Identification of Biological Pathways to Alzheimer’s Disease Using Polygenic Scores

Author(s):  
Judith Harrison
2017 ◽  
Vol 41 (S1) ◽  
pp. S166-S167
Author(s):  
J. Harrison ◽  
E. Baker ◽  
L. Hubbard ◽  
D. Linden ◽  
J. Williams ◽  
...  

IntroductionSingle nucleotide polymorphisms (SNPs) contribute small increases in risk for late-onset Alzheimer's disease (LOAD). LOAD SNPs cluster around genes with similar biological functions (pathways). Polygenic risk scores (PRS) aggregate the effect of SNPs genome-wide. However, this approach has not been widely used for SNPs within specific pathways.ObjectivesWe investigated whether pathway-specific PRS were significant predictors of LOAD case/control status.MethodsWe mapped SNPs to genes within 8 pathways implicated in LOAD. For our polygenic analysis, the discovery sample comprised 13,831 LOAD cases and 29,877 controls. LOAD risk alleles for SNPs in our 8 pathways were identified at a P-value threshold of 0.5. Pathway-specific PRS were calculated in a target sample of 3332 cases and 9832 controls. The genetic data were pruned with R2 > 0.2 while retaining the SNPs most significantly associated with AD. We tested whether pathway-specific PRS were associated with LOAD using logistic regression, adjusting for age, sex, country, and principal components. We report the proportion of variance in liability explained by each pathway.ResultsThe most strongly associated pathways were the immune response (NSNPs = 9304, = 5.63 × 10−19, R2 = 0.04) and hemostasis (NSNPs = 7832, P = 5.47 × 10−7, R2 = 0.015). Regulation of endocytosis, hematopoietic cell lineage, cholesterol transport, clathrin and protein folding were also significantly associated but accounted for less than 1% of the variance. With APOE excluded, all pathways remained significant except proteasome-ubiquitin activity and protein folding.ConclusionsGenetic risk for LOAD can be split into contributions from different biological pathways. These offer a means to explore disease mechanisms and to stratify patients.Disclosure of interestThe authors have not supplied their declaration of competing interest.


2018 ◽  
Vol 14 (7) ◽  
pp. 848-857 ◽  
Author(s):  
Shahzad Ahmad ◽  
Christian Bannister ◽  
Sven J. van der Lee ◽  
Dina Vojinovic ◽  
Hieab H.H. Adams ◽  
...  

2016 ◽  
Vol 12 ◽  
pp. P886-P887 ◽  
Author(s):  
Ana Paula MendesM Silva ◽  
Eduardo de SouzaS Nicolau ◽  
Kelly Silva Pereira ◽  
Kenia Kelly Fiaux do Nascimento ◽  
Camila Moreira SilvaMS Ferreira ◽  
...  

2021 ◽  
Author(s):  
Atul Kumar ◽  
Maryam Shoai ◽  
Sebastian Palmqvist ◽  
Erik Stomrud ◽  
John Hardy ◽  
...  

Abstract Background Cognitive decline in early-stage Alzheimer’s disease (AD) may depend on genetic variability. Methods In the Swedish BioFINDER study, we used polygenic scores (PGS) (for AD, intelligence and educational attainment), and genetic variants (in a genome-wide association study [GWAS]) to predict longitudinal cognitive change (measured by MMSE) over a mean of 4.2 years. We included 555 β-amyloid (Aβ) negative cognitively unimpaired (CU) individuals, 206 Aβ-positive CU (preclinical AD), 110 Aβ-negative mild cognitive impairment (MCI) patients, and 146 Aβ-positive MCI patients (prodromal AD). Results Polygenic scores for AD (in Aβ-positive individuals) and intelligence (independent of Aβ-status) were associated with cognitive decline. Eight genes were associated with cognitive decline in GWAS (3 independent of Aβ-status). Conclusions AD risk genes may influence cognitive decline in early AD, while genes related to intelligence may modulate cognitive decline irrespective of disease. Therapies targeting the implicated biological pathways may modulate the clinical course of AD.


