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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 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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Liang X, Duan Q, Li B, Wang Y, Bu Y, Zhang Y, Kuang Z, Mao L, An X, Wang H, Yang X, Wan N, Feng Z, Shen W, Miao W, Chen J, Liu S, Storz JF, Liu J, Nevo E, Li K. Genomic structural variation contributes to evolved changes in gene expression in high-altitude Tibetan sheep. Proc Natl Acad Sci U S A 2024; 121:e2322291121. [PMID: 38913905 PMCID: PMC11228492 DOI: 10.1073/pnas.2322291121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/06/2024] [Indexed: 06/26/2024] Open
Abstract
Tibetan sheep were introduced to the Qinghai Tibet plateau roughly 3,000 B.P., making this species a good model for investigating genetic mechanisms of high-altitude adaptation over a relatively short timescale. Here, we characterize genomic structural variants (SVs) that distinguish Tibetan sheep from closely related, low-altitude Hu sheep, and we examine associated changes in tissue-specific gene expression. We document differentiation between the two sheep breeds in frequencies of SVs associated with genes involved in cardiac function and circulation. In Tibetan sheep, we identified high-frequency SVs in a total of 462 genes, including EPAS1, PAPSS2, and PTPRD. Single-cell RNA-Seq data and luciferase reporter assays revealed that the SVs had cis-acting effects on the expression levels of these three genes in specific tissues and cell types. In Tibetan sheep, we identified a high-frequency chromosomal inversion that exhibited modified chromatin architectures relative to the noninverted allele that predominates in Hu sheep. The inversion harbors several genes with altered expression patterns related to heart protection, brown adipocyte proliferation, angiogenesis, and DNA repair. These findings indicate that SVs represent an important source of genetic variation in gene expression and may have contributed to high-altitude adaptation in Tibetan sheep.
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Affiliation(s)
- Xiaolong Liang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Qijiao Duan
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Bowen Li
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Yinjia Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Yueting Bu
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Yonglu Zhang
- Fengjia Town Health Center, Rushan City, Weihai City264200, China
| | - Zhuoran Kuang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Leyan Mao
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Xuan An
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Huihua Wang
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing100193, China
| | - Xiaojie Yang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Na Wan
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Zhilong Feng
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Wei Shen
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Weilan Miao
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Jiaqi Chen
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Sanyuan Liu
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Jay F. Storz
- School of Biological Sciences, University of Nebraska, Lincoln, NE68588
| | - Jianquan Liu
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
| | - Eviatar Nevo
- Institute of Evolution, University of Haifa, Haifa3498838, Israel
| | - Kexin Li
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou730000, China
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Yue T, Guo Y, Qi X, Zheng W, Zhang H, Wang B, Liu K, Zhou B, Zeng X, Ouzhuluobu, He Y, Su B. Sex-biased regulatory changes in the placenta of native highlanders contribute to adaptive fetal development. eLife 2024; 12:RP89004. [PMID: 38869160 PMCID: PMC11175615 DOI: 10.7554/elife.89004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Compared with lowlander migrants, native Tibetans have a higher reproductive success at high altitude though the underlying mechanism remains unclear. Here, we compared the transcriptome and histology of full-term placentas between native Tibetans and Han migrants. We found that the placental trophoblast shows the largest expression divergence between Tibetans and Han, and Tibetans show decreased immune response and endoplasmic reticulum stress. Remarkably, we detected a sex-biased expression divergence, where the male-infant placentas show a greater between-population difference than the female-infant placentas. The umbilical cord plays a key role in the sex-biased expression divergence, which is associated with the higher birth weight of the male newborns of Tibetans. We also identified adaptive histological changes in the male-infant placentas of Tibetans, including larger umbilical artery wall and umbilical artery intima and media, and fewer syncytial knots. These findings provide valuable insights into the sex-biased adaptation of human populations, with significant implications for medical and genetic studies of human reproduction.
