Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms

Abstract

Genome-Wide Association Studies (GWAS) have elucidated the genetic components of Parkinson's Disease (PD). However, because the vast majority of GWAS association signals fall within non-coding regions, translating these results into an interpretable, mechanistic understanding of the disease etiology remains a major challenge in the field. In this review, we provide an overview of the approaches to prioritize putative causal variants and genes as well as summarise the primary findings of previous studies. We then discuss recent efforts to integrate multi-omics data to identify likely pathogenic cell types and biological pathways implicated in PD pathogenesis. We have compiled full summary statistics of cell-type, tissue, and phentoype enrichment analyses from multiple studies of PD GWAS and provided them in a standardized format as a resource for the research community (https://github.com/RajLabMSSM/PD_omics_review). Finally, we discuss the experimental, computational, and conceptual advances that will be necessary to fully elucidate the effects of functional variants and genes on cellular dysregulation and disease risk.

Keywords: Genome-wide association study (GWAS); Meta-analysis; Multi-omics; Neurodegeneration; Parkinson's Disease; Phenome; Quantitative trait loci (QTL).

Figure 1.. Common workflows in the investigation of PD.

Generalized workflow of PD genomics research, from GWAS to validated functional mechanisms. We provide this as a summary of what has commonly been done to date, with the understanding that the development of novel, innovative approaches not described in this diagram will be vital to progressing neurodegenerative disease research.

Figure 2.. Meta-analysis of cell-types and tissues enriched in PD GWAS.

We compiled enrichment test summary statistics across several studies using differing methodologies (Table S1). Only results pertaining to PD were extracted, and false discovery rate (FDR) was re-calculated from nominal p-values within each study. −log(FDR) was then rescaled from 0-1 to provide a Score that is more comparable across studies. Where p-values were not available (e.g. SNP-regulatory element overlap counts and colocalization posterior probabilities in Schilder et al. (2020)) we instead computed deciles from the relevant metric and then rescaled the values to from 0-1. Within each study, rows are ordered from highest to lowest median Score. Only up to the top 50 rows are shown per study to improve plot legibility.