Dataset Information

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease.

ABSTRACT:

Background

Identifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding with age-related neurodegeneration.

Methods

A gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 reproducible (i.e., established) AD (RAD) genes. Genome-wide association study summary data were integrated with the gene co-expression results and phenotypic information (i.e., memory and aging-related outcomes) from gene knockout studies in Drosophila to generate rankings for other genes that may have a role in AD.

Results

We found that co-expression of the RAD genes is strongest in the cortical regions where neurodegeneration due to AD is most severe. There was significant evidence for two novel AD-related genes including EPS8 (FDR p?=?8.77?×?10^-3) and HSPA2 (FDR p?=?0.245).

Conclusions

Our findings indicate that AD-related risk factors are potentially associated with brain region-specific effects on gene expression that can be detected using a gene network approach.

SUBMITTER: Lancour D

PROVIDER: S-EPMC7469336 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease.

Lancour Daniel D Dupuis Josée J Mayeux Richard R Haines Jonathan L JL Pericak-Vance Margaret A MA Schellenberg Gerard C GC Crovella Mark M Farrer Lindsay A LA Kasif Simon S

Alzheimer's research & therapy 20200902 1

<h4>Background</h4>Identifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding with age-related neurodegeneration.<h4>Methods</h4>A gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 rep ...[more]

PMID: 32878640

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Project description:IntroductionThis study aimed to investigate the association between brain networks and epilepsy development in patients with Alzheimer disease (AD).MethodsWe enrolled patients newly diagnosed with AD at our hospital who underwent three-dimensional T1-weighted magnetic resonance imaging at the time of AD diagnosis and included healthy controls. We obtained the cortical, subcortical, and thalamic nuclei structural volumes using FreeSurfer and applied graph theory to obtain the global brain network and intrinsic thalamic network based on the structural volumes using BRAPH.ResultsWe enrolled 25 and 56 patients with AD with and without epilepsy development, respectively. We also included 45 healthy controls. The global brain network differed between the patients with AD and healthy controls. The local efficiency (2.026 vs. 3.185, p = .048) and mean clustering coefficient (0.449 vs. 1.321, p = .024) were lower, whereas the characteristic path length (0.449 vs. 1.321, p = .048) was higher in patients with AD than in healthy controls. Both global and intrinsic thalamic networks were significantly different between AD patients with and without epilepsy development. In the global brain network, local efficiency (1.340 vs. 2.401, p = .045), mean clustering coefficient (0.314 vs. 0.491, p = .045), average degree (27.442 vs. 41.173, p = .045), and assortative coefficient (-0.041 vs. -0.011, p = .045) were lower, whereas the characteristic path length (2.930 vs. 2.118, p = .045) was higher in patients with AD with epilepsy development than in those without. In the intrinsic thalamic network, the mean clustering coefficient (0.646 vs. 0.460, p = .048) was higher, whereas the characteristic path length (1.645 vs. 2.232, p = .048) was lower in patients with AD with epilepsy development than in those without.ConclusionWe found that the global brain network differs between patients with AD and healthy controls. In addition, we demonstrated significant associations between brain networks (both global brain and intrinsic thalamic networks) and epilepsy development in patients with AD.

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Dataset Information

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease.

Background

Methods

Results

Conclusions

Publications

Analysis of brain region-specific co-expression networks reveals clustering of established and novel genes associated with Alzheimer disease.

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