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Affiliation(s)
- Tian Yue
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life Science, University of Chinese Academy of SciencesBeijingChina
| | - Yongbo Guo
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life Science, University of Chinese Academy of SciencesBeijingChina
| | - Xuebin Qi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang HospitalKunmingChina
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and TechnologyKunmingChina
| | - Wangshan Zheng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Hui Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and TechnologyKunmingChina
| | - Bin Wang
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang HospitalKunmingChina
| | - Kai Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Bin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life Science, University of Chinese Academy of SciencesBeijingChina
| | - Xuerui Zeng
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Kunming College of Life Science, University of Chinese Academy of SciencesBeijingChina
| | - Ouzhuluobu
- Fukang Obstetrics, Gynecology and Children Branch Hospital, Tibetan Fukang HospitalKunmingChina
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
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Zhang W, Jiang Y, Ni Z, Zhou M, Liu L, Li X, Su S, Wang C. Identification of Copy Number Variations and Selection Signatures in Wannan Spotted Pigs by Whole Genome Sequencing Data: A Preliminary Study. Animals (Basel) 2024; 14:1419. [PMID: 38791637 PMCID: PMC11117326 DOI: 10.3390/ani14101419] [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: 04/07/2024] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Copy number variation (CNV) is an important structural variation used to elucidate complex economic traits. In this study, we sequenced 25 Wannan spotted pigs (WSPs) to detect their CNVs and identify their selection signatures compared with those of 10 Asian wild boars. A total of 14,161 CNVs were detected in the WSPs, accounting for 0.72% of the porcine genome. The fixation index (Fst) was used to identify the selection signatures, and 195 CNVs with the top 1% of the Fst value were selected. Eighty genes were identified in the selected CNV regions. Functional GO and KEGG analyses revealed that the genes within these selected CNVs are associated with key traits such as reproduction (GAL3ST1 and SETD2), fatty acid composition (PRKG1, ACACA, ACSL3, UGT8), immune system (LYZ), ear size (WIF1), and feed efficiency (VIPR2). The findings of this study contribute novel insights into the genetic CNVs underlying WSP characteristics and provide essential information for the protection and utilization of WSP populations.
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Affiliation(s)
- Wei Zhang
- Anhui Provincial Breeding Pig Genetic Evaluation Center, 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; (W.Z.); (M.Z.); (L.L.); (X.L.)
| | - Yao Jiang
- National Animal Husbandry Service, Beijing 100125, China;
| | - Zelan Ni
- Anhui Provincial Livestock and Poultry Genetic Resources Conservation Center, Hefei 231283, China;
| | - Mei Zhou
- Anhui Provincial Breeding Pig Genetic Evaluation Center, 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; (W.Z.); (M.Z.); (L.L.); (X.L.)
| | - Linqing Liu
- Anhui Provincial Breeding Pig Genetic Evaluation Center, 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; (W.Z.); (M.Z.); (L.L.); (X.L.)
| | - Xiaoyu Li
- Anhui Provincial Breeding Pig Genetic Evaluation Center, 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; (W.Z.); (M.Z.); (L.L.); (X.L.)
| | - Shiguang Su
- Anhui Provincial Breeding Pig Genetic Evaluation Center, 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; (W.Z.); (M.Z.); (L.L.); (X.L.)
| | - Chonglong Wang
- Anhui Provincial Breeding Pig Genetic Evaluation Center, 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; (W.Z.); (M.Z.); (L.L.); (X.L.)
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段 世, 郑 谦, 石 冰, 冯 帆. [Clinical Characteristics Analysis of 1051 Cases of Cleft Lip With/Without Cleft Palate in the Ethnic Tibetan Population]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2024; 55:397-402. [PMID: 38645847 PMCID: PMC11026881 DOI: 10.12182/20240360203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Indexed: 04/23/2024]
Abstract
Objective To examine the characteristics of the prevalence of congenital cleft lip with/without cleft palate in the ethnic Tibetan population and to provide support for the precise prevention and treatment of cleft lip with/without cleft palate in the Tibetan population. Methods The clinical data of Tibetan patients with cleft lip with/without cleft palate were collected and the clinical characteristics of the patients were analyzed. The patients' age ranged from 2 months to 51 years old. All the subjects were admitted to West China Stomatology Hospital, Sichuan University for the treatment of cleft lip with/without cleft palate between January 2016 and August 2023. Most of the subjects came from Sichuan Province and the Tibet Autonomous Region. Results A total of 1051 patients were enrolled and children aged under 12 months (460 cases) accounted for the largest proportion. Among the subjects, 383 had cleft lip only (36.44%), 140 had cleft palate only (13.32%), and 528 had cleft lip with cleft palate (50.24%). The male-to-female ratios of patients with cleft lip only (0.99∶1), cleft palate only (0.54∶1), and cleft lip with cleft palate (1.67∶1) exhibited significant differences (P<0.001). However, there was no significant difference in the male-to-female ratio in patients with cleft lip only or those with cleft lip with cleft palate when the subjects were divided into two groups according to whether they had unilateral or bilateral cleft lip with/without cleft palate. Most of the patients with bilateral cleft lip were female, while most of the patients with unilateral cleft lip and unilateral or bilateral cleft lip with cleft palate were male. The unilateral cleft lip with/without cleft palate was located predominantly on the left side. Syndromic cleft lip with/without cleft palate accounted for 3.43% of all the cases and the most common concomitant deformity was congenital heart disease. 3.81% (40 cases) of the patients had a family history. In the patients with cleft lip only and those with cleft palate only, the proportion of patients having parents with corresponding phenotypes was higher than those of other phenotypes of cleft lip with/without cleft palate. Regarding the birth time distribution of the children with cleft lip with/without cleft palate, Spring saw the highest number of births of these children (311 cases, 29.59%), while Winter saw the lowest number of births (231 cases, 21.98%). Conclusion The cases of cleft lip with/without cleft palate in the ethnic Tibetan population are predominantly cleft lip and palate. Unilateral cleft lip only or cleft lip with palate is predominantly located on the left side. Lip disease phenotypes may be more heritable.
