Tohoku University Tohoku Medical Megabank Organization (Soichi Ogishima)
2021.03.05

1.   Hozawa Atsushi, Tanno Kozo, Nakaya Naoki, et al. Study Profile of the Tohoku Medical Megabank Community-Based Cohort Study. Journal of Epidemiology. 2021; 31 (1): 65-76. doi:10.2188/jea.JE20190271  
2.   Tokunaga Hideki, Iida Keita, Hozawa Atsushi, et al. Novel candidates of pathogenic variants of the BRCA1 and BRCA2 genes from a dataset of 3,552 Japanese whole genomes (3.5KJPNv2). PLOS ONE. 2021; 16 (1): e0236907. doi:10.1371/journal.pone.0236907  
3.   Matsunaga Hiroshi, Ito Kaoru, Akiyama Masato, et al. Transethnic Meta-Analysis of Genome-Wide Association Studies Identifies Three New Loci and Characterizes Population-Specific Differences for Coronary Artery Disease. Circulation: Genomic and Precision Medicine. 2020; 13 (3): . doi:10.1161/CIRCGEN.119.002670  
4.   Kuriyama Shinichi, Metoki Hirohito, Kikuya Masahiro, et al. Cohort Profile: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study): rationale, progress and perspective. International Journal of Epidemiology. 2020; 49 (1): 18-19m. doi:10.1093/ije/dyz169  
5.   Sugawara Junichi, Ishikuro Mami, Obara Taku, et al. Maternal Baseline Characteristics and Perinatal Outcomes: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. Journal of Epidemiology. 2020; : . doi:10.2188/jea.JE20200338  
6.   Wagata Maiko, Ishikuro Mami, Obara Taku, et al. Low birth weight and abnormal pre-pregnancy body mass index were at higher risk for hypertensive disorders of pregnancy. Pregnancy Hypertension. 2020; 22 : 119-125. doi:10.1016/j.preghy.2020.08.001  
7.   Nishizawa Ayako, Kumada Kazuki, Tateno Keiko, et al. Analysis of HLA-G long-read genomic sequences in mother–offspring pairs with preeclampsia. Scientific Reports. 2020; 10 (1): 20027. doi:10.1038/s41598-020-77081-3  
8.   Tsuboi Akito, Matsui Hiroyuki, Shiraishi Naru, et al. Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project. The Tohoku Journal of Experimental Medicine. 2020; 251 (2): 97-115. doi:10.1620/tjem.251.97  
9.   Narita Akira, Nagai Masato, Mizuno Satoshi, et al. Clustering by phenotype and genome-wide association study in autism. Translational Psychiatry. 2020; 10 (1): 290. doi:10.1038/s41398-020-00951-x  
10.   Takahashi Yuta, Ueki Masao, Yamada Makoto, et al. Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection. Translational Psychiatry. 2020; 10 (1): 157. doi:10.1038/s41398-020-0831-9  
11.   Takahashi Yuta, Ueki Masao, Tamiya Gen, et al. Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes. Translational Psychiatry. 2020; 10 (1): 294. doi:10.1038/s41398-020-00957-5  
12.   Sakurai-Yageta Mika, Kawame Hiroshi, Kuriyama Shinichi, et al. A training and education program for genome medical research coordinators in the genome cohort study of the Tohoku Medical Megabank Organization. BMC Medical Education. 2019; 19 (1): 297. doi:10.1186/s12909-019-1725-5  
13.   Sugawara Junichi, Ochi Daisuke, Yamashita Riu, et al. Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy. BMJ Open. 2019; 9 (2): e025939. doi:10.1136/bmjopen-2018-025939  
14.   Nagasaki Masao, Kuroki Yoko, Shibata Tomoko F., et al. Construction of JRG (Japanese reference genome) with single-molecule real-time sequencing. Human Genome Variation. 2019; 6 (1): 27. doi:10.1038/s41439-019-0057-7  
15.   Shido Kosuke, Kojima Kaname, Yamasaki Kenshi, et al. Susceptibility Loci for Tanning Ability in the Japanese Population Identified by a Genome-Wide Association Study from the Tohoku Medical Megabank Project Cohort Study. Journal of Investigative Dermatology. 2019; 139 (7): 1605-1608.e13. doi:10.1016/j.jid.2019.01.015  
