Identification of genetic risk factors for Parkinson’s disease

Parkinson's disease (PD) is a common progressive neurodegenerative disorder with a complex and heterogeneous genetic landscape. Approximately 90% of all PD cases are driven by the cumulative effect of several common low-risk genetic variants. Over the last years, genetic studies of familial and sporadic PD cases identified a range of high and low-risk variants, representing approximately 40% of estimated heritability. However, the role of structural variants (SV) in the PD missing heritability remains understudied. Therefore, we investigated SVs in the human cohort enriched for the PD phenotype to expand our knowledge about the putative PD genetic risk factors. We leveraged the matching omics datasets obtained from 95 iPSC lines differentiated into the dopaminergic neuronal-like state to run the SV calling and to directly assess their impact on the gene and transcript expression. We demonstrated a conceptual approach for the genome-wide SV annotation and pathogenicity assessment, addressing the challenges of functional SV effect prediction based on the known properties of genome regions and available multi-omics data. Using this approach, we prioritized a group of non-coding SVs absent in the healthy controls with a strong association with the differential expression of genes whose dysregulation can trigger the development of PD or PD-related phenotype. Discovered variation impacts molecular mechanisms involved in the regulation of signaling processes, oxidative stress response, and neuronal DNA reparation. Additional analysis on the larger PD patient and control cohort has to be conducted for variant-expression association validation and exploration of the allele effect size and penetrance of the prioritized hits. The dataset is publicly available to facilitate the further discovery of SV PD risk association as well as to study sequence signatures and neurological disease-specific SV hot spots

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

publishedVersio

The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson’s disease data

Open science and collaboration are necessary to facilitate the advancement of Parkinson's disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities, thus we coordinated a virtual 3-day hackathon event, during which 49 early-career scientists from 12 countries built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools. Each team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools. Hackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early-career researchers. The resources generated can be used to accelerate research on the genetics of PD

Author Correction: Elucidating causative gene variants in hereditary Parkinson’s disease in the Global Parkinson’s Genetics Program (GP2)

Correction to: s41531-023-00526-9 npj Parkinson’s Disease, published online 27 June 2023 In this article the Global Parkinson’s Genetics Program (GP2) members names and affiliations were missing in the main author list of the Original article which are listed in the below

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations