Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

Myeloid ectopic viral integration site 2 accelerates the progression of Alzheimer's disease

Amyloid plaques, a major pathological hallmark of Alzheimer's disease (AD), are caused by an imbalance between the amyloidogenic and non-amyloidogenic pathways of amyloid precursor protein (APP). BACE1 cleavage of APP is the rate-limiting step for amyloid-β production and plaque formation in AD. Although the alteration of BACE1 expression in AD has been investigated, the underlying mechanisms remain unknown. In this study, we determined MEIS2 was notably elevated in AD models and AD patients. Alterations in the expression of MEIS2 can modulate the levels of BACE1. MEIS2 downregulation improved the learning and memory retention of AD mice and decreased the number of amyloid plaques. MEIS2 binds to the BACE1 promoter, positively regulates BACE1 expression, and accelerates APP amyloid degradation in vitro. Therefore, our findings suggest that MEIS2 might be a critical transcription factor in AD, since it regulates BACE1 expression and accelerates BACE1-mediated APP amyloidogenic cleavage. MEIS2 is a promising early intervention target for AD treatment.

Feature Fusion and Detection in Alzheimer's Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

Voxel-based morphometry provides an opportunity to study Alzheimer's disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10-6) and cell adhesion molecules (corrected p-value = 5.44 × 10-4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.

Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease

Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.

Imaging Genetics Study Based on a Temporal Group Sparse Regression and Additive Model for Biomarker Detection of Alzheimer's Disease

Imaging genetics is an effective tool used to detect potential biomarkers of Alzheimer's disease (AD) in imaging and genetic data. Most existing imaging genetics methods analyze the association between brain imaging quantitative traits (QTs) and genetic data [e.g., single nucleotide polymorphism (SNP)] by using a linear model, ignoring correlations between a set of QTs and SNP groups, and disregarding the varied associations between longitudinal imaging QTs and SNPs. To solve these problems, we propose a novel temporal group sparsity regression and additive model (T-GSRAM) to identify associations between longitudinal imaging QTs and SNPs for detection of potential AD biomarkers. We first construct a nonparametric regression model to analyze the nonlinear association between QTs and SNPs, which can accurately model the complex influence of SNPs on QTs. We then use longitudinal QTs to identify the trajectory of imaging genetic patterns over time. Moreover, the SNP information of group and individual levels are incorporated into the proposed method to boost the power of biomarker detection. Finally, we propose an efficient algorithm to solve the whole T-GSRAM model. We evaluated our method using simulation data and real data obtained from AD neuroimaging initiative. Experimental results show that our proposed method outperforms several state-of-the-art methods in terms of the receiver operating characteristic curves and area under the curve. Moreover, the detection of AD-related genes and QTs has been confirmed in previous studies, thereby further verifying the effectiveness of our approach and helping understand the genetic basis over time during disease progression.

Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review

Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.

A Systematic Analysis Revealed the Potential Gene Regulatory Processes of ATRA-Triggered Neuroblastoma Differentiation and Identified a Novel RA Response Sequence in the NTRK2 Gene

Retinoic acid- (RA-) triggered neuroblastoma cell lines are widely used cell modules of neuronal differentiation in neurodegenerative disease studies, but the gene regulatory mechanism underlying differentiation is unclear now. In this study, system biological analysis was performed on public microarray data from three neuroblastoma cell lines (SK-N-SH, SH-SY5Y-A, and SH-SY5Y-E) to explore the potential molecular processes of all-trans retinoic acid- (ATRA-) triggered differentiation. RT-qPCR, functional genomics analysis, western blotting, chromatin immunoprecipitation (ChIP), and homologous sequence analysis were further performed to validate the gene regulation processes and identify the RA response element in a specific gene. The potential disturbed biological pathways (111 functional GO terms in 14 interactive functional groups) and gene regulatory network (10 regulators and 71 regulated genes) in neuroblastoma differentiation were obtained. 15 of the 71 regulated genes are neuronal projection-related. Among them, NTRK2 is the only one that was dramatically upregulated in the RT-qPCR test that we performed on ATRA-treated SH-SY5Y-A cells. We further found that the overexpression of the NTRK2 gene can trigger differentiation-like changes in SH-SY5Y-A cells. Functional genomic analysis and western blotting assay suggested that, in neuroblastoma cells, ATRA may directly regulate the NTRK2 gene by activating the RA receptor (RAR) that binds in its promoter region. A novel RA response DNA element in the NTRK2 gene was then identified by bioinformatics analysis and chromatin immunoprecipitation (ChIP) assay. The novel element is sequence conservation and position variation among different species. Our study systematically provided the potential regulatory information of ATRA-triggered neuroblastoma differentiation, and in the NTRK2 gene, we identified a novel RA response DNA element, which may contribute to the differentiation in a human-specific manner.

Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection

MOTIVATION

The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues.

RESULTS

We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial-anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer's Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Meiyan88/SASM-VGWAS.