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Kim MJ, Heo M, Kim SJ, Song HE, Lee H, Kim NE, Shin H, Do AR, Kim J, Cho YM, Hong YS, Kim WJ, Won S, Yoo HJ. Associations between plasma metabolites and heavy metal exposure in residents of environmentally polluted areas. ENVIRONMENT INTERNATIONAL 2024; 187:108709. [PMID: 38723457 DOI: 10.1016/j.envint.2024.108709] [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: 12/14/2023] [Revised: 04/08/2024] [Accepted: 04/26/2024] [Indexed: 05/19/2024]
Abstract
Heavy metals are commonly released into the environment through industrial processes such as mining and refining. The rapid industrialization that occurred in South Korea during the 1960s and 1970s contributed significantly to the economy of the country; however, the associated mining and refining led to considerable environmental pollution, and although mining is now in decline in South Korea, the detrimental effects on residents inhabiting the surrounding areas remain. The bioaccumulation of toxic heavy metals leads to metabolic alterations in human homeostasis, with disruptions in this balance leading to various health issues. This study used metabolomics to explore metabolomic alterations in the plasma samples of residents living in mining and refining areas. The results showed significant increases in metabolites involved in glycolysis and the surrounding metabolic pathways, such as glucose-6-phosphate, phosphoenolpyruvate, lactate, and inosine monophosphate, in those inhabiting polluted areas. An investigation of the associations between metabolites and blood clinical parameters through meet-in-the-middle analysis indicated that female residents were more affected by heavy metal exposure, resulting in more metabolomic alterations. For women, inhabiting the abandoned mine area, metabolites in the glycolysis and pentose phosphate pathways, such as ribose-5-phosphate and 3-phosphoglycerate, have shown a negative correlation with albumin and calcium. Finally, Mendelian randomization(MR) was used to determine the causal effects of these heavy metal exposure-related metabolites on heavy metal exposure-related clinical parameters. Metabolite biomarkers could provide insights into altered metabolic pathways related to exposure to toxic heavy metals and improve our understanding of the molecular mechanisms underlying the health effects of toxic heavy metal exposure.
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Affiliation(s)
- Mi Jeong Kim
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Min Heo
- Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Su Jung Kim
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Ha Eun Song
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyoyeong Lee
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Nam-Eun Kim
- Department of Public Health Sciences, Seoul National University, Seoul, South Korea
| | - Hyeongyu Shin
- Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, South Korea
| | - Ah Ra Do
- Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, South Korea; RexSoft Corp, Seoul, South Korea
| | - Jeeyoung Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, South Korea
| | - Yong Min Cho
- Department of Nano Chemical and Biological Engineering, Seokyeong University, Seoul, Republic of Korea
| | - Young-Seoub Hong
- Department of Preventive Medicine, College of Medicine, Dong-A University, 32, Daesin Gongwon-ro, Seo-gu, Busan 49201, Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, South Korea
| | - Sungho Won
- Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, South Korea; Department of Public Health Sciences, Seoul National University, Seoul, South Korea; Institute of Health and Environment, Seoul National University, Seoul, South Korea; RexSoft Corp, Seoul, South Korea.
| | - Hyun Ju Yoo
- Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea; Department of Convergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea; Department of Digital Medicine, University of Ulsan College of Medicine, Seoul, South Korea.
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2
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Liu X, Koyama S, Tomizuka K, Takata S, Ishikawa Y, Ito S, Kosugi S, Suzuki K, Hikino K, Koido M, Koike Y, Horikoshi M, Gakuhari T, Ikegawa S, Matsuda K, Momozawa Y, Ito K, Kamatani Y, Terao C. Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing. SCIENCE ADVANCES 2024; 10:eadi8419. [PMID: 38630824 PMCID: PMC11023554 DOI: 10.1126/sciadv.adi8419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 03/07/2024] [Indexed: 04/19/2024]
Abstract
We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.
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Affiliation(s)
- Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
| | - Satoshi Koyama
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Sadaaki Takata
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuki Ishikawa
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shuji Ito
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Department of Orthopedic Surgery, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Shunichi Kosugi
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kunihiko Suzuki
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Keiko Hikino
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshinao Koike
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Department of Orthopedic Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takashi Gakuhari
- Institute for the Study of Ancient Civilizations and Cultural Resources, College of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
| | - Kochi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
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3
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Shi M, Tanikawa C, Munter HM, Akiyama M, Koyama S, Tomizuka K, Matsuda K, Lathrop GM, Terao C, Koido M, Kamatani Y. Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels. Brief Bioinform 2023; 25:bbad509. [PMID: 38221906 PMCID: PMC10788679 DOI: 10.1093/bib/bbad509] [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: 07/14/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024] Open
Abstract
Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.
