Leveraging single-cell multi-omics to investigate rare noncoding variants in Parkinson's Disease - PROJECT SUMMARY/ABSTRACT Parkinson’s disease (PD) is a complex neurodegenerative disorder, with numerous genetic and environmental features that modify disease risk and progression. While genome-wide association studies (GWAS) have identified many genetic loci that are associated with PD risk, there is still a large portion of heritability that is unexplained. This current study highlights the significance of rare noncoding variants, which have been largely overlooked in previous research that primarily focused on common polymorphisms or rare coding variants, by characterizing the impact of these variants on gene regulation. I hypothesize that rare noncoding variants play a substantial functional role in the genetic component of PD by influencing chromatin accessibility and gene expression in a cell type-specific manner. We utilize machine learning approaches that learn patterns in cell type-specific chromatin accessibility signals to identify functional rare noncoding variants in PD patients. We have used these deep learning models to predict how a noncoding variant may perturb chromatin accessibility, and thus gene regulation. In Aim 1, we will analyze the impact of ML-nominated candidate noncoding variants on gene regulation using a comprehensive atlas of matched single-nuclei chromatin accessibility and gene expression data from PD patients and cognitively healthy control individuals. We will investigate how select rare noncoding variants affect gene regulation and disease-associated pathways in a cell type-specific manner. In Aim 2, using massively parallel reporter assays, we will then experimentally validate the functional effects of ML-prioritized rare noncoding variants and analyze their allelic effects using in vitro models of brain cell types. By comparing rare and common putative functional noncoding variants, the research aims to understand potential differences in their mechanisms of action. This project will fill a critical knowledge gap in the understanding of the genetic basis of PD. By providing insights into how rare and common noncoding variants influence gene regulation in specific brain cell types, this research will serve as a valuable roadmap for studying noncoding variants in other heritable diseases as well. Through this project, I will develop skills in computational and experimental functional genomics approaches that will allow for meaningful contributions to our understanding of PD, ultimately improve PD diagnosis and treatment and strengthen my training as an aspiring physician-scientist.
