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dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project

Authors :
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)
Publication Year :
2021
Publisher :
Nature Publishing Group, 2021.

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.

Subjects

Subjects :
Genetics
QH426-470
Life
QH501-531

Details

Language :
English
ISSN :
2054345X
Volume :
8
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Human Genome Variation
Publication Type :
Academic Journal
Accession number :
edsdoj.3744eb34ff14a6d9dc6c6eaaef77789
Document Type :
article
Full Text :
https://doi.org/10.1038/s41439-021-00175-5