scholarly journals Biobank-wide association scan identifies risk factors for late-onset Alzheimer’s disease and endophenotypes

2018 ◽  
Author(s):  
Donghui Yan ◽  
Bowen Hu ◽  
Burcu F. Darst ◽  
Shubhabrata Mukherjee ◽  
Brian W. Kunkle ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Family History ◽  
Late Onset ◽  
Association Studies ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
High Cholesterol ◽  
Multiple Traits

AbstractDense genotype data and thousands of phenotypes from large biobanks, coupled with increasingly accessible summary association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide scans for disease-trait associations. Compared to traditional regression approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured on the same cohort. We applied BADGERS to two independent datasets for Alzheimer’s disease (AD; N=61,212). Among the polygenic risk scores (PRS) for 1,738 traits in the UK Biobank, we identified 48 significant trait PRSs associated with AD after adjusting for multiple testing. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Further, we identified 41 significant PRSs associated with AD endophenotypes. While family history and high cholesterol were strongly associated with neuropathologies and cognitively-defined AD subgroups, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

scholarly journals Microglia, Alzheimer's Disease, and Complement

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Helen Crehan ◽  
John Hardy ◽  
Jennifer Pocock
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Association Studies ◽  
Receptor Expression ◽  
Genome Wide Association Studies ◽  
Complement Cascade ◽  
Complement Factor ◽  
Factor Production

Microglia, the immune cell of the brain, are implicated in cascades leading to neuronal loss and cognitive decline in Alzheimer’s disease (AD). Recent genome-wide association studies have indicated a number of risk factors for the development of late-onset AD. Two of these risk factors are an altered immune response and polymorphisms in complement receptor 1. In view of these findings, we discuss how complement signalling in the AD brain and microglial responses in AD intersect. Dysregulation of the complement cascade, either by changes in receptor expression, enhanced activation of different complement pathways or imbalances between complement factor production and complement cascade inhibitors may all contribute to the involvement of complement in AD. Altered complement signalling may reduce the ability of microglia to phagocytose apoptotic cells and clear amyloid beta peptides, modulate the expression by microglia of complement components and receptors, promote complement factor production by plaque-associated cytokines derived from activated microglia and astrocytes, and disrupt complement inhibitor production. The evidence presented here indicates that microglia in AD are influenced by complement factors to adopt protective or harmful phenotypes and the challenge ahead lies in understanding how this can be manipulated to therapeutic advantage to treat late onset AD.

scholarly journals The clinical utility of gene testing for Alzheimer’s disease

2011 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Emily R. Atkins ◽  
Peter K. Panegyres
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Onset ◽  
Late Onset ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Genome Wide Association Studies ◽  
Apoe Ε4 ◽  
Gene Testing ◽  
A Genome

Alzheimer’s disease (AD) is the largest cause of dementia, affecting 35.6 million people in 2010. Amyloid precursor protein, presenilin 1 and presenilin 2 mutations are known to cause familial early-onset AD, whereas apolipoprotein E (APOE) ε4 is a susceptibility gene for late-onset AD. The genes for phosphatidylinositol- binding clathrin assembly protein, clusterin and complement receptor 1 have recently been described by genome-wide association studies as potential risk factors for lateonset AD. Also, a genome association study using single neucleotide polymorphisms has identified an association of neuronal sortilin related receptor and late-onset AD. Gene testing, and also predictive gene testing, may be of benefit in suspected familial early-onset AD however it adds little to the diagnosis of lateonset AD and does not alter the treatment. We do not recommend APOE ε4 genotyping.

scholarly journals Prediction of Alzheimer’s disease biomarker status defined by the ‘ATN framework’ among cognitively healthy individuals: results from the EPAD longitudinal cohort study

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Catherine M. Calvin ◽  
◽  
Casper de Boer ◽  
Vanessa Raymont ◽  
John Gallacher ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Blood Pressure ◽  
Alzheimer's Disease ◽  
Family History ◽  
Risk Scores ◽  
Healthy Individuals ◽  
Disease Biomarker ◽  
Composite Risk ◽  
Composite Scores

