Funded Studies
Objective/Rationale:
Estimates for development of Parkinson’s disease (PD) and age of onset in carriers of LRRK2 G2019S mutations vary widely (24 to 100% by age 80). Factors including ethnic group, gender, family history of PD and presence of other genetic and environmental risks may influence how many people with the mutation exhibit clinical symptoms (a proportion called penetrance). We will use new methodology that incorporates clinical information on each subject to refine these statistical estimates. The goal is to develop personalized prediction models that can aid in genetic counseling
Project Description:
We will administer a reliable, valid family history interview about PD and related neurodegenerative disorders with individuals with PD who carry LRRK2 mutations. These subjects will report on their parents, siblings and children. We will use link this information to clinical and genetic information when available. We will specifically test whether ethnic background plays a role in predicting PD risk. For example, we will explore the hypothesis that PD risk is lower in family members of Ashkenazi Jewish background than in family members of non-Ashkenazi Jewish background, using the same methodology and adjusting for the demographic and clinical characteristics that can increase penetrance. If penetrance is lower in the Ashkenazi groups, it suggests that there are genetic or environmental factors that delay the onset of PD.
Relevance to Diagnosis/Treatment of Parkinson’s Disease:
While a genetic test now can quickly inform an individual that he/she is at risk for development of PD, it does not say how likely that risk is by a specific age, e.g. risk of developing PD by age 70. We also cannot factor in characteristics that might reduce that risk, e.g. gender, smoking or family history of PD. Providing more accurate estimates can help genetic counselors communicate about an individual’s risk.
Anticipated Outcome:
We would like to determine if the risk for development of PD differs among people from different ethnic groups, and, if so, what factors contribute to the different estimates. We hope that this will lead to accurate personalized prediction models that can use patient and family member data to provide more refined prediction algorithms. Ultimately these may be used in clinical trials to test whether disease-modifying therapies can modify age at onset in unaffected family members who carry G2019S mutations.