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Affiliation(s)
- Mingyang Shi
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hans Markus Munter
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Satoshi Koyama
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Gregory Mark Lathrop
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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4
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Liu X, Matsunami M, Horikoshi M, Ito S, Ishikawa Y, Suzuki K, Momozawa Y, Niida S, Kimura R, Ozaki K, Maeda S, Imamura M, Terao C. Natural Selection Signatures in the Hondo and Ryukyu Japanese Subpopulations. Mol Biol Evol 2023; 40:msad231. [PMID: 37903429 PMCID: PMC10615566 DOI: 10.1093/molbev/msad231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/20/2023] [Accepted: 10/06/2023] [Indexed: 11/01/2023] Open
Abstract
Natural selection signatures across Japanese subpopulations are under-explored. Here we conducted genome-wide selection scans with 622,926 single nucleotide polymorphisms for 20,366 Japanese individuals, who were recruited from the main-islands of Japanese Archipelago (Hondo) and the Ryukyu Archipelago (Ryukyu), representing two major Japanese subpopulations. The integrated haplotype score (iHS) analysis identified several signals in one or both subpopulations. We found a novel candidate locus at IKZF2, especially in Ryukyu. Significant signals were observed in the major histocompatibility complex region in both subpopulations. The lead variants differed and demonstrated substantial allele frequency differences between Hondo and Ryukyu. The lead variant in Hondo tags HLA-A*33:03-C*14:03-B*44:03-DRB1*13:02-DQB1*06:04-DPB1*04:01, a haplotype specific to Japanese and Korean. While in Ryukyu, the lead variant tags DRB1*15:01-DQB1*06:02, which had been recognized as a genetic risk factor for narcolepsy. In contrast, it is reported to confer protective effects against type 1 diabetes and human T lymphotropic virus type 1-associated myelopathy/tropical spastic paraparesis. The FastSMC analysis identified 8 loci potentially affected by selection within the past 20-150 generations, including 2 novel candidate loci. The analysis also showed differences in selection patterns of ALDH2 between Hondo and Ryukyu, a gene recognized to be specifically targeted by selection in East Asian. In summary, our study provided insights into the selection signatures within the Japanese and nominated potential sources of selection pressure.
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Affiliation(s)
- Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
| | - Masatoshi Matsunami
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shuji Ito
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuki Ishikawa
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kunihiko Suzuki
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shumpei Niida
- Core Facility Administration, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Ryosuke Kimura
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
| | - Kouichi Ozaki
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Shiro Maeda
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Minako Imamura
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara-Cho, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
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5
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Choi J, Kim S, Kim J, Son HY, Yoo SK, Kim CU, Park YJ, Moon S, Cha B, Jeon MC, Park K, Yun JM, Cho B, Kim N, Kim C, Kwon NJ, Park YJ, Matsuda F, Momozawa Y, Kubo M, Kim HJ, Park JH, Seo JS, Kim JI, Im SW. A whole-genome reference panel of 14,393 individuals for East Asian populations accelerates discovery of rare functional variants. SCIENCE ADVANCES 2023; 9:eadg6319. [PMID: 37556544 PMCID: PMC10411914 DOI: 10.1126/sciadv.adg6319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023]
Abstract
Underrepresentation of non-European (EUR) populations hinders growth of global precision medicine. Resources such as imputation reference panels that match the study population are necessary to find low-frequency variants with substantial effects. We created a reference panel consisting of 14,393 whole-genome sequences including more than 11,000 Asian individuals. Genome-wide association studies were conducted using the reference panel and a population-specific genotype array of 72,298 subjects for eight phenotypes. This panel yields improved imputation accuracy of rare and low-frequency variants within East Asian populations compared with the largest reference panel. Thirty-nine previously unidentified associations were found, and more than half of the variants were East Asian specific. We discovered genes with rare protein-altering variants, including LTBP1 for height and GPR75 for body mass index, as well as putative regulatory mechanisms for rare noncoding variants with cell type-specific effects. We suggest that this dataset will add to the potential value of Asian precision medicine.
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Affiliation(s)
- Jaeyong Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | - Juhyun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ho-Young Son
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Seong-Keun Yoo
- The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Young Jun Park
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungji Moon
- Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Bukyoung Cha
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Min Chul Jeon
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyunghyuk Park
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Jae Moon Yun
- Department of Family Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Belong Cho
- Department of Family Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Family Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | | | | | - Young Joo Park
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | - Hyun-Jin Kim
- National Cancer Control Institute, National Cancer Center, Goyang, Republic of Korea
| | - Jin-Ho Park
- Department of Family Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Family Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jeong-Sun Seo
- Macrogen Inc., Seoul, Republic of Korea
- Asian Genome Center, Seoul National University Bundang Hospital, Gyeonggi, Republic of Korea
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sun-Wha Im
- Department of Biochemistry and Molecular Biology, Kangwon National University School of Medicine, Gangwon, Republic of Korea
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6
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Lee W, Lee S, Yoon JK, Lee D, Kim Y, Han YB, Kim R, Moon S, Park YJ, Park K, Cha B, Choi J, Kim J, Ha NY, Kim K, Cho S, Cho NH, Desai TJ, Chung JH, Lee JH, Kim JI. A single-cell atlas of in vitro multiculture systems uncovers the in vivo lineage trajectory and cell state in the human lung. Exp Mol Med 2023; 55:1831-1842. [PMID: 37582976 PMCID: PMC10474282 DOI: 10.1038/s12276-023-01076-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/14/2023] [Accepted: 04/26/2023] [Indexed: 08/17/2023] Open
Abstract
We present an in-depth single-cell atlas of in vitro multiculture systems on human primary airway epithelium derived from normal and diseased lungs of 27 individual donors. Our large-scale single-cell profiling identified new cell states and differentiation trajectories of rare airway epithelial cell types in human distal lungs. By integrating single-cell datasets of human lung tissues, we discovered immune-primed subsets enriched in lungs and organoids derived from patients with chronic respiratory disease. To demonstrate the full potential of our platform, we further illustrate transcriptomic responses to various respiratory virus infections in vitro airway models. Our work constitutes a single-cell roadmap for the cellular and molecular characteristics of human primary lung cells in vitro and their relevance to human tissues in vivo.