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Affiliation(s)
- 世均 段
- 口腔疾病防治全国重点实验室 国家口腔疾病临床医学研究中心 四川大学华西口腔医院 口腔颌面外科 (成都 610041)State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - 谦 郑
- 口腔疾病防治全国重点实验室 国家口腔疾病临床医学研究中心 四川大学华西口腔医院 口腔颌面外科 (成都 610041)State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - 冰 石
- 口腔疾病防治全国重点实验室 国家口腔疾病临床医学研究中心 四川大学华西口腔医院 口腔颌面外科 (成都 610041)State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - 帆 冯
- 口腔疾病防治全国重点实验室 国家口腔疾病临床医学研究中心 四川大学华西口腔医院 口腔颌面外科 (成都 610041)State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
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Zhang W, Xu C, Zhou M, Liu L, Ni Z, Su S, Wang C. Copy number variants selected during pig domestication inferred from whole genome resequencing. Front Vet Sci 2024; 11:1364267. [PMID: 38505001 PMCID: PMC10950068 DOI: 10.3389/fvets.2024.1364267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/19/2024] [Indexed: 03/21/2024] Open
Abstract
Over extended periods of natural and artificial selection, China has developed numerous exceptional pig breeds. Deciphering the germplasm characteristics of these breeds is crucial for their preservation and utilization. While many studies have employed single nucleotide polymorphism (SNP) analysis to investigate the local pig germplasm characteristics, copy number variation (CNV), another significant type of genetic variation, has been less explored in understanding pig resources. In this study, we examined the CNVs of 18 Wanbei pigs (WBP) using whole genome resequencing data with an average depth of 12.61. We identified a total of 8,783 CNVs (~30.07 Mb, 1.20% of the pig genome) in WBP, including 8,427 deletions and 356 duplications. Utilizing fixation index (Fst), we determined that 164 CNVs were within the top 1% of the Fst value and defined as under selection. Functional enrichment analyses of the genes associated with these selected CNVs revealed genes linked to reproduction (SPATA6, CFAP43, CFTR, BPTF), growth and development (NR6A1, SMYD3, VIPR2), and immunity (PARD3, FYB2). This study enhances our understanding of the genomic characteristics of the Wanbei pig and offers a theoretical foundation for the future breeding of this breed.
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Affiliation(s)
- Wei Zhang
- Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Anhui Provincial Breeding Pig Genetic Evaluation Center, Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Hefei, China
| | - Chengliang Xu
- Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Anhui Provincial Breeding Pig Genetic Evaluation Center, Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Hefei, China
| | - Mei Zhou
- Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Anhui Provincial Breeding Pig Genetic Evaluation Center, Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Hefei, China
| | - Linqing Liu
- Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Anhui Provincial Breeding Pig Genetic Evaluation Center, Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Hefei, China
| | - Zelan Ni
- Anhui Provincial Livestock and Poultry Genetic Resources Conservation Center, Hefei, China
| | - Shiguang Su
- Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Anhui Provincial Breeding Pig Genetic Evaluation Center, Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Hefei, China
| | - Chonglong Wang
- Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Anhui Provincial Breeding Pig Genetic Evaluation Center, Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Hefei, China
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7
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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: 1] [Impact Index Per Article: 1.0] [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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8
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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: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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9
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Gao Y, Yang X, Chen H, Tan X, Yang Z, Deng L, Wang B, Kong S, Li S, Cui Y, Lei C, Wang Y, Pan Y, Ma S, Sun H, Zhao X, Shi Y, Yang Z, Wu D, Wu S, Zhao X, Shi B, Jin L, Hu Z, Lu Y, Chu J, Ye K, Xu S. A pangenome reference of 36 Chinese populations. Nature 2023:10.1038/s41586-023-06173-7. [PMID: 37316654 PMCID: PMC10322713 DOI: 10.1038/s41586-023-06173-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 05/05/2023] [Indexed: 06/16/2023]
Abstract
Human genomics is witnessing an ongoing paradigm shift from a single reference sequence to a pangenome form, but populations of Asian ancestry are underrepresented. Here we present data from the first phase of the Chinese Pangenome Consortium, including a collection of 116 high-quality and haplotype-phased de novo assemblies based on 58 core samples representing 36 minority Chinese ethnic groups. With an average 30.65× high-fidelity long-read sequence coverage, an average contiguity N50 of more than 35.63 megabases and an average total size of 3.01 gigabases, the CPC core assemblies add 189 million base pairs of euchromatic polymorphic sequences and 1,367 protein-coding gene duplications to GRCh38. We identified 15.9 million small variants and 78,072 structural variants, of which 5.9 million small variants and 34,223 structural variants were not reported in a recently released pangenome reference1. The Chinese Pangenome Consortium data demonstrate a remarkable increase in the discovery of novel and missing sequences when individuals are included from underrepresented minority ethnic groups. The missing reference sequences were enriched with archaic-derived alleles and genes that confer essential functions related to keratinization, response to ultraviolet radiation, DNA repair, immunological responses and lifespan, implying great potential for shedding new light on human evolution and recovering missing heritability in complex disease mapping.
