SummaryBiobanks 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.

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Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.