scholarly journals Cell type-specific histone acetylation profiling of Alzheimer’s Disease subjects and integration with genetics

2020 ◽  
Author(s):  
Easwaran Ramamurthy ◽  
Gwyneth Welch ◽  
Jemmie Cheng ◽  
Yixin Yuan ◽  
Laura Gunsalus ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Cell Types ◽  
Brain Cell ◽  
Nucleotide Polymorphisms ◽  
Risk Genes ◽  
Genome Wide ◽  
Dorsolateral Prefrontal ◽  
Genomic Regions

We profile genome-wide histone 3 lysine 27 acetylation (H3K27ac) of 3 major brain cell types from hippocampus and dorsolateral prefrontal cortex (dlPFC) of subjects with and without Alzheimer’s Disease (AD). We confirm that single nucleotide polymorphisms (SNPs) associated with late onset AD (LOAD) prefer to reside in the microglial histone acetylome, which varies most strongly with age. We observe acetylation differences associated with AD pathology at 3,598 peaks, predominantly in an oligodendrocyte-enriched population. Strikingly, these differences occur at the promoters of known early onset AD (EOAD) risk genes (APP, PSEN1, PSEN2, BACE1), late onset AD (LOAD) risk genes (BIN1, PICALM, CLU, ADAM10, ADAMTS4, SORL1 and FERMT2), and putative enhancers annotated to other genes associated with AD pathology (MAPT). More broadly, acetylation differences in the oligodendrocyte-enriched population occur near genes in pathways for central nervous system myelination and oxidative phosphorylation. In most cases, these promoter acetylation differences are associated with differences in transcription in oligodendrocytes. Overall, we reveal deregulation of known and novel pathways in AD and highlight genomic regions as therapeutic targets in oligodendrocytes of hippocampus and dlPFC.

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 Genetic meta-analysis identifies 9 novel loci and functional pathways for Alzheimer’s disease risk

2018 ◽  
Author(s):  
Iris E Jansen ◽  
Jeanne E Savage ◽  
Kyoko Watanabe ◽  
Julien Bryois ◽  
Dylan M Williams ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Late Onset ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Cell Types ◽  
Mendelian Randomisation ◽  
Genome Wide

AbstractLate onset Alzheimer’s disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

Association of GWAS Top Genes With Late-Onset Alzheimer’s Disease in Colombian Population

2017 ◽  
Vol 32 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Diana Jennifer Moreno ◽  
Susana Ruiz ◽  
Ángela Ríos ◽  
Francisco Lopera ◽  
Henry Ostos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Late Onset ◽  
European Ancestry ◽  
Gene Interactions ◽  
Nucleotide Polymorphisms ◽  
Significant Interaction Effect ◽  
Single Nucleotide ◽  
Genome Wide

Objective: The association of variants in CLU, CR1, PICALM, BIN1, ABCA7, and CD33 genes with late-onset Alzheimer’s disease (LOAD) was evaluated and confirmed through genome-wide association study. However, it is unknown whether these associations can be replicated in admixed populations. Methods: The association of 14 single-nucleotide polymorphisms in those genes was evaluated in 280 LOAD cases and 357 controls from the Colombian population. Results: In a multivariate analysis using age, gender, APOE∊4 status, and admixture covariates, significant associations were obtained ( P < .05) for variants in BIN1 (rs744373, odds ratio [OR]: 1.42), CLU (rs11136000, OR: 0.66), PICALM (rs541458, OR: 0.69), ABCA7 (rs3764650, OR: 1.7), and CD33 (rs3865444, OR: 1.12). Likewise, a significant interaction effect was observed between CLU and CR1 variants with APOE. Conclusion: This study replicated the associations previously reported in populations of European ancestry and shows that APOE variants have a regulatory role on the effect that variants in other loci have on LOAD, reflecting the importance of gene–gene interactions in the etiology of neurodegenerative diseases.

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 PLINK, an established method. 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 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.

Recent studies on cellular and molecular mechanisms in Alzheimer’s disease: focus on epigenetic factors and histone deacetylase

2018 ◽  
Vol 29 (3) ◽  
pp. 241-260 ◽  
Author(s):  
Harikesh Dubey ◽  
Kavita Gulati ◽  
Arunabha Ray
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Association Studies ◽  
Hdac Inhibitors ◽  
Epigenetic Modifications ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Genome Wide

AbstractAlzheimer’s disease (AD) is one of the most common neurodegenerative disorders mainly affecting elderly people. It is characterized by progressive loss of memory and cognitive function. More than 95% of AD cases are related to sporadic or late-onset AD (LOAD). The etiology of LOAD is still unclear. It has been reported that environmental factors and epigenetic alterations play a significant role in AD pathogenesis. Furthermore, recently, genome-wide association studies (GWAS) identified 10 novel risk genes:ABCA7,APOE,BIN1,CD2AP,CD33,CLU,CR1,MS4A6A,MS4A4E, andPICALM, which play an important role for LOAD. In this review, the therapeutic approaches of AD by epigenetic modifications have been discussed. Nowadays, HDAC inhibitors have clinically proven its activity for epigenetic modifications. Furthermore, we try to establish the relationship between HDAC inhibitors and above mentioned LOAD risk genes. Finally, we are hoping that this review may open new area of research for AD treatment.

scholarly journals Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study

Brain ◽  
2020 ◽  
Author(s):  
Longfei Jia ◽  
Fangyu Li ◽  
Cuibai Wei ◽  
Min Zhu ◽  
Qiumin Qu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Predictive Models ◽  
Genome Wide Association Study ◽  
Disease Onset ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Risk Variants ◽  
Genome Wide

Abstract Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer’s disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer’s disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer’s disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10−19, 2.49 × 10−23, 1.35 × 10−67, and 4.81 × 10−9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P &lt; 5.0 × 10−8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer’s disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer’s disease, suggesting that our models can predict Alzheimer’s disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer’s disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer’s disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

scholarly journals Genes, pathways and risk prediction in Alzheimer’s disease

2019 ◽  
Author(s):  
John Hardy ◽  
Valentina Escott-Price
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Membrane Damage ◽  
Disease Process ◽  
Complete Understanding ◽  
Induced Membrane ◽  
Genome Wide ◽  
Key Factor ◽  
Risk Of Disease

Abstract The failure of recent clinical trials in Alzheimer's disease has highlighted the need for the development of a more complete understanding of the pathogenesis of the disorder and also a belief that therapies may only work if given very early in the disease process before overt symptoms occur. The rare, early onset forms of the disease are all caused by mutations which make amyloid deposition a more likely event. Here we discuss the recent data showing that, in contrast, much of the risk of late onset disease is encoded by loci involved in lipid metabolism and/or encoded by microglia. We discuss these finding and suggest that amyloid induced membrane damage may be a key factor in disease and also review the evidence that genome wide genetic analysis can substantially help in the prediction of those individuals at high risk of disease in the general population.

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