scholarly journals Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lanlan Li ◽  
Yeying Yang ◽  
Qi Zhang ◽  
Jiao Wang ◽  
Jiehui Jiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Association Studies ◽  
Genomic Research ◽  
Genome Wide Association Studies ◽  
Genetic Biomarkers ◽  
Gwas Analysis

Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

scholarly journals PVRL2 rs6859 Modifies the Effect of Triglyceride on Progression of Mild Cognitive Impairment: The Shanghai Aging Study

2020 ◽  
Author(s):  
Ruru Wang ◽  
Ding Ding ◽  
Abuduaili Atibaike ◽  
Jianxiong Xi ◽  
Qianhua Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cox Model ◽  
Association Studies ◽  
Interactive Effect ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Aging Study

Abstract Background Mild cognitive impairment (MCI) is an intermediate stage between normal cognition and Alzheimer’s disease (AD). Genome-wide association studies (GWAS) have identified many AD-risk variants and indicated the important role of lipid metabolism pathway in AD progression. This study aimed to investigate the effects of triglyceride (TG) and genetic risk factors on progression from MCI to AD (MCI-AD progression).Methods The current study sample comprised of 305 MCI subjects aged 50 and over who were prospectively followed up for average 4.5 years in a sub-cohort of the Shanghai Aging Study. A consensus diagnosis of incident AD was conducted according to Diagnostic and Statistical Manual of Mental Disorders-IV and the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association criteria. Fasting blood samples were obtained at baseline for analyzing serum TG. Single nucleotide polymorphisms (SNPs) genotyping was performed using a MassARRAY system. The effect of TG, genetic variants and their interaction on MCI-AD progression were analyzed using Cox proportional hazards regression model.Results During a mean (±SD) follow-up period of 4.5±1.3 y, 58 subjects developed incident AD. The SNP, rs6859 in the Poliovirus Receptor–Related 2 (PVRL2) gene, was significantly associated with incident AD (false discovery rate (FDR)-adjusted P = 0.018). In multivariate cox model, the PVRL2 rs6859 AG, AA and AG+AA genotypes were associated with significantly increased incident AD, compared with the GG genotype (hazard ratio [HR] = 2.29, P = 0.029, and HR = 2.92, P = 0.013, and HR = 2.47, P =0.012, respectively). In PVRL2 rs6859 AG/AA carriers, higher ln TG was significantly associated with increased risk of incident AD (adjusted HR =2.64, P = 0.034). Ln TG and PVRL2 rs6859 had interactive effect on the MCI-AD progression (P Ln TG × rs6859 = 0.001). Conclusion The present study indicated that PVRL2 rs6859 modified the effect of TG on MCI-AD progression. Precision prevention in MCI population based on genetic information should be considered to avoid progression to AD.

Joint Effect of ABCA7 rs4147929 and Body Mass Index on Progression from Mild Cognitive Impairment to Alzheimer’s Disease: The Shanghai Aging Study

2020 ◽  
Vol 17 (2) ◽  
pp. 185-195
Author(s):  
Jianxiong Xi ◽  
Ding Ding ◽  
Qianhua Zhao ◽  
Xiaoniu Liang ◽  
Li Zheng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Body Mass Index ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Body Mass ◽  
Joint Effect ◽  
Genome Wide Association Studies ◽  
Risk Genotype ◽  
Aging Study

Background: Approximately 40 independent Single Nucleotide Polymorphisms (SNPs) have been associated with Alzheimer’s Disease (AD) or cognitive decline in genome-wide association studies. Methods: We aimed to evaluate the joint effect of genetic polymorphisms and environmental factors on the progression from Mild Cognitive Impairment (MCI) to AD (MCI-AD progression) in a Chinese community cohort. Conclusion: Demographic, DNA and incident AD diagnosis data were derived from the follow-up of 316 participants with MCI at baseline of the Shanghai Aging Study. The associations of 40 SNPs and environmental predictors with MCI-AD progression were assessed using the Kaplan-Meier method with the log-rank test and Cox regression model. Results: Rs4147929 at ATP-binding cassette family A member 7 (ABCA7) (AG/AA vs. GG, hazard ratio [HR] = 2.43, 95% confidence interval [CI] 1.24-4.76) and body mass index (BMI) (overweight vs. non-overweight, HR = 0.41, 95% CI 0.22-0.78) were independent predictors of MCI-AD progression. In the combined analyses, MCI participants with the copresence of non-overweight BMI and the ABCA7 rs4147929 (AG/AA) risk genotype had an approximately 6-fold higher risk of MCI-AD progression than those with an overweight BMI and a non-risk genotype (HR = 6.77, 95% CI 2.60-17.63). However, a nonsignificant result was found when participants carried only one of these two risk factors (nonoverweight BMI and AG/AA of ABCA7 rs4147929). Conclusion: ABCA7 rs4147929 and BMI jointly affect MCI-AD progression. MCI participants with the rs4147929 risk genotype may benefit from maintaining an overweight BMI level with regard to their risk for incident AD.

scholarly journals A Network-based Deep Learning Framework Catalyzes GWAS and Multi-Omics Findings to Biology and Drug Repurposing for Alzheimer's Disease

2021 ◽  
Author(s):  
Jielin Xu ◽  
Yuan Hou ◽  
Yadi Zhou ◽  
Ming Hu ◽  
Feixiong Cheng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Risk ◽  
Association Studies ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Learning Framework ◽  
Protein Interactome

