scholarly journals Metabolome-wide association study on ABCA7 demonstrates a role for ceramide metabolism in impaired cognitive performance and Alzheimer's disease

2021 ◽  
Author(s):  
Abbas Dehghan ◽  
Rui Pinto ◽  
Ibrahim Karaman ◽  
Jian Hung ◽  
Brenan Durainayagam ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Cognitive Performance ◽  
Association Studies ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Underlying Mechanisms

Genome-wide association studies (GWAS) have identified genetic loci associated with risk of Alzheimer's disease (AD), but underlying mechanisms are largely unknown. Using untargeted mass spectrometry, we conducted a metabolome-wide association study (MWAS) that identified the association of lactosylceramides (LacCer)s with AD-related single nucleotide polymorphisms (SNPs) in ABCA7 (P = 5.0x 10-5 to 1.3 x 10-44). Independent support for the association came through the discovery of differences in concentrations of sphingomyelins, ceramides, and hexose-ceramides in brain tissue from ABCA7-null mice compared to wild type (P =0.049 -1.44 x10-5). We showed that plasma LacCer concentrations are associated with cognitive performance in humans. We found evidence for a potentially causal association of LacCer with AD risk using Mendelian randomisation analysis. Our work suggests that AD risks arising from functional variations in ABCA7 expression are mediated at least in part through ceramides, the metabolism or downstream signalling of which offers new therapeutic opportunities.

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 Cross–trait analysis of Alzheimer’s disease and gastrointestinal tract disorders identifies shared loci highlighting immune-related and statin pathways

2021 ◽  
Author(s):  
Emmanuel Adewuyi ◽  
Eleanor O’Brien ◽  
Dale Nyholt ◽  
Tenielle Porter ◽  
Simon Laws
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gastrointestinal Tract ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Ulcer Disease ◽  
Genome Wide ◽  
Underlying Mechanisms ◽  
Irritable Bowel

Abstract Several observational studies suggest a relationship between Alzheimer’s disease (AD) and gastrointestinal tract (GIT) disorders; however, their underlying mechanisms remain unclear. Here, we analysed several genome-wide association studies (GWAS) summary statistics (N = 34,652 – 456,327) to assess AD and GIT disorders relationships. We found a significant genetic overlap and correlation between AD and each of gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), medications for GERD or PUD (PGM), gastritis-duodenitis, irritable bowel syndrome and diverticulosis, but not inflammatory bowel disease. Our analysis suggests a partial causal association between AD and gastritis-duodenitis, diverticulosis and medication for PUD. GWAS meta-analysis identified seven loci (P < 5 × 10-8, PDE4B, CD46, SEMA3F, HLA-DRA, MTSS2, PHB, and APOE) shared by AD and PGM, six of which are novel. These loci were replicated using GERD and PUD GWAS and reinforced in gene-based analyses. Lipid metabolism, autoimmune system, lipase inhibitors, PD-1 signalling, and statin pathways were significantly enriched for AD and GIT disorders. These findings support shared genetic susceptibility in AD and GIT disorders. Lipase inhibitors and statins may provide novel therapeutic avenues for AD, GIT disorders, or their comorbidity.

scholarly journals Shared Genetic Architecture Between Alzheimer’s Disease and Gastrointestinal Tract Disorders: A Large-scale Genome-wide Cross-trait Analysis

