CuNA: Cumulant-based Network Analysis of genotype-phenotype associations in Parkinson's Disease

Abstract

Parkinsons Disease (PD) is a progressive neurodegenerative movement disorder characterized by loss of striatal dopaminergic neurons. Progression of PD is usually captured by a host of clinical features represented in different rating scales. PD diagnosis is associated with a broad spectrum of non-motor symptoms such as depression, sleep disorder as well as motor symptoms such as movement impairment, etc. The variability within the clinical phenotype of PD makes detection of the genes associated with early onset PD a difficult task. To address this issue, we developed CuNA, a cumulant-based network analysis algorithm that creates a network from higher-order relationships between eQTLs and phenotypes as captured by cumulants. We also designed a multi-omics simulator, CuNAsim to test CuNAs qualitative accuracy. CuNA accurately detects communities of clinical phenotypes and finds genes associated with them. When applied on PD data, we find previously unreported genes INPP5J, SAMD1 and OR4K13 associated with symptoms of PD affecting the kidney, muscles and olfaction. CuNA provides a framework to integrate and analyze RNA-seq, genotype and clinical phenotype data from complex diseases for more targeted diagnostic and therapeutic solutions in personalized medicine. CuNA and CuNAsim binaries are available upon request.