Project Summary/Abstract Parkinson's disease (PD) is the most common neurodegenerative movement disorder, affecting more than 6 million people worldwide, with the prevalence projected to double in the next few decades. PD is a heterogeneous disorder with identifiable clinical-pathological subtypes based on symptom severity and predominance. An accurate molecular profile could reduce clinical heterogeneity among PD patients. Few studies have applied a proteogenomic approach to samples from PD and PD dementia (PDD) patients to identify proteins associated with clinical, neuroimaging, or neuropathological subtypes. This proposal aims to create a framework for uncovering proteins, genes, pathways, and potential biomarkers that will improve our understanding of underlying disease mechanisms, predict disease course, and design clinical trials. We propose leveraging a unique resource that includes quantitative proteomic analysis of ~5,000 proteins from cerebrospinal fluid (CSF) and plasma of clinically diagnosed PD patients coupled with brain samples from autopsy-confirmed cases. This large-scale screening of ~3,110 samples could identify differentially expressed protein levels of known molecular pathways involved in PD or with a clear genetic connection to PD risk. To achieve these goals, we plan to carry out a three-stage study design: discovery, replication, and meta-analyses using SOMAscan of plasma (n=1,244), CSF (n=1,215), and brain tissue (n=659) from healthy individuals, PD, and Alzheimer's disease patients (Aim 1). For replication studies, we have accessed and processed data from studies in plasma (n=8,873), brain (n=144), and CSF (n=232). We plan to use disease status, age-at-onset, and clinical scales of motor impairment to find a proteomic profile that could be used to create a biomarker-driven clinical-molecular phenotype in PD patients (Aim 1A). We also plan to find associations of proteomic profiles with cognitive test scores, CSF biomarkers, and neuroimaging (Pittsburgh compound B) to uncover proteins associated with amyloid pathology in living PD dementia patients (Aim 1B). We will integrate neuropathology and proteomic data to identify a divergent molecular signature or share similar aberrant pathways in PD, PDD, and Alzheimer's disease. (Aim 1C). Finally, we will integrate proteomic and GWAS data to identify pQTLs and apply polygenic risk scores and Mendelian Randomization approaches to determine proteins involved in the causal pathway of PD, which are potential novel PD biomarkers (Aim 2). Using this approach, we will be able to select reliable PD biomarker candidates for validation. We expect to uncover a genome-proteome network that will provide a basis for novel approaches to diagnostic and pharmacotherapeutic applications in PD.