The age of onset and penetrance (likelihood of disease) of individuals with the LRRK2 G2019S mutation varies considerably, the latter ranging in some families from as high as 100 percent to as low as 22 percent. This variation suggests that genetic modifiers contribute to LRRK2 pathogenesis in Parkinson’s disease (PD). The objective of this project is to collect and sequence the genomes of multiple LRRK2 families and use innovative technology and computational approaches to identify and validate novel genetic modifiers of LRRK2-mediated neurodegeneration.
The overarching goal is to identify genetic modifiers of LRRK2 G2019S–induced neurodegeneration in PD. To do this, researchers propose a four phase plan, including the (1) identification and collection of samples from LRRK2 families, (2) integrating analysis of existing genetic data on PD patients with LRRK2 G2019S mutations to confirm the identity of candidate genetic modifiers, together with (3) whole genome sequencing data on LRRK2 G2019S families to identify novel genetic modifiers that will be (4) funneled into a validation scheme to directly test potential genetic modifiers for modifying LRRK2 G2019S-induced neurodegeneration in induced pluripotent stem cell (iPSC)-derived neurons from patients with the LRRK2 G2019S mutation.
Relevance to Diagnosis/Treatment of Parkinson’s Disease:
Given the variable penetrance of LRRK2 mutations, identifying novel genetic variants, might lead to better genetic testing and predictions of the likely onset or PD progression of LRRK2 carriers. Furthermore, bioinformatic approaches, such as pathway analysis of these genetic modifiers, may identify specific components that are modulated during neurodegeneration, leading to potential druggable targets that can be used to test available therapeutics to block LRRK2-mediated neurodegeneration.
This project hopes to identify genes that are important for the progression of LRRK2 associated PD. By sequencing the genomes of individuals from many families harboring LRRK2 mutations, investigators will use computational approaches to pinpoint specific genetic mutations that either enhance or lessen the onset and/or progression of LRRK2-associated PD. They will then use neurons, derived from patients with LRRK2 mutations, to validate and understand the role of these genetic mutations within cells. This work would not only increase understanding of what goes wrong in the cells of LRRK2 patients, but also help with genetic testing and in identifying potential therapeutic targets.