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Affiliation(s)
- Yang Gao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xiaofei Yang
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xinjiang Tan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Zhaoqing Yang
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, China
| | - Lian Deng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Baonan Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Shuang Kong
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Songyang Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Yuhang Cui
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Chang Lei
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Yimin Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuwen Pan
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Sen Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Hao Sun
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, China
| | - Xiaohan Zhao
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Yingbing Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Ziyi Yang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - Dongdong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Shaoyuan Wu
- Jiangsu Key Laboratory of Phylogenomics & Comparative Genomics, International Joint Center of Genomics of Jiangsu Province School of Life Sciences, Jiangsu Normal University, Xuzhou, China
| | - Xingming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education Key (MOE) Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science Fudan University, Shanghai, China
| | - Binyin Shi
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China.
| | - Jiayou Chu
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, China.
| | - Kai Ye
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China.
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
- Jiangsu Key Laboratory of Phylogenomics & Comparative Genomics, International Joint Center of Genomics of Jiangsu Province School of Life Sciences, Jiangsu Normal University, Xuzhou, China.
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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10
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Lin YC, Sahoo BK, Gau SS, Yang RB. The biology of SCUBE. J Biomed Sci 2023; 30:33. [PMID: 37237303 PMCID: PMC10214685 DOI: 10.1186/s12929-023-00925-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
The SCUBE [Signal peptide-Complement C1r/C1s, Uegf, Bmp1 (CUB)-Epithelial growth factor domain-containing protein] family consists of three proteins in vertebrates, SCUBE1, 2 and 3, which are highly conserved in zebrafish, mice and humans. Each SCUBE gene encodes a polypeptide of approximately 1000 amino acids that is organized into five modular domains: (1) an N-terminal signal peptide sequence, (2) nine tandem epidermal growth factor (EGF)-like repeats, (3) a large spacer region, (4) three cysteine-rich (CR) motifs, and (5) a CUB domain at the C-terminus. Murine Scube genes are expressed individually or in combination during the development of various tissues, including those in the central nervous system and the axial skeleton. The cDNAs of human SCUBE orthologs were originally cloned from vascular endothelial cells, but SCUBE expression has also been found in platelets, mammary ductal epithelium and osteoblasts. Both soluble and membrane-associated SCUBEs have been shown to play important roles in physiology and pathology. For instance, upregulation of SCUBEs has been reported in acute myeloid leukemia, breast cancer and lung cancer. In addition, soluble SCUBE1 is released from activated platelets and can be used as a clinical biomarker for acute coronary syndrome and ischemic stroke. Soluble SCUBE2 enhances distal signaling by facilitating the secretion of dual-lipidated hedgehog from nearby ligand-producing cells in a paracrine manner. Interestingly, the spacer regions and CR motifs can increase or enable SCUBE binding to cell surfaces via electrostatic or glycan-lectin interactions. As such, membrane-associated SCUBEs can function as coreceptors that enhance the signaling activity of various serine/threonine kinase or tyrosine kinase receptors. For example, membrane-associated SCUBE3 functions as a coreceptor that promotes signaling in bone morphogenesis. In humans, SCUBE3 mutations are linked to abnormalities in growth and differentiation of both bones and teeth. In addition to studies on human SCUBE function, experimental results from genetically modified mouse models have yielded important insights in the field of systems biology. In this review, we highlight novel molecular discoveries and critical directions for future research on SCUBE proteins in the context of cancer, skeletal disease and cardiovascular disease.
