Project 458783

Parkinson's disease (PD) is the second most common neurodegenerative disease, affecting 1 to 2% of the population older than 60 years. It belongs to a group of diseases called synucleinopathies which mainly include PD, dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). REM sleep behavior disorder (RBD), in which patients enact their dreams during REM sleep, is the best predictor for diagnosis of synucleinopathies. More than 80% of RBD cases will convert to PD, DLB or MSA within 10-15 years after onset. Previous studies have shown that synucleinopathies are highly heritable. However, in PD, only a third of PD heritability has been explained, suggesting that other genetic factors have yet to be identified. In this research proposal, I aim to identify genetic factors contributing to the missing heritability in synucleinopathies using bioinformatic and machine learning. I will be examining the association of human leukocyte antigen (HLA) alleles, copy number variations (CNV) (large genetic deletions, duplications) and perform transcriptome-wide association studies (TWAS) in synucleinopathies. I will utilize machine learning algorithms to prioritize candidate genes to facilitate downstream analyses and functional studies. By uncovering missing heritability, we will have a better understanding of the disease mechanism of synucleinopathies. This study will uncover new genetic therapeutic targets and enable researchers for follow-up analysis.