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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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Tynkkynen NP, Törmäkangas T, Palviainen T, Hyvärinen M, Klevjer M, Joensuu L, Kujala U, Kaprio J, Bye A, Sillanpää E. Associations of polygenic inheritance of physical activity with aerobic fitness, cardiometabolic risk factors and diseases: the HUNT study. Eur J Epidemiol 2023; 38:995-1008. [PMID: 37603226 PMCID: PMC10501929 DOI: 10.1007/s10654-023-01029-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the associations between higher levels of PA and better aerobic fitness and a lower risk for CMDs. We aimed to study how PA genotype associates with self-reported PA, aerobic fitness, cardiometabolic risk factors and diseases. PA genotype, which combined variation in over one million of gene variants, was composed using the SBayesR polygenic scoring methodology. First, we constructed a polygenic risk score for PA in the Trøndelag Health Study (N = 47,148) using UK Biobank single nucleotide polymorphism-specific weights (N = 400,124). The associations of the PA PRS and continuous variables were analysed using linear regression models and with CMD incidences using Cox proportional hazard models. The results showed that genotypes predisposing to higher amount of PA were associated with greater self-reported PA (Beta [B] = 0.282 MET-h/wk per SD of PRS for PA, 95% confidence interval [CI] = 0.211, 0.354) but not with aerobic fitness. These genotypes were also associated with healthier cardiometabolic profile (waist circumference [B = -0.003 cm, 95% CI = -0.004, -0.002], body mass index [B = -0.002 kg/m2, 95% CI = -0.004, -0.001], high-density lipoprotein cholesterol [B = 0.004 mmol/L, 95% CI = 0.002, 0.006]) and lower incidence of hypertensive diseases (Hazard Ratio [HR] = 0.97, 95% CI = 0.951, 0.990), stroke (HR = 0.94, 95% CI = 0.903, 0.978) and type 2 diabetes (HR = 0.94, 95 % CI = 0.902, 0.970). Observed associations were independent of self-reported PA. These results support earlier findings suggesting small pleiotropic effects between PA and CMDs and provide new evidence about associations of polygenic inheritance of PA and intermediate cardiometabolic risk factors.
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Affiliation(s)
- Niko Paavo Tynkkynen
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Timo Törmäkangas
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, Helsinki, Finland
| | - Matti Hyvärinen
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Marie Klevjer
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Laura Joensuu
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Urho Kujala
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, Helsinki, Finland
| | - Anja Bye
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Elina Sillanpää
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland.
- The Wellbeing Services County of Central Finland, Jyväskylä, Finland.
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Hashimoto C, Yorifuji T, Murakami K, Suganami S. Disease and Injury Trends following Heavy Rains in Western Japan in 2018. JMA J 2023; 6:129-137. [PMID: 37179731 PMCID: PMC10169277 DOI: 10.31662/jmaj.2022-0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/17/2022] [Indexed: 05/15/2023] Open
Abstract
Introduction Torrential rains occurred in Okayama in western Japan in July 2018, forcing local residents to evacuate. Few studies have reported early-phase disease and injury trends among patients following torrential rains. Thus, in this study, we assessed the illness and injury trends among patients who visited temporary medical facilities located in the areas affected by the 2018 torrential rains; these facilities opened 10 d after the disaster. Methods We evaluated the trends among patients who visited a medical clinic located in the area in western Japan affected by heavy rains in 2018. We reviewed medical charts related to 1,301 outpatient visits and conducted descriptive analyses. Results More than half of the patients were over 60 years old. The patients experienced mild injuries (7.9% of total visits) as well as common diseases such as hypertensive diseases (30%), diabetes mellitus (7.8%), acute upper respiratory infections (5.4%), skin diseases (5.4%), and eye diseases (4.8%). Hypertensive diseases were the main cause of a visit in any week. Eye problems were the second-highest reason for a visit in the first week, but there was a relative decrease from the first to the third week. Additionally, the proportion of injuries and skin diseases increased from the first to the second week, from 7.9% to 11.1% for injuries, and from 3.9% to 6.7% for skin diseases. Conclusions The types of diseases changed on a weekly basis. Older adults needed medical support for longer than other age groups. Prior preparedness such as earlier deployment of such temporary clinics can help mitigate the damage to the victims.
