Quantitative assessment of the effect of ABCA1 gene polymorphism on the risk of Alzheimer’s disease

Tybjærg‐Hansen

et al. 2015

Alzheimer's & Dementia

111

Section: Discussioncontrasting

confidence: 52%

Loss‐of‐function mutation in ABCA1 and risk of Alzheimer's disease and cerebrovascular disease

Tybjærg‐Hansen

et al. 2015

Alzheimer's & Dementia

111

Alzheimer's Disease - Challenges for the Future

“…In 2013, a meta-analysis was conducted on 13 independent studies totaling 6034 controls and 6214 AD patients that examined whether the ABCA1 variants R219K rs2230806, I883M rs4149313 and R1587K rs2230808 were associated with AD risk. No significant association was found even after adjusting by ethnicity and sample size [233]. This is consistent with ABCA1 failing to appear in GWAS [216].…”

Section: Cholesterol and Phospholipid Transporters In Adsupporting

confidence: 70%

Lipids and Lipoproteins in Alzheimer’s Disease

Stukas¹,

Kulic²,

Zareyan³

et al. 2015

Cholesterol is a key structural component of the brain, and cholesterol transport and distribution within the central nervous system CNS is mediated by a lipid metabolic cycle that includes generation of apolipoproteins as lipid carriers, lipidation by cholesterol and phospholipid transporters, enzyme remodeling of these particles and their receptor-mediated uptake and turnover in cells. It is becoming increasingly appreciated that "lzheimer's Disease "D patients often have comorbid conditions such as cardiovascular disease, type II diabetes mellitus, or hypertension, each of which can greatly affect lipoprotein metabolism, especially at the vessel wall and thereby possibly contribute to "D pathogenesis. Here we review the known biology of lipids and lipoproteins in the CNS and discuss how alterations in lipid metabolism may impact "D pathogenesis. "polipoprotein E APOE is the best established genetic risk factor for "D and the major apolipoprotein expressed in the brain. In addition, genome-wide association studies GW"S have identified several other genes associated with "D risk that function in lipid or lipoprotein metabolism, including clusterin CLU , "TP binding cassette ""C transporter " ABCA7 , and apoE receptors. Understanding how lipid/lipoprotein metabolism in the brain and body affect cognitive function may therefore offer new insights in developing more effective therapeutic approaches for dementia.

“…Recent studies pointed out that ABCA1 mediates the beneficial effects of the liver X receptor (LXR) agonist GW3965 on object recognition memory and amyloid burden in APP/PS1mice [99, 100]. Based on strong evidence the LXR-ABCA1-APOE regulatory axis is now considered a promising therapeutic target in AD [101]. However, a meta-analysis report of 13 studies involving a total of 12,248 subjects failed to find association of common SNPs in ABCA1 with AD risk [102].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

BackgroundAlzheimer’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.ResultsOur approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR 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.ConclusionsWith 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.