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/.

scholarly journals Sex differences in the genetic predictors of Alzheimer’s pathology

Brain ◽  
2019 ◽  
Vol 142 (9) ◽  
pp. 2581-2589 ◽  
Author(s):  
Logan Dumitrescu ◽  
Lisa L Barnes ◽  
Madhav Thambisetty ◽  
Gary Beecham ◽  
Brian Kunkle ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Disease Risk ◽  
Association Studies ◽  
Age At Onset ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Data Repositories ◽  
Genome Wide

Abstract Autopsy measures of Alzheimer’s disease neuropathology have been leveraged as endophenotypes in previous genome-wide association studies (GWAS). However, despite evidence of sex differences in Alzheimer’s disease risk, sex-stratified models have not been incorporated into previous GWAS analyses. We looked for sex-specific genetic associations with Alzheimer’s disease endophenotypes from six brain bank data repositories. The pooled dataset included 2701 males and 3275 females, the majority of whom were diagnosed with Alzheimer’s disease at autopsy (70%). Sex-stratified GWAS were performed within each dataset and then meta-analysed. Loci that reached genome-wide significance (P < 5 × 10−8) in stratified models were further assessed for sex interactions. Additional analyses were performed in independent datasets leveraging cognitive, neuroimaging and CSF endophenotypes, along with age-at-onset data. Outside of the APOE region, one locus on chromosome 7 (rs34331204) showed a sex-specific association with neurofibrillary tangles among males (P = 2.5 × 10−8) but not females (P = 0.85, sex-interaction P = 2.9 × 10−4). In follow-up analyses, rs34331204 was also associated with hippocampal volume, executive function, and age-at-onset only among males. These results implicate a novel locus that confers male-specific protection from tau pathology and highlight the value of assessing genetic associations in a sex-specific manner.

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 Alzheimer’s disease risk gene BIN1 induces Tau-dependent network hyperexcitability

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Yuliya Voskobiynyk ◽  
Jonathan R Roth ◽  
J Nicholas Cochran ◽  
Travis Rush ◽  
Nancy VN Carullo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hippocampal Neurons ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Multielectrode Arrays ◽  
Neuronal Function ◽  
Genome Wide ◽  
Neuronal Physiology

Genome-wide association studies identified the BIN1 locus as a leading modulator of genetic risk in Alzheimer’s disease (AD). One limitation in understanding BIN1’s contribution to AD is its unknown function in the brain. AD-associated BIN1 variants are generally noncoding and likely change expression. Here, we determined the effects of increasing expression of the major neuronal isoform of human BIN1 in cultured rat hippocampal neurons. Higher BIN1 induced network hyperexcitability on multielectrode arrays, increased frequency of synaptic transmission, and elevated calcium transients, indicating that increasing BIN1 drives greater neuronal activity. In exploring the mechanism of these effects on neuronal physiology, we found that BIN1 interacted with L-type voltage-gated calcium channels (LVGCCs) and that BIN1–LVGCC interactions were modulated by Tau in rat hippocampal neurons and mouse brain. Finally, Tau reduction prevented BIN1-induced network hyperexcitability. These data shed light on BIN1’s neuronal function and suggest that it may contribute to Tau-dependent hyperexcitability in AD.

scholarly journals Ethnic and trans-ethnic genome-wide association studies identify new loci influencing Japanese Alzheimer’s disease risk

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Risa Mitsumori ◽  
Shintaro Akiyama ◽  
Akinori Miyashita ◽  
Takashi Morizono ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Japanese Population ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
A Genome

AbstractAlzheimer’s disease (AD) has no cure, but early detection and risk prediction could allow earlier intervention. Genetic risk factors may differ between ethnic populations. To discover novel susceptibility loci of AD in the Japanese population, we conducted a genome-wide association study (GWAS) with 3962 AD cases and 4074 controls. Out of 4,852,957 genetic markers that passed stringent quality control filters, 134 in nine loci, including APOE and SORL1, were convincingly associated with AD. Lead SNPs located in seven novel loci were genotyped in an independent Japanese AD case–control cohort. The novel locus FAM47E reached genome-wide significance in a meta-analysis of association results. This is the first report associating the FAM47E locus with AD in the Japanese population. A trans-ethnic meta-analysis combining the results of the Japanese data sets with summary statistics from stage 1 data of the International Genomics of Alzheimer’s Project identified an additional novel susceptibility locus in OR2B2. Our data highlight the importance of performing GWAS in non-European populations.