2018 ◽  
Author(s):  
Baruh Polis ◽  
Kolluru D Srikanth ◽  
Vyacheslav Gurevich ◽  
Hava Gil-Henn ◽  
Abraham O. Samson

AbstractAlzheimer’s disease (AD) is a slowly progressive neurodegenerative disorder with an insidious onset. The disease is characterized by cognitive impairment and a distinct pathology with neuritic plaques and neurofibrillary tangles.Growing evidence highlights the role of arginase activity in the manifestation of AD. Upregulation of arginase was shown to contribute to endothelial dysfunction, atherosclerosis, diabetes, and neurodegeneration. Regulation of arginase activity appears to be a promising approach for interfering with the pathogenesis of AD and other metabolic disorders. Therefore, the enzyme represents a novel therapeutic target.Here, we administer an arginase inhibitor L-norvaline to a mouse model of AD. Then, we evaluate the neuroprotective effect of L-norvaline using immunohistochemistry, proteomics, and quantitative polymerase chain reaction assays. Finally, we identify the biological pathways activated by the treatment.Remarkably, we find that L-norvaline treatment reverses the cognitive decline in AD mice. We show the treatment is neuroprotective as indicated by reduced beta-amyloidosis, alleviated microgliosis, and TNFα transcription levels. Moreover, elevated levels of neuroplasticity related protein PSD-95 were detected in the hippocampi of mice treated with L-norvaline. Furthermore, we disclose several biological pathways, which are involved in cell survival and neuroplasticity and are activated by the treatment.Through these modes of action, L-norvaline has the potential to improve the symptoms of AD and even interfere with its pathogenesis. As such, L-norvaline is a promising neuroprotective molecule that might be tailored for the treatment of a range of neurodegenerative disorders.


2018 ◽  
Author(s):  
Hamel Patel ◽  
Richard J.B Dobson ◽  
Stephen J Newhouse

ABSTRACTBackgroundMicroarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer’s disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations. We are yet to identify robust transcriptomic changes specific to AD brains.Methods and ResultsTwenty-two AD, eight Schizophrenia, five Bipolar Disorder, four Huntington's disease, two Major Depressive Disorder and one Parkinson’s disease dataset totalling 2667 samples and mapping to four different brain regions (Temporal lobe, Frontal lobe, Parietal lobe and Cerebellum) were analysed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction. This identified 323, 435, 1023 and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe and cerebellum brain regions respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. Biological pathways involved in the “metabolism of proteins” and viral components were significantly enriched across AD brains.ConclusionThis study solely relied on publicly available microarray data, which too often lacks appropriate phenotypic information for robust data analysis and needs to be addressed by future studies. Nevertheless, with the information available, we were able to identify specific transcriptomic changes in AD brains which could make a significant contribution towards the understanding of AD disease mechanisms and may also provide new therapeutic targets.


2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andre Altmann ◽  
Marzia A Scelsi ◽  
Maryam Shoai ◽  
Eric de Silva ◽  
Leon M Aksman ◽  
...  

Abstract Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer’s disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer’s disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer’s disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer’s disease and their association (beyond the APOE locus) with a broad range of Alzheimer’s disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.


2020 ◽  
Vol 16 (S3) ◽  
Author(s):  
Ellen M. Wijsman ◽  
Tyler R. Day ◽  
Timothy A. Thornton ◽  
Andrea R. Horimoto ◽  
Elizabeth E. Blue ◽  
...  

2018 ◽  
Vol 12 ◽  
Author(s):  
Pan Cui ◽  
Xiaofeng Ma ◽  
He Li ◽  
Wenjing Lang ◽  
Junwei Hao

2020 ◽  
Author(s):  
Di Liu ◽  
Xiaoyu Zhang ◽  
Weijie Cao ◽  
Jie Zhang ◽  
Manshu Song ◽  
...  

Background In observational studies, Alzheimer's disease (AD) has been associated with an increased risk of Coronavirus disease 2019 (COVID-19), and the prognosis of COVID-19 can affect nervous systems. However, the causality between these conditions remains to be determined. Methods This study sought to investigate the bidirectional causal relations of AD with COVID-19 using two-sample Mendelian randomization (MR) analysis. Results We found that genetically predicted AD was significantly associated with higher risk of severe COVID-19 (odds ratio [OR], 3.329; 95% confidence interval [CI], 1.139-9.725; P=0.028). It's interesting that genetically predicted severe COVID-19 was also significantly associated with higher risk of AD (OR, 1.004; 95% CI, 1.001-1.007; P=0.018). In addition, the two strong genetic variants associated with severe COVID-19 was associated with higher AD risk (OR, 1.018; 95% CI, 1.003-1.034; P=0.018). There is no evidence to support that genetically predicted AD was significantly associated with COVID-19 susceptibility, and vice versa. No obvious pleiotropy bias and heterogeneity were observed. Conclusion Overall, AD may causally affect severe COVID-19, and vice versa, performing bidirectional regulation through independent biological pathways.


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