Phases
 

 

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Pilot

The pilot analysis examined six endpoints to showcase the capabilities of joint activity. This included validating biobank association results against previous genome-wide association studies (GWAS)  data for two common diseases: asthma and primary open angle glaucoma (POAG). Additionally, it highlighted the power of the platform for four rare or understudied diseases: idiopathic pulmonary fibrosis, uterine cancer, thyroid cancer, and abdominal aortic aneurysm. The primary outcomes demonstrated that consistent endpoint definitions for genetic investigation can be successfully established, even when utilizing data sets and medical systems with diverse underlying raw data.

Round 1

Researchers conducted large-scale GWAS across various populations, including individuals from diverse ancestries such as African, East Asian, and European. By pooling data from over 2 million participants, they significantly enhanced statistical power compared to studies conducted within a single biobank. The results demonstrated that global meta-analysis could be conducted on a global scale through the collaboration of international biobanks.

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Round 2

In round two, the GBMI shifted its focus to phenotype-based analysis. Members of the community were encouraged to propose phenotypes for further exploration using the methodologies established in round one. Each subgroup was led by a GBMI member. The phenotypes analyzed included: glaucoma, prostate cancer, Benign Prostatic Hyperplasia (BPH), skin cancer, thyroid cancer, lung cancer,  IBS, cervical spondylosis, endometriosis, pregnancy phenotypes, allergic disease, RSV bronchiolitis, bacterial sepsis, ASD and related neurodevelopmental disorders, anxiety disorders, and venous thromboembolism. 

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Round 3

GBMI in round three is concentrating on developing novel pipelines to enhance genomic analysis on a large scale. This round comprises three working groups: longitudinal, social determinants of health, and all-by-all. The longitudinal working group employs GWAS for time-to-event (TTE) phenotypes to comprehend the genetic signals underlying the transition from onset to primary disease over time. The social determinants of health working group integrates social determinants of health variables into polygenic risk scores and polygenic exposure scores to improve disease prediction. The all-by-all group collaborates with biobanks where all summary statistics are publicly accessible for meta-analyses of all overlapping phenotypes within the biobanks. All-by-all has partnered with UKBB, FinnGen, and MVP for this analysis.