Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebrospinal fluid (CSF) biomarkers, such as alpha-synuclein seed amplification assays (alphaSyn-SAA), offer diagnostic potential, their clinical utility is limited by invasiveness and incomplete specificity. Plasma biomarkers provide a minimally invasive alternative, but their mechanistic role in PD remains unclear. A major challenge is distinguishing whether plasma biomarkers causally reflect primary neurodegenerative processes or are downstream consequences of disease progression. To address this, we leverage the Parkinson's Progression Markers Initiative (PPMI) Project 9000, containing 2,924 plasma and CSF biomarkers, to systematically infer causal relationships with disease status. However, only a sparse subset of these biomarkers and their interconnections are actually relevant for the disease. Existing causal discovery algorithms, such as Fast Causal Inference (FCI) and its variants, struggle with the high dimensionality of biomarker datasets under sparsity, limiting their scalability. We propose Penalized Fast Causal Inference (PFCI), a novel approach that incorporates sparsity constraints to efficiently infer causal structures in large-scale biological datasets. By applying PFCI to PPMI data, we aim to identify biomarkers that are causally linked to PD pathology, enabling early diagnosis and patient stratification. Our findings will facilitate biomarker-driven clinical trials and contribute to the development of neuroprotective therapies.