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Affiliation(s)
- Woochan Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Seyoon Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Jung-Ki Yoon
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Dakyung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Yuri Kim
- Institute of Endemic Diseases, Medical Research Center, Seoul National University, Seoul, Korea
| | - Yeon Bi Han
- Department of Pathology and Translational Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Rokhyun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Sungji Moon
- Interdisciplinary Program in Cancer Biology, College of Medicine, Seoul National University, Seoul, Korea
| | - Young Jun Park
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Kyunghyuk Park
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea
| | - Bukyoung Cha
- Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea
| | - Jaeyong Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Juhyun Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Na-Young Ha
- Institute of Endemic Diseases, Medical Research Center, Seoul National University, Seoul, Korea
| | - Kwhanmien Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Sukki Cho
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Nam-Hyuk Cho
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
- Institute of Endemic Diseases, Medical Research Center, Seoul National University, Seoul, Korea
- Department of Microbiology and Immunology, Seoul National University College of Medicine, Seoul, Korea
| | - Tushar J Desai
- Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jin-Haeng Chung
- Department of Pathology and Translational Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
| | - Joo-Hyeon Lee
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.
- Interdisciplinary Program in Cancer Biology, College of Medicine, Seoul National University, Seoul, Korea.
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Korea.
- Cancer Research Institute, Seoul National University, Seoul, Korea.
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7
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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: 0] [Impact Index Per Article: 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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8
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Kim HJ, Baek JH, Lim YR, Kim JH, Yang SM, Koh JS, Oh SM, Shin MK. An analysis of shapes and location of anterior hairline in Asian men. Arch Dermatol Res 2022; 315:1233-1239. [DOI: 10.1007/s00403-022-02505-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/08/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
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9
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Hong EP, Kim BJ, Youn DH, Lee JJ, Jeon HJ, Choi HJ, Cho YJ, Jeon JP. Updated Genome-Wide Association Study of Intracranial Aneurysms by Genotype Correction and Imputation in Koreans. World Neurosurg 2022; 166:e109-e117. [PMID: 35792225 DOI: 10.1016/j.wneu.2022.06.113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Compared to European, Japanese, and Chinese populations, genetic studies on intracranial aneurysms (IAs) in Koreans are lacking. We conducted an updated genome-wide association study (GWAS) to more accurately identify candidate variations predicting IA by genotype correction and imputation than in the first Korean GWAS. METHODS We performed a high-throughput imputation of single-nucleotide polymorphisms (SNPs) and genotype missing values for 250 IA and 296 controls. Out of a total of 7,333,746 sites with an imputation R2 score of ≥0.5, 6,105,212 SNPs were analyzed. A high-throughput GWAS was performed after adjusting for clinical variables and 4 principal component analysis values. RESULTS A total of 39 SNPs reached a significant genome-wide threshold (P < 5 × 10-8). After pruning by pairwise linkage disequilibrium (r2 < 0.8), 11 SNPs were consistently associated with IA. Six tagging SNPs, including rs3120004, rs1851347, rs1522095, rs7779989, rs12935558, rs3826442, and rs2440154, showed strong linkage disequilibrium tower tagging haplotype structures. Among them, rs3120004 tagged a large and strong haplotype structure between LOC440704 and RGS18 genes in 1q31.2 (odds ratio, 2.34; 95% confidence interval, 1.74-3.14; P = 1.4 × 10-8). The rs2440154 (SLC47A1, 17p11.2) SNP increased the risk of IA most significantly (odds ratio, 2.90; 95% confidence interval, 2.07-4.08; P = 8.2 × 10-10). The region encompassing rs3826442 (MYH13, 17p13.1) showed a high recombination rate of approximately 70 cM/Mbp. CONCLUSIONS Our updated GWAS using high-throughput imputation approaches can be an informative milestone in understanding IA formation via susceptibility loci in this stage before large-scale genome-wide association meta-analysis.
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Affiliation(s)
- Eun Pyo Hong
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Bong Jun Kim
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Dong Hyuk Youn
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Hong Jun Jeon
- Department of Neurosurgery, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Hyuk Jai Choi
- Department of Neurosurgery, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Yong Jun Cho
- Department of Neurosurgery, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Jin Pyeong Jeon
- Department of Neurosurgery, Hallym University College of Medicine, Chuncheon, Republic of Korea.