Human genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer's disease (AD). Translating human genetic findings (i.e., genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery, however, remains a major challenge. To address this critical problem, we present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). NETTAG is capable of integrating multi-genomics data along with the protein-protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leverage non-coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone-QTLs, and transcription factor binding-QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The key premises of NETTAG are that the disease risk genes exhibit distinct functional characteristics compared to non-risk genes and therefore can be distinguished by their aggregated genomic features under the human protein interactome. Applying NETTAG to the latest AD GWAS data, we identified 156 putative AD-risk genes (i.e., APOE, BIN1, GSK3B, MARK4, and PICALM). We showed that predicted risk genes are: 1) significantly enriched in AD-related pathobiological pathways, 2) more likely to be differentially expressed regarding transcriptome and proteome of AD brains, and 3) enriched in druggable targets with approved medicines (i.e., choline and ibudilast). In summary, our findings suggest that understanding of human pathobiology and therapeutic development could benefit from a network-based deep learning methodology that utilizes GWAS findings under the multimodal genomic analyses.

scholarly journals A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

NeuroImage ◽  
2019 ◽  
Vol 189 ◽  
pp. 276-287 ◽  
Author(s):  
Simeon Spasov ◽  
Luca Passamonti ◽  
Andrea Duggento ◽  
Pietro Liò ◽  
Nicola Toschi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Approach

scholarly journals Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment

Biomedicines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 658
Author(s):  
Shao-Xun Yuan ◽  
Hai-Tao Li ◽  
Yu Gu ◽  
Xiao Sun
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Quantitative Traits ◽  
Association Studies ◽  
Specific Gene ◽  
Snp Analysis ◽  
Subcortical Structures

Transcriptome–wide association studies (TWAS) have identified several genes that are associated with qualitative traits. In this work, we performed TWAS using quantitative traits and predicted gene expressions in six brain subcortical structures in 286 mild cognitive impairment (MCI) samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. The six brain subcortical structures were in the limbic region, basal ganglia region, and cerebellum region. We identified 9, 15, and 6 genes that were stably correlated longitudinally with quantitative traits in these three regions, of which 3, 8, and 6 genes have not been reported in previous Alzheimer’s disease (AD) or MCI studies. These genes are potential drug targets for the treatment of early–stage AD. Single–Nucleotide Polymorphism (SNP) analysis results indicated that cis–expression Quantitative Trait Loci (cis–eQTL) SNPs with gene expression predictive abilities may affect the expression of their corresponding genes by specific binding to transcription factors or by modulating promoter and enhancer activities. Further, baseline structure volumes and cis–eQTL SNPs from correlated genes in each region were used to predict the conversion risk of MCI patients. Our results showed that limbic volumes and cis–eQTL SNPs of correlated genes in the limbic region have effective predictive abilities.

Diagnosis of Alzheimer's disease based on Deeply-Fused Nets

2020 ◽  
Vol 23 ◽  
Author(s):  
ChangZu Chen ◽  
Qi Wu ◽  
ZuoYong Li ◽  
Lei Xiao ◽  
Zhong Yi Hu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Diagnosis ◽  
Automatic Diagnosis ◽  
Single Base ◽  
Base Network ◽  
Normal Controls

Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of automatic diagnosis of AD. Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity. Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard base network. Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

scholarly journals Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 80 (3) ◽  
pp. 1079-1090
Author(s):  
Sanjay Nagaraj ◽  
Tim Q. Duong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Selection Methods ◽  
Deep Learning Algorithm ◽  
Neurocognitive Tests ◽  
Risk Stratification Score

Background: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning. Objective: The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis. Methods: Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods. Results: The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset. Conclusion: The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

scholarly journals Preliminary Data on the Interaction between Some Biometals and Oxidative Stress Status in Mild Cognitive Impairment and Alzheimer’s Disease Patients

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Ioana-Miruna Balmuș ◽  
Stefan-Adrian Strungaru ◽  
Alin Ciobica ◽  
Mircea-Nicusor Nicoara ◽  
Romeo Dobrin ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lipid Peroxidation ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Association Studies ◽  
Metal Ion Homeostasis ◽  
Brain Functions ◽  
And Oxidative Stress

Increased interest regarding the biometal mechanisms of action and the pathways in which they have regulatory roles was lately observed. Particularly, it was shown that biometal homeostasis dysregulation may lead to neurodegeneration including Alzheimer’s disease, Parkinson disease, or prion protein disease, since important molecular signaling mechanisms in brain functions implicate both oxidative stress and redox active biometals. Oxidative stress could be a result of a breakdown in metal-ion homeostasis which leads to abnormal metal protein chelation. In our previous work, we reported a strong correlation between Alzheimer’s disease and oxidative stress. Consequently, the aim of the present work was to evaluate some of the biometals’ levels (magnesium, manganese, and iron), the specific activity of some antioxidant enzymes (superoxide dismutase and glutathione peroxidase), and a common lipid peroxidation marker (malondialdehyde concentration), in mild cognitive impairment (n=15) and Alzheimer’s disease (n=15) patients, compared to age-matched healthy subjects (n=15). We found increased lipid peroxidation effects, low antioxidant defense, low magnesium and iron concentrations, and high manganese levels in mild cognitive impairment and Alzheimer’s disease patients, in a gradual manner. These data could be relevant for future association studies regarding the prediction of Alzheimer’s disease development risk or circling through stages by analyzing both active redox metals, oxidative stress markers, and the correlations in between.

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