2021 ◽  
Author(s):  
Emmanuel O Adewuyi ◽  
Eleanor K O’Brien ◽  
Dale R Nyholt ◽  
Tenielle Porter ◽  
Simon M Laws
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gastrointestinal Tract ◽  
Large Scale ◽  
Association Studies ◽  
Biological Pathways ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background: Consistent with the concept of the gut-brain phenomenon, observational studies have reported a pattern of co-occurring relationship between Alzheimer’s disease (AD) and a range of gastrointestinal tract (GIT) traits. However, it is not clear whether the reported association reflects a causal or shared genetic aetiology of GIT disorders with AD. While AD has no known curative treatments, and its pathogenesis is not clearly understood, a comprehensive assessment of its shared genetics with diseases (comorbidities) can provide a deeper understanding of its underlying biological mechanisms and enhance potential therapy development. Methods: We analysed large-scale genome-wide association studies (GWAS) summary data (sample size = 34,652 – 456,327) to comprehensively assess shared genetic overlap and causality of GIT disorders with the risk of AD. Further, we performed meta-analyses, pairwise GWAS analysis; and investigated genes and biological pathways shared by AD and GIT disorders.Results: Our analyses reveal significant concordance of SNP risk effects across AD and GIT disorders (Ppermuted = 9.99 × 10−4). Also, we found a significant positive genetic correlation between AD and each of gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), medications for GERD or PUD (PGM), gastritis-duodenitis, irritable bowel syndrome, and diverticular disease, but not inflammatory bowel disease. Mendelian randomisation analyses found no evidence for a significant causal association between AD and GIT disorders. However, shared independent genome-wide significant (Pmeta-analysis < 5 × 10-8) loci (including 1p31.3 [near gene, PDE4B], 1q32.2 [CD46], 3p21.31 [SEMA3F], 16q22.1 [MTSS2], 17q21.33 [PHB], and 19q13.32 [APOE]) were identified for AD and PGM, six of which are putatively novel. These loci were replicated using GERD and PUD GWAS and reinforced in pairwise GWAS (colocalisation) as well as gene-based analyses. Lipid metabolism, autoimmune system, lipase inhibitors, PD-1 signalling, and statin mechanisms were significantly enriched in pathway-based analyses. Conclusions: These findings support shared genetic susceptibility of GIT disorders with AD risk and provide new insights into their observed association. The identified loci and genes—PDE4B, CD46 and APOE, especially—and biological pathways—statins and lipase inhibitors, in particular—may provide novel therapeutic avenues or targets for further investigation in AD, GIT disorders, or their comorbidity.

scholarly journals A transcriptome-wide association study of Alzheimer’s disease using prediction models of relevant tissues identifies novel candidate susceptibility genes

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yanfa Sun ◽  
Jingjing Zhu ◽  
Dan Zhou ◽  
Saranya Canchi ◽  
Chong Wu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Target Genes ◽  
Prediction Models ◽  
Association Studies ◽  
Strong Support ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer’s disease (AD), but the genes responsible for these associations remain largely unknown. Methods We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry. Results We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10−6). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal. Conclusions Our study provides new insights into the etiology and underlying genetic architecture of AD.

scholarly journals Identification of functionally connected multi-omic biomarkers for Alzheimer’s Disease using modularity-constrained Lasso

2019 ◽  
Author(s):  
Linhui Xie ◽  
Pradeep Varathan ◽  
Kwangsik Nho ◽  
Andrew J. Saykin ◽  
Paul Salama ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Performance ◽  
Large Scale ◽  
Association Studies ◽  
Interaction Network ◽  
Real Data ◽  
Genome Wide Association Studies ◽  
Penalty Term ◽  
Genome Wide

AbstractIn the past decade, a large number of genetic biomarkers have been discovered through large-scale genome wide association studies (GWASs) in Alzheimer’s disease (AD), such as APOE, TOMM40 and CLU. Despite this significant progress, existing genetic findings are largely passengers not directly involved in the driver events, which presents challenges for replication and translation into targetable mechanisms. In this paper, leveraging the protein interaction network, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data in ROS/MAP cohort for discovery of biomarkers related to cognitive performance. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed, suggesting that cognitive performance can be potentially affected by the genetic mutations due to their cascade effect on the expression of downstream genes and proteins.

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.

scholarly journals Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2303
Author(s):  
Laura Madrid ◽  
Sandra C. Labrador ◽  
Antonio González-Pérez ◽  
María E. Sáez ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Studies ◽  
Tissue Expression ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Current Availability ◽  
Meta Analyses

There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. Candidate Single Nucleotide Polymorphisms (SNPs) from large, genome-wide association studies (GWAS), meta-analyses exploring AD (case-control design), and quantitative measures for cortical structure and general cognitive performance were selected. The Genotype-Tissue Expression (GTEx) database was used for identifying expression quantitative trait loci (eQTls) among candidate SNPs. Genes significantly regulated by candidate SNPs were investigated for differential expression in AD cases versus controls in the brain and plasma, both at the mRNA and protein level. This approach allowed us to identify candidate susceptibility factors and biomarkers of AD, facing experimental validation with more evidence than with genetics alone.

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.

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