New Parkinson’s meta-GWAS of over 1.8M samples!
Proud to announce the preprint of next generation of Parkinson’s disease genome-wide association study meta-analyes is here.
This study really shows the power of unified data generation and analysis processes deployed globally in the cloud! Open science and open source coming through with a win.
This study is massive, including …
63,555 cases
17,700 proxy cases with a family history of Parkinson's disease
1,746,386 controls
… making it the largest investigation of Parkinson's disease genetic risk to date.
Study design snapshot.
Quick summary of the findings include …
Identified 134 risk loci (59 novel)
A total of 157 independent risk variants across these loci, significantly expanding our understanding of Parkinson’s disease
risk.
Multi-omic data integration revealed that expression of the nominated risk genes are highly enriched in brain tissues, particularly in neuronal and astrocyte cell types.
We prioritized 33 high-confidence genes across these 134 loci for future follow-up studies.
… actionable results expanding our understanding of Parkinson’s etiology!
A very nice “Manhattan plot” summarizing the results.
This study is as transparent as I’ve ever seen …
Data is available from the team at the Global Parkinson’s Genetics Program (https://gp2.org/).
All code generated for this article, and the identifiers for all software programs and packages used, are available on GitHub (https://github.com/GP2code/GP2-EUR-metaGWAS) and were given a persistent identifier via Zenodo (DOI: 10.5281/zenodo.15013321).
Summary statistics from every ancestry-level meta-analysis are available on NDPK (https://ndkp.hugeamp.org/research.html?pageid=a2f_downloads_280).
Obligatory shout outs from the paper, a huge team effort comprised of hundreds of contributors around the world …
Hampton L. Leonard, Mary B. Makarious, Dan Vitale, and Mike A. Nalls designed the analysis, analyzed the data, and drafted the initial manuscript.
Astros Th. Skuladottir, Vala Palmadottir, Hreinn Stefansson, and Kari Stefansson designed the analysis and analyzed the data for the Iceland (ICE) ancestry GWAS from deCODE.
Kristin Levine, Hampton L. Leonard, Nicole Kuznetsov, Mary B. Makarious, Dan Vitale, Mat Koretsky contributed to quality control of GP2 genetic data and making it available to researchers.
Cornelis Blauwendraat and Andrew B. Singleton led study design, data logistics, and funding of the study.
Huw Morris, Manuela Tan, Hirotaka Iwaki, Simona Jasaityte, Ellie Stafford, Lietsel Jones, Shannon Ballard, J Solle, and Claire Wegel (Complex and Compliance Working Groups) designed the GP2 complex study, clinical and genetic data generation and preparation, and legal / data sharing logistics.
All other members of GP2 (contributors) contributed data and made critical revisions to this article.
Additional shout out to Noel Burtt and her team for hosting the summary stats months in advance!