Glucocerebrosidase gene mutations associated with Parkinson's disease: a meta-analysis in a Chinese population.

Mutations of glucocerebrosidase (GBA) confer susceptibility to Parkinson's disease in several ethnical populations, with a high incidence especially in the Ashkenazi Jewish population. Although there are several studies that have investigated a similar association in a Chinese population, small...

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Bibliographic Details
Main Authors: Jia Chen, Wei Li, Tao Zhang, Yan-jiang Wang, Xiao-jiang Jiang, Zhi-qiang Xu
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4275276?pdf=render
Description
Summary:Mutations of glucocerebrosidase (GBA) confer susceptibility to Parkinson's disease in several ethnical populations, with a high incidence especially in the Ashkenazi Jewish population. Although there are several studies that have investigated a similar association in a Chinese population, small sample sizes and few positive outcomes have made it difficult to obtain conclusive results from these individual studies. Therefore, the present study used a meta-analysis approach, pooling the appropriate data from published studies to investigate the association of GBA mutations and Parkinson's disease in a Chinese population. Nine studies containing 6536 Chinese subjects (3438 cases and 3098 healthy controls) and examining the GBA mutations of L444P, N370S and several other mutations were included. Review Manager 5.2 software was applied to analyze the pooled odds ratios (ORs) and 95% confidence intervals (CIs). The results showed a significant association of Parkinson's disease risk with overall GBA mutations (OR = 6.34, 95% CI = 3.77-10.68, p<0.00001), and with the subgroup of L444P mutation (OR = 11.68, 95% CI = 5.23-26.06, p<0.00001). No such association was observed for the subgroup with N370S mutation or other mutations, in part because of the small sample size or rare events. Thus, for the rare occurrence of GBA mutations, studies with larger sample size are necessary to minimize the sampling error and to obtain convincing results.
ISSN:1932-6203