P4-140: Biological knowledge-driven approach to gene-gene interaction analysis in the Alzheimer's Disease Genetics Consortium

AbstractAngiotensin-converting enzyme (ACE) has been reported to show altered activity in patients with neurological diseases. The recent studies found that a 287 bp insertion/deletion (I/D) polymorphism of the ACE gene may be associated with susceptibility to Alzheimer’s disease (AD) but the results have been heterogenous between studies in Europe. In the present study we examined for the first time the association of ACE I/D polymorphism along with APOE genotype in 70 sporadic AD and 126 control subjects in Slovak Caucasians (Central Europe). An increased risk for AD was observed in subjects with at least one APOE*E4 allele (OR=3.99, 95% CI=1.97–8.08). No significant differences for the genotype distribution or the allele frequency were revealed comparing controls and patients for ACE gene. Gene-gene interaction analysis showed increase of the risk to develop AD in subjects carrying both the ACE DD genotype and the APOE*E4 allele (OR=10.32, 95% C.I. 2.67–39.81).

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A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer’s Disease

BioMed Research International ◽

10.1155/2021/8893553 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Qi Jing ◽

Hui Zhang ◽

Xiaoru Sun ◽

Yaru Xu ◽

Silu Cao ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Interaction Analysis ◽

Gene Interaction ◽

The Elderly ◽

Machine Learning Algorithms ◽

Sustained Remission ◽

Protein Protein Interaction ◽

Interaction Database ◽

Normal Individuals

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly and has become a growing global health problem causing great concern. However, the pathogenesis of AD is unclear and no specific therapeutics are available to provide the sustained remission of the disease. In this study, we used comprehensive bioinformatics to determine 158 potential genes, whose expression levels changed between the entorhinal and temporal lobe cortex samples from cognitively normal individuals and patients with AD. Then, we clustered these genes in the protein-protein interaction analysis and identified six significant genes that had more biological functions. Besides, we conducted a drug-gene interaction analysis of module genes in the drug-gene interaction database and obtained 26 existing drugs that might be applied for the prevention and treatment of AD. In addition, a predictive model was built based on the selected genes using different machine learning algorithms to identify individuals with AD. These findings may provide new insights into AD therapy.

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Hippocampal Proteomics Profiling Along with Protein-Protein Interaction Analysis Elucidates Alzheimer's Disease Pathways and Genes

SSRN Electronic Journal ◽

10.2139/ssrn.3382403 ◽

2019 ◽

Author(s):

Mohammad Azizur Rahman ◽

Noorlidah Abdullah ◽

Norhaniza Aminudin

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Protein Interaction ◽

Interaction Analysis ◽

Protein Protein Interaction ◽

Disease Pathways

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Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

Current Gene Therapy ◽

10.2174/1566523219666190924113737 ◽

2019 ◽

Vol 19 (4) ◽

pp. 216-223 ◽

Cited By ~ 2

Author(s):

Tianyi Zhao ◽

Donghua Wang ◽

Yang Hu ◽

Ningyi Zhang ◽

Tianyi Zang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Targets ◽

Molecular Mechanisms ◽

Feature Vector ◽

Mirna Gene ◽

Interaction Network ◽

Gene Interaction ◽

Proteinprotein Interaction ◽

Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

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