-altitude individuals. To our knowledge, our study is the first hinting to a possible role of transposable elements in the adaptation of Tibetan and Sherpa highlanders.
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Affiliation(s)
- Giorgia Modenini
- Dept. of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy.
| | - Paolo Abondio
- IRCCS Istituto Delle Scienze Neurologiche Di Bologna, Bologna, Italy
| | - Marco Sazzini
- Dept. of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy; Interdepartmental Centre - Alma Mater Research Institute on Global Changes and Climate Change, University of Bologna, Italy
| | - Alessio Boattini
- Dept. of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
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15
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Wu Z, Li T, Jiang Z, Zheng J, Gu Y, Liu Y, Liu Y, Xie Z. Human pangenome analysis of sequences missing from the reference genome reveals their widespread evolutionary, phenotypic, and functional roles. Nucleic Acids Res 2024; 52:2212-2230. [PMID: 38364871 PMCID: PMC10954445 DOI: 10.1093/nar/gkae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/18/2024] Open
Abstract
Nonreference sequences (NRSs) are DNA sequences present in global populations but absent in the current human reference genome. However, the extent and functional significance of NRSs in the human genomes and populations remains unclear. Here, we de novo assembled 539 genomes from five genetically divergent human populations using long-read sequencing technology, resulting in the identification of 5.1 million NRSs. These were merged into 45284 unique NRSs, with 29.7% being novel discoveries. Among these NRSs, 38.7% were common across the five populations, and 35.6% were population specific. The use of a graph-based pangenome approach allowed for the detection of 565 transcript expression quantitative trait loci on NRSs, with 426 of these being novel findings. Moreover, 26 NRS candidates displayed evidence of adaptive selection within human populations. Genes situated in close proximity to or intersecting with these candidates may be associated with metabolism and type 2 diabetes. Genome-wide association studies revealed 14 NRSs to be significantly associated with eight phenotypes. Additionally, 154 NRSs were found to be in strong linkage disequilibrium with 258 phenotype-associated SNPs in the GWAS catalogue. Our work expands the understanding of human NRSs and provides novel insights into their functions, facilitating evolutionary and biomedical researches.
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Affiliation(s)
- Zhikun Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zehang Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Jingjing Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yizhou Gu
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
- University of Wisconsin-Madison, WI, USA
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yun Liu
- MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences and Shanghai Xuhui Central Hospital, Fudan University, Shanghai, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
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16
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An X, Mao L, Wang Y, Xu Q, Liu X, Zhang S, Qiao Z, Li B, Li F, Kuang Z, Wan N, Liang X, Duan Q, Feng Z, Yang X, Liu S, Nevo E, Liu J, Storz JF, Li K. Genomic structural variation is associated with hypoxia adaptation in high-altitude zokors. Nat Ecol Evol 2024; 8:339-351. [PMID: 38195998 DOI: 10.1038/s41559-023-02275-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/20/2023] [Indexed: 01/11/2024]
Abstract
Zokors, an Asiatic group of subterranean rodents, originated in lowlands and colonized high-elevational zones following the uplift of the Qinghai-Tibet plateau about 3.6 million years ago. Zokors live at high elevation in subterranean burrows and experience hypobaric hypoxia, including both hypoxia (low oxygen concentration) and hypercapnia (elevated partial pressure of CO2). Here we report a genomic analysis of six zokor species (genus Eospalax) with different elevational ranges to identify structural variants (deletions and inversions) that may have contributed to high-elevation adaptation. Based on an assembly of a chromosome-level genome of the high-elevation species, Eospalax baileyi, we identified 18 large inversions that distinguished this species from congeners native to lower elevations. Small-scale structural variants in the introns of EGLN1, HIF1A, HSF1 and SFTPD of E. baileyi were associated with the upregulated expression of those genes. A rearrangement on chromosome 1 was associated with altered chromatin accessibility, leading to modified gene expression profiles of key genes involved in the physiological response to hypoxia. Multigene families that underwent copy-number expansions in E. baileyi were enriched for autophagy, HIF1 signalling and immune response. E. baileyi show a significantly larger lung mass than those of other Eospalax species. These findings highlight the key role of structural variants underlying hypoxia adaptation of high-elevation species in Eospalax.
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Affiliation(s)
- Xuan An
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Leyan Mao
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Yinjia Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Qinqin Xu
- Department of Medical Oncology, Qinghai Provincial People's Hospital, Xining, China
| | - Xi Liu
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Shangzhe Zhang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Zhenglei Qiao
- College of Life Sciences and Technology, Mudanjiang Normal University, Mudanjiang, China
| | - Bowen Li
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Fang Li
- College of Life Sciences and Technology, Mudanjiang Normal University, Mudanjiang, China
| | - Zhuoran Kuang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Na Wan
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Xiaolong Liang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Qijiao Duan
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Zhilong Feng
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Xiaojie Yang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Sanyuan Liu
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
| | - Eviatar Nevo
- Institute of Evolution, University of Haifa, Haifa, Israel
| | - Jianquan Liu
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China.
| | - Jay F Storz
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
| | - Kexin Li
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, China.
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17
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Shi J, Jia Z, Sun J, Wang X, Zhao X, Zhao C, Liang F, Song X, Guan J, Jia X, Yang J, Chen Q, Yu K, Jia Q, Wu J, Wang D, Xiao Y, Xu X, Liu Y, Wu S, Zhong Q, Wu J, Cui S, Bo X, Wu Z, Park M, Kellis M, He K. Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing. Nat Commun 2023; 14:8282. [PMID: 38092772 PMCID: PMC10719358 DOI: 10.1038/s41467-023-44034-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.
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Affiliation(s)
- Jinlong Shi
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
- Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
| | - Zhilong Jia
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
- Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
| | - Jinxiu Sun
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China
| | - Xiaoreng Wang
- Laboratory of Nuclear and Radiation Injury, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- State Key Laboratory of Experimental Hematology, Beijing, 100853, China
| | - Xiaojing Zhao
- Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
- Translational Medicine Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
| | - Chenghui Zhao
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
- Research Center for Biomedical Engineering, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
| | - Fan Liang
- NextOmics Biosciences Inc, Wuhan, 430000, China
| | - Xinyu Song
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
- Medical Artificial Intelligence Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
| | - Jiawei Guan
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China
| | - Xue Jia
- Laboratory of Nuclear and Radiation Injury, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
| | - Jing Yang
- Laboratory of Nuclear and Radiation Injury, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
| | - Qi Chen
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China
| | - Kang Yu
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Qian Jia
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Jing Wu
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China
| | - Depeng Wang
- NextOmics Biosciences Inc, Wuhan, 430000, China
| | - Yuhui Xiao
- NextOmics Biosciences Inc, Wuhan, 430000, China
| | - Xiaoman Xu
- NextOmics Biosciences Inc, Wuhan, 430000, China
| | - Yinzhe Liu
- NextOmics Biosciences Inc, Wuhan, 430000, China
| | - Shijing Wu
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Qin Zhong
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China
| | - Jue Wu
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Saijia Cui
- Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, 100850, China
| | | | | | - Manolis Kellis
- Massachusetts Institute of Technology; MIT Computer Science and Artificial Intelligence Laboratory, Broad Institute of MIT and Harvard, Cambridge, 02139, MA, USA
| | - Kunlun He
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China.
- National Engineering Research Center of Medical Big Data, Chinese PLA General Hospital, Beijing, 100853, China.
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, 100853, China.
- Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China.
