학술논문
dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project
Document Type
article
Author
Soichi Ogishima; Satoshi Nagaie; Satoshi Mizuno; Ryosuke Ishiwata; Keita Iida; Kazuro Shimokawa; Takako Takai-Igarashi; Naoki Nakamura; Sachiko Nagase; Tomohiro Nakamura; Naho Tsuchiya; Naoki Nakaya; Keiko Murakami; Fumihiko Ueno; Tomomi Onuma; Mami Ishikuro; Taku Obara; Shunji Mugikura; Hiroaki Tomita; Akira Uruno; Tomoko Kobayashi; Akito Tsuboi; Shu Tadaka; Fumiki Katsuoka; Akira Narita; Mika Sakurai; Satoshi Makino; Gen Tamiya; Yuichi Aoki; Ritsuko Shimizu; Ikuko N. Motoike; Seizo Koshiba; Naoko Minegishi; Kazuki Kumada; Takahiro Nobukuni; Kichiya Suzuki; Inaho Danjoh; Fuji Nagami; Kozo Tanno; Hideki Ohmomo; Koichi Asahi; Atsushi Shimizu; Atsushi Hozawa; Shinichi Kuriyama; the Tohoku Medical Megabank Project Study Group; Nobuo Fuse; Teiji Tominaga; Shigeo Kure; Nobuo Yaegashi; Kengo Kinoshita; Makoto Sasaki; Hiroshi Tanaka; Masayuki Yamamoto
Source
Human Genome Variation, Vol 8, Iss 1, Pp 1-8 (2021)
Subject
Language
English
ISSN
2054-345X
Abstract
Databases: Integrating megadata for disease research A database integrating 1.3 trillion genome cohort data entries from 157,191 individuals in Japan will facilitate research into the gene-environment interactions underlying common diseases. The Tohoku Medical Megabank integrated database called dbTMM was developed by Soichi Ogishima, Masayuki Yamamoto and colleagues at Tohoku University in Japan. It incorporates the genome, metabolome, proteome, clinical, sociodemographic, lifestyle and environmental data from 84,073 adults, and 73,529 pregnant women and their families, including children. Blood and urine samples were collected from participants and analysed, then obtained genome/multiomics data were stored in dbTMM. Users can stratify the entire population into smaller populations based on multiple data variables, including whole genome variants, to search for statistically significant differences that might warrant further research. The dbTMM is expected to help clarify the genes and gene-environment interactions underlying common diseases and improve disease risk prediction.