scholarly journals Genome-wide significant, replicated and functional risk variants for Alzheimer’s disease

2017 ◽  
Vol 124 (11) ◽  
pp. 1455-1471 ◽  
Author(s):  
Xiaoyun Guo ◽  
Wenying Qiu ◽  
Rolando Garcia-Milian ◽  
Xiandong Lin ◽  
Yong Zhang ◽  
...  
Brain ◽  
2020 ◽  
Author(s):  
Longfei Jia ◽  
Fangyu Li ◽  
Cuibai Wei ◽  
Min Zhu ◽  
Qiumin Qu ◽  
...  

Abstract Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer’s disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer’s disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer’s disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10−19, 2.49 × 10−23, 1.35 × 10−67, and 4.81 × 10−9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10−8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer’s disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer’s disease, suggesting that our models can predict Alzheimer’s disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer’s disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer’s disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hung-Hsin Chen ◽  
Lauren E. Petty ◽  
Jin Sha ◽  
Yi Zhao ◽  
Amanda Kuzma ◽  
...  

AbstractLate-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer’s Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer’s Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.


2018 ◽  
Vol 77 (1) ◽  
pp. 8-12 ◽  
Author(s):  
Vincenzo De Luca ◽  
Gianfranco Spalletta ◽  
Renan P. Souza ◽  
Ariel Graff ◽  
Luciana Bastos-Rodrigues ◽  
...  

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Joseph S. Reddy ◽  
Mariet Allen ◽  
Charlotte C. G. Ho ◽  
Stephanie R. Oatman ◽  
Özkan İş ◽  
...  

AbstractCerebral amyloid angiopathy (CAA) contributes to accelerated cognitive decline in Alzheimer’s disease (AD) dementia and is a common finding at autopsy. The APOEε4 allele and male sex have previously been reported to associate with increased CAA in AD. To inform biomarker and therapeutic target discovery, we aimed to identify additional genetic risk factors and biological pathways involved in this vascular component of AD etiology. We present a genome-wide association study of CAA pathology in AD cases and report sex- and APOE-stratified assessment of this phenotype. Genome-wide genotypes were collected from 853 neuropathology-confirmed AD cases scored for CAA across five brain regions, and imputed to the Haplotype Reference Consortium panel. Key variables and genome-wide genotypes were tested for association with CAA in all individuals and in sex and APOEε4 stratified subsets. Pathway enrichment was run for each of the genetic analyses. Implicated loci were further investigated for functional consequences using brain transcriptome data from 1,186 samples representing seven brain regions profiled as part of the AMP-AD consortium. We confirmed association of male sex, AD neuropathology and APOEε4 with increased CAA, and identified a novel locus, LINC-PINT, associated with lower CAA amongst APOEε4-negative individuals (rs10234094-C, beta = −3.70 [95% CI −0.49—−0.24]; p = 1.63E-08). Transcriptome profiling revealed higher LINC-PINT expression levels in AD cases, and association of rs10234094-C with altered LINC-PINT splicing. Pathway analysis indicates variation in genes involved in neuronal health and function are linked to CAA in AD patients. Further studies in additional and diverse cohorts are needed to assess broader translation of our findings.


2016 ◽  
Vol 12 (8) ◽  
pp. 872-881 ◽  
Author(s):  
Eva Louwersheimer ◽  
Steffen Wolfsgruber ◽  
Ana Espinosa ◽  
André Lacour ◽  
Stefanie Heilmann-Heimbach ◽  
...  

2021 ◽  
Vol 19 ◽  
Author(s):  
Md. Sahab Uddin ◽  
Md. Tanvir Kabir ◽  
Maroua Jalouli ◽  
Md. Ataur Rahman ◽  
Philippe Jeandet ◽  
...  

: Alzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by the formation of intracellular neurofibrillary tangles (NFTs) and extracellular amyloid plaques. Growing evidence has suggested that AD pathogenesis is not only limited to the neuronal compartment but also strongly interacts with immunological processes in the brain. On the other hand, aggregated and misfolded proteins can bind with pattern recognition receptors located on astroglia and microglia and can in turn induce an innate immune response, characterized by the release of inflammatory mediators, ultimately playing a role in both the severity and the progression of the disease. It has been reported by genome-wide analysis that several genes which elevate the risk for sporadic AD encode for factors controlling the inflammatory response and glial clearance of misfolded proteins. Obesity and systemic inflammation are examples of external factors which may interfere with the immunological mechanisms of the brain and can induce disease progression. In this review, we discussed the mechanisms and essential role of inflammatory signaling pathways in AD pathogenesis. Indeed, interfering with immune processes and modulation of risk factors may lead to future therapeutic or preventive AD approaches.


2019 ◽  
Author(s):  
Ying Sheng ◽  
Chiung-Yu Huang ◽  
Siarhei Lobach ◽  
Lydia Zablotska ◽  
Iryna Lobach ◽  
...  

ABSTRACTLarge-scale genome-wide analyses scans provide massive volumes of genetic variants on large number of cases and controls that can be used to estimate the genetic effects. Yet, the sets of non-genetic variables available in publicly available databases are often brief. It is known that omitting a continuous variable from a logistic regression model can result in biased estimates of odds ratios (OR) (e.g., Gail et al (1984), Neuhaus et al (1993), Hauck et al (1991), Zeger et al (1988)). We are interested to assess what information is needed to recover the bias in the OR estimate of genotype due to omitting a continuous variable in settings when the actual values of the omitted variable are not available. We derive two estimating procedures that can recover the degree of bias based on a conditional density of the omitted variable or knowing the distribution of the omitted variable. Importantly, our derivations show that omitting a continuous variable can result in either under- or over-estimation of the genetic effects. We performed extensive simulation studies to examine bias, variability, false positive rate, and power in the model that omits a continuous variable. We show the application to two genome-wide studies of Alzheimer’s disease.Data Availability StatementThe data that support the findings of this study are openly available in the Database of Genotypes and Phenotypes at [https://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs000372.v1.p1], reference number [phs000372.v1.p1] and at the Alzheimer’s Disease Neuroimaging Initiative http://adni.loni.usc.edu/.


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