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18
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Zong W, Wang J, Zhao R, Niu N, Su Y, Hu Z, Liu X, Hou X, Wang L, Wang L, Zhang L. Associations of genome-wide structural variations with phenotypic differences in cross-bred Eurasian pigs. J Anim Sci Biotechnol 2023; 14:136. [PMID: 37805653 PMCID: PMC10559557 DOI: 10.1186/s40104-023-00929-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/03/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND During approximately 10,000 years of domestication and selection, a large number of structural variations (SVs) have emerged in the genome of pig breeds, profoundly influencing their phenotypes and the ability to adapt to the local environment. SVs (≥ 50 bp) are widely distributed in the genome, mainly in the form of insertion (INS), mobile element insertion (MEI), deletion (DEL), duplication (DUP), inversion (INV), and translocation (TRA). While studies have investigated the SVs in pig genomes, genome-wide association studies (GWAS)-based on SVs have been rarely conducted. RESULTS Here, we obtained a high-quality SV map containing 123,151 SVs from 15 Large White and 15 Min pigs through integrating the power of several SV tools, with 53.95% of the SVs being reported for the first time. These high-quality SVs were used to recover the population genetic structure, confirming the accuracy of genotyping. Potential functional SV loci were then identified based on positional effects and breed stratification. Finally, GWAS were performed for 36 traits by genotyping the screened potential causal loci in the F2 population according to their corresponding genomic positions. We identified a large number of loci involved in 8 carcass traits and 6 skeletal traits on chromosome 7, with FKBP5 containing the most significant SV locus for almost all traits. In addition, we found several significant loci in intramuscular fat, abdominal circumference, heart weight, and liver weight, etc. CONCLUSIONS: We constructed a high-quality SV map using high-coverage sequencing data and then analyzed them by performing GWAS for 25 carcass traits, 7 skeletal traits, and 4 meat quality traits to determine that SVs may affect body size between European and Chinese pig breeds.
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Affiliation(s)
- Wencheng Zong
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jinbu Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Runze Zhao
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- College of Animal Science, Shanxi Agricultural University, Jinzhong, 030801, China
| | - Naiqi Niu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yanfang Su
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Ziping Hu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, 266109, China
| | - Xin Liu
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Xinhua Hou
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Ligang Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Lixian Wang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Longchao Zhang
- State Key Laboratory of Animal Biotech Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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19
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Xia X, Zhang F, Li S, Luo X, Peng L, Dong Z, Pausch H, Leonard AS, Crysnanto D, Wang S, Tong B, Lenstra JA, Han J, Li F, Xu T, Gu L, Jin L, Dang R, Huang Y, Lan X, Ren G, Wang Y, Gao Y, Ma Z, Cheng H, Ma Y, Chen H, Pang W, Lei C, Chen N. Structural variation and introgression from wild populations in East Asian cattle genomes confer adaptation to local environment. Genome Biol 2023; 24:211. [PMID: 37723525 PMCID: PMC10507960 DOI: 10.1186/s13059-023-03052-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/07/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Structural variations (SVs) in individual genomes are major determinants of complex traits, including adaptability to environmental variables. The Mongolian and Hainan cattle breeds in East Asia are of taurine and indicine origins that have evolved to adapt to cold and hot environments, respectively. However, few studies have investigated SVs in East Asian cattle genomes and their roles in environmental adaptation, and little is known about adaptively introgressed SVs in East Asian cattle. RESULTS In this study, we examine the roles of SVs in the climate adaptation of these two cattle lineages by generating highly contiguous chromosome-scale genome assemblies. Comparison of the two assemblies along with 18 Mongolian and Hainan cattle genomes obtained by long-read sequencing data provides a catalog of 123,898 nonredundant SVs. Several SVs detected from long reads are in exons of genes associated with epidermal differentiation, skin barrier, and bovine tuberculosis resistance. Functional investigations show that a 108-bp exonic insertion in SPN may affect the uptake of Mycobacterium tuberculosis by macrophages, which might contribute to the low susceptibility of Hainan cattle to bovine tuberculosis. Genotyping of 373 whole genomes from 39 breeds identifies 2610 SVs that are differentiated along a "north-south" gradient in China and overlap with 862 related genes that are enriched in pathways related to environmental adaptation. We identify 1457 Chinese indicine-stratified SVs that possibly originate from banteng and are frequent in Chinese indicine cattle. CONCLUSIONS Our findings highlight the unique contribution of SVs in East Asian cattle to environmental adaptation and disease resistance.
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Affiliation(s)
- Xiaoting Xia
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Fengwei Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Shuang Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Xiaoyu Luo
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Lixin Peng
- National Engineering Research Center for Non-Food Biorefinery, Guangxi Academy of Sciences, 98 Daling Road, Nanning, China
| | - Zheng Dong
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Danang Crysnanto
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Shikang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Bin Tong
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Johannes A Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Jianlin Han
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, Kenya
- CAAS-ILRI Joint Laboratory On Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agriculture Sciences (CAAS), Beijing, China
| | - Fuyong Li
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Tieshan Xu
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Lihong Gu
- Institute of Animal Science & Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou, China
| | - Liangliang Jin
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Ruihua Dang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Yongzhen Huang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Xianyong Lan
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Gang Ren
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Yu Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Yuanpeng Gao
- College of Veterinary Medicine, Northwest A&F University, Xianyang, Yangling, China
| | - Zhijie Ma
- Qinghai Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining, China
| | - Haijian Cheng
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Shandong Key Lab of Animal Disease Control and Breeding, Jinan, China
| | - Yun Ma
- Key Laboratory of Ruminant Molecular and Cellular Breeding of Ningxia Hui Autonomous Region, School of Agriculture, Ningxia University, Yinchuan, China
| | - Hong Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Weijun Pang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China.
| | - Chuzhao Lei
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China.
| | - Ningbo Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China.
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20
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Dai X, Bian P, Hu D, Luo F, Huang Y, Jiao S, Wang X, Gong M, Li R, Cai Y, Wen J, Yang Q, Deng W, Nanaei HA, Wang Y, Wang F, Zhang Z, Rosen BD, Heller R, Jiang Y. A Chinese indicine pangenome reveals a wealth of novel structural variants introgressed from other Bos species. Genome Res 2023; 33:1284-1298. [PMID: 37714713 PMCID: PMC10547261 DOI: 10.1101/gr.277481.122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/30/2023] [Indexed: 09/17/2023]
Abstract
Chinese indicine cattle harbor a much higher genetic diversity compared with other domestic cattle, but their genome architecture remains uninvestigated. Using PacBio HiFi sequencing data from 10 Chinese indicine cattle across southern China, we assembled 20 high-quality partially phased genomes and integrated them into a multiassembly graph containing 148.5 Mb (5.6%) of novel sequence. We identified 156,009 high-confidence nonredundant structural variants (SVs) and 206 SV hotspots spanning ∼195 Mb of gene-rich sequence. We detected 34,249 archaic introgressed fragments in Chinese indicine cattle covering 1.93 Gb (73.3%) of the genome. We inferred an average of 3.8%, 3.2%, 1.4%, and 0.5% of introgressed sequence originating, respectively, from banteng-like, kouprey-like, gayal-like, and gaur-like Bos species, as well as 0.6% of unknown origin. Introgression from multiple donors might have contributed to the genetic diversity of Chinese indicine cattle. Altogether, this study highlights the contribution of interspecies introgression to the genomic architecture of an important livestock population and shows how exotic genomic elements can contribute to the genetic variation available for selection.