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Affiliation(s)
- Chiaki Hashimoto
- Department of Epidemiology, Graduate School of Medicine, Density and Pharmaceutical Sciences Okayama University, Okayama, Japan
| | - Takashi Yorifuji
- Department of Epidemiology, Graduate School of Medicine, Density and Pharmaceutical Sciences Okayama University, Okayama, Japan
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Hollister BM, Schopp EM, Telaak SH, Buscetta AJ, Dolwick AP, Fortney CJ, Bonham VL, Persky S. Educational considerations based on medical student use of polygenic risk information and apparent race in a simulated consultation. Genet Med 2022:S1098-3600(22)00895-4. [PMID: 36053286 DOI: 10.1016/j.gim.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/03/2022] [Accepted: 08/04/2022] [Indexed: 11/21/2022] Open
Abstract
PURPOSE To craft evidence-based educational approaches related to polygenic risk score (PRS) implementation, it is crucial to forecast issues and biases that may arise when PRS are introduced in clinical care. METHODS Medical students (N = 84) were randomized to a simulated primary care encounter with a Black or White virtual reality-based patient and received either a direct-to-consumer-style PRS report for 5 common complex conditions or control information. The virtual patient inquired about 2 health concerns and her genetic report in the encounter. Data sources included participants' verbalizations in the simulation, care plan recommendations, and self-report outcomes. RESULTS When medical students received PRSs, they rated the patient as less healthy and requiring more strict advice. Patterns suggest that PRSs influenced specific medical recommendations related to the patient's concerns, despite student reports that participants did not use it for that purpose. We observed complex patterns regarding the effect of patient race on recommendations and behaviors. CONCLUSION Educational approaches should consider potential unintentional influences of PRSs on decision-making and evaluate ways that they may be applied inconsistently across patients from different racial groups.
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Havrilla JM, Liu C, Dong X, Weng C, Wang K. PhenCards: a data resource linking human phenotype information to biomedical knowledge. Genome Med 2021; 13:91. [PMID: 34034817 PMCID: PMC8147460 DOI: 10.1186/s13073-021-00909-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/13/2021] [Indexed: 02/07/2023] Open
Abstract
We present PhenCards ( https://phencards.org ), a database and web server intended as a one-stop shop for previously disconnected biomedical knowledge related to human clinical phenotypes. Users can query human phenotype terms or clinical notes. PhenCards obtains relevant disease/phenotype prevalence and co-occurrence, drug, procedural, pathway, literature, grant, and collaborator data. PhenCards recommends the most probable genetic diseases and candidate genes based on phenotype terms from clinical notes. PhenCards facilitates exploration of phenotype, e.g., which drugs cause or are prescribed for patient symptoms, which genes likely cause specific symptoms, and which comorbidities co-occur with phenotypes.
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Affiliation(s)
- James M Havrilla
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Xiangchen Dong
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. .,Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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Lipner EM, Greenberg DA. The Rise and Fall and Rise of Linkage Analysis as a Technique for Finding and Characterizing Inherited Influences on Disease Expression. Methods Mol Biol 2018; 1706:381-397. [PMID: 29423810 DOI: 10.1007/978-1-4939-7471-9_21] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
For many years, family-based studies using linkage analysis represented the primary approach for identifying disease genes. This strategy is responsible for the identification of the greatest number of genes proven to cause human disease. However, technical advancements in next generation sequencing and high throughput genotyping, coupled with the apparent simplicity of association testing, led to the rejection of family-based studies and of linkage analysis. At present, genetic association methods, using case-control comparisons, have become the exclusive approach for detecting disease-related genes, particularly those underlying common, complex diseases. In this chapter, we present a historical overview of linkage analysis, including a description of how the approach works, as well as its strengths and weaknesses. We discuss how the transition from family-based studies to population comparison association studies led to a critical loss of information with respect to genetic etiology and inheritance, and we present historical and contemporary examples of linkage analysis "success stories" in identifying genes contributing to the development of human disease. Currently, linkage analysis is re-emerging as a useful approach for identifying disease genes, determining genetic parameters, and resolving genetic heterogeneity. We posit that the combination of linkage analysis, association testing, and high throughput sequencing provides a powerful approach for identifying disease-causing genes.
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Affiliation(s)
- Ettie M Lipner
- Center for Genes, Environment, and Health, National Jewish Health, 1400 Jackson Street, Denver, CO, 80602, USA.
- Department of Pharmacology, University of Colorado Denver, School of Medicine, Aurora, CO, USA.
