scholarly journals Gene Expression of Quaking in Sporadic Alzheimer’s Disease Patients is Both Upregulated and Related to Expression Levels of Genes Involved in Amyloid Plaque and Neurofibrillary Tangle Formation

2016 ◽  
Vol 53 (1) ◽  
pp. 209-219 ◽  
Author(s):  
Bryn Farnsworth ◽  
Christiane Peuckert ◽  
Bettina Zimmermann ◽  
Elena Jazin ◽  
Petronella Kettunen ◽  
...  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Masataka Kikuchi ◽  
Norikazu Hara ◽  
Mai Hasegawa ◽  
Akinori Miyashita ◽  
Ryozo Kuwano ◽  
...  

Abstract Background Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. Methods To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. Results We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. Conclusion Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.


2014 ◽  
Vol 6 (4) ◽  
pp. 39 ◽  
Author(s):  
Mariet Allen ◽  
Michaela Kachadoorian ◽  
Zachary Quicksall ◽  
Fanggeng Zou ◽  
High Chai ◽  
...  

2017 ◽  
Vol 56 ◽  
pp. 212.e5-212.e10 ◽  
Author(s):  
Qing-Qing Tao ◽  
Zhi-Jun Liu ◽  
Yi-Min Sun ◽  
Hong-Lei Li ◽  
Ping Yang ◽  
...  

2009 ◽  
Vol 16 (3) ◽  
pp. 627-634 ◽  
Author(s):  
Edna Grünblatt ◽  
Jasmin Bartl ◽  
Sonja Zehetmayer ◽  
Thomas M. Ringel ◽  
Peter Bauer ◽  
...  

2019 ◽  
Author(s):  
Wei Liu ◽  
Mo Li ◽  
Wenfeng Zhang ◽  
Geyu Zhou ◽  
Xing Wu ◽  
...  

AbstractTo increase statistical power to identify genes associated with complex traits, a number of transcriptome-wide association study (TWAS) methods have been proposed using gene expression as a mediating trait linking genetic variations and diseases. These methods first predict expression levels based on inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic effects on diseases by associating phenotypes with predicted expression levels. The success of these methods critically depends on the identification of eQTLs, which may not be functional in the corresponding tissue, due to linkage disequilibrium (LD) and the correlation of gene expression between tissues. Here, we introduce a new method called T-GEN (Transcriptome-mediated identification of disease-associatedGens withEpigenetic aNnotation) to identify disease-associated genes leveraging epigenetic information. Through prioritizing SNPs with tissue-specific epigenetic annotation, T-GEN can better identify SNPs that are both statistically predictive and biologically functional. We found that a significantly higher percentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional by ChromHMM and more are deleterious based on their Combined Annotation Dependent Depletion (CADD) scores. Applying T-GEN to 207 complex traits, we were able to identify more trait-associated genes (ranging from 7.7 % to 102%) than those from existing methods. Among the identified genes associated with these traits, T-GEN can better identify genes with high (>0.99) pLI scores compared to other methods. When T-GEN was applied to late-onset Alzheimer’s disease, we identified 96 genes located at 15 loci, including two novel loci not implicated in previous GWAS. We further replicated 50 genes in an independent GWAS, including one of the two novel loci.Author summaryTWAS-like methods have been widely applied to understand disease etiology using eQTL data and GWAS results. However, it is still challenging to discriminate the true disease-associated genes from those in strong LD with true genes, which is largely due to the misidentification of eQTLs. Here we introduce a novel statistical method named T-GEN to identify disease-associated genes considering epigenetic information. Compared to current TWAS methods, T-GEN can not only identify eQTLs with higher CADD scores and function potentials in gene-expression imputation models, but also identify more disease-associated genes across 207 traits and more genes with high (>0.99) pLI scores. Applying T-GEN in late-onset Alzheimer’s disease identified 96 genes at 15 loci with two novel loci. Among 96 identified genes, 50 genes were further replicated in an independent GWAS.


2008 ◽  
Vol 173 (3) ◽  
pp. 762-772 ◽  
Author(s):  
Jennifer B. Paulson ◽  
Martin Ramsden ◽  
Colleen Forster ◽  
Mathew A. Sherman ◽  
Eileen McGowan ◽  
...  

2021 ◽  
Author(s):  
Jinping Xu ◽  
Chao Wang ◽  
Jinhuan Zhang ◽  
Junjie Zhuo ◽  
Qingmao Hu

Abstract Background:Previous studies showed no obvious symptoms but subtle structural brain changes in a long preclinical stage of Alzheimer's disease (AD), then localized cortical and sub-cortical atrophy in MCI, and spread aggressively to nearly whole brain neurodegeneration in AD. However, the neurobiological and pathogenic substrates underlying these structural changes across AD spectrum remain largely understood.Methods: We obtained structural MRI imaging from ADNI datasets, including 83 early-stage mild cognitive impairments (EMCI), 83 late-stage mild cognitive impairments (LMCI), 83 AD, and 83 normal controls (NC), and aimed to explore structural changes across the full clinical AD spectrum and their genetic mechanism. Partial least square regressions and Spearman correlations were performed to explore how these changes associated with gene expression level obtained from Allen Human Brain Atlas. Finally, functional enrichment analyses were conducted using Metascape analysis to explore ontological pathways of the consistent genes. Results:We identified significant volume atrophy in left thalamus, left cerebellum, and bilateral middle frontal gyrus across AD spectrum. These structural changes were positively associated with gene expression levels of ABCA7, SORCS1, SORL1, PILRA, PFDN1, PLXNA4, TRIP4, and CD2AP, whereas were negatively associated with gene expression levels of CD33, PLCG2, APOE, and ECHDC3 for all three groups. Moreover, these results were verified in sub-groups of converted and stable EMCI and LMCI. Further gene enrichment analyses revealed that these positively associated genes were mainly involved in positive regulation of cellular protein localization and negative regulation of cellular component organization, whereas the negatively associated genes were mainly involved in positive regulation of iron transport. Conclusions:Overall, these results suggested that structural changes in prodromal and clinical AD might result from interaction of the same gene lists, which offered a better understanding of biological mechanisms underlying structural changes in prodromal and clinical AD.


2018 ◽  
Author(s):  
Masataka Kikuchi ◽  
Norikazu Hara ◽  
Mai Hasegawa ◽  
Akinori Miyashita ◽  
Ryozo Kuwano ◽  
...  

AbstractBackground:Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops.Results:We examined enhancer locations that were predicted in 127 human tissues or cell types, including ten brain tissues, and identified chromatin-chromatin interactions by Hi-C experiments. We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes.Conclusion:Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.


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