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Affiliation(s)
- Xuelei Dai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Peipei Bian
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Dexiang Hu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Funong Luo
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yongzhen Huang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Shaohua Jiao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xihong Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mian Gong
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Ran Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yudong Cai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jiayue Wen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Qimeng Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weidong Deng
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Hojjat Asadollahpour Nanaei
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran 1983969412, Iran
| | - Yu Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Fei Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zijing Zhang
- Institute of Animal Husbandry and Veterinary Science, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, Maryland 20705, USA
| | - Rasmus Heller
- Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark;
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China;
- Key Laboratory of Livestock Biology, Northwest A&F University, Yangling, Shaanxi 712100, China
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21
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Soto DC, Uribe-Salazar JM, Shew CJ, Sekar A, McGinty S, Dennis MY. Genomic structural variation: A complex but important driver of human evolution. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2023; 181 Suppl 76:118-144. [PMID: 36794631 PMCID: PMC10329998 DOI: 10.1002/ajpa.24713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 01/21/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
Structural variants (SVs)-including duplications, deletions, and inversions of DNA-can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single-nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well-documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single-nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever-expanding SV compendium propelled by biotechnology advancements.
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Affiliation(s)
- Daniela C. Soto
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - José M. Uribe-Salazar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Colin J. Shew
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Aarthi Sekar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Sean McGinty
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Megan Y. Dennis
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
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22
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Zheng W, He Y, Guo Y, Yue T, Zhang H, Li J, Zhou B, Zeng X, Li L, Wang B, Cao J, Chen L, Li C, Li H, Cui C, Bai C, Baimakangzhuo, Qi X, Ouzhuluobu, Su B. Large-scale genome sequencing redefines the genetic footprints of high-altitude adaptation in Tibetans. Genome Biol 2023; 24:73. [PMID: 37055782 PMCID: PMC10099689 DOI: 10.1186/s13059-023-02912-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/29/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Tibetans are genetically adapted to high-altitude environments. Though many studies have been conducted, the genetic basis of the adaptation remains elusive due to the poor reproducibility for detecting selective signatures in the Tibetan genomes. RESULTS Here, we present whole-genome sequencing (WGS) data of 1001 indigenous Tibetans, covering the major populated areas of the Qinghai-Tibetan Plateau in China. We identify 35 million variants, and more than one-third of them are novel variants. Utilizing the large-scale WGS data, we construct a comprehensive map of allele frequency and linkage disequilibrium and provide a population-specific genome reference panel, referred to as 1KTGP. Moreover, with the use of a combined approach, we redefine the signatures of Darwinian-positive selection in the Tibetan genomes, and we characterize a high-confidence list of 4320 variants and 192 genes that have undergone selection in Tibetans. In particular, we discover four new genes, TMEM132C, ATP13A3, SANBR, and KHDRBS2, with strong signals of selection, and they may account for the adaptation of cardio-pulmonary functions in Tibetans. Functional annotation and enrichment analysis indicate that the 192 genes with selective signatures are likely involved in multiple organs and physiological systems, suggesting polygenic and pleiotropic effects. CONCLUSIONS Overall, the large-scale Tibetan WGS data and the identified adaptive variants/genes can serve as a valuable resource for future genetic and medical studies of high-altitude populations.
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Affiliation(s)
- Wangshan Zheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Yongbo Guo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Tian Yue
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Hui Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Jun Li
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China
| | - Bin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Xuerui Zeng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Liya Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Bin Wang
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China
| | - Jingxin Cao
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China
| | - Li Chen
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China
| | - Chunxia Li
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China
| | - Hongyan Li
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China
| | - Chaoying Cui
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China
| | - Caijuan Bai
- 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
| | - Xuebin Qi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China.
| | - Ouzhuluobu
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang Hospital, Lhasa, 850000, China.
- High Altitude Medical Research Center, School of Medicine, Tibetan University, Lhasa, 850000, China.
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
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23
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Jin S, Han Z, Hu Y, Si Z, Dai F, He L, Cheng Y, Li Y, Zhao T, Fang L, Zhang T. Structural variation (SV)-based pan-genome and GWAS reveal the impacts of SVs on the speciation and diversification of allotetraploid cottons. MOLECULAR PLANT 2023; 16:678-693. [PMID: 36760124 DOI: 10.1016/j.molp.2023.02.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/22/2023] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
Structural variations (SVs) have long been described as being involved in the origin, adaption, and domestication of species. However, the underlying genetic and genomic mechanisms are poorly understood. Here, we report a high-quality genome assembly of Gossypium barbadense acc. Tanguis, a landrace that is closely related to formation of extra-long-staple (ELS) cultivated cotton. An SV-based pan-genome (Pan-SV) was then constructed using a total of 182 593 non-redundant SVs, including 2236 inversions, 97 398 insertions, and 82 959 deletions from 11 assembled genomes of allopolyploid cotton. The utility of this Pan-SV was then demonstrated through population structure analysis and genome-wide association studies (GWASs). Using segregation mapping populations produced through crossing ELS cotton and the landrace along with an SV-based GWAS, certain SVs responsible for speciation, domestication, and improvement in tetraploid cottons were identified. Importantly, some of the SVs presently identified as associated with the yield and fiber quality improvement had not been identified in previous SNP-based GWAS. In particular, a 9-bp insertion or deletion was found to associate with elimination of the interspecific reproductive isolation between Gossypium hirsutum and G. barbadense. Collectively, this study provides new insights into genome-wide, gene-scale SVs linked to important agronomic traits in a major crop species and highlights the importance of SVs during the speciation, domestication, and improvement of cultivated crop species.
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Affiliation(s)
- Shangkun Jin
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China; Hainan Institute of Zhejiang University, Sanya 572025, China
| | - Zegang Han
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China; Hainan Institute of Zhejiang University, Sanya 572025, China
| | - Yan Hu
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China; Hainan Institute of Zhejiang University, Sanya 572025, China
| | - Zhanfeng Si
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Fan Dai
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Lu He
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yu Cheng
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yiqian Li
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Ting Zhao
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Lei Fang
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China; Hainan Institute of Zhejiang University, Sanya 572025, China
| | - Tianzhen Zhang
- Zhejiang Provincial Engineering Center for Crop Precision Breeding, Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China; Hainan Institute of Zhejiang University, Sanya 572025, China.
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24
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Jiang YF, Wang S, Wang CL, Xu RH, Wang WW, Jiang Y, Wang MS, Jiang L, Dai LH, Wang JR, Chu XH, Zeng YQ, Fang LZ, Wu DD, Zhang Q, Ding XD. Pangenome obtained by long-read sequencing of 11 genomes reveal hidden functional structural variants in pigs. iScience 2023; 26:106119. [PMID: 36852268 PMCID: PMC9958381 DOI: 10.1016/j.isci.2023.106119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/21/2022] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
Long-read sequencing (LRS) facilitates both the genome assembly and the discovery of structural variants (SVs). Here, we built a graph-based pig pangenome by incorporating 11 LRS genomes with an average of 94.01% BUSCO completeness score, revealing 206-Mb novel sequences. We discovered 183,352 nonredundant SVs (63% novel), representing 12.12% of the reference genome. By genotyping SVs in an additional 196 short-read sequencing samples, we identified thousands of population stratified SVs. Particularly, we detected 7,568 Tibetan specific SVs, some of which demonstrate significant population differentiation between Tibetan and low-altitude pigs, which might be associated with the high-altitude hypoxia adaptation in Tibetan pigs. Further integrating functional genomic data, the most promising candidate genes within the SVs that might contribute to the high-altitude hypoxia adaptation were discovered. Overall, our study generates a benchmark pangenome resource for illustrating the important roles of SVs in adaptive evolution, domestication, and genetic improvement of agronomic traits in pigs.