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Affiliation(s)
- Yuh-Charn Lin
- Department of Physiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Binay K Sahoo
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Shiang-Shin Gau
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Ruey-Bing Yang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan.
- Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
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11
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Gonzalez-Garcia L, Guevara-Barrientos D, Lozano-Arce D, Gil J, Díaz-Riaño J, Duarte E, Andrade G, Bojacá JC, Hoyos-Sanchez MC, Chavarro C, Guayazan N, Chica LA, Buitrago Acosta MC, Bautista E, Trujillo M, Duitama J. New algorithms for accurate and efficient de novo genome assembly from long DNA sequencing reads. Life Sci Alliance 2023; 6:e202201719. [PMID: 36813568 PMCID: PMC9946810 DOI: 10.26508/lsa.202201719] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/24/2023] Open
Abstract
Building de novo genome assemblies for complex genomes is possible thanks to long-read DNA sequencing technologies. However, maximizing the quality of assemblies based on long reads is a challenging task that requires the development of specialized data analysis techniques. We present new algorithms for assembling long DNA sequencing reads from haploid and diploid organisms. The assembly algorithm builds an undirected graph with two vertices for each read based on minimizers selected by a hash function derived from the k-mer distribution. Statistics collected during the graph construction are used as features to build layout paths by selecting edges, ranked by a likelihood function. For diploid samples, we integrated a reimplementation of the ReFHap algorithm to perform molecular phasing. We ran the implemented algorithms on PacBio HiFi and Nanopore sequencing data taken from haploid and diploid samples of different species. Our algorithms showed competitive accuracy and computational efficiency, compared with other currently used software. We expect that this new development will be useful for researchers building genome assemblies for different species.
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Affiliation(s)
- Laura Gonzalez-Garcia
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | | | - Daniela Lozano-Arce
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Juanita Gil
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR, USA
| | - Jorge Díaz-Riaño
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Erick Duarte
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Germán Andrade
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Juan Camilo Bojacá
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | | | - Christian Chavarro
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Natalia Guayazan
- Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Luis Alberto Chica
- Research Group on Computational Biology and Microbial Ecology, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
- Max Planck Tandem Group in Computational Biology, Universidad de los Andes, Bogotá, Colombia
| | | | - Edwin Bautista
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Miller Trujillo
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Jorge Duitama
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
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12
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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, Qi X, 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: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [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
| | - 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.
| | - 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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13
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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: 3] [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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14
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Muñoz-Barrera A, Rubio-Rodríguez LA, Díaz-de Usera A, Jáspez D, Lorenzo-Salazar JM, González-Montelongo R, García-Olivares V, Flores C. From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research. Life (Basel) 2022; 12:1939. [PMID: 36431075 PMCID: PMC9695713 DOI: 10.3390/life12111939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/12/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.
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Affiliation(s)
- Adrián Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Ana Díaz-de Usera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - David Jáspez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - José M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Rafaela González-Montelongo
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Víctor García-Olivares
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Fernando de Pessoa Canarias, 35450 Las Palmas de Gran Canaria, Spain
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15
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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: 0] [Impact Index Per Article: 0] [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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16
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Population Structure and Selection Signatures Underlying Domestication Inferred from Genome-Wide Copy Number Variations in Chinese Indigenous Pigs. Genes (Basel) 2022; 13:genes13112026. [PMID: 36360263 PMCID: PMC9690591 DOI: 10.3390/genes13112026] [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: 10/18/2022] [Revised: 10/28/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Single nucleotide polymorphism was widely used to perform genetic and evolution research in pigs. However, little is known about the effect of copy number variation (CNV) on characteristics in pigs. This study performed a genome-wide comparison of CNVs between Wannan black pigs (WBP) and Asian wild boars (AWB), using whole genome resequencing data. By using Manta, we detected in total 28,720 CNVs that covered approximately 1.98% of the pig genome length. We identified 288 selected CNVs (top 1%) by performing Fst statistics. Functional enrichment analyses for genes located in selected CNVs were found to be muscle related (NDN, TMOD4, SFRP1, and SMYD3), reproduction related (GJA1, CYP26B1, WNT5A, SRD5A2, PTPN11, SPEF2, and CCNB1), residual feed intake (RFI) related (MAP3K5), and ear size related (WIF1). This study provides essential information on selected CNVs in Wannan black pigs for further research on the genetic basis of the complex phenotypic and provides essential information for direction in the protection and utilization of Wannan black pig.
