Genome-wide expression and genomic data integration analyses in sporadic Parkinson Disease
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Parkinson disease (PD) is the second most common neurodegenerative disorder, affecting an estimated 2% of the population above 65 years of age. Although familial forms of PD have been linked to specific mutations responsible for the onset of the disease, the majority of PD cases is still of unknown etiology. PD has been traditionally studied using individual genetic methods, such as linkage analysis, genome-wide association (GWAS), or microarray expression studies. Nevertheless, the intrinsic disease genetic variability, and the unilateral analysis approach of available datasets made the detection of robust gene or pathway signals difficult. Studies of PD that combine a range of systems genetics approaches, and integrate complementary disease-relevant genetic datasets, represent a promising approach for accommodating prior inconsistent, as well as diverse results. To investigate the genetics of idiopathic PD, I performed the largest genome-wide expression study in brain tissue to date. The study was carried out on the 1-color Agilent 60-mer Whole Human Genome Microarray, and included 26 neurologically healthy control and 27 PD samples from the frontal cortex Brodmann 9 area (BA9). The selected brain samples were of high quality (high pH and RNA integrity, no significant signs of Alzheimer disease pathology), and had rich documentation of neuropathological and clinical information available. I analyzed the microarray expression results in combination with genotyping data for PD-associated single nucleotide polymorphisms obtained for the microarray brain samples, and detected a pathway of interest for PD involving the FOXO1 (Forkhead box protein O1) gene. This result was verified in additional publically available expression datasets. I then performed a network-based canonical pathway analysis of PD, combining results from available GWAS, microarray expression, and animal model expression studies. The used analysis framework was a human functional-linkage network (FLN), consisting of genes as nodes, and weighted links indicating the confidence of gene-pair involvement in similar biological processes. I demonstrated the relevance of the used FLN for studying PD. Additionally, I ranked genes and pathways based on the available disease datasets. The frontal cortex BA9 study, and an additional non-PD microarray study were used as the positive and negative controls, respectively, for the obtained results.
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