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10
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Zhou HZW, Qiao LY, Zhang YJ, Kang WW, Yan X, Jiang YL, Ke YL, Rao YT, Liu GZ, Wang MY, Wang H, Xi YF, Wang SF. Association of Ethnicity, Sex, and Age With Cancer Diagnoses and Health Care Utilization Among Children in Inner Mongolia, China. JAMA Netw Open 2022; 5:e2231182. [PMID: 36094504 PMCID: PMC9468889 DOI: 10.1001/jamanetworkopen.2022.31182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE China is experiencing a sustained increase in childhood cancer. However, whether differences exist in disease burden by ethnicity remains unclear. OBJECTIVE To compare differences in cancer diagnoses and health care utilization in Inner Mongolia among children subgrouped by ethnicity (Han vs Mongolian), sex, and age. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study in Inner Mongolia, China, used data on children aged 0 to 14 years with cancer from the Inner Mongolia Regional Health Information Platform, which comprises the National Basic Medical Insurance database and the Inner Mongolia cause-of-death reporting system, from January 1, 2013, to December 31, 2019. Ethnicities analyzed included Han and Mongolian; patients of other ethnicities were not included in the analysis because of the small sample size. Cancer was broadly defined as a primary malignant tumor or hematologic cancer; benign central nervous system tumors were also included. A 2-year washout period was used to exclude prevalent cases. After diagnosis, the patients were followed up until the date of death or the end of the insured status, whichever came first. EXPOSURES Ethnicity (Han vs Mongolian), sex (male vs female), and age (0-4, 5-9, and 10-14 years). MAIN OUTCOMES AND MEASURES Crude incidence, 5-year prevalence, and survival rates at 1 year and 3 years after diagnosis; health care utilization, represented by medical costs during the first year and first 3 years after diagnosis; and hospital attendance with level (tertiary vs secondary and lower-level hospitals) and location of each unique visit. RESULTS From 2013 to 2019, 1 106 684 (2013), 1 330 242 (2014), 1 763 746 (2015), 2 400 343 (2016), 2 245 963 (2017), 2 901 088 (2018), and 2 996 580 (2019) children aged 0 to 14 years were registered in the NBMI database. Among the 2 996 580 children enrolled in 2019, the mean (SD) age was 6.8 (4.3) years, of whom 1 572 096 (52.5%) were male, 2 572 091 (85.8%) were Han, and 369 400 (12.3%) were Mongolian. A total of 1910 patients with cancer were identified (1048 were male [54.9%]; 1559 were Han [81.6%], and 300 were Mongolian [15.7%]). There were 764 hematologic cancers (40.0%) and 1146 solid tumors (60.0%). The overall crude incidence of cancer from 2015 to 2019 was 129.85 per million children (95% CI, 123.63-136.06), with a higher incidence among Mongolian than among Han children (155.12 [95% CI, 136.81-173.43] vs 134.39 [95% CI, 127.46-141.32]). The 5-year prevalence was 428.97 per million (95% CI, 405.52-452.42) in 2020, with a higher prevalence among Mongolian than among Han children (568.49 [95% CI, 91.62-645.36] vs 404.34 [95% CI, 379.77-428.91]). The combined 1-year (2015-2019) and 3-year (2015-2017) survival rates were 72.5% (95% CI, 67.5%-77.5%) and 66.8% (95% CI, 61.6%-71.9%), respectively. The 1-year (median [IQR], $1991 [$912-$10 181] vs $3991 [$1171-$15 425]) and 3-year (median [IQR], $2704 [$954-$13 909] vs $5375 [$1283-$22 466]) postdiagnosis costs were lower among Mongolian than among Han children. A higher proportion of Mongolian patients attended low-level hospitals (45.9% vs 17.4%). CONCLUSIONS AND RELEVANCE In this cohort study, Mongolian children had a higher incidence and prevalence of cancer but a lower demand for medical care, suggesting that further investigations are needed to identify mechanisms underlying ethnic disparities and ensure that care is equitable.