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17
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Abstract
Strong ultraviolet (UV) radiation at high altitude imposes a serious selective pressure, which may induce skin pigmentation adaptation of indigenous populations. We conducted skin pigmentation phenotyping and genome-wide analysis of Tibetans in order to understand the underlying mechanism of adaptation to UV radiation. We observe that Tibetans have darker baseline skin color compared with lowland Han Chinese, as well as an improved tanning ability, suggesting a two-level adaptation to boost their melanin production. A genome-wide search for the responsible genes identifies GNPAT showing strong signals of positive selection in Tibetans. An enhancer mutation (rs75356281) located in GNPAT intron 2 is enriched in Tibetans (58%) but rare in other world populations (0 to 18%). The adaptive allele of rs75356281 is associated with darker skin in Tibetans and, under UVB treatment, it displays higher enhancer activities compared with the wild-type allele in in vitro luciferase assays. Transcriptome analyses of gene-edited cells clearly show that with UVB treatment, the adaptive variant of GNPAT promotes melanin synthesis, likely through the interactions of CAT and ACAA1 in peroxisomes with other pigmentation genes, and they act synergistically, leading to an improved tanning ability in Tibetans for UV protection.
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18
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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: 3.0] [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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19
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Mammalian eIF4E2-GSK3β maintains basal phosphorylation of p53 to resist senescence under hypoxia. Cell Death Dis 2022; 13:459. [PMID: 35568694 PMCID: PMC9107480 DOI: 10.1038/s41419-022-04897-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 04/25/2022] [Accepted: 04/29/2022] [Indexed: 12/14/2022]
Abstract
Hypoxia modulates senescence, but their physiological link remains unclear. Here, we found that eIF4E2, a hypoxia-activated translation initiation factor, interacted with GSK3β to maintain phosphorylation of p53, thus resisting senescence under hypoxia. RNA-binding protein RBM38 interacted with eIF4E to inhibit the translation of p53, but GSK3β-mediated Ser195 phosphorylation disrupted the RBM38-eIF4E interaction. Through investigation of RBM38 phosphorylation, we found that the eIF4E2-GSK3β pathway specifically regulated proline-directed serine/threonine phosphorylation (S/T-P). Importantly, peptides e2-I or G3-I that blocking eIF4E2-GSK3β interaction can inhibit the basal S/T-P phosphorylation of p53 at multiple sites, therby inducing senescence through transcriptional inhibition. Additionally, a nanobody was screened via the domain where eIF4E2 bound to GSK3β, and this nanobody inhibited S/T-P phosphorylation to promote senescence. Furthermore, hypoxia inhibited eIF4E2-GSK3β pathway by mediating S-Nitrosylation of GSK3β. Blocking eIF4E2-GSK3β interaction promoted liver senescence under hypoxia, thus leading to liver fibrosis, eventually accelerating N, N-diethylnitrosamine (DEN)-induced tumorigenesis. Interestingly, eIF4E2 isoforms with GSK3β-binding motif exclusively exist in mammals, which protect zebrafish heart against hypoxia. Together, this study reveals a mammalian eIF4E2-GSK3β pathway that prevents senescence by maintaining basal S/T-P phosphorylation of p53, which underlies hypoxia adaptation of tissues.
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20
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Deng Y, Qian Y, Meng M, Jiang H, Dong Y, Fang C, He S, Yang L. Extensive sequence divergence between the reference genomes of two zebrafish strains Tuebingen and AB. Mol Ecol Resour 2022; 22:2148-2157. [DOI: 10.1111/1755-0998.13602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 01/14/2022] [Accepted: 02/15/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Yu Deng
- State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China
- Academy of Plateau Science and Sustainability Qinghai Normal University Xining 810016 P. R. China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Yuting Qian
- State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Minghui Meng
- Diggers (Wuhan) Biotechnology Co., Ltd Wuhan 430070 China
| | - Haifeng Jiang
- State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Yang Dong
- State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan Yunnan Agricultural University Kunming 650201 China
| | - Chengchi Fang
- State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China
- Academy of Plateau Science and Sustainability Qinghai Normal University Xining 810016 P. R. China
| | - Shunping He
- State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China
- Academy of Plateau Science and Sustainability Qinghai Normal University Xining 810016 P. R. China
- Institute of Deep Sea Science and Engineering Chinese Academy of Sciences Sanya China
- Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming 650223 China
| | - Liandong Yang
- State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China
- Academy of Plateau Science and Sustainability Qinghai Normal University Xining 810016 P. R. China
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21
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Lou H, Gao Y, Xie B, Wang Y, Zhang H, Shi M, Ma S, Zhang X, Liu C, Xu S. Haplotype-resolved de novo assembly of a Tujia genome suggests the necessity for high-quality population-specific genome references. Cell Syst 2022; 13:321-333.e6. [PMID: 35180379 DOI: 10.1016/j.cels.2022.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 11/09/2021] [Accepted: 01/27/2022] [Indexed: 12/17/2022]
Abstract
Even though the human reference genome assembly is continually being improved, it remains debatable whether a population-specific reference is necessary for every ethnic group. Here, we de novo assembled an individual genome (TJ1) from the Tujia population, an ethnic minority group most closely related to the Han Chinese. TJ1 provided a high-quality haplotype-resolved assembly of chromosome-scale with a scaffold N50 size >78 Mb. Compared with GRCh38 and other de novo assemblies, TJ1 improved short-read mapping, enhanced calling precision for structural variants, and detected rare and low-frequency variants. This revealed fine-scale differences between the closely related Han and Tujia populations, such as population-stratified variants of LCT and UBXN8, and improved screening for ancestry informative markers. We demonstrated that TJ1 could reduce false positives in clinical diagnosis and analyzed the PRSS1-PRSS2 locus as a test case. Our results suggest that population-specific assemblies are necessary for genetic and medical analysis, especially when closely related populations are studied. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Haiyi Lou
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.
