scholarly journals Lipid associated polygenic enrichment in Alzheimer’s disease

2018 ◽  
Author(s):  
Iris J. Broce ◽  
Chin Hong Tan ◽  
Chun Chieh Fan ◽  
Aree Witoelar ◽  
Natalie Wen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variants ◽  
Plasma Lipids ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Pleiotropy ◽  
Common Genetic Variants

ABSTRACTCardiovascular (CV) and lifestyle associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ∊4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV associated genes also increase risk for AD (genetic pleiotropy). Using large genome-wide association studies (GWASs) (total n > 500,000 cases and controls) and validated tools to quantify genetic pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 57 SNPs on 19 different chromosomes that were jointly associated with AD and CV outcomes including APOA4, ABCA1, ABCG5, LIPG, and MTCH2/SPI1. We found that common genetic variants influencing AD are associated with multiple CV RFs, at times with a different directionality of effect. Expression of these AD/CV pleiotropic genes was enriched for lipid metabolism processes, over-represented within astrocytes and vascular structures, highly co-expressed, and differentially altered within AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid associated RFs. Rather than a single causal link between genetic loci, RF and the outcome, we found that common genetic variants influencing AD are associated with multiple CV RFs. Our collective findings suggest that a network of genes involved in lipid biology also influence Alzheimer’s risk.

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 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 Pleiotropy of Alzheimer’s Disease and Educational Attainment: Insights from the Summary Statistics

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 986-986
Author(s):  
Yury Loika ◽  
Elena Loiko ◽  
Irina Culminskaya ◽  
Alexander Kulminski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Educational Attainment ◽  
Large Scale ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Nucleotide Polymorphisms ◽  
Omnibus Test

Abstract Epidemiological studies report beneficial associations of higher educational attainment (EDU) with Alzheimer’s disease (AD). Prior genome-wide association studies (GWAS) also reported variants associated with AD and EDU separately. The analysis of pleiotropic predisposition to these phenotypes may shed light on EDU-related protection against AD. We examined pleiotropic predisposition to AD and EDU using Fisher’s method and omnibus test applied to summary statistics for single nucleotide polymorphisms (SNPs) associated with AD and EDU in large-scale univariate GWAS at suggestive-effect (5×10-8

scholarly journals Functional Genetic Biomarkers of Alzheimer’s Disease and Gene Expression from Peripheral Blood

2021 ◽  
Author(s):  
Andrew Ni ◽  
Amish Sethi ◽  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Genetic Variants ◽  
Cell Activation ◽  
Association Studies ◽  
Gene Set Enrichment Analysis ◽  
Machine Learning Techniques ◽  
Genome Wide Association Studies

AbstractDetecting Alzheimer’s Disease (AD) at the earliest possible stage is key in advancing AD prevention and treatment but is challenged by normal aging processes in addition to other confounding neurodegenerative diseases. Recent genome-wide association studies (GWAS) have identified associated alleles, but it has been difficult to transition from non-coding genetic variants to underlying mechanisms of AD. Here, we sought to reveal functional genetic variants and diagnostic biomarkers underlying AD using machine learning techniques. We first developed a Random Forest (RF) classifier using microarray gene expression data sampled from the peripheral blood of 744 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. After initial feature selection, 5-fold cross-validation of the 100-gene RF classifier achieved an accuracy of 99.04%. The high accuracy of the RF classifier supports the possibility of a powerful and minimally invasive tool for screening of AD. Next, unsupervised clustering was used to validate and identify relationships among differentially expressed genes (DEGs) the RF selected revealing 3 distinct AD clusters. Results suggest downregulation of global sulfatase and oxidoreductase activities in AD through mutations in SUMF1 and SMOX respectively. Then, we used Greedy Fast Causal Inference (GFCI) to find potential causes of AD within DEGs. In the causal graph, HLA-DPB1 and CYP4A11 emerge as hub genes, furthering the discussion of the immune system’s role in AD. Finally, we used Gene Set Enrichment Analysis (GSEA) to determine the biological pathways and processes underlying the DEGs that were highly correlated with AD. Cell activation in the immune system, glycosaminoglycan (GAG) binding, vascular dysfunction, oxidative stress, and the neuronal apoptotic process were revealed to be significantly enriched in AD. This study further advances the possibility of low-cost and noninvasive genetic screening for AD while also providing potential gene targets for further experimentation.

scholarly journals Physical activity and risk of Alzheimer’s disease: a two-sample Mendelian randomization study

2019 ◽  
Author(s):  
Sebastian E Baumeister ◽  
André Karch ◽  
Martin Bahls ◽  
Alexander Teumer ◽  
Michael F Leitzmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Observational Studies ◽  
Protective Factor ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Inverse Variance

ABSTRACTIntroductionEvidence from observational studies for the effect of physical activity on the risk of Alzheimer’s disease (AD) is inconclusive. We performed Mendelian randomization analysis to examine whether physical activity is a protective factor for AD.MethodsSummary data of genome-wide association studies on physical activity and AD were identified using PubMed and the GWAS catalog. The study population included 21,982 AD cases and 41,944 cognitively normal controls. Eight single nucleotide polymorphisms (SNP) known at P < 5×10−8 to be associated with accelerometer-assessed physical activity served as instrumental variables.ResultsGenetically predicted accelerometer-assessed physical activity had no effect on the risk of AD (inverse variance weighted odds ratio [OR] per standard deviation (SD) increment: 1.03, 95% confidence interval: 0.97-1.10, P=0.332).DiscussionThe present study does not support a relationship between physical activity and risk of AD, and suggests that previous observational studies might have been biased.

scholarly journals Centenarian Controls Increase Variant Effect-sizes by an average two-fold in an Extreme Case-Extreme Control Analysis of Alzheimer’s Disease

