Year: 2017
Type: article
Abstract: BACKGROUND: are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks. RESULTS: ratio using quality controlled genotype data, including 563,980 single nucleotide polymorphisms (SNPs), with age, sex and diagnosis as covariates. Gene-level p-values were obtained by VEGAS2. Genes with p-value ≤ 0.05 were mapped on to a protein-protein interaction (PPI) network (9,617 nodes, 39,240 edges, from the HPRD Database). We integrated a consensus model strategy into the iPINBPA network analysis framework, and named it as CM-iPINBPA. Four consensus modules (CMs) were discovered by CM-iPINBPA, and were functionally annotated using the pathway analysis tool Enrichr. The intersection of four CMs forms a common subnetwork of 29 genes, including those related to tau phosphorylation (GSK3B, SUMO1, AKAP5, CALM1 and DLG4), amyloid beta production (CASP8, PIK3R1, PPA1, PARP1, CSNK2A1, NGFR, and RHOA), and AD (BCL3, CFLAR, SMAD1, and HIF1A). CONCLUSIONS: This study coupled a consensus module (CM) strategy with the iPINBPA network analysis framework, and applied it to the GWAS of CSF t-tau/Aβ1-42 ratio in an AD study. The genome-wide network analysis yielded 4 enriched CMs that share not only genes related to tau phosphorylation or amyloid beta production but also multiple genes enriching several KEGG pathways such as Alzheimer's disease, colorectal cancer, gliomas, renal cell carcinoma, Huntington's disease, and others. This study demonstrated that integration of gene-level associations with CMs could yield statistically significant findings to offer valuable biological insights (e.g., functional interaction among the protein products of these genes) and suggest high confidence candidates for subsequent analyses.
Source: BMC Genomics
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
Funders: National Science Foundation, U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Brigham and Women's Hospital, Yale University, Biogen, University of Pennsylvania, Ohio State University, Emory University, F. Hoffmann-La Roche, Northwestern University, Wake Forest University, Cleveland Clinic, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Johns Hopkins University, University of Pittsburgh, Georgetown University, University of Rochester, Case Western Reserve University, Dartmouth College, McGill University, Rush University, University of South Florida, USF Health Byrd Alzheimer's Institute, York University, Eisai, Jewish General Hospital, Natural Science Foundation of Heilongjiang Province, IXICO, National Institutes of Health, Directorate for Computer and Information Science and Engineering, Genentech, University of California, Los Angeles, University of California, Davis, University of California, San Francisco, University of California, Irvine, Medical Center, University of Rochester, National Key Scientific Instrument and Equipment Development Projects of China, Fundamental Research Funds for the Central Universities, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine
Awards: G1012222616, G104586243, G1286217783, G1308337445, Indiana Alzheimer Disease Center, Bioinformatics Strategies for Multidimensional Brain Imaging Genetics, G1554142629, G156407016, G1650819308, III: Small: Collaborative Research: A Large-Scale Data Mining Framework for Genome-Wide Mapping of Multi-Modal Phenotypic Biomarkers and Outcome Prediction, Alzheimer's Disease Neuroimaging Initiative, G2022000225, G2105457377, G2189010064, G2340207221, G2423387114, G2522063444, G2569262687, G2604286014, G2835601961, G29951154, G3023751968, G3130427885, G3131374986, G3146424788, G3216574877, G3553156159, G3582805754, G3710456697, G3847352259, G3870774570, G3966393351, G4119534169, G43180419, G4475667458, G4501223628, Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics, G4696819275, G4800177743, G4860885537, G5207404921, G5283650145, G5324093878, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5389965057, G5520316217, G5654639750, G5850682332, G5922885564, G6210868510, G6371410416, G6412110892, G6482260980, G6719828067, G6792506723, G6899163935, G6948409098, G7268282379, G7298697282, Memory Circuitry in MCI and Early Alzheimerâs Disease Prodrome: Molecular Drivers, G7619289898, G764699806, Amyloid Imaging, VMCI, and Analysis for ADNI, G781725486, G8228018387, G8301434335, G8586263799, G8895907669, G8917700693, G8957659042, G9000039018, G9000074849, G9000122335, G900122319, G920654007
OpenAlex ID: W2618908102