Effect of Complement CR1 on Brain Amyloid Burden During Aging and Its Modification by APOE Genotype
ABSTRACT BACKGROUND: The rs3818361 single nucleotide polymorphism in complement component (3b/4b) receptor-1 (CR1) is associated with increased risk of Alzheimer's disease (AD). Although this novel variant is associated with a small effect size and is unlikely to be useful as a predictor of AD risk, it might provide insights into AD pathogenesis. We examined the association between rs3818361 and brain amyloid deposition in nondemented older individuals. METHODS: We used (11)C-Pittsburgh Compound-B positron emission tomography to quantify brain amyloid burden in 57 nondemented older individuals (mean age 78.5 years) in the neuroimaging substudy of the Baltimore Longitudinal Study of Aging. In a replication study, we analyzed (11)C-Pittsburgh Compound-B positron emission tomography data from 22 cognitively normal older individuals (mean age 77.1 years) in the Alzheimer's Disease Neuroimaging Initiative dataset. RESULTS: Risk allele carriers of rs3818361 have lower brain amyloid burden relative to noncarriers. There is a strikingly greater variability in brain amyloid deposition in the noncarrier group relative to risk carriers, an effect explained partly by APOE genotype. In noncarriers of the CR1 risk allele, APOE ε4 individuals showed significantly higher brain amyloid burden relative to APOE ε4 noncarriers. We also independently replicate our observation of lower brain amyloid burden in risk allele carriers of rs3818361 in the Alzheimer's Disease Neuroimaging Initiative sample. CONCLUSIONS: Our findings suggest complex mechanisms underlying the interaction of CR1, APOE, and brain amyloid pathways in AD. Our results are relevant to treatments targeting brain Aβ in nondemented individuals at risk for AD and suggest that clinical outcomes of such treatments might be influenced by complex gene-gene interactions.
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ABSTRACT: Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks. Copyright © 2015 Elsevier Inc. All rights reserved.NeuroImage 01/2015; 109. DOI:10.1016/j.neuroimage.2015.01.029 · 6.13 Impact Factor
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ABSTRACT: Amyloid imaging has been clinically approved for measuring β amyloid plaque load in patients being evaluated for Alzheimer's disease or other causes of cognitive decline. Here we explore a multidimensional approach to cognitive decline, where we situate amyloid plaque burden among a number of other relevant dimensions, such as aging, volume loss, other proteinopathies such as TDP43 and Lewy bodies, and functional reorganisation of cognitive brain systems. The multidimensional model incorporates a 'pure AD' trajectory, corresponding to e.g. monogenic Alzheimer's disease, but leaves room for other combinations of biomarker abnormalities (e.g. volume loss without amyloid positivity) and other trajectories. More tools will become available in the future that allow one to carve out a causal-mechanistic space for explaing cognitive decline in a personalized manner, enhancing progress towards more efficacious interventions.Current Neurology and Neuroscience Reports 11/2014; 14(11):498. DOI:10.1007/s11910-014-0498-9 · 3.67 Impact Factor
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ABSTRACT: Presenilin 1 (PSEN1) encodes the catalytic subunit of γ-secretase, and PSEN1 mutations are the most common cause of early onset familial Alzheimer's disease (FAD). In order to elucidate pathways downstream of PSEN1, we characterized neural progenitor cells (NPCs) derived from FAD mutant PSEN1 subjects. Thus, we generated induced pluripotent stem cells (iPSCs) from affected and unaffected individuals from two families carrying PSEN1 mutations. PSEN1 mutant fibroblasts, and NPCs produced greater ratios of Aβ42 to Aβ40 relative to their control counterparts, with the elevated ratio even more apparent in PSEN1 NPCs than in fibroblasts. Molecular profiling identified 14 genes differentially-regulated in PSEN1 NPCs relative to control NPCs. Five of these targets showed differential expression in late onset AD/Intermediate AD pathology brains. Therefore, in our PSEN1 iPSC model, we have reconstituted an essential feature in the molecular pathogenesis of FAD, increased generation of Aβ42/40, and have characterized novel expression changes.PLoS ONE 01/2014; 9(1):e84547. DOI:10.1371/journal.pone.0084547 · 3.53 Impact Factor