scholarly journals Leveraging functional annotation to identify genes associated with complex diseases

2019 ◽  
Author(s):  
Wei Liu ◽  
Mo Li ◽  
Wenfeng Zhang ◽  
Geyu Zhou ◽  
Xing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Traits ◽  
Statistical Power ◽  
Late Onset ◽  
Disease Etiology ◽  
Expression Levels ◽  
Epigenetic Information ◽  
Disease Associated Genes

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.

scholarly journals Leveraging functional annotation to identify genes associated with complex diseases

2020 ◽  
Vol 16 (11) ◽  
pp. e1008315
Author(s):  
Wei Liu ◽  
Mo Li ◽  
Wenfeng Zhang ◽  
Geyu Zhou ◽  
Xing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linkage Disequilibrium ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Functional Annotation ◽  
Statistical Power ◽  
Late Onset ◽  
Expression Levels ◽  
Disease Associated Genes

To 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-associated Genes with Epigenetic 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.

scholarly journals Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Masataka Kikuchi ◽  
Norikazu Hara ◽  
Mai Hasegawa ◽  
Akinori Miyashita ◽  
Ryozo Kuwano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Chromatin Loops ◽  
Coding Regions ◽  
Affect Gene Expression ◽  
Gene Expression Levels

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.

scholarly journals Association of MAPT haplotypes with Alzheimer’s disease risk and MAPT brain gene expression levels

2014 ◽  
Vol 6 (4) ◽  
pp. 39 ◽  
Author(s):  
Mariet Allen ◽  
Michaela Kachadoorian ◽  
Zachary Quicksall ◽  
Fanggeng Zou ◽  
High Chai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Expression Levels ◽  
Brain Gene Expression ◽  
Gene Expression Levels

scholarly journals Probing the role of PPARγ in the regulation of late-onset Alzheimer’s disease-associated genes

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196943 ◽  
Author(s):  
Julio Barrera ◽  
Shobana Subramanian ◽  
Ornit Chiba-Falek
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Disease Associated Genes

scholarly journals Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease

2018 ◽  
Author(s):  
Stephen A. Semick ◽  
Rahul A. Bharadwaj ◽  
Leonardo Collado-Torres ◽  
Ran Tao ◽  
Joo Heon Shin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Neurodegenerative Disorder ◽  
Late Onset ◽  
Brain Regions ◽  
Epigenetic Mechanism ◽  
Novel Genes ◽  
Age Related

AbstractBackgroundLate-onset Alzheimer’s disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD represented as variation in DNA methylation (DNAm), we surveyed 420,852 DNAm sites from neurotypical controls (N=49) and late-onset AD patients (N=24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum).ResultsWe identified 858 sites with robust differential methylation, collectively annotated to 772 possible genes (FDR<5%, within 10kb). These sites were overrepresented in AD genetic risk loci (p=0.00655), and nearby genes were enriched for processes related to cell-adhesion, immunity, and calcium homeostasis (FDR<5%). We analyzed corresponding RNA-seq data to prioritize 130 genes within 10kb of the differentially methylated sites, which were differentially expressed and had expression levels associated with nearby DNAm levels (p<0.05). This validated gene set includes previously reported (e.g. ANK1, DUSP22) and novel genes involved in Alzheimer’s disease, such as ANKRD30B.ConclusionsThese results highlight DNAm changes in Alzheimer’s disease that have gene expression correlates, implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex disease.

Novel Late-Onset Alzheimer's Disease Loci Variants Associate with Brain Gene Expression (S54.001)

Neurology ◽  
2012 ◽  
Vol 78 (Meeting Abstracts 1) ◽  
pp. S54.001-S54.001
Author(s):  
M. Allen ◽  
F. Zou ◽  
H. S. Chai ◽  
C. Younkin ◽  
J. Crook ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Brain Gene Expression ◽  
Disease Loci

scholarly journals Neuropsychiatric Symptoms, Endophenotypes, and Syndromes in Late-Onset Alzheimer's Disease: Focus on APOE Gene

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Francesco Panza ◽  
Davide Seripa ◽  
Grazia D'Onofrio ◽  
Vincenza Frisardi ◽  
Vincenzo Solfrizzi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Statistical Power ◽  
Psychological Symptoms ◽  
Late Onset ◽  
Neuropsychiatric Symptoms ◽  
Apoe Gene ◽  
Late Life Depression ◽  
Underlying Mechanisms ◽  
Apoe Genotypes

Neuropsychiatric symptoms, previously denominated as behavioural and psychological symptoms of dementia, are common features of Alzheimer's disease (AD) and are one of the major risk factors for institutionalization. At present, the role of the apolipoprotein E (APOE) gene in the development of neuropsychiatric symptoms in AD patients is unclear. In this paper, we summarized the findings of the studies of neuropsychiatric symptoms and neuropsychiatric syndromes/endophenotypes in AD in relation to APOE genotypes, with special attention to the possible underlying mechanisms. While some studies failed to find a significant association between APOE and neuropsychiatric symptoms in late-onset AD, other studies reported a significant association between the APOE ε4 allele and an increase in agitation/aggression, hallucinations, delusions, and late-life depression or anxiety. Furthermore, some negative studies that focused on the distribution of APOE genotypes between AD patients with or without neuropsychiatric symptoms further emphasized the importance of subgrouping neuropsychiatric symptoms in distinct neuropsychiatric syndromes. Explanations for the variable findings in the existing studies included differences in patient populations, differences in the assessment of neuropsychiatric symptomatology, and possible lack of statistical power to detect associations in the negative studies.

scholarly journals Genetic dependency of Alzheimer’s disease-associated genes across cells and tissue types

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suraj K. Jaladanki ◽  
Abdulkadir Elmas ◽  
Gabriel Santos Malave ◽  
Kuan-lin Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cell Lines ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Disease Etiology ◽  
Genome Wide ◽  
Disease Associated Genes ◽  
Network Studies

AbstractEffective treatments targeting disease etiology are urgently needed for Alzheimer’s disease (AD). Although candidate AD genes have been identified and altering their levels may serve as therapeutic strategies, the consequence of such alterations remain largely unknown. Herein, we analyzed CRISPR knockout/RNAi knockdown screen data for over 700 cell lines and evaluated cellular dependencies of 104 AD-associated genes previously identified by genome-wide association studies (GWAS) and gene expression network studies. Multiple genes showed widespread cell dependencies across tissue lineages, suggesting their inhibition may yield off-target effects. Meanwhile, several genes including SPI1, MEF2C, GAB2, ABCC11, ATCG1 were identified as genes of interest since their genetic knockouts specifically affected high-expressing cells whose tissue lineages are relevant to cell types found in AD. Overall, analyses of genetic screen data identified AD-associated genes whose knockout or knockdown selectively affected cell lines of relevant tissue lineages, prioritizing targets for potential AD treatments.

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