scholarly journals Computational Methods Dedicated for Neurological Disorder Detection through Epistasis Analysis: A Review

2020 ◽  
Vol 01 ◽  
Author(s):  
Sridharan Priya ◽  
Radhakrishnan Manavalana
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurological Disorders ◽  
Neurological Disorder ◽  
Disease Risk ◽  
Neurological Diseases ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies

Background: Neurological disorders diseases such as ALS, Alzheimer’s, epilepsy, Parkinson’s Disease, Autism, Atrial Fibrillation, and Sclerosis affect the central nervous system, including the brain, nerves, spinal cords, muscles, and Neuromuscular joint. These disorders are investigated by detecting the genetic variations in Single Nucleotide Polymorphism (SNP) in Genome-Wide Association Studies (GWAS). In the human genome sequence, one SNP influence the effects of another SNP. These SNP-SNP interactions or Gene-Gene interaction (Epistasis) significantly increases the risk of disease susceptibility to neurological disorders. Objective: The manual analyzes of various genetic interactions related to Neurological diseases are cumbersome. Hence, the computational system is effective for the discovery of Epistasis effects in Neurological syndromes. This study aims to explore various techniques of statistical, machine learning, optimization, so far applied to find the epistasis effect for neurological-disorder. Conclusion: This study finds several genetic interactions models involving different loci, various candidate genes, and SNP interactions involved in numerous neurological diseases. The gene APOE and its polymorphism increase Alzheimer's disease pathology. The gene GAB2 and its SNPs play a vital role in Alzheimer’s disease. The genes GABRA4, ITGB3, and SLC64A highly influence the genetic interactions for Autism disorder. In schizophrenia, the SNPs of NRG1 increases the disease risk. The benefits, limitations, and issues of the various computational techniques implemented for epistasis evaluation of neurological disease are deeply discussed.

scholarly journals A Network-based Deep Learning Framework Catalyzes GWAS and Multi-Omics Findings to Biology and Drug Repurposing for Alzheimer's Disease

2021 ◽  
Author(s):  
Jielin Xu ◽  
Yuan Hou ◽  
Yadi Zhou ◽  
Ming Hu ◽  
Feixiong Cheng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Risk ◽  
Association Studies ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Learning Framework ◽  
Protein Interactome

Human genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer's disease (AD). Translating human genetic findings (i.e., genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery, however, remains a major challenge. To address this critical problem, we present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). NETTAG is capable of integrating multi-genomics data along with the protein-protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leverage non-coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone-QTLs, and transcription factor binding-QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The key premises of NETTAG are that the disease risk genes exhibit distinct functional characteristics compared to non-risk genes and therefore can be distinguished by their aggregated genomic features under the human protein interactome. Applying NETTAG to the latest AD GWAS data, we identified 156 putative AD-risk genes (i.e., APOE, BIN1, GSK3B, MARK4, and PICALM). We showed that predicted risk genes are: 1) significantly enriched in AD-related pathobiological pathways, 2) more likely to be differentially expressed regarding transcriptome and proteome of AD brains, and 3) enriched in druggable targets with approved medicines (i.e., choline and ibudilast). In summary, our findings suggest that understanding of human pathobiology and therapeutic development could benefit from a network-based deep learning methodology that utilizes GWAS findings under the multimodal genomic analyses.

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 Novel Alzheimer’s disease risk variants identified based on whole-genome sequencing of APOE ε4 carriers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jong-Ho Park ◽  
Inho Park ◽  
Emilia Moonkyung Youm ◽  
Sejoon Lee ◽  
June-Hee Park ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Disease Risk ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Ε4 Allele

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disease associated with a complex genetic etiology. Besides the apolipoprotein E ε4 (APOE ε4) allele, a few dozen other genetic loci associated with AD have been identified through genome-wide association studies (GWAS) conducted mainly in individuals of European ancestry. Recently, several GWAS performed in other ethnic groups have shown the importance of replicating studies that identify previously established risk loci and searching for novel risk loci. APOE-stratified GWAS have yielded novel AD risk loci that might be masked by, or be dependent on, APOE alleles. We performed whole-genome sequencing (WGS) on DNA from blood samples of 331 AD patients and 169 elderly controls of Korean ethnicity who were APOE ε4 carriers. Based on WGS data, we designed a customized AD chip (cAD chip) for further analysis on an independent set of 543 AD patients and 894 elderly controls of the same ethnicity, regardless of their APOE ε4 allele status. Combined analysis of WGS and cAD chip data revealed that SNPs rs1890078 (P = 6.64E−07) and rs12594991 (P = 2.03E−07) in SORCS1 and CHD2 genes, respectively, are novel genetic variants among APOE ε4 carriers in the Korean population. In addition, nine possible novel variants that were rare in individuals of European ancestry but common in East Asia were identified. This study demonstrates that APOE-stratified analysis is important for understanding the genetic background of AD in different populations.

scholarly journals Genetic variability in response to Aβ deposition influences Alzheimer’s risk

2018 ◽  
Author(s):  
Dervis A. Salih ◽  
Sevinc Bayram ◽  
Manuel S. Guelfi ◽  
Regina Reynolds ◽  
Maryam Shoai ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variability ◽  
Disease Risk ◽  
Late Onset ◽  
Association Studies ◽  
Amyloid Deposition ◽  
Genome Wide Association Studies ◽  
Statistical Comparison ◽  
Microglial Response