2018 ◽  
Author(s):  
Niccolò Tesi ◽  
Sven J. van der Lee ◽  
Marc Hulsman ◽  
Iris E. Jansen ◽  
Najada Stringa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Effect Size ◽  
Genetic Variants ◽  
Association Studies ◽  
Effect Sizes ◽  
Small Samples ◽  
Control Analysis ◽  
Genome Wide Association Studies ◽  
Variant Effect

AbstractThe detection of genetic loci associated with Alzheimer’s disease (AD) requires large numbers of cases and controls because variant effect-sizes are mostly small. We hypothesized that variant effect-sizes should increase when individuals who represent the extreme ends of a disease spectrum are considered, as their genomes are assumed to be maximally enriched or depleted with disease-associated genetic variants.We used 1,073 extensively phenotyped AD cases with relatively young age at onset as extreme cases (66.3±7.9 years), 1,664 age-matched controls (66.0±6.5 years) and 255 cognitively healthy centenarians as extreme controls (101.4±1.3 years). We estimated the effect-size of 29 variants that were previously associated with AD in genome-wide association studies.Comparing extreme AD-cases with centenarian-controls increased the variant effect-size relative to published effect-sizes by on average 1.90-fold (SE=0.29,p=9.0×10−4). The effect-size increase was largest for the rare high-impactTREM2 (R74H)variant (6.5-fold), and significant for variants in/nearECHDC3(4.6-fold),SLC24A4-RIN3(4.5-fold),NME8(3.8-fold),PLCG2(3.3-fold),APOE-ε2(2.2-fold) andAPOE-ε4(2.0-fold). Comparing extreme phenotypes enabled us to replicate the AD association for 10 variants (p<0.05) in relatively small samples. The increase in effect-sizes depended mainly on using centenarians as extreme controls: the average variant effect-size was not increased in a comparison of extreme AD cases and age-matched controls (0.94-fold,p=6.8×10−1), suggesting that on average the tested genetic variants did not explain the extremity of the AD-cases. Concluding, using centenarians as extreme controls in AD case-controls studies boosts the variant effect-size by on average two-fold, allowing the replication of disease-association in relatively small samples.

scholarly journals Deep learning-based identification of genetic variants: Application to Alzheimer's disease classification

2021 ◽  
Author(s):  
Taeho Jo ◽  
Kwangsik Nho ◽  
Paula Bice ◽  
Andrew J Saykin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Genetic Variants ◽  
Association Studies ◽  
Classification Model ◽  
High Dimensional ◽  
Optimal Size ◽  
Genome Wide Association Studies ◽  
Genome Wide

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Although deep learning has been used in several genetic studies, it is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) and develop accurate classification models. In the first step, we divided the whole genome into non-overlapping fragments of an optimal size and then ran Convolutional Neural Network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using an overlapping window approach, we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using genome-wide genotyping data for Alzheimer's disease (AD) (N=981; cognitively normal older adults (CN) =650 and AD=331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which outperformed traditional machine learning approaches, Random Forest and XGBoost. By using a novel deep learning-based GWAS approach, we were able to identify AD-associated SNPs and develop a better classification model for AD.

scholarly journals Alzheimer's disease variant portal (ADVP): a catalog of genetic findings for Alzheimer's disease

2020 ◽  
Author(s):  
Pavel P Kuksa ◽  
Chia-Lun Lui ◽  
Wei Fu ◽  
Liming Qu ◽  
Yi Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Genomics ◽  
Genetic Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Disease Variant

Background: Alzheimer's disease (AD) genetic findings span progressively larger genome-wide association studies (GWASs) for various outcomes and populations. These genetic findings are obtained from a single GWAS, joint- or meta- analyses of multiple GWAS datasets. However, no single resource provides harmonized and searchable information on all AD genetic associations obtained from these analyses, nor linking the identified genetic variants and reported genes with other supporting functional genomic evidence. Methods: We created the Alzheimer's Disease Variant Portal (ADVP), which provides unified access to a uniquely extensive collection of high-quality GWAS association results for AD. Records in ADVP are curated from the genome-wide significant and suggestive loci reported in AD genetics literature. ADVP contains curated results from all AD GWAS publications by Alzheimer's Disease Genetics Consortium (ADGC) since 2009 and AD GWAS publications identified from other public catalogs (GWAS catalog). Genetic association information was systematically extracted from these publications, harmonized, and organized into three types of tables. These tables included structured publication, variant, and association categories to ensure consistent representation of all AD genetic findings. All extracted AD genetic associations were further annotated and integrated with NIAGADS Genomics DB in order to provide extensive biological and functional genomics annotations. Results: Currently, ADVP contains 6,990 AD-association records curated from >200 AD GWAS publications corresponding to >900 unique genomic loci and >1,800 unique genetic variants. The ADVP collection contains genetic findings from >80 cohorts and across various populations, including Caucasians, Hispanics, African-Americans, and Asians. Of all the association records, 46% are disease-risk, 13% are related to expression quantitative trait analyses, and 27% are related to AD endophenotypes and neuropathology. ADVP web interface allows accessing AD association records by individual variants, genes, publications, genomic regions of interest, and genome-wide interactive variant views. ADVP is integrated with the NIAGADS Alzheimer's Genomics Database. Researchers can explore additional biological annotations at the genetic variant or gene level and view cross-reference functional genomics evidence provided by other public resources. Conclusions: ADVP is the largest, most up-to-date, and comprehensive literature-derived collection of AD genetic associations. All records have been systematically curated, harmonized, and comprehensively annotated. ADVP is freely accessible at https://advp.niagads.org/.

Sign in / Sign up

Export Citation Format

Share Document