A cross-population atlas of genetic associations for 220 human phenotypes
Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage)...
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| Vydané v: | Nature genetics Ročník 53; číslo 10; s. 1415 - 1424 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Nature Publishing Group US
01.10.2021
Nature Publishing Group |
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| ISSN: | 1061-4036, 1546-1718, 1546-1718 |
| On-line prístup: | Získať plný text |
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| Abstract | Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (
n
= 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (
n
total
= 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.
Genome-wide analyses in BioBank Japan, UK Biobank and FinnGen identify ~5,000 new loci associated with 220 human traits. Statistical decomposition of matrices of phenome-wide summary statistics further highlights variants underpinning diseases across populations. |
|---|---|
| AbstractList | Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal = 628,000) identified -5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesi -free re-investigation of human diseases through genetics. Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n.sub.total = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan ( n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen ( n total = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Genome-wide analyses in BioBank Japan, UK Biobank and FinnGen identify ~5,000 new loci associated with 220 human traits. Statistical decomposition of matrices of phenome-wide summary statistics further highlights variants underpinning diseases across populations. Current genome-wide association studies (GWASs) do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype GWASs (diseases, biomarkers, and medication usage) in BioBank Japan (n=179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal=628,000) identified ~5,000 novel loci, which improved the resolution of genomic map of human traits. This atlas elucidated landscape of pleiotropy as represented by MHC locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically-informed subtyping of similar diseases (e.g., allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n.sub.total = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Genome-wide analyses in BioBank Japan, UK Biobank and FinnGen identify ~5,000 new loci associated with 220 human traits. Statistical decomposition of matrices of phenome-wide summary statistics further highlights variants underpinning diseases across populations. Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. |
| Audience | Academic |
| Author | Ishigaki, Kazuyoshi Akiyama, Masato Terao, Chikashi Takahashi, Yasuo Yamauchi, Toshimasa Komuro, Issei Yamamoto, Kazuhiko Kamatani, Yoichiro Sakaue, Saori Ito, Kaoru Kubo, Michiaki Kadowaki, Takashi Nakamura, Yusuke Karjalainen, Juha Suzuki, Takao Koretsune, Yukihiro Rivas, Manuel A. Masumoto, Akihide Tamiya, Gen Kurki, Mitja Shinozaki, Nobuaki Yamamoto, Masayuki Yamamoto, Kenichi Suzuki, Ken Higashiyama, Masahiko Takahashi, Kazuhisa Obata, Daisuke Yoshimori, Kozo Daly, Mark J. Yamaji, Ken Palotie, Aarno Murayama, Shigeo Matsuda, Koichi Kanai, Masahiro Obara, Wataru Okada, Yukinori Yamaguchi, Hiroki Konuma, Takahiro Narita, Akira Nagayama, Satoshi Tanigawa, Yosuke Koshiba, Seizo Suzuki, Akari Murakami, Yoshinori Asai, Satoshi Minami, Shiro |
| AuthorAffiliation | 10. Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA 7. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA 1. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan 6. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA 37. Graduate School of Medicine, Tohoku University, Sendai, Japan 44. Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA 42. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan 8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA 17. Department of Urology, Iwate Medical University, Iwate, Japan 24. Department of Hematology, Nippon Medical School, Tokyo, Japan 46. Integ |
| AuthorAffiliation_xml | – name: 39. Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan – name: 46. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan – name: 20. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan – name: 32. National Hospital Organization Osaka National Hospital, Osaka, Japan – name: 30. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan – name: 3. Center for Data Sciences, Harvard Medical School, Boston, MA, USA – name: 13. Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan – name: 19. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan – name: 37. Graduate School of Medicine, Tohoku University, Sendai, Japan – name: 31. Aso Iizuka Hospital, Fukuoka, Japan – name: 45. Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, the University of Tokyo, Tokyo, Japan – name: 17. Department of Urology, Iwate Medical University, Iwate, Japan – name: 36. Toranomon Hospital, Tokyo, Japan – name: 24. Department of Hematology, Nippon Medical School, Tokyo, Japan – name: 26. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan – name: 29. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Otsu, Japan – name: 41. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan – name: 42. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan – name: 16. Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan – name: 44. Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, MA, USA – name: 47. These authors contributed equally: S Sakaue and M Kanai – name: 15. Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan – name: 33. Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan – name: 1. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan – name: 5. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA – name: 8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA – name: 6. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA – name: 22. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan – name: 28. The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan – name: 38. Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan – name: 40. Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan – name: 4. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA – name: 11. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan – name: 18. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan – name: 21. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan – name: 43. Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan – name: 23. Tokushukai Group, Tokyo, Japan – name: 7. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA – name: 10. Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA – name: 27. Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan – name: 9. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland – name: 25. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan – name: 2. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan – name: 14. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan – name: 12. The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Sendai, Japan – name: 34. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan – name: 35. Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34594039$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Author Contributions S.S., M. Kanai, and Y.O. conceived the study. S.S., M. Kanai, Y. Tanigawa., M.A.R., and Y.O. wrote the manuscript. S.S., M. Kanai, J.K., M. Kurki, T.Konuma, Kenichi Yamamoto, M.A., K.Ishigaki, Kazuhiko Yamamoto, Y. Kamatani, A.P., M.J.D., and Y.O. conducted GWAS data analysis. S.S., Y. Tanigawa., and M.A.R. conducted statistical decomposition analysis. S.S., S.T., A.N., G.T., and Y.O. conducted metabolome analysis. A.S., K.S., W.O., Ken Yamaji, K.T., S.A., Y.Takahashi, T.S., N.S., H.Y., S.Minami, S.Murayama, Kozo Yoshimori, S.N., D.O., M.H., A.M., Y.Koretsune, K.Ito, C.T., T.Y., I.K., T.Kadowaki, M.Y., Y.N., M.Kubo, Y.M., Kazuhiko Yamamoto, and K.M. collected and managed samples and data. A.P. and M.J.D. coordinated collaboration with FinnGen. |
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| Snippet | Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic... Current genome-wide association studies (GWASs) do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic... |
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| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1415 |
| SubjectTerms | 45/23 45/43 45/61 631/114/2785 631/208/205/2138 692/308/2056 ABO Blood-Group System - genetics Agriculture Allergic diseases Animal Genetics and Genomics Biobanks Biological Specimen Banks Biomarkers Biomedical and Life Sciences Biomedicine Cancer Research Classification Data mining Decomposition Disease Electronic health records Electronic medical records Gene Function Gene mapping Genetic Association Studies Genetic Loci Genetic Pleiotropy Genetic Predisposition to Disease Genetic research Genetics Genome-wide association studies Genome-Wide Association Study Genomes Genotype & phenotype Histocompatibility antigen HLA Human Genetics Humans Loci Major histocompatibility complex Major Histocompatibility Complex - genetics Medical records Meta-Analysis as Topic Mutation - genetics Pathogenesis Phenotype Phenotypes Pleiotropy Population Population genetics Populations Tuberculosis Ulcers |
| Title | A cross-population atlas of genetic associations for 220 human phenotypes |
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| Volume | 53 |
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