Transcriptomics

Dataset Information

9

Expression data from human brain amygdala - including control samples and samples with major depression disorders (42 samples MD3_AMY)


ABSTRACT: Major depressive disorder is a heterogeneous illness with a mostly uncharacterized pathology. Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases (See publication for details). 42 total samples in 21 pairs were analyzed in postmortem tissue from the amygdala.

ORGANISM(S): Homo sapiens  

SUBMITTER: Etienne Sibille  

PROVIDER: E-GEOD-54564 | ArrayExpress | 2014-05-16

SECONDARY ACCESSION(S): GSE54564PRJNA237166

REPOSITORIES: GEO, ArrayExpress

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Publications

A conserved BDNF, glutamate- and GABA-enriched gene module related to human depression identified by coexpression meta-analysis and DNA variant genome-wide association studies.

Chang Lun-Ching LC   Jamain Stephane S   Lin Chien-Wei CW   Rujescu Dan D   Tseng George C GC   Sibille Etienne E  

PloS one 20140307 3


Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of hum  ...[more]

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