AlzRiskMR database: an online database for the impact of exposure factors on Alzheimer’s disease

2020 ◽  
Author(s):  
Zhe Wang ◽  
Lei Meng ◽  
Hong Liu ◽  
Liang Shen ◽  
Hong-Fang Ji
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Studies ◽  
Modifiable Risk Factors ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Inverse Variance ◽  
Weighted Median ◽  
The Impact

Abstract In view of great difficulties in the pathogenesis analysis of Alzheimer’s disease (AD) presently, profiling the modifiable risk factors is crucial for early detection and intervention of AD. However, the causal associations among them have yet to be identified, and the effective integration and application of these data also remain considerable challenges due to the lack of efficient collection and analysis procedures. To address this issue, we performed comprehensive analyses by two-sample Mendelian randomization (2SMR) and established the AlzRiskMR database (https://github.com/SDBMC/RiskFactors2AD). Four 2SMR analysis methods, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode, were used for the complementary calculation to test the reliability of the results. The database currently comprises 1870 sets of data of Genome-Wide Association Studies (GWAS) from the MR-Base and NHGRI-EBI GWAS Catalog database. AlzRiskMR database not only estimates causal associations between modifiable risk factors and AD but also offers a useful and timely resource for early intervention of AD development incidence.

scholarly journals Genetic Insights into the Impact of Complement in Alzheimer’s Disease

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1990
Author(s):  
Megan Torvell ◽  
Sarah M. Carpanini ◽  
Nikoleta Daskoulidou ◽  
Robert A. J. Byrne ◽  
Rebecca Sims ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complement Activation ◽  
Association Studies ◽  
Screening Tests ◽  
Diagnostic Tools ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Activation Products ◽  
The Impact

The presence of complement activation products at sites of pathology in post-mortem Alzheimer’s disease (AD) brains is well known. Recent evidence from genome-wide association studies (GWAS), combined with the demonstration that complement activation is pivotal in synapse loss in AD, strongly implicates complement in disease aetiology. Genetic variations in complement genes are widespread. While most variants individually have only minor effects on complement homeostasis, the combined effects of variants in multiple complement genes, referred to as the “complotype”, can have major effects. In some diseases, the complotype highlights specific parts of the complement pathway involved in disease, thereby pointing towards a mechanism; however, this is not the case with AD. Here we review the complement GWAS hits; CR1 encoding complement receptor 1 (CR1), CLU encoding clusterin, and a suggestive association of C1S encoding the enzyme C1s, and discuss difficulties in attributing the AD association in these genes to complement function. A better understanding of complement genetics in AD might facilitate predictive genetic screening tests and enable the development of simple diagnostic tools and guide the future use of anti-complement drugs, of which several are currently in development for central nervous system disorders.

scholarly journals Genomics: New Light on Alzheimer’s Disease Research

2018 ◽  
Vol 19 (12) ◽  
pp. 3771 ◽  
Author(s):  
Yeong Jung ◽  
Yoon Kim ◽  
Mridula Bhalla ◽  
Sung Lee ◽  
Jinsoo Seo
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Studies ◽  
Memory Loss ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
And Behavior ◽  
Generation Sequencing

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that represents a major cause of death in many countries. AD is characterized by profound memory loss, disruptions in thinking and reasoning, and changes in personality and behavior followed by malfunctions in various bodily systems. Although AD was first identified over 100 years ago, and tremendous efforts have been made to cure the disease, the precise mechanisms underlying the onset of AD remain unclear. The recent development of next-generation sequencing tools and bioinformatics has enabled us to investigate the role of genetics in the pathogenesis of AD. In this review, we discuss novel discoveries in this area, including the results of genome-wide association studies (GWAS) that have implicated a number of novel genes as risk factors, as well as the identification of epigenetic regulators strongly associated with the onset and progression of AD. We also review how genetic risk factors may interact with age-associated, progressive decreases in cognitive function in patients with AD.

scholarly journals Polygenic risk for Alzheimer's disease shapes hippocampal scene-selectivity

2020 ◽  
Vol 45 (7) ◽  
pp. 1171-1178
Author(s):  
Hannah L. Chandler ◽  
Carl J. Hodgetts ◽  
Xavier Caseras ◽  
Kevin Murphy ◽  
Thomas M. Lancaster
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Function ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Risk Scoring ◽  
Asymptomatic Individuals ◽  
The Impact

AbstractPreclinical models of Alzheimer’s disease (AD) suggest APOE modulates brain function in structures vulnerable to AD pathophysiology. However, genome-wide association studies now demonstrate that AD risk is shaped by a broader polygenic architecture, estimated via polygenic risk scoring (AD-PRS). Despite this breakthrough, the effect of AD-PRS on brain function in young individuals remains unknown. In a large sample (N = 608) of young, asymptomatic individuals, we measure the impact of both (i) APOE and (ii) AD-PRS on a vulnerable cortico-limbic scene-processing network heavily implicated in AD pathophysiology. Integrity of this network, which includes the hippocampus (HC), is fundamental for maintaining cognitive function during ageing. We show that AD-PRS, not APOE, selectively influences activity within the HC in response to scenes, while other perceptual nodes remained intact. This work highlights the impact of polygenic contributions to brain function beyond APOE, which could aid potential therapeutic/interventional strategies in the detection and prevention of AD.

