The multi-modality neuroimaging analysis identified an essential genetic variant associated with Parkinson's disease
Chen, Z.; Wu, B.; Li, G.; Zhou, L.; Zhang, L.; Liu, J.
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BackgroundOver 90 genetic variants have been found to be associated with Parkinsons disease (PD) in genome-wide association studies, however, the neural mechanisms of previously identified risk variants in PD were largely unexplored. The objective of this study was to evaluate the associations between PD-associated genetic variants and brain gene expressions, clinical features, and brain networks. MethodsPD patients (n = 198) receiving magnetic resonance imaging examinations from Parkinsons Progression Markers Initiative (PPMI) database were included in the analysis. The effects of PD-associated genetic variants assayed in PPMI database on clinical manifestations and brain networks of PD patients were systematically evaluated. FindingsMost associations between 14 PD-associated risk variants and clinical manifestations of PD patients failed to reach the stringent p-value threshold of 0.00026 (0.05/14 clinical variables x 14 variants). Shared and distinct brain network metrics were significantly shaped by PD-associated genetic variants. Small-worldness properties at the global level and nodal metrics in caudate and putamen of basal ganglia network were preferentially modified. Small-worldness properties in gray matter covariance network mediated the effects of OGFOD2/CCDC62 rs11060180 on motor assessments of PD patients. InterpretationOur findings support that both shared and distinct brain network metrics are shaped by PD-associated risk variants. Small-worldness properties modified by OGFOD2/CCDC62 rs11060180 in gray matter covariance network are associated with motor severity of PD patients. Future studies are encouraged to explore the underlying mechanisms of PD-associated risk variants in PD pathogenesis. FundingThis work was supported by grants from the National Key Research and Development Program (2016YFC1306505) and the National Natural Science Foundation of China (81471287, 81071024, 81171202).
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