Overdispersed gene expression in schizophrenia.


Journal

NPJ schizophrenia
ISSN: 2334-265X
Titre abrégé: NPJ Schizophr
Pays: United States
ID NLM: 101657919

Informations de publication

Date de publication:
03 Apr 2020
Historique:
received: 31 08 2019
accepted: 13 02 2020
entrez: 5 4 2020
pubmed: 5 4 2020
medline: 5 4 2020
Statut: epublish

Résumé

Schizophrenia (SCZ) is a severe, highly heterogeneous psychiatric disorder with varied clinical presentations. The polygenic genetic architecture of SCZ makes identification of causal variants a daunting task. Gene expression analyses hold the promise of revealing connections between dysregulated transcription and underlying variants in SCZ. However, the most commonly used differential expression analysis often assumes grouped samples are from homogeneous populations and thus cannot be used to detect expression variance differences between samples. Here, we applied the test for equality of variances to normalized expression data, generated by the CommonMind Consortium (CMC), from brains of 212 SCZ and 214 unaffected control (CTL) samples. We identified 87 genes, including VEGFA (vascular endothelial growth factor) and BDNF (brain-derived neurotrophic factor), that showed a significantly higher expression variance among SCZ samples than CTL samples. In contrast, only one gene showed the opposite pattern. To extend our analysis to gene sets, we proposed a Mahalanobis distance-based test for multivariate homogeneity of group dispersions, with which we identified 110 gene sets with a significantly higher expression variability in SCZ, including sets of genes encoding phosphatidylinositol 3-kinase (PI3K) complex and several others involved in cerebellar cortex morphogenesis, neuromuscular junction development, and cerebellar Purkinje cell layer development. Taken together, our results suggest that SCZ brains are characterized by overdispersed gene expression-overall gene expression variability among SCZ samples is significantly higher than that among CTL samples. Our study showcases the application of variability-centric analyses in SCZ research.

Identifiants

pubmed: 32245959
doi: 10.1038/s41537-020-0097-5
pii: 10.1038/s41537-020-0097-5
pmc: PMC7125213
doi:

Types de publication

Journal Article

Langues

eng

Pagination

9

Subventions

Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 61803320
Organisme : National Science Foundation of China | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund)
ID : 61803320

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Auteurs

Guangzao Huang (G)

Department of Automation, Xiamen University, Xiamen, 361005, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.
College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China.

Daniel Osorio (D)

Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, 77843, USA.

Jinting Guan (J)

Department of Automation, Xiamen University, Xiamen, 361005, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.

Guoli Ji (G)

Department of Automation, Xiamen University, Xiamen, 361005, China. glji@xmu.edu.cn.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China. glji@xmu.edu.cn.
Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, 361005, China. glji@xmu.edu.cn.

James J Cai (JJ)

Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, 77843, USA. jcai@tamu.edu.
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA. jcai@tamu.edu.
Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX, 77843, USA. jcai@tamu.edu.

Classifications MeSH