Abstract Background The Amyloid/Tau/Neurodegeneration (ATN) framework has been proposed as a means of evidencing the biological state of Alzheimer’s disease (AD). Predicting ATN status in pre-dementia individuals therefore provides an important opportunity for targeted recruitment into AD interventional studies. We investigated the extent to which ATN-defined biomarker status can be predicted by known AD risk factors as well as vascular-related composite risk scores. Methods One thousand ten cognitively healthy older adults were allocated to one of five ATN-defined biomarker categories. Multinomial logistic regression tested risk factors including age, sex, education, APOE4, family history of dementia, cognitive function, vascular risk indices (high systolic blood pressure, body mass index (BMI), high cholesterol, physical inactivity, ever smoked, blood pressure medication, diabetes, prior cardiovascular disease, atrial fibrillation and white matter lesion (WML) volume), and three vascular-related composite scores, to predict five ATN subgroups; ROC curve models estimated their added value in predicting pathology. Results Age, APOE4, family history, BMI, MMSE and white matter lesions (WML) volume differed between ATN biomarker groups. Prediction of Alzheimer’s disease pathology (versus normal AD biomarkers) improved by 7% after adding family history, BMI, MMSE and WML to a ROC curve that included age, sex and APOE4. Risk composite scores did not add value. Conclusions ATN-defined Alzheimer’s disease biomarker status prediction among cognitively healthy individuals is possible through a combination of constitutional and cardiovascular risk factors but established dementia composite risk scores do not appear to add value in this context.

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.

scholarly journals Genome-Wide Meta-Analysis of Late-Onset Alzheimer's Disease Using Rare Variant Imputation in 324,809 Subjects Identifies Novel Rare Variant Locus NCK2: The International Genomics of Alzheimer's Project (IGAP)

2021 ◽  
Author(s):  
Adam C. Naj ◽  
Ganna Leonenko ◽  
Xueqiu Jian ◽  
Benjamin Grenier-Boley ◽  
Maria Carolina Dalmasso ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variant ◽  
Late Onset ◽  
Sequence Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Uk

Risk for late-onset Alzheimer's disease (LOAD) is driven by multiple loci primarily identified by genome-wide association studies, many of which are common variants with minor allele frequencies (MAF)>0.01. To identify additional common and rare LOAD risk variants, we performed a GWAS on 25,170 LOAD subjects and 41,052 cognitively normal controls in 44 datasets from the International Genomics of Alzheimer's Project (IGAP). Existing genotype data were imputed using the dense, high-resolution Haplotype Reference Consortium (HRC) r1.1 reference panel. Stage 1 associations of P<10-5 were meta-analyzed with the European Alzheimer's Disease Biobank (EADB) (n=20,301 cases; 21,839 controls) (stage 2 combined IGAP and EADB). An expanded meta-analysis was performed using a GWAS of parental AD/dementia history in the UK Biobank (UKBB) (n=35,214 cases; 180,791 controls) (stage 3 combined IGAP, EADB, and UKBB). Common variant (MAF≥0.01) associations were identified for 29 loci in stage 2, including novel genome-wide significant associations at TSPAN14 (P=2.33×10-12), SHARPIN (P=1.56×10-9), and ATF5/SIGLEC11 (P=1.03[mult]10-8), and newly significant associations without using AD proxy cases in MTSS1L/IL34 (P=1.80×10-8), APH1B (P=2.10×10-13), and CLNK (P=2.24×10-10). Rare variant (MAF<0.01) associations with genome-wide significance in stage 2 included multiple variants in APOE and TREM2, and a novel association of a rare variant (rs143080277; MAF=0.0054; P=2.69×10-9) in NCK2, further strengthened with the inclusion of UKBB data in stage 3 (P=7.17×10-13). Single-nucleus sequence data shows that NCK2 is highly expressed in amyloid-responsive microglial cells, suggesting a role in LOAD pathology.

scholarly journals Alzheimer’s disease Phospholipase C-gamma-2 (PLCG2) protective variant is a functional hypermorph

2018 ◽  
Author(s):  
Lorenza Magno ◽  
Christian B Lessard ◽  
Marta Martins ◽  
Pedro Cruz ◽  
Matilda Katan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Phospholipase C ◽  
Cell Function ◽  
Immune Cell ◽  
Late Onset ◽  
Association Studies ◽  
Enzyme Function ◽  
Genome Wide Association Studies ◽  
Phospholipase C Gamma

