Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

• Published in: BMC genomics Vol. 15; no. 1; p. 199
• Main Authors: Talwar, Puneet, Silla, Yumnam, Grover, Sandeep, Gupta, Meenal, Agarwal, Rachna, Kushwaha, Suman, Kukreti, Ritushree
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.03.2014; BioMed Central Ltd; Springer Nature B.V
• Subjects: Advertising executives; Algorithms; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer's disease; Animal Genetics and Genomics; Biology; Biomarkers; Biomedical and Life Sciences; Computational Biology - methods; Convergence; Cyclin-dependent kinases; Design; Environmental factors; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Genetic aspects; Genetic Linkage; Genome-Wide Association Study; Genomes; Genomics; Health risk assessment; Human and rodent genomics; Humans; Industrial research; Life Sciences; Methods; Microarrays; Microbial Genetics and Genomics; Molecular Sequence Annotation; Ontology; Pathogenesis; Physiological aspects; Plant Genetics and Genomics; Plasma; Polymorphism, Single Nucleotide; Protein Interaction Mapping - methods; Protein Interaction Maps; Protein-protein interactions; Proteins; Proteomics; Reproducibility of Results; Research Article; Risk factors; Studies; Type 2 diabetes; Functional annotation; Protein-protein interaction; Gene prioritization; Clustering
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-15-199

Background Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. Results Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S R ) based method followed by prioritization using clusters derived from PPI network. S R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. Conclusions With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.