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

Soichi OgishimaSatoshi NagaieSatoshi MizunoRyosuke IshiwataKeita IidaKazuro ShimokawaTakako Takai-IgarashiNaoki NakamuraSachiko NagaseTomohiro NakamuraNaho TsuchiyaNaoki NakayaKeiko MurakamiFumihiko UenoTomomi OnumaMami IshikuroTaku ObaraShunji MugikuraHiroaki TomitaAkira UrunoTomoko KobayashiAkito TsuboiShu TadakaFumiki KatsuokaAkira NaritaMika SakuraiSatoshi MakinoGen TamiyaYuichi AokiRitsuko ShimizuIkuko N MotoikeSeizo KoshibaNaoko MinegishiKazuki KumadaTakahiro NobukuniKichiya SuzukiInaho DanjohFuji NagamiKozo TannoHideki OhmomoKoichi AsahiAtsushi ShimizuAtsushi HozawaShinichi Kuriyamanull nullNobuo FuseTeiji TominagaShigeo KureNobuo YaegashiKengo KinoshitaMakoto SasakiHiroshi TanakaMasayuki Yamamoto
Published in: Human genome variation (2021)
To reveal gene-environment interactions underlying common diseases and estimate the risk for common diseases, the Tohoku Medical Megabank (TMM) project has conducted prospective cohort studies and genomic and multiomics analyses. To establish an integrated biobank, we developed an integrated database called "dbTMM" that incorporates both the individual cohort/clinical data and the genome/multiomics data of 157,191 participants in the Tohoku Medical Megabank project. To our knowledge, dbTMM is the first database to store individual whole-genome data on a variant-by-variant basis as well as cohort/clinical data for over one hundred thousand participants in a prospective cohort study. dbTMM enables us to stratify our cohort by both genome-wide genetic factors and environmental factors, and it provides a research and development platform that enables prospective analysis of large-scale data from genome cohorts.
Keyphrases
  • genome wide
  • electronic health record
  • healthcare
  • big data
  • dna methylation
  • copy number
  • adverse drug
  • emergency department
  • machine learning
  • high throughput
  • deep learning