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Affiliation(s)
- Hu-Zi-Wei Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Li-Ying Qiao
- Department of Chronic Noncommunicable Diseases Prevention and Control, The Inner Mongolia Autonomous Region Comprehensive Center for Disease Control and Prevention, Inner Mongolia, China
| | - Yun-Jing Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wei-Wei Kang
- Department of Chronic Noncommunicable Diseases Prevention and Control, The Inner Mongolia Autonomous Region Comprehensive Center for Disease Control and Prevention, Inner Mongolia, China
| | - Xue Yan
- School of Public Health, Baotou Medical College, Baotou, China
| | - Yu-Ling Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Ya-Lei Ke
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Ying-Ting Rao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Guo-Zhen Liu
- Bigdata Division, Innovation Center, Peking University Health Information Technology, Beijing, China
| | - Ming-Yuan Wang
- Bigdata Division, Innovation Center, Peking University Health Information Technology, Beijing, China
| | - Hui Wang
- Department of Maternal and Child Health, School of Public Health, Peking University, Beijing China
| | - Yun-Feng Xi
- Department of Chronic Noncommunicable Diseases Prevention and Control, The Inner Mongolia Autonomous Region Comprehensive Center for Disease Control and Prevention, Inner Mongolia, China
| | - Sheng-Feng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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11
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Genomic analyses of 10,376 individuals in the Westlake BioBank for Chinese (WBBC) pilot project. Nat Commun 2022; 13:2939. [PMID: 35618720 PMCID: PMC9135724 DOI: 10.1038/s41467-022-30526-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/05/2022] [Indexed: 01/04/2023] Open
Abstract
We initiate the Westlake BioBank for Chinese (WBBC) pilot project with 4,535 whole-genome sequencing (WGS) individuals and 5,841 high-density genotyping individuals, and identify 81.5 million SNPs and INDELs, of which 38.5% are absent in dbSNP Build 151. We provide a population-specific reference panel and an online imputation server (https://wbbc.westlake.edu.cn/) which could yield substantial improvement of imputation performance in Chinese population, especially for low-frequency and rare variants. By analyzing the singleton density of the WGS data, we find selection signatures in SNX29, DNAH1 and WDR1 genes, and the derived alleles of the alcohol metabolism genes (ADH1A and ADH1B) emerge around 7,000 years ago and tend to be more common from 4,000 years ago in East Asia. Genetic evidence supports the corresponding geographical boundaries of the Qinling-Huaihe Line and Nanling Mountains, which separate the Han Chinese into subgroups, and we reveal that North Han was more homogeneous than South Han. Biobanks of genetic data have been primarily in European populations, which gives us an incomplete understanding of complex traits across populations. Here, the authors initiate the Westlake BioBank for Chinese (WBBC) pilot project with 4,535 whole genome sequences and 5,841 high-density genotypes from China, characterizing large-scale genomic variation in Chinese populations.
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12
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Jung J, Kim H. Shared genetic etiology and antagonistic relationship of plasma renin activity and systolic blood pressure in a Korean cohorts. Genomics 2022; 114:110334. [PMID: 35278618 DOI: 10.1016/j.ygeno.2022.110334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/11/2022] [Accepted: 03/06/2022] [Indexed: 01/14/2023]
Abstract
Despite extensive studies on blood pressure, its genetic risk factors remain uncertain. Even one of the most researched blood pressure-related traits - renin - is not fully understood genetically. Here, we determine the genetic relationship and associated predisposition between blood pressure and baseline renin. In 8840 Korean individuals, we observed a strong negative genome-wide genetic correlation (rg = -0.484) between systolic blood pressure (SBP) and plasma renin activity (PRA), suggesting that antagonistic genetic signals explain the variance in the two traits. We found 51 significant pleiotropic SNPs affecting the two traits, which could contribute to the Renin-Angiotensin-Aldosterone System (RAAS). Our findings provide insight into studies on RAAS by identifying the genome-wide relationship and susceptibility loci of SBP and PRA.
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Affiliation(s)
- Jaehoon Jung
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Republic of Korea; eGnome, 26 Beobwon-ro, Songpa-gu, Seoul 05836, Republic of Korea.
| | - Heebal Kim
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Republic of Korea; eGnome, 26 Beobwon-ro, Songpa-gu, Seoul 05836, Republic of Korea; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea.
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13
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Ou M, Leung HCM, Leung AWS, Luk HM, Yan B, Liu CM, Tong TMF, Mok MTS, Ko WMY, Law WC, Lam TW, Lo IFM, Luo R. HKG: an open genetic variant database of 205 Hong Kong cantonese exomes. NAR Genom Bioinform 2022; 4:lqac005. [PMID: 35156024 PMCID: PMC8826781 DOI: 10.1093/nargab/lqac005] [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: 06/15/2021] [Revised: 12/04/2021] [Accepted: 01/06/2022] [Indexed: 11/23/2022] Open
Abstract
HKG is the first fully accessible variant database for Hong Kong Cantonese, constructed from 205 novel whole-exome sequencing data. There has long been a research gap in the understanding of the genetic architecture of southern Chinese subgroups, including Hong Kong Cantonese. HKG detected 196 325 high-quality variants with 5.93% being novel, and 25 472 variants were found to be unique in HKG compared to three Chinese populations sampled from 1000 Genomes (CHN). PCA illustrates the uniqueness of HKG in CHN, and the admixture study estimated the ancestral composition of HKG and CHN, with a gradient change from north to south, consistent with their geological distribution. ClinVar, CIViC and PharmGKB annotated 599 clinically significant variants and 360 putative loss-of-function variants, substantiating our understanding of population characteristics for future medical development. Among the novel variants, 96.57% were singleton and 6.85% were of high impact. With a good representation of Hong Kong Cantonese, we demonstrated better variant imputation using reference with the addition of HKG data, thus successfully filling the data gap in southern Chinese to facilitate the regional and global development of population genetics.