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Affiliation(s)
- Yi-Fan Jiang
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Chong-Long Wang
- 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
| | - Ru-Hai Xu
- Key Laboratory of Animal Genetics and Breeding of Zhejiang Province, Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Wen-Wen Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian 271001, China
| | - Yao Jiang
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
- 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
| | - Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Li Jiang
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Li-He Dai
- Key Laboratory of Animal Genetics and Breeding of Zhejiang Province, Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Jie-Ru Wang
- 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
| | - Xiao-Hong Chu
- Key Laboratory of Animal Genetics and Breeding of Zhejiang Province, Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Yong-Qing Zeng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian 271001, China
| | - Ling-Zhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, Denmark
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian 271001, China
| | - Xiang-Dong Ding
- National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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25
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Li R, Gong M, Zhang X, Wang F, Liu Z, Zhang L, Yang Q, Xu Y, Xu M, Zhang H, Zhang Y, Dai X, Gao Y, Zhang Z, Fang W, Yang Y, Fu W, Cao C, Yang P, Ghanatsaman ZA, Negari NJ, Nanaei HA, Yue X, Song Y, Lan X, Deng W, Wang X, Pan C, Xiang R, Ibeagha-Awemu EM, Heslop-Harrison PJS, Rosen BD, Lenstra JA, Gan S, Jiang Y. A sheep pangenome reveals the spectrum of structural variations and their effects on tail phenotypes. Genome Res 2023; 33:463-477. [PMID: 37310928 PMCID: PMC10078295 DOI: 10.1101/gr.277372.122] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/21/2023] [Indexed: 03/29/2023]
Abstract
Structural variations (SVs) are a major contributor to genetic diversity and phenotypic variations, but their prevalence and functions in domestic animals are largely unexplored. Here we generated high-quality genome assemblies for 15 individuals from genetically diverse sheep breeds using Pacific Biosciences (PacBio) high-fidelity sequencing, discovering 130.3 Mb nonreference sequences, from which 588 genes were annotated. A total of 149,158 biallelic insertions/deletions, 6531 divergent alleles, and 14,707 multiallelic variations with precise breakpoints were discovered. The SV spectrum is characterized by an excess of derived insertions compared to deletions (94,422 vs. 33,571), suggesting recent active LINE expansions in sheep. Nearly half of the SVs display low to moderate linkage disequilibrium with surrounding single-nucleotide polymorphisms (SNPs) and most SVs cannot be tagged by SNP probes from the widely used ovine 50K SNP chip. We identified 865 population-stratified SVs including 122 SVs possibly derived in the domestication process among 690 individuals from sheep breeds worldwide. A novel 168-bp insertion in the 5' untranslated region (5' UTR) of HOXB13 is found at high frequency in long-tailed sheep. Further genome-wide association study and gene expression analyses suggest that this mutation is causative for the long-tail trait. In summary, we have developed a panel of high-quality de novo assemblies and present a catalog of structural variations in sheep. Our data capture abundant candidate functional variations that were previously unexplored and provide a fundamental resource for understanding trait biology in sheep.
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Affiliation(s)
- Ran Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mian Gong
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xinmiao Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Fei Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zhenyu Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Lei Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Qimeng Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yuan Xu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mengsi Xu
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, Xinjiang 832000, China
| | - Huanhuan Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yunfeng Zhang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, Xinjiang 832000, China
| | - Xuelei Dai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yuanpeng Gao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zhuangbiao Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wenwen Fang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yuta Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weiwei Fu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chunna Cao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Peng Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, Xinjiang 832000, China
| | - Zeinab Amiri Ghanatsaman
- Department of Animal Science, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Shiraz 7155863511, Iran
| | | | | | - Xiangpeng Yue
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
| | - Yuxuan Song
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xianyong Lan
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weidong Deng
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Xihong Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chuanying Pan
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Ruidong Xiang
- Faculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, 3052 Victoria, Australia
| | - Eveline M Ibeagha-Awemu
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, Quebec J1M 0C8, Canada
| | - Pat J S Heslop-Harrison
- Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, Maryland 20705, USA
| | - Johannes A Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht 3508 TD, The Netherlands
| | - Shangquan Gan
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi, Xinjiang 832000, China;
- College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China;
- Key Laboratory of Livestock Biology, Northwest A&F University, Yangling, Shaanxi 712100, China
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26
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Cheng H, Zhang Z, Wen J, Lenstra JA, Heller R, Cai Y, Guo Y, Li M, Li R, Li W, He S, Wang J, Shao J, Song Y, Zhang L, Billah M, Wang X, Liu M, Jiang Y. Long divergent haplotypes introgressed from wild sheep are associated with distinct morphological and adaptive characteristics in domestic sheep. PLoS Genet 2023; 19:e1010615. [PMID: 36821549 PMCID: PMC9949681 DOI: 10.1371/journal.pgen.1010615] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023] Open
Abstract
The worldwide sheep population comprises more than 1000 breeds. Together, these exhibit a considerable morphological diversity, which has not been extensively investigated at the molecular level. Here, we analyze whole-genome sequencing individuals of 1,098 domestic sheep from 154 breeds, and 69 wild sheep from seven Ovis species. On average, we detected 6.8%, 1.0% and 0.2% introgressed sequence in domestic sheep originating from Iranian mouflon, urial and argali, respectively, with rare introgressions from other wild species. Interestingly, several introgressed haplotypes contributed to the morphological differentiations across sheep breeds, such as a RXFP2 haplotype from Iranian mouflon conferring the spiral horn trait, a MSRB3 haplotype from argali strongly associated with ear morphology, and a VPS13B haplotype probably originating from urial and mouflon possibly associated with facial traits. Our results reveal that introgression events from wild Ovis species contributed to the high rate of morphological differentiation in sheep breeds, but also to individual variation within breeds. We propose that long divergent haplotypes are a ubiquitous source of phenotypic variation that allows adaptation to a variable environment, and that these remain intact in the receiving population probably due to reduced recombination.
