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

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

doi:10.1016/j.xgen.2022.100192

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

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

Massachusetts General Hospital
Broad Institute
Harvard University
The University of Osaka
University of Michigan, Ann Arbor
Norwegian University of Science and Technology
University of Oslo
University of Bristol
Vanderbilt University
University of California at Los Angeles
University of Groningen
Amsterdam University Medical Centers
Uppsala University
University of Tartu
Ontario Institute for Cancer Research
University of Helsinki
University of Colorado Anschutz Medical Campus
National Health Research Institutes Taiwan
National Taiwan University
University of Pennsylvania
Queensland Institute of Medical Research
Queensland University of Technology
Qatar Foundation HQ
University of Oxford
Peking University
Inc.
Seoul National University
Icahn School of Medicine at Mount Sinai
deCODE Genetics
VA Medical Center
Qatar Biobank
University of Edinburgh
Queen Mary University of London
Amphora Health
Chinese Academy of Medical Sciences
Stanford University
Japan Tobacco
Genomelink, Inc.
Biogen IDEC
NIHR Bristol Biomedical Research Centre
University of Cambridge
Department of Veterans Affairs
University of Copenhagen
The University of Tokyo
King's College London
Bradford Teaching Hospitals NHS Foundation Trust
University of Toronto
Chan Zuckerberg Biohub
The African Computational Genomics (TACG) Research Group
London School of Hygiene and Tropical Medicine
National Yang Ming Chiao Tung University
National Cheng Kung University
Wellcome Trust Sanger Institute
RIKEN
National Research Council of Italy

Research output: Contribution to journal › Article › peer-review

237 Citations (Scopus)

Abstract

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 language	English
Article number	100192
Journal	Cell Genomics
Volume	2
Issue number	10
DOIs	https://doi.org/10.1016/j.xgen.2022.100192
Publication status	Published - 12 Oct 2022
Externally published	Yes

Keywords

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

Access to Document

10.1016/j.xgen.2022.100192

Cite this

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, ... Colorado Center for Personalized Medicine (2022). Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease. Cell Genomics, 2(10), Article 100192. https://doi.org/10.1016/j.xgen.2022.100192

@article{4e700a426f6045daad4d417661be5e87,

title = "Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease",

abstract = "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.",

keywords = "GWAS, ancestry diversity, biobank, genetic association studies, meta-analysis, phenotype harmonization",

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

note = "Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = oct,

day = "12",

doi = "10.1016/j.xgen.2022.100192",

language = "English",

volume = "2",

journal = "Cell Genomics",

issn = "2666-979X",

publisher = "Cell Press",

number = "10",

}

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 2022, 'Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease', Cell Genomics, vol. 2, no. 10, 100192. https://doi.org/10.1016/j.xgen.2022.100192

TY - JOUR

T1 - Global Biobank Meta-analysis Initiative

T2 - Powering genetic discovery across human disease

AU - deCODE Genetics

AU - Estonian Biobank

AU - FinnGen

AU - Generation Scotland

AU - Genes & Health Research Team

AU - Lifelines

AU - Mass General Brigham Biobank

AU - Michigan Genomics Initiative

AU - National Biobank of Korea

AU - Penn Medicine Biobank

AU - Qatar Biobank

AU - The QSkin Sun and Health Study

AU - Taiwan Biobank

AU - The HUNT Study

AU - UCLA ATLAS Community Health Initiative

AU - Uganda Genome Resource

AU - UK Biobank

AU - Biobank of the Americas

AU - Biobank Japan Project

AU - BioMe

AU - BioVU

AU - CanPath - Ontario Health Study

AU - China Kadoorie Biobank Collaborative Group

AU - Colorado Center for Personalized Medicine

AU - Zhou, Wei

AU - Kanai, Masahiro

AU - Wu, Kuan Han H.

AU - Rasheed, Humaira

AU - Tsuo, Kristin

AU - Hirbo, Jibril B.

AU - Wang, Ying

AU - Bhattacharya, Arjun

AU - Zhao, Huiling

AU - Namba, Shinichi

AU - Surakka, Ida

AU - Wolford, Brooke N.

AU - Lo Faro, Valeria

AU - Lopera-Maya, Esteban A.

AU - Läll, Kristi

AU - Favé, Marie Julie

AU - Partanen, Juulia J.

AU - Chapman, Sinéad B.

AU - Karjalainen, Juha

AU - Kurki, Mitja

AU - Maasha, Mutaamba

AU - Brumpton, Ben M.

AU - Chavan, Sameer

AU - Chen, Tzu Ting

AU - Daya, Michelle

AU - Ding, Yi

AU - Feng, Yen Chen A.

AU - Guare, Lindsay A.

AU - Gignoux, Christopher R.

AU - Graham, Sarah E.

AU - Hornsby, Whitney E.

AU - Ingold, Nathan

AU - Ismail, Said I.

AU - Johnson, Ruth

AU - Laisk, Triin

AU - Lin, Kuang

AU - Lv, Jun

AU - Millwood, Iona Y.

AU - Moreno-Grau, Sonia

AU - Nam, Kisung

AU - Palta, Priit

AU - Pandit, Anita

AU - Preuss, Michael H.

AU - Saad, Chadi

AU - Setia-Verma, Shefali

AU - Thorsteinsdottir, Unnur

AU - Uzunovic, Jasmina

AU - Verma, Anurag

AU - Zawistowski, Matthew

AU - Zhong, Xue

PY - 2022/10/12

Y1 - 2022/10/12

N2 - 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.

AB - 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.

KW - GWAS

KW - ancestry diversity

KW - biobank

KW - genetic association studies

KW - meta-analysis

KW - phenotype harmonization

UR - https://www.scopus.com/pages/publications/85139994619

U2 - 10.1016/j.xgen.2022.100192

DO - 10.1016/j.xgen.2022.100192

M3 - Article

AN - SCOPUS:85139994619

SN - 2666-979X

VL - 2

JO - Cell Genomics

JF - Cell Genomics

IS - 10

M1 - 100192

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this