scholarly journals System-Level Analysis of Alzheimer’s Disease Prioritizes Candidate Genes for Neurodegeneration

2021 ◽  
Vol 12 ◽  
Author(s):  
Jeffrey L. Brabec ◽  
Montana Kay Lara ◽  
Anna L. Tyler ◽  
J. Matthew Mahoney
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Neurodegenerative Disorder ◽  
Disease Status ◽  
Cumulative Distribution ◽  
System Level ◽  
Risk Genes ◽  
Genome Wide ◽  
Prioritization Method

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.

scholarly journals Genetic Interactions Explain Variance in Cingulate Amyloid Burden: An AV-45 PET Genome-Wide Association and Interaction Study in the ADNI Cohort

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jin Li ◽  
Qiushi Zhang ◽  
Feng Chen ◽  
Jingwen Yan ◽  
Sungeun Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Neurodegenerative Disorder ◽  
Genome Wide Association ◽  
Interaction Study ◽  
Gwas Analysis ◽  
Genome Wide ◽  
A Genome ◽  
Single Marker

Alzheimer’s disease (AD) is the most common neurodegenerative disorder. Using discrete disease status as the phenotype and computing statistics at the single marker level may not be able to address the underlying biological interactions that contribute to disease mechanism and may contribute to the issue of “missing heritability.” We performed a genome-wide association study (GWAS) and a genome-wide interaction study (GWIS) of an amyloid imaging phenotype, using the data from Alzheimer’s Disease Neuroimaging Initiative. We investigated the genetic main effects and interaction effects on cingulate amyloid-beta (Aβ) load in an effort to better understand the genetic etiology of Aβdeposition that is a widely studied AD biomarker. PLINK was used in the single marker GWAS, and INTERSNP was used to perform the two-marker GWIS, focusing only on SNPs withp≤0.01for the GWAS analysis. Age, sex, and diagnosis were used as covariates in both analyses. Correctedpvalues using the Bonferroni method were reported. The GWAS analysis revealed significant hits within or proximal toAPOE,APOC1, andTOMM40genes, which were previously implicated in AD. The GWIS analysis yielded 8 novel SNP-SNP interaction findings that warrant replication and further investigation.

scholarly journals Genome-wide analysis identifies a novel LINC-PINT splice variant associated with vascular amyloid pathology in Alzheimer’s disease

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Joseph S. Reddy ◽  
Mariet Allen ◽  
Charlotte C. G. Ho ◽  
Stephanie R. Oatman ◽  
Özkan İş ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Transcriptome Profiling ◽  
Brain Regions ◽  
Common Finding ◽  
Amyloid Angiopathy ◽  
Genome Wide ◽  
A Genome ◽  
Male Sex

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.

scholarly journals Summary-Based Methylome-Wide Association Analyses Suggest Potential Genetically Driven Epigenetic Heterogeneity of Alzheimer’s Disease

2020 ◽  
Vol 9 (5) ◽  
pp. 1489
Author(s):  
Alireza Nazarian ◽  
Anatoliy I. Yashin ◽  
Alexander M. Kulminski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Tissue Samples ◽  
Significance Level ◽  
Association Analyses ◽  
Functional Studies ◽  
Genome Wide ◽  
Summary Data

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with no curative treatment available. Exploring the genetic and non-genetic contributors to AD pathogenesis is essential to better understand its underlying biological mechanisms, and to develop novel preventive and therapeutic strategies. We investigated potential genetically driven epigenetic heterogeneity of AD through summary data-based Mendelian randomization (SMR), which combined results from our previous genome-wide association analyses with those from two publicly available methylation quantitative trait loci studies of blood and brain tissue samples. We found that 152 probes corresponding to 113 genes were epigenetically associated with AD at a Bonferroni-adjusted significance level of 5.49E-07. Of these, 10 genes had significant probes in both brain-specific and blood-based analyses. Comparing males vs. females and hypertensive vs. non-hypertensive subjects, we found that 22 and 79 probes had group-specific associations with AD, respectively, suggesting a potential role for such epigenetic modifications in the heterogeneous nature of AD. Our analyses provided stronger evidence for possible roles of four genes (i.e., AIM2, C16orf80, DGUOK, and ST14) in AD pathogenesis as they were also transcriptionally associated with AD. The identified associations suggest a list of prioritized genes for follow-up functional studies and advance our understanding of AD pathogenesis.

scholarly journals Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study

Brain ◽  
2020 ◽  
Author(s):  
Longfei Jia ◽  
Fangyu Li ◽  
Cuibai Wei ◽  
Min Zhu ◽  
Qiumin Qu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Predictive Models ◽  
Genome Wide Association Study ◽  
Disease Onset ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Risk Variants ◽  
Genome Wide

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.

Genome-Wide Association Study of Brain Alzheimer’s Disease-Related Metabolic Decline as Measured by [18F] FDG-PET Imaging

2020 ◽  
Vol 77 (1) ◽  
pp. 401-409
Author(s):  
Rong-Ze Wang ◽  
Yu-Xiang Yang ◽  
Hong-Qi Li ◽  
Xue-Ning Shen ◽  
Shi-Dong Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Fdg Pet ◽  
Genome Wide Association Study ◽  
Genetic Model ◽  
Genome Wide Association ◽  
Angular Gyrus ◽  
Genome Wide ◽  
18F Fdg

Background: Hypometabolism detected by fluorodeoxyglucose F18 positron emission tomography ([18F] FDG PET) is an early neuropathologic changes in Alzheimer’s disease (AD) and provides important pathologic staging information. Objective: This study aimed to discover genetic interactions that regulate longitudinal glucose metabolic decline in AD-related brain regions. Methods: A total of 586 non-Hispanic white individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 1/GO/2 cohorts that met all quality control criteria were included in this study. Genome-wide association study of glucose metabolic decline in regions of interest (ROIs) was performed with linear regression under the additive genetic model. Results: We identified two novel variants that had a strong association with longitudinal metabolic decline in different ROI. Rs4819351-A in gene 1-acylglycerol-3-phosphate O-acyltransferase 3 (AGPAT3) demonstrated reduced metabolic decline in right temporal gyrus (p = 3.97×10–8, β= –0.016), while rs13387360-T in gene LOC101928196 demonstrated reduced metabolic decline in left angular gyrus (p = 1.69×10–8, β= –0.027). Conclusion: Our results suggest two genome-wide significant SNPs (rs4819351, rs13387360) in AGPAT3 and LOC101928196 as protective loci that modulate glucose metabolic decline. These two genes should be further investigated as potential therapeutic target for neurodegeneration diseases.

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