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Affiliation(s)
- Hong Cheng
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhuangbiao Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Jiayue Wen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Johannes A. Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Rasmus Heller
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yudong Cai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Yingwei Guo
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Ran Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Wenrong Li
- Key Laboratory of Ruminant Genetics, Breeding & Reproduction, Ministry of Agriculture, China
- Key Laboratory of Animal Biotechnology of Xinjiang, Institute of Biotechnology, Xinjiang Academy of Animal Science, Urumqi, Xinjiang, China
| | - Sangang He
- Key Laboratory of Ruminant Genetics, Breeding & Reproduction, Ministry of Agriculture, China
- Key Laboratory of Animal Biotechnology of Xinjiang, Institute of Biotechnology, Xinjiang Academy of Animal Science, Urumqi, Xinjiang, China
| | - Jintao Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Junjie Shao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Yuxuan Song
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Lei Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Masum Billah
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Xihong Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Mingjun Liu
- Key Laboratory of Ruminant Genetics, Breeding & Reproduction, Ministry of Agriculture, China
- Key Laboratory of Animal Biotechnology of Xinjiang, Institute of Biotechnology, Xinjiang Academy of Animal Science, Urumqi, Xinjiang, China
- * E-mail: (ML); (YJ)
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
- * E-mail: (ML); (YJ)
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27
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Analysis of dog breed diversity using a composite selection index. Sci Rep 2023; 13:1674. [PMID: 36717599 PMCID: PMC9886904 DOI: 10.1038/s41598-023-28826-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 01/25/2023] [Indexed: 01/31/2023] Open
Abstract
During breed development, domestic dogs have undergone genetic bottlenecks and sustained selective pressures, as a result distinctive genomic diversity occurs to varying degrees within and between breed groups. This diversity can be identified using standard methods or combinations of these methods. This study explored the application of a combined selection index, composite selection signals (CSS), derived from multiple methods to an existing genotype dataset from three breed groups developed in distinct regions of Asia: Qinghai-Tibet plateau dogs (adapted to living at altitude), Xi dogs (with superior running ability) and Mountain hounds (used for hunting ability). The CSS analysis confirmed top ranked genomic regions on CFA10 and CFA21 in Qinghai-Tibet plateau dogs, CFA1 in Xi dogs and CFA5 in Mountain hounds. CSS analysis identified additional significant genomic regions in each group, defined by a total of 1,397, 1,475 and 1,675 significant SNPs in the Qinghai-Tibetan Plateau dogs, Xi dogs and Mountain hounds, respectively. Chitinase 3 Like 1 (CHI3L1) and Leucine Rich Repeat Containing G Protein-Coupled Receptor 6 (LGR6) genes were located in the top ranked region on CFA7 (0.02-1 Mb) in the Qinghai-Tibetan Plateau dogs. Both genes have been associated with hypoxia responses or altitude adaptation in humans. For the Xi dogs, the top ranked region on CFA25 contained the Transient Receptor Potential Cation Channel Subfamily C Member 4 (TRPC4) gene. This calcium channel is important for optimal muscle performance during exercise. The outstanding signals in the Mountain dogs were on CFA5 with 213 significant SNPs that spanned genes involved in cardiac development, sight and generation of biochemical energy. These findings support the use of the combined index approach for identifying novel regions of genome diversity in dogs. As with other methods, the results do not prove causal links between these regions and phenotypes, but they may assist in focusing future studies that seek to identify functional pathways that contribute to breed diversity.
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28
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Zhang L, Zhang R, Zhang F, Yin X, Liu Y, Guo Y, Sun P. Comparison of Cardiorespiratory Fitness of Chinese Tibetan Adolescents with Their Han Counterparts: A Cross-Sectional Retrospective Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16526. [PMID: 36554405 PMCID: PMC9779579 DOI: 10.3390/ijerph192416526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Cardiorespiratory fitness (CRF) is a core element of healthy physical fitness. Foreign attention to CRF in adolescents at different altitudes is high, while less research has been conducted on Chinese adolescents. In order to compare the CRF of Chinese Tibetan adolescents with their Han counterparts born and raised at high altitude and Chinese Han adolescents at sea level. A total of 2748 participants, including Chinese Tibetan adolescents, Chinese Han adolescents born and raised at high altitudes, and Chinese Han adolescents at sea level aged 12-18 years old, were obtained using convenience sampling and random cluster sampling. The method of the 20 m shuttle run test (20 m SRT) test was used to derive VO2max by equation. One-way ANOVA and LSD methods were conducted, and effect sizes were calculated to compare the CRF of the three types of adolescents. Regression analysis was used to analyze the relationship between altitude and VO2max. The VO2max scores of Chinese Tibetan adolescents and Chinese Han adolescents at sea level were higher than Chinese Han adolescents born and raised at high altitudes. For both boys and girls, the VO2max scores of Chinese Tibetan adolescents exceeded Chinese Han adolescents at sea level after the age of 16 years old. Regression analysis showed that altitude was inversely associated with VO2max. The pace of lung growth may distinguish Chinese Tibetan adolescents from Chinese Han adolescents born and raised at high altitudes. The results of the study suggest that we should focus on the changes in CRF in adolescents at different altitudes and should adopt different CRF interventions for adolescents at different altitudes.
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Affiliation(s)
- Li Zhang
- Department of Physical Education, China University of Mining and Technology, Beijing 100083, China
| | - Ruming Zhang
- Department of Physical Education, China University of Mining and Technology, Beijing 100083, China
| | - Feng Zhang
- Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, East China Normal University, Shanghai 200241, China
- College of Physical Education and Health, East China Normal University, Shanghai 200241, China
| | - Xiaojian Yin
- College of Economics and Management, Shanghai Institute of Technology, Shanghai 201418, China
| | - Yuan Liu
- Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, East China Normal University, Shanghai 200241, China
- College of Physical Education and Health, East China Normal University, Shanghai 200241, China
| | - Yaru Guo
- Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, East China Normal University, Shanghai 200241, China
- College of Physical Education and Health, East China Normal University, Shanghai 200241, China
| | - Pengwei Sun
- Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, East China Normal University, Shanghai 200241, China
- College of Physical Education and Health, East China Normal University, Shanghai 200241, China
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29
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Pokrovac I, Pezer Ž. Recent advances and current challenges in population genomics of structural variation in animals and plants. Front Genet 2022; 13:1060898. [PMID: 36523759 PMCID: PMC9745067 DOI: 10.3389/fgene.2022.1060898] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 11/15/2022] [Indexed: 05/02/2024] Open
Abstract
The field of population genomics has seen a surge of studies on genomic structural variation over the past two decades. These studies witnessed that structural variation is taxonomically ubiquitous and represent a dominant form of genetic variation within species. Recent advances in technology, especially the development of long-read sequencing platforms, have enabled the discovery of structural variants (SVs) in previously inaccessible genomic regions which unlocked additional structural variation for population studies and revealed that more SVs contribute to evolution than previously perceived. An increasing number of studies suggest that SVs of all types and sizes may have a large effect on phenotype and consequently major impact on rapid adaptation, population divergence, and speciation. However, the functional effect of the vast majority of SVs is unknown and the field generally lacks evidence on the phenotypic consequences of most SVs that are suggested to have adaptive potential. Non-human genomes are heavily under-represented in population-scale studies of SVs. We argue that more research on other species is needed to objectively estimate the contribution of SVs to evolution. We discuss technical challenges associated with SV detection and outline the most recent advances towards more representative reference genomes, which opens a new era in population-scale studies of structural variation.
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Affiliation(s)
| | - Željka Pezer
- Laboratory for Evolutionary Genetics, Division of Molecular Biology, Ruđer Bošković Institute, Zagreb, Croatia
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30
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Li C, Wu Y, Chen B, Cai Y, Guo J, Leonard AS, Kalds P, Zhou S, Zhang J, Zhou P, Gan S, Jia T, Pu T, Suo L, Li Y, Zhang K, Li L, Purevdorj M, Wang X, Li M, Wang Y, Liu Y, Huang S, Sonstegard T, Wang MS, Kemp S, Pausch H, Chen Y, Han JL, Jiang Y, Wang X. Markhor-derived Introgression of a Genomic Region Encompassing PAPSS2 Confers High-altitude Adaptability in Tibetan Goats. Mol Biol Evol 2022; 39:6830663. [PMID: 36382357 PMCID: PMC9728798 DOI: 10.1093/molbev/msac253] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding the genetic mechanism of how animals adapt to extreme conditions is fundamental to determine the relationship between molecular evolution and changing environments. Goat is one of the first domesticated species and has evolved rapidly to adapt to diverse environments, including harsh high-altitude conditions with low temperature and poor oxygen supply but strong ultraviolet radiation. Here, we analyzed 331 genomes of domestic goats and wild caprid species living at varying altitudes (high > 3000 m above sea level and low < 1200 m), along with a reference-guided chromosome-scale assembly (contig-N50: 90.4 Mb) of a female Tibetan goat genome based on PacBio HiFi long reads, to dissect the genetic determinants underlying their adaptation to harsh conditions on the Qinghai-Tibetan Plateau (QTP). Population genomic analyses combined with genome-wide association studies (GWAS) revealed a genomic region harboring the 3'-phosphoadenosine 5'-phosphosulfate synthase 2 (PAPSS2) gene showing strong association with high-altitude adaptability (PGWAS = 3.62 × 10-25) in Tibetan goats. Transcriptomic data from 13 tissues revealed that PAPSS2 was implicated in hypoxia-related pathways in Tibetan goats. We further verified potential functional role of PAPSS2 in response to hypoxia in PAPSS2-deficient cells. Introgression analyses suggested that the PAPSS2 haplotype conferring the high-altitude adaptability in Tibetan goats originated from a recent hybridization between goats and a wild caprid species, the markhor (Capra falconeri). In conclusion, our results uncover a hitherto unknown contribution of PAPSS2 to high-altitude adaptability in Tibetan goats on QTP, following interspecific introgression and natural selection.