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Affiliation(s)
- Min Ou
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | | | | | - Ho-Ming Luk
- Clinical Genetic Service, Department of Health, Hong Kong
| | - Bin Yan
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | - Chi-Man Liu
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | | | | | | | | | - Tak-Wah Lam
- Department of Computer Science, The University of Hong Kong, Hong Kong
| | | | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong
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14
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Ohn J, Son HY, Yu DA, Kim MS, Kwon S, Park WS, Kim JI, Kwon O. Early onset female pattern hair loss: a case–control study for analyzing clinical features and genetic variants. J Dermatol Sci 2022; 106:21-28. [DOI: 10.1016/j.jdermsci.2022.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/13/2022] [Accepted: 02/27/2022] [Indexed: 11/26/2022]
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15
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Hwang MY, Choi NH, Won HH, Kim BJ, Kim YJ. Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population. Front Genet 2022; 13:1008646. [PMID: 36506321 PMCID: PMC9731225 DOI: 10.3389/fgene.2022.1008646] [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: 08/01/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Genotype imputation is essential for enhancing the power of association-mapping and discovering rare and indels that are missed by most genotyping arrays. Imputation analysis can be more accurate with a population-specific reference panel or a multi-ethnic reference panel with numerous samples. The National Institute of Health, Republic of Korea, initiated the Korean Reference Genome (KRG) project to identify variants in whole-genome sequences of ∼20,000 Korean participants. In the pilot phase, we analyzed the data from 1,490 participants. The genetic characteristics and imputation performance of the KRG were compared with those of the 1,000 Genomes Project Phase 3, GenomeAsia 100K Project, ChinaMAP, NARD, and TOPMed reference panels. For comparison analysis, genotype panels were artificially generated using whole-genome sequencing data from combinations of four different ancestries (Korean, Japanese, Chinese, and European) and two population-specific optimized microarrays (Korea Biobank Array and UK Biobank Array). The KRG reference panel performed best for the Korean population (R 2 = 0.78-0.84, percentage of well-imputed is 91.9% for allele frequency >5%), although the other reference panels comprised a larger number of samples with genetically different background. By comparing multiple reference panels and multi-ethnic genotype panels, optimal imputation was obtained using reference panels from genetically related populations and a population-optimized microarray. Indeed, the reference panels of KRG and TOPMed showed the best performance when applied to the genotype panels of KBA (R 2 = 0.84) and UKB (R 2 = 0.87), respectively. Using a meta-imputation approach to merge imputation results from different reference panels increased the imputation accuracy for rare variants (∼7%) and provided additional well-imputed variants (∼20%) with comparable imputation accuracy to that of the KRG. Our results demonstrate the importance of using a population-specific reference panel and meta-imputation to assess a substantial number of accurately imputed rare variants.
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Affiliation(s)
- Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea.,Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
| | - Nak-Hyeon Choi
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Hong Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
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16
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Yamada M, Motoike IN, Kojima K, Fuse N, Hozawa A, Kuriyama S, Katsuoka F, Tadaka S, Shirota M, Sakurai M, Nakamura T, Hamanaka Y, Suzuki K, Sugawara J, Ogishima S, Uruno A, Kodama EN, Fujino N, Numakura T, Ichikawa T, Mitsune A, Ohe T, Kinoshita K, Ichinose M, Sugiura H, Yamamoto M. Genetic loci for lung function in Japanese adults with adjustment for exhaled nitric oxide levels as airway inflammation indicator. Commun Biol 2021; 4:1288. [PMID: 34782693 PMCID: PMC8593164 DOI: 10.1038/s42003-021-02813-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 10/27/2021] [Indexed: 11/08/2022] Open
Abstract
Lung function reflects the ability of the respiratory system and is utilized for the assessment of respiratory diseases. Because type 2 airway inflammation influences lung function, genome wide association studies (GWAS) for lung function would be improved by adjustment with an indicator of the inflammation. Here, we performed a GWAS for lung function with adjustment for exhaled nitric oxide (FeNO) levels in two independent Japanese populations. Our GWAS with genotype imputations revealed that the RNF5/AGER locus including AGER rs2070600 SNP, which introduces a G82S substitution of AGER, was the most significantly associated with FEV1/FVC. Three other rare missense variants of AGER were further identified. We also found genetic loci with three candidate genes (NOS2, SPSB2 and RIPOR2) associated with FeNO levels. Analyses with the BioBank-Japan GWAS resource revealed genetic links of FeNO and asthma-related traits, and existence of common genetic background for allergic diseases and their biomarkers. Our study identified the genetic locus most strongly associated with airway obstruction in the Japanese population and three genetic loci associated with FeNO, an indicator of type 2 airway inflammation in adults.