| | - David A Greenberg
- Battelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, Wexner Medical Center, Ohio State University, Columbus, OH, USA
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Hirata M, Nagai A, Kamatani Y, Ninomiya T, Tamakoshi A, Yamagata Z, Kubo M, Muto K, Kiyohara Y, Mushiroda T, Murakami Y, Yuji K, Furukawa Y, Zembutsu H, Tanaka T, Ohnishi Y, Nakamura Y, Matsuda K. Overview of BioBank Japan follow-up data in 32 diseases. J Epidemiol 2017; 27:S22-S28. [PMID: 28190660 PMCID: PMC5363789 DOI: 10.1016/j.je.2016.12.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 12/15/2016] [Indexed: 01/23/2023] Open
Abstract
Background We established a patient-oriented biobank, BioBank Japan, with information on approximately 200,000 patients, suffering from any of 47 common diseases. This follow-up survey focused on 32 diseases, potentially associated with poor vital prognosis, and collected patient survival information, including cause of death. We performed a survival analysis for all subjects to get an overview of BioBank Japan follow-up data. Methods A total of 141,612 participants were included. The survival data were last updated in 2014. Kaplan–Meier survival analysis was performed after categorizing subjects according to sex, age group, and disease status. Relative survival rates were estimated using a survival-rate table of the Japanese general population. Results Of 141,612 subjects (56.48% male) with 1,087,434 person-years and a 97.0% follow-up rate, 35,482 patients died during follow-up. Mean age at enrollment was 64.24 years for male subjects and 63.98 years for female subjects. The 5-year and 10-year relative survival rates for all subjects were 0.944 and 0.911, respectively, with a median follow-up duration of 8.40 years. Patients with pancreatic cancer had the least favorable prognosis (10-year relative survival: 0.184) and patients with dyslipidemia had the most favorable prognosis (1.013). The most common cause of death was malignant neoplasms. A number of subjects died from diseases other than their registered disease(s). Conclusions This is the first report to perform follow-up survival analysis across various common diseases. Further studies should use detailed clinical and genomic information to identify predictors of mortality in patients with common diseases, contributing to the implementation of personalized medicine. 141,612 participants with any of 32 diseases were included in the follow-up survey. Subject characteristics at enrollment for the follow-up survey were identified. The relative survival analysis showed the worst prognosis in pancreatic cancer. The most common cause of death in all subjects was malignant neoplasms.
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Affiliation(s)
- Makoto Hirata
- Laboratory of Genome Technology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Akiko Nagai
- Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akiko Tamakoshi
- Department of Public Health, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Zentaro Yamagata
- Department of Health Sciences, University of Yamanashi, Yamanashi, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaori Muto
- Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yutaka Kiyohara
- Hisayama Research Institute for Lifestyle Diseases, Fukuoka, Japan
| | - Taisei Mushiroda
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Koichiro Yuji
- Project Division of International Advanced Medical Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoichi Furukawa
- Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Hitoshi Zembutsu
- Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan
| | - Toshihiro Tanaka
- SNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan; Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yozo Ohnishi
- SNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Shinko Clinic, Medical Corporation Shinkokai, Tokyo, Japan
| | - Yusuke Nakamura
- Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, USA
| | | | - Koichi Matsuda
- Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
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Hirata M, Kamatani Y, Nagai A, Kiyohara Y, Ninomiya T, Tamakoshi A, Yamagata Z, Kubo M, Muto K, Mushiroda T, Murakami Y, Yuji K, Furukawa Y, Zembutsu H, Tanaka T, Ohnishi Y, Nakamura Y, Matsuda K. Cross-sectional analysis of BioBank Japan clinical data: A large cohort of 200,000 patients with 47 common diseases. J Epidemiol 2017; 27:S9-S21. [PMID: 28190657 PMCID: PMC5363792 DOI: 10.1016/j.je.2016.12.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 12/15/2016] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND To implement personalized medicine, we established a large-scale patient cohort, BioBank Japan, in 2003. BioBank Japan contains DNA, serum, and clinical information derived from approximately 200,000 patients with 47 diseases. Serum and clinical information were collected annually until 2012. METHODS We analyzed clinical information of participants at enrollment, including age, sex, body mass index, hypertension, and smoking and drinking status, across 47 diseases, and compared the results with the Japanese database on Patient Survey and National Health and Nutrition Survey. We conducted multivariate logistic regression analysis, adjusting for sex and age, to assess the association between family history and disease development. RESULTS Distribution of age at enrollment reflected the typical age of disease onset. Analysis of the clinical information revealed strong associations between smoking and chronic obstructive pulmonary disease, drinking and esophageal cancer, high body mass index and metabolic disease, and hypertension and cardiovascular disease. Logistic regression analysis showed that individuals with a family history of keloid exhibited a higher odds ratio than those without a family history, highlighting the strong impact of host genetic factor(s) on disease onset. CONCLUSIONS Cross-sectional analysis of the clinical information of participants at enrollment revealed characteristics of the present cohort. Analysis of family history revealed the impact of host genetic factors on each disease. BioBank Japan, by publicly distributing DNA, serum, and clinical information, could be a fundamental infrastructure for the implementation of personalized medicine.
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Affiliation(s)
- Makoto Hirata
- Laboratory of Genome Technology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Akiko Nagai
- Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yutaka Kiyohara
- Hisayama Research Institute for Lifestyle Diseases, Fukuoka, Japan
| | - Toshiharu Ninomiya
- Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akiko Tamakoshi
- Department of Public Health, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Zentaro Yamagata
- Department of Health Sciences, University of Yamanashi, Yamanashi, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaori Muto
- Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Taisei Mushiroda
- Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Koichiro Yuji
- Project Division of International Advanced Medical Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yoichi Furukawa
- Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Hitoshi Zembutsu
- Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan
| | - Toshihiro Tanaka
- SNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan; Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yozo Ohnishi
- SNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Shinko Clinic, Medical Corporation Shinkokai, Tokyo, Japan
| | - Yusuke Nakamura
- Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, USA
| | | | - Koichi Matsuda
- Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
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