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Affiliation(s)
| | | | | | | | | | | | - Peter Kalds
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Shiwei Zhou
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China,College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Jingchen Zhang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Ping Zhou
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China,State Key Laboratory of Sheep Genetic Improvement and Healthy Breeding, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
| | - Shangqu Gan
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China,State Key Laboratory of Sheep Genetic Improvement and Healthy Breeding, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
| | - Ting Jia
- Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China
| | - Tianchun Pu
- Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China
| | - Langda Suo
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Yan Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ke Zhang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Lan Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Myagmarsuren Purevdorj
- Lab of Animal Genetics and Animal Reproductive Technology, Research Institute of Animal Husbandry, Mongolian University of Life Sciences, Ulaanbaatar 17024, Mongolia
| | - Xihong Wang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ming Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Yu Wang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Yao Liu
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Shuhong Huang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | | | - Ming-Shan Wang
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 94720
| | - Stephen Kemp
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi 30709-00100, Kenya
| | - Hubert Pausch
- Animal Genomics, ETH Zürich, 8092 Zürich, Switzerland
| | - Yulin Chen
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | | | - Yu Jiang
- Corresponding authors: E-mails: ; ;
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31
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Huang D, Feng X, Yang H, Wang J, Zhang W, Fan X, Dong X, Chen K, Yu Y, Ma X, Yi X, Li M. QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes. Nucleic Acids Res 2022; 51:D1122-D1128. [PMID: 36330927 PMCID: PMC9825467 DOI: 10.1093/nar/gkac1020] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.
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Affiliation(s)
| | | | - Hongxi Yang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jianhua Wang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wenwen Zhang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xutong Fan
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiaobao Dong
- Department of Bioinformatics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Bioinformatics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Ying Yu
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xin Ma
- Correspondence may also be addressed to Xin Ma.
| | - Xianfu Yi
- Correspondence may also be addressed to Xianfu Yi.
| | - Mulin Jun Li
- To whom correspondence should be addressed. Tel: +86 22 83336668; Fax: +86 22 83336668;
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32
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Otsuki A, Okamura Y, Ishida N, Tadaka S, Takayama J, Kumada K, Kawashima J, Taguchi K, Minegishi N, Kuriyama S, Tamiya G, Kinoshita K, Katsuoka F, Yamamoto M. Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology. Commun Biol 2022; 5:991. [PMID: 36127505 PMCID: PMC9489684 DOI: 10.1038/s42003-022-03953-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Abstract
Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale. Long-read sequencing on activated T-cells from a sample of 333 Japanese individuals (representing 111 parent-offspring trios) provides a useful reference dataset of structural variation in the Japanese population.
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Affiliation(s)
- Akihito Otsuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Yasunobu Okamura
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Noriko Ishida
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Jun Takayama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15 F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.,Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Kazuki Kumada
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Junko Kawashima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Keiko Taguchi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15 F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.,Department of AI and Innovative Medicine, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Graduate School of Information Sciences, Tohoku University, 6-3-09 Aramaki Aza-Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan. .,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. .,Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
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Gong H, Liu W, Wu Z, Zhang M, Sun Y, Ling Z, Xiao S, Ai H, Xin Y, Yang B, Huang L. Evolutionary insights into porcine genomic structural variations based on a novel constructed dataset from 24 worldwide diverse populations. Evol Appl 2022. [DOI: 10.1111/eva.13455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Huanfa Gong
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
- Key Laboratory of Molecular Animal Nutrition, Ministry of Education, College of Animal Sciences Zhejiang University Hangzhou P.R. China
- Key Laboratory of Animal Nutrition and Feed Science in Eastern China, Ministry of Agriculture, College of Animal Sciences Zhejiang University Hangzhou P.R. China
| | - Weiwei Liu
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Zhongzi Wu
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Mingpeng Zhang
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Yingchun Sun
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Ziqi Ling
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Shijun Xiao
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Huashui Ai
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Yuyun Xin
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Bin Yang
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
| | - Lusheng Huang
- State Key Laboratory of Pig Genetic Improvement and Production Technology Jiangxi Agricultural University Nanchang P.R. China
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Smith G, Manzano-Marín A, Reyes-Prieto M, Antunes CSR, Ashworth V, Goselle ON, Jan AAA, Moya A, Latorre A, Perotti MA, Braig HR. Human follicular mites: Ectoparasites becoming symbionts. Mol Biol Evol 2022; 39:msac125. [PMID: 35724423 PMCID: PMC9218549 DOI: 10.1093/molbev/msac125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
Most humans carry mites in the hair follicles of their skin for their entire lives. Follicular mites are the only metazoans tha continuously live on humans. We propose that Demodex folliculorum (Acari) represents a transitional stage from a host-injuring obligate parasite to an obligate symbiont. Here, we describe the profound impact of this transition on the genome and physiology of the mite. Genome sequencing revealed that the permanent host association of D. folliculorum led to an extensive genome reduction through relaxed selection and genetic drift, resulting in the smallest number of protein-coding genes yet identified among panarthropods. Confocal microscopy revealed that this gene loss coincided with an extreme reduction in the number of cells. Single uninucleate muscle cells are sufficient to operate each of the three segments that form each walking leg. While it has been assumed that the reduction of the cell number in parasites starts early in development, we identified a greater total number of cells in the last developmental stage (nymph) than in the terminal adult stage, suggesting that reduction starts at the adult or ultimate stage of development. This is the first evolutionary step in an arthropod species adopting a reductive, parasitic or endosymbiotic lifestyle. Somatic nuclei show underreplication at the diploid stage. Novel eye structures or photoreceptors as well as a unique human host melatonin-guided day/night rhythm are proposed for the first time. The loss of DNA repair genes coupled with extreme endogamy might have set this mite species on an evolutionary dead-end trajectory.