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Affiliation(s)
- Mitsuhiro Yamada
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ikuko N Motoike
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kaname Kojima
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Nobuo Fuse
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Atsushi Hozawa
- Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Fumiki Katsuoka
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Shu Tadaka
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Matsuyuki Shirota
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Miyuki Sakurai
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tomohiro Nakamura
- Department of Health Record Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yohei Hamanaka
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kichiya Suzuki
- Department of Biobank, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Junichi Sugawara
- Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Soichi Ogishima
- Department of Health Record Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Japan
| | - Akira Uruno
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Eiichi N Kodama
- Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Naoya Fujino
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tadahisa Numakura
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomohiro Ichikawa
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ayumi Mitsune
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takashi Ohe
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kengo Kinoshita
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Japan
- Department of System Bioinformatics, Tohoku University Graduate School of Information Sciences, Sendai, Japan
| | | | - Hisatoshi Sugiura
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masayuki Yamamoto
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
- Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, Sendai, Japan.
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Best practices for analyzing imputed genotypes from low-pass sequencing in dogs. Mamm Genome 2021; 33:213-229. [PMID: 34498136 PMCID: PMC8913487 DOI: 10.1007/s00335-021-09914-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/01/2021] [Indexed: 12/15/2022]
Abstract
Although DNA array-based approaches for genome-wide association studies (GWAS) permit the collection of thousands of low-cost genotypes, it is often at the expense of resolution and completeness, as SNP chip technologies are ultimately limited by SNPs chosen during array development. An alternative low-cost approach is low-pass whole genome sequencing (WGS) followed by imputation. Rather than relying on high levels of genotype confidence at a set of select loci, low-pass WGS and imputation rely on the combined information from millions of randomly sampled low-confidence genotypes. To investigate low-pass WGS and imputation in the dog, we assessed accuracy and performance by downsampling 97 high-coverage (> 15×) WGS datasets from 51 different breeds to approximately 1× coverage, simulating low-pass WGS. Using a reference panel of 676 dogs from 91 breeds, genotypes were imputed from the downsampled data and compared to a truth set of genotypes generated from high-coverage WGS. Using our truth set, we optimized a variant quality filtering strategy that retained approximately 80% of 14 M imputed sites and lowered the imputation error rate from 3.0% to 1.5%. Seven million sites remained with a MAF > 5% and an average imputation quality score of 0.95. Finally, we simulated the impact of imputation errors on outcomes for case-control GWAS, where small effect sizes were most impacted and medium-to-large effect sizes were minorly impacted. These analyses provide best practice guidelines for study design and data post-processing of low-pass WGS-imputed genotypes in dogs.
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The ChinaMAP reference panel for the accurate genotype imputation in Chinese populations. Cell Res 2021; 31:1308-1310. [PMID: 34489580 PMCID: PMC8648815 DOI: 10.1038/s41422-021-00564-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 08/26/2021] [Indexed: 11/15/2022] Open
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McInerney TW, Fulton-Howard B, Patterson C, Paliwal D, Jermiin LS, Patel HR, Pa J, Swerdlow RH, Goate A, Easteal S, Andrews SJ. A globally diverse reference alignment and panel for imputation of mitochondrial DNA variants. BMC Bioinformatics 2021; 22:417. [PMID: 34470617 PMCID: PMC8409003 DOI: 10.1186/s12859-021-04337-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 08/16/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Variation in mitochondrial DNA (mtDNA) identified by genotyping microarrays or by sequencing only the hypervariable regions of the genome may be insufficient to reliably assign mitochondrial genomes to phylogenetic lineages or haplogroups. This lack of resolution can limit functional and clinical interpretation of a substantial body of existing mtDNA data. To address this limitation, we developed and evaluated a large, curated reference alignment of complete mtDNA sequences as part of a pipeline for imputing missing mtDNA single nucleotide variants (mtSNVs). We call our reference alignment and pipeline MitoImpute. RESULTS We aligned the sequences of 36,960 complete human mitochondrial genomes downloaded from GenBank, filtered and controlled for quality. These sequences were reformatted for use in imputation software, IMPUTE2. We assessed the imputation accuracy of MitoImpute by measuring haplogroup and genotype concordance in data from the 1000 Genomes Project and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The mean improvement of haplogroup assignment in the 1000 Genomes samples was 42.7% (Matthew's correlation coefficient = 0.64). In the ADNI cohort, we imputed missing single nucleotide variants. CONCLUSION These results show that our reference alignment and panel can be used to impute missing mtSNVs in existing data obtained from using microarrays, thereby broadening the scope of functional and clinical investigation of mtDNA. This improvement may be particularly useful in studies where participants have been recruited over time and mtDNA data obtained using different methods, enabling better integration of early data collected using less accurate methods with more recent sequence data.
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Affiliation(s)
- Tim W McInerney
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Brian Fulton-Howard
- Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
| | - Christopher Patterson
- Keck School of Medicine, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Devashi Paliwal
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Lars S Jermiin
- CSIRO Land and Water, Commonwealth Scientific Industrial and Research Organization, Acton, ACT, 2601, Australia
- Research School of Biology, Australian National University, Canberra, ACT, 2601, Australia
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
- Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Hardip R Patel
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Judy Pa
- Keck School of Medicine, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, Alzheimer's Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Russell H Swerdlow
- Department of Neurology, Alzheimer's Disease Center, University of Kansas, Fairway, KS, USA
| | - Alison Goate
- Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
| | - Simon Easteal
- John Curtin School of Medical Research, Australian National University, Australian Capital Territory, Canberra, Australia
| | - Shea J Andrews
- Genetics and Genomic Sciences, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA.
