What is the GNPC?
The GNPC is a major neurodegenerative disease biomarker discovery effort. Launched in 2023, we are uniting and expanding the available molecular “fingerprinting” data for thousands of patient samples across dozens of different dementia and population cohorts, including healthy aging, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD).
Frequently asked questions
If you have additional questions, please email info@neuroproteome.org.
The GNPC is a precompetitive “safe sandbox” for researchers from both the private and public sectors to collaborate. We both incentivize and simplify data sharing, serving as a model for how big data approaches in biomedicine can be realized in a manner that respects individual patient data privacy and complies with data sharing regulations across various geographies and jurisdictions around the world.
By becoming a member of the GNPC, you will contribute to the world’s largest protein biomarker discovery effort for neurodegenerative diseases to date. The consortium’s Harmonized Data Set (HDS) includes over 40,000 patient samples from over 20 international research groups. The HDS will continue to grow on an annual basis, incorporating new cohorts and data. Consortium members get first access to each new version of the HDS before it is released to the broader research community.
After one year of intra-consortium analysis, V1 of the HDS will be released to the broader research community as a shared global resource via the AD Data Initiative data platform as a FAIR dataset.
If you are interested in learning more about eligibility to join the consortium, email info@neuroproteome.org.
The GNPC Consortium Agreement for data contributors and funders provides detailed information on harmonization methods and the consortium’s structure and governance. Please note: We have streamlined the GNPC Consortium Agreement to require completion only once per institution. Please contact your legal team to see if a cohort within your institution is already part of the GNPC.
Once you are a consortium member, you can create an account on the AD Workbench and request your own private workspace to gain access to the community workspace.
Origin story
In 2020, the COVID-19 pandemic shut down clinical trials worldwide, slowing down disease research and progress toward new drug and diagnostic development. As researchers around the world struggled to continue their work, Janssen Research & Development LLC, a Johnson & Johnson company and Gates Ventures came together in a public-private partnership to fund new analyses of existing, previously collected biosamples from healthy individuals and those with neurodegenerative diseases. By uniting existing samples across different cohorts, and using them to conduct new analyses, we were able to continue progress on biomarker discovery work during the pandemic. Together, both groups provided initial support for over 10,000 samples on the SomaScan 7k proteomics platform.
This initial data and these cohorts laid the groundwork for the consortium, initially launched in 2023. From the start, the group’s intent was to promote open data access and empower the research community with new big data for analysis. The opportunity to join this first-of-its-kind consortium in the field of neurodegenerative disorders drew in additional global researchers with existing proteomics data, increasing the Harmonized Data Set (HDS) substantially.
Our initial goal was to bring together 10,000 or more samples to be harmonized into a single data set. Today, we are delighted to have exceeded that goal at least four-fold. Thanks to our many global partnerships, the first release of the HDS will include over 40,000 samples. Moreover, the HDS is intended to grow annually, and we are already in the process of recruiting additional cohorts for our next iteration and release in 2025.
Contact Us
Please contact info@neuroproteome.org with inquiries about joining the Global Neurodegeneration Proteomics Consortium (GNPC) or questions about accessing the resulting data.