AbstractGenetic analysis of late-onset Alzheimer’s disease risk has previously identified a network of largely microglial genes that form a transcriptional network. In transgenic mouse models of amyloid deposition we have previously shown that the expression of many of the mouse orthologs of these genes are co-ordinately up-regulated by amyloid deposition. Here we investigate whether systematic analysis of other members of this mouse amyloid-responsive network predicts other Alzheimer’s risk loci. This statistical comparison of the mouse amyloid-response network with Alzheimer’s disease genome-wide association studies identifies 5 other genetic risk loci for the disease (OAS1, CXCL10, LAPTM5, ITGAM and LILRB4). This work suggests that genetic variability in the microglial response to amyloid deposition is a major determinant for Alzheimer’s risk.One Sentence SummaryIdentification of 5 new risk loci for Alzheimer’s by statistical comparison of mouse Aβ microglial response with gene-based SNPs from human GWAS

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 Alzheimer’s disease risk SNPs show no strong effect on miRNA expression in human lymphoblastoid cell lines

2018 ◽  
Author(s):  
Inken Wohlers ◽  
Colin Schulz ◽  
Fabian Kilpert ◽  
Lars Bertram
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cell Lines ◽  
Mirna Expression ◽  
Large Scale ◽  
Disease Risk ◽  
Association Studies ◽  
Lymphoblastoid Cell Lines ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies

AbstractThe role of microRNAs (miRNAs) in the pathogenesis of Alzheimer’s disease (AD) is currently extensively investigated. In this study, we assessed the potential impact of AD genetic risk variants on miRNA expression by performing large-scale bioinformatic data integration. Our analysis was based on genetic variants from three AD genome-wide association studies (GWAS). Association with miRNA expression was tested by expression quantitative trait loci (eQTL) analysis using next-generation miRNA sequencing data generated in lymphoblastoid cell lines (LCL). While, overall, we did not identify a strong effect of AD GWAS variants on miRNA expression in this cell type we highlight two notable outliers, i.e. miR-29c-5p and miR-6840-5p. MiR-29c-5p was recently reported to be involved in the regulation of BACE1 and SORL1 expression. In conclusion, despite two exceptions our large-scale assessment provides only limited support for the hypothesis that AD GWAS variants act as miRNA eQTLs.

scholarly journals An integrated genome and phenome-wide association study approach to understanding Alzheimer's disease predisposition

2022 ◽  
Author(s):  
Archita Khaire ◽  
Courtney E Wimberly ◽  
Eleanor C Semmes ◽  
Jillian H Hurst ◽  
Kyle M Walsh
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Disease Risk ◽  
Association Studies ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Distribution Width ◽  
Amyloid Toxicity ◽  
Load Risk

Background: Genome-wide association studies (GWAS) have identified common, heritable alleles that increase late-onset Alzheimer's disease (LOAD) risk. We recently published an analytic approach to integrate GWAS and phenome-wide association study (PheWAS) data, enabling identification of candidate traits and trait-associated variants impacting disease risk, and apply it here to LOAD. Methods: PheWAS was performed for 23 known LOAD-associated single nucleotide polymorphisms (SNPs) and 4:1 matched control SNPs using UK Biobank data. Traits enriched for association with LOAD SNPs were ascertained and used to identify trait-associated candidate SNPs to be tested for association with LOAD risk (17,008 cases; 37,154 controls). Results: LOAD-associated SNPs were significantly enriched for associations with 6/778 queried traits, including three platelet traits. The strongest enrichment was for platelet distribution width (PDW) (P=1.2x10-5), but no consistent direction of effect was observed between increased PDW and LOAD susceptibility across variants or in Mendelian randomization analysis. Of 384 PDW-associated SNPs identified by prior GWAS, 36 were nominally associated with LOAD risk and 5 survived false-discovery rate correction for multiple testing. Associations confirmed known LOAD risk loci near PICALM, CD2AP, SPI1, and NDUFAF6, and identified a novel risk locus in the epidermal growth factor receptor (EGFR) gene. Conclusions: Through integration of GWAS and PheWAS data, we identify substantial pleiotropy between genetic determinants of LOAD and of platelet morphology, and for the first time implicate EGFR - a mediator of Beta amyloid toxicity - in Alzheimer's disease susceptibility.

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 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 Missing lnc(RNAs) in Alzheimer’s Disease?

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 39
Author(s):  
Rafaela Policarpo ◽  
Constantin d’Ydewalle
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Neurological Diseases ◽  
Expression Profiles ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Demographic Shift ◽  
Genome Wide ◽  
Exact Etiology

With the ongoing demographic shift towards increasingly elderly populations, it is estimated that approximately 150 million people will live with Alzheimer’s disease (AD) by 2050. By then, AD will be one of the most burdensome diseases of this and potentially next centuries. Although its exact etiology remains elusive, both environmental and genetic factors play crucial roles in the mechanisms underlying AD neuropathology. Genome-wide association studies (GWAS) identified genetic variants associated with AD susceptibility in more than 40 different genomic loci. Most of these disease-associated variants reside in non-coding regions of the genome. In recent years, it has become clear that functionally active transcripts arise from these non-coding loci. One type of non-coding transcript, referred to as long non-coding RNAs (lncRNAs), gained significant attention due to their multiple roles in neurodevelopment, brain homeostasis, aging, and their dysregulation or dysfunction in neurological diseases including in AD. Here, we will summarize the current knowledge regarding genetic variations, expression profiles, as well as potential functions, diagnostic or therapeutic roles of lncRNAs in AD. We postulate that lncRNAs may represent the missing link in AD pathology and that unraveling their role may open avenues to better AD treatments.

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