| | - Yang Gao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bo Xie
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yimin Wang
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Miao Shi
- Berry Genomics, Beijing 102200, China
| | - Sen Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxi Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chang Liu
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China; Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou 221116, China; Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China; Ministry of Education Key Laboratory of Contemporary Anthropology, Human Phenome Institute, Fudan University, Shanghai 201203, China.
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22
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Quan C, Li Y, Liu X, Wang Y, Ping J, Lu Y, Zhou G. Characterization of structural variation in Tibetans reveals new evidence of high-altitude adaptation and introgression. Genome Biol 2021; 22:159. [PMID: 34034800 PMCID: PMC8146648 DOI: 10.1186/s13059-021-02382-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/14/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Structural variation (SV) acts as an essential mutational force shaping the evolution and function of the human genome. However, few studies have examined the role of SVs in high-altitude adaptation and little is known of adaptive introgressed SVs in Tibetans so far. RESULTS Here, we generate a comprehensive catalog of SVs in a Chinese Tibetan (n = 15) and Han (n = 10) population using nanopore sequencing technology. Among a total of 38,216 unique SVs in the catalog, 27% are sequence-resolved for the first time. We systematically assess the distribution of these SVs across repeat sequences and functional genomic regions. Through genotyping in additional 276 genomes, we identify 69 Tibetan-Han stratified SVs and 80 candidate adaptive genes. We also discover a few adaptive introgressed SV candidates and provide evidence for a deletion of 335 base pairs at 1p36.32. CONCLUSIONS Overall, our results highlight the important role of SVs in the evolutionary processes of Tibetans' adaptation to the Qinghai-Tibet Plateau and provide a valuable resource for future high-altitude adaptation studies.
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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, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Yuanfeng Li
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Xinyi Liu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Yahui Wang
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Jie Ping
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
| | - Yiming Lu
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
- Hebei University, Baoding, Hebei Province 071002 People’s Republic of China
| | - Gangqiao Zhou
- Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850 People’s Republic of China
- Hebei University, Baoding, Hebei Province 071002 People’s Republic of China
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province 211166 People’s Republic of China
- Medical College of Guizhou University, Guiyang, Guizhou Province 550025 People’s Republic of China
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23
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Takayama J, Tadaka S, Yano K, Katsuoka F, Gocho C, Funayama T, Makino S, Okamura Y, Kikuchi A, Sugimoto S, Kawashima J, Otsuki A, Sakurai-Yageta M, Yasuda J, Kure S, Kinoshita K, Yamamoto M, Tamiya G. Construction and integration of three de novo Japanese human genome assemblies toward a population-specific reference. Nat Commun 2021; 12:226. [PMID: 33431880 PMCID: PMC7801658 DOI: 10.1038/s41467-020-20146-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 11/17/2020] [Indexed: 12/21/2022] Open
Abstract
The complete human genome sequence is used as a reference for next-generation sequencing analyses. However, some ethnic ancestries are under-represented in the reference genome (e.g., GRCh37) due to its bias toward European and African ancestries. Here, we perform de novo assembly of three Japanese male genomes using > 100× Pacific Biosciences long reads and Bionano Genomics optical maps per sample. We integrate the genomes using the major allele for consensus and anchor the scaffolds using genetic and radiation hybrid maps to reconstruct each chromosome. The resulting genome sequence, JG1, is contiguous, accurate, and carries the Japanese major allele at most loci. We adopt JG1 as the reference for confirmatory exome re-analyses of seven rare-disease Japanese families and find that re-analysis using JG1 reduces total candidate variant calls versus GRCh37 while retaining disease-causing variants. These results suggest that integrating multiple genomes from a single population can aid genome analyses of that population.
