Genetic Risk Factors for Parkinson's Disease

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Parkinson's disease (PD) affects 1-2% of the population over 60 years of age and thus constitutes a major problem in public health. Current treatment strategies are only palliative and a better understanding of the molecular mechanisms underlying PD is necessary in order to develop more definitive neuroprotective therapies. Human genetic studies are proving to be a valuable asset in this endeavor and progress is now accelerating with the advent of genome- wide association studies (GWAS) in which the entire human genome is interrogated using hundreds of thousands of markers. The PI and his collaborators in the NeuroGenetics Research Consortium (NGRC) are currently conducting a GWAS in a large PD case-control sample; genotyping for the project will be complete by the fall of 2009. In this application, we propose to validate findings from the NGRC GWAS using dense next- generation sequencing and brain/CSF proteomics as key tools. In Aim 1, we will select a list of candidate genes from the GWAS based both on statistical grounds and on evidence of differential expression of the corresponding protein in frontal cortex and/or CSF in PD patients vs. controls. We will then sequence each gene in its entirety in 96 PD patients using array- based capture and next-generation pyrosequencing techniques. This will allow discovery of variants that were not represented or tagged in the GWAS (e.g. low-frequency coding SNPs and SNPs in singleton bins). Such variants will then be genotyped in Aim 2 in 2,000 PD patients and 2,000 controls from the NGRC. This will serve to further fine-map each candidate gene, and potentially strengthen the association for bona fide susceptibility loci. In Aim 3, we will then replicated these findings in an independent sample of 1,600 cases and 1,600 controls. Finally, in the most promising genes that remain, we will examine correlation between genotype and total levels/isoform ratios of the corresponding proteins in CSF and frontal cortex of PD patients and controls (Aim 4). Combined with bioinformatics analysis, this will assist in identifying putative functional variants within each susceptibility gene that will be suitable for future study in model systems.