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 Genome-wide meta-analysis, fine-mapping, and integrative prioritization identify new Alzheimer’s disease risk genes

2020 ◽  
Author(s):  
Jeremy Schwartzentruber ◽  
Sarah Cooper ◽  
Jimmy Z Liu ◽  
Inigo Barrio-Hernandez ◽  
Erica Bello ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Fine Mapping ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Genome Wide ◽  
Causal Genes

AbstractGenome-wide association studies (GWAS) have discovered numerous genomic loci associated with Alzheimer’s disease (AD), yet the causal genes and variants remain incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including novel associations near genes CCDC6, TSPAN14, NCK2, and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with greater than 50% probability each of being causally involved in AD risk, and others strongly suggested by functional annotation. We followed this with colocalisation analyses across 109 gene expression quantitative trait loci (eQTL) datasets, and prioritization of genes using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we find that evidence converges on likely causal genes, including the above four genes, and those at previously discovered AD loci including BIN1, APH1B, PTK2B, PILRA, and CASS4.

scholarly journals Multi-Omic Analyses Characterize the Ceramide/Sphingomyelin Pathway as a Therapeutic Target in Alzheimer's Disease

2021 ◽  
Author(s):  
Priyanka Baloni ◽  
Matthias Arnold ◽  
Herman Moreno ◽  
Kwangsik Nho ◽  
Luna Buitrago ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variants ◽  
Metabolic Flux ◽  
Association Studies ◽  
Flux Analysis ◽  
Genome Wide Association Studies ◽  
Imaging Features ◽  
Genome Wide ◽  
Lipid Species

Dysregulation of sphingomyelin (SM) and ceramide metabolism have been implicated in Alzheimer's Disease (AD). Genome-wide and transcriptome wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with AD. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. Evaluation of peripheral lipidomic profiles is useful in providing perspective on metabolic dysregulation in preclinical and clinical AD states. In this study, we focused on the sphingolipid pathway and carried out multi-omic analyses to identify central and peripheral metabolic changes in AD patients and correlate them to imaging features and cognitive performance in amyloidogenic mouse models. Our multi-omic approach was based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on 1708 context-specific metabolic networks to identify differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in SM pathway with AD pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in AD; (e) metabolite genome-wide association studies (mGWAS) to define receptors within pathway as potential drug target. Our findings from complementary approaches suggested that depletion of S1P compensated for AD cellular pathology, likely by upregulating the SM pathway, suggesting that modulation of S1P signaling may have protective effects in AD. We tested this hypothesis in APP/PS1 mice and showed that prolonged exposure to fingolimod, an S1P signaling modulator approved for treatment of multiple sclerosis, alleviated the cognitive impairment in mice. Our multi-omic approach identified potential targets in the SM pathway and suggested modulators of S1P metabolism as possible candidates for AD treatment.

scholarly journals Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Dervis A Salih ◽  
Sevinc Bayram ◽  
Sebastian Guelfi ◽  
Regina H Reynolds ◽  
Maryam Shoai ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variability ◽  
Disease Risk ◽  
Association Studies ◽  
Amyloid Deposition ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Genome Wide

Abstract Genome-wide association studies of late-onset Alzheimer’s disease risk have previously identified genes primarily expressed in microglia that form a transcriptional network. Using transgenic mouse models of amyloid deposition, we previously showed that many of the mouse orthologues of these risk genes are co-expressed and associated with amyloid pathology. In this new study, we generate an improved RNA-seq-derived network that is expressed in amyloid-responsive mouse microglia and we statistically compare this with gene-level variation in previous human Alzheimer’s disease genome-wide association studies to predict at least four new risk genes for the disease (OAS1, LAPTM5, ITGAM/CD11b and LILRB4). Of the mouse orthologues of these genes Oas1a is likely to respond directly to amyloid at the transcriptional level, similarly to established risk gene Trem2, because the increase in Oas1a and Trem2 transcripts in response to amyloid deposition in transgenic mice is significantly higher than both the increase of the average microglial transcript and the increase in microglial number. In contrast, the mouse orthologues of LAPTM5, ITGAM/CD11b and LILRB4 (Laptm5, Itgam/CD11b and Lilra5) show increased transcripts in the presence of amyloid plaques similar in magnitude to the increase of the average microglial transcript and the increase in microglia number, except that Laptm5 and Lilra5 transcripts increase significantly quicker than the average microglial transcript as the plaque load becomes dense. This work suggests that genetic variability in the microglial response to amyloid deposition is a major determinant for Alzheimer’s disease risk, and identification of these genes may help to predict the risk of developing Alzheimer’s disease. These findings also provide further insights into the mechanisms underlying Alzheimer’s disease for potential drug discovery.

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