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Affiliation(s)
- Gilbert Smith
- School of Natural Sciences, Bangor University, Bangor, Wales, United Kingdom
| | - Alejandro Manzano-Marín
- Centre for Microbiology and Environmental Systems Science (CMESS), University of Vienna, Vienna, Austria
| | - Mariana Reyes-Prieto
- Institute of Integrative Systems Biology (I2Sysbio), Universitat de València and Spanish Research Council (CSIC), València, Spain
- Foundation for the Promotion of Health and Biomedical Research of the Valencian Community (FISABIO), València, Spain
| | | | - Victoria Ashworth
- School of Natural Sciences, Bangor University, Bangor, Wales, United Kingdom
| | - Obed Nanjul Goselle
- School of Natural Sciences, Bangor University, Bangor, Wales, United Kingdom
| | | | - Andrés Moya
- Institute of Integrative Systems Biology (I2Sysbio), Universitat de València and Spanish Research Council (CSIC), València, Spain
- Foundation for the Promotion of Health and Biomedical Research of the Valencian Community (FISABIO), València, Spain
- Center for Networked Biomedical Research in Epidemiology and Public Health (CIBEResp), Madrid, Spain
| | - Amparo Latorre
- Institute of Integrative Systems Biology (I2Sysbio), Universitat de València and Spanish Research Council (CSIC), València, Spain
- Foundation for the Promotion of Health and Biomedical Research of the Valencian Community (FISABIO), València, Spain
- Center for Networked Biomedical Research in Epidemiology and Public Health (CIBEResp), Madrid, Spain
| | - M Alejandra Perotti
- School of Biological Sciences, University of Reading, Reading, United Kingdom
| | - Henk R Braig
- School of Natural Sciences, Bangor University, Bangor, Wales, United Kingdom
- Institute and Museum of Natural Sciences, National University of San Juan, San Juan, Argentina
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35
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Population Structure and Genetic Diversity of Chinese Honeybee (Apis Cerana Cerana) in Central China. Genes (Basel) 2022; 13:genes13061007. [PMID: 35741769 PMCID: PMC9222672 DOI: 10.3390/genes13061007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 12/22/2022] Open
Abstract
Central China has a rich terrain with a temperate monsoon climate and varied natural environments for the Chinese honeybee (Apis cerana cerana). However, little comprehensive research on population genetic diversity has been done in this area. A population survey of the structure and genetic diversity of Apis cerana cerana in this area is deeply needed for understanding adaptation to variable environments and providing more references for the protection of honeybee biodiversity. In this study, we present a dataset of 72 populations of Chinese honeybees collected from nine sites by whole genome sequencing in Central China. We obtained 2,790,214,878 clean reads with an average covering a depth of 22×. A total of 27,361,052 single nucleotide polymorphisms (SNPs) were obtained by mapping to the reference genome with an average mapping rate of 93.03%. Genetic evolution analysis was presented via the population structure and genetic diversity based on the datasets of SNPs. It showed that Apis cerana cerana in plains exhibited higher genetic diversity than in mountain areas. The mantel test between Apis cerana cerana groups revealed that some physical obstacles, especially the overurbanization of the plains, contributed to the differentiation. This study is conducive to elucidating the evolution of Apis cerana in different environments and provides a theoretical basis for investigating and protecting the Chinese honeybee.
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36
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Quan C, Lu H, Lu Y, Zhou G. Population-scale genotyping of structural variation in the era of long-read sequencing. Comput Struct Biotechnol J 2022; 20:2639-2647. [PMID: 35685364 PMCID: PMC9163579 DOI: 10.1016/j.csbj.2022.05.047] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 11/29/2022] Open
Abstract
Population-scale studies of structural variation (SV) are growing rapidly worldwide with the development of long-read sequencing technology, yielding a considerable number of novel SVs and complete gap-closed genome assemblies. Herein, we highlight recent studies using a hybrid sequencing strategy and present the challenges toward large-scale genotyping for SVs due to the reference bias. Genotyping SVs at a population scale remains challenging, which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases. We summarize academic efforts to improve genotype quality through linear or graph representations of reference and alternative alleles. Graph-based genotypers capable of integrating diverse genetic information are effectively applied to large and diverse cohorts, contributing to unbiased downstream analysis. Meanwhile, there is still an urgent need in this field for efficient tools to construct complex graphs and perform sequence-to-graph alignments.
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Affiliation(s)
- Cheng Quan
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
| | - Hao Lu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
| | - Yiming Lu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
- Hebei University, Baoding, Hebei Province 071002, PR China
| | - Gangqiao Zhou
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing 100850, PR China
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province 211166, PR China
- Medical College of Guizhou University, Guiyang, Guizhou Province 550025, PR China
- Hebei University, Baoding, Hebei Province 071002, PR China
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37
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Gao Y, Ma L, Liu GE. Initial Analysis of Structural Variation Detections in Cattle Using Long-Read Sequencing Methods. Genes (Basel) 2022; 13:828. [PMID: 35627213 PMCID: PMC9142105 DOI: 10.3390/genes13050828] [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: 04/15/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 02/01/2023] Open
Abstract
Structural variations (SVs), as a great source of genetic variation, are widely distributed in the genome. SVs involve longer genomic sequences and potentially have stronger effects than SNPs, but they are not well captured by short-read sequencing owing to their size and relevance to repeats. Improved characterization of SVs can provide more advanced insight into complex traits. With the availability of long-read sequencing, it has become feasible to uncover the full range of SVs. Here, we sequenced one cattle individual using 10× Genomics (10 × G) linked read, Pacific Biosciences (PacBio) continuous long reads (CLR) and circular consensus sequencing (CCS), as well as Oxford Nanopore Technologies (ONT) PromethION. We evaluated the ability of various methods for SV detection. We identified 21,164 SVs, which amount to 186 Mb covering 7.07% of the whole genome. The number of SVs inferred from long-read-based inferences was greater than that from short reads. The PacBio CLR identified the most of large SVs and covered the most genomes. SVs called with PacBio CCS and ONT data showed high uniformity. The one with the most overlap with the results obtained by short-read data was PB CCS. Together, we found that long reads outperformed short reads in terms of SV detections.
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Affiliation(s)
- Yahui Gao
- Animal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD 20705, USA;
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA;
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA;
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD 20705, USA;
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38
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Yuan L, Zhang X, Luo B, Li X, Tian F, Yan W, Ni Y. Ethnic Specificity of Species and Strain Composition of Lactobacillus Populations From Mother–Infant Pairs, Uncovered by Multilocus Sequence Typing. Front Microbiol 2022; 13:814284. [PMID: 35387090 PMCID: PMC8979337 DOI: 10.3389/fmicb.2022.814284] [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: 11/13/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
The maternal gut is thought to be the principal source of potential probiotic bacteria in the infant gut during the lactation stage. It is not clear whether facultative symbiont lactobacilli strictly follow vertical transmission from mother to infant and display the ethnic specificity in terms of species and strain composition in mother–infant cohorts. In the present study, a total of 16 former Lactobacillus species (365 strains) and 11 species (280 strains) were retrieved from 31 healthy mother–infant pairs of two ethnic groups, which have never intermarried, respectively. The result showed that the composition and number of Lactobacillus species between the two ethnic groups varied. Among 106 Lacticaseibacillus paracasei strains isolated, 64 representative strains were classified into 27 sequence types (ST) by means of multilocus sequence typing (MLST), of which 20 STs derived from 33 Uighur strains and 7 STs from 31 Li strains, and no homologous recombination event of genes was detected between strains of different ethnic groups. A go-EBURST analysis revealed that except for a few mother–infant pairs in which more than one STs were detected, L. paracasei isolates from the same mother–infant pair were found to be monophyletic in most cases, confirming vertical transfer of Lactobacillus at the strain level. More notably, L. paracasei isolates from the same ethnic group were more likely than strains from another to be incorporated into a specific phylogenetic clade or clonal complex (CC) with similar metabolic profile of glycan, supporting the hypothesis of ethnic specificity to a large degree. Our study provides evidence for the development of personalized probiotic tailored to very homogenous localized populations from the perspective of maternal and child health.
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Affiliation(s)
- Lixia Yuan
- School of Food Science and Technology, Shihezi University, Shihezi, China
| | - Xueling Zhang
- School of Food Science and Technology, Shihezi University, Shihezi, China
| | - Baolong Luo
- School of Food Science and Technology, Shihezi University, Shihezi, China
| | - Xu Li
- School of Food Science and Technology, Shihezi University, Shihezi, China
| | - Fengwei Tian
- School of Food Science and Technology, Jiangnan University, Wuxi, China
| | - Wenli Yan
- School of Food Science and Technology, Shihezi University, Shihezi, China
- *Correspondence: Wenli Yan,
| | - Yongqing Ni
- School of Food Science and Technology, Shihezi University, Shihezi, China
- Yongqing Ni,
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Affiliation(s)
- Kin Fai Au
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
- Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH, 43210, USA.