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Affiliation(s)
- Jun Takayama
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Tohoku Medical Megabank Organization, 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 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Kenji Yano
- Tohoku Medical Megabank Organization, 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 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
| | - Fumiki Katsuoka
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Chinatsu Gocho
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Takamitsu Funayama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Satoshi Makino
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Yasunobu Okamura
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Atsuo Kikuchi
- Department of Pediatrics, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Sachiyo Sugimoto
- 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
| | - Akihito Otsuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Mika Sakurai-Yageta
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
| | - Jun Yasuda
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Division of Molecular and Cellular Oncology, Miyagi Cancer Center Research Institute, 47-1, Nodayama, Medeshima-Shiode, Natori, Miyagi, 981-1293, Japan
| | - Shigeo Kure
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan
- Department of Pediatrics, Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan
| | - Kengo Kinoshita
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Tohoku Medical Megabank Organization, 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.
| | - Masayuki Yamamoto
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
| | - Gen Tamiya
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan.
- Tohoku Medical Megabank Organization, 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 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
- Tohoku University Graduate School of Medicine, 2-1, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
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24
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Abstract
East Asia constitutes one-fifth of the global population and exhibits substantial genetic diversity. However, genetic investigations on populations in this region have been largely under-represented compared with European populations. Nonetheless, the last decade has seen considerable efforts and progress in genome-wide genotyping and whole-genome sequencing of the East-Asian ethnic groups. Here, we review the recent studies in terms of ancestral origin, population relationship, genetic differentiation, and admixture of major East- Asian groups, such as the Chinese, Korean, and Japanese populations. We mainly focus on insights from the whole-genome sequence data and also include the recent progress based on mitochondrial DNA (mtDNA) and Y chromosome data. We further discuss the evolutionary forces driving genetic diversity in East-Asian populations, and provide our perspectives for future directions on population genetics studies, particularly on underrepresented indigenous groups in East Asia.
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Affiliation(s)
- Ziqing Pan
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuhua Xu
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
- School of Life Science and Technology, ShanghaiTech Universit, Shanghai, 201210, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China.
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25
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Lee J, Nishiyama T, Shigenobu S, Yamaguchi K, Suzuki Y, Shimada T, Katsuma S, Kiuchi T. The genome sequence of Samia ricini, a new model species of lepidopteran insect. Mol Ecol Resour 2020; 21:327-339. [PMID: 32985129 DOI: 10.1111/1755-0998.13259] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 08/24/2020] [Accepted: 09/01/2020] [Indexed: 01/15/2023]
Abstract
Samia ricini, a gigantic saturniid moth, has the potential to be a novel lepidopteran model species. Samia ricini is far more resistant to diseases than the current model species Bombyx mori, and therefore can be more easily reared. In addition, genetic resources available for S. ricini rival those for B. mori: at least 26 ecoraces of S. ricini are reported and S. ricini can hybridize with wild Samia species, which are distributed throughout Asian countries, and produce fertile progenies. Physiological traits such as food preference, integument colour and larval spot pattern differ among S. ricini strains and wild Samia species so that those traits can be targeted in forward genetic analyses. To facilitate genetic research in S. ricini, we determined its whole genome sequence. The assembled genome of S. ricini was 458 Mb with 155 scaffolds, and the scaffold N50 length of the assembly was ~ 21 Mb. In total, 16,702 protein coding genes were predicted. While the S. ricini genome was mostly collinear with that of B. mori with some rearrangements and few S. ricini-specific genes were discovered, chorion genes and fibroin genes seemed to have expanded in the S. ricini lineage. As the first step of genetic analyses, causal genes for "Blue," "Yellow," "Spot," and "Red cocoon" phenotypes were mapped to chromosomes.
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Affiliation(s)
- Jung Lee
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.,Department of Life Science, Faculty of Science/Graduate School of Science, Gakushuin University, Tokyo, Japan
| | - Tomoaki Nishiyama
- Advanced Science Research Center, Kanazawa University, Kanazawa, Japan
| | - Shuji Shigenobu
- Functional Genomics Facility, National Institute for Basic Biology, Okazaki, Japan
| | - Katsushi Yamaguchi
- Functional Genomics Facility, National Institute for Basic Biology, Okazaki, Japan
| | - Yutaka Suzuki
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Toru Shimada
- Department of Life Science, Faculty of Science/Graduate School of Science, Gakushuin University, Tokyo, Japan
| | - Susumu Katsuma
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Takashi Kiuchi
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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