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Kim S, Shin JY, Kwon NJ, Kim CU, Kim C, Lee CS, Seo JS. Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson's disease. Hum Genomics 2021; 15:58. [PMID: 34454617 PMCID: PMC8403377 DOI: 10.1186/s40246-021-00357-w] [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: 06/25/2021] [Accepted: 08/22/2021] [Indexed: 12/02/2022] Open
Abstract
Background Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.
Results Using eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered. Conclusions Overall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-021-00357-w.
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Affiliation(s)
- Sungjae Kim
- Precision Medicine Institute, Seoul, 08511, Republic of Korea.,Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, 03080, Republic of Korea
| | - Jong-Yeon Shin
- Precision Medicine Institute, Seoul, 08511, Republic of Korea
| | - Nak-Jung Kwon
- Precision Medicine Institute, Seoul, 08511, Republic of Korea
| | | | - Changhoon Kim
- Precision Medicine Institute, Seoul, 08511, Republic of Korea
| | - Chong Sik Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Pungnap 2(i)-dong, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Jeong-Sun Seo
- Precision Medicine Institute, Seoul, 08511, Republic of Korea. .,Asian Genome Institute, Seoul National University Bundang Hospital, 172 Dolma-ro, Seongnam, Bundang-gu, Gyeonggi-do, 13605, Republic of Korea.
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21
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Kojima K, Tadaka S, Katsuoka F, Tamiya G, Yamamoto M, Kinoshita K. A genotype imputation method for de-identified haplotype reference information by using recurrent neural network. PLoS Comput Biol 2020; 16:e1008207. [PMID: 33001993 PMCID: PMC7529210 DOI: 10.1371/journal.pcbi.1008207] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 07/30/2020] [Indexed: 02/02/2023] Open
Abstract
Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario.
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Affiliation(s)
- Kaname Kojima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
- RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
- RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
- School of Medicine, Tohoku University, Sendai, Miyagi, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Miyagi, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Miyagi, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Miyagi, Japan
- * E-mail:
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Abstract
The prevalence and clinical characteristics of depressive disorders differ between women and men; however, the genetic contribution to sex differences in depressive disorders has not been elucidated. To evaluate sex-specific differences in the genetic architecture of depression, whole exome sequencing of samples from 1000 patients (70.7% female) with depressive disorder was conducted. Control data from healthy individuals with no psychiatric disorder (n = 72, 26.4% female) and East-Asian subpopulation 1000 Genome Project data (n = 207, 50.7% female) were included. The genetic variation between men and women was directly compared using both qualitative and quantitative research designs. Qualitative analysis identified five genetic markers potentially associated with increased risk of depressive disorder in females, including three variants (rs201432982 within PDE4A, and rs62640397 and rs79442975 within FDX1L) mapping to chromosome 19p13.2 and two novel variants (rs820182 and rs820148) within MYO15B at the chromosome 17p25.1 locus. Depressed patients homozygous for these variants showed more severe depressive symptoms and higher suicidality than those who were not homozygotes (i.e., heterozygotes and homozygotes for the non-associated allele). Quantitative analysis demonstrated that the genetic burden of protein-truncating and deleterious variants was higher in males than females, even after permutation testing. Our study provides novel genetic evidence that the higher prevalence of depressive disorders in women may be attributable to inherited variants.
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An exome-wide rare variant analysis of Korean men identifies three novel genes predisposing to prostate cancer. Sci Rep 2019; 9:17173. [PMID: 31748686 PMCID: PMC6868235 DOI: 10.1038/s41598-019-53445-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 10/25/2019] [Indexed: 01/26/2023] Open
Abstract
Since prostate cancer is highly heritable, common variants associated with prostate cancer have been studied in various populations, including those in Korea. However, rare and low-frequency variants have a significant influence on the heritability of the disease. The contributions of rare variants to prostate cancer susceptibility have not yet been systematically evaluated in a Korean population. In this work, we present a large-scale exome-wide rare variant analysis of 7,258 individuals (985 cases with prostate cancer and 6,273 controls). In total, 19 rare variant loci spanning 7 genes contributed to an association with prostate cancer susceptibility. In addition to replicating previously known susceptibility genes (e.g., CDYL2, MST1R, GPER1, and PARD3B), 3 novel genes were identified (FDR q < 0.05), including the non-coding RNAs ENTPD3-AS1, LOC102724438, and protein-coding gene SPATA3. Additionally, 6 pathways were identified based on identified variants and genes, including estrogen signaling pathway, signaling by MST1, IL-15 production, MSP-RON signaling pathway, and IL-12 signaling and production in macrophages, which are known to be associated with prostate cancer. In summary, we report novel genes and rare variants that potentially play a role in prostate cancer susceptibility in the Korean population. These observations demonstrated a path towards one of the fundamental goals of precision medicine, which is to identify biomarkers for a subset of the population with a greater risk of disease than others.
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