Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases
Authors: Zhou Wei , Kanai Masahiro , Wu Kuan-Han H , Humaira Rasheed , Tsuo Kristin , Hirbo Jibril B , Wang Ying , Bhattacharya Arjun , Zhao Huiling , Namba Shinichi , Surakka Ida , Wolford Brooke N , Faro Valeria Lo , Lopera-Maya Esteban A , Läll Kristi , Favé Marie-Julie , Chapman Sinéad B , Karjalainen Juha , Kurki Mitja , Mutaamba Maasha , Brumpton Ben M , Chavan Sameer , Chen Tzu-Ting , Daya Michelle , Ding Yi , Feng Yen-Chen A , Gignoux Christopher R , Graham Sarah E , Hornsby Whitney E , Ingold Nathan , Johnson Ruth , Laisk Triin , Lin Kuang , Lv Jun , Millwood Iona Y , Palta Priit , Pandit Anita , Preuss Michael , Thorsteinsdottir Unnur , Uzunovic Jasmina , Zawistowski Matthew , Zhong Xue , Campbell Archie , Crooks Kristy , De Bock Geertruida h , Douville Nicholas J , Finer Sarah , Fritsche Lars G , Griffiths Christopher J , Guo Yu , Hunt Karen A , Konuma Takahiro , Marioni Riccardo E , Nomdo Jansonius , Patil Snehal , Rafaels Nicholas , Richmond Anne , Shortt Jonathan A , Straub Peter , Tao Ran , Vanderwerff Brett , Barnes Kathleen C , Boezen Marike , Chen Zhengming , Chen Chia-Yen , Cho Judy , Smith George Davey , Finucane Hilary K , Franke Lude , Gamazon Eric , Ganna Andrea , Gaunt Tom R , Ge Tian , Huang Hailiang , Huffman Jennifer , Lajonchere Clara , Law Matthew H , Li Liming , Lindgren Cecilia M , Loos Ruth JF , MacGregor Stuart , Matsuda Koichi , Olsen Catherine M , Porteous David J , Shavit Jordan A , Snieder Harold , Trembath Richard C , Vonk Judith M , Whiteman David , Wicks Stephen J , Wijmenga Cisca , Wright John , Zheng Jie , Zhou Xiang , Awadalla Philip , Boehnke Michael , Cox Nancy J , Geschwind Daniel H , Hayward Caroline , Hveem Kristian , Kenny Eimear E , Lin Yen-Feng , Mägi Reedik , Martin Hilary C , Medland Sarah E , Okada Yukinori , Palotie Aarno V , Pasaniuc Bogdan , Sanna Serena , Smoller Jordan W , Stefansson Kari , van Heel David A , Walters Robin G , Zoellner Sebastian ,   Biobank Japan,   BioMe,   BioVU,   Canadian Partnership for Tomorrow,   China Kadoorie Biobank Collaborative Group,   Colorado Center for Personalized Medicine,   deCODE Genetics,   Estonian Biobank,   FinnGen,   Generation Scotland,   Genes & Health,   LifeLines,   Mass General Brigham Biobank,   Michigan Genomics Initiative,   QIMR Berghofer Biobank,   Taiwan Biobank,   The HUNT Study,   UCLA ATLAS Community Health Initiative,   UK Biobank, Martin Alicia R , Willer Cristen J , Daly Mark J , Neale Benjamin M 
Published: November 2021

Biobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of diverse ancestries - with most biobanks and preceding GWAS studies composed of individuals of European ancestries. Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI) -- a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: approximately 33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) individuals. In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks. We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve 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 the studied traits.