ABSTRACTRecent Genome Wide Association Studies (GWAS) have identified novel rare coding variants in immune genes associated with late onset AD (LOAD). Amongst these, a polymorphism in Phospholipase C-gamma 2 (PLCG2) P522R, has been reported to be protective against LOAD. PLC enzymes are key elements in signal transmission networks and are potentially druggable targets. PLCG2 is highly expressed in the hematopoietic system. Hypermorphic mutations in PLCG2 in humans have been reported to cause autoinflammation and immune disorders, suggesting a key role for this enzyme in the regulation of immune cell function.We confirmed that PLCG2 expression is restricted primarily to microglia in both the healthy and AD brain. Functional analysis of the P522R variant in heterologous systems demonstrated a small hypermorphic effect of the mutation on enzyme function. PLCγ2 is therefore a potential target for modulating microglia function in AD, and a small molecule drug that weakly activates PLCγ2 may be one potential therapeutic approach.SUMMARYThe PLCG2 P522R variant is protective against Alzheimer’s disease (AD). We show that PLCG2 is expressed in CNS-resident myeloid cells, and the P522R polymorphism weakly activates enzyme function. These data suggest that activation of PLCG2 and not inhibition could be therapeutically beneficial in AD.

scholarly journals Alzheimer's disease: the large gene instability hypothesis

2017 ◽  
Author(s):  
Sourena Soheili-Nezhad
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Association Studies ◽  
Phase Iii ◽  
Testable Hypothesis ◽  
Genome Wide Association Studies ◽  
Evolutionary Forces ◽  
Large Gene ◽  
Genome Wide

All drug trials of the Alzheimer's disease (AD) have failed to slow the progression of dementia in phase III studies, and the most effective therapeutic strategy remains controversial due to the poorly understood disease mechanisms. For AD drug design, amyloid beta (Aβ) and its cascade have been the primary focus since decades ago, but mounting evidence indicates that the underpinning molecular pathways of AD are more complex than the classical reductionist models. Several genome-wide association studies (GWAS) have recently shed light on dark aspects of AD from a hypothesis-free perspective. Here, I use this novel insight to suggest that the amyloid cascade hypothesis may be a wrong model for AD therapeutic design. I review 23 novel genetic risk loci and show that, as a common theme, they code for receptor proteins and signal transducers of cell adhesion pathways, with clear implications in synaptic development, maintenance, and function. Contrary to the Aβ-based interpretation, but further reinforcing the unbiased genome-wide insight, the classical hallmark genes of AD including the amyloid precursor protein (APP), presenilins (PSEN), and APOE also take part in similar pathways of growth cone adhesion and contact-guidance during brain development. On this basis, I propose that a disrupted synaptic adhesion signaling nexus, rather than a protein aggregation process, may be the central point of convergence in AD mechanisms. By an exploratory bioinformatics analysis, I show that synaptic adhesion proteins are encoded by largest known human genes, and these extremely large genes may be vulnerable to DNA damage accumulation in aging due to their mutational fragility. As a prototypic example and an immediately testable hypothesis based on this argument, I suggest that mutational instability of the large Lrp1b tumor suppressor gene may be the primary etiological trigger for APOE-dab1 signaling disruption in late-onset AD. In conclusion, the large gene instability hypothesis suggests that evolutionary forces of brain complexity have led to emergence of large and fragile synaptic genes, and these unstable genes are the bottleneck etiology of aging disorders including senile dementias. A paradigm shift is warranted in AD prevention and therapeutic design.

scholarly journals Identifying Genetic Interactions Associated with Late-Onset Alzheimer’s Disease

2013 ◽  
Author(s):  
Charalampos S Floudas ◽  
Nara Um ◽  
M. Ilyas Kamboh ◽  
Michael M Barmada ◽  
Shyam Visweswaran
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Association Studies ◽  
Univariate Analysis ◽  
Genetic Interactions ◽  
Supporting Evidence ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide

Background Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease. Results We applied BCM to two late-onset Alzheimer’s disease (LOAD) GWAS datasets to identify SNP-SNP interactions between a set of known SNP associations and the dataset SNPs. For evaluation we compared our results with those from logistic regression, as implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs. Conclusion BCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.

scholarly journals Meta-analysis of genetic association with diagnosed Alzheimer’s disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing

2018 ◽  
Author(s):  
BW Kunkle ◽  
B Grenier-Boley ◽  
R Sims ◽  
JC Bis ◽  
AC Naj ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variants ◽  
Late Onset ◽  
Association Studies ◽  
Meta Analysis ◽  
The Elderly ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Load Risk

IntroductionLate-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly1, and risk is partially driven by genetics2. Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS)3–8. To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7) indicating that additional rare variants remain to be identified.

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