News Release

Global Biobank Meta-analysis Initiative making genome-wide association studies more diverse and representative

Peer-Reviewed Publication

Cell Press

GBMI biobanks

image: This figure shows the 23 biobanks across four continents that have joined GBMI as of April 2022, bringing the total number of samples with matched health data and genotypes to more than 2.2 million. Biobanks are colored based on the sample recruiting strategies. view more 

Credit: Zhou et al./Cell Genomics

Human genetic discoveries have historically focused on individuals of European descent, so how well these findings transfer to other non-European populations has remained an open question. A collaborative network of 23 biobanks from 4 continents holding genomic data for over 2 million consenting individuals is now revealing the gaps caused by this lack of diversity, such as missed mutations that cause genetic diseases. The first studies from the Global Biobank Meta-analysis Initiative (GBMI), published October 12 in the journal Cell Genomics, offer guidance on how and why to make genome-wide association studies (GWASs) more representative.

“The aims of the GBMI are to increase the power to discover genetic variation associated with phenotypes for GWAS analyses, increase replication power, and determine more accurate polygenic risk scores,” says Cell Genomics Editor-in-Chief Laura Zahn. “Their work is helping to provide new insights into the underlying biology of human diseases and traits.”

Cell Genomics features seven initial studies from the GBMI:

1. GWASs in different biobanks worldwide can be successfully integrated

Utilizing most of the biobanks represented in GBMI, researchers generated GWASs that identified 317 known and 183 new genes associated with 14 diseases, from asthma and gout to certain cancers. The pilot studies also reflected consistent results despite differences among biobanks, encouraging the sharing and integration of their unique genomic data, thus making it possible to conduct some of the largest GWAS analyses of certain diseases to date.

Zhou et al.: “Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.”

2. Looking across ancestries can identify more drug targets for genetic diseases

Genetic tools provide a cost-effective way to understand whether drug targets for genetic diseases may have similar or different effects across ancestries. In this study, researchers used biobank samples to screen about 1,300 proteins, each measured in populations of African and European ancestry, for their role in 8 complex diseases. They identified 45 proteins that could potentially be involved in both ancestries and 7 pairs with specific effects in the two ancestries separately, with 16 of these prioritized for investigation in future drug trials.

Zhao et al.: “Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases.”

3. Introducing a drug discovery framework for cross-population GWAS meta-analyses

GWASs have the potential to identify and evaluate drug candidates and drug targets. This research team created guidelines that utilizes three techniques for in-depth, genomics-driven drug discovery that work across populations. They applied this framework to 13 common diseases to nominate promising drug candidates targeting the genes involved in the coagulation process for a certain type of blood clot as well as in immune signaling pathways for gout. 

Namba and Konuma et al.: “A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis.”

4. Forty years of genetic data comes with advantages

Since 1984, around 229,000 people from Trøndelag County, Norway, have taken part in the Trøndelag Health Study (HUNT), providing health records and biological samples with nearly 40 years of follow-up. Of the HUNT participants, approximately 88,000 individuals have provided genetic data, which have been used to generate insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases. This resource acts as inspiration to conduct similar longitudinal studies across more diverse populations.

Brumptom, Graham, and Surakka et al.: “The HUNT study: A population-based cohort for genetic research.”

5. New opportunities to combine data to study rare diseases

By combining data from 13 biobanks around the globe, this research team performed a multi-ancestry GWAS to look at thousands of patients with idiopathic pulmonary fibrosis (IPF), a rare disease characterized by lung tissue scarring. The researchers identified seven new gene markers linked to IPF, including those involved in lung function and COVID-19 response, as well as sex-specific effects. Only one of these gene markers would have been identified had the analysis been limited to European ancestry individuals.

Partanen et al.: “Leveraging global multi-ancestry meta-analysis in the study of idiopathic  pulmoary fibrosis genetics.”

6. Overcoming statistical challenges studying ancestry-specific genetic associations

Transcriptome-wide association studies (TWASs) boost detection power and provide biological context to genetic associations by integrating genetic variant-to-trait associations with predictive models of gene expression. In this paper, researchers highlight practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. This provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.

Bhattacharya and Hirbo et al.: “Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative.”

7. The Taiwan Biobank offers East Asian population diversity in genetics research

The Taiwan Biobank is an ongoing prospective population study of over 150,000 people of predominantly Han Chinese ancestry. Through physical examinations and biological samples, researchers are tracing more than 1,000 genetic traits, as well as lifestyle traits and environmental factors, that are more specific to populations in East and Southeast Asia. Their membership in the GMBI is an example of the population diversity possible with a global genetics research effort.

Feng et al.: “Taiwan Biobank: A rich biomedical research database of the Taiwanese population.”


Funding information and declarations of interest can be found in the manuscripts.

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