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Metadata

Name
CSF pQTL study in the Japanese population [somascan]
Repository
Gene Expression Omnibus
Identifier
geo.series:GSE83710
Description
Cerebrospinal fluid (CSF) is virtually the only one accessible source of proteins derived from the central nervous system (CNS) of living humans and possibly reflects the pathophysiology of a variety of neuropsychiatric diseases. However, little is known regarding the genetic basis of variation in protein levels of human CSF. We examined CSF levels of 1,126 proteins in 133 subjects and performed genome-wide association analysis of 514,227 single nucleotide polymorphisms (SNPs) to detect protein quantitative trait loci (pQTLs). Spearman’s correlation was used to identify association between genotypes of SNPs and protein levels. A total of 421 cis and 25 trans SNP-protein pairs were significantly correlated at a false discovery rate (FDR) of less than 0.01 (nominal P < 7.66 x 10 ?9). Cis-only analysis revealed additional 580 significant cis SNP-protein pairs with FDR < 0.01 (nominal P < 2.13 x 10 ?5). pQTL SNPs were more likely, compared to non-pQLT SNPs, to be disease/trait-associated variants identified by previous genome-wide association studies. The present findings suggest that genetic variations play a major role in the regulation of protein expression in the CNS. The obtained database will serve as a valuable resource for future neuropsychiatric research.
Data or Study Types
Other
Source Organization
National Center for Biotechnology Information
Access Conditions
available
Year
2016
Access Hyperlink
http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GSE83710

Distributions

  • Encoding Format: TXT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE83710/matrix/
  • Encoding Format: MINiML ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE83710/miniml/
  • Encoding Format: SOFT ; URL: ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE83710/soft/
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.