ASAP is committed to accelerating the pace of discovery and informing a path to a cure for Parkinson’s disease through collaboration, research-enabling resources, and data sharing. We’ve created this catalog to showcase the research outputs and tools developed by ASAP-funded programs.


Towards a phenome-wide view of Parkinson’s disease

Preprint: The authors examine that relationship between PD and the environment by holistically characterizing environmental, health, and pharmacological traits associated with PD patients. They found numerous traits that were positively and negatively associated with PD.


Chemically induced senescence in human stem cell‐derived neurons promotes phenotypic presentation of neurodegeneration

Published: Using embryonic stem-cell derived neurons to model age-related neurodegenerative diseases is inherently difficult. The authors thereby developed a chemical cocktail to induce cellular senescence (without causing DNA damage), thereby inducing embryonic cells to exhibit features characteristic of aged cells. The cocktail can help enhance disease-related phenotypes in iPSCs.


Nicotine-mediated rescue of α-synuclein toxicity requires synaptic vesicle glycoprotein 2

Preprint: Parkinson’s disease likely reflects a complex interaction among genetic and environmental factors. Here, the role of nicotine, SV2 and the alpha-synuclein were examined. The study suggests that SV2 may be needed for the protection nicotine provides from Parkinson’s-related neurotoxicity.


powereQTL – An R package for calculating sample size and power of bulk tissue and single-cell eQTL analysis

Power and sample size calculation for bulk tissue and single-cell eQTL analysis based on ANOVA, simple linear regression, or linear mixed-effects model. It can also calculate power/sample size for testing the association of an SNP to a continuous type phenotype.


powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis

Published: Genome-wide association studies (GWAS) reveal thousands of genetic loci. To understand the causality of the genetic variants, many researchers employ expression of quantitative trait locus (eQTL) analysis. The authors created a simplified, user-friendly R package called powerEQTL to aid in performing the power calculations needed for eQTL analysis. View original preprint.