Mining genetic, transcriptomic and imaging data in Parkinson’s disease

Parkinson’s Disease (PD) is one of the most common and diffused neurodegenerative diseases, with a prevalence of 0.3% in the general population of industrialized countries and ~1% in subjects over the age of 60 [1]. PD is associated with both genetic and neuroimaging factors. Despite the factors causing PD are still not completely clear, during the last decade there has been many significant advances in early clinical diagnosis, via brain imaging, and in our understanding of the genetics of Parkinson’s disease. Imaging genetics is an emerging longitudinal research field bridging genetic insights into the biology of complex diseases with quantitative neuroimaging phenotypes. Imaging genetics primarily focuses on identifying and characterizing how genes and genetic variation influence neuroanatomical and neurophysiological traits exploiting brain images. Until now have been proposed several imaging genetics methods to improve our knowledge on different complex neurodegenerative disease, such as Alzheimer’s disease, but only few studies focused on PD. Using PD as a case study, here we exploit the advantages of existing methods to analyze heterogeneous datasets. To do so, we designed and propose a multi-view imaging genetics workflow interpolating genotyping, transcriptomic data, and functional and morphological brain images. We show how to process and interpret data to retrieve several potential genetic variants, which could constitute potential genetic biomarkers of PD onset and progression. The method consists of three steps. Each phase explores different aspects and uses different tools to handle the different data type. We will move from the classical quality control phase, which corrects potential problems in the dataset, to the more technical and core part of our method, consisting of principal component analysis (PCA), Genome Wide Association Study (GWAS), and results merge, validation and interpretation. Each step carries out the analysis, by using different state-of-the-art tools and scripts written in various programming language like R, Python and Bash.