16.   Tanikawa Chizu, Kamatani Yoichiro, Terao Chikashi, et al. Novel Risk Loci Identified in a Genome-Wide Association Study of Urolithiasis in a Japanese Population. Journal of the American Society of Nephrology. 2019; 30 (5): 855-864. doi:10.1681/ASN.2018090942  
17.   Yasuda Jun, Kinoshita Kengo, Katsuoka Fumiki, et al. Genome analyses for the Tohoku Medical Megabank Project towards establishment of personalized healthcare. The Journal of Biochemistry. 2019; 165 (2): 139-158. doi:10.1093/jb/mvy096  
18.   Minegishi Naoko, Nishijima Ichiko, Nobukuni Takahiro, et al. Biobank Establishment and Sample Management in the Tohoku Medical Megabank Project. The Tohoku Journal of Experimental Medicine. 2019; 248 (1): 45-55. doi:10.1620/tjem.248.45  
19.   Kogetsu Atsushi, Ogishima Soichi, Kato Kazuto. Authentication of Patients and Participants in Health Information Exchange and Consent for Medical Research: A Key Step for Privacy Protection, Respect for Autonomy, and Trustworthiness. Frontiers in Genetics. 2018; 9 : 167. doi:10.3389/fgene.2018.00167  
20.   Ogishima Soichi. [Human Genome Data and Drug Development]. Gan to kagaku ryoho. Cancer & chemotherapy. 2018; 45 (4): 597-600. http://www.ncbi.nlm.nih.gov/pubmed/29650811  
21.   Koshiba Seizo, Motoike Ikuko, Saigusa Daisuke, et al. Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project. Genes to Cells. 2018; 23 (6): 406-417. doi:10.1111/gtc.12588  
22.   Yamaguchi-Kabata Yumi, Yasuda Jun, Tanabe Osamu, et al. Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals. Journal of Human Genetics. 2018; 63 (2): 213-230. doi:10.1038/s10038-017-0347-1  
23.   Takai-Igarashi Takako, Kinoshita Kengo, Nagasaki Masao, et al. Security controls in an integrated Biobank to protect privacy in data sharing: rationale and study design. BMC Medical Informatics and Decision Making. 2017; 17 (1): 100. doi:10.1186/s12911-017-0494-5  
24.   Shido K., Kojima K., Hozawa A., et al. 503 Genome-wide association study identifies novel susceptibility loci for tanning ability in Japanese population. Journal of Investigative Dermatology. 2017; 137 (5): S86. doi:10.1016/j.jid.2017.02.523  
25.   Low Siew-Kee, Takahashi Atsushi, Ebana Yusuke, et al. Identification of six new genetic loci associated with atrial fibrillation in the Japanese population. Nature Genetics. 2017; 49 (6): 953-958. doi:10.1038/ng.3842  
26.   Köhler Sebastian, Vasilevsky Nicole A, Engelstad Mark, et al. The Human Phenotype Ontology in 2017. Nucleic Acids Research. 2017; 45 (D1): D865-D876. doi:10.1093/nar/gkw1039  
27.   Noguchi Shuhei, Arakawa Takahiro, Fukuda Shiro, et al. FANTOM5 CAGE profiles of human and mouse samples. Scientific Data. 2017; 4 : 170112. doi:10.1038/sdata.2017.112  
28.   Kuriyama Shinichi, Yaegashi Nobuo, Nagami Fuji, et al. The Tohoku Medical Megabank Project: Design and Mission. Journal of Epidemiology. 2016; 26 (9): 493-511. doi:10.2188/jea.JE20150268  
29.   Ogishima Soichi, Mizuno Satoshi, Kikuchi Masataka, et al. AlzPathway, an Updated Map of Curated Signaling Pathways: Towards Deciphering Alzheimer’s Disease Pathogenesis. Methods in molecular biology (Clifton, N.J.). 2016; 1303 : 423-432. doi:10.1007/978-1-4939-2627-5_25  
30.   Kikuchi Masataka, Ogishima Soichi, Mizuno Satoshi, et al. Network-Based Analysis for Uncovering Mechanisms Underlying Alzheimer’s Disease. Methods in molecular biology (Clifton, N.J.). 2016; 1303 : 479-491. doi:10.1007/978-1-4939-2627-5_29  
31.   Mizuno Satoshi, Ogishima Soichi, Kitatani Kazuyuki, et al. Network Analysis of a Comprehensive Knowledge Repository Reveals a Dual Role for Ceramide in Alzheimer’s Disease. PLOS ONE. 2016; 11 (2): e0148431. doi:10.1371/journal.pone.0148431  
