Back

Parietal Cortex Transcriptomics Refines Parkinson Disease GWAS Nomination and Highlights STAT3 as a Putative Upstream Glial Regulator

Alfradique-Dunham, I.; Sanford, J.; Liu, M.; Perrin, R. J.; Franklin, E. E.; Norris, S.; Kotzbauer, P. T.; Perlmutter, J. S.; Budde, J. P.; Cruchaga, C.; Ibanez, L.; Minaya, M.

2026-08-21 neuroscience
10.64898/2026.08.13.744719 bioRxiv
Show abstract

Parkinson disease (PD) affects more than 1.1 million individuals in the United States and around 12 million worldwide. Although Genome Wide Association Studies (GWAS) have substantially advanced our understanding of PD genetic architecture, the regulatory mechanisms linking PD risk loci to disease-relevant gene expression remain incompletely characterized, limiting our ability to infer disease mechanisms from genetic associations. Here, we integrated disease-state parietal cortex transcriptomics with the International Parkinsons Disease Genomics Consortium (iPDGC) locus prioritization to refine PD gene nomination and identify biologically plausible candidates missed by GWAS-only approaches. Using bulk RNA-seq from 99 neuropathologically confirmed PD cases and 30 neuropathologically confirmed controls, we prioritized candidate genes across 78 loci and classified them according to concordance between genetic evidence and differential expression in diseased cortices. This integrative approach recovered candidate genes not captured by external GWAS-based prioritization methods and highlighted synaptic, lysosomal, and proteostasis pathways as major components of PD risk biology. Network and transcription factor analyses further suggested coordinated regulation of these genes, with STAT3 emerging as a putative upstream glial regulator. Together, these findings suggest that integrating disease-state transcriptomics with genetic prioritization can refine PD risk-gene nomination and uncover regulatory programs that may be missed by GWAS alone.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.