Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

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  • Wei Zhou
  • Masahiro Kanai
  • Kuan Han H. Wu
  • Humaira Rasheed
  • Kristin Tsuo
  • Jibril B. Hirbo
  • Ying Wang
  • Arjun Bhattacharya
  • Huiling Zhao
  • Shinichi Namba
  • Ida Surakka
  • Brooke N. Wolford
  • Valeria Lo Faro
  • Esteban A. Lopera-Maya
  • Kristi Läll
  • Marie Julie Favé
  • Juulia J. Partanen
  • Sinéad B. Chapman
  • Juha Karjalainen
  • Mitja Kurki
  • Mutaamba Maasha
  • Ben M. Brumpton
  • Sameer Chavan
  • Tzu Ting Chen
  • Michelle Daya
  • Yi Ding
  • Yen Chen A. Feng
  • Lindsay A. Guare
  • Christopher R. Gignoux
  • Sarah E. Graham
  • Whitney E. Hornsby
  • Nathan Ingold
  • Said I. Ismail
  • Ruth Johnson
  • Triin Laisk
  • Kuang Lin
  • Jun Lv
  • Iona Y. Millwood
  • Sonia Moreno-Grau
  • Kisung Nam
  • Priit Palta
  • Anita Pandit
  • Michael H. Preuss
  • Chadi Saad
  • Shefali Setia-Verma
  • Unnur Thorsteinsdottir
  • Jasmina Uzunovic
  • Anurag Verma
  • Matthew Zawistowski
  • Loos, Ruth
  • deCODE Genetics
  • Estonian Biobank
  • FinnGen
  • Generation Scotland
  • Genes & Health Research Team
  • LifeLines
  • Mass General Brigham Biobank
  • Michigan Genomics Initiative
  • National Biobank of Korea
  • Penn Medicine BioBank
  • Qatar Biobank
  • The QSkin Sun and Health Study
  • Taiwan Biobank
  • The HUNT Study
  • UCLA ATLAS Community Health Initiative
  • Uganda Genome Resource
  • UK Biobank
  • Biobank of the Americas
  • BioBank Japan Project
  • BioMe
  • BioVU
  • CanPath - Ontario Health Study
  • China Kadoorie Biobank Collaborative Group
  • Colorado Center for Personalized Medicine

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Original languageEnglish
Article number100192
JournalCell Genomics
Issue number10
Number of pages20
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022

    Research areas

  • ancestry diversity, biobank, genetic association studies, GWAS, meta-analysis, phenotype harmonization

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