32.   Mizuno Satoshi, Ogishima Soichi, Nishigori Hidekazu, et al. The Pre-Eclampsia Ontology: A Disease Ontology Representing the Domain Knowledge Specific to Pre-Eclampsia. PLOS ONE. 2016; 11 (10): e0162828. doi:10.1371/journal.pone.0162828  
33.   Koshiba Seizo, Motoike Ikuko, Kojima Kaname, et al. The structural origin of metabolic quantitative diversity. Scientific Reports. 2016; 6 (1): 31463. doi:10.1038/srep31463  
34.   Nagaie Satoshi, Ogishima Soichi, Nakaya Jun, Tanaka Hiroshi. A method to associate all possible combinations of genetic and environmental factors using GxE landscape plot. Bioinformation. 2015; 11 (3): 161-4. doi:10.6026/97320630011161  
35.   Ogishima Soichi, Tanaka Hiroshi, Nakaya Jun. Modularity in the evolution of yeast protein interaction network. Bioinformation. 2015; 11 (3): 127-30. doi:10.6026/97320630011127  
36.   Aoki-Kinoshita Kiyoko F, Kinjo Akira R, Morita Mizuki, et al. Implementation of linked data in the life sciences at BioHackathon 2011. Journal of Biomedical Semantics. 2015; 6 (1): 3. doi:10.1186/2041-1480-6-3  
37.   Tanaka Hiroshi, Ogishima Soichi. Network biology approach to epithelial–mesenchymal transition in cancer metastasis: three stage theory. Journal of Molecular Cell Biology. 2015; 7 (3): 253-266. doi:10.1093/jmcb/mjv035  
38.   Nagasaki Masao, Yasuda Jun, Katsuoka Fumiki, et al. Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals. Nature Communications. 2015; 6 (1): 8018. doi:10.1038/ncomms9018  
39.   Arner E., Daub C. O., Vitting-Seerup K., et al. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science. 2015; 347 (6225): 1010-1014. doi:10.1126/science.1259418  
40.   Ogishima Soichi, Takai Takako, Shimokawa Kazuro, et al. Integrated Database And Knowledge Base For Genomic Prospective Cohort Study In Tohoku Medical Megabank Toward Personalized Prevention And Medicine. Studies in health technology and informatics. 2015; 216 : 1057. doi:10.3233/978-1-61499-564-7-1057  
41.   Katayama Toshiaki, Wilkinson Mark D, Aoki-Kinoshita Kiyoko F, et al. BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains. Journal of Biomedical Semantics. 2014; 5 (1): 5. doi:10.1186/2041-1480-5-5  
42.   FANTOM Consortium and the RIKEN PMI and CLST (DGT) The FANTOM Consortium and the RIKEN PMI and CLST, Forrest Alistair R R, Kawaji Hideya, et al. A promoter-level mammalian expression atlas. Nature. 2014; 507 (7493): 462-70. doi:10.1038/nature13182  
43.   Miyashita A, Hatsuta H, Kikuchi M, et al. Genes associated with the progression of neurofibrillary tangles in Alzheimer’s disease. Translational Psychiatry. 2014; 4 (6): e396-e396. doi:10.1038/tp.2014.35  
44.   Nakaya J., Kimura M., Ogishima S., et al. Future Direction of IMIA Standardization. Yearbook of Medical Informatics. 2014; 23 (01): 105-109. doi:10.15265/IY-2014-0010  
45.   Nishio Yousuke, Ogishima Soichi, Ichikawa Masao, et al. Analysis of l-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli. BMC Systems Biology. 2013; 7 (1): 92. doi:10.1186/1752-0509-7-92  
46.   Ogishima S, Mizuno S, Kikuchi M, et al. A Map of Alzheimer’s Disease–Signaling Pathways: A Hope for Drug Target Discovery. Clinical Pharmacology & Therapeutics. 2013; 93 (5): 399-401. doi:10.1038/clpt.2013.37  
47.   Kikuchi Masataka, Ogishima Soichi, Miyamoto Tadashi, et al. Identification of Unstable Network Modules Reveals Disease Modules Associated with the Progression of Alzheimer’s Disease. PLoS ONE. 2013; 8 (11): e76162. doi:10.1371/journal.pone.0076162  
48.   Böck Matthias, Ogishima Soichi, Tanaka Hiroshi, et al. Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination. PLoS ONE. 2012; 7 (5): e35077. doi:10.1371/journal.pone.0035077