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40
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Wang M, Du W, Tang R, Liu Y, Zou X, Yuan D, Wang Z, Liu J, Guo J, Yang X, Chen J, Yang M, Zhang X, Wei LH, Yuan H, Yeh HY, Wang CC, Liu C, He G. Genomic history and forensic characteristics of Sherpa highlanders on the Tibetan Plateau inferred from high-resolution InDel panel and genome-wide SNPs. Forensic Sci Int Genet 2021; 56:102633. [PMID: 34826721 DOI: 10.1016/j.fsigen.2021.102633] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/13/2021] [Accepted: 11/10/2021] [Indexed: 11/18/2022]
Abstract
Sherpa people, one of the high-altitude hypoxic adaptive populations, mainly reside in Nepal and the southern Tibet Autonomous Region. The genetic origin and detailed evolutionary profiles of Sherpas remain to be further explored and comprehensively characterized. Here we analyzed the newly-generated InDel genotype data from 628 Dingjie Sherpas by merging with 4222 worldwide InDel profiles and collected genome-wide SNP data (approximately 600K SNPs) from 1612 individuals in 191 modern and ancient populations to explore and reconstruct the fine-scale genetic structure of Sherpas and their relationships with nearby modern and ancient East Asians based on the shared alleles and haplotypes. The forensic parameters of 57 autosomal InDels (A-InDels) included in our used new-generation InDel amplification system showed that this focused InDel panel is informative and polymorphic in Dingjie Sherpas, suggesting that it can be used as the supplementary tool for forensic personal identification and parentage testing in Dingjie Sherpas. Descriptive findings from the PCA, ADMIXTURE, and TreeMix-based phylogenies suggested that studied Nepal Sherpas showed excess allele sharing with neighboring Tibeto-Burman Tibetans. Furthermore, patterns of allele sharing in f-statistics demonstrated that Nepal Sherpas had a different evolutionary history compared with their neighbors from Nepal (Newar and Gurung) but showed genetic similarity with 2700-year-old Chokhopani and modern Tibet Tibetans. QpAdm/qpGraph-based admixture sources and models further showed that Sherpas, core Tibetans, and Chokhopani formed one clade, which could be fitted as having the main ancestry from late Neolithic Qijia millet farmers and other deep ancestries from early Asians. Chromosome painting profiles and shared IBD fragments inferred from fineSTRUCTURE and ChromoPainter not only confirmed the abovementioned genomic affinity patterns but also revealed the fine-scale genetic microstructures among Sino-Tibetan speakers. Finally, natural-selection signals revealed via iHS, nSL and iHH12 showed natural selection signatures associated with disease susceptibility in Sherpas. Generally, we provided the comprehensive landscape of admixture and evolutionary history of Sherpa people based on the shared alleles and haplotypes from the InDel-based genotype data and high-density genome-wide SNP data. The more detailed genetic landscape of Sherpa people should be further confirmed and characterized via ancient genomes or single-molecule real-time sequencing technology.
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Affiliation(s)
- Mengge Wang
- Guangzhou Forensic Science Institute, Guangzhou 510030, PR China; Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan Road II, Guangzhou 510080, PR China
| | - Weian Du
- AGCU ScienTech Incorporation, Wuxi 214174, PR China; School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Yan Liu
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan 637100, PR China
| | - Xing Zou
- College of Basic Medicine, Chongqing University, Chongqing 400016, PR China; Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610000, PR China
| | - Didi Yuan
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, PR China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610000, PR China
| | - Jing Liu
- Institute of Forensic Medicine, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610000, PR China
| | - Jianxin Guo
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, PR China
| | - Xiaomin Yang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, PR China
| | - Jing Chen
- Department of Forensic Medicine, Guizhou Medical University, Guiyang 550000, PR China
| | - Meiqing Yang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang 550000, PR China
| | - Xianpeng Zhang
- Institute of Biological Anthropology, Jinzhou Medical University, Jinzhou 121000, PR China
| | - Lan-Hai Wei
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, PR China
| | - Haibing Yuan
- National Demonstration Center for Experimental Archaeology Education and Department of Archaeology, Sichuan University, Chengdu 610200, PR China; School of Archaeology and Museology & National Demonstration Center for Experimental Archaeology Education, Sichuan University, Chengdu, Sichuan 610064, PR China.
| | - Hui-Yuan Yeh
- School of Humanities, Nanyang Technological University, Nanyang 639798, Singapore.
| | - Chuan-Chao Wang
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, PR China.
| | - Chao Liu
- Guangzhou Forensic Science Institute, Guangzhou 510030, PR China; Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, No. 74 Zhongshan Road II, Guangzhou 510080, PR China; School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.
| | - Guanglin He
- Department of Anthropology and Ethnology, Institute of Anthropology, National Institute for Data Science in Health and Medicine, State Key Laboratory of Marine Environmental Science, State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, PR China; School of Humanities, Nanyang Technological University, Nanyang 639798, Singapore.
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Wu Z, Jiang Z, Li T, Xie C, Zhao L, Yang J, Ouyang S, Liu Y, Li T, Xie Z. Structural variants in the Chinese population and their impact on phenotypes, diseases and population adaptation. Nat Commun 2021; 12:6501. [PMID: 34764282 PMCID: PMC8586011 DOI: 10.1038/s41467-021-26856-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 10/21/2021] [Indexed: 02/05/2023] Open
Abstract
A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing has improved the sensitivity of structural variant discovery. Here, we conduct the long-read sequencing-based structural variant analysis for 405 unrelated Chinese individuals, with 68 phenotypic and clinical measurements. We discover a landscape of 132,312 nonredundant structural variants, of which 45.2% are novel. The identified structural variants are of high-quality, with an estimated false discovery rate of 3.2%. The concatenated length of all the structural variants is approximately 13.2% of the human reference genome. We annotate 1,929 loss-of-function structural variants affecting the coding sequence of 1,681 genes. We discover rare deletions in HBA1/HBA2/HBB associated with anemia. Furthermore, we identify structural variants related to immunity which differentiate the northern and southern Chinese populations. Our study describes the landscape of structural variants in the Chinese population and their contribution to phenotypes and disease.
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Affiliation(s)
- Zhikun Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zehang Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chuanbo Xie
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
| | - Jiaqi Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Shuai Ouyang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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Layton KKS, Bradbury IR. Harnessing the power of multi-omics data for predicting climate change response. J Anim Ecol 2021; 91:1064-1072. [PMID: 34679193 DOI: 10.1111/1365-2656.13619] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/11/2021] [Indexed: 01/19/2023]
Abstract
Predicting how species will respond to future climate change is of central importance in the midst of the global biodiversity crisis, and recent work has demonstrated the utility of population genomics for improving these predictions. Here, we suggest a broadening of the approach to include other types of genomic variants that play an important role in adaptation, like structural (e.g. copy number variants) and epigenetic variants (e.g. DNA methylation). These data could provide additional power for forecasting response, especially in weakly structured or panmictic species. Incorporating structural and epigenetic variation into estimates of climate change vulnerability, or maladaptation, may not only improve prediction power but also provide insight into the molecular mechanisms underpinning species' response to climate change.
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Affiliation(s)
- Kara K S Layton
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
| | - Ian R Bradbury
- Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John's, Canada
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