Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases

Abstract

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.

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

“…We accessed protein quantitative trait loci (pQTL) data from the ARIC, AASK cohorts 22 . Incorporating samples with diverse ancestries in the biobank meta-analysis enables comparison of effect sizes of genomic loci across ancestry.…”

Polygenic risk score from a multi-ancestry GWAS uncovers susceptibility of heart failure

“…We next compared PRS generated from the present GBMI HF meta-analysis with the PRS generated from the previous HERMES GWAS (47,309 cases and 930,014 controls) to examine the change in PRS prediction with increasing GWAS sample size and evaluate the performance of genetic research utilizing large scale EHR-linked biobank 5 . The GBMI PRS outperformed the HERMES PRS, which is the largest publicly available heart failure GWAS to date.…”

“…Heart failure cases in the GBMI training dataset were defined based upon ICD codes (phecode 428.2: heart failure, not otherwise specified), which did not distinguish between heart failure subtypes (Reference to GBMI flagship). 5 In the GBMI discovery dataset, genetic data was analyzed from a total of 67,049 HF patients from 1,305,592 samples from 6 ancestral populations: 25.4% of the samples were of non-European ancestry (Supplementary Figure 1; Supplementary Table 1).…”

“…To expand upon our current understanding of the genetics underpinning heart failure we calculated a polygenic risk score (PRS) derived from a new genome-wide association study (GWAS) for overall heart failure from the Global Biobank Meta-analysis Initiative (GBMI) 5 . GBMI is a global collaboration among 21 biobanks across the world with diverse ancestries.…”

Leveraging global multi-ancestry meta-analysis in the study of Idiopathic Pulmonary Fibrosis genetics

“…For GBMI, GWASs stratified by ancestry and sex were conducted in each biobank after standard sample-level and variant-level quality control and fixed-effect meta-analyses based on inversevariance weighting were performed for all biobanks across all ancestries, and all biobanks by sex, detailed description elsewhere 35 . The GBMI meta-analysis was performed for both sexes, and for males and females separately.…”