Parkinson’s disease (PD) is a devastating neurodegenerative disorder that affects an estimated ~5 million people worldwide and is associated with intracellular α-synuclein protein inclusions and progressive neuronal cell loss. There are currently no disease-modifying therapies for PD due in part to lack of clinically useful biomarkers and clearly intervenable therapeutic targets. Despite recent advances made with genome-wide association studies (GWAS), there is an unmet need in the PD field for (1) generation of diagnostic and prognostic biomarkers that can distinguish PD from non-PD and parse considerable clinical heterogeneity, and (2) mechanistic follow-up of GWAS-identified leads to elucidate their role in neurodegeneration. This dissertation addresses these two gaps in therapeutic development in PD. First, we perform an unbiased, ~1000-protein screen in plasma samples from over 500 PD patients and neurologically normal controls across three different clinical sites, identifying novel proteins associated with PD. We show that our top PD-associated biomarkers – particularly, plasma levels of growth hormone receptor (GHR) – replicate their associations with disease status across multiple cohorts, are robust to common sources of variability in a clinical setting (specifically, sample handling and dopaminergic medication), and predict future cognitive decline across cohorts, cognitive measures, and statistical test used. Second, to “widen the net” for potential pathophysiological mechanisms and therapeutic targets in PD, we dissect the mechanism behind the non-coding chromosome 7 locus first associated with PD by GWAS. We identify the glycoprotein non-metastatic melanoma protein B gene (GPNMB) as the target of this locus by mining existing “-omic” datasets and analyzing the probability of a shared causal variant behind both the association to PD risk and expression levels of potential target genes. We then confirm these in silico analyses with allele-specific expression experiments using patient-derived brain samples. We explore interactions between GPNMB and α-synuclein by immunofluorescence, co-immunoprecipitation, immunoblotting, and RNA-seq-based transcriptomic profiling in immortalized cell lines and iPSC-derived neurons with normal levels, partial loss, or full loss of GPNMB. We find that GPNMB interacts with α-synuclein and that loss of GPNMB leads to decreased synaptic α-synuclein and broad transcriptomic changes among synaptic genes. Lastly, we test the clinical utility of GPNMB as a biomarker in plasma and CSF samples from >800 PD patients and controls, finding that plasma GPNMB is elevated in PD and higher levels are found in PD patients with more severe disease. These results suggest a model whereby the rs199347 risk allele results in increased GPNMB expression which, through interactions with α-synuclein, leads to synaptic changes, thereby impacting risk for neurodegeneration. More broadly, this dissertation exemplifies an investigative approach combining computational analyses of large datasets with targeted bench-based experiments. Through this work, we nominate GHR as a promising PD biomarker and GPNMB as both a biomarker and potential therapeutic target in PD.