Life Course Adiposity and Alzheimer’s Disease: A Mendelian Randomization Study

2021 ◽  
pp. 1-10
Author(s):  
Xian Li ◽  
Yan Tian ◽  
Yu-Xiang Yang ◽  
Ya-Hui Ma ◽  
Xue-Ning Shen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Life Course ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Fat Percentage ◽  
Regression Methods ◽  
Weighted Median

Background: Several studies showed that life course adiposity was associated with Alzheimer’s disease (AD). However, the underlying causality remains unclear. Objective: We aimed to examine the causal relationship between life course adiposity and AD using Mendelian randomization (MR) analysis. Methods: Instrumental variants were obtained from large genome-wide association studies (GWAS) for life course adiposity, including birth weight (BW), childhood body mass index (BMI), adult BMI, waist circumference (WC), waist-to-hip ratio (WHR), and body fat percentage (BFP). A meta-analysis of GWAS for AD including 71,880 cases and 383,378 controls was used in this study. MR analyses were performed using inverse variance weighted (IVW), weighted median, and MR-Egger regression methods. We calculated odds ratios (ORs) per genetically predicted standard deviation (1-SD) unit increase in each trait for AD. Results: Genetically predicted 1-SD increase in adult BMI was significantly associated with higher risk of AD (IVW: OR = 1.03, 95% confidence interval [CI] = 1.01–1.05, p = 2.7×10–3) after Bonferroni correction. The weighted median method indicated a significant association between BW and AD (OR = 0.94, 95% CI = 0.90–0.98, p = 1.8×10–3). We also found suggestive associations of AD with WC (IVW: OR = 1.03, 95% CI = 1.00–1.07, p = 0.048) and WHR (weighted median: OR = 1.04, 95% CI = 1.00–1.07, p = 0.029). No association was detected of AD with childhood BMI and BFP. Conclusion: Our study demonstrated that lower BW and higher adult BMI had causal effects on increased AD risk.

scholarly journals Pyk2 overexpression in postsynaptic neurons blocks amyloid β1–42-induced synaptotoxicity in microfluidic co-cultures

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Devrim Kilinc ◽  
Anaïs-Camille Vreulx ◽  
Tiago Mendes ◽  
Amandine Flaig ◽  
Diego Marques-Coelho ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Precursor Protein ◽  
Genetic Risk ◽  
Association Studies ◽  
Amyloid Β ◽  
Precursor Protein ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
The Impact

Abstract Recent meta-analyses of genome-wide association studies identified a number of genetic risk factors of Alzheimer’s disease; however, little is known about the mechanisms by which they contribute to the pathological process. As synapse loss is observed at the earliest stage of Alzheimer’s disease, deciphering the impact of Alzheimer’s risk genes on synapse formation and maintenance is of great interest. In this article, we report a microfluidic co-culture device that physically isolates synapses from pre- and postsynaptic neurons and chronically exposes them to toxic amyloid β peptides secreted by model cell lines overexpressing wild-type or mutated (V717I) amyloid precursor protein. Co-culture with cells overexpressing mutated amyloid precursor protein exposed the synapses of primary hippocampal neurons to amyloid β1–42 molecules at nanomolar concentrations and induced a significant decrease in synaptic connectivity, as evidenced by distance-based assignment of postsynaptic puncta to presynaptic puncta. Treating the cells with antibodies that target different forms of amyloid β suggested that low molecular weight oligomers are the likely culprit. As proof of concept, we demonstrate that overexpression of protein tyrosine kinase 2 beta—an Alzheimer’s disease genetic risk factor involved in synaptic plasticity and shown to decrease in Alzheimer’s disease brains at gene expression and protein levels—selectively in postsynaptic neurons is protective against amyloid β1–42-induced synaptotoxicity. In summary, our lab-on-a-chip device provides a physiologically relevant model of Alzheimer’s disease-related synaptotoxicity, optimal for assessing the impact of risk genes in pre- and postsynaptic compartments.

Alzheimer's disease genetics: lessons to improve disease modelling

2011 ◽  
Vol 39 (4) ◽  
pp. 910-916 ◽  
Author(s):  
Rita J. Guerreiro ◽  
John Hardy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Complete Understanding ◽  
Genome Wide ◽  
History Of ◽  
Ad Alzheimer’S Disease

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

scholarly journals Predicting late-onset Alzheimer’s disease from genomic data using deep neural networks

2019 ◽  
Author(s):  
Javier de Velasco Oriol ◽  
Edgar E. Vallejo ◽  
Karol Estrada ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Genetic Variants ◽  
Deep Neural Networks ◽  
Late Onset ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Clinical Markers ◽  
Genome Wide

AbstractAlzheimer’s disease (AD) is the leading form of dementia. Over 25 million cases have been estimated worldwide and this number is predicted to increase two-fold every 20 years. Even though there is a variety of clinical markers available for the diagnosis of AD, the accurate and timely diagnosis of this disease remains elusive. Recently, over a dozen of genetic variants predisposing to the disease have been identified by genome-wide association studies. However, these genetic variants only explain a small fraction of the estimated genetic component of the disease. Therefore, useful predictions of AD from genetic data could not rely on these markers exclusively as they are not sufficiently informative predictors. In this study, we propose the use of deep neural networks for the prediction of late-onset Alzheimer’s disease from a large number of genetic variants. Experimental results indicate that the proposed model holds promise to produce useful predictions for clinical diagnosis of AD.

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